Autism spectrum disorder

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Future Directions for Infant Identification and
Intervention for Autism Spectrum Disorder from a
Transdiagnostic Perspective

Meagan R. Talbott & Meghan R. Miller

To cite this article: Meagan R. Talbott & Meghan R. Miller (2020): Future Directions for Infant
Identification and Intervention for Autism Spectrum Disorder from a Transdiagnostic Perspective,
Journal of Clinical Child & Adolescent Psychology, DOI: 10.1080/15374416.2020.1790382

To link to this article: https://doi.org/10.1080/15374416.2020.1790382

Published online: 23 Jul 2020.

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Future Directions for Infant Identification and Intervention for Autism Spectrum
Disorder from a Transdiagnostic Perspective
Meagan R. Talbott and Meghan R. Miller

MIND Institute and Department of Psychiatry & Behavioral Sciences, University of California, Davis

ABSTRACT
By the time they are typically detected, neurodevelopmental disorders like autism spectrum
disorder (ASD) are already challenging to treat. Preventive and early intervention strategies in
infancy are critical for improving outcomes over the lifespan with significant cost savings.
However, the impact of prevention and early intervention efforts is dependent upon our ability
to identify infants most appropriate for such interventions. Because there may be significant
overlap between prodromal symptoms across neurodevelopmental disorders and child psycho-
pathology more broadly which may wax and wane across development, we contend that the
impact of prevention and early intervention efforts will be heightened by identifying early
indicators that may overlap across ASD and other commonly co-occurring disorders. This paper
summarizes the existing literature on infant symptoms and identification of ASD to demonstrate
the ways in which a transdiagnostic perspective could expand the impact of early identification
and intervention research and clinical efforts, and to outline suggestions for future empirical
research programs addressing current gaps in the identification-to-treatment pipeline. We pro-
pose four recommendations for future research that are both grounded in developmental and
clinical science and that are scalable for early intervention systems: (1) development of fine-
grained, norm-referenced measures of ASD-relevant transdiagnostic behavioral domains; (2)
identification of shared and distinct mechanisms influencing the transition from risk to disorder;
(3) determination of key cross-cutting treatment strategies (both novel and extracted from
existing approaches) effective in targeting specific domains across disorders; and (4) integration
of identified measures and treatments into existing service systems.

By the time they are typically detected, neurodevelopmen-
tal disorders like autism spectrum disorder (ASD) are
already challenging to treat. ASD is increasingly prevalent,
emerges early in development, and is associated with sig-
nificant long-term impairment (Bal et al., 2015; Howlin &
Magiati, 2017; Howlin et al., 2013). The economic burden
resulting from elevated health-care costs, costs to families,
and costs associated with lost work represents an issue of
considerable public health concern (Lavelle et al., 2014).
Preventive and early intervention strategies are likely to be
the most effective approaches to improving outcomes over
the lifespan, with significant cost savings (Chasson et al.,
2007; Cidav et al., 2017; Kim et al., 2018; Knudsen et al.,
2006). However, the impact of these efforts is dependent
upon our ability to identify infants and very young children
most appropriate for such interventions.

Over the past two decades, there have been substan-
tial efforts to uncover the earliest emerging signs of ASD.
One of the key clinical implications of these early

identification studies is earlier referral to evidence-
based early intervention. While reliable diagnoses of
ASD can be made as early as 18 months of age in some
cases (Ozonoff et al., 2015; Zwaigenbaum et al., 2016),
and although there are evidence-based treatments for
toddlers with ASD (e.g., Carter et al., 2011; Dawson
et al., 2010; Kaiser & Roberts, 2013; Kasari et al., 2006;
R. L. Koegel et al., 1999; Lovaas, 1987; McEachin et al.,
1993; see Sandbank et al., 2020 for a recent meta-
analysis), the development of ASD-relevant and specific
screening and intervention programs for infants has
been hampered by a number of methodological and
conceptual disagreements and a relative lack of fine-
grained cross-disorder longitudinal comparisons. These
issues have limited our understanding of early beha-
vioral indicators and treatment targets that may be
shared across, or distinct between, ASD and other emer-
ging neurodevelopmental disorders (and, perhaps, child
psychopathology more broadly).

CONTACT Meagan R. Talbott mtalbott@ucdavis.edu; Meghan R. Miller mrhmiller@ucdavis.edu MIND Institute and Department of Psychiatry &
Behavioral Sciences, University of California, Davis, Sacramento, CA 95817
The authors contributed equally to this work.

JOURNAL OF CLINICAL CHILD & ADOLESCENT PSYCHOLOGY
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While it is critical for diagnostic purposes to identify
disorder-specific early behavioral indicators, we con-
tend that the impact of prevention and early interven-
tion efforts will be heightened by also identifying early
indicators that may overlap across ASD and other
commonly co-occurring disorders. Indeed, a range of
other conditions are frequently comorbid with ASD,
including attention-deficit/hyperactivity disorder
(ADHD), anxiety disorders, externalizing disorders,
and mood disorders (Abdallah et al., 2011; Houghton
et al., 2017). Transdiagnostic approaches focus on iden-
tifying processes that are shared across disorders and
that underlie and maintain symptoms (Harvey et al.,
2004; Nolen-Hoeksema & Watkins, 2011). This frame-
work is frequently being utilized in the study of adult
psychopathology, but has less often been applied to
neurodevelopmental disorders, particularly in infancy
and prior to diagnosis. These approaches could have
wide-reaching effects, leading to treatments targeting
impaired processes that can be applied across indivi-
duals with, or at risk for, various disorders, thereby
more efficiently leveraging the limited funding allo-
cated to early intervention services. Indeed, if key
shared factors can be identified early in life across
children at high risk for a range of atypical develop-
ment including ASD and common comorbidities, pre-
vention and intervention programs targeting such
factors may have wider-reaching applications than
those targeting disorder-specific early indicators.
Transdiagnostic prevention and intervention efforts
would be especially impactful during the period in
infancy when a child’s outcome is still unclear and
symptoms are in the process of emerging. Intervening
before symptoms have become clearly instantiated is
closer to a true “prevention” model, which seeks to
reduce the likelihood of symptoms emerging, in con-
trast to a diagnostic-based approach which attempts to
reduce or ameliorate clinically significant symptoms
already present (Dawson, 2008; Dryden & Dryden,
2018).

The goals of this Future Directions paper are to
summarize the existing literature on infant symptoms
and identification of ASD, to demonstrate the ways in
which a transdiagnostic perspective could address cur-
rent challenges in early identification and intervention
research and clinical efforts, and to outline suggestions
for future empirical research programs addressing cur-
rent gaps in the identification-to-treatment pipeline. Of
note, we primarily focus on the first year of life,
a period during which there is massive developmental
plasticity and potential benefit from efficacious inter-
ventions, but a period that is also characterized by
significant phenotypic heterogeneity and overlap in

prodromal symptoms across neurodevelopmental dis-
orders. We advocate for the development of a unified
approach to early identification of ASD, neurodevelop-
mental disorders, and child psychopathology more
broadly that is both grounded in developmental and
clinical science and scalable for early intervention
systems.

Current Approaches to Infant Identification and
Intervention: What Do We Know?

Infant Identification

Fulfilling the promise of early intervention requires
efficacious early screening and identification of infants
who will benefit. Initial studies focused on early identi-
fication of ASD relied on retrospective analysis of
infant/toddler home videos of diagnosed children
(Osterling & Dawson, 1994; Osterling et al., 2002;
Ozonoff, Iosif et al., 2011; Werner et al., 2000, 2005),
providing crucial insights into early markers and pat-
terns of symptom emergence and paving the way for
prospective studies. However, although these studies
offered some of the first opportunities to examine the
early development of autism symptoms, there were
significant methodological limitations (Palomo et al.,
2006), resulting in a shift toward prospective high-risk
infant studies given the high rates of recurrence within
families, which near 20% (Ozonoff, Young et al., 2011).
These prospective studies involve the recruitment of
infants at familial risk for ASD – younger siblings of
diagnosed children – from early in life in order to
identify early indicators. Notably, they do not come
without limitations themselves, the most prominent
being questions around generalizability to non-familial
cases of ASD.

Despite herculean efforts, more than a decade of
research employing these prospective ‘infant sibling’
designs have failed to find robust behavioral markers
specific to ASD risk in infants between the ages of 0 to
12 months (Zwaigenbaum et al., 2015). Rather, differ-
ences at a group level have been documented most
consistently between 12 and 24 months of age (Landa
et al., 2013; Ozonoff et al., 2008, 2010; Szatmari et al.,
2016), with rare exceptions of group differences prior
to 12 months (Miller et al., 2017; Nyström et al., 2019).
Thus, although a small number of infants demonstrate
overt behavioral symptoms of ASD as early as the
first year of life (Bryson et al., 2007; Chawarska et al.,
2013; L. K. Koegel et al., 2013; Rogers et al., 2014), the
emergence of ASD symptoms is most often insidious,
consisting of gradual declines in core social commu-
nication behaviors (Ozonoff et al., 2018; Ozonoff &

2 M. R. TALBOTT AND M. R. MILLER

Iosif, 2019). Beyond early behavioral indicators,
a number of studies suggest that it may be possible to
identify brain-based differences that capture increased
risk before behavioral symptoms are present, including
the presence of increased extra-axial cerebrospinal
fluid (Shen et al., 2013, 2018), altered brain morphol-
ogy (Wolff et al., 2015), differences in functional con-
nectivity patterns (Pruett et al., 2015), and differences
in microstructural properties of white matter fiber
tracts (Wolff et al., 2012). The degree to which such
measurements will be scalable and translatable to rou-
tine clinical practice is unclear, however.

Because many psychiatric disorders share some
overlapping risk factors (Gandal et al., 2018),
a growing body of research has begun to focus on the
intersection between ASD and other psychiatric condi-
tions. For example, ADHD commonly co-occurs with
ASD (Leitner, 2014) and is more prevalent among
family members of individuals with ASD than in the
general population (Ghirardi et al., 2018; Jokiranta-
Olkoniemi et al., 2016; Miller et al., 2019). Research
has suggested shared genetic influences (Stergiakouli
et al., 2017), and studies comparing neural, cognitive,
and behavioral profiles of ASD and ADHD have
revealed some similarities (Geurts et al., 2004; Di
Martino et al., 2013; Semrud-Clikeman et al., 2010).
Additionally, some children who meet criteria for
ASD in preschool “evolve” to exhibit behavioral phe-
notypes more consistent with ADHD by middle child-
hood (Fein et al., 2005). In a seminal review, Johnson
et al. (2015) highlighted key behavioral domains that
may be disrupted in the early development of both ASD
and ADHD, including attention regulation, tempera-
ment and self-regulation, social interaction and com-
munication, motor skills, and sensory processing/
perception (Johnson et al., 2015). Recent work from
one of our own research groups has found that reduced
orienting to name – a behavior typically thought to
serve as a specific early indicator of ASD (and one of
the earliest-documented behavioral differences among
infants developing ASD; Miller et al., 2017) – may in
fact be a general marker for ASD and risk for ADHD
earlier in infancy but become a more specific indicator
of ASD by 24 months of age (Hatch et al., 2020).
Likewise, we recently described a mixture of overlap-
ping and distinct early markers of preschool ASD- and
ADHD-like latent profiles which can be difficult to
disentangle early in life (Miller et al., in press). These
challenges may have significant clinical implications
with respect to early identification and referral to
early intervention; one study showed that diagnoses of
ASD were delayed by an average of 3 years among

children who received initial diagnoses of ADHD
(Miodovnik et al., 2015).

Similarly, anxiety disorders also frequently co-occur
with ASD (Kirsch et al., 2020; see Kerns & Kendall,
2012 for a review), and anxiety symptom levels tend to
be higher, on average, among unaffected family mem-
bers of individuals with ASD (Howlin et al., 2015;
Shephard et al., 2017). Recent work seeking to under-
stand the overlap in early predictors of ASD and anxi-
ety symptoms in middle childhood has highlighted
some shared predictors based on parent ratings of
infant temperament, including high levels of fearful-
ness/shyness (Shephard et al., 2019).

These examples are intended to be illustrative, not
exhaustive, but they highlight the point that it can be
challenging to distinguish neurodevelopmental disor-
ders and child psychopathology as they are emerging
due to phenotypic and, possibly, etiological overlap, at
least during certain periods of development. Some early
behavioral indicators may overlap across these popula-
tions serving as general indices of atypical development
that could be leveraged for transdiagnostic treatment
development efforts. Indeed, it is likely that transdiag-
nostic or cross-disorder approaches to identification of
early markers and disrupted processes could include
ASD and a number of other conditions (e.g., schizo-
phrenia). In our view, the overarching goals of this
research are (1) to develop a deeper understanding of
the pathogenesis of these conditions, and (2) to identify
factors that could be tested as relevant targets of pre-
vention and early intervention programs. Ultimately,
for now we are still left asking, when and for whom
should we intervene in infancy?

Infant Intervention

As noted at the outset, preventive and early intervention
strategies are critical to improving outcomes over the
lifespan for child psychopathology and neurodevelop-
mental disorders (Jaffee, 2018; Sonuga-Barke &
Halperin, 2010). With respect to ASD, our knowledge of
early intervention in infancy is based on single-subject
trials, small groups, and randomized controlled trials
(RCTs) of both general developmental and ASD-specific
interventions. The current early intervention system in
the United States consists of programs for infants aged 0–
3 years, funded via Part C of the Individuals with
Disabilities Education Act of 2004 (IDEA). States have
some latitude in determining specific eligibility criteria,
but basic criteria include documented developmental
delays in one of the five specified developmental domains
(cognition, motor, social-emotional, communication, or

JOURNAL OF CLINICAL CHILD & ADOLESCENT PSYCHOLOGY 3

adaptive behavior) or diagnosis that typically results in
developmental delays (e.g., Down syndrome, deafness,
autism). States can provide services to infants deemed at-
risk for delays, but only four states currently do so
(Rosenberg et al., 2013). Part C programs are mandated
to include parents and to be delivered in natural settings;
as such, most families receive low-intensity (1–2 h per
week) services delivered via parent coaching in the home.
The parent-mediated approach is also developmentally
appropriate for infants when compared to other, more
intensive ASD-specific approaches for toddlers.

To date, there have been only a handful of trials of ASD-
specific treatment in infancy, all delivered via parent coach-
ing. They have each focused on different groups of infants,
utilized different intervention targets in terms of both child
behavior and parent strategies, and used different outcome
measures. Green and colleagues (Green et al., 2013, 2015,
2017) conducted a series of single-subject and randomized
controlled trials testing the effects of a general develop-
mental parent-mediated intervention, the Video
Interaction for Promoting Positive Parenting (VIPP;
Juffer et al., 2008) for infants at familial risk, irrespective
of behavioral symptoms at enrollment. These trials found
some effects on target parent behaviors and proximal child
measures of attentiveness and communication initiations,
but no effects on standardized language measures or diag-
nostic classification at 3 years. In a subsequent RCT,
Whitehouse et al. (2019) used the same intervention
approach but targeted infants identified as at-risk for
ASD based on a screening checklist. Again, they found no
effects for the primary outcome on standardized measures
of ASD symptoms, or secondary outcomes using standar-
dized measures of development, behavior coding of par-
ent–child interactions, or parent questionnaire measures of
infant gesture, adaptive social functioning, or parenting
sense of competence. There were some positive effects on
parent-reported measures of expressive and functional
language.

Other studies have focused on increasing specific “pivo-
tal” infant behaviors, with or without also targeting par-
ental responsivity, for symptomatic infants (Baranek et al.,
2015; L. K. Koegel et al., 2013; Steiner et al., 2013; Watson
et al., 2017). Two single-subject studies in this area demon-
strated positive effects on specific target infant behaviors:
functional communication, response to name, avoidance of
eye contact, and positive affect (L. K. Koegel et al., 2013;
Steiner et al., 2013). Watson et al. (2017) conducted an
RCT with 87 one-year-olds identified as ‘at risk’ via com-
munity screening to test the effects of a responsive parent
coaching model targeting specific pivotal child skills across
two domains: social communication and sensory-regula-
tory. They found no significant main effect on primary
child outcomes using standardized measures of ASD

symptoms, adaptive functioning, or language. However,
there were significant increases in parental responsiveness,
one of the hypothesized mediators of developmental
change; changes in parent responsiveness mediated change
on the majority of child outcome measures.

Finally, in a small pilot study, Rogers et al. (2014),
coached parents of infants with significant early symptoms
in strategies to address 6 targeted infant ASD symptoms.
Treated infants were compared to groups of infants con-
structed from existing datasets and matched to initial
symptom level: infants with a known ASD outcome from
a prior cohort, infants with ASD outcomes initially referred
to treatment but who declined to participate, and high and
low familial risk infant siblings with known non-ASD out-
comes. Findings were mixed with respect to trajectories on
standardized measures of language and cognitive develop-
ment, but at 36 months, infants in the treatment group had
lower scores on standardized measures of ASD symptoms,
and a smaller proportion of infants with developmental
quotients less than 70 or who received clinical best estimate
diagnoses of ASD. In general, infants in the treatment
group had more positive outcomes than infants who
declined treatment but still differed significantly from the
non-ASD outcome comparison groups.

Together, these studies suggest that although there
are positive impacts on various domains of functioning,
general developmental interventions are not likely to be
effective in reducing core ASD symptoms (Kasari,
2019). They also suggest the need for better alignment
between behavioral treatment targets and active ingre-
dients of interventions, consistent with transdiagnostic,
process-focused research into early markers of neuro-
developmental disorders and child psychopathology.

Future Directions for Transdiagnostic Early
Identification and Intervention: Challenges,
Next Steps, and Implications

The fields of infant identification and intervention,
reviewed above, face a number of challenges under
the current diagnostically oriented framework. Here,
we highlight what we perceive to be the most signifi-
cant barriers and describe the ways in which a rigorous
and systematic transdiagnostic approach may better
address these challenges. We then propose future direc-
tions for transdiagnostic early identification and inter-
vention that will address these challenges.

Current Challenges in Infant Identification and
Intervention

Measurement challenges. Although there exist a number
of high-quality ASD screening measures with appropriate

4 M. R. TALBOTT AND M. R. MILLER

sensitivity and specificity beginning in the second year of
life (e.g., Modified Checklist for Autism in Toddlers,
Revised with Follow-Up, Robins et al., 2020; Infant-
Toddler Checklist, Wetherby et al., 2008; First Year
Inventory, Reznick et al., 2007; see Petrocchi et al., 2020
for a recent systematic review), a key issue is the relative
paucity of robust universal screening and evaluation tools
for use within the first year (to be sure, several such
measures do exist, but sensitivity and specificity values
tend not to be adequate until the second year of life;
Parikh et al., 2020; Wetherby et al., 2008). Extending
universal screening measures downward into infancy is
challenging for a number of reasons. First, longitudinal
data from high-risk infant siblings of children with ASD
have revealed that although symptoms begin to emerge
toward the end of the first year of life, there are not robust
group differences between infants ultimately diagnosed
with ASD and those with typical or other outcomes in the
first year (for relevant reviews, see Elsabbagh & Johnson,
2016; Zwaigenbaum et al., 2015). Second, although bio-
markers (e.g., EEG, MRI) may potentially be more sensi-
tive to group differences before behavioral differences are
evident (Bosl et al., 2018; Shen et al., 2013, 2017; Emerson
et al., 2017; Hazlett et al., 2017), they are unlikely to be
scalable or used universally in community-based settings.
Third, there may be significant overlap between prodro-
mal symptoms across neurodevelopmental disorders and
child psychopathology more broadly (e.g., ASD, ADHD,
anxiety) which may wax and wane across development
(Begum Ali et al., 2020; Hatch et al., 2020; Miller et al., in
press; Shephard et al., 2019), calling into question the
specificity and long-term predictive validity of these
tools and ultimately necessitating the shift to
a transdiagnostic perspective.

Definitional Challenges
Currently, there is a lack of consensus regarding the
definition of elevated risk for ASD or optimal thresh-
olds of prodromal features significant enough to war-
rant intervention. Some have argued that all infants
belonging to ’selective’ risk groups (e.g., infant siblings
of children with ASD, infants with specific genetic
syndromes, infants born prematurely or very low birth-
weight) should be referred for preemptive intervention,
regardless of individual behavioral symptoms (Green
et al., 2017). Others suggest that infants with some
degree of social communication delays with emerging
restricted and repetitive behaviors be considered for
early intervention (Watson et al., 2017). The most
stringent definitions suggest that early intervention be
reserved only for infants with clear, specific symptoms
of ASD (Rogers et al., 2014) – an increasingly difficult
threshold to meet the younger the infant’s age.

Currently under IDEA Part C, infants with significant
social communication delays typically receive a mixture
of speech therapy, developmental, and other allied
health services (Hallam et al., 2009; Hebbeler et al.,
2007). These developmental services are not ASD-
specific and thus are unlikely to exert substantial effects
on core symptoms (Ingersoll et al., 2014; Kasari, 2019).

Accessibility Challenges
Accessibility challenges can be separated into two cate-
gories: Those related to the measurement and defini-
tional challenges described above, and those related to
equity and geographical context. Given the lack of clear
screening and assessment tools for evaluating specific
ASD risk in infancy, families with concerns about ASD
(or other neurodevelopmental disorders) often face
extended delays between initial concerns and formal
diagnosis or initiation of ASD-specific services
(Zuckerman et al., 2015). In terms of contextual factors,
there are clear disparities in racial and ethnic minori-
ties’ access to specialists and to early, evidence-based
evaluations for developmental delays, ASD, and other
childhood disorders (Rosenberg et al., 2008; Smith
et al., 2020). These issues are compounded in rural
areas with limited access to specialists and high-
quality university-based diagnostic and intervention
services (Kalkbrenner et al., 2011; Nahmias et al.,
2019). These accessibility issues are even more pro-
nounced when considering infants at risk.

Recently, the vulnerability of our current system for
evidence-based assessments – which typically involve
the direct administration of standardized tools – has
been highlighted under the extreme conditions of the
COVID-19 global pandemic. The sudden cessation of
in-person services has made it abundantly clear that
new methods utilizing emerging technologies such as
telehealth are desperately needed to maintain and
expand access to services. There are currently very
few norm-referenced assessment tools meeting IDEA
requirements that can be administered remotely (Early
Childhood Technical Assistance Center, 2020). These
events have underscored the vulnerability of current
early intervention services for very young children,
who are in the developmental period most sensitive to
benefits of early intervention delivery (L. K. Koegel
et al., 2014).

Conceptual Challenges
As alluded to previously, there is variability in concep-
tual frameworks driving research into early markers of
ASD and other conditions. Most have taken a disorder-
specific approach, but it is becoming increasingly
apparent that cross-disorder approaches may add

JOURNAL OF CLINICAL CHILD & ADOLESCENT PSYCHOLOGY 5

value both scientifically and clinically. The NIMH
RDoC aims to reduce our reliance on diagnostic cate-
gories and emphasize psychopathology-relevant beha-
viors in an effort to enhance knowledge of underlying
processes and mechanisms, and to support personalized
medicine. This framework is becoming increasingly
utilized in the study of adult psychopathology but has
less-often been applied to child psychopathology and
neurodevelopmental disorders. As a result, there have
been growing calls to incorporate development into the
RDoC framework (Garber & Bradshaw, 2020; Mittal &
Wakschlag, 2017). There are a number of benefits to
this framework, and also some challenges in utilizing
this approach in early life including the need for the
development of clinically relevant and translational
measures which are appropriate to the infant and tod-
dler periods across key transdiagnostic processes and
which represent the full range of relevant behavior,
from atypical to supranormal.

For example, disrupted attentional processes have
been implicated in a number of neurodevelopmental
and psychiatric disorders including autism, ADHD,
and anxiety (Racer & Dishion, 2012). However, few
measures exist to capture individual differences in this
domain early in life in a psychometrically sound way
that would allow for identification of clinically signifi-
cant differences on the behavioral level (which, we note,
is the level at which clinical decisions are currently
made, including within the early intervention system).
The measures that do exist are largely parent rated
temperament questionnaires or more invasive methods
such as eye-tracking, EEG/ERP, and MRI, which are
limited with respect to clinical utility. If we are to make
progress toward the goal of infant identification of
a range of atypical development consistent with the
RDoC framework, new measures are needed.

Expanding Behavioral Dimensions and
Measurement Approaches within a Developmental
Framework

Developing fine-grained measurement tools across
a broader range of ASD-relevant behaviors (and beyond)
has the potential to uncover distinct behavioral profiles
across neurodevelopmental disorders and child psycho-
pathology in general. This has implications for the devel-
opment of process-focused interventions rather than
treatments tied to specific diagnostic categories and
may facilitate future randomized trials testing the effects
of both existing and future treatments on specific
mechanisms, underling processes, and/or domains of
behavior. Novel measures of prodromal symptoms and
relevant underlying processes should be designed within

an integrated clinical-developmental framework. That is,
they should not merely represent downward extensions
of DSM symptoms, which are inherently not develop-
mentally oriented. Instead, good measures of core pro-
cesses implicated across these childhood conditions
(e.g., attention, self-regulation, social behavior) are
needed.

As an example, rather than an “autism screener” for
infants, the field may do well to move toward screeners
or direct assessments of attention or self-regulation that
could reveal early risk for a range of atypical develop-
mental outcomes. Novel measures should be devised in
a way that allows for comparison against other, same-
aged infants or toddlers. Indeed, the development of
norm-referenced direct assessments, including those
that can be administered via distance technology, has
the potential to move the field forward in clinically
relevant ways. Such measures would allow not only
for the measurement of deviance from same-aged
peers in symptoms, behavior, and functioning but also
a better understanding of the development of funda-
mental processes. Similar approaches are widely used in
a post-hoc fashion among older children and adoles-
cents (e.g., neuropsychological testing); they are not
diagnostic in and of themselves but provide one win-
dow into a child’s functioning. The ability to obtain
comparable data early in life – prior to the onset of the
full symptom set – may provide an opportunity to
identify which infants are at the highest risk for
a range of atypical developmental outcomes. Relevant
efforts are underway to develop an “NIH Infant and
Toddler Toolbox” in the domains of cognition, social
functioning, language, numeracy, self-regulation, and
executive function (75N94019D00005, PI: Gershon).
We suggest there may be subdomains of ASD-relevant
behavior, such as social attention, that could be
explored within the RDoC framework with an eye
toward clinical relevance and integration with screen-
ing and interventions around those targets. We recog-
nize that the range of normative behavior early in
development is wide, and the development of new
clinical tools as described above runs the risk of over-
pathologizing. Many ASD-relevant behaviors have high
base rates within typically developing infant samples.
For example, motor overflow movements, wherein
motor behavior from intentional actions ‘spills over’
into other incidental actions, share surface-level fea-
tures with motor stereotypies. These overflow move-
ments are observed nearly universally at some stages in
infancy (Soska et al., 2012). Other restricted and repe-
titive behaviors, such as intense preoccupations with
specific topics, are also highly prevalent in normative
samples throughout toddlerhood (Leekam et al., 2007).

6 M. R. TALBOTT AND M. R. MILLER

Thus, the development of ASD-relevant transdiagnostic
measures will necessarily rely on large longitudinal
investigations from infancy through adolescence in
order to establish predictive validity to clinically mean-
ingful outcomes.

Expanding the Scope and Delivery of Targeted
Interventions for Infants

Development of fine-grained measurement tools across
ASD-relevant behavioral domains has implications for
treatment, in that such information may form the basis
for treatment targets and goals. This would also have
significant and far-reaching impacts on the develop-
ment, validation, and implementation of novel inter-
vention approaches. First, these measures could be
utilized to develop formal process theories to guide
targeted intervention approaches. Second, expanded
measurement capabilities would support the identifica-
tion of specific elements of existing interventions (i.e.,
‘active ingredients’) likely to be shared across infants, as
well as the evaluation of the effects of those compo-
nents on infants’ development in terms of both direct
and collateral effects. Such an approach would be con-
sistent with transdiagnostic, process-focused research
into early markers of neurodevelopmental disorders
and child psychopathology. As an example, some have
begun to test whether attention training among infants
at risk for disrupted attentional processing (i.e., infants
at familial risk for ADHD, infants at familial risk for
ASD, infants who are born preterm) is feasible and
effective (Forssman & Wass, 2018; Goodwin et al.,
2016; Perra et al., 2020). These types of mechanisms-
or process-focused approaches have shown initial evi-
dence of generalizability to non-trained dimensions
such as social communication among infants who are
not at known risk, at least in the short term (Forssman
& Wass, 2018). Whether long-term effects related to
clinical outcomes exist is an important area for future
investigations.

Many existing behavioral treatment approaches for
toddlers and several of the candidate treatments for
infants described in earlier sections address domains
like social attention and affect regulation that are likely
to be widely relevant. There is emerging work examin-
ing the efficacy of existing ASD-specific interventions
in toddlers with other clinical diagnoses/genetic syn-
dromes (e.g., Fragile X Syndrome, Tuberous Sclerosis;
McDonald et al., 2020; Vismara et al., 2019). Many
evidence-based interventions for toddlers with ASD
(ESDM, Rogers & Dawson, 2010; JASPER, Kasari
et al., 2015; Project IMPACT, Ingersoll & Wainer,
2013) belong to class of interventions termed

“naturalistic, developmental, behavioral interventions”
(NDBIs, Schreibman et al., 2015). Core shared compo-
nents of various NDBIs include (1) a focus on a broad
array of developmental domains including cognition,
play, language, social, and motor development; (2)
learning embedded within daily living or play routines;
(3) use of specific behavior analytic techniques such as
a three-part learning contingencies, reinforcement,
modeling, and prompting techniques; and (4) increas-
ing balanced turn-taking and social attention of the
child. NDBIs are manualized approaches with clear
fidelity guidelines and measurement of child progress
on specific treatment objectives. The primary difference
between them is the extent to which they target specific
domains, versus more comprehensive approaches tar-
geting most developmental domains. These interven-
tions provide fertile ground for identification of specific
treatment components likely to impact infant behavior
across a range of ASD-relevant domains. One of our
research groups has begun to take this approach in
infants with early ASD symptoms, dismantling an exist-
ing treatment (Infant Start, Rogers et al., 2014), and
evaluating the direct and collateral effects of three tar-
get parent interaction techniques (“Step into the
Spotlight”, “Imitation”, and “Talking to Baby”) on
a range of target child behaviors (eye contact, directed
vocalizations, gestures, play, irritability; Dufek et al.,
2020). These were tested in a multiple-baseline single-
subjects study of six infants with significant ASD symp-
toms and their caregivers. Results were promising, with
significant increases in parent fidelity scores and
decreases in child ASD symptoms.

A third critical direction is the integration of imple-
mentation science approaches such as community-
based participatory research into the transdiagnostic
framework we are proposing. As noted by Stahmer
et al. (2017), these approaches have been used most
often in the domain of intervention research, helping
to highlight shared goals between researchers and com-
munities of increasing capacity and effectiveness of
available community services (Drahota et al., 2016).
There is a need to develop both the measures and
treatments we are proposing within the context of the
system that will ultimately deliver such services.
Developing and testing approaches from the outset
that fit within the existing service system will help to
support the implementation of evidence-based prac-
tices in community settings. The transdiagnostic frame-
work may better reflect the heterogenous populations
seen in community settings.

Finally, there is a need to expand the accessibility of
both identification and intervention services to families.
Telehealth has an enormous potential to increase the

JOURNAL OF CLINICAL CHILD & ADOLESCENT PSYCHOLOGY 7

reach of early intervention services into rural and low-
resource areas, where barriers to service include long
travel times for families or providers, inclement
weather, and a shortage of professionals with appropri-
ate expertise. Approximately one-quarter of the costs of
early intervention services are related to transportation
(Johnson et al., 2011). Reducing some of these costs via
telehealth has the potential to increase the capacity to
serve more children and deliver more frequent services.
Use of telehealth has rapidly expanded within existing
state Part C early intervention programs, even prior to
the COVID-19 pandemic (Cason et al., 2012; Cole
et al., 2019). Telehealth has been used most frequently
and successfully for parent coaching models versus
direct instruction to children (Bearss et al., 2018;
Lindgren et al., 2016; Vismara et al., 2019).
Additionally, several promising tools for diagnostic
screening and referral of toddlers with suspected ASD
currently exist (Tele-ASD-PEDS, Corona et al., 2020;
NODA, Nazneen et al., 2015). Within our own research
group, we are developing a telehealth-based assessment
of ASD symptoms and social communication rates for
infants 6–12months (Talbott et al., 2019). We are cur-
rently testing an expanded protocol that includes
a developmental curriculum assessment including ver-
bal and nonverbal domains and scoring of
a standardized and norm-referenced measure of gross
motor skills. Encouragingly, telehealth approaches have
generally resulted in high family acceptability, cost-
savings for systems, and increased use of evidence-
based, family-centered parent coaching practices by

therapists (Behl et al., 2017; Sutherland et al., 2018)
suggesting this is a feasible route to increasing access
to intervention services.

Policy and Practice Implications

We would be remiss if we did not highlight relevant
policy and practice implications, which are related in
some way to each of the key challenges we previously
identified. A dimensionally oriented approach over the
first year of life, grounded within a developmental fra-
mework, is likely scalable within existing community-
based early intervention programs which, for infants,
are generally oriented around domains of delay or
deficit. We have provided an illustrative model
(Figure 1) overlaying our proposed changes onto the
existing Part C framework as well as the interaction
between basic research and implementation.

While we contend that our proposed approach will
ultimately more efficiently serve infants with prodromal
risk signs, we cannot ignore the significant impact these
changes may require to states’ evaluation and eligibility
criteria. Identification of additional domains would
require clear evidence of clinically relevant need (e.g.,
adverse developmental outcomes) in order for states to
expand programs. Integrating norm-referenced assess-
ment tools in additional domains into the existing system,
as we suggest, would require alignment with the five
content areas already required to be evaluated by Part
C regulations: (i) Physical development, (ii) Cognitive
development, (iii) Communication development, (iv)

Figure 1. Modern illustrating integration of transdiagnostic assessment and intervention for prodromal infants within the current
early intervention system. Gray boxes depict the current system; while boxes depict the proposed addictions.

8 M. R. TALBOTT AND M. R. MILLER

Social or emotional development, or (v) Adaptive devel-
opment, or policy changes at the federal level to expand
eligibility criteria.

Another key consideration is balancing the benefits
and burden of early identification and intervention on
families when we cannot predict the diagnostic future.
On the one hand, some infants and toddlers who are
not ultimately diagnosed with a neurodevelopmental
disorder will receive treatment. If the delivered inter-
vention is unwanted or burdensome, the cost for
families could be high. On the other hand, many
infants and toddlers who do need treatment are not
currently being served, despite parents’ early concerns
and seeking of services (Zuckerman et al., 2015). There
is some evidence that supportive parent coaching deliv-
ered through parent-mediated treatment for toddlers
with ASD may in fact reduce parental anxiety, depres-
sion, and stress (Estes et al., 2014); these effects on
parent mental health and overall family functioning
should not be underestimated. Still, many children
meeting eligibility criteria for Part C services face sig-
nificant adversity and have low rates of participation in
intervention services (Rosenberg et al., 2008). For
example, in a sample of 1,997 toddlers being investi-
gated for possible maltreatment, 47% had delays sig-
nificant enough to quality for Part C services
(Rosenberg & Smith, 2008). Finally, states already face
significant challenges delivering services to eligible
infants and toddlers eligible under the existing criteria
(Rosenberg et al., 2013). Only 9% of 9-month-olds with
delays that would make them eligible actually receive
services (Feinburg et al., 2011). Increased funding and
capacity of the existing system is urgently needed. As
a result, the costs to develop, validate, and disseminate
additional norm-referenced tools that Part C is man-
dated to use for evaluation will likely be borne by
research. It is our hope that identifying specific process-
oriented mechanisms and efficacious interventions to
support those mechanisms will ultimately help to
improve the efficiency and capacity of the early inter-
vention system.

Summary

A transdiagnostic approach to infant identification of
ASD, neurodevelopmental disorders, and child psycho-
pathology more broadly that is both grounded in devel-
opmental and clinical science and scalable for early
intervention systems has the potential for wide-
reaching impacts on research and clinical practice.
Key barriers to the full implementation of such
approaches at both the research and clinical levels
span measurement challenges, definitional challenges,

accessibility challenges, and conceptual challenges.
Addressing these barriers will require an integrated
effort from both clinical and developmental perspec-
tives. We propose four recommendations for future
research efforts: (1) development of fine-grained,
norm-referenced measures of ASD-relevant transdiag-
nostic behavioral domains; (2) identification of shared
and distinct mechanisms influencing the transition
from risk to disorder; (3) determination of key, cross-
cutting treatment strategies (both novel and extracted
from existing approaches) effective in targeting specific
domains across disorders; and (4) integration of iden-
tified measures and treatments into existing service
systems.

Disclosure Statement

No potential conflict of interest was reported by the authors.

Funding

This work was supported by the Eunice Kennedy Shriver
National Institute of Child Health and Human
Development [R21 HD100372]; National Institute of Mental
Health [R00 MH106642].

ORCID

Meagan R. Talbott http://orcid.org/0000-0001-5480-1549
Meghan R. Miller http://orcid.org/0000-0002-1260-4149

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https://doi.org/10.1002/aur.1585

JOURNAL OF CLINICAL CHILD & ADOLESCENT PSYCHOLOGY 15

https://doi.org/10.1007/s10803-019-04314-4

https://doi.org/10.1007/s10803-018-3833-1

https://doi.org/10.1007/s10803-017-3268-0

https://doi.org/10.1007/s10803-005-3301-6

https://doi.org/10.1023/A:1005463707029

https://doi.org/10.1177/1362361308094501

https://doi.org/10.1016/S2352-4642(19)30184-1

https://doi.org/10.1093/brain/awv118

https://doi.org/10.1176/appi.ajp.2011.11091447

https://doi.org/10.1016/j.jpeds.2015.03.007

https://doi.org/10.1542/peds.2014-3667B

https://doi.org/10.1002/aur.1585

Abstract
Current Approaches to Infant Identification and Intervention: What Do We Know?

Infant Identification
Infant Intervention

Future Directions for Transdiagnostic Early Identification and Intervention: Challenges, Next Steps, and Implications

Current Challenges in Infant Identification and Intervention
Definitional Challenges
Accessibility Challenges
Conceptual Challenges
Expanding Behavioral Dimensions and Measurement Approaches within aDevelopmental Framework
Expanding the Scope and Delivery of Targeted Interventions for Infants
Policy and Practice Implications
Summary
Disclosure Statement
Funding
References

Vol.:(0123456789)

1 3

J Autism Dev Disord

DOI 10.1007/s10803-017-3339-2

ORIGINAL PAPER

Teaching Parents Behavioral Strategies for Autism Spectrum
Disorder (ASD): Effects on Stress, Strain, and

Competence

Suzannah Iadarola1,7 · Lynne Levato1 · Bryan Harrison1 · Tristram Smith1 ·
Luc Lecavalier2 · Cynthia Johnson3 · Naomi Swiezy4 · Karen Bearss5 ·
Lawrence Scahill6

parental competence while reducing parental stress and
parental strain.

Keywords Autism spectrum disorder · Parent training ·
Parental stress · Parental competence

Introduction

Parents of young children with ASD face many challenges.
Children with ASD often require specialized care coordi-
nation across several providers and multiple meetings on
school placement. Parents may become isolated from friends
and family who may not understand the child’s behavior
and disability (Abbeduto et al. 2004; Kogan et al. 2008;
Rao and Beidel 2009). These parental challenges may be
influenced by the child’s age, timing of diagnosis, and level

Abstract We report on parent outcomes from a rand-
omized clinical trial of parent training (PT) versus psychoe-
ducation (PEP) in 180 children with autism spectrum disor-
der (ASD) and disruptive behavior. We compare the impact
of PT and PEP on parent outcomes: Parenting Stress Index
(PSI), Parent Sense of Competence (PSOC), and Caregiver
Strain Questionnaire (CGSQ). Mixed-effects linear models
evaluated differences at weeks 12 and 24, controlling for
baseline scores. Parents in PT reported greater improvement
than PEP on the PSOC (ES = 0.34), CGSQ (ES = 0.50), and
difficult child subdomain of the PSI (ES = 0.44). This is
the largest trial assessing PT in ASD on parent outcomes.
PT reduces disruptive behavior in children, and improves

Electronic supplementary material The online version of this
article (doi:10.1007/s10803-017-3339-2) contains supplementary
material, which is available to authorized users.

* Suzannah Iadarola
suzannah_iadarola@urmc.rochester.edu

Lynne Levato
lynne_levato@urmc.rochester.edu

Bryan Harrison
bryan_harrison@urmc.rochester.edu

Tristram Smith
Tristram_smith@urmc.rochester.edu

Luc Lecavalier
luc.lecavalier@osumc.edu

Cynthia Johnson
Cynthia.johnson@chp.edu

Naomi Swiezy
nswiezy@iupui.edu

Karen Bearss
kbearss@u.washington.edu

Lawrence Scahill
Lawrence.scahill@emory.edu

1 University of Rochester Medical Center, Rochester, NY,
USA

2 Ohio State University, Columbus, OH, USA
3 University of Florida, Gainesville, FL, USA
4 Indiana University, Indianapolis, IN, USA
5 University of Washington, Seattle, WA, USA
6 Emory University, Atlanta, GA, USA
7 Department of Pediatrics, University of Rochester Medical

Center, 601 Elmwood Avenue, Box 671, Rochester,
NY 14642, USA

http://orcid.org/0000-0001-6828-8379

http://crossmark.crossref.org/dialog/?doi=10.1007/s10803-017-3339-2&domain=pdf

http://dx.doi.org/10.1007/s10803-017-3339-2

J Autism Dev Disord

1 3

of symptom severity (Greenberg et al. 2006; Hastings et al.
2005). Disruptive behaviors, including tantrums, noncompli-
ance, aggression, and self-injury are common, affecting as
many as 50% of children with ASD. These behaviors in the
child may amplify caregiving burden (Hastings et al. 2005)
and contribute to parental stress and strain (Boström et al.
2011; Hsiao 2016; Vasilopoulou and Nisbet 2016). Disrup-
tive behavior in the child may also erode parental compe-
tence, perceived self-efficacy, and problem-solving skills
(Benson 2014; Falk et al. 2014; Pottie and Ingram 2008;
Rezendes and Scarpa 2011). Interventions that reduce dis-
ruptive behavior may also reduce parental stress and strain.
In addition, the association between child behavior and
parenting stress may be bidirectional, with stress reducing
parents’ ability to address disruptive behavior (Greenberg
et al. 2006).

Several parent training (PT) interventions have been
developed (Smith and Iadarola 2015) to teach new skills,
address skill deficits, or decrease disruptive behavior in
children with ASD (Bearss et al. 2015; Strauss et al. 2012;
Tonge et al. 2014). Studies on PT for children with ASD
and disruptive behavior have shown decreases in child dis-
ruptive behavior (Postorino et al. 2017). Few studies have
reported on parental distress and related outcomes (e.g., self-
efficacy). Findings on parental outcomes in parent training
studies are inconclusive due to study design limitations or
small sample size (Coolican et al. 2010; Tonge et al. 2006;
Whittingham et al. 2009). Parent variables that moderate
or mediate the effect of PT on child disruptive behavior or
parental stress have not been examined (Smith and Iadarola
2015). Observational, cross-sectional studies suggest that
parental cognitions such as perceived efficacy (or related
cognitions such as perceived locus of control, perceived
competence, engagement, and problem-solving) are associ-
ated with lower parenting stress (Benson 2014; Falk et al.
2014; Pottie and Ingram 2008; Rezendes and Scarpa 2011).

Although PT is a well-established intervention for dis-
ruptive behavior in non-ASD pediatric populations (Dretzke
et al. 2009), findings on parental outcomes are also limited
and equivocal. Common parent outcomes of interest include
perceived parenting ability and satisfaction (often referred
to as parental self-efficacy or parental competence) as well
as stress and internalizing symptoms (i.e., anxiety, depres-
sion, somatization). A meta-analysis reported medium effect
sizes (0.42–0.53) of PT on measures of parental stress and
competence (Lundahl et al. 2006). Associations between
parenting programs for disruptive behavior and reduced
stress, decreased depression, and increased locus of control
(Chacko et al. 2009; Danforth et al. 2006; Moreland et al.
2016) have also been documented. However, in two large-
scale studies of children with ADHD, parents reported no
reduction in stress after PT (Abikoff et al. 2007; Wells et al.
2000). Thus, although PT provides specific tools to help

parents manage disruptive behavior, it is not clear that PT
reduces parental stress and strain. Indeed, the task demands
of applying PT could contribute to parental stress (Karst and
Van Hecke 2012). It may be expected that parents who learn
and practice parent training techniques will achieve a greater
sense of competence. Whether increases in competence will
contribute to decreased parental stress and strain is not clear.

In prior work, we built a structured PT manual that inte-
grated behavior change strategies developed and tested in
single subject studies for disruptive behavior in children
with ASD (Johnson et al. 2007). We conducted a series of
studies showing that this program is acceptable to parents,
can be delivered with fidelity by trained therapists, and can
augment the therapeutic effects of medication (Aman et al.
2009; Bearss et al. 2013). In a rigorous test of PT, we con-
ducted a six-site, 24-week randomized controlled trial (RCT)
in 180 children with ASD and disruptive behavior, aged
3–7 (Bearss et al. 2015; Lecavalier et al. 2017; Scahill et al.
2016). Children were randomized to an 11-session, struc-
tured PT program or a 12-session parent education program
(PEP) that controlled for time and attention. PT was superior
to PEP in reducing parent-rated child disruptive behavior.
The positive response rate on the Clinical Global Impres-
sion Improvement Score completed by clinicians who were
blind to treatment assignment was 68.5% in PT compared
to 39.6% in PEP (39.6%). PT was also superior to PEP on
a standardized measure of child daily living skills (Scahill
et al. 2016). In a follow-up paper we identified that modera-
tors of positive child outcomes in PT versus PEP included
lower ADHD and anxiety symptoms and higher household
income (Lecavalier et al. 2017).

The current report focuses on parent outcomes in this
RCT. Our primary hypothesis was that self-reported paren-
tal competence would show significant improvement and
whether self-reported measures of parental stress would
show significant decreases in PT compared to PEP. Based
upon previous findings that parental cognitions may influ-
ence parental stress (Falk et al. 2014), we also explored
whether change in parental cognitions (i.e., competence)
would predict improvement in parental stress and caregiver
strain, as well as child disruptive behavior. The study design
allowed examination of these questions because it included
multiple parent self-report measures collected at midpoint
(week 12) and endpoint (week 24).

Methods

Design

The original RCT was conducted at six sites (Emory Univer-
sity, Indiana University, Ohio State University, University of
Pittsburgh, University of Rochester, and Yale University) from

J Autism Dev Disord
1 3

September, 2010 to February, 2014. The trial was approved
by the institutional review boards at each site, and informed
consent was obtained from a parent or legal guardian. Partici-
pants who met eligibility criteria were randomly assigned to 24
weeks of PT or PEP using permutated blocks with concealed
allocation to investigators. Randomization was performed
within site and further stratified by educational intensity to
ensure that groups contained an equal number of participants
in high intensity school/therapeutic programming. High inten-
sity service was defined as 15 h or more per week of 1:1 or 1:2
specialized instruction for ASD. Therapists held a minimum of
a master’s degree and had been certified to deliver each study
intervention after completing training and demonstrating fidel-
ity (> 80% correct implementation of content in all sessions,
rated by a senior investigator). Therapists received weekly
local supervision and monthly during cross-site case reviews.
Parent ratings on child disruptive behavior were completed
every 4 weeks and every 12 weeks for parent measures. The
measures included several parent-report questionnaires. The
background, methods, and main child outcomes are reported
in detail in Bearss et al. (2015).

Participants

One-hundred-eighty children with ASD and moderate
or greater disruptive behavior between the ages of 3 and
7 years inclusive participated in the 24-week study. One
parent from each household was enrolled in PT or PEP and
was the informant on all outcome measures. Other parents
and family members with caregiving responsibilities were
invited to join therapy sessions. Eligibility required: an ASD
diagnosis, a score ≥ 15 on the Irritability subscale of the
Aberrant Behavior Checklist (described below), and a CGI
Severity (CGI-S) score ≥ 4. Additional interventions and
medications were required to be stable for 6 weeks, with no
planned changes for the course of the study. Children with
serious medical conditions or another psychiatric disorder
in need of treatment, receptive language skills ≤ 18 months
(as determined by standardized cognitive and developmen-
tal assessments), or those with current or past treatment in
structured PT for disruptive behavior were excluded. Clini-
cal diagnosis of ASD was based on DSM-IV-TR criteria
(American Psychiatric Association 2000) corroborated by
the Autism Diagnostic Interview-Revised (ADI-R; Rutter
et al. 2003) and the Autism Diagnostic Observation Sched-
ule (ADOS; Lord et al. 2002).

Measures

Characterization Measures

The Autism Diagnostic Observation Schedule (ADOS) is
an investigator-based assessment conducted in naturalistic

social situations demanding specific social, communication
and restricted/repetitive responses. Behaviors are scored in
the areas of social communication, social relatedness, play
and imagination, and repetitive behaviors. An ADOS score
above the cutoff for either autism or autism spectrum disor-
der was used to support the diagnosis of ASD.

The Autism Diagnostic Interview, Revised (ADI-R), is a
structured parent interview that is designed to obtain rel-
evant information about a child’s early communication and
language development, social development and play, and
unusual interests and behaviors. The ADI-R was conducted
with parents to corroborate the information collected dur-
ing the ADOS and was also used to confirm diagnosis for
eligibility.

Developmental/Cognitive Functioning The Stanford-
Binet Fifth Edition (SB-V; Roid 2003) or the Mullen Scales
of Early Learning (MSEL; Mullen 1995) were used to assess
cognitive functioning. The abbreviated SB-V was attempted
with all children. The Mullen was administered to children
who were unable to the abbreviated SB-V. Standard scores
obtained from these measures were used to determine eli-
gibility and to characterize the cognitive functioning of the
sample.

Outcome Measures

Parenting Stress Index-Short Form (PSI; Abidin 1995) The
PSI is a 36-item measure completed by parents of children
3 months to 10 years of age designed to assess parental
stress. Each item is rated on a 5-point scale (from “Strongly
Disagree” = 0 to “Strongly Agree” = 5). The PSI yields a
total stress score and subscale scores across three factors:
parental distress, parent–child dysfunctional interaction, and
difficult child characteristics. Example statements include, “I
feel trapped by my responsibilities as a parent,” “Sometimes
I feel my child doesn’t like me and doesn’t want to be close
to me,” and “I feel that my child is very moody and easily
upset.” The PSI has good test–retest reliability (ICC = 0.77)
and internal consistency (IC; 0.91 (Barroso et al. 2016)).
A PSI total score of ≥ 88 (85th percentile) is considered
clinically significant. This measure was used to differentiate
among subtypes of stress, including stress related to child
behavior and interactions, as well as stress related to the
parents’ internal emotional state.

Caregiver Strain Questionnaire (CGSQ; Brannan et al.
1997) The CGSQ is a 21-item, parent self-report on the
burdens associated with raising a child with ASD and
perceived interference with family activities. Parents rate
caregiver strain on items such as “Interruption of personal
time,” “Financial strain,” and “Feeling sad or unhappy” on
a 1–5 scale (“Not a problem” to “Very much a problem”).
This measure yields objective strain, internalized strain, and
externalized strain subscales and a global score. The CGSQ

J Autism Dev Disord
1 3

demonstrates acceptable-to-high internal consistency for
objective strain (0.91), externalized strain (0.74), internal-
ized strain (0.86), and the global score (0.93). The subscales
are negatively correlated with established measures of fam-
ily functioning, such as the Family Assessment Device. The
original sample from Brannan et al. (1997) included parents
of children with unspecified emotional/behavioral disorders
and reported mean scores of 2.0 (objective strain), 3.4 (inter-
nalized strain) and 2.3 (externalized strain).

Parenting Sense of Competence (PSOC) The PSOC
(Gibaud-Wallston and Wandersman 1978) is a 17-item,
parent self-report reflecting parental satisfaction and effi-
cacy. The satisfaction subscale measures parental motivation
and frustration (e.g., “Even though being a parent could be
rewarding, I am frustrated now while my child is at her/
her present age”). The efficacy subscale measures perceived
capacity to change the child’s behavior (e.g., “I meet my
own personal expectations for expertise in caring for my
child”). The PSOC also yields a Total Competence score,
with higher scores reflecting higher competence. In a norma-
tive community sample of mothers (Gilmore and Cuskelly
2009), subscale mean scores of Satisfaction (22.72) and Effi-
cacy (22.03) were reported.

Aberrant Behavior Checklist (ABC; Aman et al. 1985)
is a reliable and valid 58-item parent-rated scale with dem-
onstrated sensitivity to change (Kaat et al. 2014). Each item
is rated from 0 (not a problem) to 3 (severe in degree). The
ABC contains five subscales: Irritability (15 items), Social
Withdrawal (16 items), Stereotypic Behavior (7 items),
Hyperactivity/Noncompliance (16 items), and Inappropri-
ate Speech (4 items). It was completed at baseline and every
4 weeks thereafter. For the current analysis, we used only
the Irritability subscale, which was the primary outcome
measure in the RCT.

Treatments

Parent Training (PT)

PT consisted of 11 core sessions of 60–90 min delivered
individually that included direct instruction, video vignettes,
practice examples, and role playing between parent and ther-
apist. Weekly homework assignments gave the participat-
ing parent opportunities to practice the strategies learned
in session with the child in natural settings. The program
included two home visits and up to two optional sessions.
Sessions were conducted over 16 weeks. Therapists followed
a treatment manual that included scripts and suggestions for
engaging the family. Fidelity to the manual was assessed
with a session-specific checklist of the required elements
of the session. The PT intervention instructed parents on
the application of behavioral strategies to manage behavio-
ral problems in the home and community. Session content

focused on the situations and events that preceded disruptive
behavior (antecedents) and the environmental responses that
reinforced the behavior. Briefly, the PT program included
use of visual daily schedules, positive reinforcement,
planned ignoring as well as techniques to promote compli-
ance and daily living skills.

Psychoeducation Program (PEP)

PEP was an active condition to control for time and therapist
attention. It was also a structured intervention consisting of
12 individually-delivered sessions and one home visit. The
manual covered useful topics for parents of young children
with ASD, including etiology of ASD, educational plan-
ning, advocacy, and information on how to select effective
treatments. As with PT, PEP was delivered over 16 weeks
and included regular fidelity checks. Unlike PT, PEP did not
include any direct instruction in behavioral management.
Each session comprised didactics, discussion, and informa-
tional handouts at the end of each visit (see Bearss et al.
2015 for more detailed information about PT and PEP).

Statistical Analysis

Treatment Effects

Mixed-effects linear regression models were used to evaluate
within-group changes over time from baseline to week 24
(endpoint) and between-group differences at week 12 (mid-
point) and week 24 for total scores on the PSI, CGSQ, and
PSOC. Exploratory analyses examined subscales on each
measure. Fixed effects included treatment group, time, site,
intervention intensity (i.e., whether the child was receiv-
ing more or less than 15 h of direct, individual service per
week), and time-by-treatment. Within-group effects were
ascertained by regressing the PSI, CGSQ, and PSOC scores
against time. For between-group effects, the average slopes
of the regression lines were compared (PT

versus PEP)

.
Effect sizes were calculated on each measure by taking the
difference in the least squares means at weeks 12 and 24
and dividing by the standard deviation at baseline for the
entire sample. We assumed that missing data were missing
at random (Little 1988).

Exploratory Analyses

Structural equation modeling (SEM) was employed to evalu-
ate the relationships between change in PSOC in the first 12
weeks and change in the PSI, CGSQ and ABC Irritability
subscale in the next 12 weeks. SEM permits examination
of the model while simultaneously controlling for shared
variance across measures and informants at each assessment
wave. Within the SEM framework, latent difference scores

J Autism Dev Disord
1 3

(LDS; McArdle 2009) were calculated to model true change
in predictors and outcomes over time. The LDS scores gen-
erated for the PSOC (the predictor of change from baseline
to week 12) and outcomes (change in PSI, CGSQ and ABC-I
from week 12 to week 24). This method accounted for base-
line scores on each variable.

Models (see Fig. 1) examined (a) the extent to which
change in parental competence (PSOC) predicted change
in outcome measures (PSI, CGSQ, and ABC-I) and (b)
whether this predictive relationship significantly differed
across the two treatment groups (PT and PEP). (McArdle
2009). Structural equation models were estimated using the
Amos 18.0 software system (Arbuckle 2006). To maximize
statistical power, we used full-information maximum likeli-
hood (FIML) in AMOS and included the full sample in the
analyses (Enders 2001). The model adequately represented
the data, χ2 (2) = 3.53, p = .17, RMSEA = 0.07. To test for
treatment-related differences, AMOS’s critical ratio (CR) of
Differences was used. Pairwise parameter comparisons cal-
culated the difference between the two estimates divided by
the estimated standard error of the difference. The resulting
difference statistic is normally distributed and tested against
the z-score distribution (CR > 1.96). Therefore, the CR pro-
vides an explicit test of the modifying effect of treatment
group.

Results

Parent respondents were primarily female (93%), in their
mid-30s (see Table 1). Children were 79% male, with a mean
age of 4.2 years (SD = 1.1), and 74% had an IQ of 70 or
above on the SB-V.

Treatment Effects

Table 2 presents data on parent outcome measures at base-
line and week 24 within each treatment group. Baseline

scores on the PSI were elevated. On the CGSQ, scores were
similar or elevated compared to the sample described in
Brannan et al. (1997). Scores on the PSOC were comparable
or higher than those reported in community samples. Over
time, both PT and PEP showed improvements in the PSI,
CGSQ, and PSOC. On the PSI total score, PT showed a 14%
reduction, and PEP showed 9.3% reduction. On the CGSQ
global score, PT showed 17.2% reduction, and PEP showed
7.1% reduction. For PSOC total score, PT showed 16.4%
increase, and PEP showed 7.4% increase. See Supplemental
Materials for figures of the total score and subscale scores
for the PSI, CGSQ, and PSOC.

Table 3 shows differences in least squared means from
baseline to weeks 12 and 24 between PT and PEP. PT did
not show a significant advantage over PEP on the PSI total
score, the parent–child interaction or the child distress scales
at week 12. On the PSI difficult child factor, however, PT
produced greater reductions than PEP at week 12 and week
24. The reduction in the PSI total score was greater in PT
than PEP at week 24, but the difference was not significant.
At week 12 and week 24, PT was superior to PEP on the
CGSQ global score and Internalized subscale. There was
no difference in CGSQ Externalized or objective strain sub-
scales at week 12. The CGSQ Objective subscale reached
significance at week 24. On the PSOC, parents in the PT
group reported greater gains than parents in PEP at week
12 on the satisfaction subscale but not the efficacy subscale
or total score. Improvement was significantly greater in PT
compared to PEP on the PSOC total score and the efficacy
subscale at week 24. The difference on the satisfaction sub-
scale was no longer significant.

Exploratory Analyses

The LDS models confirmed findings from the mixed-effects
linear models that parents in both treatment groups showed
significant change in competence from baseline to week
12 (PT: β = .68, p < .001; PEP: β = .49, p < .001). Pairwise

Fig. 1 Model predicting change
in parental stress and strain and
child irritability, as predicted by
change in parental competence

Change in
Parent

Competence
Change in
Parent

Stress/ Strain
and Child
Irritability

Competence
at Baseline

Competence
at Week 12

Treatment
Group (PT

versus PEP)

J Autism Dev Disord
1 3

parameter comparisons indicated that parents in the PT
group reported significantly greater gains on the PSOC than
the PEP group during the first 12 weeks of the interven-
tion (z = 2.72, p < .01). Parents in both groups also reported significant decrease in stress (PT: β = −0.38, p = .009; PEP: β = −0.39, p = .006) and strain (PT: β = −0.50, p < .001; PEP: β = −0.45, p < .001) from week 12 to week 24. However,

change in stress and strain from week 12 to 24 did not sig-
nificantly differ across groups (z = 1.15 and z = 0.74, respec-
tively). Similarly, parents in both groups reported significant
change in child disruptive behavior on the ABC-I from week
12 to week 24 (PT: β −0.54; PEP: β −0.50, p < .001), and there was no difference across treatment groups (z = 0.983).

This exploratory analysis examined whether change in
competence predicted change in stress, strain, or child irri-
tability, and whether treatment groups differed. The results
revealed that change in competence did not significantly pre-
dict change in PSI total score, CGSQ global score, or child
disruptive behavior (ABC-I). Furthermore, the magnitude
of this relationship did not significantly vary across groups.

Discussion

To our knowledge, this is the largest randomized controlled
study to date of PT in children with ASD and disruptive
behavior. Here we examined the impact of PT on multiple
parent outcomes, including parental stress, caregiver strain,
and parental competence. The improvements in parent
self-reports in both groups suggest non-specific treatment
effects (e.g., therapist attention). However, there may also be
unique effects for each intervention: increased proficiency in
behavioral strategies in PT and increased knowledge about
ASD in PEP. Still, the larger improvements for PT suggest
that addressing child behavior was an especially effective
intervention component. Although both groups improved,
PT showed greater increase on perceived parental compe-
tence than PEP. Compared to PEP, PT also showed greater
reduction in several indices of parental strain and stress due
to the challenges of raising a child with ASD and disruptive
behavior. Effect sizes ranged from small to medium. Positive
effects for difficult child behavior, global caregiver strain,
and satisfaction with parental competence were evident at
week 12, suggesting that change in parent self-reported out-
comes occurred during the first half of treatment. This is
consistent with the differential improvements between treat-
ment groups that also emerged for child outcomes at week
12 (Bearss et al. 2015). Other dimensions, such as parental
efficacy, overall parental competence, and externalized car-
egiver strain, required the full 24 weeks to show differential
change. These findings contribute to the growing evidence
for PT in ASD and lend support to the broader finding that
PT reduces disruptive behavior in many different child popu-
lations (Dretzke et al. 2009; Postorino et al. 2017; Skotarc-
zak and Lee 2015).

The PSI difficult child subscale, which includes disruptive
behavior problems, was significantly different between treat-
ment groups at week 24. Given that disruptive behavior is the
target of PT, this finding is not surprising. On the PSI total
score and other PSI factors (parental distress, parent–child

Table 1 Participant characteristics

No. (%)

Parent training
(n = 89)

Parent education
(n = 91)

Study center
Emory/Yale University 17 (19.1) 18 (19.8)
Indiana University 14 (15.7) 14 (15.4)
Ohio State University 19 (21.4) 20 (22.0)
University of Pittsburgh 19 (21.4) 18 (19.8)
University of Rochester 20 (22.5) 21 (23.1)

Parent demographics
Gender of primary informant
Female 79 (88.7) 87 (95.6)
Male 10 (11.3) 4 (4.4)

Mother age (years) 35.4 (6.6) 35.9 (6.1)
Father age (years) 38.4 (7.7) 38.5 (7.2)
2-parent family 77 (86.5) 81 (89.0)
Education
  Some high school 1 (1.1) 0
  High school degree 9 (10.1) 5 (5.5)
  Some college 28 (31.5) 26 (28.6)
  College diploma 22 (24.7) 37 (40.7)
  Advanced degree 29 (32.6) 23 (25.3)

Family income
<$20,000 8 (9.0) 7 (7.7) $20,001–$40,000 19 (21.3) 17 (18.7) $40,001–$60,000 17 (19.1) 19 (20.9) $60,001–$90,000 15 (16.9) 21 (23.1) >$90,000 29 (32.6) 27 (29.7)

Child demographics
Gender
Female 10 (11.2) 12 (13.2)
Male 79 (88.8) 79 (86.8)

Age (years) 4.8 (1.2) 4.7 (1.1)
IQ
< 70 13 (14.6) 16 (17.6) ≥ 70 66 (74.2) 68 (74.7) Missing 10 (11.2) 7 (7.7)

Ethnicity
  Black/African-American 9 (10.1) 6 (6.6)
  Asian/Pacific Islander 2 (2.3) 6 (6.6)
  White/Caucasian 78 (87.6) 78 (85.7)
  Other 0 1 (1.10)

J Autism Dev Disord
1 3

dysfunctional interaction), both groups improved and there
were no significant differences between groups. The greater
improvements in parental competence and parental stress
for PT over PEP suggest that decreases in child disruptive
behavior are associated with positive parent outcomes.

Although the PEP group improved over time, PT was
superior to PEP on measures of difficult child behavior, car-
egiver strain, and parent perceived competence at week 24.
The finding that psychoeducation programs, such as PEP,
can improve child outcomes (Bearss et al. 2015; Kasari et al.
2015) suggests that a better understanding of the behavior in
children with ASD may promote reductions in parental stress
and strain, although this implication cannot conclusively
be drawn from the data. However, our results suggest that

providing parents with specific tools to reduce disruptive
behavior reduces parental stress and strain, and improves
parental competence. Furthermore, the task demands of
applying parent training did not appear to contribute to
parental stress.

The finding that a decrease in child disruptive behavior
via PT promotes improvement in parental competence, are
consistent with previous research in non-ASD populations
(Dretzke et al. 2009). We used structural equation models
(SEM) to explore the predictive role of improved parent
competence in PT on parent and child outcomes. Our pre-
diction that improved parental competence would predict
subsequent reduction in parental stress and strain as well
as disruptive behavior in the child in PT versus PEP was

Table 2 Parenting stress, parent competence, and caregiver strain raw scores by group and timepoint

Parent training (PT)
(n = 89)

Parent education (PEP)
(n = 91)

Baseline Week 12 Week 24 Baseline Week 12 Week 24

Parenting Stress Index-short form total 106.2 (19.0) 96.5 (20.7) 91.4 (19.6) 103.5 (17.9) 96.9 (17.6) 93.9 (19.4)
Parental distress 33.3 (8.6) 30.7 (8.8) 28.9 (8.1) 32.7 (8.8) 29.9 (8.1) 28.7 (9.6)
Parent/child difficult interaction 29.1 (7.3) 26.8 (7.4) 25.4 (7.0) 28.6 (7.7) 26.8 (7.0) 25.7 (6.8)
Difficult child 43.6 (6.9) 38.9 (8.4) 37.0 (8.1) 42.2 (6.8) 40.1 (7.2) 39.1 (8.0)

Caregiver strain questionnaire global 2.9 (0.6) 2.5 (0.6) 2.4 (0.6) 2.8 (0.6) 2.5 (0.6) 2.6 (0.6)
Objective strain 2.9 (0.7) 2.5 (0.7) 2.4 (0.7) 2.8 (0.7) 2.5 (0.7) 2.6 (0.7)
Internalized strain 3.4 (0.7) 2.8 (0.8) 2.6 (0.8) 3.3 (0.7) 2.9 (0.74) 2.9 (0.9)
Externalized strain 2.2 (0.6) 2.0 (0.5) 1.9 (0.49) 2.0 (0.6) 2.0 (0.6) 2.0 (0.6)

Parenting sense of competence total 61.4 (11.8) 68.3 (10.4) 71.5 (9.7) 63.5 (10.7) 66.9 (11.8) 68.2 (12.2)
Satisfaction 35.2 (7.4) 38.7 (6.8) 40.1 (6.1) 36.7 (6.6) 38.1 (7.8) 39.2 (7.5)
Efficacy 26.2 (6.6) 29.6 (5.4) 31.4 (5.3) 26.8 (5.9) 28.7 (5.9) 29.0 (6.2)

Aberrant behavior checklist—irritability 23.7 (6.4) 16.1 (7.3) 11.9 (6.5) 23.9 (6.2) 18.7 (7.4) 16.6 (7.6)

Table 3 Differences between
parent training and parent
education program in least
square means, p values, and
standardized effect sizes for
change from baseline at 12 and
24 weeks

Effect sizes represented as Cohen’s d
LSM-Diff difference in least square means (PT-PEP)
*All trends show an advantage for PT over PEP

Change from baseline to week
12*

Change from baseline to week
24*

LSM-Diff p Effect size LSM-Diff p Effect size

Parenting Stress Index-short form total −3.50 .15 0.19 − 4.51 .07 0.25
Parental distress − 0.21 .83 0.02 − 0.52 .63 0.06
Parent/child difficult Interaction − 0.41 .65 0.05 − 0.52 .56 0.07
Difficult child −2.75 .008 0.40 −3.02 .004 0.44

Caregiver strain questionnaire global − 0.16 .05 0.32 − 0.29 < 0.001 0.50 Objective strain − 0.14 .14 0.20 − 0.28 .005 0.40 Internalized strain − 0.27 .01 0.35 − 0.38 < 0.001 0.49 Externalized strain − 0.01 .89 0.90 − 0.14 .08 0.23

Parenting sense of competence total 2.41 .07 0.21 3.76 .01 0.34
Satisfaction 1.41 .04 0.20 1.32 .08 0.19
Efficacy 0.97 .18 0.15 2.40 .001 0.38

J Autism Dev Disord
1 3

not confirmed. We observed significant increases on the
PSOC in the PT group from baseline to week 12, but this
did not significantly predict more change on the PSI total
score, CGSQ global score or the ABC-I from week 12 to
week 24. A limitation of this analysis is that, although
SEM controls for shared variance across measures, these
measures are not entirely separate constructs and were
highly inter-correlated in our sample, reducing our ability
to separate change in competence from change in stress
and strain.

The positive child and parent outcomes demonstrated in
this trial add to the empirical support for PT and suggest
that PT is ready for wider application for young children
with ASD and disruptive behavior. Challenges ahead include
identification of barriers that may hinder the broader appli-
cation of PT in community settings. One obvious challenge
is training a wide range of practitioners (e.g., psychologists,
special educators, social workers and child psychiatric nurse
practitioners), which would require institutional commitment
to provide space and funding. Evaluation of ancillary effects
on families (e.g., parenting styles, sibling behavior), child
behavior in other settings (e.g., classrooms), and longer-
term outcomes could help indicate whether PT has broader,
clinically significant effects. Given the improvements in both
PT and PEP, a blended intervention that includes content
from both approaches may yield additive effects, but would
also increase the number of treatment sessions. Alternative
approaches such as group PT or PT by telehealth also war-
rant further development.

The present findings should be considered in light of
several limitations. Primary outcomes on child behavior
and parent outcomes are based on parent report. We did
not measure objective outcomes, such as physiological
markers of parental stress. However, parental perception
of stress and caregiver strain is testimony from parents
directly facing the challenges of raising a child with ASD
and disruptive behavior. We also note that parents were
not blind to group assignment. Perceptions about the two
treatments may have influenced their ratings. To date, few
studies with other measures of parental stress and well-
ness (e.g., physiological recordings) have been reported
in PT studies, and this is an area in need of future devel-
opment. We also note the use of multiple comparisons,
which inflated the probability of Type I error. Finally, the
parents who participated in this study were mostly white,
middle and upper-middle class, and were well-educated.
Accordingly, our findings may not necessarily extend to
racially or ethnically diverse or under-resourced parents,
who may face additional daily stressors not directly related
to parenting. Implementation studies are needed to extend
the reach of PT to more diverse and under-resourced
populations. Despite these limitations, this study demon-
strated the superiority of PT on reducing child disruptive

behavior, improving child adaptive behavior, decreasing
parental stress and strain and improving parental sense of
competence.

Acknowledgments We would like to thank our team for their con-
tributions to this project: Jill Pritchett at Ohio State University; Laura
Simone at Yale TrialDB; Yanhong Deng, Saankari Anusha Challa,
Denis Sukhodolsky, James Dziura, and Allison Gavaletz at Yale; Car-
rie McGinnis at Indiana University; Rachael Davis, David McAdam,
Bridget Reynolds, Melissa Sturge-Apple, and Amit Chowdhry at Uni-
versity of Rochester Medical Center. We also thank the Data and Safety
Monitoring Board: Gerald Golden, M.D. (retired pediatric neurologist),
Christopher Young, M.D. (Medical Director of Wellmore Behavioral
Health, Waterbury, CT and Martin Schwartzman father of a child with
autism).

Funding This work was funded by the National Institute of Men-
tal Health by the following grants: Yale University/Emory University
MH081148 (principal investigator: L. Scahill); University of Pitts-
burgh/University of Florida MH080965 (principal investigator: C.
Johnson); Ohio State University MH081105 (principal investigator:
L. Lecavalier); Indiana University MH081221 (principal investigator:
N. Swiezy); University of Rochester MH080906 (principal investigator:
T. Smith). Additional support was provided by MH079130 (princi-
pal investigator: D Sukhodolsky), the National Center for Advancing
Translational Sciences of the National Institutes of Health under Award
Numbers UL1 TR000454 (Emory University), UL1 TR000042 (Uni-
versity of Rochester), UL1 RR024139 (Yale University) and the Mar-
cus Foundation. We thank the families who participated in this study.

Author Contributions SI participated conceived of the current study
analyses, participated in its design and coordination, and drafted the
manuscript; LL conceived of the current study analyses and drafted the
manuscript; BH participated in the study design, performed the statisti-
cal analyses, and assisted in drafting the manuscript; TS conceived of
the original study, participated in its design and coordination, assisted
with data interpretation and helped to draft the manuscript; LL con-
ceived of the original study, participated in its design and coordination,
assisted with data interpretation and helped to draft the manuscript; CJ
conceived of the original study, participated in its design and coordi-
nation, assisted with data interpretation and helped to draft the manu-
script; NS conceived of the original study, participated in its design
and coordination, assisted with data interpretation and helped to draft
the manuscript; KB conceived of the original study, participated in its
design and coordination, assisted with data interpretation and helped to
draft the manuscript; LS conceived of the original study, participated
in its design and coordination, assisted with data interpretation and
helped to draft the manuscript. All authors read and approved the final
manuscript.

Compliance with Ethical Standards

Conflict of interest The authors declare that they have no conflict
of interest.

Ethical Approval All procedures performed in studies involving
human participants were in accordance with the ethical standards of
the institutional and/or national research committee and with the 1964
Helsinki declaration and its later amendments or comparable ethical
standards.

Informed Consent Informed consent was obtained from all indi-
vidual participants included in the study.

J Autism Dev Disord
1 3

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http://dx.doi.org/10.1155/2011/395190

Teaching Parents Behavioral Strategies for Autism Spectrum Disorder (ASD): Effects on Stress, Strain, and Competence

Abstract
Introduction
Methods
Design
Participants
Measures
Characterization Measures
Outcome Measures
Treatments
Parent Training (PT)
Psychoeducation Program (PEP)
Statistical Analysis
Treatment Effects
Exploratory Analyses

Results
Treatment Effects
Exploratory Analyses
Discussion
Acknowledgments
References

Vol.:(0123456789)

1 3

Journal of Autism and Developmental Disorders
https://doi.org/10.1007/s10803-020-04705-y

L E T T E R TO T H E E D I TO R

Insomnia and Treatment Strategies: Improving Quality of Life
in Children with Autism Spectrum Disorder

Bárbara Virginia Vitti‑Ruela1 · Vinícius Dokkedal‑Silva2 · Priscila Kalil Morelhão2 · Sandra Doria Xavier2 ·
Sergio Tufik2 · Monica Levy Andersen2

Dear Editor,
Autism spectrum disorder (ASD) is a complex develop-

mental disorder that involves impairments in social com-
munication and interaction, sensory abnormalities and
restricted and repetitive behaviors (Vahia 2013). Insomnia
is commonly observed in children with ASD, reaching a
prevalence as high as 60–86%, two to three times greater
when compared to typically developing (TD) children (Rich-
dale and Schreck 2009; Posar and Visconti 2020). The main
cause of sleep disorders include the interaction of several
factors, such as poor sleep hygiene and abnormalities in the
melatonin system (Mazurek and Sohl 2016; Gagnon and
Godbout 2018).

A systematic review assessing objective and subjective
sleep studies in individuals with ASD found a significantly
higher bedtime resistance, sleep onset delay, sleep anxiety,
night awakenings, parasomnias, sleep-disordered breathing,
daytime sleepiness, sleep onset latency (in min), restora-
tive value of sleep and general sleep problems (Diaz-Roman
et al. 2018; Mazurek and Sohl 2016). Moreover, sleep depri-
vation effects seem to be more intense in ASD children than
in TD ones (Mazurek and Sohl 2016). As a consequence of
these sleep problems there is a worsening of behavior, which
may include aggression, irritability, inattention and hyper-
activity (Mazurek and Sohl 2016; Posar and Visconti 2020).

The neurological mechanisms underlying insomnia in
ASD are not yet fully understood. However, it is known that
sleep plays an important role in neurological development,
since many of the processes involved occur at ages when

sleep is the predominant brain state (Wintler et al. 2020).
The balance between excitatory and inhibitory neurotrans-
mission, especially with regard to the paradoxical mecha-
nism of GABA, seem to influence the pathophysiology and
be related to the dysfunction of the ASD network (Wintler
et al. 2020).

One possible mechanism involved in insomnia in ASD
children could be sensorial dysregulation, which is very fre-
quent. Reynolds et al. (2011) found a higher prevalence of
atypical sensory behaviors and sleep disturbances in children
with ASD aged between 6 and 12 years when compared
to TD. As suggested by Souders et al. (2017), sleep dif-
ficulties may be connected to localized problems in differ-
ent sensory areas and a higher sensory threshold (that is, a
reduced sensibility to sensory stimuli) would be related to
better sleep. In this sense, a focus must be given to reduc-
ing sensory hyper-reactivity by making alterations in the
bedroom, which include reduction of environmental stimuli
and appliance of calming strategies (Souders et al. 2017).

A case–control study has shown that there might be an
interruption of sleep homeostasis in ASD children, leading
to reduced sleep pressure and consequently problems in ini-
tiating and maintaining sleep, besides being correlated with
the severity of the disorders presented (Arazi et al. 2019).
This characteristic is expressed by weaker delta power dur-
ing slow wave sleep (SWS) and less N3 sleep during the first
2 h of sleep (Arazi et al. 2019).

ASD may also be associated with a dysregulation of the
circadian cycle, marked by a disturbance in melatonin pro-
duction, which can contribute to sleep disturbances, such as
insomnia (Gagnon and Godbout 2018). A possible strategy
to reduce this sleep problem could be to administer mela-
tonin to help initiate and maintain sleep. Melatonin acts by
activating 2 high-affinity membrane receptors MT1 and
MT2, which are distributed, mainly, by the brain and spinal
cord (Gagnon and Godbout 2018; Atkin et al. 2018). Mela-
tonin is already used as a treatment for insomnia in ASD and

* Monica Levy Andersen
ml.andersen12@gmail.com

1 Faculdade de Medicina de Marília, Avenida Monte Carmelo,
800, Marília, Brazil

2 Departamento de Psicobiologia, Universidade Federal de
São Paulo (UNIFESP), Rua Napoleão de Barros, 925, Vila
Clementino, São Paulo 04024-002, Brazil

http://crossmark.crossref.org/dialog/?doi=10.1007/s10803-020-04705-y&domain=pdf

Journal of Autism and Developmental Disorders

1 3

evidence suggests that it may also be useful for treatment of
comorbidities that contribute to further impairment of sleep
(Gagnon and Godbout 2018; Atkin et al. 2018).

Whereas melatonin acts only by modifying time to facili-
tate sleep and has no effect on sleep architecture, another
possible treatment could be the use of the Z-drugs (zolpi-
dem, zaleplon, zopiclone and eszopiclone) (Atkin et al.
2018). Among the Z-drugs, zolpidem and zaleplon alter the
structure of sleep and reduce latency for the onset of sleep.
Zolpidem also increases total sleep time (Sukys-Claudino
et al. 2010). The Z-drugs are FDA-approved for insomnia
disorders and act as positive allosteric modulators at the
GABA binding site, potentiating the inhibitory effect of
GABA. These drugs have a greater affinity for the alpha
1 receptor, which seems to be related to sleep (Atkin et al.
2018). However, careful attention should be paid to the pos-
sible side effects of these drugs, which include coordination
and cognitive impairment, tolerance and abuse. (Atkin et al.
2018). Caution is also needed regarding the administration
of these drugs in patients with ASD, since a long-term treat-
ment may be necessary and Z drugs are generally used for a
restricted period. To the best of our knowledge, no investiga-
tion of the efficacy of Z-drugs in the treatment of insomnia
in ASD children has been carried out. Randomized clinical
trials should be conducted to verify the safety, efficacy and
possible side effects of Z-drugs in the treatment of these
patients.

Combining the use of Z-drugs with melatonin might help
to reduce the most common sleep disturbance presented in
ASD, insomnia (Richdale and Schreck 2009; Mazurek and
Sohl 2016). We believe that further randomized controlled
trials should be carried out to assess the possible interactions
and benefits of the polytherapy with Z-drugs and melatonin.
As treatment with Z-drugs may have potential side effects,
it is worth investigating if their use with melatonin might
allow a reduction in dosage and improve sleep outcomes
while reducing or avoiding these side effects.

Given the need to deliver appropriate treatment for
insomnia in children with ASD, we would like to highlight
the importance of behavioral and pharmacological treatment
strategies, especially those including Z-drugs, melatonin and
sensory profile interventions aimed at improving the qual-
ity of life of these individuals and ameliorating behavio-
ral symptoms. Nevertheless, clinical trials are necessary to
investigate the effect of these medications as an intervention
measure in children with ASD.

Acknowledgments Our studies are supported by grants from the Asso-
ciação Fundo de Incentivo à Pesquisa (AFIP). M.L.A. and S.T. are
CNPq fellowship recipients.

Author Contributions BVV-R was responsible for the conception and
writing of the manuscript and the literature search. VD-S participated
in the writing and revision of the manuscript. PKM contributed to the

conception of the manuscript and participated in its writing and revision.
SDX revised the manuscript and contributed to the literature search. ST
has supervised the production of the manuscript and provided funding.
ML Andersen was the main supervisor of the manuscript and participated
in its revision.

Compliance with Ethical Standards

Conflict of interest The authors declare that they have no conflict of in-
terest.

References

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atic review and meta-analysis of subjective and objective stud-
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development, and autism spectrum disorders: Insights from animal
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Insomnia and Treatment Strategies: Improving Quality of Life in Children with Autism Spectrum Disorder

Acknowledgments
References

Research in Developmental Disabilities 107 (2020) 10379

Available online 24 October 2020
0891-4222/© 2020 Elsevier Ltd. All rights reserved.

Barriers and facilitators to treating insomnia in children with
autism spectrum disorder and other neurodevelopmental
disorders: Parent and health care professional perspectives

Kim M. Tan-MacNeill a, Isabel M. Smith a, b, c, Anastasija Jemcov a, Laura Keeler a,
Jill Chorney a, c, d, e, Shannon Johnson a, Shelly K. Weiss f, Esmot Ara Begum a,
Cary A. Brown g, Evelyn Constantin h, Roger Godbout i, Ana Hanlon-Dearman j,
Osman Ipsiroglu k, Graham J. Reid l, m, n, o, Sarah Shea b, c, Penny V. Corkum a, c, p,*
a Department of Psychology & Neuroscience, Dalhousie University, Canada
b Department of Pediatrics, Dalhousie University, Canada
c IWK Health Centre, Canada
d Department of Anesthesia, Pain, and Perioperative Medicine, Dalhousie University, Canada
e Centre for Pediatric Pain Research, IWK Health Centre, Canada
f Division of Neurology, Department of Paediatrics, University of Toronto, Canada
g Faculty of Rehabilitation Medicine, University of Alberta, Canada
h Department of Pediatrics, McGill University, Canada
i Department of Psychiatry, Université de Montréal, Canada
j Faculty of Health Sciences, University of Manitoba, Canada
k Faculty of Medicine, University of British Columbia, Canada
l Department of Psychology, Western University, Canada
m Department of Family Medicine, Schulich School of Medicine and Dentistry, Western University, Canada
n Department of Paediatrics, Schulich School of Medicine and Dentistry, Western University, Canada
o Children’s Health Research Institute & Lawson Health Research Institute, Canada
p Department of Psychiatry, Dalhousie University, Canada

A R T I C L E I N F O

Number of reviews completed is 2

Keywords:
Children
Insomnia
Neurodevelopmental disorders
Treatment accessibility

A B S T R A C T

Background/aims: Insomnia is highly prevalent in children with neurodevelopmental disorders
(NDDs), yet little research exists on sleep treatment access, utilization, and provision in this
population. This study explores barriers and facilitators to access, use, and provision of treatment
for sleep problems as experienced by parents of children with NDDs, including Autism Spectrum
Disorder (ASD), Attention-Deficit/Hyperactivity Disorder (ADHD), Cerebral Palsy (CP) and Fetal
Alcohol Spectrum Disorder (FASD), and health care professionals who work with children with
these conditions.

Abbreviations: BNBD, Better Nights, Better Days (name of intervention); BNBD-NDD, Better Nights, Better Days for Children with Neuro-
developmental Disorders (name of intervention); TD, typically developing; NDD, neurodevelopmental disorder; ADHD, attention-deficit/hyperac-
tivity disorder; ASD, autism spectrum disorder; CP, cerebral palsy; FASD, fetal alcohol spectrum disorder; HCP, health care professional; BCBA,
Board-Certified Behaviour Analyst; BIQ, Behavioural Insomnia Questionnaire; SILS, Single Item Literacy Scale; PSQ, Pediatric Sleep Questionnaire;
RCT, randomized controlled trial.

* Corresponding author at: Department of Psychology & Neuroscience, Dalhousie University, Life Sciences Centre, Rm 2521, 1355 Oxford Street,
Halifax, NS, B3H 4R2, Canada.

E-mail address: penny.corkum@dal.ca (P.V. Corkum).

Contents lists available at ScienceDirect

Research in Developmental Disabilities

journal homepage: www.elsevier.com/locate/redevdis

https://doi.org/10.1016/j.ridd.2020.103792
Received 15 May 2020; Received in revised form 22 September 2020; Accepted 5 October 2020

mailto:penny.corkum@dal.ca

www.sciencedirect.com/science/journal/08914222

https://www.elsevier.com/locate/redevdis

https://doi.org/10.1016/j.ridd.2020.103792

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https://doi.org/10.1016/j.ridd.2020.103792

Research in Developmental Disabilities 107 (2020) 103792

Barriers
Parents Method: Transcripts from online focus groups and interviews, conducted separately with parents

of children with NDDs (n = 43) and health care professionals (n = 44), were qualitatively
analyzed using content analysis for key themes.
Results: Barriers included limited access to/availability of treatment, lack of knowledge/training,
NDD-specific factors (e.g., symptoms, medications, and comorbidities), parent factors (e.g., ca-
pacity to implement treatment, exhaustion), and the challenging, intensive nature of sleep
treatment. Facilitators included positive beliefs and attitudes, education, support, and ability to
modify treatments for NDD symptoms. Barriers and facilitators were similar across all four NDDs.
Conclusions: Results highlight a need for more education about sleep in NDDs and to develop
accessible interventions, as well as the potential of a transdiagnostic approach to sleep treatment
in this population.

What this paper adds

This paper contributes to our understanding of parents’ experiences of seeking, accessing, and using treatments for behavioural
sleep problems, such as insomnia, for their children with neurodevelopmental disorders (NDDs). At the same time, this paper provides
information on front-line health care professionals’ experiences with providing treatments for insomnia for children with NDDs. By
engaging these key stakeholders, this study informs our understanding of unmet needs in the areas of sleep treatment accessibility,
delivery, and use, as well as professional development and training needs related to sleep treatment. Findings from this study also add
to a growing body of evidence that supports a transdiagnostic approach to treating sleep problem in children with NDDs.

1. Introduction

1.1. Background

Neurodevelopmental disorders (NDDs) emerge in early childhood and are linked to disturbances in central nervous system func-
tioning, which can cause impaired cognition, communication, motor skills, and/or behaviour, and functional impairment in a variety
of daily life domains (American Psychiatric Association, 2013). Sleep problems are highly prevalent in children with NDD, with rates
ranging from 40 to 86% (Robinson-Shelton & Malow, 2016; Romeo et al., 2014). Insomnia, the most common sleep problem expe-
rienced by children with NDD, includes difficulty falling and staying asleep (American Academy of Sleep Medicine, 2014). Throughout
this paper, we will use the terms insomnia and sleep problems interchangeably.

Sleep problems have been shown to increase the severity of NDD symptoms as well as behavioural and emotional problems, and to
have negative effects on children’s daytime functioning (Tudor, Hoffman, & Sweeney, 2012; Goldman et al., 2011; Newman, O’Regan,
& Hensey, 2006). Children’s sleep problems occur within a broad psychosocial context and may affect the whole family; for example,
parents of children with NDDs and sleep problems experience high levels of stress (Doo & Wing, 2006).

Development of effective treatments for insomnia in children with NDDs is important, given the high prevalence and negative
effects of sleep problems. Behavioural interventions are the first-line recommendation for pediatric insomnia in both NDD and typi-
cally developing (TD) populations (Malow et al., 2012). Research on effective sleep interventions for children with NDDs is expanding,
with several recent randomized controlled trials (RCTs) (e.g., Hiscock, Sciberras, & Mensah, 2015). A recent systematic review found
support for a transdiagnostic behavioural approach to treating sleep problems in children with NDDs (Rigney et al., 2018), wherein the
same behavioural treatment principles are applied across multiple diagnoses, with minor modification of strategies originally
developed for TD children (e.g., psychoeducation, healthy sleep practices, extinction).

Emerging research suggests that access to and uptake of behavioural sleep interventions by families of children with NDDs is
limited (e.g., Bessey, Coulombe, Smith, & Corkum, 2013; Boerner, Coulombe, & Corkum, 2014). Additionally, front-line health care
professionals (HCPs) are generally not well trained to provide sleep interventions (e.g., Boerner et al., 2014), much less for special
populations such as children with NDDs. There is a great need to explore factors influencing families’ seeking of, access to, and uptake
of treatment for sleep problems in their children with NDDs, as well as the factors influencing HCPs’ ability to provide such treatment.
This information will provide a foundation for the development of effective sleep interventions for this population.

Focus groups were conducted (or interviews when participants were not able to attend focus groups) to gather the perspectives of
parents of children with NDDs and HCPs on barriers and facilitators to access, uptake, and provision of sleep treatments for children.
Four prevalent NDDs that encompass a range of symptoms and functional impairments were included: Attention-Deficit/Hyperactivity
Disorder (ADHD), Autism Spectrum Disorder (ASD), Cerebral Palsy (CP), and Fetal Alcohol Spectrum Disorder (FASD). The results of
the study will identify unmet needs in the areas of treatment delivery and use, accessibility, and professional development and training
in order to inform the development of a sleep intervention for children with these four NDDs.

1.2. Research objectives

The research objectives were to explore the barriers and facilitators experienced by 1) parents, in seeking, accessing, utilizing, and
implementing treatments for sleep problems in children with NDDs; and 2) HCPs, in their access to information about and provision of

K.M. Tan-MacNeill et al.

Research in Developmental Disabilities 107 (2020) 103792

sleep treatments for children with NDDs. We predicted that lack of knowledge, training, and time may be barriers reported by HCPs.
We expected that both parents’ and HCPs’ beliefs and attitudes about the nature of sleep problems in NDDs and their treatability would
influence responses regarding treatment seeking, access, uptake, and provision.

2. Method

2.1. Participants

This study was approved by the Research Ethics Board of the IWK Health Centre in Halifax, Nova Scotia, Canada. Informed consent
was obtained from all participants, who were recruited online via social media, through sharing of recruitment advertisements by
NDD-related parent and health organizations, and through the authors’ professional networks. Conducting individual interviews
became necessary for some participants, due to difficulty accommodating time zones and schedules.

Fig. 1. Parent study flow diagram.

K.M. Tan-MacNeill et al.

Research in Developmental Disabilities 107 (2020) 103792

2.1.1. Parent participants
The final sample included 43 parents or caregivers (hereafter, parents) of children aged 4–12 years with parent-reported diagnoses

of ADHD (n = 9), ASD (n = 20), CP (n = 6), and/or FASD (n = 8), as well as behavioural sleep problems confirmed by a screening

Table 1
Demographic Information for Parent Participants and their Children.

Total Primary NDD Group

Parent Participant Demographics N = 43 ASD
(n = 20)

ADHD
(n = 9)

CP
(n = 6)

FASD
(n = 8)

Participants’ relationship to child
Biological Mother 32 (74.4 %) 18 (90 %) 9 (100 %) 5 (83.3 %)
Biological Father 3 (7%) 2 (10 %) 1 (16.7 %)
Adoptive Mother 8 (18.6 %) 8 (100 %)

Participant (Parent/caregiver) Mean Age in Years (SD,
range)

38.5 (SD = 7.1,
25− 65)

36.5 (SD = 5.2,
25− 47)

38.2 (SD = 4.7,
32− 45)

38.7 (SD = 8.5,
28− 52)

43.6 (SD =
10.5, 32− 65)

Participants’ relationship status
Married/Common-law 33 (75.7 %) 15 (75 %) 6 (66.7 %) 5 (83.3 %) 7 (87.5 %)
Single/Never legally married 3 (7%) 1 (5%) 1 (16.7 %) 1 (12.5 %)
Separated/Divorced 6 (14.0 %) 4 (20 %) 2 (22.2 %) 1 (16.7 %) 1 (12.5 %)

Community of residence
Rural 12 (27.9 %) 5 (25 %) 2 (22.2 %) 1 (16.7 %) 4 (50 %)
Town 5 (11.6 %) 3 (15 %) 1 (11.1 %) 1 (16.7 %) 1 (12.5 %)
City 26 (60.4 %) 12 (60 %) 6 (66.7 %) 4 (66.7 %) 3 (37.5 %)

Ethnic or Cultural Heritage
White/Caucasian 39 (90.7 %) 19 (95 %) 8 (88.9 %) 6 (100 %) 6 (75 %)
Aboriginal – Metis 1 (2.3 %) 1 (12.5 %)
Other 2 (4.7 %) 1 (5%) 1 (12.5 %)

Highest Level of Education
High school equivalent or less 3 (7%) 2 (10 %) 1 (16.7 %)
Diploma or certificate from college, university,
trade/technical/vocational school, or less

18 (41.2 %) 7 (35 %) 5 (55.5 %) 2 (33.3 %) 4 (50 %)

Bachelor’s/Undergraduate Degree (e.g., BA, BSc,
BEd)

13 (30.2 %) 7 (35 %) 2 (22.2 %) 2 (33.3 %) 2 (25 %)

Graduate degree (e.g., MA, MSc, MEd, PhD, DSc,
EdD)

8 (18.6 %) 4 (20 %) 1 (11.1 %) 1 (16.7 %) 2 (25 %)

Participant’s Current Employment Status
Full Time 21 (48.8 %) 7 (35 %) 6 (66.7 %) 4 (66.7 %) 4 (50 %)
Part Time 7 (16.3 %) 6 (30 %) 1 (12.5 %)
Unemployed 1 (2.3 %) 1 (12.5 %)
Student 1 (2.3 %) 1 (5%)
Homemaker 8 (18.6 %) 6 (30 %) 2 (33.3 %)
Other 4 (9.3 %) 2 (22.2 %) 2 (25 %)

Estimated Household Income
Less than $30,000 3 (7%) 2 (10 %) 1 (16.7 %)
$30,000 – $59,999 7 (16.3 %) 3 (15 %) 1 (11.1 %) 1 (16.7 %) 2 (25 %)
$60,000 – $99,999 16 (37.2 %) 6 (30 %) 4 (44.4 %) 1 (16.7 %) 3 (37.5 %)
$100,000 + 16 (37.2 %) 9 (45 %) 2 (22.2 %) 2 (33.3 %) 3 (37.5 %)
$100,000 – $149,999 9 (20.9 %) 6 (30 %) 1 (11.1 %) 2 (33.3 %)
$150,000 – $199,999 5 (11.6 %) 1 (5%) 1 (11.1 %) 3 (37.5 %)
$200,000 and over 2 (4.7 %) 2 (10 %)

Average Number of Other Children in Home (mean; SD;
range)

1.95 (.90, 1− 5) 1.85 (.49; 1− 3) 2.44 (1.13, 1− 5) 1.50 (.55, 1− 2) 2.00 (1.41,
1− 5)

Child Demographics N = 43 ASD (n = 20) ADHD (n = 9) CP (n = 6) FASD (n = 8)
Child Sex

Male 29 (67.4 %) 15 (75 %) 6 (66.7 %) 3 (50 %) 5 (62.5 %)
Female 14 (32.6 %) 5 (25 %) 3 (33.3 %) 3 (50 %) 3 (37.5 %)

Child Mean Age in years (SD, range) 8.5 years (SD =
2.5, 4.3–12.6)

9 years (SD =
2.5, 4.8–12.6)

6.9 years (SD =
2.3, 4.3–11.4)

8.3 years (SD =
2.3, 4.9–11.7)

9 years (SD =
2.3, 4.8–11.5)

Years Since NDD Diagnosis (SD, range) 4.2 (2.5, 1− 10) 4.1 (SD = 1.8,
2− 8)

2.6 (SD = 2.3,
1− 7)

5.8 (SD = 1.9,
4− 9)

5 (SD = 3.6,
1− 10)

Comorbid Diagnoses: Additional NDD, Mental Health, and
Physical Disorders (may have multiple diagnoses)
Presence of Parent-reported Diagnosis 30 (69.8 %) 12 (60 %) 5 (55.6 %) 5 (83.3 %) 8 (100 %)

Another NDD (ADHD, ASD, CP, or FASD) 9 (20.1 %) 2 (10 %) 1 (16.7 %) 6 (75 %)
Learning Disability 7 (16.3 %) 2 (10 %) 1 (11.1 %) 2 (33.3 %) 2 (25 %)
Intellectual Disability / Developmental Delay 9 (20.9 %) 3 (15 %) 2 (33.3 %) 4 (50 %)
Mental Health disorder (e.g., at least one of:
anxiety, mood, obsessive compulsive disorder,
oppositional defiant disorder)

26 (60.5 %) 10 (50 %) 5 (55.6 %) 4 (66.7 %) 7 (87.5 %)

Note: One participant did not complete; multiple participants missed or skipped questions.

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questionnaire; only participants whose children’s diagnoses were made by a physician or psychologist (self-reported) were eligible to
participate. In cases where children had comorbid ADHD with ASD, CP, or FASD, the ASD/CP/FASD diagnoses were considered
primary for assigning them to a disorder group (e.g., comorbid FASD and ADHD = FASD group). As such, children of parents in the
ADHD group could not have comorbid ASD, CP, or FASD. Parents were required to live in Canada, have access to a computer, internet,
web-camera and microphone (or telephone), and be comfortable speaking/reading English. Parent-reported formal diagnoses of sleep
disorders other than insomnia (e.g., sleep apnea or sleep-disordered breathing) were an exclusion criterion due to the potential
confound with behavioural insomnia. Information about children’s comorbid diagnoses (e.g., NDD, neurological, physiological,
mental health) and medication use was recorded but not used as exclusionary criteria.

Fig. 1 depicts parent participation, and Table 1 contains demographic information. Twenty-seven parents participated in focus
groups (which ranged from 2 to 5 participants each) and 16 parents completed individual interviews. Most parents were biological
mothers (74.4 %). The mean age of parents was 38.5 years (SD = 7.1, range = 25− 65), and most parents were married/common-law (n
= 33, 75.7 %). Most lived in cities (n = 26, 60.4 %), were of Caucasian heritage (n = 39, 90.7 %), and had completed high school and
some post-secondary education (n = 39, 90.7 %). The average reported number of other children in the home was 1.95 (SD = .9, range
1–5). Most parents were from Ontario (n = 17, 39.5 %), British Columbia (n = 8, 18.6 %), and Alberta (n = 7, 16.3 %), with the
remainder from Nova Scotia (n = 4), New Brunswick (n = 2), Newfoundland and Labrador (n = 2), and Prince Edward Island,
Manitoba, and Quebec (each n = 1).

Fig. 2. HCP study flow diagram.

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Most children were male (n = 29, 67.4 %) and mean age was 8.5 years (SD = 2.5, range 4.3–12.6). Most children had at least one
other parent-reported diagnosis (n = 30, 69.8 %), including other comorbid NDDs (ASD, ADHD, CP, or FASD; n = 9, 20.1 %) or mental
health diagnoses (n = 26, 60.5 %); anxiety was common (n = 16, 37.2 %). Children also had a range of parent-reported physical health
conditions (n = 19, 44.2 %), most frequently epilepsy/seizure disorders (n = 5, 11.6 %), other neurological disorders (n = 7, 16.3 %),
gastrointestinal disorders (n = 6, 14 %), and respiratory disorders (n = 4, 9.3 %).

In terms of behavioural insomnia (Anders & Dahl, 2007), fifteen (34.9 %) children met criteria for bedtime resistance/sleep onset
problems, six (14 %) met criteria for night waking problems, and 18 (41.9 %) met criteria for both. Four children (9.3 %) were below
threshold for behavioural insomnia, but were included as their parents reported high severity/impact of sleep problems. Twenty
parents (46.5 %) reported that their children woke too early in the morning. Frequently reported problems were: problems falling
asleep (n = 38, 88.4 %), lying awake in bed after lights out for more than 20 min (n = 38, 88.4 %), problems staying asleep (n = 34,
79.1 %), getting out of bed once expected to stay in bed for the night (n = 32, 74.4 %), and waking during the night with difficulty
falling back asleep (n = 32, 74.4 %).

2.1.2. Health care professional participants
The final sample included 44 credentialed Canadian HCPs who practiced with 4- to 12-year-olds with NDDs. As many HCPs

practiced with more than one NDD group, they were asked to choose the NDD with which they worked most often for the focus group/
interview. The breakdown of HCPs by NDD was as follows: ADHD (n = 8), ASD (n = 21), CP (n = 8), and FASD (n = 7). Eligible
professions for participation included physicians, psychologists, nurses, social workers, occupational therapists, and Board-Certified
Behaviour Analysts (BCBAs; certified behaviour analysts who primarily work with children with ASD and provide behavioural in-
terventions). Fig. 2 depicts HCP participation. HCPs required access to a computer/internet, web camera and microphone (or tele-
phone), and fluency in English. To ensure a diverse sample of HCPs, no minimum percentage of practice time was specified for working
with children with NDDs or with sleep problems.

Twenty-one HCPs participated in focus groups (ranging from 2 to 4 participants each), whereas 23 participated in individual in-
terviews. Table 2 shows demographic information. Professions included occupational therapists (n = 15), clinical psychologists (n =
10), general paediatricians (n = 1)/developmental paediatricians (n = 6), nurses (n = 4), BCBAs (n = 4; ASD only), family physicians/
general practitioners (n = 2), and social workers (n = 2). The majority of HCPs were from Ontario (n = 16, 36.4 %) and Nova Scotia (n

Table 2
Demographic Information for Health Care Professionals (HCPs).

Total Primary NDD Group

(N = 44) ASD
(n = 21)

ADHD
(n = 8)

CP
(n = 8)

FASD
(n = 7)

HCP Sex
Male 3 (7%) 1 (4.8 %) 0 0 2 (28.6)
Female 41 (93 %) 20 (95.2 %) 8 (100 %) 8 (100 %) 5 (71.4 %)

Highest Level of Education
Bachelor’s Degree 9 (20.9 %) 3 (14.3 %) 2 (25 %) 3 (37.5 %) 1 (14.3 %)
Master’s Degree 18 (41.9 %) 12 (47.1 %) 1 (12.5 %) 3 (37.5 %) 2 (28.6 %)
MD 5 (11.6 %) 2 (9.5 %) 2 (25 %) 1 (14.3 %)
PhD 8 (18.6 %) 3 (14.3 %) 2 (25 %) 1 (12.5 %) 3 (42.9 %)

Years of Practice (Mean, SD, range) 14.5 (SD = 10.9,
0.5–38)

12.7 (SD = 10.8,
1− 33)

13.9 (SD = 9.2,
1.5–31)

19.0 (SD = 13.8,
2− 38)

15.4 (SD = 9.8,
0.5–30)

Practice Area
Primarily health 27 (61.4 %) 12 (57.1 %) 5 (62.5 %) 7 (87.5 %) 3 (42.9 %)
Primarily mental health 9 (20.5 %) 7 (33.3 %) 1 (12.5 %) 1 (14.3 %)
Evenly split between health/
mental health

3 (6.8 %) 1 (4.8 %) 2 (28.6 %)

Practice Setting
Private practice 9 (20.5 %) 3 (14.3 %) 3 (37.5 %) 3 (42.9 %)
Community health or mental
health centre

7 (15.9 %) 5 (23.8 %) 2 (28.6 %)

Hospital 14 (31.8 %) 11 (52.4 %) 1 (12.5 %) 2 (25 %)
University 1 (2.3 %) 1 (12.5 %)
Other (e.g. non-profit, rehab,
treatment centre)

8 (18.2 %) 1 (4.8 %) 1 (12.5 %) 5 (62.5 %) 1 (14.3 %)

Years of Experience Working with
Children with NDDs (Mean, SD,
range)

13.9 (SD = 9.9,
2− 35)

10.5 (SD = 6.5,
2− 30)

15.0 (SD = 11.7,
2− 30)

16.2 (SD = 31.1,
2.5–33)

20.3 (SD = 10.9,
8–35)

Self-reported estimated percentage of
practice time working with children
with NDD (Mean, SD, range)

48.6 % (SD = 31.1,
0.2–100)

59.7 % (SD = 28.6,
3–100)

25.4 % (SD = 30.3,
0.2–75)

38.6 % (SD = 22.5,
5− 70)

47.0 % (SD = 36.7,
5− 90)

Specialize in NDDs?
Yes 34 (77.3 %) 19 (90.5 %) 3(37.5 %) 6 (75 %) 6 (85.7 %)
No 10 (22.7 %) 2 (9.5 %) 5 (62.5 %) 2 (25 %) 1 (14.3 %)

Note: Three participants did not complete and one only partially completed the demographic / background information questionnaires.

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= 13, 29.5 %), followed by Alberta (n = 5, 11.4 %), British Columbia (n = 4, 11.4 %), New Brunswick (n = 3, 6.8 %), and Quebec (n =
3, 6.8 %). Most HCPs were female (n = 41, 93 %), had a Master’s or higher degree (n = 31, 70.5 %), and practiced primarily in
healthcare settings (n = 27, 61.4 %), most commonly in hospitals (n = 14, 31.8 %). HCPs averaged 13.9 years of experience working
with children (SD = 9.9, range 2–35 years); most specialized in working with children with NDDs (n = 34, 77.3 %).

2.2. Screening, eligibility, demographic, and background information measures

2.2.1. Parents
Two-step screening was completed online: 1) Parents completed an author-made questionnaire targeting inclusion and exclusion

criteria. 2) Parents who met initial inclusion/exclusion criteria then completed a questionnaire consisting of general diagnostic in-
formation, the Behavioural Insomnia Questionnaire (BIQ; Anders & Dahl, 2007; modified by authors) to assess the presence of
behavioural sleep problems, and the first six items of the Pediatric Sleep Questionnaire (PSQ; Chervin, Hedger, Dillon, & Pituch, 2000)
to screen for sleep apnea. The BIQ provides a cut-off score to determine presence of sleep onset and night-waking problems over the
previous month; author additions included parent ratings of the perceived severity and impact of their children’s sleep problems across
multiple domains (e.g., school, fatigue, family life), as well as reports of co-sleeping. Eligible parents then completed a Demographic
Information Questionnaire (author-developed; based on Canadian census).

2.2.2. Health care professionals
Health care professionals completed an author-made questionnaire that asked about inclusion and exclusion criteria, identifying

their professional group and the NDD group(s) with which they worked. Eligible HCPs completed a Health Care Professionals’ De-
mographic Information and Training Questionnaire (author-adapted from measures in Meltzer, Phillips, & Mindell, 2009), which
collected information on HCPs’ professional practice with NDDs, sleep-related training/education, practice setting, and self-rated
competence in treating sleep problems.

2.3. Focus groups and interviews

After eligible parents and HCPs were enrolled in the study and scheduled for a focus group session or interview, participants were
instructed in using the video-conferencing software and required to test the software prior to participation.

2.3.1. Description of focus groups/interviews
Separate focus groups and interviews were held for parents (10 focus groups, 16 interviews) and HCPs (8 focus groups, 22 in-

terviews). Groups/interviews were separated by NDD (e.g., ASD-only parent focus group). Within HCP focus groups, HCPs of different
disciplines were combined. Groups/interviews were conducted using encrypted video-conferencing software (Blackboard Collabo-
rate/Collaborate Ultra) that displayed PowerPoint slides showing discussion questions for the participants. A minority of participants
(parent n = 5, HCP n = 7) required teleconferencing (i.e., integrating a phone without video into the software) due to technical
difficulties. One local HCP was interviewed and recorded in-person (at their request). Due to software constraints, the present study set
a maximum of 5 participants per group plus a moderator, which is consistent with online focus group guidelines (Tuttas, 2014). Each
focus group (approximate duration 1.5 hours) was facilitated by the first author (K.T.M.). Volunteer research assistants acted as second
moderators and were available for technical support during focus groups. All interviews (approximate duration 1 hour) were con-
ducted solely by the first author using the same software as the focus groups.

2.3.2. Topic guides
Semi-structured topic guides for focus groups/interviews focused on the experience of treatment, from seeking to implementing.

Parent topics included knowledge of sleep in children with NDDs, experience of seeking treatment for insomnia, uptake/use of
treatments (separated into medications, over-the-counter treatments such as melatonin/natural remedies, and behavioural treat-
ments). HCP topics included familiarity with and extent of involvement with sleep treatment for children with NDDs, knowledge about
and access to sleep treatments, and provision of sleep treatment. At the end of each session, participants were asked what they felt was
the most important issue discussed and if anything had been missed. Participants were not asked to review transcripts.

2.4. Analysis

Focus group/interview sessions were audio-/video-recorded, transcribed, and de-identified. Transcripts were analyzed in NVivo
software (QSR International, NVivo for Mac, version 12.4.0), using qualitative content analysis (Schreier, 2012). The first author (K.T.
M.) developed separate coding frames for parents and HCPs in consultation with authors I.S. and P.C. and trained a second coder (L.K.).
Transcripts were reviewed and recoded multiple times to ensure coding agreement and that the coding frames were suitable. Parent
and HCP data were coded separately.

As transcripts were reviewed, the smallest units of analysis that contained a coherent meaning (typically a sentence, group of
sentences, or a single response from a participant) were identified as separate codes. Given the complexity of responses, some sections
of text yielded several different codes. Following the initial round of coding to identify individual barrier and facilitator codes, the
codes were grouped into broader themes and sub-themes. These themes constituted the final barriers and facilitators and are presented
in Tables 3–6. Frequency data (i.e., number of participants who endorsed each code) are available upon request. To examine group

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differences, complete lists of codes and frequencies were generated for all parent data and all HCP data respectively, then separate lists
were generated for each NDD group (e.g., parents – ASD, ADHD, CP, FASD). Similarities and differences were noted in the presence of
codes across NDD groups (within parent or HCP data overall).

3. Results

3.1. Parents

3.1.1. Barriers
Four barriers were identified for parents, consisting of 34 individual codes (see Table 3): 1. Access to and Availability of Services, 2.

Experience with Service (HCPs) and Treatment Implementation, 3. Parent Factors (a. Beliefs and attitudes, b. Experience and impact of
sleep problems, c. Knowledge), and 4. NDD-Specific factors.

Lack of knowledge about sleep, combined with limited availability of services and difficulty accessing available treatments, were
frequently reported barriers by parents. When parents were able to access treatment, some reported negative experiences with HCPs
such as feeling unheard or perceiving their HCPs as not knowledgeable about sleep and NDD. For example, a parent commented, “I

Table 3
Parent Barriers and Codes.

Barrier Codes

1. Access to/Availability of Services
1 Long wait times
2 NDD specialists difficult to access or not available
3 Need to access multiple HCPs or disciplines
4 Not able to attend appointments
5 Sleep treatment not affordable
6 Lack of available information & resources

2. Experience with Service (HCPs) and
Treatment Implementation

7 HCPs lack knowledge about sleep & NDD
8 Perceptions of HCPs as not helpful
9 Negative interpersonal experience with HCPs
10 Behavioural treatment can lead to a behaviour burst or dysregulation (unwanted)
11 Inconsistent response to treatment
12 Individualization – no one size fits all treatment
13 Treatment not working
14 Trial and error (don’t know what will work / why)
15 Treatment is hard (challenging)

3. Parent Factors
3A. Beliefs & Attitudes

16 Reluctance to stop using what works even if problematic (e.g., co-sleeping)
17 Reluctance to use medication for sleep
18 Reluctance to use melatonin for sleep
19 Cultural beliefs – co-sleeping acceptable
20 Expectation of negative outcome
21 Belief that child’s brain is wired differently in NDD
22 Belief that sleep tips for TD don’t apply for NDD

3B. Experience and Impact of Sleep Problems
23 Parental guilt/self-blame/anxiety for sleep problem
24 Feeling judged/stigmatized by others
25 Sleep is not first priority
26 Caregivers have different perspectives about sleep
27 Negative impact on family
28 Parental exhaustion & stress

3C. Knowledge
29 Lack of awareness about sleep in NDD
30 Lack of knowledge of underlying cause of sleep problem
31 Lack of knowledge of where to go for help or what to ask

4. NDD-Specific Factors
32 Complexity and comorbidity associated with NDD complicates sleep treatment (e.g., child

anxiety, attachment concerns, trauma history, physiological issues)
33 NDD medications negatively affect sleep
34 NDD symptoms make sleep problems harder to treat (e.g.,
needing to wind down; limited communication ability; pain/physical symptoms in CP; difficulty taking
medication; level of functioning; rigidity/difficulty with transitions; sensory sensitivities)

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don’t think there’s a lot of information available to doctors around this. It seems to be an area that doesn’t have a lot of research” (P32,
ADHD). Some parents reported that HCPs only seemed to offer melatonin and medication as treatment options, and other parents
expressed reluctance to use such treatments for sleep. A parent shared, “our doctors just automatically wanted to medicate [for sleep
problems]” (P31, CP). Another parent stated, “The pediatrician who diagnosed my daughter with ADHD simply said as an aside, ‘Oh
for sleep, you know you can give her melatonin and you can do it long term,’ and that was all that was ever said by him in the course of
discussing her treatment” (P28, ADHD).

The negative impact of sleep problems on parents and families also acted as a barrier that influenced parents’ decision to seek
treatment and ability to implement treatment, as did their own feelings of self-blame, anxiety, and exhaustion. One parent felt that
implementing strategies was difficult because, “we’re kind of empty. We have no more gas left in the tank after five years of sleep
deprivation” (P39, ADHD). Another parent said that it was hard to “be consistent with anything initially because you’re just so tired
that, even though you know what you should do, and you know what needs to be done […] you just do whatever you can to […] get
them to bed, or get them to go back to bed in the night. It’s kind of hard to be logical” (P20, ASD). Furthermore, addressing sleep
problems was not always described as a priority; a parent shared, “My child’s needs are so high and it’s so intense all of the time […]
we’ve never just made an appointment for sleep because we’re really in the throes of the crises every day” (P29, FASD). For some
parents, the complexity and comorbidity associated with their children’s NDD diagnoses was reported to act as a barrier, especially
NDD symptoms, NDD medications (especially stimulants and seizure medications), children’s anxiety, attachment concerns, trauma
history, and medical issues (e.g., seizures, feeding problems).

3.1.2. Facilitators
Three facilitators were identified, comprised of 24 individual codes (see Table 4): 1. Experience with Service (HCPs) and Treatment

Implementation, 2. Parent Factors (a. Beliefs and attitudes, b. Education), and 3. Support. Overall, parents were able to identify some
aspects of their experiences with HCPs and treatment that had facilitated their seeking or use of treatment: supportive and caring HCPs,
a behavioural approach to treatment, and consistency with treatment implementation were particularly helpful. Some parents also
reported that individualization of treatment (i.e., tailoring treatment to both child and parent needs) was helpful. Trying out different
types of treatment was also reported to be helpful; one parent noted, “You just try different things, I guess. See what works” (P43, CP).
Specific parental beliefs and attitudes were also reported to be facilitators, including being persistent, hopeful, self-advocating, and
experiencing success. Parents reported self-education to be a facilitator, with some either doing their own research on sleep or drawing
on their own specialized experience. One parent offered this perspective, “The books and the education and the establishing routines,
those have all been quite helpful, or helpful to varying degrees. […] none of them have been perfect, but […] picking away at it from all

Table 4
Parent Facilitators and Codes.

Facilitator Codes

1. Experience with Service (HCPs) and Treatment Implementation
1 Supportive, caring HCPs
2 Behavioural approach to treatment
3 Consistency
4 Incorporating medication
5 Incorporating melatonin
6 Incorporating sensory or physiological components
7 Individualization of treatment to child’s needs
8 Involving child in treatment
9 Nutrition
10 Practicing healthy sleep habits
11 Same strategies work for TD
12 Trying out different treatments
13 Understanding what’s comfortable for both child and parent
14 Using bedtime routines

2. Parent Factors
2A. Beliefs and Attitudes

15 Hope or past experience of success
16 Persistence or keeping going
17 Willing to try anything
18 Self-advocacy

2B. Education
19 Discovering cause of sleep problem (e.g., by assessment)
20 Drawing on own specialized experience
21 Getting psychoeducation about sleep
22 Self-education & doing own research

3. Support
23 Having support
24 Support from other parents

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directions has helped” (P15, ASD). Finally, support, especially from other parents, was identified as a key facilitator, with a parent
sharing, “The parents are the people who help you the most. Because you learn from them. […] You learn not to give up” (P3b, ASD).

3.1.3. Differences across NDD groups
Most themes were common across all four NDD groups, and most differences were reported within the NDD-Specific Factors

barrier. Some parents of children with FASD reported believing that sleep problems in their children were more complex to treat than
in other NDDs, whether due to a history of trauma and attachment concerns, or because they perceived their children as less responsive
to behavioural treatments due to neurological impairment. Parents of children with CP reported pain and medical problems (e.g.,
muscle tightness, limited mobility) as barriers to sleep more often than did parents of children with other NDDs; for example, some
parents reported that pain appeared to cause their children’s sleep problems.

Table 5
HCP Barriers and Codes.

Barrier Codes

1. Access to/Availability of Services
1 Lack of/limited specialist evidence-based sleep treatment & NDD services
2 Lack of information and resources

2. HCP Factors
2A. Education, Training & Experience

3 Lack of experience or training with sleep
4 Limited awareness of importance of sleep
5 Perceived self-efficacy – not sleep experts

2B. Beliefs & Attitudes
6 Different approaches from different HCPs
7 Relying on anecdotal data rather than functional behaviour analysis
8 Some strategies work, some don’t (hit or miss)

3. Individual Practice Factors
3A. Time

9 Lack of time and availability to provide treatment
10 Lack of time to access information and educate self

3B. Supporting Families
11 Unable to provide adequate or direct support

3C. Nature of Role/Practice
12 Nature of role/service = limited involvement or capacity for sleep treatment
13 Outside scope of practice

4. Parent Factors
4A. Parent Ability to Implement & Follow Through with

Treatment
14 Caregivers lack support
15 Challenging to get parents to implement strategies/follow through consistently
16 Concern that parents to not have capacity to implement treatment (treatment not

feasible)
17 Lack of stable home environment
18 Parental mental health concerns
19 Parents are exhausted/stressed/burned out

4B. Parents’ Access to Treatment
20 Language & communication are treatment barriers
21 Parents not able to physically attend appointments
22 Treatments not affordable/cost too great

4C. Parent Beliefs & Attitudes
23 Cultural norms conflict with recommended behavioural strategies (e.g., co-

sleeping)
24 Parents not ready for treatment
25 Parents don’t know that sleep problems can be treated/think they are normal
26 Medications are preferred/more frequently used
27 Parents are concerned about/resistant to using medication/melatonin
28 Parents are desperate for immediate solution
29 Sleep is not parents’ main priority for treatment

5. NDD-Specific Factors
30 Complexity and comorbidity associated with NDD complicates sleep treatment
31 Medication for NDD symptoms negatively impacts sleep
32 NDD symptoms make sleep problems harder to treat

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3.2. Health care professionals

3.2.1. Barriers
Five barriers were identified for HCPs, comprised of 32 individual codes (see Table 5): 1. Access to/Availability of Services, 2. HCP

Factors (a. Education, training, and experience; b. Beliefs and attitudes), 3. Individual Practice Factors (a. Time, b. Supporting families,
c. Nature of role/practice), 4. Parent Factors (a. Parent ability to implement/follow through with treatment, b. Parents’ access to
treatment, c. Parental beliefs and attitudes), and 5. NDD-Specific Factors.

Health care professionals reported that both their own limited access to resources needed to provide sleep treatments, as well as
their patients’ limited access to sleep- and NDD-related services, could act as barriers to treatment provision. One HCP noted that a
“lack of resources” about sleep and NDD meant there was “nowhere for parents to get […] help when they need it” (H34, CP). Some
HCPs also highlighted their lack of experience and training with sleep as potential barriers, saying: “It doesn’t really feel like we’re
experts in sleep… [because we] weren’t trained through school to think about sleep as a targeted intervention or a targeted goal” (H8,
ASD). Lack of time and availability to provide treatment and conduct follow-up appointments was another barrier. An HCP noted,
“Continued support I think is the most important thing, but it’s the hardest thing to do, given […] a clinic setting and availability of
clinicians” (H11, ASD).

Health care professionals also identified parents’ exhaustion, stress, and capacity for implementing treatment as barriers, noting
that when children do not sleep, neither do their parents. An HCP indicated that if parents “don’t identify [sleep] as a problem, then it’s
not really something I’m gonna tackle at that point for them,” because treatment depended on parents “going through a pretty rough
sleep to improve sleep behaviours” (H4, ASD). An HCP said, “It’s not an easy fix and it’s also not something that is fixed quickly, so
that’s difficult when parents are exhausted by the time they bring these problems to light […] their ability to cope is compromised from
the get go” (H25, ADHD). Finally, some HCPs noted that specific NDD-related factors could be barriers to treatment, including
comorbidities (mental health and medical) and use of medications that target NDD symptoms but may compromise sleep.

Table 6
HCP Facilitators and Codes.

Facilitator Codes

1. HCP Factors
1A. Education, Training, &

Experience
1 Professional development or formal training in sleep
2 Self-education
3 Accessing evidence-based literature
4 Accessing & using pre-existing resources

2. Individual Practice Factors
2A. Supporting Families

5 Ability to provide direct support to families
6 Ability to work in-home (e.g., BCBAs)

2B. Collaboration
7 Consultation with other colleagues
8 Multidisciplinary team approach

3. Treatment Approaches and
Experience

3A. Family-Centered Approach
9 Accommodating and understanding that caregivers may be on different pages
10 Making treatment manageable for parents and preparing them for difficulties
11 Taking into account family values and parents’ perspective and understanding of sleep
12 Help families experience success & positive affirmation

3B. Helpful Treatment Strategies
13 Behavioural approach to treatment
14 Consistency (helping families maintain)
15 Generalization of strategies across diagnoses
16 Psychoeducation about sleep to parents
17 Using assessment to inform sleep treatment
18 Using coaching, modelling, and teaching of strategies to parents

3C. Modifications to Treatment
19 Addressing physiological or physical factors affecting sleep
20 Individualization of treatment to the child
21 Modifying NDD medication regimen
22 Modifying treatments for NDD symptoms is helpful (e.g., accommodating functional level, adapting

strategies for NDD severity, addressing rigidity/difficulty with transitions, helping parents adjust
expectations, addressing feeding/swallowing issues, focusing on routines, modifying environment,
accommodating sensory sensitivities, using visual supports)

K.M. Tan-MacNeill et al.

Research in Developmental Disabilities 107 (2020) 103792

3.2.2. Facilitators
Three facilitators were identified, comprised of 22 individual codes (see Table 6): 1. HCP Factors (a. Education, training, and

experience), 2. Individual Practice Factors (a. Supporting families, b. Collaboration), and 3.Treatment Approaches and Experience (a.
Family-centered approach, b. Helpful treatment strategies, c. Modifications to treatment). In general, facilitators were related to HCPs’
acquisition of knowledge and education about sleep problems in NDD, working collaboratively with colleagues, their perceived ability
to adequately support families, and a variety of specific approaches to treatment (including strategies and treatment modifications).
Some HCPs reported that self-education was very helpful. Several mentioned using sleep/NDD resources such as the Autism Speaks
Sleep Toolkit (https://www.autismspeaks.org/sleep). One HCP commented, “I don’t have any formal training or education in [sleep].
It’s more what I’ve learned through experience and what I’ve picked up in supporting families” (H2, ASD).

A family-centered treatment approach that incorporates parents’ values and perspectives was recommended as facilitating pro-
vision. One HCP described treating sleep problems as “a partnership with parents” (H27, CP). Health care professionals reported that a
behavioural approach to treatment could be a facilitator, particularly when psychoeducation about sleep was combined with the use of
coaching and modelling strategies for parents. One HCP expressed that education was extremely important, saying, “Many families
these days under-value sleep and under-appraise the importance of sleep and what the implications of lack of sleep are for children,”
and noting that the “number one” recommendation would be “educating families on how to better set up sleep hygiene and routines to
accomplish that” (H38, ADHD). Finally, some HCPs noted that in addition to individualizing treatment to the child, addressing medical
factors, adjusting children’s NDD medication regimens (e.g., stimulants and anti-epileptics), and modifying treatments to accom-
modate NDD symptoms such as rigidity and other factors such as children’s functional levels and feeding/swallowing problems were
helpful.

3.2.3. Differences across NDD groups
Few differences across NDD groups were reported. Some HCPs suggested that more resources are available for sleep problems in the

context of ASD than other NDDs. Similar to parents, a few differences emerged for FASD and CP. For example, some HCPs felt that sleep
problems were harder to treat in children with FASD, because of the presence of dysregulation, brain damage, and history of trauma/
attachment problems. Professionals working with children with CP also identified sleep problems as being primarily related to pain
and medical factors, compared to the behavioural factors endorsed by the other HCPs.

3.3. Similarities and differences between parent and health care professionals

Lack of information, awareness, and accessible services for sleep were reported to be barriers by both parents and HCPs. Parents
and HCPs expressed concerns about each other, with some parents reporting that their experiences with HCPs could act as barriers or
facilitators, and HCPs reporting concerns about not wanting to burden parents with unfeasible treatments or commenting on parents’
inconsistent implementation. Both parents and HCPs acknowledged the difficulty of sleep treatment, emphasizing parental stress and
exhaustion as potential barriers. Both parents and HCPs reported that in some cases, sleep problems were not prioritized for treatment
amongst children’s other behaviour problems (e.g., disruptive behaviours).

Knowledge and education were endorsed by both parents and HCPs as facilitators to treatment. Both also found the same treatment
approaches helpful – primarily behavioural approaches, emphasis on consistency, use of bedtime routines, and healthy sleep habits,
with incorporation of melatonin or medication as needed. Individualization of treatment also emerged as a theme amongst both
parents and HCPs; for example, some parents reported needing to take an individualized, trial and error approach to treatment (i.e.,
trying out multiple treatments to find one that worked). From HCPs’ perspectives, the ability to individualize and modify treatments to
children’s and parents’ needs facilitated treatment provision (e.g., using more visual supports, addressing environmental sensitivities,
accommodating functional level, adjusting time expectations). Similar core behavioural treatment strategies and modifications were
identified as helpful across all four NDDs by parents and HCPs.

4. Discussion

The main purpose of this study was to identify barriers and facilitators experienced by parents and HCPs in accessing and utilizing
treatment for sleep problems in children with NDDs, in order to better inform our understanding of treatment needs from both parents’
and HCPs’ perspectives, and to inform the development of a sleep intervention for children with NDDs. Key themes that emerged from
the data were similar for both parents and HCPs. There is a general lack of knowledge and awareness about sleep problems among both
parents and HCPs, combined with inaccessible or limited services and evidence-based treatments. Sleep problems and their treatment
appear to be especially challenging, demanding, and intensive due to the negative impact on parents and the need to individualize
treatment to children’s needs within a complex array of NDD symptoms and comorbidities. Treatments often require already-tired
parents to implement difficult strategies consistently night after night with tired, uncooperative children and little support from
professionals. However, parents who had implemented sleep treatments and HCPs who provided sleep treatments for their patients
with NDDs reported that perseverance with behavioural treatment, particularly consistent use of bedtime routines and healthy sleep
habits, combined with melatonin or medication as needed, were effective and helpful. Given the intensity of sleep treatments, ensuring
that families feel supported by their HCPs, motivated, and hopeful before beginning and throughout treatment is critical.

When the four NDD groups were compared, very few differences in barriers and facilitators emerged. The primary differences
related to specific aspects of FASD and CP that could act as barriers to sleep treatment. However, across all NDD diagnoses, the same
core behavioural strategies were reported to be used, with modifications to accommodate specific NDD symptoms. Although this

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Research in Developmental Disabilities 107 (2020) 103792

transdiagnostic use of strategies initially appears to contradict the need for individualization of treatment, it should be noted that
parents and HCPs understood individualization as tailoring treatment to a child’s needs. The actual treatment approaches and specific
strategies that they used and recommended were the same across all four disorders. This suggests that exploring a transdiagnostic
approach to treatment may be useful, consistent with existing literature on sleep interventions for children with NDDs (Rigney et al.,
2018).

4.1. Clinical implications

Canadian parents of children with NDDs and HCPs working with these children reported that neither sleep treatments nor in-
formation and education about sleep are easily accessible. In particular, standard face-to-face treatment modalities may not be
accessible or feasible, with HCPs sharing that they are not able to follow up adequately with parents. Online intervention delivery (i.e.,
eHealth) may offer a solution to these barriers, as it is more accessible and wider reaching than conventional face-to-face interventions
(Breitenstein, Gross, & Christophersen, 2014). Another solution to reducing HCP time and involvement is parent-implemented in-
terventions, wherein parents are trained to deliver treatments to their children directly. Such interventions have been shown to be
effective for a wide range of NDD concerns (e.g., Althoff, Dammann, Hope, & Ausderau, 2019). Self-directed eHealth
parent-implemented interventions may be an ideal vehicle for delivering sleep psychoeducation and behavioural strategies directly to
parents. However, given the challenges that both parents and HCPs noted about being stressed and having difficulty following through
with intervention implementation, it will be important to explore how to provide adequate support to parents. Although we asked
participants in the present study about their experiences of seeking and using treatment from a range of sources, future research could
explore the barriers and facilitators affecting parents who are actively seeking treatment from a specific service or intervention
program.

Given emerging evidence that effective sleep treatment strategies are transdiagnostic across NDDs (Rigney et al., 2018), a modular
transdiagnostic eHealth intervention likely has great potential (e.g., Sauer-Zavala et al., 2017). For example, such an intervention
could offer general psychoeducation about sleep in the context of NDD, and recommend core behavioural strategies, healthy sleep
habits and bedtime routines (e.g., Rigney et al., 2018). If more specific NDD diagnostic information is required, parents could choose to
access a module specifically about sleep in the context of their child’s diagnosis.

The results of the present study have been used to inform the modification of the Better Nights, Better Days (BNBD) intervention for
TD children with insomnia (Corkum et al., 2018) into Better Nights, Better Days for Children with Neurodevelopmental Disorders
(BNBD-NDD). The original BNBD was recently the subject of a Canada-wide RCT (NCT02243501, clinicaltrials.gov). Based on the
current research, along with the extant literature (see Rigney et al., 2018), BNBD-NDD was developed as a modular transdiagnostic
parent-implemented eHealth intervention for parents of children with ASD, ADHD, CP, and FASD (see Tan-MacNeill et al., 2020 for
results of usability testing).

4.2. Limitations

This sample of participants may have been more interested in or knowledgeable about sleep than other parents and HCPs, given
their willingness to participate in an online study about sleep. Likewise, the study may have appealed to participants with greater
internet literacy. Although we aimed to recruit a diverse and representative sample, parents of children with more severe sleep
problems or other behavioural symptoms may have been less able to participate. Additionally, our sample was largely Caucasian,
reflecting lack of diversity. Difficulties in scheduling necessitated the administration of interviews, as well as focus groups that varied
in size, in order to accommodate participants. While emergent themes were consistent across interviews and focus groups during
coding, nevertheless different information may have been gained from these two approaches. The themes that emerged from the data
may also have been influenced by the questions asked in the topic guides. Finally, the study was expanded from originally only
including ASD-specific participants to include the other three NDDs to inform the development of the BNBD-NDD intervention. As
such, ASD-specific participants are overrepresented in the sample and recruitment of groups was non-concurrent (but all completed
within a two-year window).

4.3. Conclusion

Overall, these findings suggest a great need for more awareness about the importance of healthy sleep for children with NDDs, more
education about how to treat sleep problems, and more evidence-based interventions that are readily accessible. Similar barriers,
facilitators, and effective treatment strategies were identified across all four NDDs, suggesting that a transdiagnostic approach to
treatment would be helpful. An eHealth intervention would address many of the reported barriers to treatment.

Funding

This research was supported in part by scholarships and funding to Kim Tan-MacNeill from the Social Sciences and Humanities
Research Council (SSHRC), the Nova Scotia Health Research Foundation (NSHRF), the Nova Scotia Graduate Research (NSGS) pro-
gram, the Autism Research Training (ART) Program, and the Better Nights, Better Days Trainee Program. Dr. Isabel Smith was sup-
ported by the Joan & Jack Craig Chair in Autism Research. The study falls under the umbrella of both the Better Nights, Better Days
(BNBD) study (supported by the Canadian Institutes of Health Research Team Grant FRN-TGS 109221), and the Better Nights, Better

K.M. Tan-MacNeill et al.

Research in Developmental Disabilities 107 (2020) 103792

Days for Children with Neurodevelopmental Disorders (BNBD-NDD) project, supported by the Kids Brain Health Network (formerly
NeuroDevNet), a Canadian Network of Centres of Excellence.

Declaration of Competing Interest

Should the Better Nights, Better Days (BNBD) or Better Nights, Better Days for Children with Neurodevelopmental Disorders (BNBD-NDD)
interventions prove to be effective after being tested in randomized controlled trials, we plan to pursue commercialization of the
interventions to ensure their sustainability and accessibility.

Acknowledgements

The authors thank all families and health care professionals who participated in the study. A very special thank you to all those who
assisted in recruitment, management, transcription, data collection, and data analysis, especially Nicole Ali, Sydney Dale-McGrath,
Jason Isaacs, Amanda Young, Josh Mugford, Braeden Jennings, Sarah Campbell, and Ainsley Lofstedt.

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Barriers and facilitators to treating insomnia in children with autism spectrum disorder and other neurodevelopmental disor …

What this paper adds
1 Introduction
1.1 Background
1.2 Research objectives
2 Method
2.1 Participants
2.1.1 Parent participants
2.1.2 Health care professional participants
2.2 Screening, eligibility, demographic, and background information measures
2.2.1 Parents
2.2.2 Health care professionals
2.3 Focus groups and interviews
2.3.1 Description of focus groups/interviews
2.3.2 Topic guides
2.4 Analysis
3 Results
3.1 Parents
3.1.1 Barriers
3.1.2 Facilitators
3.1.3 Differences across NDD groups
3.2 Health care professionals
3.2.1 Barriers
3.2.2 Facilitators
3.2.3 Differences across NDD groups
3.3 Similarities and differences between parent and health care professionals
4 Discussion
4.1 Clinical implications
4.2 Limitations
4.3 Conclusion
Funding
Declaration of Competing Interest
Acknowledgements
References

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