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77
5
Progress Monitoring During
theTreatment ofAutism
andDevelopmental Disorders
CelesteTevis, MeganCallahan,
andJohnnyL.Matson
Abstract
Autism spectrum disorder (ASD) is character-
ized by decits in social interaction and com-
munication, as well as repetitive and restricted
behaviors. As the prevalence rates of ASD
have steadily increased since the 1990s, there
is a growing need to understand the optimal
treatment options available for ASD and their
efcacy. Monitoring the progress of individu-
als’ response to interventions is crucial to
manage any developmental challenges those
with ASD may encounter throughout the life
span. However, autism and developmental dis-
orders are multifaceted, often presenting with
a wide range of symptoms, impacting several
developmental domains. Additionally, there
are currently no standardized measures that
can be utilized for the purpose of progress
monitoring, further adding complications. The
aims, challenges, limitations, and measures
utilized for progress monitored among indi-
viduals with ASD are discussed.
Keywords
Progress monitoring · ASD · Developmental
disorders · Comorbidities · Treatment
outcomes · Response to treatment · Autism ·
Medical home
Autism spectrum disorder (ASD) is a neurodevel-
opmental disorder characterized by decits in
social interaction and communication, as well as
the presence of repetitive and restricted behaviors
(American Psychiatric Association, 2013). Since
the 1990s, prevalence rates of ASD have steadily
risen (Matson & Kozlowski, 2011; Bolte & Diehl,
2013) and are most currently estimated to impact
1in 54 children (CDC, 2020). As a result, there is
an increased need to identify optimal treatments
for ASD.Previously, the focal point of research
regarding ASD measurement tools was on their
use in early diagnosis, as opposed to their use in
assessing progress throughout interventions (Bolte
& Diehl, 2013). While accurate identication of
ASD serves as a crucial initial step, monitoring
progress throughout interventions is necessary for
managing developmental challenges that arise
throughout the life span (Eapen etal., 2016).
ASD is multifaceted and commonly presents
with a wide array of heterogeneous symptoms
(Eapen et al., 2016; Bolte & Diehl, 2013). The
C. Tevis (*) · M. Callahan · J. L. Matson
Department of Psychology, Louisiana State
University, Baton Rouge, LA, USA
e-mail: ctevis1@lsu.edu
© Springer Nature Switzerland AG 2022
P. McPherson (ed.), Handbook of Treatment Planning for Children with Autism and Other
Neurodevelopmental Disorders, Autism and Child Psychopathology Series,
https://doi.org/10.1007/978-3-031-06120-2_5
78
level of impairment across social, cognitive,
behavioral, and communication skills differs
among individuals with ASD. In addition to the
core symptoms, comorbid conditions, like atten-
tion decit hyperactivity disorder (ADHD),
oppositional deant disorder, and intellectual dis-
ability (ID), commonly co-occur in those with
ASD (Frazier etal., 2011; Simonoff etal., 2008).
Consequently, the central features of ASD may
not be the exclusive targets of intervention for
children. For example, challenging behaviors,
like aggression and self-injury, commonly require
management. More detailed progress monitoring
is essential for children with multiple co-
occurring conditions or behaviors compared to
those with fewer comorbidities, making progress
monitoring a highly individualized and complex
process (Eapen etal., 2016). For children receiv-
ing care in a pediatric medical home, progress
monitoring should be addressed in the shared
plan of care (Todorow et al., 2018). Chapters 1
and 2 can be referenced for a detailed review of
the medical home model of care.
Additionally, there are a variety of ways to
measure progress among those with ASD
(Hanratty et al., 2015). While several measures
exist to assess symptoms of ASD, there are cur-
rently no standardized measures expressly
designed for progress monitoring purposes (Bolte
& Diehl, 2013; Copeland & Buch, 2013). As a
result, diagnostic measures are often utilized to
meet monitoring requirements (Copeland &
Buch, 2013). The importance of progress moni-
toring, existing challenges, and future directions
will be discussed. The literature surrounding cur-
rent tools used to assess treatment response tar-
geting ASD and related comorbid conditions is
discussed below. The psychometric properties of
these measurement tools in individuals with ASD
are provided in the later part of the chapter.
Progress Monitoring Comorbid
Conditions
Anxiety Disorders
According to a recent meta-analysis, as many as
40% of children with ASD meet criteria for one
or more comorbid anxiety disorders (Van Steensel
etal., 2011; White etal., 2009). Specic phobia
(30–44%) is the most common anxiety disorder
in those with ASD, followed by obsessive com-
pulsive disorder (OCD) (17–37%), generalized
anxiety disorder (15–35%), and separation anxi-
ety (9–38%) (Zaboski & Storch, 2018). Comorbid
anxiety among those with ASD can be associated
with an increased likelihood of sleep problems,
self-injurious behaviors, gastrointestinal illness,
parental stress, and symptoms of depression
(Mazurek & Petroski, 2015; Kerns etal., 2015;
Mazurek et al., 2013; Kerns et al., 2017).
However, the relationship between anxiety disor-
ders and ASD is multifaceted, as the symptoms
of anxiety can be difcult to extricate from the
core symptoms of ASD.There is some intersec-
tion between symptoms of anxiety and ASD,
such as social avoidance and compulsive behav-
ior (Kerns etal., 2017; Lecavalier et al., 2014).
Additionally, a high prevalence of children with
ASD, estimated between 50% and 80%, also
have accompanying intellectual disabilities
(Mpaka etal., 2016). Impairments in expressive
language skills make self- and parent-report mea-
sures less reliable among children with ASD and
lower cognitive skills (Lecavalier et al., 2014).
Comorbid anxiety can impact the severity of
ASD symptoms; thus, it is crucial to monitor the
treatment of anxiety symptoms among those with
ASD (Kerns etal., 2017).
Despite the high prevalence of comorbid anxi-
ety disorders among those with ASD, there is a
lack of consensus surrounding how anxiety
symptoms should be measured. Approximately
36 parent-, child-, and teacher-report measures
have been used to measure anxiety symptoms in
children with ASD, though very few measures
have been validated on children with ASD
(Lecavalier et al., 2014). In a recent review,
Lecavalier et al. (2014) reviewed 10 measures
detailed in over 38 research studies. Of the mea-
sures, a select few were considered to be appro-
priate for monitoring anxiety among children
with ASD.The rst is the Child and Adolescent
Symptom Inventory—5th Edition (CASI-5,
Gadow & Sprafkin, 2002) which is a behavior
rating scale for DSM-5 behavioral and emotional
disorders utilized for children between 5 and
C. Tevis et al.
79
18years of age. As part of the CASI-5, there are
individual items that assess across several anxi-
ety disorders including generalized anxiety disor-
der, separation anxiety, and social anxiety. In a
randomized controlled trial, White etal. (2013)
used the Child and Adolescent Symptom
Inventory—4th Edition Revised (CASI-4R;
Gadow & Sprafkin, 2002; Hallett et al., 2013)
anxiety scale as the primary outcome measure of
anxiety symptoms among children with ASD and
at least one comorbid anxiety disorder. The
Multidimensional Anxiety Scale for Children
(MASC; March etal., 1997) was also found to be
useful for measuring anxiety symptoms in those
with ASD.The MASC can be completed by care-
givers or as a self-report measure for children
between 8 and 19years of age. The items assess
several subscales including the physical symp-
toms scale, social anxiety scale, harm avoidance
scale, separation/panic scale, and total anxiety
scale. The MASC, especially the self-report
form, is dependent on language abilities; how-
ever, the total score on the parent report form of
the MASC has shown to be an effective treatment
outcome measure among children with ASD
(Wood et al., 2009; Storch et al., 2013). Lastly,
the Pediatric Anxiety Rating Scale (PARS;
Ginsburg etal., 2011) is a measure that can be
utilized to assess severity of anxiety symptoms in
children between 6 and 17 years of age. The
PARS is a semi-structured interview for children
and caregivers. Similarly to the MASC, the child-
interview section of the PARS requires uent
expressive language skills, which may limit its
utility among children with ASD (Lecavalier
etal., 2014). While the PARS was found to have
acceptable validity when used to measure symp-
toms of anxiety among children with ASD, its
internal consistency reliability was low
(Lecavalier etal., 2014). However, a study com-
pleted by Maddox etal. (2020) utilized a modi-
ed PARS for children with ASD aged 6–17years
of age. This modied PARS is a child and care-
giver interview that clinicians can complete with
both individuals together. This modied version
of the PARS is not as dependent on the child’s
verbal skills and expression of anxiety symptoms
but rather emphasizes observable signs of anxiety
that caregivers may witness.
Sleep Problems
Children with ASD are at a higher risk for expe-
riencing sleep problems than their typically
developing peer counterparts. As many as
50–80% of children with ASD experience sleep
problems, such as frequent awakenings during
the night, early wake times, or fragmented sleep
(Abel etal., 2017; Hodge etal., 2012). Prior stud-
ies have suggested that sleep difculties among
children with ASD may worsen the core symp-
toms of ASD and contribute to challenging
behaviors, like aggression and self-injury (Fadini
etal., 2015; Abel etal., 2017). Thus, sleep prob-
lems are essential for clinicians to identify and
monitor, so that they do not impact the progress
of treatment (Abel etal., 2017).
Sleep questionnaires can be used to both
assess and monitor sleep problems in children
with ASD.One common measure, the Children’s
Sleep Habits Questionnaire (CSHQ; Owens
etal., 2000), is a caregiver-report questionnaire
that assesses bedtime resistance, sleep-onset
delay, sleep anxiety, nighttime waking, parasom-
nias, sleep-disordered breathing, and daytime
sleepiness (Owens etal., 2000). On the CSHQ,
parents rate how frequently their children experi-
enced sleep problems within the past week.
While the CSHQ was originally developed to
identify sleep problems in typically developing
children aged 4–10 years old, it is also used in
clinical populations (Moore et al., 2017).
Currently, the CSHQ is the most widely used
standardized measure designed to identify and
monitor sleep problems in children with ASD
(Hodge et al., 2012; Moore et al., 2017).
According to a study completed by Giannotti
et al. (2008), consisting of 104 children with
ASD and 162 typically developing children, the
CSHQ was found to demonstrate adequate inter-
nal consistency among participants with ASD.
The Modied Simonds and Parraga Sleep
Questionnaire (MSPSQ; Simonds & Parraga,
5 Progress Monitoring During theTreatment ofAutism andDevelopmental Disorders
80
1982) is another measure that identies sleep dis-
turbances in children between 5 and 18years of
age. The MSPSQ has been modied and used to
both assess and monitor treatment outcomes for
children with ASD and other developmental dis-
abilities (Moore et al., 2017; Wiggs & Stores,
2004). The MSPSQ includes 51 items organized
across two parts, one used to assess sleep quality
and quantity, and another used to identify specic
sleep difculties. While it is similar to the CSHQ
in that it assesses a range of sleep problems, it
also obtains information advantageous to treat-
ment planning (Moore etal., 2017). Additionally,
the Family Inventory of Sleep Habits (FISH;
Malow et al., 2009) is a measure designed to
assess sleep hygiene of individuals with ASD
aged 3–10. It probes for information related to
children’s habits before bedtime, bedtime rou-
tines, sleep environment, and caregiver behaviors
surrounding bedtime. A study completed by
Malow etal. (2015) examined whether caregiver
sleep education provided in either an individual
or group format would be most effective for care-
givers of children with ASD.Caregiver question-
naires, including the FISH and CSHQ, were
utilized to identify the initial sleep difculties
and posttreatment outcomes of the children with
ASD. The FISH can be particularly useful in
treatment planning and monitoring treatment out-
comes for children with ASD, as it was designed
to be utilized in children with ASD (Moore etal.,
2017).
Additionally, sleep diaries, also known as
sleep logs, can be used to record and monitor
sleep problems in children with ASD.Sleep dia-
ries require caregivers to report on their child’s
sleep quality over the previous night for a period
of 2weeks or longer to ensure adequate validity.
Typically, sleep diaries will include information
regarding the child’s bed time and waking times,
daytime sleeping, the onset time of sleep, and any
sleep-waking activity (Hodge et al., 2012).
Several studies using samples of children with
ASD have supported the use of sleep diaries to
assess and monitor variables impacting sleep.
Prior studies that examined children with ASD
found strong correlations between parent sleep
diary reports and data from actigraphy (i.e., an
objective measure that monitors an individual’s
nighttime movement) (Hodge et al., 2012;
Goodlin-Jones et al., 2008; Allik et al., 2006).
The ndings from the aforementioned studies
suggest that sleep diaries can be particularly use-
ful in assessing and monitoring sleep and wake
times, though they may not provide as accurate
information regarding sleep latency compared to
objective measures, like actigraphy (Hodge etal.,
2012).
Challenging Behaviors
Challenging behaviors, also referred to as prob-
lem behaviors, are behaviors that impact an indi-
vidual’s ability to function and often can cause
harm, such as aggression, property destruction,
and self-injurious behavior (Minshawi et al.,
2014). While challenging behaviors are not a
core feature of ASD, they are prevalent in an esti-
mated 64.3–94.3% of children with ASD
(Murphy etal., 2009). The rate at which individu-
als engage in challenging behaviors is associated
with increased ASD severity, impairments in
adaptive skills, and comorbid intellectual disabil-
ity (ID) (Baeza-Velasco et al., 2014; Baghdadli
etal., 2008). A range of negative effects of chal-
lenging behaviors have been identied. Some of
these effects include bodily injury, limited social
relationships, impairments in adaptive behavior,
poor academic skills, and decreased quality of
life (Baghdadli et al., 2008; Herzinger &
Campbell, 2007). As a result, frequent progress
monitoring of challenging behaviors throughout
evidence-based treatment is essential to ensure
decreased frequency and intensity of behaviors.
Considering the high prevalence of challeng-
ing behaviors in those with ASD, there is a need
for validated measures that assess and progress
monitor challenging behaviors in children with
ASD (Minshawi etal., 2014). Currently, there are
several caregiver-rated measures that are used to
identify challenging behaviors in those with ASD
and developmental delays, as well as typically
developing individuals. The Aberrant Behavior
Checklist (ABC; Aman etal., 1985a) can be used
to both assess the core symptoms of ASD and
C. Tevis et al.
81
various comorbid emotional and behavioral prob-
lems across several domains including irritabil-
ity, agitation and crying, lethargy/social
withdrawal, stereotypic behavior, hyperactivity/
noncompliance, and inappropriate speech (Aman
etal., 1985a). It was originally developed to be
utilized as a measure of treatment outcome in
individuals with ID but has since been used and
normed on individuals with a range of develop-
mental delays associated with behavioral prob-
lems such as ASD, Down syndrome, and Fragile
X syndrome (Kat et al., 2020). It has been uti-
lized extensively in both pediatric and adult pop-
ulations due to the high reliability and validity of
the measure (Aman etal., 1985b; Schmidt etal.,
2013). The ABC is especially advantageous for
monitoring treatment outcomes, as it is more
comprehensive than other measures used to iden-
tify behavior problems in individuals with ASD
like the Autism Behavior Checklist or Social
Response Scale (Kat etal., 2020).
While the ABC was originally developed for
use among individuals in residential facilities,
revisions to the measure have been made to allow
for more appropriate usage among individuals in
home and school settings; this measure is referred
to as the Aberrant Behavior Checklist—
Community (ABC-C) (Aman & Singh, 1994;
Schmidt etal., 2013). The ABC has been used to
monitor the outcomes of behavioral interventions
in both research (Aman etal., 2009) and in prac-
tice. For example, the ABC has been a useful tool
in identifying participants for forms of research
interventions, as well as monitoring comorbid
behavioral difculties among those with genetic
syndromes or neurodevelopmental disorders.
While the ABC has mainly been used to assess
children, adolescents, and adults, some studies
have evaluated behavioral and pharmacological
treatment effects with the ABC in children under
5years of age; this is most likely due to the lack
of other available measures for young children
(Schmidt et al., 2013; Brown et al., 2002;
Chadwick etal., 2000). However, in a study com-
pleted by Schmidt etal. (2013), researchers found
that the original 5-factor structure of the ABC-C
was not supported in children under 5 years of
age, and it may either underestimate or overesti-
mate behavior problems in this population. Thus,
the utility of the ABC-C for young children under
5years of age is not fully supported and estab-
lished currently (Schmidt etal., 2013).
Additionally, the Behavior Problems
Inventory-01 (BPI-01; Rojahn etal., 2001) is a
narrowband measure that assesses behavioral
problems in individuals with ID and develop-
mental disabilities. It contains items that measure
self-injurious behavior, stereotypic behavior, and
aggressive/destructive behavior on both a ve-
point frequency scale (never=0, monthly = 1,
weekly = 2, daily = 3, hourly =4) and a four-
point severity scale (no problem=0, slight prob-
lem = 1, moderate problem = 2, severe
problem = 3). It was designed to assess behav-
ioral problems in individuals, measure treatment
outcomes, and assist with surveys used for
administrative decision-making (Rojahn et al.,
2001). While the BPI-01 was found to have ade-
quate reliability in adults with intellectual dis-
abilities (González et al., 2009), it was not
specically developed or validated on infants and
toddlers (Rojahn et al., 2013; Minshawi et al.,
2014).
There are limited measures that identify and
monitor behavioral difculties in children with
ASD specically, especially infants and toddlers
with ASD. While two common measures, the
Behavior Assessment System for Children Third
Edition (BASC-3; Reynolds & Kamphaus, 2015)
and the Child Behavior Checklist (CBCL;
Achenbach & Rescorla, 2000) both assess behav-
ior problems in young children under 5years of
age, they were both designed for typically devel-
oping populations; there is also a lack of research
on contact validity for use of the these measures
among children with ASD (Hanratty etal., 2015).
However, infants and toddlers with ASD are at a
greater risk for developing challenging behaviors
than young children with other developmental
disabilities (Hartley etal., 2008). The Baby and
Infant Screen for Children with Autism Traits,
Part 3 (BISCUIT-Part 3; Matson etal., 2009) is a
caregiver-rated measure that was designed to be
used in children between 17 and 37 months of
age with this purpose in mind. The BISCUIT-Part
3 includes 17 different items that assess a range
5 Progress Monitoring During theTreatment ofAutism andDevelopmental Disorders
82
of behavioral topographies including self-
injurious behaviors, stereotypic behaviors, dis-
ruptive behaviors, and aggressive behaviors.
Clinical cutoff scores are presented on the mea-
sure for no to minimal impairment, moderate
impairment, and severe impairment. Additionally,
the Autism Spectrum Disorder—Behavior
Problems for Children (ASD-BPC; Matson etal.,
2008) and Autism Spectrum Disorder—Behavior
Problems for Adults (ASD-BPA; Matson &
Rivet, 2007) are measures that identify behav-
ioral problems in individuals with ASD speci-
cally. The ASD-BPC is a measure completed by
caregivers of children between 2 and 16years of
age made up of 18 items that are rated on a three-
point Likert scale (0= not a problem or impair-
ment, 1=mild problem or impairment, 2=severe
problem or impairment). The ASD-BPC is made
up of two domains including internalizing and
externalizing behaviors. The ASD-BPA is a
caregiver- rated screening measure that can be
used to determine the frequency, intensity, func-
tion, and duration of challenging behaviors in
adults with ASD.It is made up of 19 items across
3 subscales including aggression/destruction,
disruptive behavior, and self-injurious behavior,
and a total score. The items are rated on a binary
scale (0 = not a problem, no impairment,
1=problem, impairment). The ASD-BPA can be
followed up by the BP-01, direct measures, or
functional assessment depending upon the neces-
sity of the individual. The ASD-BPA identies a
range of challenging behaviors in adults with
ASD and can be administered in a short amount
of time. As a result, it can serve as a method of
monitoring treatment outcomes in adults with
ASD (Matson & Rivet, 2007).
Depression
Depression is a common diagnosis in children
and adolescents, with a prevalence rate of nearly
12% of children in the population (DeFilippis,
2018). Depression is associated with increased
risk of medical illness, suicidality, and poor func-
tioning in children and adolescents alike
(Chandrasekhar & Sikich, 2015). Prior research-
ers have suggested that individuals with ASD are
nearly four times more likely to develop depres-
sion than their typically developing peers, sug-
gesting the importance of regular screening,
monitoring, and treatment of depression (Hudson
et al., 2019). However, comorbid depression
diagnoses can be difcult to identify in those
with ASD, as depression symptoms can present
differently and overlap with ASD. For example,
depression can be accompanied by social with-
drawal and interpersonal difculties, which is
also characteristic of ASD (DeFilippis, 2018).
Additionally, there is a lack of validated diagnos-
tic tools that can be used among individuals with
ASD (Chandrasekhar & Sikich, 2015; DeFilippis,
2018).
Several questionnaires have been utilized to
identify and monitor depression symptoms in
those with ASD, though there is a lack of stan-
dardized progress monitoring tools that can be
utilized in this population (DeFilippis, 2018).
The Children’s Depression Inventory—Second
Edition (CDI-2; Kovacs, 2010) is utilized com-
monly among children with ASD. It is a brief
self-, parent-, or teacher-report measure for chil-
dren between 7 and 17years of age that can be
used to identify the cognitive, affective, and
behavioral symptoms of depression. The self-
report version of the CDI-2 is a clinically vali-
dated measure of depression with adequate
sensitivity (80%) and specicity (84%) in typi-
cally developing children (Kovacs, 2010; Kovacs
& Staff, 2003). While the specicity and sensitiv-
ity of the measure have been found to be lower
when utilized among children between 10 and 17
with ASD, it has been used to identify and moni-
tor depression symptoms among them (Mazefsky
& Oswald, 2011). The parent-report version of
the CDI-2 has also been utilized to measure and
monitor depressive symptoms among children
and adolescents with ASD and developmental
delays (Gotham etal., 2015). However, Mazefsky
and Oswald (2011) suggest that self-report mea-
sures, like the CDI-2, should be interpreted with
caution, even though they do provide useful
information regarding depressive symptoms. As
a result, clinicians may choose to use both the
self-report and parent-report version of the CDI-2
C. Tevis et al.
83
when evaluating and monitoring depressive
symptoms.
The BASC-3 is also commonly used to iden-
tify symptoms of depression and anxiety, though
it was not created to be a diagnostic tool
(Reynolds & Kamphaus, 2015). Prior studies
have compared the use of the BASC in individu-
als with ASD and typically developing children
on the internalizing composite and its related
subscales. For example, Mahan and Matson
(2011) examined the differences on the BASC-2
parent-rating scales between a typically develop-
ing sample and children with ASD between 6 and
16years of age. The children with ASD scored
signicantly higher on the depression subscale;
however, there were not signicant differences
between the scores on the anxiety subscales
among those with ASD and those typically devel-
oping individuals. This study supports the valid
use of the BASC among those with ASD when
screening and monitoring symptoms of depres-
sion, as it replicated the ndings of higher inter-
nalizing symptoms in children with ASD.
Attention Decit/Hyperactivity
Disorder
While approximately 7% of the population has a
diagnosis of attention decit/hyperactivity disor-
der (ADHD), between 30% and 80% of individu-
als with ASD have a comorbid diagnosis of
ADHD (Hollingdale et al., 2020; Young et al.,
2020). ADHD is characterized by decits in
attention, as well as symptoms of hyperactivity
and impulsivity (APA, 2013). While the condi-
tions independently have established standards
on their identication and treatment, the under-
standing of ASD and ADHD as comorbid
conditions is less founded (Young et al., 2020).
ASD and ADHD were not formally recognized
as co- occurring conditions until the Diagnostic
and Statistical Manual of Mental Disorders (5th
ed.; DSM-5; APA, 2013). Prior researchers sug-
gested that lower cognitive functioning, increased
social impairments, and delays in adaptive skills
are more prevalent among individuals with
comorbid ASD and ADHD compared to individ-
uals with ASD only (Rao & Landa, 2013). Thus,
there is an increased need for an understanding of
the identication and monitoring of the condi-
tions together (Young etal., 2020).
Several narrowband rating scales can be used
to both screen for symptoms of ADHD and moni-
tor response to interventions (Young etal., 2020).
One such rating scale is called the ADHD Rating
Scale—5 for Children and Adolescents
(ADHD-RS-5; Dupaul et al., 2016). The
ADHD-RS-5 contains both parent- and teacher-
rating scales that can be used with children
between the ages of 5 and 17years of age (Dupaul
etal., 2016). The items on the ADHD-RS-5 are
closely linked to the symptom domains specied
in the DSM-5, including both inattention and
hyperactivity symptoms (Dupaul et al., 2016;
Epstein & Weiss, 2012). One study completed by
Yerys et al. (2017) examined the use of the
ADHD-RS-IV among children with ASD, as it is
a rating scale that is regularly used to screen for
ADHD symptoms in children with ASD.However,
limitations existed with factorial validity when
used in individuals with ASD. Thus, caution is
advised when using narrowband rating scales,
particularly the ADHD-RS-IV/ADHD-RS-5, to
measure response to treatment (Yerys et al.,
2017). The Conners’ Rating Scales—Revised
(CRS-R; Conners, 1997) contains parent, teacher,
and adolescent self-report scales for individuals
between 6 and 18years of age. This measure con-
tains several content, index, and symptom scales
that can be used to identify specic difculties
related to ADHD symptomology and related
comorbidities. The CRS-R provides large norma-
tive samples and t-scores based on age and sex.
The CRS-R is a valuable option for clinicians to
consider as symptom-based outcome measures,
as it has adequate psychometric properties
(Epstein & Weiss, 2012). The Vanderbilt ADHD
Diagnostic Rating Scale (VADRS; Collett etal.,
2003) is another rating scale for children between
6 and 12years of age that assesses both symp-
toms of ADHD and their impact on behavior and
academic performance. In addition to the ADHD
symptom scales, there are performance scales
included in the measure to assess impairment in
math, reading, writing, and interpersonal rela-
5 Progress Monitoring During theTreatment ofAutism andDevelopmental Disorders
84
tionships (Epstein & Weiss, 2012). Two versions
of the VADRS are available, including a parent-
and teacher-rated scale. Clinicians be inclined to
use the VADRS to monitor changes in the fre-
quency of ADHD symptoms and any medication
side effects, as the measure is available in the
public domain (Epstein & Weiss, 2012).
Progress Monitoring Methods
Progress monitoring can be dened as “the simple
repeated measurement of student performance
toward a long-range goal” (Deno, 1985). While
children with ASD could benet from ongoing
evaluation of academic skills, like math and read-
ing, impairment in social communication is a
dening characteristic of ASD (APA, 2013;
Witmer etal., 2015). Interventions aimed at the
core features of ASD, as well as any accompany-
ing challenging behaviors, are most likely to be
benecial. As described by Salvia etal. (2013),
several methods are commonly utilized to moni-
tor ongoing intervention. These methods include
interview techniques, rating scales, and system-
atic observations, all of which are accompanied
by both advantages and disadvantages (Witmer
etal., 2015). While interview techniques can pro-
vide qualitative information, they risk susceptibil-
ity to bias. Rating scales completed by informants,
such as parents, teachers, or children, can provide
a rating of a particular behavior based on the
informant’s observations over a period of time.
While they can be completed relatively quickly
and assist in the detection of low base rate behav-
iors, they may not specically target the behavior
of interest and may be susceptible to response
bias. Systematic observation techniques allow for
a target behavior to be measured at the time and
place of its occurrence. These techniques are a
central approach to data collection in applied
behavior analysis (ABA) to treat children with
ASD, as they are less vulnerable to bias; however,
it is debatable whether lay people, like parents
and teachers, can use these techniques reliably to
monitor progress regularly across settings
(Witmer etal., 2015; Fisher etal., 2011).
Progress Monitoring Challenges
While ABA treatment techniques are well sup-
ported by research, children with ASD do not all
experience uniform success; this further high-
lights the need for effective measures to assess
response to treatment among children. Measuring
progress in areas of social communication can be
difcult as the changes often vary across children
and interventions (Grzadzinski etal., 2020). For
example, changes in social communication
behaviors may be related to either an increased
quantity of the behavior or its overall quality
(Grzadzinski et al., 2020; Kasari et al., 2015).
Consider two social communication goals that
are often targeted in ABA therapy: prosody and
manding. For treatments targeting language pros-
ody (e.g., rhythm and intonation of speech), clini-
cians may measure the quality of a child’s spoken
phrases. Contrarily, changes in communicative
behaviors that are expected to increase in fre-
quency, such as mands (e.g., requests), would
most adequately be examined in terms of the
behavior’s quantity. Additionally, interventions
vary in their focal points, with some emphasizing
behaviors across symptoms of ASD and others
focusing on more general social communication
skills, like joint attention. Thus, it is crucial for
measures used to assess progress to account for
“subtle, though clinically meaningful” changes
across many different areas of social communica-
tion, including the quality and quantity of behav-
iors (Grzadzinski etal., 2020, p. 2; Anagnostou
etal., 2015).
As discussed by Bolte and Diehl (2013),
there is very limited consistency in the measures
used to assess changes in treatment response for
individuals with ASD. After the review of 195
intervention trials, researchers found that 289
different measurement tools were utilized in an
attempt to assess behavior changes related to
treatment. Only three measures, including the
Aberrant Behavior Checklist (ABC), the
Clinical Global Impression Scale (CGI), and the
Vineland Adaptive Behavior Scales (VABS),
were consistently included in more than 2% of
the studies.
C. Tevis et al.
85
Progress Monitoring Limitations
Limited Validity
One of the primary challenges associated with
progress monitoring is the lack of valid and reli-
able tools that can assess change across time. As
a result, clinicians often utilize tools designed for
diagnostic purposes which are not typically sen-
sitive to changes that occur as a result of inter-
vention. Also, many of these measures were
developed for use with typically developing chil-
dren, often making delayed developmental tra-
jectories difcult to monitor across time (Eapen
et al., 2016). Several measures have been criti-
cized for their lack of validity as tools used to
assess treatment progress (Grzadzinski et al.,
2020; Eapen et al., 2016). For example,
McConachie etal. (2015) completed a review of
tools to measure outcome for children with ASD,
nding that three of the most commonly utilized
measures for progress monitoring, the ABC,
CGI, and VABS, along with several others, were
not recommended. These tools were found to be
limited in their ability to detect change in
response to intervention, as they were not devel-
oped for the purpose of progress monitoring and
often required extensive training (Grzadzinski
etal., 2020).
Contrarily, after reviewing 38 measures,
Anagnostou etal. (2015) found 6, including the
ABC and VABS, to be sufcient at measuring
treatment response among children with ASD,
though some limitations were noted (Grzadzinski
etal., 2020). While the ABC was found to ade-
quately measure treatment effects in children
with ASD, Fragile X, and related developmental
delays, it was not able to measure some decits in
social communication (e.g., impairments in non-
verbal communication and quality of social initi-
ations). Additionally, while the VABS was found
to adequately characterize social and communi-
cation skills of those with ASD, it has not been
found to be consistently sensitive to change over
time; this is particularly a challenge for interven-
tions lasting shorter than 6 months (Anagnostou
etal., 2015; Grzadzinski etal., 2020).
Potential Bias
Several current measures utilized for progress
monitoring purposes rely on either caregiver or
clinician report. For example, the CGI is com-
monly used as an outcome measure, particularly
regarding medication therapy. Similarly, mea-
sures designed to assess adaptive functioning,
such as the Adaptive Behavior Assessment
System (ABAS) and the VABS, require a care-
giver report of the child’s current skills. These
measures, as well as other similar measures, can
emphasize placebo effects, exceeding any small
changes that may be related to treatment
(Guastella etal., 2015; Grzadzinski etal., 2020).
Because clinicians and caregivers often take an
active role in treatment, the threat of biased
results can be high. For instance, when complet-
ing a caregiver-rated measure, any changes
observed may be the result of caregiver’s under-
standing of whether or not the child was receiv-
ing treatment, as opposed to the actual treatment
effects (Guastella etal., 2015; Grzadzinski etal.,
2020).
Considering this limitation, one structured
observation measure, the Early Social-
Communication Scales (ESCS), has been
designed to adequately assess changes in verbal
and nonverbal communication skills among typi-
cally developing or developmentally delayed
children (Mundy etal., 2013; Anagnostou etal.,
2015). It has been found to be an adequate mea-
sure for treatment outcome. However, it requires
considerable training for reliable administration,
and it may only be utilized for typically develop-
ing children between 8 and 30months or devel-
opmentally delayed children with a verbal age
that falls in the aforementioned range
(Grzadzinski etal., 2020; Mundy etal., 2013).
Limited Measures that Evaluate ASD
Symptoms
While most interventions for children with ASD
target ASD-specic symptoms, many of the com-
monly utilized treatment outcome measures were
5 Progress Monitoring During theTreatment ofAutism andDevelopmental Disorders
86
not designed to assess ASD-specic symptoms
(Grzadzinski et al., 2020; Kasari et al., 2002;
Green etal., 2010). For example, the VABS mea-
sures global adaptive functioning in individuals
across several domains, including communica-
tion, socialization, daily living, and motor skills
(Sparrow et al., 2016). Additionally, the ABC
assesses a range of challenging behaviors across
various settings, such as irritability, social with-
drawal, stereotypic behavior, noncompliance,
and inappropriate speech (Aman et al., 1985a).
Children with ASD can have associated adaptive
functioning difculties or challenging behaviors,
so measuring any improvements in these decits
as a result of treatment can be clinically informa-
tive. However, measures that directly assess
ASD-specic symptoms, including decits in
social communication and repetitive behaviors,
are required to evaluate whether there are
improvements of the core features of ASD as a
result of treatment (Grzadzinski etal., 2020).
In order to assess changes in the core symp-
toms of ASD, previous studies have utilized the
ADOS (Lord et al., 2000; Grzadzinski et al.,
2020). However, the ADOS was designed to be
utilized as a diagnostic tool, rather than a mea-
sure of treatment outcomes. Prior researchers
have used both the raw scores from the subscales
and the Calibrated Severity Scores (CSS), which
are considered to be more sensitive to changes
over time. The raw scores were generally not able
to meaningfully identify changes associated with
treatment, as changes in raw scores were observed
even among children in control groups
(Grzadzinski et al., 2020; Green et al., 2010;
Gutstein etal., 2007). The ADOS CSS was found
to effectively evaluate change over the course of
several years; however, it does not adequately
measure changes associated with brief interven-
tions (Anagnostou etal., 2015; Grzadzinski etal.,
2020). Considering these limitations, it is recom-
mended that diagnostic measures, like the ADOS,
not be utilized to measure treatment outcomes
(Grzadzinski etal., 2020).
Individualized Progress Monitoring
According to the National Institute for Health
and Care Excellence (2013), intervention and
treatment plans for ASD should be individual-
ized, research based, continually monitored for
progress, and frequently revised. As children
with ASD develop into adolescents and young
adults, it becomes increasingly difcult to deliver
efcacious treatments as their particular goals
and needs evolve (Eapen etal., 2016). As early as
the elementary school years, children with ASD
may be educated in ABA therapy clinics or exclu-
sively special education schools, while others
may be integrated into the general education cur-
riculum. However, as children age, they may be
exposed to an increasing variety of environments
including advanced educational placements,
work settings, or supported employment settings.
Clinicians should aim to include input from indi-
viduals with ASD into treatment planning, along
with the goals of any caregivers that may be
involved. While elements of an individualized
treatment plan are expected to change as the indi-
vidual progresses, there are crucial core ele-
ments. These elements include long-term goals
for the individual with ASD including a transition
plan, assessment of current performance of adap-
tive skills, measurable goals over a specic inter-
val of time, a method in which to monitor
progress, and a review and revision of the mental
health and medical provider treatment plans
which are captured in the overall shared plan of
care (Eapen etal., 2016).
Progress Monitoring Throughout
theLife Span
There are four primary stages throughout the life
span that are often accompanied with different
goals, as individuals’ abilities and environments
change. Generally, these developmental stages
include the preschool years, primary years, high
C. Tevis et al.
87
school years, and adult years. During the pre-
school years, treatment goals typically focus on
impairments in receptive and expressive lan-
guage, social skills, behavioral difculties, motor
skills, and increasing adaptive independence.
However, as individuals with ASD age, treatment
goals typically “shift from assessing specic
developmental domains and abilities to assessing
participation in education, employment, or civic
life” (Eapen etal., 2016, p.90). Changes in prog-
ress monitoring should also be considered during
periods of assessment and reassessment of indi-
viduals with ASD.For example, there are several
signicant transitions throughout the life span
that may be stressful for individuals with ASD
and their families; these include the time immedi-
ately following a diagnosis, the start of school or
educational programs, the transition from one
educational placement to another, and post high
school as students move into vocational place-
ments. During these time periods in particular,
goal-specic assessment and progress monitor-
ing are crucial. Topics related to independent liv-
ing, daily living skills, personal and sexual
relationships, and driving may also be consid-
ered. Contact information to any relevant
resources or services that may assist individuals
with ASD and their families during major life
transitions should be incorporated into the prog-
ress monitoring framework (Eapen etal., 2016).
Developments inProgress
Monitoring
When choosing appropriate treatment outcome
measurements, there are several factors that
should be considered. Clinicians should recog-
nize that not all children will respond to treat-
ments in the same way (Vivanti et al., 2014;
Grzadzinski etal., 2020). There are currently no
measurement tools that are comprehensive
enough to assess all changes. Additionally, mea-
surement tools may overidentify children that did
not change as a result of intervention, or, con-
versely, underidentify children that underwent
changes as a result of the intervention. Below are
several highlighted measures that can be espe-
cially useful in assessing response to intervention
among individuals with ASD (Grzadzinski etal.,
2020).
Treatment Outcome Measures
forASD
Change across the core symptoms of ASD is
among the most crucial measure of change for
interventions aimed to improve ASD (Matson,
2007). However, according to Rogers (1998), the
vast majority of research investigating treatment
outcomes for ASD did not utilize one of the main
ASD outcome measures. A review conducted by
Bolte and Diehl (2013) investigated specic mea-
surement tools used to identify response to treat-
ment in ASD interventions between 2001 and
2010. In the review, 195 articles investigating
clinical trials involved in the treatment of ASD
were included. Over 289 unique measurement
tools were utilized to measure treatment out-
comes in the included articles. From these mea-
surement tools, approximately 62% were only
used in one study over the 10-year period of time.
The most utilized measurement tools were the
Aberrant Behavior Checklist, Clinical Global
Inventory, and Vineland Adaptive Behavior
Scales, present in nearly 3% of the included stud-
ies. Within the articles, over 600 various target
skills were also identied to monitor; these skills
were divided into different categories, such as
autism severity, adaptive functioning, communi-
cation/language, and behavioral difculties.
While core features of ASD, like social skills and
communication/language, were frequently moni-
tored in the studies, there was very little consis-
tency in the measures used.
In another review completed by McConachie
et al. (2015), 184 journal articles published
between 1992 and 2013 were identied for
review. The articles included all investigated
measures used to assess treatment outcomes for
early intervention among children with ASD up
to 6years of age. Throughout the review of the
articles, over 130 measurement tools were identi-
ed for use in assessing response to treatment.
While there were no studies that met inclusion
5 Progress Monitoring During theTreatment ofAutism andDevelopmental Disorders
88
criteria for 75 of the measures, psychometric
properties for 57 of the measurement tools were
examined. Measures assessing several target
skills, including autism severity, global outcome,
social skills, and cognitive ability, were included.
In a recent review by Brugha et al. (2015),
researchers examined 31 articles discussing treat-
ment outcome measures in treatment trials for
adolescents and adults with ASD.In the review,
the researchers considered outcome measures
that identied the core symptoms of ASD, com-
monly comorbid symptoms, like anxiety, chal-
lenging behaviors, and cognitive impairments,
and overall quality of life. A range of different
studies were included, such as retrospective
assessment studies, case series, and randomized
and placebo-controlled trials. Researchers found
that there was a lack of focus on the assessment
of outcomes related to the core symptoms of
ASD overall. The Ritvo-Freeman Real-life
Rating Scale (RF-RLRS; Freeman et al., 1986)
was used most commonly in the studies to mea-
sure treatment outcomes across the core symp-
toms of ASD, with the social relatedness subscale
showing the most change. Overall, the RF-RLRS
was found to have low interrater reliability,
though it may have utility when it is used in phar-
macological studies and for direct observation
and monitoring. The Social Responsiveness
Scale (SRS; Constantino & Gruber, 2005) was
also used to measure outcomes in the core symp-
toms of ASD and two studies noted improve-
ments in social skills across time. None of the
included studies used the ADOS or other related
observational tools to assess change in ASD
severity, as these instruments were designed for
diagnostic purposes. However, most of the out-
come measures included in the review were not
specic to ASD symptoms, like the CGI and the
VABS.
Additionally, Howard (2019) evaluated tools
that were intended to measure treatment out-
comes for parent-mediated interventions for
ASD. Parent-mediated interventions are particu-
larly crucial for early intervention and often
involve trained professionals teaching parents
intervention techniques that be used in home and
community settings. In prior studies, researchers
have found improved communication and social
skills among children involved in parent-
mediated interventions (McConachie & Diggle,
2007). In the studies reviewed, several different
measurement tools were used to identify response
to treatment, including measures that emphasize
child outcomes, parent outcomes, and parent-
child outcomes. In general, the results of the
review were variable, as no one measure showed
treatment effects that were consistent. However,
within the categories of tools reviewed, particular
measures performed better than others. For
example, with respect to parent-mediated inter-
ventions, measures assessing social skills and
adaptive behavior were more likely to show a
treatment effect, though it was not consistent.
Additionally, the parent-child interaction vari-
ables, like child initiations, showed signicant
increases in response to treatment in various
studies. As demonstrated by the review, the utili-
zation of various measurement tools can make it
increasingly difcult to assess treatment out-
comes. An overview of the aforementioned
reviews can be found in Table5.1.
ASD-Specic Measures
Autism Treatment Evaluation
Checklist
The Autism Treatment Evaluation Checklist
(ATEC; Rimland & Edelson, 1999) is a checklist
to monitor improvements due to interventions in
children with ASD.There are a total of 72 items
and 4 subtests: Speech/Language Communication
(14 items), Sociability (20 items), Sensory/
Cognitive Awareness (18 items), and Health/
Physical/Behavior (25 items). The measure is
one page to be completed by parents, caregivers,
and/or teachers.
A study by Magiati etal. (2011) investigated
the ATEC utility in monitoring progress with
promising ndings. The ATEC was found to have
good content validity, as the total and subscale
scores were signicantly correlated with age
C. Tevis et al.
89
Table 5.1 Review of treatment outcome measures for ASD
Authors Intervention type Number of studies included Measures used
Bolte and Diehl
(2013)
Global
functioning
Core ASD
symptoms
Behavior
problems
8
3
9
5
10
Vineland Adaptive Behavior Scales
(VABS)
Bayley Scales of Infant Development
(Bayley)
Clinical Global Impressions (CGI)
Childhood Autism Rating Scale (CARS)
Aberrant Behavior Checklist (ABC)
McConachie
etal. (2015)
Core ASD
symptoms
Behavior
problems
Global
functioning
13; 3 intervention evaluation
studies
8; 4 intervention evaluation
studies
1
1
2; 1 intervention evaluation
study
1
3
1
1; 1 intervention evaluation
study
67; 24 intervention
evaluation studies
9; 4 intervention evaluation
studies
Childhood Autism Rating Scale (CARS)
Gilliam Autism Rating Scale (GARS)
BISCUIT-Part 1
Autism Impact Measure (AIM)
Social Communication Questionnaire
(SCQ)
Autism Treatment Evaluation Checklist
(ATEC)
Aberrant Behavior Checklist (ABC)
BISCUIT-Part 3
Home Situations Questionnaire-Pervasive
Developmental Disorders Version
Vineland Adaptive Behavior Scales
(VABS)
Psychoeducational Prole—Revised/
Psychoeducational Prole—Third Edition
Brugha etal.
(2015)
Core ASD
symptoms
Global
functioning
3; used primarily for
pharmacological research
1
2
1
6
1
9
Ritvo-Freeman Real-life Rating Scale
(RF-RLRS)
Autism Behavior Checklist
Social Responsiveness Scale
Childhood Autism Rating Scale (CARS)
Yale-Brown Obsessive-Compulsive Scale
(Y-BOCS)
The Repetitive Behavior Scale
Clinical Global Impression (CGI)
Behaviors/Mood 2
3
2
Aberrant Behavior Checklist (ABC)
Maladaptive Subscale of Vineland Adaptive
Behavior Scale (VABS)
Positive and Negative Affect Schedule
(PANAS)
Howard (2019) Child outcome
Communication
Adaptive
behavior
Behaviors
Parent outcome
Parental stress
Family
functioning
1
1
6
2
1
2
4
1
1
Early Social Communication Scales
Vineland Adaptive Behavior Scales
(VABS) (Communication Domain)
Vineland Adaptive Behavior Scales
(VABS)
Child Behavior Checklist (CBCL)
Preschool Behavior Checklist
Developmental Behavior Checklist
Parenting Stress Index (PSI)
Stress-Arousal Checklist
Mc Master Family Assessment Device
equivalences and raw scores from standardized
measures. It was also found to have good predic-
tive validity, as the initial score predicted the
progress made over time and the overall outcome
at the follow-up administration.
5 Progress Monitoring During theTreatment ofAutism andDevelopmental Disorders
90
Childhood Autism Rating Scale
The Childhood Autism Rating Scale, Second
Edition (CARS2; Schopler etal., 2010) is a diag-
nostic measure for children with ASD, aged
2 years and older. It consists of three forms:
CARS-2 Standard Version (CARS2-ST),
CARS-2 High Functioning Version (CARS-
2- HF), and CARS-2 Questionnaire for Parents or
Caregivers (CARS2-QPC). The Standard Version
is for individuals below the age of 6 or individu-
als with an IQ score below 79. The High
Functioning Version is for children 6 years and
older with higher intellectual functioning (IQ
above 80) and verbal abilities. Unlike the rst
two, which are scored by a clinician, the
Questionnaire for Parents or Caregivers is not
scored but rather is used to inform the CARS2-ST
and CARS2-HF. The Standard and High
Functioning Versions have 15 categories, rated
between 1 and 4, with .5 increments. The catego-
ries for the CARS-ST include relating to people,
imitation, emotional response, body use, object
use, adaptation to change, visual response, listen-
ing response, taste, smell, and touch response and
use, fear or nervousness, verbal communication,
nonverbal communication, activity level, and
level and consistency of intellectual response.
The CARS-HF categories include social-
emotional understanding, emotional expression
and regulation of emotions, relating to people,
body use, object use in play, adaptation to change/
restricted interests, visual response, listening
response, taste, smell, and touch response and
use, fear or anxiety, verbal communication, non-
verbal communication, thinking/cognitive inte-
gration skills, and level and consistency of
intellectual response. The raw score is calculated
by adding the domains, which is then converted
to a T-score and percentile. It is a level C measure
that takes approximately 5–10minutes to score.
The CARS2-ST was found to have an internal
consistency of .93 and item-to-total correlations
between .43 and .81. The CARS2-HF had item-
to- total correlations between .53 and .88. The
CARS2-HF was found to have an interrater reli-
ability of .95, while interrater reliability for the
CARS2-ST was not provided. A factor analysis
supported a two-factor solution for the CARS-ST:
(1) communication and sensory issues and (2)
emotional issues, while a three-factor solution
was found for the CARS2-HF: (1) social and
emotional issues, (2) cognitive functioning and
verbal abilities, and (3) sensory issues. Both the
CARS2-ST and CARS2-HF were found to have
convergent validity with the ADOS (Lord etal.,
1999; .79 and .77 respectively). However, corre-
lations were .38 and .47 for the standard and
High Functioning version, respectively, when
compared to the Social Responsiveness Scale
(Constantino & Gruber, 2005).
Psychoeducational Prole
The Psychoeducational Prole, Third Edition
(PEP-3; Schopler et al., 2005) is a measure for
individuals 2–7 years of age with autism spec-
trum disorder and other communication disorders
to assist with individualized educational pro-
grams (IEP), conrm diagnoses, and assess
results of educational interventions. The measure
has a performance portion which assesses ten
areas: cognitive verbal/preverbal, expressive lan-
guage, receptive language, ne motor, gross
motor, visual-motor imitation, affective expres-
sion, social reciprocity, characteristic motor
behaviors, and characteristic verbal behaviors.
This section consists of 172 total items, which
yield composite scores for each of the 3 factors:
communication, motor, and maladaptive behav-
iors (Schopler et al., 2005). Additionally, the
PEP-3 has a parent/caregiver report, which con-
sists of three subtests: problem behaviors, per-
sonal self-care, and adaptive behavior. The PEP-3
is a level B measure, for individuals with a mas-
ter’s degree in psychology, education, or related
eld, and takes 45–90minutes to administer.
The PEP-3 has demonstrated a high reliability,
ranging between .92 and .97 for developmental
subtests, .90 and .93 for maladaptive subtests,
and .84 and .90 for caregiver report subtests
(Conrod & Marcus, 2013). The parent/caregiver
report also had high interrater reliability, ranging
from .70 to 91 for problem behavior, .65 to 1.00
for personal self-care, and .52 to .90 for adaptive
C. Tevis et al.
91
behavior (Conrod & Marcus, 2013). Convergent
validity was supported with the Vineland subtests
related to developmental skills, self-care skills,
and behaviors related to ASD.On the other hand,
it showed divergent validity with lower correla-
tions with VABS motor and daily living skills
scores. The PEP-3 was negatively correlated with
the CARS and Autism Behavior Checklist-
Second Edition (Conrod & Marcus, 2013).
Social Responsiveness Scale
The Social Responsiveness Scale, Second Edition
(SRS-2; Constantino & Gruber, 2012) measures
ASD symptom severity in children and adoles-
cents 4–18 years of age. There are four rating
forms, based on age of the individual: the
Preschool Form (2 years 6 months to 4 years
6months), School-Age Form (4–18years), and
Adult Form (19–89years). The Adult Form has
two versions for self-report and for parents,
spouses, friends, and relatives to complete. The
SRS-2 consists of 65 items and 5 subscales:
Social Awareness (8 items), Social Cognition (12
items), Social Communication (22 items), Social
Motivation (11 items), and Restricted Interests
and Repetitive Behavior (12 items). The items
are rated on a Likert-type scale ranging from 1
(not true) to 4 (always true). It takes approxi-
mately 15–30minutes to complete.
The SRS-2 was found to have an internal con-
sistency which ranged between .94 and .96 across
all age groups, with high internal consistency
across age, gender, and clinical subgroups (Bruni,
2014). The Preschool Form was found to have an
interrater reliability of .61, while the School-Age
Form had higher interrater reliability at .77. The
interrater reliability for the Adult Self-Report
Form compared with various familial raters
ranged between .61 and .92 (Bruni, 2014). A con-
rmatory factor analysis found a good t for the
two-factor structure: (1) social communication
and interaction and (2) restricted interests and
repetitive behaviors. The School-Age Form was
found to have concurrent validity with Social
Communication Questionnaire (Rutter et al.,
2001) and CARS (Schopler etal., 1980), among
other rating scales of social communication
(Bruni, 2014).
Developmental Measures
Bayley Scales ofInfant andToddler
Development
The Bayley Scales of Infant and Toddler
Development, Third Edition (Bayley-III; Bayley,
2006) is a standardized test, which consists of
ve scales for children between 1 and 42months
of age. The scale consists of ve scales to assess
and measure developmental functioning: cogni-
tive, language, motor, social-emotional, and
adaptive behavior. The rst three scales are direct
assessments, while the latter two are parent-
informed measures. The Bayley-III provides
scaled scores, composite scores, percentile ranks,
condence intervals, and developmental age
equivalences for the scales and subtests. In addi-
tion, the test kit contains most of the needed stim-
ulus materials. The Bayley-III is a B-level test
and takes approximately 50–90 minutes to
administer, based on the child’s age.
The cognitive scale contains a total of 91
items. The language scale contains a total of 97
items, with a Receptive Communication subtest
with 49 items and an Expressive Communication
subtest with 48 items. The Motor scale contains
138 items, also with 2 subtests. The Fine Motor
subtest contains 66 items, and the Gross Motor
subtest contains 72 items. The Social-Emotional
scale consists of a questionnaire of 35 items,
rated from 0 (can’t tell) to 5 (all of the time).
Lastly, the Adaptive Behavior scale consists of a
questionnaire of 241 items, rated from 0 (is not
able) to 3 (always when needed).
The Bayley-III has demonstrated good inter-
nal consistency with reliability ranging from .91
to .93 for the Cognitive, Language, and Motor
scales and .86 to .91 for the subtests (Albers &
Grieve, 2007). The internal consistency ranged
from .76 to .94 for the Social-Emotional scale
and ranged from .79 to .86 for the Adaptive
Behavior scale. Test-retest reliability of the
Cognitive, Language, and Motor scales was .67
5 Progress Monitoring During theTreatment ofAutism andDevelopmental Disorders
92
to .80 for children 2–4months of age and .83 to
.94 for children 33–42months of age and had an
average stability higher than .80 across all age
groups (Albers & Grieve, 2007). The Adaptive
Behavior scale had a test-retest reliability of .80
and higher for the domains, with stability increas-
ing with age.
A conrmatory factor analysis supports a
three-factor model of the Cognitive, Language,
and Motor scales, except in the 0–6-month age
group in which a two-factor model was also sup-
ported (Albers & Grieve, 2007). The Bayley-III
had a correlation of .60 between the Cognitive
composite and the Mental Index Score on previ-
ous edition of the Bayley, the Bayley Scales of
Infant and Toddler Development, Second Edition
(BSID-II; Bayley, 1993). It also had a correlation
of .60 between the motor composite scores on the
Bayley-III and BSID-II (Albers & Grieve, 2007).
The Bayley-III also showed high correlations
between the Cognitive and Language composite
scores and the Verbal, Performance, and Full-
Scale scores of the Wechsler Preschool and
Primary Scale of Intelligence-Third Edition
(Wechsler, 2002).
The fourth edition of the Bayley Scales of Infant
and Toddler Development (Bayley-4; Bayley &
Aylward, 2019) was released in the United States in
2019. The Bayley-4 adopts polytomous scoring, on
a scale of 0–2, rather than 0 or 1 in the Bayley-
III.The new version has kept the same 5 scales (i.e.,
Cognitive, Language, Motor, Social-Emotional,
and Adaptive Behavior). The adaptive Behavior
scale reduced the items to 120 and the total admin-
istration time for the Bayley was reduced from
30–90minutes to 30–70minutes. Lastly, the Social-
Emotional and Adaptive Behavior scales have
remote options for caregivers.
For the Cognitive, Language, and Motor
scales, Pearson (2019a) reports the internal con-
sistency ranges between .93 and .95 for the sub-
tests and between .95 and .96 for the composite
scores. The internal consistency ranges between
.85 and .91 for the Social-Emotional scale and
between .91 and .98 for the Adaptive Behavior
scale (Pearson, 2019a). They also reported test-
retest reliability ranged between .81 and .85 for
the Cognitive, Language and Motor subtests and
composites and between .72 and .87 for the
Adaptive Behavior scale (Pearson, 2019a).
Pearson also reports a classication accuracy of
.82 for developmental delay and .89 for language
delay (Pearson, 2019b).
Adaptive Measure
Vineland Adaptive Behavior Scales
The Vineland Adaptive Behavior Scales, Third
Edition (Vineland-3; Sparrow et al., 2016) is a
measure of adaptive functioning for individuals
birth to 90years of age. There are three forms:
Interview, Parent/Caregiver, and Teacher, which
each has a Comprehensive and Domain-Level
versions. The two versions have three domains,
which include Communication, Daily Living
Skills, and Socialization. The three domains
comprise the Adaptive Behavior Composite
(ABC). The Comprehensive version includes an
additional nine core and ve optional subdo-
mains. Adaptive raw scores of the subdomains
are converted to v-scale scores and determine
standard scores for the subdomain. The Parent
Caregiver has 502 and 180 items for the
Comprehensive and Domain-level versions,
respectively; the Teacher Form has 333 items.
Each item is scored using a Likert-type scale
from 0 (never) to 2 (usually or often); however,
some questions require only yes (2) or no (0).
The Vineland-3 is a level B measure and takes
10–40 minutes to administer, depending on the
form.
The Vineland-3 has demonstrated excellent
reliability, with the internal consistency between
.94 and. 99 for the Comprehensive Form adaptive
domains and ABCs and .86 and .99 for the
Domain-Level adaptive domains and ABCs,
across all age groups (Pepperdine & McCrimmon,
2018). The test-retest reliability ranged between
.64 and .94 for the Comprehensive Form adaptive
domains and ABCs and between .63 and .92 for
the Domain-Level adaptive domains and ABCs
(Pepperdine & McCrimmon, 2018). Further,
interrater reliability ranged from .61 to .87 for the
Comprehensive Form adaptive domains and
C. Tevis et al.
93
ABCs, with the exception of the Socialization
adaptive domain the in the Teacher Form which
had an interrater reliability of .46 for individuals
3–5years of age. The interrater reliability ranged
from .58 to .93 for the Domain-Level adaptive
domains and ABCs.
The Parent/Caregiver Comprehensive form
has demonstrated concurrent validity with the
Bayley-III, with moderate to high correlations,
ranging from .67 to .81 (Pepperdine &
McCrimmon, 2018). The Parent/Caregiver and
Teacher Forms also demonstrated concurrent
validity with the Adaptive Behavior Assessment
System-Third Edition (ABAS-3; Harrison &
Oakland, 2015), with correlations between .75
and .88 for the Teacher Forms and .41 and .98 for
the Parent/Caregiver forms.
Behavioral Measures
Aberrant Behavior Checklist
The Aberrant Behavior Checklist– Community
(Aman & Singh, 1994) is a behavior rating scale
to evaluate treatment effects in individuals with
developmental and intellectual disabilities. The
ABC consists of 58 items rated from 0, indicating
not at all a problem to 3, indicating a severe prob-
lem. There are ve factors: Irritability (15 items),
Lethargy/Social Withdrawal (16 items),
Stereotypy (7 items), Hyperactivity/
Noncompliance (16 items), and Inappropriate
Speech (4 items). Scores are generated for each
factor, but a total score is not recommended
(Aman & Singh, 2017).
Internal consistency of the ABC was found to
be very good, ranging from .86 to .95, with
test- retest reliability ranging from .96 to .99
(Gaddis & Grill, 1995). Interrater reliability is
found to be lower, ranging from .55 to .69 (Gaddis
& Grill, 1995).
A study by Norris et al. (2019) investigated
the structural validity in a sample of individuals
2–14years of age with ASD.A conrmatory fac-
tor analysis supported the original ve-factor
solution. The factors, excluding Inappropriate
Speech, were all negatively correlated with the
Socialization, Daily Living Skills, and
Communication domains of the Vineland
Adaptive Behavior Scales, Second Edition
(VABS-II; Sparrow etal., 2005).
Behavior Assessment System
forChildren
The Behavior Assessment System for Children,
Third Edition (BASC-3; Reynolds & Kamphaus,
2015) is a behavioral assessment for individuals
between 2 and 21 years of age. There are three
rating scales: Parent Rating Scale, Teacher Rating
Scales, and Self-Report of Personality. The Parent
and Teacher scales are for individuals between 2
and 21years of age, while the Self- Report scale is
for ages 6 through 25years. The BASC-3 includes
validity indicators, composite scales, and four
indexes. In addition, there is a Parenting
Relationship Questionnaire (BASC-3 PRQ),
Behavioral and Emotional Screening System
(BASC-3 BESS), and Flex Monitor. The Flex
Monitor (BASC-3 Flex Monitor) is a method to
monitor behavioral and emotional functioning
changes in response to treatment and intervention
for individuals 2–18 years of age. The Flex
Monitor also has three forms for self, teacher, and
parent. It takes 5minutes to complete each stan-
dard form, with custom forms varying in comple-
tion time.
Reliability coefcients for the BASC-3 PRS
ranged from .76 to .97in the general sample and
between .71 and .98 for the clinical sample.
Similarly, reliability coefcients for the TRS
ranged from .77 to .98in the general sample and
.78 to .98in the clinical sample. Lastly, the coef-
cients for the SRP ranged from .71 to .97 for the
general sample and .57 to .96in the clinical sam-
ple. Test-retest reliability was found to be greater
than .80 for the PRS and TRS, and lower for the
SRP, which ranged from .59 to .87in the young-
est group of respondents. Additionally, the
BASC-3 had a wide range for internal reliability,
between .32 and .84 for the TRS and between .47
and .87 for the PRS.
5 Progress Monitoring During theTreatment ofAutism andDevelopmental Disorders
94
Children’s Sleep Habits Questionnaire
The Children’s Sleep Habits Questionnaire
(CSHQ; Owens etal., 2000) is a parent question-
naire for children between 4 and 10years of age
experiencing sleep difculties. There are 35
items which cover 8 sleep domains such as bed-
time behavior and sleep onset, sleep duration,
anxiety around sleep, behavior occurring during
sleep and night waking, sleep-disordered breath-
ing, parasomnias, and morning waking/daytime
sleepiness. Each item is rated as “rarely” (occur-
ring 0–1 time per week), “sometimes” (occurring
2–4 times per week), or “usually” (5–7 times per
week).
Owens etal. (2000) found the internal consis-
tency to be .68in a community sample, with the
internal consistency for the subdomains ranging
between .36 and .70. The internal consistency for
the clinical sample was found to be .78, ranging
from .44 to .83 for the subdomains. Test-retest
reliability was acceptable, ranging from .62 to
.79 in a community sample. The CBCL was
found to have a sensitivity of .80 and specicity
of .72 with a cutoff score of 41 (Owens etal.,
2000).
Anxiety andDepression Measures
Child Depression Inventory
The Child Depression Inventory, Second Edition
(CDI 2; Kovacs & Staff, 2011) is a self-report
measure to assess depression symptoms in chil-
dren and adolescents 7–17years of age. There is
a full version (28 items) as well as a short ver-
sion, primarily used for screening. In addition,
there is a Teacher Form (12 items) and a Parent
Form (17 items). All forms except for the short
form have two subscales: Emotional Problems,
with Negative Mood/Physical Symptoms and
Negative Self-esteem as subscales, and Functional
Problems, with Ineffectiveness and Interpersonal
Problems as subscales.
The internal consistency was found to range
between .67 and .91 for total and subscales across
all age and sex groups (Bae, 2012). The CDI 2
was found to have convergent validity with the
Beck Depression Inventory – Youth version
(BDI-Y; Beck et al., 2001) and Conners
Comprehensive Behavior Rating Scales (Conners
CBRS; Conners, 2008).
Multidimensional Anxiety Scale
forChildren
The Multidimensional Anxiety Scale for Children,
Second Edition (MASC; March, 2013) is a self-
report and parent-report measure to identify and
treat anxiety in children between 8 and 19years of
age. It consists of six scales and four subscales for
a total of 50 items. The scales include Separation
Anxiety/Phobias, Generalized Anxiety Disorder
(GAD) Index, Social Anxiety: Total, Obsessions
and Compulsions, Physical Symptoms: total, and
Harm Avoidance. The subscales are Humiliation/
Rejection and Performance Fears under Social
Anxiety: Total and Panic and Tense/Restless under
Physical Symptoms. The scores yield a total score,
Anxiety Probability scale, scale scores, and an
Inconsistency index.
Exploratory factor analyses yielded a four-
factor solution: Physical Symptoms, Social
Anxiety, Separation/Panic, and Harm Avoidance
(Fraccaro etal., 2015). The MASC-2 was found
to have acceptable internal consistency, with a
coefcient alpha for the Total Score of .92 for the
self-report and .89 for the parent report. Test-
retest reliability was found to be between .80 and
.94 for both forms. The MASC-2 demonstrates
discriminative validity, as individuals with sepa-
ration anxiety disorder, GAD, and social phobia
scored highest on the respective scales (Fraccaro
et al., 2015). It also demonstrated convergent
validity with the Beck Youth Inventory– Anxiety
(BYI-A; Beck etal., 2001), the GAD Scale of the
Conners Comprehensive Behaviour Rating
Scales – Self Report (Conners CBRS-SR;
Conners, 2008) and parent report.
Pediatric Anxiety Rating Scales
The Pediatric Anxiety Rating Scales (PARS;
RUPP, Research Units on Pediatric
Psychopharmacology Anxiety Study Group,
C. Tevis et al.
95
2002) is a clinician-rated measure of the severity
of anxiety symptoms in children between 6 and
17years of age. Clinicians rate a total of 50 items,
informed by separate child and parent interviews.
Each item is rated on a 6-point scale ranging
from 0 (none) to 1–5 (minimal to extreme) on
seven dimensions: number of symptoms, fre-
quency, severity of distress associated with anxi-
ety symptoms, severity of physical symptoms,
avoidance, interference at home, and interference
out-of-home. A 5-item total is also available,
which excludes number of symptoms and physi-
cal symptoms, which may be used for medication
trials.
A study by Storch and colleagues (2012)
investigated the reliability and validity of the
PARS in children 7–17years of age with autism.
They found the internal consistency to be .59.
Test-retest reliability was .83 and .86 for the
interrater reliability. The PARS demonstrated
convergent validity with the CGI-Severity, the
MASC-P, and the internalizing and anxiety scales
of the CBCL.Further, it demonstrated divergent
validity with the ADOS, and the externalizing,
attention, delinquent, and aggressive scales of the
CBCL.
Attention Decit/Hyperactivity
Disorder Measures
ADHD Symptoms Rating Scale
The ADHD Symptoms Rating Scale (ADHD-
SRS; Holland etal., 1998) is a measure to assess
ADHD in school-aged individuals (K − 12).
There is a total of 56 items on both the parent and
Teacher Forms. An Exploratory Factor Analysis
revealed a two-factor solution: Hyperactive-
Impulsive and Inattention.
The ADHD-SRS was found to have excellent
reliability with an internal consistency of .98 for
the Parent Form and .99 for the Teacher Form
(Holland etal., 1998). The test-retest reliability
ranged between .95 and .97 for the total and sub-
scale scores. Further, the ADHD-SRS was found
to have convergent validity with other measures
of ADHD (e.g., Attention Decit Disorders
Evaluation Scale (ADDES; McCarney, 1995a, b)
and the Conners’ Teacher Rating Scale (CTRS))
(Table5.2).
Summary
With the prevalence of ASD rising since the
1990s, there is an increasing need for effective
treatment options. In order to maximize treat-
ment benets and track response to treatments,
ongoing progress monitoring is essential. ASD is
multidimensional and can present with a wide
range of symptoms (Eapen et al., 2016). Core
symptoms of ASD, like impairments in social
communication, are often targeted in interven-
tions (Bolte & Diehl, 2013). However, decits
across various domains, including communica-
tion, behavioral and social skills, and cognitive
abilities, vary among individuals with ASD. In
addition to the core symptoms, comorbid condi-
tions like challenging behaviors, sleep problems,
anxiety, depression, and ADHD can be present in
those with ASD (Frazier etal., 2011; Simonoff
et al., 2008). As a result, symptoms of co-
occurring conditions are often targets of interven-
tion for children with ASD. More detailed
progress monitoring is often necessary for those
with multiple comorbid conditions compared to
those with few (Eapen etal., 2016).
Since additional symptoms can present in
conjunction with core ASD symptoms, interven-
tions and treatment plans for ASD should be
highly individualized, research based, progress
monitored, and revised often (National Institute
for Health and Care Excellence, 2013). Clinicians
should aim to include input from caregivers in
treatment planning goals and progress monitor-
ing. While there are several different methods
and tools that are used to monitor response to
interventions among individuals with ASD, there
are currently no standardized measures expressly
designed for progress monitoring purposes (Bolte
& Diehl, 2013). In spite of the challenges and
limitations associated with progress monitoring,
several evidence- based tools can be used to
assess response to treatment in individuals from
infancy to late adulthood.
5 Progress Monitoring During theTreatment ofAutism andDevelopmental Disorders
96
Table 5.2 Progress monitoring measures
Domain Measure Age Informant Target behavior(s)
ASD Autism Treatment Evaluation
Checklist (Rimland & Edelson,
1999)
Children, unspecied Parents/caregiver
and/or teachers
Speech/language communication, sociability, sensory/cognitive
awareness, health/physical/behavior
Childhood Autism Rating Scale
(Schopler etal., 2010)
2+ years Clinician Relating to people, imitation, emotional response, object use,
body use, adaptation to change, visual response, listening
response, taste, smell, and touch response and use, fear or
nervousness, communication, activity level, intellectual response
Psychoeducational Prole, Third
Edition (Schopler etal., 2005)
2–7years Parent/caregiver,
clinician
Language, motor, imitation, affect
Social Responsiveness Scale,
Second Edition (Constantino &
Gruber, 2012)
4–18years Self-report, parents,
spouse, friends,
relatives, teachers
Social awareness, social cognition, social communication, social
motivation, restricted interests and repetitive behavior
Developmental Bayley Scales of Infant and Toddler
Development, Third Edition
(Bayley, 2006
1–42months Parent/caregiver,
clinician
Cognitive, language, motor, social-emotional, and adaptive
behavior
Adaptive Vineland Adaptive Behavior Scales,
Third Edition (Sparrow etal., 2016)
Birth−90years Parent/caregiver,
teacher, clinician
Communication, daily living skills, socialization, and motor
Challenging
behaviors
Aberrant Behavior Checklist
(Amanetal., 1985a)
Individuals with
developmental and
intellectual disabilities
Parent/caregiver Irritability, lethargy/social withdrawal, stereotypy, hyperactivity/
noncompliance, and inappropriate speech
Behavior Assessment for Children,
Third Edition (Reynolds &
Kamphaus,2015)
2–21years Self-report, parent/
caregiver, teacher
Externalizing problems, internalizing problems, adaptive skills
Sleep
difculties
Children’s Sleep Habits
Questionniare (Owens etal., 2000)
4–10years Parent/caregiver Bedtime behavior, sleep onset, duration, anxiety around sleep,
parasomnias, sleepiness
Depression Child Depression Inventory (Kovacs
& Staff, 2011)
7–17years Self-report, parent/
caregiver, teacher
Emotional problems and functional problems
Anxiety Multidimensional Anxiety Scale for
Children, Second Edition (March,
2013)
8–19years Self-report, parent/
caregiver
Anxiety/phobias, generalized anxiety disorder, social anxiety,
obsessions and compulsions, and physical symptoms
Pediatric Anxiety Rating Scales
(RUPP, 2002)
6–17years Clinician Number of symptoms, frequency, severity of distress, severity of
physical symptoms, avoidance, interference
ADHD ADHD Symptoms Rating Scale
(Holland etal., 1998)
K−12 Parent/caregiver,
teacher
Hyperactive-impulsive and inattention
C. Tevis et al.
97
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