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Autism Spectrum Disorder: Investigating Predictive Adaptive Behavior Skill Deficits in Young Children

Autism Spectrum Disorder: Investigating Predictive Adaptive Behavior Skill Deficits in Young... Hindawi Autism Research and Treatment Volume 2021, Article ID 8870461, 9 pages https://doi.org/10.1155/2021/8870461 Research Article Autism Spectrum Disorder: Investigating Predictive Adaptive Behavior Skill Deficits in Young Children 1 1 1,2 1 Emma Feige, Rhonda Mattingly, Teresa Pitts, and Alan F. Smith Department of Otolaryngology-Head/Neck Surgery-and Communicative Disorders, University of Louisville, Louisville, KY, USA Department of Neurological Surgery; Kentucky Spinal Cord Research Centre, University of Louisville, Louisville, KY, USA Correspondence should be addressed to Alan F. Smith; afsmit01@louisville.edu Received 20 July 2020; Revised 31 December 2020; Accepted 22 January 2021; Published 31 January 2021 Academic Editor: Daniel Rossignol Copyright © 2021 Emma Feige et al. &is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Autism spectrum disorder (ASD) is a lifelong neurodevelopmental disorder that consists of difficulties with social communication and language, as well as the presence of restricted and repetitive behaviors. &ese deficits tend to present in early childhood and usually lead to impairments in functioning across various settings. Moreover, these deficits have been shown to negatively impact adaptive behavior and functioning. &us, early diagnosis and intervention is vital for future success within this population. &e purpose of this study was to further examine the subscales that comprise the adaptive behavior section of the Bayley -III to determine which of the ten subscales are predictive of ASD in young children (i.e.,≤ three years of age). A retrospective file review of 273 children participating in Kentucky’s early intervention program, First Steps, was completed. &e children ranged in age from 18 to 35 months. A binary logistic regression was used to assess the subscales that comprise the adaptive behavior of the section of the Bayley -III to determine which of the ten subscales are predictive of ASD in young children (i.e., ≤ three years of age). &e results indicated that individual lower raw scores in communication, community use, functional preacademics, home living, health and safety, leisure, self-care, self-direction, and social subscales were predictive of an autism diagnosis. females may “mask sociocommunicative impairments due 1. Introduction to increased sensitivity to social pressure to fit in, gendered Autism spectrum disorder (ASD) is a lifelong neuro- expectations for social behavior, and strengths in some developmental disorder that consists of deficits in social social-communication skills” [5]. &is could result in fe- communication and language, as well as the presence of males possibly being “missed by current diagnostic proce- restricted and repetitive behaviors [1, 2]. ASD is described as dures” [5]. Nonetheless, diagnosis of ASD appears to be 4 a spectrum disorder as it presents differently in each indi- times more common in males than in females [3]. vidual. &ese deficits tend to present in early childhood and Secondary to the “heterogeneity of affected individuals usually lead to impairments in functioning across various and the genetic complexity” of the disorder, it has been settings [2]. difficult to identify the cause(s) of ASD [2]. Previous re- &e Centers for Disease Control and Prevention report search has suggested several possible etiologies; however, the that approximately 1 in 59 children are diagnosed with ASD literature remains inconclusive [6]. Bölte, Girdler, and crossing all racial, ethnic, and socioeconomic groups [3]. Marschik suggest that many genetic and environmental Previous research has reported a steady increase in the factors and their interactions may contribute to autism prevalence of ASD over the past 2 decades [4]. Probable phenotypes, but their specific causal mechanisms remain reasons for the increase include the “broadening of diag- poorly understood. Yates and Le Couteur [2] suggest that nostic criteria and improved case recognition” [2]. More- significant genetic variations have been found in approxi- over, symptomology of ASD tends to present differently in mately 10% of individuals diagnosed with ASD. Increased males and females [2]. “Camouflaging theory” suggests that paternal and maternal age has also been associated with 2 Autism Research and Treatment intervention implemented before age three has been asso- higher risk of having a child with autism, possibly due to “de novo spontaneous mutations and/or alterations in genetic ciated with better communicative, academic, and behavioral outcomes at school age [17]. Several studies have concluded imprinting” [7]. Moreover, strong heritability has been linked with ASD as recurrence rates for siblings have been that children with autism make greater gains in intervention reported to be up to 18.7% [2]. “Research continues to study when it begins earlier, between the ages of two and four, as neurobiological differences in ASD considering variation in compared to older children receiving the same interven- neurotransmitters, volumetric and functioning differences tions, including those with other neurodevelopmental dis- of various regions within the brain, but the relevance to orders [18]. More recent emerging evidence supports the clinical practice of most identified abnormalities has not idea that earlier and more intensive treatment results in more favorable outcomes [19]. been established” [2]. Environmental factors may also play a role in possible Early intervention services often address the needs of children across five developmental areas, including cogni- ASD diagnosis. Previous research [8] found that exposure to environmental neurotoxicants during prenatal, perinatal, tive, motor, social-emotional, communication, and adaptive development [14]. Children referred for early intervention and postnatal development has been shown to influence the biochemical brain development, resulting in “neuro- services typically undergo an in-depth evaluation process to developmental abnormalities that may contribute to ASD.” assess their therapeutic needs prior to intervention. Various More specifically, prenatal exposures to “air pollution, heavy assessment measures may be used during this process with metals, pesticides, and toxic substances in consumer differing requirements from state-to-state. Nonetheless, the products” could bring about atypical brain development, assessment process should be comprised of a comprehensive resulting in possible neural pathologies such as ASD [9]. set of activities to (1) identify a child’s strengths and weaknesses, (2) address the families concerns and priorities, &rough growing research, it has become more evident that the etiology associated with ASD is multifactorial with ge- and (3) develop a plan for ongoing treatment strategies for the child [20, 21]. netic and environmental factors playing a role [7]. &e heterogeneity of ASD is evident in the early years of IDEA requires that the evaluation/assessment be com- pleted using a range of tools in a variety of contexts [14]. &e development as well [10, 11]. Kanner first described autism as being one of an “infantile” type, suggesting that the onset instruments used may include both criterion-referenced of symptoms occurred throughout the early ages of life [7]. and/or standardized properties. One tool, in particular, that Another study examined three possible types/developmental is often utilized within early intervention circles is the Bayley rd trajectories of ASD in children [12]. &ese three types in- Scales of Infant and Toddler Development (3 Edition) or clude early onset, regression, and plateau [12]. ASD the Bayley -III. &e Bayley -III is a comprehensive as- ® ® symptoms manifest soon after birth in children with the sessment tool used to identify developmental issues in early early onset type, whereas children with the regressive type childhood [22]. begin to develop normally until around two years of age Previous research has shown that individual lower proceeded by a regression in development [12]. &is re- subscale scores within the cognitive, language, adaptive gression is most evident in the child’s language and social behavior, and social-emotional developmental domains on skills [12]. Last, children with the plateau type develop the Bayley -III were predictive of an ASD diagnosis in normally until approximately six months of age and cease to children three years of age and younger [23]. Due to current make any developmental advances [12]. For example, Rogers literature and ASD diagnostic criteria, this outcome is not [13] describes a halting of development where “babbling was surprising with regards to language and social-emotional present but did not continue to develop into speech.” Re- domains. A direct connection with the cognitive and garding ongoing development and future outcomes, evi- adaptive behavior sections, however, may be less clear. dence suggests that children who present with the regressive Adaptive behavior appears strongly associated with in- developmental trajectory tend to have more severe deficits telligence in neurotypical individuals; however, “cognitively across time and in a variety of areas [10]. able individuals with ASD fail to acquire adaptive skills at While the DSM-V provides guidelines and criter- rates corresponding with gains” in intelligence [24]. ia—including severity levels—for diagnosing ASD, it also Moreover, the “gap in daily living skills (i.e., adaptive skills) highlights the fact that symptoms must also be present between children with ASD and typically developing chil- during early childhood. Under the Individuals with Dis- dren increased across early childhood” [24] including poorer abilities Education Act (IDEA), specifically Part C, the law planning abilities and cognitive flexibility [25]. Nonetheless, defines the age range for children eligible for early inter- a review of the literature examining ASD and adaptive vention serves as birth to three years of age [14]. &e functioning conclude that individuals with ASD tend to American Speech-Language-Hearing Association defines present with adaptive functioning difficulties as compared to early intervention as providing families, toddler, and infants their same-age peers [24, 26, 27]. who have or are at risk of a developmental delay, disability, Harris and Oakland [28] define adaptive behavior skills or other health condition that inhibits typical development as “practical, everyday skills needed to function and meet the with intervention services [15]. demands of one’s environment, including the skills neces- Evidence suggests that the earlier a child receives in- sary to effectively and independently take care of oneself and tervention, the greater the likelihood of an improved de- to interact with other people.” Within the subscale of the velopmental trajectory [16]. In general, intensive adaptive behavior (ADP) skills portion of the Bayley -III, ® Autism Research and Treatment 3 there are ten subscales. &e subscales are comprised of were retrieved from each file (for children diagnosed as communication (CO), community use (CU), functional having ASD) and randomly for children with developmental preacademics (FA), home living (HL), health and safety delay. &e developmental delay sample served as a type of (HS), leisure (L), self-care (SC), self-direction (SD), social the control group. &e raw scores for the ten adaptive be- (S), and motor (M) [22]. &ese subscales “assess the daily havior subsections and the overall standard deviation scores functional skills of a child, measuring what the child actually for the overall adaptive behavior section were anonymously does, in addition to what he or she may be able to do” [22]. compiled into a Microsoft Excel spreadsheet and then Scores are provided via parent report and are based on the exported to IBM SPSS for Windows, version 25 (IBM Corp., frequency (e.g., is not able, never when needed, sometimes Armonk, N.Y., USA) for statistical analyses. Separate when needed, and always when needed) with which the child spreadsheets were created for children diagnosed with ASD performs the behavior when it is needed and without help and those that did not carry the diagnosis. &e data were provided [22]. stored on a password protected computer behind a locked &e purpose of the study was to further examine the door; a master-code was never created. Gender was coded, subscales that comprise the adaptive behavior section of the where 1 � male and 2 � female. ASD diagnosis was coded in Bayley -III to determine which of the ten subscales are the same manner, where 1 � not diagnosed and 2 � diagnosed. No identifying information was recorded. predictive of ASD in young children (i.e., ≤ three years of age). Improved knowledge of the predictive value of each A binary logistic regression was used to assess the subscales subscale or combination thereof may contribute to an im- that comprise the adaptive behavior section of the Bayley -III proved understanding of the role adaptive behavior plays in to determine which of the ten subscales (e.g., communication, the diagnosis of ASD. community use, functional preacademics, home living, health and safety, leisure, self-care, self-direction, social, and motor) are predictive of ASD in young children (i.e., ≤ three years of 2. Methods and Materials age). A binary logistic regression analysis was used, as the &is study utilized a retrospective file review of children criterion variable—ASD diagnosis—is dichotomous [31]. (N � 273) that participated in Kentucky’s early intervention Descriptive statistics, assumption testing, and the results of the program, First Steps, between 1/1/2012 and 6/1/2019. &e logistic regression analyses follow. sample included children between the ages of 18 and 35 months and comprised 203 males and 70 females. 3. Results Tabachnick and Fidell [29] recommended a sample size of at least 80, where N> 50 + 8m (m is the number of predictor &is study comprised a retrospective file review of 273 variables). Moreover, Babyak [30] suggested a minimum children in the state of Kentucky: 74.4% (n � 203) was male sample size of 10–15 observations per predictor variable. and 25.6% (n � 70) was female. &e ages ranged from 18 to Children with and without ASD diagnosis were represented. 35 months (M � 24.04, SD � 5.30). Forty-eight percent ASD diagnosis was determined by the intensive level of (n � 131) of the children were diagnosed with ASD; 52% evaluation (ILE) as completed by the University of Louis- (n � 142) did not have an ASD diagnosis. ville, Weisskopf Child Evaluation Center (WCEC). For the Table 1 presents the mean and standard deviations for purpose of this study, an ILE is equivalent to a multidis- the ten subscales of the adaptive behavior section of the ciplinary evaluation that typically involves—in Kentucky—a Bayley -III [22]. Consistent with regression-based analyses, speech-language pathologist, psychologist, and develop- the ten subscales are referenced as predictor variables. ASD mental pediatrician. An occupational therapist may also be diagnosis served as the criterion variable. involved on a case-by-case basis. Diagnosis is based on majority opinion of the team. Per this study, possible ILE diagnoses included autism with developmental delay or 3.1. Logistic Regressions are Sensitive to Multicollinearity. developmental delay. Approval for this study, including the “When data are not centered, the regression coefficients that retrospective file review, was granted by the Institutional are estimated and tested may be irrelevant and misleading. Review Boards (IRB) of the University of Louisville and the Centering, thoughtfully done, can diminish the almost in- Kentucky Cabinet for Health and Family Services. evitable multicollinearity problems in regression, thus in- &e researchers were granted access to the Technology- creasing both the precision of parameter estimation and the assisted Observation and Teaming Support (TOTS) data- power of statistical testing of those parameters” [32]. base, an electronic record used by the Kentucky Department As previously suggested, the continuous variables were of Public Health to track children as they are referred, mean centered by subtracting the mean from the value for evaluated, and—in some cases—receive services through the each variable. &e dichotomous variable—ASD diag- early intervention program. &e researchers used TOTS to nosis—was also centered. &is was completed by changing query children referred to—and evaluated by—First Steps the values of 0 to −0.5 and 1 to 0.5. Variables were centered between the aforementioned date range. Again, specific as a strategy to prevent errors in statistical inference. interest centered on ASD diagnosis. Demographic infor- A correlation matrix (Pearson) was calculated to assess mation included each child’s age (in months) at evaluation multicollinearity presence. Mukaka [33] was used to in- and gender. Paper-based files were reviewed at the Ken- terpret the size of the correlation coefficient. Tabachnick and tuckiana Point of Entry office. &e Bayley -III protocols Fidell [29] suggest that as long as correlation coefficients ® 4 Autism Research and Treatment Table 1: Descriptive statistics adaptive behavior subscale raw Table 2: Pearson product-moment correlation matrix (N � 273). scores (N � 273). ADP CO CU FA HL HS LS SC SD SOC Subscale M SD ADP Communication 25.0 10.0 CO 0.57 Community use 9.6 8.4 CU 0.47 0.56 Functional preacademics 6.6 7.9 FA 0.36 0.55 0.49 Home living 22.7 15.3 HL 0.51 0.59 0.72 0.49 Health and safety 23.6 11.5 HS 0.44 0.62 0.61 0.40 0.78 Leisure 28.5 10.2 LS 0.55 0.64 0.49 0.41 0.64 0.68 Self-care 35.8 9.4 SC 0.42 0.58 0.49 0.26 0.63 0.69 0.74 Self-direction 29.1 11.0 SD 0.49 0.54 0.57 0.29 0.70 0.75 0.81 0.74 Social 31.6 10.0 SOC 0.59 0.76 0.57 0.44 0.70 0.70 0.76 0.70 0.75 Motor 51.5 11.0 MO 0.17 0.40 0.45 0.23 0.60 0.68 0.61 0.64 0.67 0.62 Moderate positive (negative) correlation |r � 0.50–0.70| in italics. High positive (negative) correlation |r> 0.70| in bold. among independent variables are less than 0.90, multi- collinearity is less likely to have occurred. &e results are Table 3: Predicting ASD diagnosis based on adaptive behavior presented in Table 2. scale standard deviation. Individual logistic regression analyses were used to examine the relationship between the overall adaptive be- Subscale Odds ratio 95% (CI) % variance p havior scale and the associated subscale raw scores with the Adaptive behavior 0.12 0.08–0.20 53 <0.001 diagnosis of ASD. Logistic regression allows the use of outcome variables that are categorical and predictor vari- ables that are continuous or categorical. Logistic regression Table 4: Predicting ASD diagnosis based on adaptive behavior analysis is the most appropriate statistical measure since the subscale raw scores. criterion variable is dichotomous. Table 3 shows the results Subscale Odds ratio 95% (CI) % variance p of the logistic regression analysis examining the overall Communication 0.86 0.83–0.90 34 <0.001 adaptive behavior scale as a predictor of ASD. Community use 0.91 0.88–0.95 15 <0.001 &e complete results of the logistic regression analyses Preacademics 0.93 0.89–0.97 8 <0.001 for the individual subscales that comprise the adaptive Home living 0.96 0.94–0.98 11 <0.001 behavior scale are presented in Table 4. Health/safety 0.95 0.93–0.98 9 <0.001 Leisure 0.92 0.89–0.95 18 <0.001 Self-care 0.93 0.90–0.96 13 <0.001 3.2. Bayley -III Adaptive Behavior Scale and ASD Diagnosis. Self-direction 0.95 0.92–0.97 10 <0.001 Logistic regression—step 1—entered the adaptive behavior Social 0.88 0.85–0.91 31 <0.001 scale standard deviation scores as a predictor of ASD di- Motor 0.98 0.96–1.01 1 0.14 agnosis. &e results were statistically significant (odds ratio � 0.12, 95% CI � 0.08–0.20, p< 0.001) and explained 53% (Nagelkereke R ) of the variance of ASD diagnosis. &e a predictor of ASD diagnosis. &e results were statistically results suggest that children who receive lower standard significant (odds ratio � 0.91, 95% CI � 0.88–0.95, p< 0.001) deviation scores on the Bayley -III adaptive behavior scale and explained 15% (Nagelkereke R ) of the variance of ASD are more likely to receive an ASD diagnosis than children diagnosis. &e results suggest that children who scored lower with higher standard deviation scores. (raw scores) on the community use subscale on the Bayley - III adaptive behavior scale are more likely to receive an ASD diagnosis than children with higher community use subscale 3.3. Bayley -III Adaptive Behavior Communication Subscale ® raw scores. and ASD Diagnosis. Logistic regression—step 1a—entered the adaptive behavior communication subscale raw scores as a predictor of ASD diagnosis. &e results were statistically 3.5. Bayley -III Adaptive Behavior Preacademics Subscale and significant (odds ratio � 0.86, 95% CI � 0.83–0.90, p< 0.001) ASD Diagnosis. Logistic regression—step 1c—entered the and explained 34% (Nagelkereke R ) of the variance of ASD adaptive behavior functional preacademics subscale raw diagnosis. &e results suggest that children who scored lower scores as a predictor of ASD diagnosis. &e results were (raw scores) on the communication subscale on the Bayley - ® statistically significant (odds ratio � 0.93, 95% III adaptive behavior scale are more likely to receive an ASD CI � 0.89–0.97, p< 0.001) and explained 8% (Nagelkereke diagnosis than children with higher communication sub- 2 R ) of the variance of ASD diagnosis. &e results suggest that scale raw scores. children who scored lower (raw scores) on the functional preacademics subscale on the Bayley -III adaptive behavior scale are more likely to receive an ASD diagnosis than 3.4. Bayley -III Adaptive Behavior Community Use Subscale children with higher functional preacademics subscale raw and ASD Diagnosis. Logistic regression—step 1b—entered scores. the adaptive behavior community use subscale raw scores as Autism Research and Treatment 5 3.6. Bayley -III Adaptive Behavior Home Living Subscale and diagnosis than children with higher self-direction subscale ASD Diagnosis. Logistic regression—step 1d—entered the raw scores. adaptive behavior home living subscale raw scores as a predictor of ASD diagnosis. &e results were statistically 3.11. Bayley -III Adaptive Behavior Social Subscale and ASD significant (odds ratio � 0.96, 95% CI � 0.94–0.98, p< 0.001) Diagnosis. Logistic regression—step 1i—entered the adap- and explained 11% (Nagelkereke R ) of the variance of ASD tive behavior social subscale raw scores as a predictor of ASD diagnosis. &e results suggest that children who scored lower diagnosis. &e results were statistically significant (odds (raw scores) on the home living subscale on the Bayley -III ratio � 0.88, 95% CI � 0.85–0.91, p< 0.001) and explained adaptive behavior scale are more likely to receive an ASD 31% (Nagelkereke R ) of the variance of ASD diagnosis. &e diagnosis than children with higher home living subscale results suggest that children who scored lower (raw scores) raw scores. on the social subscale on the Bayley -III adaptive behavior scale are more likely to receive an ASD diagnosis than children with higher social subscale raw scores. 3.7. Bayley -III Adaptive Behavior Health and Safety Subscale and ASD Diagnosis. Logistic regression—step 1e—entered the adaptive behavior health and safety subscale raw scores 3.12. Bayley -III Adaptive Behavior Motor Subscale and ASD as a predictor of ASD diagnosis. &e results were statistically Diagnosis. Logistic regression—step 1j—entered the adap- significant (odds ratio � 0.95, 95% CI � 0.93–0.98, p< 0.001) tive behavior motor subscale raw scores as a predictor of and explained 9% (Nagelkereke R ) of the variance of ASD ASD diagnosis. &e results were not statistically significant diagnosis. &e results suggest that children who scored lower (odds ratio � 0.98, 95% CI � 0.96–1.01, p � 0.14). Although (raw scores) on the health and safety subscale on the statistical significance was not achieved, the model explained Bayley -III adaptive behavior scale are more likely to receive 2 1% (Nagelkereke R ) of the variance of ASD diagnosis. an ASD diagnosis than children with higher health and Motor subscale raw scores do not seem to vary substantially safety subscale raw scores. across ASD diagnostic categories. Per this sample, children with an ASD diagnosis did not appear to have significantly lower motor subscale raw scores than their non-ASD peers. 3.8. Bayley -III Adaptive Behavior Leisure Subscale and ASD &e intent of this study sought to examine the subscales Diagnosis. Logistic regression—step 1f—entered the adap- that comprise the adaptive behavior section of the Bayley tive behavior leisure subscale raw scores as a predictor of III to determine which of the ten subscales are predictive of ASD diagnosis. &e results were statistically significant (odds ASD in young children (i.e.,≤ three years of age). &e results ratio � 0.92, 95% CI � 0.89–0.95, p< 0.001) and explained 2 found that lower standard deviation scores on the adaptive 18% (Nagelkereke R ) of the variance of ASD diagnosis. &e behavior scale on the Bayley -III was a statistically signif- results suggest that children who scored lower (raw scores) icant predictor of ASD in young children. Moreover, lower on the leisure subscale on the Bayley -III adaptive behavior raw scores on the communication, community use, func- scale are more likely to receive an ASD diagnosis than tional preacademics, home living, health and safety, leisure, children with higher leisure subscale raw scores. self-care, self-direction, and social subscales of the adaptive behavior scale of the Bayley -III were found to be statis- tically significant predictors of ASD in young children. &e 3.9. Bayley -III Adaptive Behavior Self-Care Subscale and communication and social subscales were found to con- ASD Diagnosis. Logistic regression—step 1g—entered the tribute the greatest amount of variance in predicting ASD at adaptive behavior self-care subscale raw scores as a predictor 34% and 31%, respectively. of ASD diagnosis. &e results were statistically significant (odds ratio � 0.93, 95% CI � 0.90–.96, p< 0.001) and explained 13% (Nagelkereke R ) of the variance of ASD 4. Discussion and Conclusions diagnosis. &e results suggest that children who scored lower &e purpose of this study was to further examine the (raw scores) on the self-care subscale on the Bayley -III subscales that comprise the adaptive behavior section of the adaptive behavior scale are more likely to receive an ASD Bayley -III to determine which of the subscales are pre- diagnosis than children with higher self-care subscale raw dictive of ASD in young children (i.e.,≤ three years of age) in scores. hope to contribute to the specificity of autism characteristics in early childhood as they relate to adaptive behavior. 3.10. Bayley -III Adaptive Behavior Self-Direction Subscale &e current study examined individual logistic regres- and ASD Diagnosis. Logistic regression—step 1h—entered sion analyses which determined that lower standard devi- the adaptive behavior self-direction subscale raw scores as a ation scores on the adaptive behavior scale on the Bayley - predictor of ASD diagnosis. &e results were statistically III was a statistically significant predictor of ASD in young significant (odds ratio � 0.95, 95% CI � 0.92–0.97, p< 0.001) children. Moreover, lower raw scores on the communica- and explained 10% (Nagelkereke R ) of the variance of ASD tion, community use, functional preacademics, home living, diagnosis. &e results suggest that children who scored lower health and safety, leisure, self-care, self-direction, and social (raw scores) on the self-direction subscale on the Bayley -III subscales were found to be statistically significant predictors adaptive behavior scale are more likely to receive an ASD of ASD in young children. &e social and communication 6 Autism Research and Treatment individual subscale scores contributed the greatest amount presence of restricted and repetitive behaviors can also be of variance when predicting the diagnosis of ASD. As these linked to deficits in executive function [43]. In a study conducted by Pennington and Ozonoff [44], individuals two deficits are specified within the current diagnostic criteria and there is a vast amount of literature discussing with autism completed executive functioning tasks with a these deficits among the ASD population, these results come higher number of perseverative errors as well as exhibited as no surprise. rigid and inflexible problem-solving strategies. Social and communicative deficits have been diagnostic Executive functioning (EF) closely pertains to the cog- hallmarks since the first clinical accounts of ASD were nitive domains of attention, reasoning, and problem-solving recorded [34]. &e first clinical accounts were recorded by [44]. Particularly, “set-shifting and set-maintenance, inter- Dr. Kanner [35], wherein he referenced difficulties with ference control, inhibition, integration across space and socialization among the observed group of children [34]. time, planning, and working memory” are that of a few Present, one of the first symptoms that is commonly found executive functions [44]. Liss et al. [41] further included the in children with ASD is their lack of social interaction [36]. processes of “forming abstract concepts, having a flexible sequenced plan of action, focusing and sustaining attention Studies examining the relationship between communication skills and corresponding levels of adaptive behavior in in- and mental effort, rapidly retrieving relevant information, dividuals with ASD are limited [37]. However, Kjellmer et al. being able to self-monitor and self-correct as a task is [37] concluded that nonverbal communication skills may be performed, and being able to inhibit impulsive responses” as related to severity of autism symptoms as well as adaptive EF components (p. 261). An individual’s level of executive functioning. functioning has been shown to correlate with academic skills &e lack of communication skills displayed by children [41]. Wenz-Gross et al. [45] affirm that EF comprises of with autism is the greatest cause of concern for parents [17]. “cognitive processes thought to support academic achieve- As limited communication skills are associated with ASD, ment through top-down control of attention and behavior” these individuals are more likely to display challenging (p. 2). In general, learning is characterized by the executive behaviors and/or aggression as this may be their only means functioning tasks of “seeing relationships between pieces of information, identifying central patterns or themes, dis- of communication, indirectly resulting in increased parental psychological distress [38]. One study examined how par- tinguishing relevant from irrelevant information, and de- ents modified the environment in order to meet the needs of riving meaning” [44]. As it relates to the present study, the their child with ASD who demonstrated challenging be- functional preacademic domain within the Bayley -III as- haviors [39]. &e study revealed that parents limited social sesses preacademic skills such as letter recognition, count- activities and outings with the child (i.e., shopping and ing, and drawling simple shapes [22]. &e results of this visiting restaurants) [39]. Furthermore, parents avoided study can be explained by the theory of executive dys- taking their child to new and different environments, lim- function, as it is known that individuals with ASD display iting their exposure into the community [39]. difficulties with EF as it pertains to academic skills [44]. &e community use and home living subscales of the Conceptual understanding of the main idea or big picture of Bayley -III measure a child’s ability to participate in ac- a topic is often lacking among this group of individuals [44]. tivities and interests throughout the community as well as Matson et al. [10] state that individuals with ASD exhibit completing household tasks and taking care of personal difficulties “executing the mental control necessary for possessions [22]. According to parent interviews, factors maintaining a problem-solving strategy to obtain a future contributing to decreased community and home partici- goal” as well as deficits in cognitive flexibility and planning pation include, but are not limited to, displaying tantrums in (p. 445). community settings as well as demonstrating difficulty with &e self-care and health and safety domains encompass following directions [40]. the skills used in order to complete functional tasks of daily One study examined participation patterns in preschool living in addition to the ability to complete those tasks safely children with ASD, specifically within the domains of and avoid physical dangers [22]. Cavkaytar and Pollard [46] community mobility and domestic chores [40]. &e results report that many individuals with autism require multiple indicated that children with ASD participate in significantly repetitions of instructions and demonstrate deficits in in- fewer activities in all domains compared to typically de- dependently completing daily living skills. One study ex- veloping children [40]. Furthermore, the presence of re- plored possible reasons for these deficits and included the following: lack of motivation, habits/performance patterns, stricted and repetitive behaviors (RRBs) has been shown to set these individuals apart resulting in an increased risk for communication abilities, sensory processing difficulties, and reduced participation in everyday activities [40]. Liss et al. variability in performance [47]. Individuals with autism may [41] studied individuals with ASD as they completed the not find the value in the self-care task itself nor its outcome Wisconsin card sorting task (WCST) and observed frequent and are unlikely to become motivated to finish the task perseverative behaviors throughout the task that ultimately merely to “please an adult or conform to social standards” affected their accuracy and completion. Whereas this task [47]. With these individuals demonstrating perseverative was completed for an experimental purpose, it can em- and stereotyped behaviors, this population tends to stick to phasize the role repetitive, and perseverative behaviors play strict rituals and routines [1]. &erefore, incorporating new on the accuracy and completion of everyday tasks such as routines to complete tasks of daily living may be difficult to domestic chores and self-care routines [42]. Moreover, the an individual with autism [47]. Additionally, difficulty Autism Research and Treatment 7 understanding the task at the hand and the inability for the found no significant association between a diagnosis of ASD child to express his/her own needs can affect the completion and motor deficits. Furthermore, within this study, only 14 and/or accuracy of said task [47]. children (9%) among the sample group had a history of a gross Additionally, it is common for individuals with autism to motor delay, and all 14 of the children achieved gross motor demonstrate difficulties regarding sensory processing [48]. milestones by the enrollment of the study [53]. Additionally, Sensory difficulties may interfere in with self-care tasks in a Hanaie et al. [54] investigated the relationship between ab- number of ways, one of which being unable to teach the child normal corpus callosum connectivity and its effect on socio- the self-care task [47]. Hand-over-hand assistance will likely communicative and motor deficits in children with ASD. &is be resisted by the child with sensory processing deficits [47]. study displayed abnormal corpus callosum connectivity relative Last, a variability in performance demonstrated by the child to sociocommunicative deficits but not as it related to motor and the inconsistencies of adult responses can influence both deficits in children with ASD [54]. Previously, a study was “task performance and trajectories of progress” in the realm conducted examining a predictive relationship between the five of completing tasks of daily living [47]. main developmental domains within the Bayley -III assess- &e self-direction and leisure subscales pertain to skills ment and a diagnosis of ASD [23]. &e results indicated that the such as self-control, following directions and rules, making motor standard deviation subscale was not significant as an choices, playing, and participating in recreational activities individual predictor of an ASD diagnosis, supportive of the within the home [22]. A study conducted by Bachevalier and present study’s findings [23]. Loveland [49] found that individuals with ASD demonstrate Several factors within this study pose possible limita- difficulties with self-regulation of social-emotional behavior. tions. &e adaptive behavior portion of the Bayley -III is Self-regulation is defined in the aforementioned study as assessed based on a questionnaire that is to be filled out by “the ability to select and initiate complex behaviors in re- the child’s parent, guardian, and/or clinician. &is could sponse to the specific condition of the social environment” result in biased data and understanding of the participants. [49]. &e ability to self-regulate depends greatly on making In this case, self-reporting bias may be present [55]. Self- inferences about the people and the environment sur- reporting is a common approach utilized by researchers to rounding one’s self [49]. With these individuals demon- obtain data and can include questionnaires, surveys, or strating deficits in social communication and social- interviews [55]. Two different types of bias can result from emotional behavior, self-regulation then becomes difficult self-reporting—social desirability bias and recall bias [55]. [23, 49]. &e results of an additional study concluded that When researchers use self-reporting as a means of data children with autism had significant deficits in the “stability collection, the questions asked may concern private or of self-regulation and affective expression” as compared to sensitive topics; in this case, questions were asked regarding that in individuals with Down syndrome [50]. Furthermore, the child of the participants’ development [55]. &us, an- with measures assessing attention, flexibility, engagement, swers to these questions can be “affected by an external bias and goal-directedness during play activities, individuals with caused by social desirability or approval” [55]. Furthermore, ASD demonstrated greater deficits within these realms self-reporting measures may require participants to recall relative to the group of individuals with Down syndrome past events resulting in a recall error [55]. [50]. More specifically, the ASD group exhibited difficulties Additionally, the evaluation and diagnostic processes for in the ability to sustain attention and concentration to fa- early intervention vary by state. &is study obtained files and cilitate appropriate play activity [50]. data from Kentucky’s early intervention program—First When examining the participation patterns in pre- Steps [56]. Other states may have different protocols and school-aged children with autism, parent interviews revealed procedures in place when assessing children of three years of children with ASD participate in fewer preschool activities of age and younger for autism. &ere are various tools available vigorous leisure [40]. Specific factors affecting decreased to early interventionists for the assessment of children of participation in leisure include, but are not limited to, the three years of age and younger. &is study utilized results child’s inability to follow directions as well as the child’s from the Bayley -III due to availability. While this is a disinterest in the leisure activity [40]. popular tool utilized by early interventionists, opportunities &e motor component assesses a child’s locomotive abil- for future research can include results from other stan- ities as well as his/her ability to manipulate his/her environ- dardized assessments. ment [22]. Contrarily, the motor subscale raw score on the Currently, the literature regarding motor deficits within Bayley -III did not significantly contribute to the variance in this population is varied and limited. Future research among predicting autism spectrum disorder diagnosis in children ≤ this realm will allow for increased specificity in motor three years of age. Present, the literature is mixed on whether or characteristics in young children with ASD. As previously not motor deficits are a diagnostic characteristic of ASD. mentioned, future research can incorporate other popular Within various studies examining motor coordination, arm assessment tools to examine the different domains and movements, gait, and postural stability deficits, individuals with determine if they are predictive of an autism diagnosis. &is ASD were found to have significant deficits among these motor can allow for a more descriptive analysis of early diagnostic domains [51]. Likewise, difficulties with postural control, fine characteristics of autism in young children. and gross motor coordination, and gait abnormalities have &e intent of this study was primarily to contribute to the been shown to co-occur with an ASD diagnosis [52]. However, specificity of early diagnostic characteristics in young chil- in contrast to the aforementioned literature, Ming et al. [53] dren with ASD. More specifically, the study’s focus was on 8 Autism Research and Treatment [9] C. T. Wong, J. Wais, and D. A. Crawford, “Prenatal exposure to the diagnostic characteristics relative to that of adaptive common environmental factors affects brain lipids and increases behavior skills. &e study encompassed children of three risk of developing autism spectrum disorders,” European Journal years of age and younger. &e findings were consistent with of Neuroscience, vol. 42, no. 10, pp. 2742–2760, 2015. the current body of literature on ASD with respect to deficits [10] J. L. Matson, J. 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Autism Spectrum Disorder: Investigating Predictive Adaptive Behavior Skill Deficits in Young Children

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Copyright © 2021 Emma Feige et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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10.1155/2021/8870461
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Hindawi Autism Research and Treatment Volume 2021, Article ID 8870461, 9 pages https://doi.org/10.1155/2021/8870461 Research Article Autism Spectrum Disorder: Investigating Predictive Adaptive Behavior Skill Deficits in Young Children 1 1 1,2 1 Emma Feige, Rhonda Mattingly, Teresa Pitts, and Alan F. Smith Department of Otolaryngology-Head/Neck Surgery-and Communicative Disorders, University of Louisville, Louisville, KY, USA Department of Neurological Surgery; Kentucky Spinal Cord Research Centre, University of Louisville, Louisville, KY, USA Correspondence should be addressed to Alan F. Smith; afsmit01@louisville.edu Received 20 July 2020; Revised 31 December 2020; Accepted 22 January 2021; Published 31 January 2021 Academic Editor: Daniel Rossignol Copyright © 2021 Emma Feige et al. &is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Autism spectrum disorder (ASD) is a lifelong neurodevelopmental disorder that consists of difficulties with social communication and language, as well as the presence of restricted and repetitive behaviors. &ese deficits tend to present in early childhood and usually lead to impairments in functioning across various settings. Moreover, these deficits have been shown to negatively impact adaptive behavior and functioning. &us, early diagnosis and intervention is vital for future success within this population. &e purpose of this study was to further examine the subscales that comprise the adaptive behavior section of the Bayley -III to determine which of the ten subscales are predictive of ASD in young children (i.e.,≤ three years of age). A retrospective file review of 273 children participating in Kentucky’s early intervention program, First Steps, was completed. &e children ranged in age from 18 to 35 months. A binary logistic regression was used to assess the subscales that comprise the adaptive behavior of the section of the Bayley -III to determine which of the ten subscales are predictive of ASD in young children (i.e., ≤ three years of age). &e results indicated that individual lower raw scores in communication, community use, functional preacademics, home living, health and safety, leisure, self-care, self-direction, and social subscales were predictive of an autism diagnosis. females may “mask sociocommunicative impairments due 1. Introduction to increased sensitivity to social pressure to fit in, gendered Autism spectrum disorder (ASD) is a lifelong neuro- expectations for social behavior, and strengths in some developmental disorder that consists of deficits in social social-communication skills” [5]. &is could result in fe- communication and language, as well as the presence of males possibly being “missed by current diagnostic proce- restricted and repetitive behaviors [1, 2]. ASD is described as dures” [5]. Nonetheless, diagnosis of ASD appears to be 4 a spectrum disorder as it presents differently in each indi- times more common in males than in females [3]. vidual. &ese deficits tend to present in early childhood and Secondary to the “heterogeneity of affected individuals usually lead to impairments in functioning across various and the genetic complexity” of the disorder, it has been settings [2]. difficult to identify the cause(s) of ASD [2]. Previous re- &e Centers for Disease Control and Prevention report search has suggested several possible etiologies; however, the that approximately 1 in 59 children are diagnosed with ASD literature remains inconclusive [6]. Bölte, Girdler, and crossing all racial, ethnic, and socioeconomic groups [3]. Marschik suggest that many genetic and environmental Previous research has reported a steady increase in the factors and their interactions may contribute to autism prevalence of ASD over the past 2 decades [4]. Probable phenotypes, but their specific causal mechanisms remain reasons for the increase include the “broadening of diag- poorly understood. Yates and Le Couteur [2] suggest that nostic criteria and improved case recognition” [2]. More- significant genetic variations have been found in approxi- over, symptomology of ASD tends to present differently in mately 10% of individuals diagnosed with ASD. Increased males and females [2]. “Camouflaging theory” suggests that paternal and maternal age has also been associated with 2 Autism Research and Treatment intervention implemented before age three has been asso- higher risk of having a child with autism, possibly due to “de novo spontaneous mutations and/or alterations in genetic ciated with better communicative, academic, and behavioral outcomes at school age [17]. Several studies have concluded imprinting” [7]. Moreover, strong heritability has been linked with ASD as recurrence rates for siblings have been that children with autism make greater gains in intervention reported to be up to 18.7% [2]. “Research continues to study when it begins earlier, between the ages of two and four, as neurobiological differences in ASD considering variation in compared to older children receiving the same interven- neurotransmitters, volumetric and functioning differences tions, including those with other neurodevelopmental dis- of various regions within the brain, but the relevance to orders [18]. More recent emerging evidence supports the clinical practice of most identified abnormalities has not idea that earlier and more intensive treatment results in more favorable outcomes [19]. been established” [2]. Environmental factors may also play a role in possible Early intervention services often address the needs of children across five developmental areas, including cogni- ASD diagnosis. Previous research [8] found that exposure to environmental neurotoxicants during prenatal, perinatal, tive, motor, social-emotional, communication, and adaptive development [14]. Children referred for early intervention and postnatal development has been shown to influence the biochemical brain development, resulting in “neuro- services typically undergo an in-depth evaluation process to developmental abnormalities that may contribute to ASD.” assess their therapeutic needs prior to intervention. Various More specifically, prenatal exposures to “air pollution, heavy assessment measures may be used during this process with metals, pesticides, and toxic substances in consumer differing requirements from state-to-state. Nonetheless, the products” could bring about atypical brain development, assessment process should be comprised of a comprehensive resulting in possible neural pathologies such as ASD [9]. set of activities to (1) identify a child’s strengths and weaknesses, (2) address the families concerns and priorities, &rough growing research, it has become more evident that the etiology associated with ASD is multifactorial with ge- and (3) develop a plan for ongoing treatment strategies for the child [20, 21]. netic and environmental factors playing a role [7]. &e heterogeneity of ASD is evident in the early years of IDEA requires that the evaluation/assessment be com- pleted using a range of tools in a variety of contexts [14]. &e development as well [10, 11]. Kanner first described autism as being one of an “infantile” type, suggesting that the onset instruments used may include both criterion-referenced of symptoms occurred throughout the early ages of life [7]. and/or standardized properties. One tool, in particular, that Another study examined three possible types/developmental is often utilized within early intervention circles is the Bayley rd trajectories of ASD in children [12]. &ese three types in- Scales of Infant and Toddler Development (3 Edition) or clude early onset, regression, and plateau [12]. ASD the Bayley -III. &e Bayley -III is a comprehensive as- ® ® symptoms manifest soon after birth in children with the sessment tool used to identify developmental issues in early early onset type, whereas children with the regressive type childhood [22]. begin to develop normally until around two years of age Previous research has shown that individual lower proceeded by a regression in development [12]. &is re- subscale scores within the cognitive, language, adaptive gression is most evident in the child’s language and social behavior, and social-emotional developmental domains on skills [12]. Last, children with the plateau type develop the Bayley -III were predictive of an ASD diagnosis in normally until approximately six months of age and cease to children three years of age and younger [23]. Due to current make any developmental advances [12]. For example, Rogers literature and ASD diagnostic criteria, this outcome is not [13] describes a halting of development where “babbling was surprising with regards to language and social-emotional present but did not continue to develop into speech.” Re- domains. A direct connection with the cognitive and garding ongoing development and future outcomes, evi- adaptive behavior sections, however, may be less clear. dence suggests that children who present with the regressive Adaptive behavior appears strongly associated with in- developmental trajectory tend to have more severe deficits telligence in neurotypical individuals; however, “cognitively across time and in a variety of areas [10]. able individuals with ASD fail to acquire adaptive skills at While the DSM-V provides guidelines and criter- rates corresponding with gains” in intelligence [24]. ia—including severity levels—for diagnosing ASD, it also Moreover, the “gap in daily living skills (i.e., adaptive skills) highlights the fact that symptoms must also be present between children with ASD and typically developing chil- during early childhood. Under the Individuals with Dis- dren increased across early childhood” [24] including poorer abilities Education Act (IDEA), specifically Part C, the law planning abilities and cognitive flexibility [25]. Nonetheless, defines the age range for children eligible for early inter- a review of the literature examining ASD and adaptive vention serves as birth to three years of age [14]. &e functioning conclude that individuals with ASD tend to American Speech-Language-Hearing Association defines present with adaptive functioning difficulties as compared to early intervention as providing families, toddler, and infants their same-age peers [24, 26, 27]. who have or are at risk of a developmental delay, disability, Harris and Oakland [28] define adaptive behavior skills or other health condition that inhibits typical development as “practical, everyday skills needed to function and meet the with intervention services [15]. demands of one’s environment, including the skills neces- Evidence suggests that the earlier a child receives in- sary to effectively and independently take care of oneself and tervention, the greater the likelihood of an improved de- to interact with other people.” Within the subscale of the velopmental trajectory [16]. In general, intensive adaptive behavior (ADP) skills portion of the Bayley -III, ® Autism Research and Treatment 3 there are ten subscales. &e subscales are comprised of were retrieved from each file (for children diagnosed as communication (CO), community use (CU), functional having ASD) and randomly for children with developmental preacademics (FA), home living (HL), health and safety delay. &e developmental delay sample served as a type of (HS), leisure (L), self-care (SC), self-direction (SD), social the control group. &e raw scores for the ten adaptive be- (S), and motor (M) [22]. &ese subscales “assess the daily havior subsections and the overall standard deviation scores functional skills of a child, measuring what the child actually for the overall adaptive behavior section were anonymously does, in addition to what he or she may be able to do” [22]. compiled into a Microsoft Excel spreadsheet and then Scores are provided via parent report and are based on the exported to IBM SPSS for Windows, version 25 (IBM Corp., frequency (e.g., is not able, never when needed, sometimes Armonk, N.Y., USA) for statistical analyses. Separate when needed, and always when needed) with which the child spreadsheets were created for children diagnosed with ASD performs the behavior when it is needed and without help and those that did not carry the diagnosis. &e data were provided [22]. stored on a password protected computer behind a locked &e purpose of the study was to further examine the door; a master-code was never created. Gender was coded, subscales that comprise the adaptive behavior section of the where 1 � male and 2 � female. ASD diagnosis was coded in Bayley -III to determine which of the ten subscales are the same manner, where 1 � not diagnosed and 2 � diagnosed. No identifying information was recorded. predictive of ASD in young children (i.e., ≤ three years of age). Improved knowledge of the predictive value of each A binary logistic regression was used to assess the subscales subscale or combination thereof may contribute to an im- that comprise the adaptive behavior section of the Bayley -III proved understanding of the role adaptive behavior plays in to determine which of the ten subscales (e.g., communication, the diagnosis of ASD. community use, functional preacademics, home living, health and safety, leisure, self-care, self-direction, social, and motor) are predictive of ASD in young children (i.e., ≤ three years of 2. Methods and Materials age). A binary logistic regression analysis was used, as the &is study utilized a retrospective file review of children criterion variable—ASD diagnosis—is dichotomous [31]. (N � 273) that participated in Kentucky’s early intervention Descriptive statistics, assumption testing, and the results of the program, First Steps, between 1/1/2012 and 6/1/2019. &e logistic regression analyses follow. sample included children between the ages of 18 and 35 months and comprised 203 males and 70 females. 3. Results Tabachnick and Fidell [29] recommended a sample size of at least 80, where N> 50 + 8m (m is the number of predictor &is study comprised a retrospective file review of 273 variables). Moreover, Babyak [30] suggested a minimum children in the state of Kentucky: 74.4% (n � 203) was male sample size of 10–15 observations per predictor variable. and 25.6% (n � 70) was female. &e ages ranged from 18 to Children with and without ASD diagnosis were represented. 35 months (M � 24.04, SD � 5.30). Forty-eight percent ASD diagnosis was determined by the intensive level of (n � 131) of the children were diagnosed with ASD; 52% evaluation (ILE) as completed by the University of Louis- (n � 142) did not have an ASD diagnosis. ville, Weisskopf Child Evaluation Center (WCEC). For the Table 1 presents the mean and standard deviations for purpose of this study, an ILE is equivalent to a multidis- the ten subscales of the adaptive behavior section of the ciplinary evaluation that typically involves—in Kentucky—a Bayley -III [22]. Consistent with regression-based analyses, speech-language pathologist, psychologist, and develop- the ten subscales are referenced as predictor variables. ASD mental pediatrician. An occupational therapist may also be diagnosis served as the criterion variable. involved on a case-by-case basis. Diagnosis is based on majority opinion of the team. Per this study, possible ILE diagnoses included autism with developmental delay or 3.1. Logistic Regressions are Sensitive to Multicollinearity. developmental delay. Approval for this study, including the “When data are not centered, the regression coefficients that retrospective file review, was granted by the Institutional are estimated and tested may be irrelevant and misleading. Review Boards (IRB) of the University of Louisville and the Centering, thoughtfully done, can diminish the almost in- Kentucky Cabinet for Health and Family Services. evitable multicollinearity problems in regression, thus in- &e researchers were granted access to the Technology- creasing both the precision of parameter estimation and the assisted Observation and Teaming Support (TOTS) data- power of statistical testing of those parameters” [32]. base, an electronic record used by the Kentucky Department As previously suggested, the continuous variables were of Public Health to track children as they are referred, mean centered by subtracting the mean from the value for evaluated, and—in some cases—receive services through the each variable. &e dichotomous variable—ASD diag- early intervention program. &e researchers used TOTS to nosis—was also centered. &is was completed by changing query children referred to—and evaluated by—First Steps the values of 0 to −0.5 and 1 to 0.5. Variables were centered between the aforementioned date range. Again, specific as a strategy to prevent errors in statistical inference. interest centered on ASD diagnosis. Demographic infor- A correlation matrix (Pearson) was calculated to assess mation included each child’s age (in months) at evaluation multicollinearity presence. Mukaka [33] was used to in- and gender. Paper-based files were reviewed at the Ken- terpret the size of the correlation coefficient. Tabachnick and tuckiana Point of Entry office. &e Bayley -III protocols Fidell [29] suggest that as long as correlation coefficients ® 4 Autism Research and Treatment Table 1: Descriptive statistics adaptive behavior subscale raw Table 2: Pearson product-moment correlation matrix (N � 273). scores (N � 273). ADP CO CU FA HL HS LS SC SD SOC Subscale M SD ADP Communication 25.0 10.0 CO 0.57 Community use 9.6 8.4 CU 0.47 0.56 Functional preacademics 6.6 7.9 FA 0.36 0.55 0.49 Home living 22.7 15.3 HL 0.51 0.59 0.72 0.49 Health and safety 23.6 11.5 HS 0.44 0.62 0.61 0.40 0.78 Leisure 28.5 10.2 LS 0.55 0.64 0.49 0.41 0.64 0.68 Self-care 35.8 9.4 SC 0.42 0.58 0.49 0.26 0.63 0.69 0.74 Self-direction 29.1 11.0 SD 0.49 0.54 0.57 0.29 0.70 0.75 0.81 0.74 Social 31.6 10.0 SOC 0.59 0.76 0.57 0.44 0.70 0.70 0.76 0.70 0.75 Motor 51.5 11.0 MO 0.17 0.40 0.45 0.23 0.60 0.68 0.61 0.64 0.67 0.62 Moderate positive (negative) correlation |r � 0.50–0.70| in italics. High positive (negative) correlation |r> 0.70| in bold. among independent variables are less than 0.90, multi- collinearity is less likely to have occurred. &e results are Table 3: Predicting ASD diagnosis based on adaptive behavior presented in Table 2. scale standard deviation. Individual logistic regression analyses were used to examine the relationship between the overall adaptive be- Subscale Odds ratio 95% (CI) % variance p havior scale and the associated subscale raw scores with the Adaptive behavior 0.12 0.08–0.20 53 <0.001 diagnosis of ASD. Logistic regression allows the use of outcome variables that are categorical and predictor vari- ables that are continuous or categorical. Logistic regression Table 4: Predicting ASD diagnosis based on adaptive behavior analysis is the most appropriate statistical measure since the subscale raw scores. criterion variable is dichotomous. Table 3 shows the results Subscale Odds ratio 95% (CI) % variance p of the logistic regression analysis examining the overall Communication 0.86 0.83–0.90 34 <0.001 adaptive behavior scale as a predictor of ASD. Community use 0.91 0.88–0.95 15 <0.001 &e complete results of the logistic regression analyses Preacademics 0.93 0.89–0.97 8 <0.001 for the individual subscales that comprise the adaptive Home living 0.96 0.94–0.98 11 <0.001 behavior scale are presented in Table 4. Health/safety 0.95 0.93–0.98 9 <0.001 Leisure 0.92 0.89–0.95 18 <0.001 Self-care 0.93 0.90–0.96 13 <0.001 3.2. Bayley -III Adaptive Behavior Scale and ASD Diagnosis. Self-direction 0.95 0.92–0.97 10 <0.001 Logistic regression—step 1—entered the adaptive behavior Social 0.88 0.85–0.91 31 <0.001 scale standard deviation scores as a predictor of ASD di- Motor 0.98 0.96–1.01 1 0.14 agnosis. &e results were statistically significant (odds ratio � 0.12, 95% CI � 0.08–0.20, p< 0.001) and explained 53% (Nagelkereke R ) of the variance of ASD diagnosis. &e a predictor of ASD diagnosis. &e results were statistically results suggest that children who receive lower standard significant (odds ratio � 0.91, 95% CI � 0.88–0.95, p< 0.001) deviation scores on the Bayley -III adaptive behavior scale and explained 15% (Nagelkereke R ) of the variance of ASD are more likely to receive an ASD diagnosis than children diagnosis. &e results suggest that children who scored lower with higher standard deviation scores. (raw scores) on the community use subscale on the Bayley - III adaptive behavior scale are more likely to receive an ASD diagnosis than children with higher community use subscale 3.3. Bayley -III Adaptive Behavior Communication Subscale ® raw scores. and ASD Diagnosis. Logistic regression—step 1a—entered the adaptive behavior communication subscale raw scores as a predictor of ASD diagnosis. &e results were statistically 3.5. Bayley -III Adaptive Behavior Preacademics Subscale and significant (odds ratio � 0.86, 95% CI � 0.83–0.90, p< 0.001) ASD Diagnosis. Logistic regression—step 1c—entered the and explained 34% (Nagelkereke R ) of the variance of ASD adaptive behavior functional preacademics subscale raw diagnosis. &e results suggest that children who scored lower scores as a predictor of ASD diagnosis. &e results were (raw scores) on the communication subscale on the Bayley - ® statistically significant (odds ratio � 0.93, 95% III adaptive behavior scale are more likely to receive an ASD CI � 0.89–0.97, p< 0.001) and explained 8% (Nagelkereke diagnosis than children with higher communication sub- 2 R ) of the variance of ASD diagnosis. &e results suggest that scale raw scores. children who scored lower (raw scores) on the functional preacademics subscale on the Bayley -III adaptive behavior scale are more likely to receive an ASD diagnosis than 3.4. Bayley -III Adaptive Behavior Community Use Subscale children with higher functional preacademics subscale raw and ASD Diagnosis. Logistic regression—step 1b—entered scores. the adaptive behavior community use subscale raw scores as Autism Research and Treatment 5 3.6. Bayley -III Adaptive Behavior Home Living Subscale and diagnosis than children with higher self-direction subscale ASD Diagnosis. Logistic regression—step 1d—entered the raw scores. adaptive behavior home living subscale raw scores as a predictor of ASD diagnosis. &e results were statistically 3.11. Bayley -III Adaptive Behavior Social Subscale and ASD significant (odds ratio � 0.96, 95% CI � 0.94–0.98, p< 0.001) Diagnosis. Logistic regression—step 1i—entered the adap- and explained 11% (Nagelkereke R ) of the variance of ASD tive behavior social subscale raw scores as a predictor of ASD diagnosis. &e results suggest that children who scored lower diagnosis. &e results were statistically significant (odds (raw scores) on the home living subscale on the Bayley -III ratio � 0.88, 95% CI � 0.85–0.91, p< 0.001) and explained adaptive behavior scale are more likely to receive an ASD 31% (Nagelkereke R ) of the variance of ASD diagnosis. &e diagnosis than children with higher home living subscale results suggest that children who scored lower (raw scores) raw scores. on the social subscale on the Bayley -III adaptive behavior scale are more likely to receive an ASD diagnosis than children with higher social subscale raw scores. 3.7. Bayley -III Adaptive Behavior Health and Safety Subscale and ASD Diagnosis. Logistic regression—step 1e—entered the adaptive behavior health and safety subscale raw scores 3.12. Bayley -III Adaptive Behavior Motor Subscale and ASD as a predictor of ASD diagnosis. &e results were statistically Diagnosis. Logistic regression—step 1j—entered the adap- significant (odds ratio � 0.95, 95% CI � 0.93–0.98, p< 0.001) tive behavior motor subscale raw scores as a predictor of and explained 9% (Nagelkereke R ) of the variance of ASD ASD diagnosis. &e results were not statistically significant diagnosis. &e results suggest that children who scored lower (odds ratio � 0.98, 95% CI � 0.96–1.01, p � 0.14). Although (raw scores) on the health and safety subscale on the statistical significance was not achieved, the model explained Bayley -III adaptive behavior scale are more likely to receive 2 1% (Nagelkereke R ) of the variance of ASD diagnosis. an ASD diagnosis than children with higher health and Motor subscale raw scores do not seem to vary substantially safety subscale raw scores. across ASD diagnostic categories. Per this sample, children with an ASD diagnosis did not appear to have significantly lower motor subscale raw scores than their non-ASD peers. 3.8. Bayley -III Adaptive Behavior Leisure Subscale and ASD &e intent of this study sought to examine the subscales Diagnosis. Logistic regression—step 1f—entered the adap- that comprise the adaptive behavior section of the Bayley tive behavior leisure subscale raw scores as a predictor of III to determine which of the ten subscales are predictive of ASD diagnosis. &e results were statistically significant (odds ASD in young children (i.e.,≤ three years of age). &e results ratio � 0.92, 95% CI � 0.89–0.95, p< 0.001) and explained 2 found that lower standard deviation scores on the adaptive 18% (Nagelkereke R ) of the variance of ASD diagnosis. &e behavior scale on the Bayley -III was a statistically signif- results suggest that children who scored lower (raw scores) icant predictor of ASD in young children. Moreover, lower on the leisure subscale on the Bayley -III adaptive behavior raw scores on the communication, community use, func- scale are more likely to receive an ASD diagnosis than tional preacademics, home living, health and safety, leisure, children with higher leisure subscale raw scores. self-care, self-direction, and social subscales of the adaptive behavior scale of the Bayley -III were found to be statis- tically significant predictors of ASD in young children. &e 3.9. Bayley -III Adaptive Behavior Self-Care Subscale and communication and social subscales were found to con- ASD Diagnosis. Logistic regression—step 1g—entered the tribute the greatest amount of variance in predicting ASD at adaptive behavior self-care subscale raw scores as a predictor 34% and 31%, respectively. of ASD diagnosis. &e results were statistically significant (odds ratio � 0.93, 95% CI � 0.90–.96, p< 0.001) and explained 13% (Nagelkereke R ) of the variance of ASD 4. Discussion and Conclusions diagnosis. &e results suggest that children who scored lower &e purpose of this study was to further examine the (raw scores) on the self-care subscale on the Bayley -III subscales that comprise the adaptive behavior section of the adaptive behavior scale are more likely to receive an ASD Bayley -III to determine which of the subscales are pre- diagnosis than children with higher self-care subscale raw dictive of ASD in young children (i.e.,≤ three years of age) in scores. hope to contribute to the specificity of autism characteristics in early childhood as they relate to adaptive behavior. 3.10. Bayley -III Adaptive Behavior Self-Direction Subscale &e current study examined individual logistic regres- and ASD Diagnosis. Logistic regression—step 1h—entered sion analyses which determined that lower standard devi- the adaptive behavior self-direction subscale raw scores as a ation scores on the adaptive behavior scale on the Bayley - predictor of ASD diagnosis. &e results were statistically III was a statistically significant predictor of ASD in young significant (odds ratio � 0.95, 95% CI � 0.92–0.97, p< 0.001) children. Moreover, lower raw scores on the communica- and explained 10% (Nagelkereke R ) of the variance of ASD tion, community use, functional preacademics, home living, diagnosis. &e results suggest that children who scored lower health and safety, leisure, self-care, self-direction, and social (raw scores) on the self-direction subscale on the Bayley -III subscales were found to be statistically significant predictors adaptive behavior scale are more likely to receive an ASD of ASD in young children. &e social and communication 6 Autism Research and Treatment individual subscale scores contributed the greatest amount presence of restricted and repetitive behaviors can also be of variance when predicting the diagnosis of ASD. As these linked to deficits in executive function [43]. In a study conducted by Pennington and Ozonoff [44], individuals two deficits are specified within the current diagnostic criteria and there is a vast amount of literature discussing with autism completed executive functioning tasks with a these deficits among the ASD population, these results come higher number of perseverative errors as well as exhibited as no surprise. rigid and inflexible problem-solving strategies. Social and communicative deficits have been diagnostic Executive functioning (EF) closely pertains to the cog- hallmarks since the first clinical accounts of ASD were nitive domains of attention, reasoning, and problem-solving recorded [34]. &e first clinical accounts were recorded by [44]. Particularly, “set-shifting and set-maintenance, inter- Dr. Kanner [35], wherein he referenced difficulties with ference control, inhibition, integration across space and socialization among the observed group of children [34]. time, planning, and working memory” are that of a few Present, one of the first symptoms that is commonly found executive functions [44]. Liss et al. [41] further included the in children with ASD is their lack of social interaction [36]. processes of “forming abstract concepts, having a flexible sequenced plan of action, focusing and sustaining attention Studies examining the relationship between communication skills and corresponding levels of adaptive behavior in in- and mental effort, rapidly retrieving relevant information, dividuals with ASD are limited [37]. However, Kjellmer et al. being able to self-monitor and self-correct as a task is [37] concluded that nonverbal communication skills may be performed, and being able to inhibit impulsive responses” as related to severity of autism symptoms as well as adaptive EF components (p. 261). An individual’s level of executive functioning. functioning has been shown to correlate with academic skills &e lack of communication skills displayed by children [41]. Wenz-Gross et al. [45] affirm that EF comprises of with autism is the greatest cause of concern for parents [17]. “cognitive processes thought to support academic achieve- As limited communication skills are associated with ASD, ment through top-down control of attention and behavior” these individuals are more likely to display challenging (p. 2). In general, learning is characterized by the executive behaviors and/or aggression as this may be their only means functioning tasks of “seeing relationships between pieces of information, identifying central patterns or themes, dis- of communication, indirectly resulting in increased parental psychological distress [38]. One study examined how par- tinguishing relevant from irrelevant information, and de- ents modified the environment in order to meet the needs of riving meaning” [44]. As it relates to the present study, the their child with ASD who demonstrated challenging be- functional preacademic domain within the Bayley -III as- haviors [39]. &e study revealed that parents limited social sesses preacademic skills such as letter recognition, count- activities and outings with the child (i.e., shopping and ing, and drawling simple shapes [22]. &e results of this visiting restaurants) [39]. Furthermore, parents avoided study can be explained by the theory of executive dys- taking their child to new and different environments, lim- function, as it is known that individuals with ASD display iting their exposure into the community [39]. difficulties with EF as it pertains to academic skills [44]. &e community use and home living subscales of the Conceptual understanding of the main idea or big picture of Bayley -III measure a child’s ability to participate in ac- a topic is often lacking among this group of individuals [44]. tivities and interests throughout the community as well as Matson et al. [10] state that individuals with ASD exhibit completing household tasks and taking care of personal difficulties “executing the mental control necessary for possessions [22]. According to parent interviews, factors maintaining a problem-solving strategy to obtain a future contributing to decreased community and home partici- goal” as well as deficits in cognitive flexibility and planning pation include, but are not limited to, displaying tantrums in (p. 445). community settings as well as demonstrating difficulty with &e self-care and health and safety domains encompass following directions [40]. the skills used in order to complete functional tasks of daily One study examined participation patterns in preschool living in addition to the ability to complete those tasks safely children with ASD, specifically within the domains of and avoid physical dangers [22]. Cavkaytar and Pollard [46] community mobility and domestic chores [40]. &e results report that many individuals with autism require multiple indicated that children with ASD participate in significantly repetitions of instructions and demonstrate deficits in in- fewer activities in all domains compared to typically de- dependently completing daily living skills. One study ex- veloping children [40]. Furthermore, the presence of re- plored possible reasons for these deficits and included the following: lack of motivation, habits/performance patterns, stricted and repetitive behaviors (RRBs) has been shown to set these individuals apart resulting in an increased risk for communication abilities, sensory processing difficulties, and reduced participation in everyday activities [40]. Liss et al. variability in performance [47]. Individuals with autism may [41] studied individuals with ASD as they completed the not find the value in the self-care task itself nor its outcome Wisconsin card sorting task (WCST) and observed frequent and are unlikely to become motivated to finish the task perseverative behaviors throughout the task that ultimately merely to “please an adult or conform to social standards” affected their accuracy and completion. Whereas this task [47]. With these individuals demonstrating perseverative was completed for an experimental purpose, it can em- and stereotyped behaviors, this population tends to stick to phasize the role repetitive, and perseverative behaviors play strict rituals and routines [1]. &erefore, incorporating new on the accuracy and completion of everyday tasks such as routines to complete tasks of daily living may be difficult to domestic chores and self-care routines [42]. Moreover, the an individual with autism [47]. Additionally, difficulty Autism Research and Treatment 7 understanding the task at the hand and the inability for the found no significant association between a diagnosis of ASD child to express his/her own needs can affect the completion and motor deficits. Furthermore, within this study, only 14 and/or accuracy of said task [47]. children (9%) among the sample group had a history of a gross Additionally, it is common for individuals with autism to motor delay, and all 14 of the children achieved gross motor demonstrate difficulties regarding sensory processing [48]. milestones by the enrollment of the study [53]. Additionally, Sensory difficulties may interfere in with self-care tasks in a Hanaie et al. [54] investigated the relationship between ab- number of ways, one of which being unable to teach the child normal corpus callosum connectivity and its effect on socio- the self-care task [47]. Hand-over-hand assistance will likely communicative and motor deficits in children with ASD. &is be resisted by the child with sensory processing deficits [47]. study displayed abnormal corpus callosum connectivity relative Last, a variability in performance demonstrated by the child to sociocommunicative deficits but not as it related to motor and the inconsistencies of adult responses can influence both deficits in children with ASD [54]. Previously, a study was “task performance and trajectories of progress” in the realm conducted examining a predictive relationship between the five of completing tasks of daily living [47]. main developmental domains within the Bayley -III assess- &e self-direction and leisure subscales pertain to skills ment and a diagnosis of ASD [23]. &e results indicated that the such as self-control, following directions and rules, making motor standard deviation subscale was not significant as an choices, playing, and participating in recreational activities individual predictor of an ASD diagnosis, supportive of the within the home [22]. A study conducted by Bachevalier and present study’s findings [23]. Loveland [49] found that individuals with ASD demonstrate Several factors within this study pose possible limita- difficulties with self-regulation of social-emotional behavior. tions. &e adaptive behavior portion of the Bayley -III is Self-regulation is defined in the aforementioned study as assessed based on a questionnaire that is to be filled out by “the ability to select and initiate complex behaviors in re- the child’s parent, guardian, and/or clinician. &is could sponse to the specific condition of the social environment” result in biased data and understanding of the participants. [49]. &e ability to self-regulate depends greatly on making In this case, self-reporting bias may be present [55]. Self- inferences about the people and the environment sur- reporting is a common approach utilized by researchers to rounding one’s self [49]. With these individuals demon- obtain data and can include questionnaires, surveys, or strating deficits in social communication and social- interviews [55]. Two different types of bias can result from emotional behavior, self-regulation then becomes difficult self-reporting—social desirability bias and recall bias [55]. [23, 49]. &e results of an additional study concluded that When researchers use self-reporting as a means of data children with autism had significant deficits in the “stability collection, the questions asked may concern private or of self-regulation and affective expression” as compared to sensitive topics; in this case, questions were asked regarding that in individuals with Down syndrome [50]. Furthermore, the child of the participants’ development [55]. &us, an- with measures assessing attention, flexibility, engagement, swers to these questions can be “affected by an external bias and goal-directedness during play activities, individuals with caused by social desirability or approval” [55]. Furthermore, ASD demonstrated greater deficits within these realms self-reporting measures may require participants to recall relative to the group of individuals with Down syndrome past events resulting in a recall error [55]. [50]. More specifically, the ASD group exhibited difficulties Additionally, the evaluation and diagnostic processes for in the ability to sustain attention and concentration to fa- early intervention vary by state. &is study obtained files and cilitate appropriate play activity [50]. data from Kentucky’s early intervention program—First When examining the participation patterns in pre- Steps [56]. Other states may have different protocols and school-aged children with autism, parent interviews revealed procedures in place when assessing children of three years of children with ASD participate in fewer preschool activities of age and younger for autism. &ere are various tools available vigorous leisure [40]. Specific factors affecting decreased to early interventionists for the assessment of children of participation in leisure include, but are not limited to, the three years of age and younger. &is study utilized results child’s inability to follow directions as well as the child’s from the Bayley -III due to availability. While this is a disinterest in the leisure activity [40]. popular tool utilized by early interventionists, opportunities &e motor component assesses a child’s locomotive abil- for future research can include results from other stan- ities as well as his/her ability to manipulate his/her environ- dardized assessments. ment [22]. Contrarily, the motor subscale raw score on the Currently, the literature regarding motor deficits within Bayley -III did not significantly contribute to the variance in this population is varied and limited. Future research among predicting autism spectrum disorder diagnosis in children ≤ this realm will allow for increased specificity in motor three years of age. Present, the literature is mixed on whether or characteristics in young children with ASD. As previously not motor deficits are a diagnostic characteristic of ASD. mentioned, future research can incorporate other popular Within various studies examining motor coordination, arm assessment tools to examine the different domains and movements, gait, and postural stability deficits, individuals with determine if they are predictive of an autism diagnosis. &is ASD were found to have significant deficits among these motor can allow for a more descriptive analysis of early diagnostic domains [51]. Likewise, difficulties with postural control, fine characteristics of autism in young children. and gross motor coordination, and gait abnormalities have &e intent of this study was primarily to contribute to the been shown to co-occur with an ASD diagnosis [52]. However, specificity of early diagnostic characteristics in young chil- in contrast to the aforementioned literature, Ming et al. [53] dren with ASD. More specifically, the study’s focus was on 8 Autism Research and Treatment [9] C. T. Wong, J. Wais, and D. A. Crawford, “Prenatal exposure to the diagnostic characteristics relative to that of adaptive common environmental factors affects brain lipids and increases behavior skills. &e study encompassed children of three risk of developing autism spectrum disorders,” European Journal years of age and younger. &e findings were consistent with of Neuroscience, vol. 42, no. 10, pp. 2742–2760, 2015. the current body of literature on ASD with respect to deficits [10] J. L. Matson, J. Wilkins, and J. C. Fodstad, “Children with in social, communication, functional preacademics, leisure, autism spectrum disorders: a comparison of those who regress self-care, self-direction, health and safety, home living, and vs. those who do not,” Developmental Neurorehabilitation, community use [23, 34, 40, 41, 44, 46, 47, 49]. vol. 13, no. 1, pp. 37–45, 2010. It is the researchers’ belief that with the increased [11] E. Werner, G. Dawson, J. Munson, and J. Osterling, “Vari- knowledge of ASD characteristics in young children, there ation in early developmental course in autism and its relation will be an increase in a definitive ASD diagnosis at an earlier with behavioral outcome at 3-4 years of age,” Journal of age. Concurrently, this will allow for these individuals and Autism and Developmental Disorders, vol. 35, no. 3, their families to benefit from early intervention services pp. 337–350, 2005. which have been shown to greatly improve the individual’s [12] W. E. Barbeau, “Neonatal and regressive forms of autism: developmental trajectory. It is our hope that the limited diseases with similar symptoms but a different etiology,” knowledge based on early ASD diagnosis in young children Medical Hypotheses, vol. 109, pp. 46–52, 2017. has been increased and the gap in the available literature [13] S. J. Rogers, “Developmental regression in autism spectrum disorders,” Mental Retardation and Developmental Disabil- narrowed. ities Research Reviews, vol. 10, no. 2, pp. 139–143, 2004. 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