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Journal of Autism and Developmental Disorders

, Volume 47, Issue 12, pp 3857–3871 | Cite as

Continuity and Change in, and Child Predictors of, Caregiver Reported Anxiety Symptoms in Young People with Autism Spectrum Disorder: A Follow-Up Study

  • Elizabeth J. Teh
  • Diana Mei-En Chan
  • Germaine Ke Jia Tan
  • Iliana Magiati
S.I. : Anxiety in Autism Spectrum Disorders

Abstract

Little is known about continuity, change and predictors of anxiety in ASD. This follow-up study investigated changes in caregiver-reported anxiety in 54 non-referred youth with ASD after 10–19 months. Earlier child predictors of later anxiety were also examined. Anxiety scores were generally stable. Time 1 ASD repetitive behavior symptoms, but not social/communication symptoms, predicted Time 2 total anxiety scores, over and above child age, gender and adaptive functioning scores, but this predictive relationship was fully mitigated by Time 1 anxiety scores when these were included as a covariate in the regression model. Exploring bi-directionality between autism and anxiety symptomatology, Time 1 anxiety scores did not predict Time 2 ASD symptoms. Preliminary clinical implications and possible future directions are discussed.

Keywords

Autism spectrum disorder Anxiety Child Symptomatology Predictors Longitudinal 

Introduction

Comorbid psychiatric and behavioral conditions are common in ASD (Fernell etal. 2013; Mannion et al. 2014) and clinically elevated anxiety-related difficulties are estimated to be present in 11–84% of young people with ASD, with up to 40% diagnosed with at least one anxiety disorder at some point in their lives (Van Steensel et al. 2011; White et al. 2009). The most common anxiety subtypes in ASD are specific phobia (29.8%), obsessive-compulsive disorder (OCD) (17.4%) and social anxiety disorder (16.6%; van Steensel et al. 2011). Studies examining child characteristics associated with anxiety in ASD have so far produced mixed findings (Vasa and Mazurek 2015; White et al. 2009). However, studies have differed in sample characteristics, measures used, and methodologies employed. Moreover, most studies have been cross-sectional in design, thus limiting our understanding of factors influencing continuity and change in anxiety symptoms in ASD over time.

Child Characteristics Affecting Anxiety in ASD

Chronological Age

Positive associations between age and anxiety have been reported in some studies (e.g., Dubin et al. 2015; Mayes et al. 2011; Vasa et al. 2013), while others report no significant age effects (Strang et al. 2012; Sukhodolsky et al. 2008). The samples’ age characteristics may have affected the associations found in different studies. Davis et al.’s (2011a) study with individuals with ASD across the lifespan reported that anxiety was higher during the toddlerhood to childhood period, then decreased through young adulthood, and was again higher in later adulthood, however their study was based on a five-item anxiety index compiled from other measures, with no reported psychometric properties. It is also possible that certain anxiety subtypes may be more common at different ages in ASD (i.e., Magiati et al. 2016; van Steensel et al. 2011) in line with the onset and course of different anxiety problems and disorders in typically developing children and young people (i.e., Rapee et al. 2009; Essau and Ollendick 2013). Thus, developmental/ age effects should continue to be considered when exploring predictors of anxiety in ASD.

Gender

Few studies in ASD have enough female participants to meaningfully examine gender differences. Overall, most existing studies report few consistent gender differences in anxiety rates (e.g., Dubin et al. 2015; Kirkovski et al. 2013; Magiati et al. 2016). May, Cornish, and Rinehart (2014) reported that only social anxiety was higher in girls as compared to boys with ASD. In contrast, Gotham, Brunwasser, and Lord (2015) found that males with ASD had higher rates of anxiety and depression than females at baseline (age 5–19 years), but anxiety (and depressive) symptoms increased at a greater rate in females, such that there were no gender differences by age 21 years. However, both studies had few females (n < 30), and further examination of gender effects in ASD is needed.

Adaptive/intellectual Functioning

Associations between adaptive functioning and anxiety in ASD may be moderated by intellectual functioning, with poorer parent-rated adaptive skills increasing likelihood of anxiety only in ASD youth with lower intellectual abilities (Dubin et al. 2015). With regards to intellectual functioning, some studies have found positive associations with anxiety in ASD (Dubin et al. 2015; Mayes et al. 2011; Mazurek and Kanne 2010), while others have reported no relationship (Eussen et al. 2013; Simonoff et al. 2008; Strang et al. 2012), or an inverse relationship (Mattila et al. 2010). One reason for the mixed findings may be that some studies used continuous IQ scores as predictors (e.g., Matilla et al. 2010; Strang et al. 2012), while others compared anxiety rates across IQ categories using cut-offs set by the researchers (e.g., Dubin et al. 2015; Eussen et al. 2013; Mayes et al. 2011). It is also possible that intellectual and adaptive functioning may be related differentially to different anxiety subtypes (i.e., Sukhodolsky et al. 2008; Magiati et al. 2016). For example, Sukhodolsky et al. (2008) found higher likelihood of generalized and separation anxiety symptoms in 5–17-year old youth with ASD with IQ > 70 compared to those with IQ < 70, but equal likelihood of social phobia and panic symptoms. Hence, further clarification of associations between intellectual/ adaptive functioning and anxiety is needed.

ASD Symptomatology

Positive associations between ASD symptom severity and anxiety have been reported in some studies (e.g., Kelly et al. 2008; Sukhodolsky et al. 2008), while others have reported no associations (Renno and Wood 2013; Simonoff et al. 2008; Strang et al. 2012). These differences may be partly due to the use of different measures for ASD symptom severity, including parent-rated measures (Kelly et al. 2008; Sukhodolsky et al. 2008), clinician-rated measures (Eussen et al. 2013; Simonoff et al. 2008), or a combination of both (Renno and Wood 2013; Strang et al. 2012). There were also differences in sample co-morbidities, for example, Sukhodolsky et al.’s sample included children with ASD across the range of intellectual functioning and a number with severe behavioral difficulties, while Eussen and colleagues’ participants were non-cognitively impaired children (IQ > 70) and with milder symptom severity. Additionally, total ASD symptom severity scores were used in some studies (Kelly et al. 2008; Renno and Wood 2013; Simonoff et al. 2008), whereas ASD clusters of social/communication and repetitive behavior symptoms may be differentially associated with anxiety (Hus et al. 2014).

ASD Social/communication Symptoms

Greater social and communication deficits in children with ASD may be associated with increased anxiety (Chang et al. 2012; Eussen et al. 2013), but some studies report that associations were significant only in individuals with ASD without intellectual disabilities (e.g., Bellini 2006; Niditch et al. 2012; Sukhodolsky et al. 2008; Wood and Gadow 2010). In individuals with ASD with co-occurring intellectual disabilities, greater communication deficits have been postulated to potentially buffer against anxiety (Davis et al. 2011b; Mazurek and Kanne 2010). Yet other studies have found no relationship between ASD social/communication symptom severity and anxiety (Hollocks et al. 2014; Rieske et al. 2012).

ASD Repetitive Speech/Stereotyped Behavior Symptoms

Greater restricted and repetitive/stereotyped behaviors have been more consistently associated with increased anxiety in ASD (Rodgers et al. 2012; Sukhodolsky et al. 2008), with increasing evidence that sensory avoidance and sensitivity, insistence on sameness or intolerance of uncertainty may be mediators (Lidstone et al. 2014; Rodgers et al. 2012; Wigham et al. 2014).

ASD Symptom Clusters and Anxiety Subtypes

Hallett et al. (2013) found that greater ASD social/communication symptoms were associated with higher separation anxiety symptoms, while more restricted and repetitive symptoms were associated with higher panic and OCD symptoms, although it is possible this relationship can be explained by overlap in the presentation between repetitive behaviors and OCD symptom presentation (Kerns et al. 2014). Similarly, Magiati and colleagues (2016) found that ASD repetitive/stereotyped behaviors explained most of the variance in separation, generalized, panic/agoraphobia and obsessive–compulsive anxiety symptoms, after controlling for other child characteristics, while social/communication symptoms cross-sectionally predicted social anxiety only.

Taken together, current findings suggest that the two core clusters of ASD symptomatology may have differential relationships with overall and subtype anxiety symptoms, and more research on these relationships is indicated.

Findings From Longitudinal Studies

The studies discussed earlier were mostly cross-sectional. Only a handful of longitudinal studies examining continuity and change in anxiety in ASD have been published. Gotham and colleagues (2015) followed up 109 individuals with ASD and 56 individuals with developmental delays (without ASD diagnoses) at regular intervals between 9 and 24 years of age, where caregiver-reported anxiety and depressive symptoms were assessed using the relevant subscales of the Child or Adult Behavior Checklist (CBCL, Achenbach and Rescorla 2001, 2003; ABCL). The researchers found significant continuity in caregiver-reported anxiety in both groups, and ASD diagnosis predicted greater increases in anxiety over time. However, whether specific clusters of ASD symptoms might have contributed most to this growth was not examined. Additionally, female gender predicted greater increases in anxiety (and depressive) symptoms in both participant groups. May and colleagues (2014) also reported overall continuity in caregiver-reported anxiety symptoms in school-aged children with ASD after 1 year, with only social anxiety being higher in girls than boys.

A number of researchers have theorized the potentially complex relationship between ASD and anxiety (i.e. Wood and Gadow 2010; Kerns and Kendall 2012) and have called for the need to empirically investigate the direction and strength of this relationship further. Kerns and Kendall (2012), for example, considered evidence for or against the proposition that anxiety is a sequel of ASD, while Wood and Gadow (2010) also suggested that anxiety in individuals with ASD may in turn potentially increase the severity of certain ASD symptoms (i.e. social avoidance and/ or repetitive behaviors) as a mechanism for emotion regulation. A small number of other longitudinal studies have explored earlier predictors of later anxiety, and the direction of the relationship between ASD symptomatology and anxiety over time. So far, mixed findings have been reported. In a presented but unpublished study, Baird and colleagues (2012) found that caregiver-reported fixed interests and repetitive behaviors at Time 1 predicted caregiver-reported mixed anxiety/ depressive symptoms on the CBCL in preschool-aged children with ASD (84% males) 1 year later, over and above other Time 1 anxiety and other child variables. Another study found that higher caregiver-reported sensory over-responsivity symptoms at Time 1 predicted increased anxiety symptoms 1 year later in toddlers with ASD, over and above Time 1 child anxiety and ASD social/communication symptoms and maternal anxiety (Green et al. 2012). Time 1 child anxiety did not predict Time 2 sensory over-responsivity symptoms, supporting a unidirectional relationship (Green et al. 2012). However, both Baird et al. and Green et al. used anxiety subscales from broader measures with only a small number of anxiety-specific items.

In contrast, Kim, Szatmari, Bryson, Streiner and Wilson (2000) found no significant associations between Time 1 clinician-rated ASD symptoms, and parent-rated anxiety and mood problems 6 years later in 4–6 year old children with ASD. Similarly, Simonoff et al. (2008) used both clinician-rated and parent-rated measures of ASD symptoms, and found that ASD symptom severity, IQ and adaptive functioning at 12 years old did not predict emotional problems at 16 years old, although some earlier family characteristics (poorer maternal health, family deprivation and lower social class) did. As these two studies did not measure anxiety at intake, continuity in anxiety over time and bi-directionality between earlier and later anxiety and autistic symptoms were not examined.

Hallett et al. (2010) adopted a dimensional perspective in measuring caregiver-rated autistic traits and internalizing symptoms in a large population sample consisting of twins without clinical diagnoses of ASD who were assessed at age 7–8 years and again at 12 years old. The study found significant continuity in both internalizing and autistic traits between the two time-points, and also reported a bi-directional relationship between autistic and internalizing traits. ASD-like traits measured at Time 1 had a stronger association with anxiety at Time 2 than vice versa. Moreover, Time 1 internalizing traits contributed nearly half the variance of Time 2 internalizing traits, while repetitive behaviors and communication (but not social) difficulties added further variance after controlling for other child variables.

In summary, the small number of longitudinal studies to date suggests that anxiety remains relatively stable over time in children and youth with ASD. However, there have been mixed findings regarding the relationship between earlier ASD symptom severity and other child variables, and later anxiety. Moreover, little is known about the role of earlier anxiety symptom severity itself as a predictor of later anxiety in ASD, and the relationship of earlier anxiety with later ASD symptomatology, while these relationships were found to be significant in a non-clinical sample (Hallett et al. 2010).

The Present Study: Rationale, Aims and Hypotheses

Currently, there are mixed findings in the literature on whether, and which, child characteristics are associated with anxiety in ASD. Moreover, few studies have examined the possibly different relationships between the two core ASD symptom clusters and overall, as well as subtype, anxiety, with only a handful of studies adopting longitudinal designs. Finally, possible bi-directional effects between ASD symptomatology and anxiety need to be further investigated.

Thus, the present study aimed:

  1. 1.

    To investigate the continuity and change in caregiver-reported anxiety symptoms in ASD; we predicted that anxiety symptoms would largely show continuity over the course of the study (e.g., Green et al. 2012; Gotham et al. 2015);

     
  2. 2.

    To examine the predictive value of earlier child characteristics in explaining later total and subscale anxiety; we hypothesized that greater T1 ASD repetitive speech/stereotyped behaviors would predict higher T2 anxiety over and above ASD social/communication symptoms and other child characteristics (Baird et al. 2012), but that T1 anxiety would explain most of the variance in T2 anxiety, given the predicted high continuity of anxiety symptoms;

     
  3. 3.

    To explore the direction of relationships between ASD symptomatology and anxiety over time; we predicted bi-directional effects between ASD repetitive speech/behaviors and anxiety (Rodgers et al. 2012; Ozsivadjian et al. 2012; Wood and Gadow 2010); and similarly between ASD social/communication symptoms and anxiety; and,

     
  4. 4.

    To examine differential relationships between T1 child characteristics and different anxiety subtypes at T2 (Hallett et al. 2013; Magiati et al. 2016). We predicted that ASD social/communication symptoms would show positive associations with separation anxiety (Hallett et al. 2013) and social anxiety (Bellini 2006), while ASD repetitive speech/behaviors would show positive associations with panic/agoraphobia and obsessive–compulsive anxiety subtypes (Hallett et al. 2013).

     

Method

Participants

Time 1 (T1) data were collected from 241 caregivers of 5–17-year-old young people with ASD (197 boys; 81.7%) in Singapore as part of an initially cross-sectional study (Magiati et al. 2016). Participants were a non-referred sample recruited through six special education schools.1 For the follow-up 10–19 months later (T2), 164 of the participants (68.0%), who had indicated in their written consent forms that they were agreeable to being contacted again for a follow-up, were invited to participate (see Fig. 1).

Fig. 1

Participant attrition rates from Time 1 to Time 2

Completed surveys were received from 54 caregivers (32.9%) of 48 boys (88.9%) and six girls (11.1%; T1 age M = 120.7 months; SD = 32.8; see Tables 1, 2 for participant and informant characteristics). The same informants, predominantly mothers, completed the measures at both time-points (N = 52; 96.3%).

Table 1

Time 1 and Time 2 participant characteristics (N = 54)

 

Time 1

N (%)

Time 2

N (%)

Age

120.7 (32.8)

132.5 (32.3)

Gender

 Female

6 (11.1)

 

 Male

48 (88.9)

 

Ethnicity

 Chinese

42 (77.6)

 

 Malay

5 (9.3)

 

 Indian

3 (5.6)

 

 Others

3 (5.6)

 

 Not reported/ missing data

1 (1.9)

 

Clinical diagnosis

 Autism spectrum disorder or autism

47 (87.0)

 

 Asperger’s syndrome

6 (11.1)

 

 Pervasive developmental disorder-not otherwise

1 (1.9)

 

 Specified (PDD-NOS)

  

Any other additional diagnosis

 No

41 (75.9)

48 (88.9)

 Yesa

12 (22.2)

6 (11.1)

 Not reported/missing data

1 (1.9)

 

Medication

 No

49 (90.7)

49 (90.7)

 Yesb

5 (9.3)

5 (9.3)

Any diagnosis of anxiety disorder

 No

49 (90.7)

51 (94.4)

 Yes, currentlyc

1(1.9)

 

 Yes, in the pastd

3 (5.6)

3 (5.6)

 Missing data/not reported

1 (1.9)

 

Caregiver reported child’scommunication level

 No speech

2 (3.7)

2 (3.7)

 Verbal

52 (96.3)

52 (96.3)

    Single words only

2 (3.7)

2 (3.7)

    2–3 word phrase speech

5 (9.3)

3 (5.6)

    Phrases/short sentences with more than 4 words

19 (35.1)

19 (35.2)

    Generally age-appropriate speech for chronological age

21 (38.9)

21 (38.9)

    Not indicated

5 (9.3)

7 (13.0)

aADHD: N = 2 (Time 1) N = 2 (Time 2); medical conditions: N = 3 (Time 1); Dyslexia/dyspraxia/specific language impairment: N = 6 (Time 1) N = 4 (Time 2)

bSinus and eczema medication: N = 1 (Time 1); Topamax, phenobarbitone, epilim: N = 1 (Time 1 & 2); Carvedilol, enlapril, trimetazidine: N = 1 (Time 1 & 2); Risperidone: N = 1 (Time 1 & 2) & N = 1 (Time 2); Imipramine: N = 1 (Time 1); Ritalin: N = 1 (Time 2)

cSpecific diagnosis not reported

dGeneralized anxiety: N = 1 (Time 1 & 2); specific phobia: N = 1 (Time 1); separation anxiety disorder: N = 1 (Time 1); social phobia and OCD: N = 1 (Time 2); separation anxiety, specific phobia, and generalized anxiety: N = 1 (Time 2)

Table 2

Characteristics of parents/families (N = 54)

 

Time 1

N (%)

Time 2

N (%)

Respondent’s relationship to child with ASD

 Mother

43 (79.6)

43 (79.6)

 Father

9 (16.6)

10 (18.5)

 Grandparent

1 (1.9)

1 (1.9)

 Not reported/ missing data

1 (1.9)

 

Family unit composition

 Two-parent family

40 (74.0)

40 (74.0)

 Single-parent family

3 (5.6)

3 (5.6)

 Nuclear family living with extended family

10 (18.5)

10 (18.5)

 Not reported/ missing data

1 (1.9)

1 (1.9)

Educational level of parents

 College and below

27 (50.0)

 

 Degree and above

26 (48.1)

 

 Not reported/ missing data

1 (1.9)

 

All the children had a clinical diagnosis of ASD, Asperger’s syndrome or PDD-NOS, made by a qualified medical or mental health professional in Singapore.2 Caregiver information on professional diagnosis, diagnostic setting/organization and professional(s) who made the diagnosis was obtained. Most participants (N = 43; 79.7%) were diagnosed in one of the three leading public diagnostic clinics in the country.3 42 participants (77.7%) were from a special school catering to children with a diagnosis of ASD and a non-verbal IQ score above 70 who are considered cognitively able to access the mainstream curriculum in a supportive autism-specific educational setting, and an additional three participants (5.6%) were formerly from this school at T1 but had progressed to a mainstream school by T2. Six participants (11.1%) were in schools for children with ASD and mild intellectual disabilities (IQ 50–70), and three (5.6%) were in schools for children with multiple disabilities including ASD. Just under 75% of the participants (N = 40) were reported by their caregivers to be able to communicate using sentences with more than four words and to have conversational speech (see Table 1).

Participants at T2 (N = 54) did not differ significantly from those who did not participate at the follow-up (N = 187) on demographic variables, T1 anxiety or ASD symptom severity scores, except that they had higher T1 adaptive functioning with a medium effect size difference (d = 0.58; Table 3). This difference in adaptive functioning may be because a higher proportion of respondents at T2 were from one special school catering to more cognitively able children with ASD (83.3%), as compared to participants from the same school at T1 (53.8%).

Table 3

Comparison of participants in the follow-up study (N = 54) and those who participated in the cross-sectional study only (N = 187)

Measures

Participants in present follow-up study (N = 54)

Participants in Time 1 study only (N = 187)

Statistics

M (SD) or N (%)

M (SD) or N (%)

t (p) or χ 2 (df)

d or φ

Educational level of parents

 College and below

27 (50.0%)

97 (51.9%)

0.02 (1), ns

0.04

 Degree and above

26 (48.1%)

89 (47.6%)

  

 Not reported

1 (1.9%)

1 (0.5%)

  

Child’s age at Time 1 (months)

120.67 (32.80)

121.05 (35.16)

− 0.07 (0.94), ns

0.01

Child’s gender

 Male

48 (88.9%)

149 (79.7%)

2.38 (1), ns

−0.09

 Female

6 (11.1%)

38 (20.3%)

  

SIB-R Total

76.57 (37.45)

53.63 (39.80)a

3.75 (0.00)***

0.58

DBC-P Total

44.44 (21.64)

45.45 (23.72)

− 0.28 (0.78), ns

0.04

 DBC-P-repetitive speech/stereotyped symptoms

9.19 (4.99)

9.20 (5.67)a

− 0.02 (0.99), ns

0.00

 DBC-P-social/communication symptoms

6.31 (3.53)

7.24 (3.95)a

−1.54 (0.12), ns

0.24

 DBC-P-anxiety

4.50 (2.79)

4.59 (2.86)a

− 0.20 (0.84), ns

0.03

 SCAS-P total

20.69 (13.36)

17.95 (11.05)

1.53 (0.13), ns

0.24

SIB-R Total scales of independent behavior-revised, total standard scores, DBC-P Total developmental behavior checklist-parent version– total (overall) raw scores; DBC-P-Repetitive speech/stereotyped behavior symptoms, DBC-P-Social/communication symptoms, DBC-P-Anxiety developmental behavior checklist-parent version–subdomain raw scores, SCAS-P Total Spence children’s anxiety scale-parent report, total raw scores

ns p > .05

*p < .05 ;**p < .01; ***p < .001,

a N=185 due to missing data for 2 cases at Time 1

Measures

Spence Children’s Anxiety Scale—Parent Report (SCAS-P; Spence 1999)

Rated on a 0–3 rating scale, with higher scores indicating more anxiety, the SCAS-P is a caregiver-rated anxiety measure with 38 items (total score range 0-114) in six subscales: panic/agoraphobia (9 items), obsessive–compulsive disorder (6 items), generalized anxiety (6 items), separation anxiety (6 items), social phobia (6 items) and fear of physical injury (5 items). Norms have been published by Nauta et al. (2004). A cut-off of 1 SD above the normative mean (i.e. total score > 24) is often applied to indicate clinically-elevated anxiety symptoms (Spence, October 2012, personal communication). The measure has good psychometric properties in typically developing children (Li et al. 2011; Nauta et al. 2004) and has been used in several studies on anxiety in ASD (see reviews by Grondhuis and Aman 2012; Wigham and McConachie 2014), with good construct, convergent and discriminant validity (Zainal et al. 2014).

Developmental Behavior Checklist—Parent Version (DBC-P; Einfeld and Tonge 2002)

Rated on a 0–2 point scale (higher scores indicate more difficulties), the DBC-P is a caregiver-rated 96-item checklist for measuring behavioral and emotional problems in 4–18-year-old children with developmental/intellectual disabilities. It provides a total behavior problem score (score range 0–192; Cronbach’s α = 0.94) and five subscales: disruptive/antisocial, self-absorbed, communication disturbance, anxiety, and social relating. The DBC-P has strong psychometric properties and is reliable and valid in discriminating between individuals with intellectual disabilities with and without ASD using a factor analysis derived 29-item score, the DBC-Autism Screening Algorithm (DBC-ASA; score range 0–58; cut-off score 14; Brereton et al. 2002; Steinhausen and Metzke 2004).

As the DBC-ASA also includes non-ASD-specific items, Magiati et al. (2016) developed a modified 26-item DBC-autism symptom score applying DSM-5 ASD criteria. These 26 items were organized into two composite ASD subdomain scores:4 Social/Communication (S/C; 10 items; score range 0–20, α T1 = 0.72, α T2 = 0.75) and Repetitive Speech and Stereotyped Behavior Symptoms (R/S; 16 items; score range 0–32; α T1 = 0.75, α T2 = 0.75). These were used in all subsequent analyses in the present study to measure ASD related symptomatology.

Scales of Independent Behavior-Revised (SIB-R; Bruininks et al. 1996)

This study used the 40-item SIB-R Short Form, which correlates highly with the SIB-R Full Form (r = .92), a standardized measure of motor, social, communication, personal living and community living adaptive behavior skills, for use with individuals aged 0–80 years (Bruininks et al. 1996). Caregivers rate items on a 0–3 scale (total raw score range 0–120), with higher scores indicating better adaptive functioning. Standard scores (M = 100; SD = 15) were used. The SIB-R has excellent reported psychometric properties and is highly correlated with the Vineland Adaptive Behavior Scales (r = .83; Middleton et al. 1990).

Demographic Questionnaire

Demographic information, including children’s age, education placement, diagnoses of ASD, anxiety and any other co-occurring conditions, and family background, was also collected.

Procedure

Ethical approval was obtained from the National University of Singapore, Institutional Review Board. T1 data was collected between July 2011 and March 2012, and T2 data was collected 10–19 months later (M = 12.1 months). Following schools’ approval, the above measures, and participants’ information sheets and consent forms, were posted to the participants together with a postage-paid return envelope. Mandarin Chinese versions of all measures were provided to participants unable to fill in the English questionnaires. Participants who returned their completed questionnaires were given a $10-dollar voucher in appreciation of their participation.

Missing Data and Statistical Analyses

Item substitution using the mean of the item’s subscale was carried out for four T1 and two T2 participants who had some SCAS-P missing, items, according to the SCAS-P manual. Additionally, a small number of participants responded with ‘not applicable’ (‘N.A.’) to some items, particularly in SIB-R items. These were reviewed and substituted with ‘0’ if they were deemed clearly not applicable for a child given their age and/or level of functioning (e.g., ‘explains the terms of a written contract’). No participant was excluded due to missing data. Further, there were no outlier scores > 3SD above the mean on any T1 or T2 measure, except for two cases in the SCAS-P total scores. Preliminary analyses showed no differences in findings with and without the outliers, thus it was decided to maintain all participants to avoid reducing sample size further.

The statistical package of social sciences (SPSS)-version 23 was used. As DBC-P and SCAS-P scores at T1 and T2 were positively-skewed, both logarithm and square-root transformations were initially conducted to correct these scores. However, analyses using the raw and transformed scores showed no differences, thus, results using the original raw scores are reported.

The reliability of the SIB-R, DBC-P and SCAS-P measures in this sample at both time-points were calculated using Cronbach’s alpha coefficients. Differences in mean scores of child characteristics between T1 and T2 were compared using paired-sample t-tests. Pearson’s r and intraclass correlations of T1 and T2 anxiety variables were calculated. Further, changes in anxiety between T1 and T2 were examined at the individual level. Next, a hierarchical linear regression model was applied to investigate whether T1 DBC R/S and DBC S/C symptoms predicted T2 SCAS-P Total anxiety, after controlling for T1 child age and adaptive functioning. A third step was added to the model to investigate the unique contribution of T1 SCAS-P Total anxiety as a predictor, over and above other T1 child variables. Lastly, we carried out hierarchical linear regressions to examine whether T1 SCAS-P Total anxiety scores predicted severity of T2 DBC-R/S and S/C symptom scores, in order to explore bi-directionality.

Results

Internal consistency at T1 was >0.70 with the exception of the SCAS-P Fear of physical injury subscale. At T2, all SCAS-P subscales had < 0.70, except for the SCAS-P Panic/agoraphobia subscale (Table 4).5

Table 4

Time 1 and Time 2 SIB-R, DBC-P and SCAS-P scores(N = 54)

Measures

Time 1

Time 2

Statistics

α

M (SD)

α

M (SD)

r

ICC [95% CI]

t

d

SIB-R total

0.89

76.57 (37.45)

0.92

79.24 (38.64)

0.80***

0.80 [0.69, 0.88]

− 0.82

0.07

DBC-P total

0.94

44.44 (21.64)

0.92

40.35 (19.16)

0.70***

0.68 [0.50, 0.80]

1.87

0.20

DBC-P- R/S

0.75

9.19 (4.99)

0.75

8.19 (4.89)

0.66***

0.65 [0.46, 0.78]

1.80

0.20

DBC-P- S/C

0.72

6.31 (3.53)

0.75

6.04 (3.61)

0.67***

0.67 [0.50, 0.80]

0.70

0.08

DBC-P-anxiety

0.72

4.50 (2.79)

0.52

3.98 (2.25)

0.52***

0.50 [0.27, 0.67]

1.51

0.21

SCAS-P total

0.91

20.69 (13.36)

0.85

19.80 (11.30)

0.69***

0.69 [0.52, 0.81]

0.66

0.07

SCAS-P panic/agoraphobia

0.76

1.94 (2.59)

0.72

2.00 (3.24)

0.71***

0.70 [0.53, 0.81]

− 0.18

0.02

SCAS-P obsessive-compulsive disorder

0.80

3.13 (3.23)

0.49

2.43 (2.07)

0.60***

0.52 [0.30, 0.69]

1.99

0.26

SCAS-P generalized anxiety disorder

0.73

3.50 (2.61)

0.57

3.20 (2.23)

0.73***

0.72 [0.56, 0.83]

1.20

0.12

SCAS-P separation anxiety

0.76

4.02 (3.55)

0.64

4.06 (3.16)

0.69***

0.69 [0.51, 0.80]

− 0.10

0.01

SCAS-P social phobia

0.71

3.09 (2.74)

0.60

3.13 (2.45)

0.46***

0.46 [0.22, 0.65]

− 0.10

0.02

SCAS-P fear of physical injury

0.44

5.00 (2.67)

0.54

5.02 (2.80)

0.68***

0.69 [0.52, 0.81]

− 0.06

0.01

All SCAS-P subscale scores are raw scores

SIB-R Total Scales of independent behavior-revised, total standard scores; DBC-P Total developmental behavior checklist-parent version–total (overall) raw scores; DBC-P-R/S developmental behavior checklist-parent version–repetitive speech/stereotyped behavior symptom scores; DBC-P-S/C developmental behavior checklist-parent version– social/communication symptom scores; SCAS-P Total Spence children’s anxiety scale-parent report, total (overall) anxiety raw scores

*p < .05; **p < .01; ***p < .001

Adaptive functioning SIB-R Standard Scores remained stable over time, with small effect size non-significant changes. DBC total problem behavior, S/C and R/S ASD-symptom scores also did not change significantly from T1 to T2, indicating overall continuity in problem behaviors and ASD symptom severity during the study (Table 4).

Continuity and Change in Anxiety Symptoms Over Time

There was no significant change in overall anxiety and anxiety subscale scores from T1 to T2 and effect sizes were small, showing overall continuity (Table 4). Rates of young people scoring above the clinical cut-off in SCAS-P also appeared unchanged from T1 to T2 (Fig. 2). However, this overall continuity masked individual differences in anxiety changes over time, as seven participants (six boys) who were rated above clinical cut-off at T1 had scores below cut-off at T2, while four participants (all boys) who were below cut-off at T1 had developed anxiety symptoms to above clinical cut-off point at T2. Hence, 11 participants (20.4%) had ratings that indicated changes in anxiety levels over time, while 80% remained stable in their anxiety status. It should, however, be noted that the cut-offs for clinical severity were based on the normative non-ASD SCAS-P sample (Nauta et al. 2004). Moreover, while individual change scores ranged from |0–38| in our sample (M change  = 6.59, SD = 7.30), a change in membership from the below to the above clinical cut-off groups could be due to raw score changes as small as −3.0 for one participant only.

Fig. 2

Number of participants scoring above and below clinical cut-off on the SCAS-P at T1 and T2

Based on individual change scores, 47 participants (87%) showed very little change or an increase/decrease in total anxiety symptoms within one normative SD for their age-group and gender (Nauta et al. 2004). However, seven participants (13%; one female) showed an increase/decrease of >1 SD, including two participants exceeding three SDs (+30 points in one and −38 in another). Thus, at an individual level, there was wider variability in total anxiety symptoms for a substantial minority of the sample, despite continuity in overall average scores from T1 to T2.

Associations Between T1 Child Variables and T2 Anxiety

Child age at T1 was not significantly correlated with T2 overall or subscale anxiety scores (all p > .05), and effect sizes were small (Table 5).

Table 5

Correlations between Time 1 child variables and Time 2 SCAS-P total and subscale scores

Time 1 child variables

Time 2 Anxiety

SCAS-P Total

SCAS-P

Separation anxiety

SCAS-P

Generalized anxiety

SCAS-P Social phobia

SCAS-P Panic /Agoraphobia

SCAS-P Fear of physical injury

SCAS-P obsessive–compulsive disorder

Chronological age

−0.02

−0.10

−0.02

0.01

−0.03

0.04

0.00

Gender

−0.06

0.01

−0.27*

0.03

−0.07

0.06

−0.05

SIB-R Total

−0.13

−0.03

−0.07

0.14

−0.36**

−0.09

−0.05

DBC-P-R/S

0.43**

0.35*

0.39**

0.02

0.47**

0.11

0.49***

DBC-P-S/C

0.27*

0.14

0.23

0.07

0.26

0.13

0.33*

SCAS-P Time 1 anxietya

0.69***

0.69***

0.73***

0.46***

0.71***

0.68***

0.60***

SIB-R Total scales of independent behavior-revised, total standard scores; DBC-P-R/S developmental behavior checklist-parent version–repetitive speech/stereotyped behavior symptom scores; DBC-P-S/C developmental behavior checklist-parent version–social/communication symptom scores; SCAS-P Spence children’s anxiety scale-parent report, total and subtype anxiety raw scores

*p < .05; **p < .01; ***p < .001

aTime 1 and Time 2 SCAS-P Anxiety scores are correlated against their corresponding baseline scores (total or subscale scores)

T1 adaptive functioning was negatively associated with T2 SCAS-P panic/agoraphobia symptoms only, with a medium effect size. The T1 DBC ASD S/C and R/S were positively correlated with overall anxiety and some of the SCAS-P anxiety subscales at T2, with small-to-medium effect sizes (Table 5). Gender was not significantly associated with T2 anxiety, except for the generalized anxiety subscale (r = −.27, p = .05), with females scoring somewhat lower than males (Ms 1.50 vs. 3.20). However, because of the small number of females in our sample (11.1%), we did not include gender as a predictor in subsequent regression analyses.

T1 Child Characteristics and Anxiety as Predictors of T2 Anxiety (SCAS-P Total)

Step 1 of the hierarchical linear regression, with T1 age and adaptive functioning as predictors, was not significant and explained only 2% of the variance in T2 anxiety (Table 6). After including the two T1 DBC ASD symptom scores, Step 2 was significant and explained an additional 17%. T1 DBC R/S was a significant predictor of T2 anxiety (β = 0.40, p = .02), but DBC S/C was not (β = 0.04, ns). With T1 SCAS-P total anxiety scores included in Step 3, a significant additional 34% of variance was explained, and T1 DBC R/S was no longer significant (β = 0.13, ns), suggesting that the T2 anxiety variance predicted by T1 DBC R/S symptoms in Step 2 was fully explained by participants’ T1 anxiety scores.

Table 6

Summary of hierarchical regression analyses for Time 1 variables predicting SCAS-P Anxiety Total Score at Time 2

Predictors

Step 1

 

Step 2

 

Step 3

B

SE B

β

 

B

SE B

β

 

B

SE B

β

Time 1 variables

 Age

−0.02

0.05

−0.05

 

−0.01

0.05

−0.03

 

−0.05

0.04

−0.14

 SIB-R Total

−0.04

0.04

−0.14

 

−0.01

0.04

−0.02

 

−0.03

0.03

−0.10

 DBC-P-R/S

    

0.91

0.36

0.40*

 

0.29

0.30

0.13

 DBC-P-S/C

    

0.14

0.51

0.04

 

−0.08

0.39

−0.03

 SCAS-P Total

        

0.56

0.10

0.67***

 R 2

0.02

  

R 2 change

0.17

  

R 2 change

0.34

  

 F

0.48

  

F for R 2 change

5.11*

  

F for R 2 change

34.23***

  

SIB-R Total scales of independent behavior-revised, total standard scores; DBC-P-R/S developmental behavior checklist-parent version–repetitive speech/stereotyped behavior symptom scores; DBC-P-S/C developmental behavior checklist-parent version–social/communication symptom scores; SCAS-P Total Spence children’s anxiety scale-parent report, total raw scores

*p < .05; **p < .01; ***p < .001

Is the Relationship Bi-directional? Examining T1 SCAS-P Total as a Predictor of T2 ASD Symptomatology

Predicting T2 DBC S/C Symptoms

T1 total anxiety did not predict T2 DBC S/C scores, after controlling for all other T1 child variables. The full model explained 47.7% of the variance in T2 DBC S/C symptoms, with T1 DBC S/C being the only significant predictor (Table 7).

Table 7

Summary of hierarchical regression analyses for Time 1 variables predicting Time 2 DBC-P-S/C scores

Predictors

Step 1

 

Step 2

 

Step 3

B

SE B

β

 

B

SE B

β

 

B

SE B

β

Time 1 variables

 

 Age

0.00

0.02

0.03

 

−0.01

0.01

−0.05

 

0.00

0.01

−0.02

 SIB-R Total

−0.01

0.01

−0.13

 

0.00

0.01

−0.01

 

0.00

0.01

0.01

 DBC-P-R/S

    

−0.04

0.10

−0.05

 

0.02

0.10

0.02

 DBC-P-S/C

    

0.72

0.13

0.70***

 

0.74

0.13

0.72***

 SCAS-P Total

        

−0.05

0.03

−0.18

 R 2

0.02

  

R 2 change

0.43

  

R 2 change

0.03

  

 F

0.48

  

F for R 2 change

19.36***

  

F for R 2 change

2.30

  

SIB-R Total scales of independent behavior-revised, total standard scores; DBC-P-R/S developmental behavior checklist-parent version–repetitive speech/stereotyped behavior symptom scores; DBC-P-S/C developmental behavior checklist-parent version–social/communication symptom scores; SCAS-P Total Spence children’s anxiety scale-parent report, total raw scores

*p < .05; **p < .01; ***p < .001

Predicting T2 DBC R/S Symptoms

Similarly, T1 anxiety did not predict T2 DBC R/S scores, over and above other T1 child variables (Table 8). Rather, T2 DBC R/S scores were significantly predicted by T1 DBC R/S and S/C scores, with the full model explaining 55% of the variance (Table 8). Thus, a bi-directional effect between severity of ASD R/S symptoms and anxiety was not supported in this study.

Table 8

Summary of hierarchical regression analyses for Time 1 variables predicting Time 2 DBC-P-R/S scores

Predictors

Step 1

 

Step 2

 

Step 3

B

SE B

β

 

B

SE B

β

 

B

SE B

β

Time 1 variables

 Age

−0.01

0.02

−0.05

 

−0.01

0.02

−0.07

 

−0.01

0.02

−0.04

 SIB-R Total

−0.05

0.02

−0.37**

 

−0.03

0.01

−0.19

 

−0.02

0.01

−0.18

 DBC-P-R/S

    

0.42

0.12

0.43**

 

0.48

0.13

0.49***

 DBC-P-S/C

    

0.43

0.17

0.31*

 

0.45

0.17

0.33**

 SCAS-P Total 

        

−0.05

0.04

−0.15

 R 2

0.13

  

R 2 change

0.40

  

R 2 change

0.02

  

 F

3.80*

  

F for R 2 change

21.16***

  

F for R 2 change

1.77

  

SIB-R Total scales of independent behavior-revised, total standard scores; DBC-P-R/S developmental behavior checklist-parent version–repetitive speech/stereotyped behavior symptom scores; DBC-P-S/C developmental behavior checklist-parent version–social/communication symptom scores; SCAS-P Total Spence children’s anxiety scale-parent report, total raw scores

*p < .05; **p < .01; ***p < .001

T1 Child Characteristics and T2 SCAS-P Anxiety Subscales

As alpha coefficients for most T2 SCAS-P anxiety subscales were below optimal levels of 0.70 (Table 4), we examined T1 and T2 SCAS-P inter-item correlations to try to understand the reduced internal consistencies of the subscales at T2. These were somewhat weaker at T2 (mean r = .17, range −0.34 < r < .89) than at T1 (mean r = .22, range −0.22 < r < .80), particularly on the T2 obsessive–compulsive subscale where the mean inter-item correlations dropped from 0.40 at T1 to 0.14 at T2.6 As respondents for most participants were the same at both time-points (98.1%), it is unclear why the internal consistencies were lower at T2. One possibility is that some items may have become less relevant or responses more heterogeneous as the participants have grown older. Moreover, as most SCAS-P subscales comprise only 5–6 items, lower inter-item correlations at T2 likely had a larger impact on subscale internal reliability. In contrast, panic/agoraphobia (9 items) and overall anxiety (38 items) were less susceptible to this effect and had acceptable alphas at both time-points. Due to questionable internal consistency at T2, we did not examine predictors of SCAS-P subscale scores at T2 (hypothesis 4), although this should be examined further in future studies.

Discussion

This follow-up study investigated continuity and change in caregiver-reported anxiety symptoms, as well as earlier predictors of later anxiety, in a non-referred sample of children and youths with ASD after 10–19 months.

Continuity and Change in Anxiety Symptoms Over Time

Average total and subscale anxiety scores in our sample were generally stable after 10–19 months, supporting hypothesis 1. While a few individual participants showed greater variability in total anxiety symptoms, the majority did not significantly change in their anxiety scores over time. This finding is in line with the small but growing number of longitudinal studies reporting overall continuity or increases in anxiety symptoms over time in ASD (Green et al. 2012; Gotham et al. 2015), as also reported in typically developing children when support and intervention are not provided (e.g., Broeren et al. 2013). While it is encouraging that most children with lower levels of anxiety remained in the non-clinically elevated range at follow-up, it is also concerning that those with clinically elevated anxiety did not appear to improve over time. Hence, our finding also supports the call for early assessment and timely management of anxiety in ASD (Baird et al. 2012; Dubin et al. 2015), since symptoms will likely not reduce or “go away” with time and anxiety has been associated with other more adverse concerns, such as self-injurious behaviors and depressive symptoms (Kerns et al. 2015).

Earlier Predictors of Later Total Anxiety Symptoms

Our follow-up study did not find any significant relationships between age or adaptive functioning and anxiety at both time-points, whereas other cross-sectional studies have reported associations between these child characteristics and at least some anxiety subscales (i.e., Dubin et al. 2015; Magiati et al. 2016; Sukhodolsky et al. 2008). However, earlier studies tended to assess anxiety in larger, often clinically anxious, samples who were more able in terms of intellectual functioning, and most significant findings for age and adaptive functioning in such studies were of small effect sizes (Dubin et al. 2015; van Steensel et al. 2011). By comparison, we followed up a small community sample of individuals with ASD with mixed abilities, and correlations between anxiety, age, and adaptive functioning, were of small effect sizes in our study.

Total anxiety scores at Time 2 were most strongly predicted by Time 1 anxiety scores, over and above the other T1 child factors examined, supporting hypothesis 2. Without T1 anxiety in the model, T1 DBC R/S scores contributed a significant and large amount of variance in explaining T2 anxiety scores, after controlling for age and adaptive functioning. This finding appears to parallel cross-sectional findings of positive relationships between ASD-related repetitive/stereotyped behaviors and anxiety in ASD (Magiati et al. 2016; Rodgers et al. 2012). Our study extends these cross-sectional findings and suggests that earlier severity of R/S predicts later anxiety when earlier anxiety is not accounted for, but does not when earlier anxiety scores are included as a covariate. As some researchers have proposed that experiences of anxiety may be associated with possible increases in repetitive and stereotyped behaviors in ASD (Rodgers et al. 2012; Oszivadjian et al. 2012), these ASD-characteristic behaviors may represent coping responses to concurrent experiences of anxiety (Kerns et al. 2014; Rodgers et al. 2012), but likely do not predict later anxiety over and above earlier anxiety symptomatology. Our results contrast somewhat with Baird et al. (2012) who reported that earlier R/S behaviors significantly predicted anxiety 1 year later, over and above earlier anxiety scores. However, they studied a much younger group of preschool-aged children (M = 39.8 months). Furthermore, their study used the CBCL anxiety subscale as a measure of anxiety, which was more limited than our anxiety measure in terms of number of items.

In the present study, T1 DBC S/C was not a significant predictor of later anxiety, consistent with some cross-sectional studies reporting no association between social/communication impairments and anxiety in ASD (Hollocks et al. 2014; Rieske et al. 2012), but contrasting others reporting either positive or negative associations (e.g. Chang et al. 2012; Davis et al. 2011b; Eussen et al. 2013). However, it is possible that our use of a 10-item subscale score derived from the DBC may not have comprehensively captured a broader range of ASD-related S/C symptoms. The predictive value of ASD-related social/communication symptoms in contributing to anxiety should be examined using more detailed and comprehensive measures of ASD symptomatology.

In sum, we found that earlier anxiety contributes strongly to maintaining anxiety over time, occluding any effects of the other examined predictors, particularly in a community sample where referral for and identification of anxiety problems was likely low. Additionally, the differential relationship of ASD R/S and S/C symptoms with later anxiety in our study supports earlier findings that ASD symptomatology may have a fractionable relationship with anxiety (Hallett et al. 2013; Hus et al. 2014). Further research will be needed to clarify this relationship.

A Uni- or Bi-directional Relationship Between Anxiety and ASD Symptomatology?

In our study, we found that T1 anxiety did not predict T2 R/S or S/C symptoms when T1 R/S and S/C symptoms were included in the model. Thus, hypothesis 3 was not supported, which is consistent with Green et al.’s (2012) finding of a unidirectional relationship between earlier ASD symptom clusters and later total anxiety scores. Our findings are different from Hallett et al.’s (2010) findings of bidirectional effects between internalizing traits and autistic-like traits over time. However, their sample was different as participants had no clinical diagnoses of ASD and also, they used a 5-item score for internalizing traits from the Strengths and Difficulties Questionnaire rather than an anxiety scale, so associations may have been stronger in their study. Moreover, it may be that our community sample was too small to detect any bi-directional effects. Thus, the direction and strength of relationships between anxiety and ASD symptomatology requires further investigation using longitudinal designs.

Limitations and Future Directions

These findings should be interpreted in light of the study’s relatively small sample size and short follow-up period. Furthermore, the study’s follow-up response rate (33%) was somewhat low, and participants at follow-up represented a subsample with higher adaptive functioning compared to the T1 participants. As participants were recruited from the community and were not a clinically-anxious referred sample, DBC-P and SCAS-P scores were mildly positively skewed at both time points, indicating more participants with fewer problem behaviors and lower anxiety scores in this sample. Because of this, it is possible that some predictive relationships or bi-directional effects may have been of too small effect size or strength to be detected. Also, the small sample size prevented us from examining trends by age-groups. Future longitudinal studies should follow-up larger numbers of children over a longer time-period, and include a larger proportion of females to enable more meaningful examination of any possible gender differences. Multiple-informant scales, rather than relying on parent-report, and scales which can capture more atypical anxiety experiences and presentations in ASD (i.e. Rodgers et al. 2016), should be used to better capture possible variability in the presentation and frequency of ASD and anxiety symptoms in different contexts and by different informants/ methods (Dubin et al. 2015; Gotham et al. 2015).

Further, our DBC-derived S/C symptom score may not have been adequate to detect predictive relationships in this study. As our preliminary results found differential relationships between anxiety and DBC ASD R/S and S/C symptoms (with more consistent relationships identified for repetitive behaviors), more comprehensive measures of each ASD symptom subdomain should be used to investigate these relationships further. Clinician-rated assessments may also provide more “objective” measures of ASD severity compared to parent-rated instruments such as the DBC.

Lastly, one of this study’s main findings was that earlier anxiety scores were the only significant predictor of later overall anxiety in ASD when all other child characteristics studied were considered together, thus supporting recent calls for timely assessment and treatment of anxiety symptoms in ASD (Dubin et al. 2015; Vasa and Mazurek 2015). Since repetitive speech/stereotyped behavioral symptoms tend to be highly associated with anxiety in cross-sectional investigations, and earlier anxiety in turn predicts later anxiety, future studies need to investigate factors influencing the development and maintenance of ASD symptomatology in relation to anxiety (Rodgers et al. 2012).

Footnotes

  1. 1.

    Special education in Singapore is provided to children with physical disabilities, those with a formal, clinical diagnosis of intellectual disability (IQ and/or adaptive skills standard scores ⩽70), those with a professional diagnosis of ASD and those with sensory impairments. Singapore has a total of 20 special education schools approved by the Ministry of Education. Of these, four cater to children and youths with cerebral palsy or visual/ hearing impairments, and the remaining 16 educate children with intellectual disabilities, ASD and/or multiple disabilities.

  2. 2.

    Participants were diagnosed before the DSM-5 was published in 2013. Hence, this study included children who had clinical diagnoses of autism, ASD, Asperger Syndrome or Pervasive Developmental Disorder – Not Otherwise Specified (PDD-NOS), using the DSM-IV-TR or ICD-10 criteria. Under current DSM-5 criteria, most of the participants would most likely be described as meeting criteria for an ASD.

  3. 3.

    These are staffed by qualified multi-disciplinary teams employing evidence-based approaches to diagnosing ASD using DSM-IV or ICD-10 criteria (Academy of Medicine Singapore-Ministry of Health (AMS-MOH) Clinical Practice Guidelines Workgroup on Autism Spectrum Disorders, 2010; Moh & Magiati, 2012).

  4. 4.

    One item from Magiati et al.’s (2016) DBC repetitive speech/behavior subscale (‘Speaks in whispers, high pitched voice or other unusual tone or rhythm’) was included in the DBC social/communication subdomain in this study, as this item was more closely related to the communication impairments described by the DSM-5 criteria (APA, 2013).

  5. 5.

    Following Nauta et al. (2004), we also applied the Spearman–Brown formula to correct for differences due to scale length, but there was little difference between the corrected and uncorrected coefficient alphas; hence, only the uncorrected alpha coefficients are reported here.

  6. 6.

    The ‘fear of physical injury’ subscale was not internally reliable in our sample at both time-points. However, this subscale also has the lowest internal consistency in other samples of typically developing children (Nauta et al. 2004).

Notes

Acknowledgments

We would like to thank all the children and caregivers for their participation and all the special schools for their collaboration. This study was funded by a start-up grant to the fourth author from the Faculty of Arts and Social Sciences, National University of Singapore.

Author Contributions

EJT took the lead in analyzing data, reviewing the literature, and writing the manuscript for publication; DMeC contributed subtantially to data entry, cleaning and analyses; GKJT collected all Time 2 data and contributed to the development of the study’s research questions and initial analyses; IM conceptualized the study, obtained research funding, supervised and oversaw all aspects of the study. All authors contributed substantially to co-writing and revising the manuscript at different stages. All auhors read and approved the final manuscript.

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of PsychologyNational University of SingaporeSingaporeSingapore
  2. 2.Autism Resource CentreSingaporeSingapore
  3. 3.Response Early Intervention and Assessment in Community (REACH) North Mental Health TeamInstitute of Mental HealthSingaporeSingapore

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