Synthesis – senior seminar – prognosis: what the future holds and the

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Read the following 3 articles and synthesize (Combine the ideas of all three sources into one overall point – DO NOT SUMMARIZE)  them into 1 and a half page word document. Also, write a well-elaborated question from each reading. Keep in mind the following points when working on this task:

*Questions must be original, thoughtful, and not easily found in the articles.

*Follow APA Rules

*Use proper citations

*Use  PAST TENSE when discussing the articles  (Research already took place)

*DO NOT USE the following words: Me, you, I, we, prove, proof.

*Refer to the articles by their AUTHORS (year of publication) 

*DO NOT USE the article name or words first, second, or third.

*DO NOT SUMMARIZE!!!

***MUST FOLLOW THE ATTACHED SAMPLE

REVIEW

CURRENTOPINION Autism spectrum disorder: outcomes in adulthood

Copyrig

0951-7367 Copyright � 2017 Wolte

a,b c

Patricia Howlin and Iliana Magiati

Purpose of review

Until recently, there has been little systematic study of adult life among individuals with autism spectrum
disorder (ASD) but recognition of the high psychological and social costs of ASD has led to an increase in
adult-focused research over the past decade. The aim of this review is to summarize recent empirical
findings on outcomes for adults with ASD.

Recent findings

Most research on adult outcomes in ASD indicates very limited social integration, poor job prospects and
high rates of mental health problems. However, studies vary widely in their methodology, choice of
measures and selection of participants. Thus, estimates of how many adults have significant social and
mental health problems are often conflicting. There is little consistent information on the individual, familial
or wider social factors that may facilitate more positive social and psychological outcomes. There is a
particular dearth of research on older individuals with ASD.

Summary

The very variable findings reported in this review reflect the problems of conducting research into lifetime
outcomes for individuals with a condition as heterogeneous as ASD. Much more systematic research is
needed to delineate different patterns of development in adulthood and to determine the factors influencing
these trajectories.

Keywords

adulthood, aging, autism, autism spectrum disorder, developmental trajectories, outcome

INTRODUCTION

The estimated prevalence of autism spectrum
disorder (ASD) in adults is high (11/1000 [1

&

]),
and there are increasing concerns about the poor
long-term outcome for so many individuals with
this condition. Health economists [2

&

] also high-
light the high financial costs, predicting that annual
medical and nonmedical costs of ASD in the United
States will be $268 billion for 2015 and $461 billion
for 2025 [2

&

]. Much of this expenditure is for adults,
largely because of the costs of medical care, residen-
tial or supported living accommodation and pro-
ductivity loss, both by caregivers and by individuals
themselves.

aDepartment of Psychology, Institute of Psychiatry, Psychology and
Neuroscience, King’s College, London, UK, bFaculty of Health Sciences,
University of Sydney, Sydney, New South Wales, Australia and

AIMS

The aim of the present article is to review data from
recent studies on adults with ASD with respect to:

cDepartment of Psychology, National University of Singapore, Singapore

Correspondence to Patricia Howlin, PhD, MSc, BA, Department of

(1)

Social outcomes

Psychology, Institute of Psychiatry, Psychology and Neuroscience, King’s

(2)

Trajectories of change over time

College, London SE58AF, UK. E-mail: [email protected]

(3)

Factors related to outcome

Curr Opin Psychiatry 2017, 30:69–76

(4)

Mental health

DOI:10.1097/YCO.0000000000000308

(5)

Mortality and aging

ht © 2017 Wolters Kluwe

rs Kluwer Health, Inc. All rights rese

Articles were selected from a literature search
(conducted in October 2016) of PsycINfo, Medline
and Google Scholar. The search was limited to
peer reviewed articles published in English from 1
January 2015 to 30 September 2016. (See references
for details of search strategy.) Articles were included
in the review if: the focus was on broader social/
lifetime outcomes in ASD (i.e. individuals with
autism, Asperger syndrome/disorder and autism
spectrum disorder); samples were of adequate size
(n >20); average cohort age was 18þ (any IQ level).
The search identified 1525 articles relating to adults
with ASD; of these, 43 met inclusion criteria for the
present review. Additional references outside the

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rved. www.co-psychiatry.com

KEY POINTS

� Research on outcomes in adult life for individuals with
ASD is growing, but findings remain inconsistent and
often conflicting.

� There is contradictory evidence on how many
individuals with ASD achieve good outcomes as adults,
and lack of agreement on how this should be measured
or how prognosis might be improved.

� In particular, data on psychiatric comorbidities in
adulthood vary widely, and it remains uncertain how
many individuals do experience serious mental
health problems.

� Nevertheless, despite lack of agreement between
studies, overall outcomes for adults with ASD in terms
of jobs, relationships, independent living and mental
health are considerably poorer than for same
age peers.

� Knowledge about factors that are associated with
good, or poor, social and psychological outcomes
remains very limited, with few studies considering
family, school or wider social influences.

Neurodevelopmental disorders

specified date range are included in order to place
current research in context.

SOCIAL OUTCOMES IN ADULTHOOD

Assessments based on ‘normative’ measures
of social functioning

Previous systematic reviews, focusing on ‘norma-
tive’ measures of social outcomes in ASD (i.e. objec-
tive measures of employment, independence, social
participation and relationships) [3] have concluded
that, even among cohorts of average intelligence,
most individuals remain highly dependent on
others for their care, social contacts are limited
and employment rates are extremely low. Moreover,
adults with ASD are more economically, education-
ally and socially disadvantaged than adults with
other developmental or intellectual disabilities [4].

A recent systematic review [5
&&

] (12 studies pub-
lished from 1967 to 2013; n ¼ 828) indicates little
change in these conclusions. Outcomes were rated
as in previous reviews [3] (i.e. ‘very good’¼high
independence and social functioning; ‘good’¼
some level of employment, some friends, largely
independent; ‘fair’¼requires some support in daily
living; ‘poor’¼requires substantial support/residen-
tial care; and ‘very poor’¼ long-stay secure care).
Forty-eight per cent of participants were rated as
having a ‘poor’/‘very poor’ outcome, and only 20%
were deemed to have a ‘good’ or ‘very good’

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70 www.co-psychiatry.com

outcome. However, the authors also highlight the
wide variability in findings. Thus, the mean esti-
mated percentage of individuals with a ‘good out-
come’ was 20% but with 95% confidence intervals
(CIs) varying from 14 to 27% across studies;
similarly, the 95% CIs for ‘poor outcome’ ranged
from 37 to 59%.

This variability is evident in subsequent studies.
Within a French cohort [6

&

] of adults of mixed IQ
and autism severity (n¼76; age 18 –54 years), two
thirds had a ‘poor’ or ‘very poor’ outcome and even
among those rated as having a ‘good outcome’,
none was living independently. A US-based, online
survey [7

&

] completed by parents/carers of 143 indi-
viduals with ASD (mean age 25 years) revealed that
only 22% were in work, 7% lived independently,
whereas 87% were on benefits.

Cohorts involving more cognitively and
verbally able adults generally report more positive
results, although, again figures vary. Among 50
adults with Asperger syndrome (mean childhood
IQ 100þ; mean current age 30 years) living in Swe-
den [8

&

], 40% were in full time education or inde-
pendent work, 62% were living independently, 48%
had two or more friends and 52% either currently or
in the past had a partner (14% of these were married/
cohabiting). In a German cohort of 50 adults with
Asperger syndrome [9

&

] (mean age 36 years), 46%
were currently employed, and 28% had a partner;
however, 50% depended on their families or state
benefits for support and 28% were still living with
their parents. The US survey [7

&

] also collected data
from 255 adults with ASD (mean age 38 years) who
were able to report on their own status. They
recorded relatively high levels of education (42%
batchelors degree or higher), employment (47%)
and independent living/living with a spouse or
partner (67%). Employment figures in this study
are considerably higher than reported for another
US cohort [10

&

] of individuals with ASD of average
IQ (n¼73, mean age 24 years), among whom only
one quarter was consistently employed. Neverthe-
less, even within the former sample [7

&

], over half
were unemployed and many (36%) were dependent
on federal or state benefits.

Subjective assessments of social outcomes
and quality of life

The wide variability in outcomes across studies has
led to concerns that standard concepts of what
constitutes a ‘good’ social outcome may not always
be relevant for people with ASD. For example,
higher social achievements for some individuals
may come at the cost of higher stress and poorer
mental health [11

&&

]. It has been suggested [11
&&

]

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Volume 30 � Number 2 � March 2017

Adult outcomes in ASD Howlin and Magiati

that when subjective factors such as satisfaction
with life, good physical and mental health, adequate
living conditions, supportive and fulfilling social
and family relationships are taken into account,
adult outcomes may be more positive than usually
reported. Thus, within a US cohort studied over
several decades [12

&&

] (n¼60; mean age 32 years),
many participants experienced relatively good sub-
jective Quality of Life (QoL) and their mean scores
on the World Health Organization Quality of Life
measure [13] were comparable with those of the
general population. In the Swedish cohort of indi-
viduals with Asperger syndrome [8

&

], scores on a
subjective quality of life measure [14] were also
within the average normative range (albeit at the
lower end of that range). Nevertheless, despite these
more positive findings, a recent meta-analysis of
studies of QoL across the lifespan [15

&&

] concluded
that individuals with ASD, including those of higher
intellectual and verbal ability, have a poorer QoL
than their non-ASD peers.

TRAJECTORIES OF CHANGE OVER TIME

Research on trajectories of development in ASD
generally indicates improvements over time
[8

&

,16
&

,17
&

,18
&&

] but, again, there is considerable
variability. An 8–10 year follow-up [16

&

,17
&

] in
the United States of over 300 individuals (mean
age at follow-up 22 years; 70% with IQ < 70) found
that one-third (35%) showed improvements in non-
verbal communication, 58% in verbal communi-
cation, 40% in social interaction and 61% in
repetitive/stereotyped behaviors and interests. Mal-
adaptive behaviors improved in 42%. Only 12%
showed a worsening in total autism symptoms
and 11% in maladaptive behaviors.

Another longitudinal study in the United States
[18

&&

] examined progress in 85 individuals first seen
as children. In early adulthood (mean age 19 years),
most (77, 91%) continued to meet ASD criteria; of
these, 53 had an IQ below 70 and 24 had an IQ of at
least 70. Eight individuals (all IQ>70) who no longer
met criteria were described as having a ‘Very Positive
Outcome’ (VPO). Trajectories of change (IQ, com-
munication, social functioning and repetitive
behaviors) among the VPO group were significantly
more positive than for participants with IQ below
70. There were also some significant differences
between the VPO group and participants of average
IQ who still met diagnostic criteria. The authors
suggest that further research focusing on different
patterns of developmental trajectories may be
important for identifying different genetic causes
as well as having implications for more individually
tailored interventions.

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Is there ‘recovery’ from autism spectrum
disorder?
The identification of individuals with ‘Very Positive
Outcomes’ [18

&&

] raises the question of whether
there can be ‘recovery’ from ASD. The Swedish
follow-up [8

&

,19
&

] also identified a small group of
adults (11 out of 50) who no longer met diagnostic
criteria for ASD. All had an IQ in the average range,
had friends and were living independently, and all
but one was employed. Nevertheless, although
eight, either currently or in the past, had a partner
or were married, romantic relationships were lower
than in the general population; three also had some
current psychiatric comorbidity. An earlier study
[20] described 34 children (mean age 12 years; mean
IQ 111) who, although initially meeting ASD
criteria, were currently functioning ‘within normal
limits’. Recent, more detailed data analyses, how-
ever, [21 –23] indicate persisting subtle difficulties in
social understanding, pragmatic communication,
attention, self-control and emotional maturity
and in psychiatric morbidity. As yet, there are no
data on this cohort in adulthood and it is uncertain
whether these remaining differences will abate or
become more evident with age.

FACTORS RELATED TO SOCIAL
OUTCOMES IN ADULTHOOD

Intellectual and verbal functioning

Intellectual and verbal functioning in childhood are
among the strongest prognostic indicators in ASD
[3]. Few individuals with a childhood IQ below 70,
or who fail to develop functional speech, live inde-
pendently as adults and job prospects and social
integation are particularly poor. In addition, they
show less improvement in cognitive or social skills,
and greater increases in ritualistic behaviors over
time than individuals with an average IQ in child-
hood [16

&

,17
&

,18
&&

]. Nonverbal mental age at 2 years
is also predictive of independence in daily living
skills at age 21 [24

&

]. Unsurprisingly, too, persisting
communication and intellectual impairments in
adulthood (especially if associated with epilepsy)
are associated with low levels of social attainments
and independence [6

&

]. Nevertheless, even among
individuals with a childhood IQ at least 70, out-
comes can vary widely. Some show very few autism
symptoms or cognitive difficulties as young adults,
whereas others continue to experience significant
problems [18

&&

]. The relationship between IQ and
outcome in higher functioning samples also
depends on the variables studied. Thus, among
the Swedish Asperger syndrome cohort [8

&

,19
&

],
current IQ correlated highly with academic success,

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Neurodevelopmental disorders

but not with independent living, friendships or
marital status.

Autism symptom severity

Another established predictor of adult outcome is
autism severity [3]. In the Swedish study [8

&

,19
&

],
higher levels of autism symptomatology, both in
childhood and adulthood, were associated with
reduced independence, lower rates of employment
and more limited social relationships. Similarly,
within the French cohort [6

&

], no adults with a
childhood diagnosis of ‘severe autism’ were judged
to have a ‘good’ outcome; in contrast, 40% of those
with ‘moderate autism’ and all those with a child-
hood diagnosis of Asperger syndrome were rated as
having a ‘good’ outcome.

Gender

The role of gender remains uncertain as most studies
involve so few women [25

&

]. There are some sugges-
tions that women with ASD have poorer social out-
comes, especially with respect to employment [10

&

]
and quality of life [11

&&

] than men. However, other
studies [16

&

,26
&&

] report no significant impact of
gender on autism symptoms, behavior problems
or social outcomes.

Family/environmental factors

In the community sample followed up over 8–10
years in the United States [16

&

,17
&

], higher levels of
inclusion in social and academic activities in school
were associated with more positive outcomes, over
and above individual characteristics such as age, IQ
and sex. Greater maternal praise/positivity in child-
hood also predicted higher levels of nonverbal com-
munication and social reciprocity in adulthood;
increases in maternal praise over time were associ-
ated with decreases in externalizing behaviors. In
addition, quality of mother –child relationship was
significantly associated with fewer maladaptive
behaviors at follow-up; improvements in mother –
child relationships were related to a decline in
behavioral and social problems. Other research
[11

&&

,12
&&

] has also highlighted the positive impact
of maternal warmth on adult quality of life, along
with factors such as physical health, greater inde-
pendence in daily living skills and better executive
function.

More recently, the specific impact of stress has
received attention. Among 25 Swedish adults (mean
age 34 years) [27

&

], greater perceived stress was
associated with more severe autism symptoms and
poorer coping in adulthood. A US study [28

&

,29
&

] has

Copyright © 2017 Wolters Kluwer

72 www.co-psychiatry.com

also explored the effects of stress in two groups of
adults with ASD (n¼38, 40; mean ages 23–24 years;
rates of employment 37–47%; and living independ-
ently 17 –21%). These adults experienced signifi-
cantly more stressful life events and stress than
community controls, and levels of global stress sig-
nificantly predicted overall social functioning and
social disability. In a long-term follow-up in the
United States [12

&&

], current levels of perceived
stress, together with frequency of bullying in child-
hood, were the two factors consistently linked
with poorer adult self-reported quality of life. In
turn, good quality of life was positively correlated
with better-developed daily living skills and good
physical health.

Mental health

Data on rates of psychiatric disorders in ASD are
highly variable. In a UK longitudinal study [30

&

] of
58 adults originally diagnosed as children (mean IQ
69; mean age 44 years), 28% had at some time
experienced mild-to-moderate mental health prob-
lems, and 28% had severe or very severe difficulties.
A retrospective case review [31

&

] of 474 adults
attending an ASD diagnostic clinic in the UK found
that around half (57%) had a comorbid psychiatric
disorder. A similar figure (54%) was reported in a
large US database study [32

&&

] (n¼1507 adults with
ASD). In the Swedish cohort with Asperger syn-
drome [19

&

], 54% had a current diagnosis but almost
all (94%) met lifetime criteria for a comorbid psy-
chiatric/neurodevelopmental disorder. In a Dutch
cohort [33

&&

] including older adults (n¼172, age
19 –79 years, IQ > 80), 79% met criteria for a psy-
chiatric disorder at least once in their lives. Among
participants with Asperger syndrome in the German
cohort [8

&

], 70% had at one or more psychiatric
comorbidities. In the online US survey [7

&

], 86%
of the self-report group and 73% of the proxy-report
group had at least one mental/behavioral comor-
bidity.

The majority of diagnoses/symptoms identified
in these studies relate to anxiety and/or depression,
but again there are many inconsistencies. Estimates
of depressive disorders range from 20 to 58% and
anxiety disorders from 22 to 39%; other commonly
reported difficulties include Attention Deficit Hyper-
activity Disorder (ADHD) (10–28%); tic disorders
(1–50%); Obsessive Compulsive Disorder (OCD)
(8–28%) and somatoform and eating disorders
(6–17%). Two studies [34,35

&

] report high rates of
social anxiety (50–52%). Estimates of psychotic
disorders tend to be relatively low (usually around
2–4%), but again there is wide variability, with a
recent review recording figures from 0 to 35% [36

&

].

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Volume 30 � Number 2 � March 2017

Adult outcomes in ASD Howlin and Magiati

Conflicting data on rates of mental health prob-
lems in ASD are due to many factors, including
differences in sampling, the range of conditions
selected for study and the diagnostic procedures
and measures used. Although, overall, the data
indicate that psychiatric morbidity is higher in
ASD that in the general population [32

&&

], until
there is greater methodological consistency across
studies, it remains impossible accurately to estimate
the true risk.

Estimates of substance abuse disorders are also
contradictory. A Swedish epidemiological study
[37

&

] (n¼26 986 individuals with ASD and 96 557
controls) reported a substantially increased risk of
drug [odds ratio (OR) 8.5] and alcohol abuse/
dependence (OR 4.0); risks of substance-related
crime (OR 1.4), or deaths linked to abuse were also
high (OR 3.0). A German study [8

&

] recorded sim-
ilarly high rates of drug abuse (12%) or alcohol-
related problems (18%). In contrast, in the US data-
base [32

&&

], alcohol abuse/dependency was ident-
ified in 3% and drug abuse/dependency in 4%.
Combined rates of these problems in the UK [31

&

]
and Swedish [19

&

] samples were also low (2 and 4%,
respectively). This inconsistency is highlighted in a
recent systematic review of substance abuse in ASD
[38

&&

] (n¼18 studies; 11 epidemiological). Although
estimates were generally low, figures ranged from
0.7 to 36%, making it impossible to establish a
reliable prevalence figure. The only consistent find-
ing noted in the review was the lack of knowledge
among professionals on how to treat this group of
patients, and the dearth of intervention research.

Variables associated with adult mental
health

Although there has been little systematic explora-
tion of factors related to mental health in adults
with ASD, there is some evidence of an association
between poor mental health and poorer social func-
tioning [30

&

], lower life satisfaction [39
&

] and higher
levels of autism symptoms [7

&

,19
&

,30
&

,33
&&

,40].
The relation between mental health and gender

is inconsistent, probably because of the small
numbers of women in most studies. Most research
suggests that, compared with men, women are at
greater risk of anxiety and mood disorders
[32

&&

,33
&&

] and of conditions such as dementia,
schizophrenia and bipolar disorder [32

&&

]; men tend
to have higher rates of OCD and ADHD [32

&&

].
However, two studies [30

&

,31
&

] identified no signifi-
cant sex differences in overall rates or types of
mental health problems and in the German cohort
[8

&

], whereas there were no differences in rates of
major depression, men had more anxiety symptoms

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(53 vs. 12%) and more mood disorders than women
(32 vs. 6%).

Data on substance abuse are, yet again, contra-
dictory. In the German cohort [8

&

], more women
than men had drug abuse problems (19 vs. 9%) but
fewer had alcohol abuse/dependence (12 vs. 20%).
In contrast, in the US sample [32

&&

], drug/alcohol
problems were more frequent in men (drugs 5 vs.
3%; alcohol 4 vs. 2%).

Findings on the relationship between mental
health and age are equally confusing. The German
study [8

&

] found that young adults (<40 years)
showed less psychopathology than those over 40.
In the US longitudinal study [39

&

], women were
more likely to show greater increases in anxiety
and depressive symptoms over time, whereas ado-
lescent males had more depressive symptoms that
were maintained into young adulthood. In contrast,
a study including a much wider age range [33

&&

]
(n¼344, age 19–79 years) concluded that psycho-
pathology declined with age, with fewer adults in
the older age group (55 –79 years) meeting criteria
for any psychiatric diagnosis and particularly social
phobia. Discrepancies here are likely because of the
very small number of older adults in the two
former samples.

Findings concerning the relationship with other
variables that are frequently associated with mental
health in the general population (i.e. cognitive
functioning, social economic status and living situ-
ations, as well as life events and family factors)
remain inconclusive and inconsistent.

MORTALITY AND SUICIDE IN ADULTHOOD

Many major chronic medical conditions occur sig-
nificantly more frequently in adults with ASD than
in the general population [32

&&

], and mortality risks
are also higher. In a Danish epidemiological study
[41

&

] (total n¼1,912 904; n ASD¼20 492), mortality
rates for young adults with ASD were double those in
the general population, and similar to the risk for
individuals with neurological or mental/behavioral
disorders. Comparable rates were identified in
Sweden [42] (27 122 individuals with ASD;
2,672 185 matched general population controls).
Mortality was over twice as high in the ASD group
(OR 2.56) and mean age of death was much lower
[controls 70.2 years; ASD 53.9 years (39.5 years in
those with Intellectual Disability (ID); 58.4 years in
those without ID)]. The most frequent causes of
death were nervous, circulatory, respiratory or diges-
tive disorders and congenital malformations.

Overall, death rates in men and women were
similar, but women were more likely to die from
endocrine disease, congenital malformations or

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Neurodevelopmental disorders

suicide, and men from diseases of the nervous and
circulatory systems. The most common cause of
death in individuals with ID and ASD was epilepsy.

This study [40] also found that death by suicide
was significantly elevated among cognitively able
individuals with ASD (OR 9.4). However, there is
little consistent information on suicidal behaviors
or ideation, or how frequently these result in serious
or fatal suicide attempts, and reported rates of
suicide in ASD vary widely (from <10 to >50%)
[41

&

]. For example, suicide attempts occurred in
only 2% of the US database cohort [32

&&

]. In the
Swedish Asperger study [19

&

] (n¼50), 13 individuals
(26%) exhibited ongoing suicidal behavior, but only
one was considered to be at high risk. This conflicts
with an earlier study [42] in which 66% of 367
individuals with a diagnosis of Asperger syndrome
reported suicidal ideation; 35% reported plans or
attempts at suicide. Recent studies in this area
[43

&&

,44] note the importance of more research into
the factors associated with suicide, especially in
more able individuals, and the need for more
reliable ways of identifying at-risk adults with ASD.

AGING

Although there has been very little investigation of
the impact of aging in ASD, current research
[45

&

,46
&&

] suggests that many cognitive skills (proc-
essing speed, attention, verbal memory, cognitive
flexibility and planning, and theory of mind) show
similar patterns of decline as in typical aging. Con-
versely, individuals with ASD may be less prone to
decline in visual and working memory than elderly
in the general population [46

&&

]. Quality of life in
ASD also seems to be less affected by age than in the
general population, but to date studies on aging in
ASD are small scale, mainly cross-sectional and con-
tain few participants aged over 60. More extensive
research in this area is crucially needed.

CONCLUSION

It is evident from this review that individuals with
ASD continue to face many challenges throughout
adulthood. Current social and health services for
adults [47,48

&

,49
&&

,50–52] are often inadequate,
resulting in high levels of stress for both individuals
themselves and their families [53

&

]. To date, how-
ever, methodological issues particularly related to
wide heterogeneity in the cohorts studied and
variability in the measures used, have resulted in
inconsistent and sometimes contradictory research
findings. We still do not know with any certainly
what proportion of individuals manage to attain
adequate levels of social integration as adults or

Copyright © 2017 Wolters Kluwer

74 www.co-psychiatry.com

how many experience a good psychological and
physical quality of life. More importantly, we are
a long way from identifying the individual, family or
environmental factors that enhance resilience and
ensure social and psychological well being in adult-
hood. High-quality adult outcome research must be
a priority if we are to meet the needs of current and
future generations of adults with ASD.

Acknowledgements

None.

Financial support and sponsorship

None.

Conflicts of interest

There are no conflicts of interest.

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&&

Steinhausen HC, Mohr Jensen C, Lauritsen MB. A systematic review and
meta-analysis of the long-term overall outcome of autism spectrum disorders
in adolescence and adulthood. Acta Psychiatrica Scandinavica 2016;
133:445 – 452.

This article describes the very variable findings from studies of prognosis in
individuals with ASD. It is the first to provide CIs for estimates of good/fair/poor
outcomes and concludes that the prognosis for almost 50% of individuals is poor.
The authors highlight the need for research on pathways and predictors of
outcome.
6.
&

Chamak B, Bonniau B. Trajectories, long-term outcomes and family experi-
ences of 76 adults with autism spectrum disorder. J Autism Dev Disord 2016;
46:1084 – 1095.

Data are based on parental reports of social functioning in adults (age 18 – 54) with
ASD. Negative prognostic variables include greater autism severity, low IQ, poor
language and the presence of epilepsy.
7.
&

Gotham K, Marvin AR, Taylor JL, et al. Characterizing the daily life, needs, and
priorities of adults with autism spectrum disorder from Interactive Autism
Network data. Autism 2015; 19:794 – 804.

This is an internet-based survey completed by individuals with autism or parents/
carers. It describes the situation for adults in the United States and highlights the
need to improve healthcare and public services.
8.
&

Helles A, Gillberg IC, Gillberg C, Billstedt E. Asperger syndrome in males over
two decades: quality of life in relation to diagnostic stability and psychiatric
comorbidity. Autism 2016; 1362361316650090 [Epub ahead of print]

Analysis of factors related to outcome in adulthood indicates the importance of
examining subjective aspects of quality of life (physical and emotional health) in
addition to objective measures (jobs, relationships and so on).
9.
&

Roy M, Prox-Vagedes V, Ohlmeier MD, Dillo W. Beyond childhood: psychia-
tric comorbidities and social background of adults with Asperger syndrome.
Psychiatria Danubina 2015; 27:50 – 59.

This German study identifies high rates of mental health problems in 50 adults (age
20 – 62). Rates of employment and close relationships were also limited.

Health, Inc. All rights reserved.

Volume 30 � Number 2 � March 2017

Adult outcomes in ASD Howlin and Magiati

10.
&

Taylor JL, Henninger NA, Mailick MR. Longitudinal patterns of employment
and postsecondary education for adults with autism and average-range IQ.
Autism 2015; 19:785 – 793.

This article focuses on the poor employment prospects of young adults with ASD.
Outcomes for women were found to be even less positive than for men.
11.
&&

Bishop-Fitzpatrick L, Hong J, Smith LE, et al. Characterizing objective quality
of life and normative outcomes in adults with autism spectrum disorder: an
exploratory latent class analysis. J Autism Dev Disord 2016; 46:2707 –
2719.

The authors draw attention to the need for broader measures of outcome in ASD,
rather than focusing on ‘normative’ measures such as rates of employment/
independent living and so on. More positive outcomes are associated with better
daily living skills, better executive function and more maternal warmth.
12.
&&

Hong J, Bishop-Fitzpatrick L, Smith LE, et al. Factors associated with sub-
jective quality of life of adults with autism spectrum disorder: self-report versus
maternal reports. J Autism Dev Disord 2016; 46:1368 – 1378.

This article compared self-rated and parent-rated quality of life among adults with
autism. The agreement between self-report and proxy report was good. Factors
related to better quality of life included independence in daily activities and physical
health. Higher levels of perceived stress and bullying were associated with poorer
quality of life.
13. Skevington SM, Lotfy M, O’Connell KA. The World Health Organization’s

WHOQOL-BREF quality of life assessment: psychometric properties and
results of the international field trial. A report from the WHOQOL group. Qual
Life Res 2004; 13:299 – 310.

14. Pallant JF, Lae L. Sense of coherence, well being, coping and personality
factors: further evaluation of the sense of coherence scale. Pers Individ Dif
2002; 33:39 – 48.

15.
&&

van Heijst BF, Geurts HM. Quality of life in autism across the lifespan: a meta-
analysis. Autism 2015; 19:158 – 167.

Meta-analysis of 10 studies (2004 – 2012; n¼486 people with autism and 17 776
controls). Data indicated that, across the lifespan, people with autism experience a
much lower quality of life compared with people without autism. However, age did
not have an effect on quality of life.
16.
&

Woodman AC, Smith LE, Greenberg JS, Mailick MR. Change in autism
symptoms and maladaptive behaviors in adolescence and adulthood: the
role of positive family processes. J Autism Dev Disord 2015; 45:111 – 126.

The authors report improvements in autism symptoms and maladaptive behaviors
over 8.5 years in a large, community-based sample of adolescents and adults.
17.
&

Woodman AC, Smith LE, Greenberg JS, Mailick MR. Contextual factors
predict patterns of change in functioning over 10 years among adolescents
and adults with autism spectrum disorders. J Autism Dev Disord 2016;
46:176 – 189.

Exploration of factors associated with positive trajectories over time identified the
importance of educational inclusion and maternal praise/warmth.
18.
&&

Lord C, Bishop S, Anderson D. Developmental trajectories as autism phe-
notypes. Am J Med Genet Part C: Semin Med Genet 2015; 169:198 – 208.

This article examined trajectories of development in 85 individuals with autism
followed into young adulthood. Three distinct groups were identified: adults with
intellectual disability and persistent ASD; adults with IQs in the average range who
continued to show ASD impairments and a small group (n¼8) with IQs in the
average range who were judged to be functioning at age appropriate levels at age
19 years, despite a previous childhood diagnosis of ASD. The findings have
potentially important implications for understanding subtypes of autism and
developing more individually tailored interventions.
19.
&

Gillberg IC, Helles A, Billstedt E, Gillberg C. Boys with Asperger syndrome
grow up: psychiatric and neurodevelopmental disorders 20 years after initial
diagnosis. J Autism Dev Disord 2016; 46:74 – 82.

The authors report very high rates (94%) of psychiatric comorbidities in 50 adult
males diagnosed with Asperger syndrome. These rates are considerably higher
than in several other adult outcome studies.
20. Fein D, Barton M, Eigsti IM, et al. Optimal outcome in individuals with a history

of autism. J Child Psychol Psychiatry 2013; 54:195 – 205.
21. Orinstein A, Tyson KE, Suh J, et al. Psychiatric symptoms in youth with a

history of autism and optimal outcome. J Autism Dev Disord 2015; 45:3703 –
3714.

22. Orinstein AJ, Suh J, Porter K, et al. Social function and communication in
optimal outcome children and adolescents with an autism history on struc-
tured test measures. J Autism Dev Disord 2015; 45:2443 – 2463.

23. Suh J, Orinstein A, Barton M, et al. Ratings of broader autism phenotype and
personality traits in optimal outcomes from autism spectrum disorder. J Autism
Dev Disord 2016; 46:3505 – 3518.

24.
&

Bal VH, Kim SH, Cheong D, Lord C. Daily living skills in individuals with autism
spectrum disorder from 2 to 21 years of age. Autism 2015; 19:774 – 784.

Authors found that nonverbal mental age, receptive language and social-commu-
nication impairment in early childhood predicted levels of daily living skills in young
adults with ASD. They suggest that daily living skills should be a focus of treatment
particularly for adolescents transitioning to adulthood.
25.
&

Halladay AK, Bishop S, Constantino JN, et al. Sex and gender differences in
autism spectrum disorder: summarizing evidence gaps and identifying emer-
ging areas of priority. Mol Autism 2015; 6:1.

Discussion article highlighting how little is known about the role of sex and gender
in ASD.

Copyright © 2017 Wolters Kluwe

0951-7367 Copyright � 2017 Wolters Kluwer Health, Inc. All rights rese

26.
&&

Kirby AV, Baranek GT, Fox L. Longitudinal predictors of outcomes for adults
with autism spectrum disorder systematic review. OTJR: Occup Particip
Health 2016; 36:55 – 64.

Systematic review of research on predictors of outcome in ASD. Most studies
were found to be of poor quality methodologically. The impact of most factors was
inconsistent, but individual variables (IQ and so on) appear to be most important.
27.
&

Hirvikoski T, Blomqvist M. High self-perceived stress and poor coping in
intellectually able adults with autism spectrum disorder. Autism 2015;
19:752 – 757.

Adults with ASD found to have significantly higher subjective stress and
poorer ability to cope with stress compared with typical adults. Higher levels
of autistic traits were associated with higher levels of stress and poorer coping
ability.
28.
&

Bishop-Fitzpatrick L, Mazefsky CA, Minshew NJ, Eack SM. The relationship
between stress and social functioning in adults with autism spectrum disorder
and without intellectual disability. Autism Res 2015; 8:164 – 173.

See note to 29
29.
&

Bishop-Fitzpatrick L, Minshew NJ, Mazefsky CA, Eack SM. Perception of life
as stressful, not biological response to stress, is associated with greater
social disability in adults with autism spectrum disorder. J Autism Dev Disord
2016; DOI: 10.1007/s10803-016-2910-6.

This and reference [28] report on high levels of stress in adults with ASD and their
greater exposure to stressful events. In turn, these are significantly associated with
social disability. The authors highlight the need for more interventions targeting
stress management.
30.
&

Moss P, Howlin P, Savage S, et al. Self and informant reports of mental health
difficulties among adults with autism findings from a long-term follow-up study.
Autism 2015; 19:832 – 841.

Self and informant reports of psychiatric comorbidities in adults with ASD in their
mid-40s indicate that around half have some mental health problems. However,
over 40% had experienced no psychiatric problems in adulthood.
31.
&

Russell AJ, Murphy CM, Wilson E, et al. The mental health of individuals
referred for assessment of autism spectrum disorder in adulthood: a clinic
report. Autism 2016; 20:623 – 627.

Clinical case note study of 474 adults with ASD. Just over half (57%) had a
history of psychiatric problems. As in the previous study [30], this rate is higher
than in the general population, but considerably less than reported in some other
studies.
32.
&&

Croen LA, Zerbo O, Qian Y, et al. The health status of adults on the autism
spectrum. Autism 2015; 19:814 – 823.

Very informative article on physical and mental health problems in a large cohort of
adults with ASD identified via an insurance database. Although such sampling is
prone to a number of biases, the large size of the sample and the detailed
information included provide valuable insight into the frequency and range of
problems experienced by individuals with ASD.
33.
&&

Lever AG, Geurts HM. Psychiatric co-occurring symptoms and disorders in
young, middle-aged, and older adults with autism spectrum disorder. J Autism
Dev Dis 2016; 46:1916 – 1930.

In this study of 344 adults with ASD aged 19 – 79 years, 79% had a history of
psychiatric disorder (mainly depression and anxiety). However, older adults were
less likely to experience mental health problems than younger individuals. The
discrepancies in reported rates of psychiatric difficulties across studies (e.g. 19,
30, 31, 32 and 37), and disagreements concerning the association with age,
highlight the need for more systematic research in this area.
34. Maddox BB, White SW. Comorbid social anxiety disorder in adults with

autism spectrum disorder. J Autism Dev Disord 2015; 45:3949 – 3960.
35.
&

Spain D, Happé F, Johnston P, et al. Social anxiety in adult males with autism
spectrum disorders. Res Autism Spect Disord 2016; 32:13 – 23.

As in reference [33], the authors report high rates of social anxiety (52%) in
adults with ASD. The article concludes that more research is needed to ascertain
the prevalence social anxiety in ASD and to identify triggers and maintaining
factors.
36.
&

Chisholm K, Lin A, Abu-Akel A, Wood SJ. The association between autism
and schizophrenia spectrum disorders: a review of eight alternate models of
co-occurrence. Neurosci Biobehav Rev 2015; 55:173 – 183.

Very useful review of studies on the association between autism and schizophre-
nia. The article highlights the large discrepancies between many studies in this
area and explores possible reasons for these inconsistencies.
37.
&

Butwicka A, Långström N, Larsson H, et al. Increased risk for substance
use-related problems in autism spectrum disorders: a population-based
cohort study. J Autism Dev Disord 2016; 1 – 0. doi:10.1007/s10803-016-
2914-2.

These authors identify very high rates of substance abuse in ASD and in their
siblings and parents. They conclude that ASD is a risk factor for substance abuse-
related problems. However, other studies (e.g. [36]) have not identified this as a
significant problem in ASD; reasons for these conflicting findings remain unclear.
38.
&&

Arnevik EA, Helverschou SB. Autism spectrum disorder and co-occurring
substance use disorder: a systematic review. Substance Abuse: Res Treat
2016; 10:69 – 75.

A systematic review (18 articles) of substance abuse in ASD. The authors
conclude that overall comorbidity rates appear to be low, but lack of agreement
across studies makes difficult to establish a general prevalence rate. The authors
highlight the lack of high-quality research in this area.

r Health, Inc. All rights reserved.

rved. www.co-psychiatry.com 75

Neurodevelopmental disorders

39.
&

Gotham K, Brunwasser SM, Lord C. Depressive and anxiety symptom
trajectories from school age through young adulthood in samples with autism
spectrum disorder and developmental delay. J Am Acad Child Adolesc
Psychiatry 2015; 54:369– 376.

Study of trajectories of psychiatric disorders in 109 individuals with ASD. Rates of
affective and anxiety disorders were high, but men and women showed different
patterns of change over time.
40. Garcı́a-Villamisar D, Rojahn J. Comorbid psychopathology and stress mediate

the relationship between autistic traits and repetitive behaviours in adults with
autism. J Intell Disabil Res 2015; 59:116 – 124.

41.
&

Schendel DE, Overgaard M, Christensen J, et al. Association of psychiatric
and neurologic comorbidity with mortality among persons with autism
spectrum disorder in a Danish population. JAMA Pediatr 2016;
170:243 – 250.

Large epidemiological study identified high risks of early mortality in individuals
with ASD. The presence of comorbid mental/behavioral or neurologic conditions
significantly increased the risk of early death (2.6-fold to 7.6-fold) compared with
individuals without ASD.
42. Cassidy S, Bradley P, Robinson J, et al. Suicidal ideation and suicide

plans or attempts in adults with Asperger’s syndrome attending a specialist
diagnostic clinic: a clinical cohort study. Lancet Psychiatry 2014; 1:142 –
147.

43.
&&

Hirvikoski T, Mittendorfer-Rutz E, Boman M, et al. Premature
mortality in autism spectrum disorder. Br J Psychiatry 2016; 208:232 –
238.

Important epidemiological study highlighting greatly increased risks of premature
mortality in ASD. The study also identifies factors that appear to be associated with
a greater risk of early death. The presence of a range of different comorbid medical/
neurological conditions was associated with a higher risk of death, especially
among women with intellectual disability. Individuals of average intellectual ability
had a high risk of suicide. The authors stress the need for more research into
factors related to early death in ASD.
44. Salvatore T, Brown J, Hastings B, et al. Suicide risk in adults with

autism spectrum disorder: an exploratory discussion. J Special Popul
2016; 1:1 – 11.

45.
&

Lever AG, Werkle-Bergner M, Brandmaier AM, et al. Atypical working memory
decline across the adult lifespan in autism spectrum disorder? J Abnormal
Psychol 2015; 124:1014.

See note to [46].

Copyright © 2017 Wolters Kluwer

76 www.co-psychiatry.com

46.
&&

Lever AG, Geurts HM. Age-related differences in cognition across the adult
lifespan in autism spectrum disorder. Autism Res 2016; 9:666 – 676.

These authors [44], [45] are among the very few who have explored cognitive and
psychological changes in ASD in later adulthood. They have identified certain
areas of cognitive difficulty (e.g. in Theory of Mind) that seem to become less
apparent in old age in ASD; other skills (e.g. visual memory) show less decline with
age than in the general population. The authors conclude that age-related cognitive
difficulties in ASD are mainly parallel to those found in typical aging, although
deterioration in some areas may actually be reduced. They suggest that ASD could
partially protect against an age-related decrease in cognitive functioning.
47. Havlicek J, Bilaver L, Beldon M. Barriers and facilitators of the transition to

adulthood for foster youth with autism spectrum disorder: perspectives of
service providers in Illinois. Child Youth Serv Rev 2016; 60:119 – 128.

48.
&

Turcotte P, Mathew M, Shea LL, et al. Service needs across the lifespan for
individuals with autism. J Autism Dev Disord 2016; 46:2480 – 2489.

US study indicating that adults with ASD were in greater need of services but had less
access to a range of different services than individuals in the general population.
49.
&&

Murphy CM, Wilson CE, Robertson DM, et al. Autism spectrum disorder in
adults: diagnosis, management, and health services development. Neurop-
sychiatric Dis Treat 2016; 12:1669.

This article highlights the lack of health services research for adults with ASD. The
authors focus on the need for more rigorous pharmacological and pharmacological
trials, and the importance of taking into account the views of individuals with ASD
themselves.
50. Vohra R, Madhavan S, Sambamoorthi U. Comorbidity prevalence, healthcare

utilization, and expenditures of Medicaid enrolled adults with autism spectrum
disorders. Autism 2016; pii: 1362361316665222. [Epub ahead of print]

51. Vohra R, Madhavan S, Sambamoorthi U. Emergency department use among
adults with autism spectrum disorders (ASD). J Autism Dev Disord 2016;
46:1441 – 1454; 50.

52. Raymaker DM, McDonald KE, Ashkenazy E, et al. Barriers to healthcare:
instrument development and comparison between autistic adults and adults
with and without other disabilities. Autism 2016; pii: 1362361316661261.
[Epub ahead of print]

53.
&

McKenzie K, Ouellette-Kuntz H, Blinkhorn A, Démoré A. Out of school and into
distress: families of young adults with intellectual and developmental dis-
abilities in transition. J Appl Res Intell Disabil 2016; doi: 10.1111/jar.12264.
[Epub ahead of print]

This study describes the high levels of stress experienced by many families during
their sons’/daughters’ transition from school.

Health, Inc. All rights reserved.

Volume 30 � Number 2 � March 2017

NEW RESEARCH

Developmental Trajectories in
Adolescents and Adults With Autism:

The Case of Daily Living Skills
Leann E. Smith, Ph.D., Matthew J. Maenner, Ph.D., Marsha Mailick Seltzer, Ph.D.

Objective: This study aimed to investigate the longitudinal course of daily living skills in a
large, community-based sample of adolescents and adults with autism spectrum disorders
(ASD) over a 10-year period. Method: Adolescents and adults with ASD (n � 397) were
drawn from an ongoing, longitudinal study of individuals with ASD and their families. A
comparison group of 167 individuals with Down syndrome (DS) were drawn from a linked
longitudinal study. The Waisman Activities of Daily Living Scale was administered four times
over a 10-year period. Results: We used latent growth curve modeling to examine change in
daily living skills. Daily living skills improved for the individuals with ASD during
adolescence and their early 20s, but plateaued during their late 20s. Having an intellectual
disability was associated with lower initial levels of daily living skills and a slower change over
time. Individuals with DS likewise gained daily living skills over time, but there was no
significant curvature in the change. Conclusions: Future research should explore what
environmental factors and interventions may be associated with continued gains in daily
living skills for adults with ASD. J. Am. Acad. Child Adolesc. Psychiatry, 2012;51(6):
622– 631. Key Words: daily living skills, autism, adolescence, adulthood, trajectories

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A utism spectrum disorders (ASDs) are life-long developmental disabilities that af-fect an estimated 1 in 110 children in the
United States.1 ASDs are characterized by im-
pairments in communication and social interac-
tion as well as the presence of repetitive behav-
iors. In recent years, increasing attention has
been given to understanding the behavioral phe-
notype of ASD during adolescence and adult-
hood. For instance, researchers have explored
how autism symptoms and behavior problems
change across adolescence and adulthood.2-4

Other work has focused on measuring educa-
tional and occupational outcomes for adults with
ASD, with results indicating that few individuals
reach high levels of independence.5,6 Virtually no
studies, however, have explored the develop-
ment of independence in daily living skills in
adolescents and adults with ASD, even though
such abilities are often cited as important factors

An interview with the author is available by podcast at

I
www.jaacap.org.

JOURN
622 www.jaacap.org

Downloaded for Anonymous User (n/a) at Florida International University – F
For personal use only. No other uses without permissi

for successful outcomes for adults with intellec-
tual and developmental disabilities (IDD).7,8 The

resent study addressed this gap by examining
rajectories of daily living skills over a 10-year
eriod in a large, community sample of adoles-
ents and adults with ASD.

aily Living Skills in Individuals With ASD
aily living skills constitute a critical domain of

daptive behaviors, which are defined as behav-
ors necessary for age-appropriate, independent
unctioning in social, communication, daily liv-
ng, or motor areas. Past research suggests that
he development of daily living skills may be
articularly challenging for individuals with
SD. Children with autism often have significant

mpairments in daily living skills compared with
ell-matched controls,9,10 and as early as 36

months of age such children display a greater
discrepancy between their adaptive behavior and
mental age than children with other develop-
mental delays.11 These delays in daily living
kills may become more pronounced over time.

n a validation study of the Vineland Adaptive

AL OF THE AMERICAN ACADEMY OF CHILD & ADOLESCENT PSYCHIATRY
VOLUME 51 NUMBER 6 JUNE 2012

lorida state consortium from ClinicalKey.com by Elsevier on January 04, 2018.
on. Copyright ©2018. Elsevier Inc. All rights reserved.

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DEVELOPMENTAL TRAJECTORIES AND AUTISM

Downloa

Behavior Scales’ supplemental norms for autism,
younger children with ASD had higher standard
scores than older children with ASD in all adap-
tive behavior domains, including daily living
skills, suggesting that as children with ASD
grow, they increasingly lag behind their same-
age peers.12 Similarly, in sample of 1,089 children
and adolescents with ASD, Kanne et al.13 recently
found that adolescents had a greater gap be-
tween their mental age and adaptive skills than
younger children, suggesting that individuals
with ASD may gain daily living skills at a pace
slower than the rate of their intellectual growth.
These cross-sectional findings highlight the need
for longitudinal studies to elucidate how daily
living skills change over time for individuals
with ASD and what factors are associated with
improvements in these skills.

Most studies that have examined within-person
change in daily living skills for individuals with
autism notably have focused on early childhood.
For instance, Freeman et al.14 explored change in
the Vineland Adaptive Behavior Scales in chil-
dren with ASD and found that daily living skills
improved with age. In addition, results indicated
that children with IQs at or above 70 improved at
a faster rate than children whose IQs were below
70.14 Similarly, in a longitudinal study of daily
living skills in preschoolers with ASD, Green and
Carter15 found a linear increase in daily living
skills over a 3-year period, with lower IQ scores
and higher levels of autism symptoms associated
with slower gains. In a recent study of children
with high-functioning autism, daily living skills
improved over time, although the rate of change
slowed as children entered adolescence.16 Taken
together, these studies suggest that daily living
skills improve during early childhood and into
adolescence, although the rate of change slows
over time, and that the presence of an intellectual
disability (ID) further slows the rate of growth.
Questions remain, however, regarding whether
daily living skills continue to improve through ado-
lescence and adulthood and the extent to which ID
may influence these later-life trajectories.

The literature on daily living skills for adults
with other types of IDD may offer insights into
possible patterns of change in daily living skills
for those with ASD. For example, Esbensen et
al.17 explored functional abilities (housekeeping,
personal care, meal-related activities, and mobil-
ity domains) over a nine-year period in a sample

of individuals with IDD, including a large sub-

JOURNAL OF THE AMERICAN ACADEMY OF CHILD & ADOLESCENT PSYCHIATRY
VOLUME 51 NUMBER 6 JUNE 2012

ded for Anonymous User (n/a) at Florida International University – Florida state con
For personal use only. No other uses without permission. Copyright

sample of individuals with Down syndrome
(DS). Results indicated that housekeeping skills
improved over time, whereas personal care and
mobility skills declined over the same period.
Improvements in housekeeping skills were fast-
est for younger individuals and declines in per-
sonal care skills were fastest for older individu-
als.17 However, this study did not examine the

ossibility of curvilinear change. It may be that
aily living skills improve for individuals with

DD during adolescence and early adulthood but
ecline later in adulthood. The present study
xamined this hypothesis by using latent growth
urve modeling to test for linear and curvilinear
hange in daily living skills for adolescents and
dults with ASD as well as for similarly aged
ndividuals with DS.

resent Study
he primary aim of the present study was to

nvestigate the longitudinal course of daily living
kills in a large, community sample of adoles-
ents and adults with ASD. Daily living skills
ere measured on four occasions over a 10-year
eriod, allowing for an examination of linear and
urvilinear change. Furthermore, due to the wide
ange of ages of participants in our study (10 –52
ears at Time 1), we were able to explore the
ffects of the age of the individual with ASD
termed “child age”) in addition to ID status on
nitial level of daily living skills as well as change
n daily living skills over time. Residential status
f the individual with ASD (living with parent
s. not living with parent) also was examined as
time-varying covariate. To provide a bench-
ark for interpreting trends among the adoles-

ents and adults with ASD, a secondary aim of
he present study was to examine change in daily
iving skills among similarly aged individuals

ith DS, again measured on four occasions over
10-year period. Although we did not conduct a
irect comparison given differences in the age
etween samples, we explored change in the DS
ample to provide additional context for inter-
reting scores in the ASD group.

Based on past studies documenting that au-
ism symptoms and behavior problems tend to
ecome less severe during adolescence and
dulthood,2 we hypothesized that there would
e concomitant improvements in daily living
kills over the course of the study. However,
ased on recent work by Taylor and Seltzer3

indicating that the rate of improvement in autism

623www.jaacap.org
sortium from ClinicalKey.com by Elsevier on January 04, 2018.

©2018. Elsevier Inc. All rights reserved.

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SMITH et al.

symptoms and behavior problems slows after
entering adulthood, we also hypothesized that
the change in daily living skills would be curvi-
linear; that is, that the rate of change would
decrease over time. Next, consistent with past
studies showing that daily living skills improve
with age for children with ASD,14,15 we hypoth-
esized that there would be a significant associa-
tion between age and initial level of daily living
skills. Given the association between intelligence
and daily living skills in other samples,10,13,18 we
hypothesized that having an ID would be associ-
ated with a lower initial level of daily living skills.
Based on the association between ID and growth in
daily living skills in studies of younger children
with ASD,14,15 we hypothesized that having an ID
would be associated with slower change in daily
living skills for adolescents and adults with ASD.

Regarding our secondary aim, based on past
work showing gains over time in functional
abilities for individuals with ID,17 we hypothe-
sized that there would be improvements in daily
living skills for individuals with DS. We also
hypothesized that individuals with DS would dis-
play curvilinear change, or a slowing of improve-
ment, consistent with findings that individuals
with DS are at risk for dementia as they age.19,20

METHOD
Autism Sample Participants
Participants were drawn an ongoing, multi-wave, lon-
gitudinal study of 406 individuals with ASD and their
families, the Adolescents and Adults with Autism
study (AAA).21 The present study focused on four of
eight points of data collection, Times 1, 4, 7, and 8.
Families were recruited via agencies, schools, diagnos-
tic clinics, and media announcements. At entry into the
AAA study, families met three criteria: the family
included a child 10 years of age or older; the child had
received a diagnosis of ASD from a medical, psycho-
logical, or educational professional; and scores on the
Autism Diagnostic Interview—Revised (ADI-R)22

were consistent with the parental report of an ASD.
Of the original sample of 406 individuals, nine

were excluded from the present study, as they did
not have complete data on activities of daily living at
Time 1. Excluded cases were not significantly differ-
ent from the full sample in child age, sex, family
income, or parental education. The individuals with
ASD in the present study ranged from 10 to 52 years
of age at the beginning of the study (Time 1: mean �
21.84 years, SD � 9.32 years; Time 4: mean � 26.25 years,
SD � 9.36 years; Time 7: mean � 29.87 years, SD � 9.19

years; and Time 8: mean � 31.23 years, SD � 9.02). The

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ajority of the sample was male (73%) and 70% of the
ample had a comorbid diagnosis of ID.

When the AAA study began in 1998, 65% of the
ndividuals with ASD lived with their families and
5% lived away from home. An increasing proportion
f individuals moved away from the family home at
ach subsequent point of data collection, such that at
imes 4, 7, and 8, 56%, 45%, and 47% were co-residing
ith their families, respectively. About half (52.2%) of
arent respondents had at least a bachelor’s degree,
nd the median annual household income was $50,000
o $59,000 in 1998 to 1999. The majority of participants
93%) were white. Most respondents were mothers
96.5%), with 14 fathers participating (3.5%).

There were no significant differences between fami-
ies who participated in all waves of data collection and
amilies with missing data in child sex or ID status.
owever, consistent with age-related morbidity and
ortality, families of older individuals were more likely

o end study participation than were their younger
ounterparts (F � 3.78, p � .05). Importantly, there were

no significant associations between complete study par-
ticipation and daily living skill scores at Time 1.

Down Syndrome Sample Participants
To benchmark changes in daily living skills in individ-
uals with ASD, a sample of individuals with DS was
drawn from a linked longitudinal study of 461 indi-
viduals with ID.23 Families were included in the study
f they met two criteria: the mother was between 55
nd 85 years of age and the son or daughter lived at
ome with her. Of the 461 target children in the study,
69 of the sons or daughters had a diagnosis of DS. Of
hese cases, 2 were excluded from the present study as
hey had missing data on activities of daily living at Time
. The individuals with DS in the present study ranged
rom 15 to 56 years of age at Time 1 (mean � 31.61 years,
D � 7.19 years). The majority were daughters (60.5%),
nd 28.8% of mothers had at least a bachelor’s degree.
he majority of participants (92%) were white.

When this longitudinal study began, all of the
ndividuals with DS lived at home with their families.

owever, some individuals changed residences at
ach subsequent point in data collection, such that at
imes 4, 7, and 8, 94%, 81%, and 92% were co-residing
ith their families, respectively. Families who partici-
ated in all waves of data collection were not signifi-
antly different from families with missing data on child
ex, but families of older individuals were more likely to
nd study participation than families of younger individ-
als (F � 6.94, p � .05). As with the ASD sample, there
as no association between complete study participation

nd Time 1 daily living skills scores.

Procedure and Measures
Procedures and measures were identical for both the

ASD and DS samples. At each time point (Times 1, 4,

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7, and 8), mothers completed self-administered ques-
tionnaires and participated in a 2- to 3-hour in-home
interview. For the ASD sample, data collection be-
tween Times 1 and 4 occurred an average of 5.00 years
apart. Approximately 3.45 years occurred between
Times 4 and 7, and 1.99 years occurred between Times
7 and 8. For the DS sample, an average of 4.44 years
elapsed between Times 1 and 4, 4.47 years between
Times 4 and 7, and 1.72 years between Times 7 and 8.

Independence in activities of daily living was mea-
sured using the Waisman Activities of Daily Living
Scale (W-ADL; Maenner, Smith, Hong, Makuch,
Greenberg, and Seltzer, unpublished data, 2011) (Table
1). Parent respondents rated their son or daughter’s
level of independence on 17 items covering the do-
mains of personal care, housekeeping, and meal-
related activities. Each item was rated on a three-point
scale of independence 0 (does not perform the task at
all), 1 (performs the task with help), or 2 (performs the
task independently); and items were summed. Coeffi-

TABLE 1 Waisman Activities of Daily Living Scale

1. Making his/her own bed
2. Doing household tasks, including picking up around the

house, putting things away, light housecleaning, etc.
3. Doing errands, including shopping in stores
4. Doing home repairs, including simple repairs around

the house, non-technical in nature; for example,
changing light bulbs or repairing a loose screw

5. Doing laundry, washing and drying
6. Washing/bathing
7. Grooming, brushing teeth, combing and/or brushing

hair
8. Dressing and undressing
9. Toileting

10. Preparing simple foods requiring no mixing or cooking,
including sandwiches, cold cereal, etc.

11. Mixing and cooking simple foods, fry eggs, make
pancakes, heat food in microwave, etc.

12. Preparing complete meal
13. Setting and clearing table
14. Drinking from a cup
15. Eating from a plate
16. Washing dishes (including using a dishwasher)
17. Banking and managing daily finances, including

keeping track of cash, checking account, paying bills,
etc. (Note: if he/she can do a portion but not all circle
‘1’ with help.)

Note: Instructions that accompanied the items: “Next we would like to
know about your son or daughter’s current level of independence in
performing activities of daily living. For each activity please tell me the
number which best describes your son/daughter’s ability to do the
task. For example, Independent would mean your son/daughter is
able to do the task without any help or assistance.”
2 � independent or does on own; 1 � does with help; 0 � does not

do at all.

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cient � values for the total score in the ASD sample
ranged from 0.90 to 0.94 for Times 1, 4, 7, and 8; for the
DS sample, the � values ranged from 0.91 to 0.93.

Child characteristics of age (continuous) and ID
status (1 � yes, 0 � no) were included as predictors of

aily living skills scores in the ASD sample analyses.
rocedures for assessing the presence of ID in our
ample have been reported in detail in previous stud-
es24 and involved a clinical consensus process using
nformation drawn from sources including direct cog-
itive testing and educational records. Residential sta-

us (0 � co-residing with parent, 1 � not co-residing
ith parent) was also included as a time-varying

ovariate.

Data Analysis
To address our primary aim, we used latent growth
curve (LGC) modeling to examine daily living skills
over a 10-year period. LGC modeling integrates indi-
vidual growth modeling (i.e., hierarchical linear mod-
eling) and structural equation modeling (SEM) ap-
proaches25 and provides estimates of mean structure
intercept and slope), reflecting the average starting
oint for all individuals and average rate of change.26

It is also possible to model nonlinear change; by
adding a quadratic parameter to a model that already
includes an intercept and linear slope, the growth
trajectory becomes curvilinear. In a purely linear
model, the rate of change is presumed to be constant
over time; in contrast, a quadratic latent curve model
allows the rate of change either to increase or decrease
over time. As an example, the magnitude of change in
repeated measures may be larger in earlier years than
in later years.26 The addition of a quadratic trend also
lters the interpretation of the linear slope, such that
he linear slope coefficient is changed to reflect the
nstantaneous rate of change at a specific point in
ime.25 If the coefficient for the quadratic factor is
egative, then the trajectory is concave to the time axis.
onversely, the presence of a positive quadratic trend

ndicates that the trajectory is convex to the time axis.25

After preliminary analyses of variance confirmed
the presence of change over time in daily living skills
without controlling for age and ID status, a multivar-
iate LCG model was assessed in which age and ID
status were included as time-invariant predictors and
residential status was included as a time-varying co-
variate. To address our secondary aim of providing an
illustrative benchmark for interpreting the patterns of
daily living skills observed in our ASD sample, we
used LGC modeling to assess change in daily living
skills in a sample of individuals with DS over a similar
period of time. All models were evaluated in terms of
measures of goodness-of-fit using the Mplus modeling
program.27 A satisfactory fit is indicated by a compar-
ative fit index (CFI) close to one and a root mean
square error of approximation (RMSEA) less than or

equal to 0.08.

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SMITH et al.

RESULTS
Primary Aim: Daily Living Skills in Adolescents
and Adults With ASD
Activities of daily living were assessed at 4 time
points (Times 1, 4, 7, and 8) in the ASD sample.
We present means, standard deviations, ranges,
and intercorrelations among study variables in
Table 2. Figure 1 depicts a mixed-effects regres-
sion model showing individual scores by age and
intellectual disability status. By the end of the
study, the average score for the group was 20.59
(SD � 8.08) on a scale in which a score of 34
reflects complete independence. Only 16.5% of
the sample had scores of 30 or above at Time 8.
Scores at each time point were significantly cor-
related with scores at all other time points.

We evaluated an LGC model of daily living
skills that specified quadratic growth over time
and included age and ID status as predictors of
all latent factors. Residential status (co-residing
vs not co-residing) also was included as a time-
varying covariate (Figure 2). In this model, the
latent intercept, linear slope, and quadratic trend
were indicated by daily living scores at Times 1,
4, 7, and 8. The factor loadings for the intercept
factor were all set to 1. The loadings for the linear
slope factor were fixed at 0, 5.0, 8.45, and 10.44,
reflecting the average length of time between
waves of data collection. The loadings for the
quadratic slope factor were the linear values
squared.

This model displayed excellent fit [�2 (15, n �
406)� 12.69, p � .63; RMSEA� 0.00; CFI � 1.0].
There was a significant positive linear slope (est. �
1.25, SE� 0.19, p�.001) and a significant negative
quadratic trend (est.� �0.07, SE� 0.02, p � .001).
However, the linear trend in a model that in-
cludes a quadratic trend is interpreted as the
instantaneous rate of change. This means that for
different snapshots of time, the rate of change
may be different. As such, to determine how
daily living skills were changing across time, we
also examined the values for the linear slope
when time was centered at Times 4, 7, and 8,
respectively. At Time 4, the linear trend was
positive (est.� 0.56, SE� 0.07, p�.001), but at
Time 7 the linear trend was nonsignificant (est. �
0.07, SE �0.15, p � .57). At Time 8, however,
there was a significant negative linear trend
(est.� �0.89, SE� 0.27, p � .001). Taken together,
these findings suggest that, on average, scores
were increasing at Times 1 and 4 but were no

longer significantly changing at Time 7. By Time

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TA

T T T T C ID M R N *

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DEVELOPMENTAL TRAJECTORIES AND AUTISM

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8, scores were beginning to decrease. In other
words, daily living skills were improving during
adolescence and the early 20s, plateaued around
the time of the late 20s, and started to decline
during the early 30s. However, we encourage
some caution in interpreting the exact nature of
change for older individuals, as the majority
of the sample was under the age of 30 at the end
of the study.

There also were significant relationships be-
tween age, ID status, and the latent factors. Age
was positively associated with the intercept of
daily living skills, with older individuals having
higher scores at the start of the study (��0.24, p
� .001). Age also was associated with the linear
factor (� � �0.31, p � .01) and the quadratic
factor (� � 0.22, p � .10), although this associa-
tion with the quadratic factor was not significant
at the .05 level. These age effects suggest that
older individuals displayed a faster rate of cur-
vature; that is, they were declining at a faster

FIGURE 1 Change in Waisman Activities of Daily Livin
sample, individual and group trajectories. Note: Quadrat

rate. In addition, ID status was a significant s

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predictor of the intercept of daily living skills,
with individuals with ID having lower initial
levels of daily living skills at the start of the study
(�� �0.52, p � .001). Having an ID was also

egatively associated with the linear factor (� �
�0.20, p � .01), suggesting that individuals with
D were gaining skills at a slower rate than
ndividuals without ID. Residential status was a
tatistically significant covariate of daily living
kills at Times 1 and 8, with coresidence between
arent and child associated with lower scores.

econdary Aim: Daily Living Skills in Individuals
ith Down Syndrome

o explore trajectories of daily living skills in the
S sample, daily living skills were assessed at

our time points (Times 1, 4, 7, and 8). We present
eans, standard deviations, ranges, and intercor-

elations among study variables in Table 3. Fig-
re 3 depicts a mixed-effects regression model

-ADL) scores over time for autism spectrum disorder
ge2) mixed-model paramaterizations displayed.

g (W
ic (a

howing individual scores by age. By the end of

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SMITH et al.

the study, the average score for the group was
23.83 (SD � 6.33) on a scale in which a score of 34
reflects complete independence. Only 19.6% of
the sample had scores of 30 or above at Time 8.
Scores at each time point were significantly cor-
related with scores at all other time points.

FIGURE 2 Latent growth curve model of Waisman
Activities of Daily Living (ADL) scores for individuals with
autism spectrum disorders (N � 397). Note: ID �
intellectual disability. *p � .05, ��p � .01, ���p �
.001, †p � .10.

TABLE 3 Descriptive Statistics and Intercorrelations Amo

Time 1 W-ADL Time 4 W-ADL

Time 1 W-ADL 1 (n � 166)
Time 4 W-ADL 0.88*** (n � 124) 1 (n � 125)
Time 7 W-ADL 0.83*** (n � 98) 0.88*** (n � 92
Time 8 W-ADL 0.85*** (n � 66) 0.86*** (n � 63
Child age 0.12 (n � 166) 0.03 (n � 125)
Mean (SD) 22.55 (6.37) 23.69 (6.56)
Range 3–33 2–33

Note: W-ADL � Waisman Activities of Daily Living.

***p � .001.

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We evaluated a LGC model of daily living
skills that specified linear and quadratic growth
over time and included age as a predictor of all
latent factors and residential status included as a
time-varying covariate. Since all individuals in
the DS sample had a diagnosis of ID, disability
status was not included in this model. In this
model, the latent intercept and linear slope were
indicated by daily living scores at Times 1, 4, 7,
and 8. The factor loadings for the intercept factor
were all set to 1. The loadings for the linear slope
factor were fixed at 0, 4.44, 8.91, and 10.63,
reflecting the average length of time between
waves of data collection. The loadings for the
quadratic slope factor were the linear values
squared.

This model displayed good fit (�2 (11, N �
67) � 16.84, p � .11; RMSEA � 0.06; CFI � 0.99).
here was a positive linear slope (est.� 0.86,
E � 0.45), indicating improvement in daily
iving skills over time, although this effect only
pproached statistical significance (p � .058).
mportantly, the average increase of 0.86 point
er year is a clinically significant change. The
uadratic slope factor was nonsignificant (est.�
0.04, SE � 0.04, p � .42), indicating that there
as no curvature in the change in daily living

kills over time. There was a trend for age to be
ositively associated with the intercept of daily

iving skills (p � .10). There were no significant
ssociations between age and the other latent
actors. Residential status was not a significant
ovariate at any of the time points.

DISCUSSION
The present study used LGC modeling to inves-
tigate trajectories of daily living skills for ado-
lescents and adults with ASD. Past research

tudy Variables for Down Syndrome Sample

Time 7 W-ADL Time 8 W-ADL Child Age

(n � 99)
0.92*** (n � 60) 1 (n � 66)

�0.02 (n � 99) 0.01 (n � 66) 1 (n � 167)
23.37 (7.41) 23.83 (6.33) 31.61 (7.19)
1–32 5–33 15–56

ng S

)
)

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examined trajectories of daily living skills for
individuals with ASD during childhood and
early adolescence but not across adolescence and
adulthood. In contrast, the present study in-
cluded a large, community-based sample of indi-
viduals with ASD with a wide age range (10 –52
years), which enabled us to examine the influ-
ence of age as well as ID status on change in daily
living skills well into adulthood. The longitudi-
nal design of the current study addressed an-
other gap in the literature by allowing for an
examination of curvilinear change over a 10-year
period. Finally, the present study included an
additional analysis of trajectories of daily living
skills in a linked longitudinal study of individu-
als with DS, providing a unique opportunity to
understand patterns of change in two different
groups of individuals with IDD.

Most notably, the present study found signif-
icant quadratic change in daily living skills for

FIGURE 3 Change in Waisman Activities of Daily Livin
individual and group trajectories. Note: Linear mixed–mo

individuals with ASD, indicating that skills im- o

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proved during adolescence and the early 20s,
plateaued around the late 20s, and began to
decline in the early 30s. This is consistent with
our hypothesis that there would be improve-
ments in skills for individuals with ASD but that
the rate of change would slow as individuals
aged. Recently, Taylor and Seltzer3 documented
hat the rate of improvement in autism symp-
oms and behavior problems for adolescents with
SD slowed down (or even stopped) after the

ndividuals with ASD exited the secondary
chool system (typically during the early 20s).
aken together, these findings suggest that ado-

escence is a time of growth and improvement for
ndividuals with ASD in a variety of domains,
ut that, on average, the period of improvement
nds by the time such individuals reach 30 years
f age. The slowing of improvement in daily

iving skills is particularly concerning given that
he plateau in gains was not due to ceiling effects

-ADL) scores over time for Down syndrome sample;
aramaterization displayed.

g (W
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SMITH et al.

lished data, 2011). In fact, by the end of the study
period, on average, the individuals with ASD
were failing to perform more than one-third of
the measured daily living skills independently.

Consistent with our hypothesis and with other
studies demonstrating an association between
age and daily skills,14,15 we found that being
older at the start of the study was associated with
higher levels of initial daily living skills. Findings
also indicated that older individuals displayed a
faster rate of curvature; that is, the older an
individual was at the start of the study, the
sooner he or she started to display a plateau and
eventual decline in daily living skills. Also con-
sistent with past studies of daily living skills in
children with ASD,14,15 having an ID was associ-
ated with lower initial levels of daily living skills
and with slower rates of change.

A secondary aim of the present study was to
examine the pattern of change in daily living
skills among individuals with DS to provide a
context for interpreting the pattern of change
observed in our ASD sample. Past research has
shown that adults with DS have higher levels of
daily living skills than individuals with other
intellectual disabilities,17,16 including individuals
with ASD and ID.7,28 Findings from the present
study indicated that individuals with DS show a
different pattern of change in daily living skills,
with these adults continuing to gain skills across
adulthood. The slowing of improvement in daily
living skills for adults with ASD may contribute
to the poorer adult outcomes observed in indi-
viduals with ASD compared with peers with DS
who had similar levels of intellectual functioning.7

The limitations of the present study point to
areas for future research. The majority of individ-
uals in our sample were white and middle-class,
highlighting a need to examine these skills in
more diverse groups. Similarly, although we
controlled for residential status, it may be that
other environmental contexts (e.g., type of day
activity) also influence change in daily living
skills. It also is important to note that the sample
size for the DS group was small for conducting
growth curve analyses, which leaves the possi-
bility that more nuanced patterns of change may
be observed in larger groups of adults with DS.
In addition, the majority of the DS sample were
already adults at the start of the study, thus
limiting the extent to which direct comparisons

between the two samples would be informative;

JOURN
630 www.jaacap.org

Downloaded for Anonymous User (n/a) at Florida International University – F
For personal use only. No other uses without permissi

a sharper increase in skills may have been ob-
served in the DS group, had we measured these
skills earlier in development. Although our ob-
servation of differences between the ASD and DS
groups was entirely exploratory and descriptive,
it does raise an intriguing question for future
investigation. Finally, the present study is limited
by the use of use of a categorical variable mea-
suring ID status, as IQ scores were not available
for all participants.

This study has important implications for fu-
ture research and clinical practice. By the end of
the study period, all individuals with ASD were
adults but, on average, these adults had not
achieved independence in many of the daily
living skills that we measured. However, as other
researchers have noted,13,29 daily living skills are
ess tied to the core symptoms of autism than
ther aspects of functioning, such as socialization
r communication, and thus may be more ame-
able to change. The improvement in daily living
kills for individuals with ASD into the late 20s
ikewise suggests that it may be possible for daily
iving skills to be gained at later points in devel-
pment, even as skills in other areas plateau. It
ill be critical for future research to explore what

nvironmental factors may be associated with
ontinued gains of daily living skills for adults
ith ASD as well as the best practices for teach-

ng these skills. Although some attention has
een given to developing behavioral and phar-
acological interventions to improve daily living

kills in younger children with ASD,30,31 new
research is needed to develop strategies for sup-
porting gains in daily living skills for individuals
with ASD at later points in the life course. &

Accepted March 7, 2012.

Drs. Smith, Maenner, and Seltzer are with the Waisman Center,
University of Wisconsin–Madison.

This research was supported by National Institute of Health (NIH)
grants R01 AG08768 (M.M.S.), T32 HD07489 (M.M.S.), and P30
HD03352 (M.M.S.).

The authors are extremely grateful to the families who participated in
this study; without their generous commitment, this research would not
have been possible.

Disclosure: Drs. Smith, Maenner, and Seltzer report no biomedical
financial interests or potential conflicts of interest.

Correspondence to: Leann E. Smith, Ph.D.,1500 Highland Avenue,
Waisman Center, Madison, WI 53705; e-mail: [email protected]
wisc.edu

0890-8567/$36.00/©2012 American Academy of Child and
Adolescent Psychiatry

DOI: 10.1016/j.jaac.2012.03.001

AL OF THE AMERICAN ACADEMY OF CHILD & ADOLESCENT PSYCHIATRY
VOLUME 51 NUMBER 6 JUNE 2012

lorida state consortium from ClinicalKey.com by Elsevier on January 04, 2018.
on. Copyright ©2018. Elsevier Inc. All rights reserved.

DEVELOPMENTAL TRAJECTORIES AND AUTISM

Downloa

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25. Willett JB, Sayer AG. Using covariance structure-analysis to
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Psychol Bull. 1994;116:363-381.

26. Bollen KA, Curran PJ. Latent Curve Models: a Structural Equation
Perspective. Hoboken, NJ: Wiley; 2006.

27. Muthen LK, Muthen BO. Mplus User’s Guide. 2nd ed. Los
Angeles, CA: Muthen and Muthen; 2001.

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29. Kraijer D. Review of adaptive behavior studies in mentally
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30. Drahota A, Wood J, Sze K, Van Dyke M. Effects of cognitive
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631www.jaacap.org
sortium from ClinicalKey.com by Elsevier on January 04, 2018.

©2018. Elsevier Inc. All rights reserved.

  • Developmental Trajectories in Adolescents and Adults With Autism: The Case of Daily Living Skills
    • Daily Living Skills in Individuals With ASD
    • Present Study
    • Method
      • Autism Sample Participants
      • Down Syndrome Sample Participants
      • Procedure and Measures
      • Data Analysis
    • Results
      • Primary Aim: Daily Living Skills in Adolescents and Adults With ASD
      • Secondary Aim: Daily Living Skills in Individuals With Down Syndrome
  • Discussion
  • References

International Journal
of Clinical and Health Psychology

www.elsevier.es/ijchp

International Journal of Clinical and Health Psychology (2013) 13, 91−100

1697-2600/$ – see front matter © 2012 Asociación Española de Psicología Conductual. Published by Elsevier España, S.L. All rights reserved.

International Journal of
Clinical and Health

Psychology

Publicación cuatrimestral / Four-monthly publication ISSN 1697-2600

Volumen 13, Número 2
Mayo – 2013

Volume 13, Number 2
May – 2013

Director / Editor:
Juan Carlos Sierra

Directores Asociados / Associate Editors:
Stephen N. Haynes
Michael W. Eysenck

Gualberto Buela-Casal

ORIGINAL ARTICLE

Prediction of treatment outcomes and longitudinal analysis in
children with autism undergoing intensive behavioral intervention

Javier Virues-Ortega*,a, Víctor Rodríguezb, C.T. Yua

aUniversity of Manitoba and St. Amant Research Centre, Canada
bFundación Planeta Imaginario, Spain

Received September 4, 2012; accepted March 18, 2013

*Corresponding author at: University of Manitoba, Psychology Department, P518 Duff Roblin Bldg., 190 Dysart Road,
MB R3T Winnipeg, Manitoba, Canada.
E-mail address: [email protected] (J. Virues-Ortega).

Abstract Outcome prediction is an important component of treatment planning and prognosis.
However, reliable predictors of intensive behavioral intervention (IBI) have not been clearly
established. IBI is an evidence-based approach to the systematic teaching of academic, social,
verbal, and daily living skills to individuals with autism spectrum disorder. Incorporating
longitudinal analysis to IBI outcome studies may help to identify outcome predictors of clinical
value. Twenty-four children with autism underwent on average two years of IBI and completed
language, daily living skills, cognitive, and motor assessments (Early Learning Accomplishment
Profile and the Learning Accomplishment Profile-Diagnostic, 3rd edition) every six months. We
used multilevel analysis to identify potential longitudinal predictors including gender, age,
intervention intensity, intervention duration, total intervention time, and pre-intervention
functioning. Results indicated that total intervention time, pre-intervention functioning, and
age caused the greatest increase in goodness-of-fit of the longitudinal multilevel models.
Longitudinal analysis is a promising analytical strategy to identify reliable predictors of the
clinical outcome of IBI.
© 2012 Asociación Española de Psicología Conductual. Published by Elsevier España, S.L.
All rights reserved.

KEYWORDS
Autism;
Predictors;
Applied behavior
analysis;
Quasi-experiment
(interrupted
time-series with
one group)

Resumen La predicción de resultados de tratamiento es un componente importante de la
planificación clínica. No obstante, no se han hallado predictores fiables de los efectos de la in-
tervención conductual intensiva en personas con trastorno del espectro autista. La incorpora-
ción de análisis longitudinales a la investigación sobre resultados de tratamiento en este área
puede contribuir a la identificación de predictores con valor clínico. En el presente estudio se
evaluaron las habilidades verbales, cognitivas y de la vida diaria (Early Learning Accomplish-
ment Profile y Learning Accomplishment Profile-Diagnostic, 3ª ed.) de 24 niños con trastorno del
espectro autista en un programa de intervención conductual intensiva. Las evaluaciones se rea-
lizaron cada seis meses y durante un periodo medio de intervención de dos años. Mediante

PALABRAS CLAVE
Autismo;
predictor
Análisis aplicado
de la conducta;
Cuasi-experimento
(serie temporal
interrumpida con
un grupo)

92 J. Virues-Ortega et al.

Autism spectrum disorder (ASD) is a pervasive developmental
disorder that affects 1 to 2.5% of children (Baio, 2012). A
number of comprehensive psychosocial interventions for
people with ASD have been developed for which preliminary
evidence exists. These include the Early Start Denver model
(ESDM, Dawson et al., 2010), the Treatment and Education
of Autistic and Related Communication Handicapped
Children (TEACCH, Welterlin, Turner-Brown, Harris, Mesibov,
& Delmolino, 2012), and intensive behavioral intervention
based on the UCLA Young Autism Project model and applied
behavior analysis (IBI, Lovaas, 1987). Although there is no
single approach to treatment for all individuals with ASD,
IBI based on applied behavior analysis is among the few
approaches to treatment that have been tested extensively
using clinical trial methodology (Rogers & Vismara, 2008;
Virués-Ortega, 2010; Wetherby & Woods, 2006).

Applied behavior analysis is devoted to the experimental
study of socially significant behavior as a function of
environmental and social variables, and is the branch of
experimental psychology that supports the conceptual
framework of IBI (Luiselli, Russo, Christian, & Wilczynski,
2008). IBI is a comprehensive and evidence-based approach
to the systematic teaching of behavioral, verbal, cognitive,
and social repertoires to individuals diagnosed with ASD
(Howlin, Magiati, & Charman, 2009). Treatment typically
involves over 20 weekly hours of one-to-one teaching
incorporating multiple learning trails and specific programs
for targeted behavioral goals. Teachers program hundreds
of learning trials per day featuring discrimination training,
prompting, generalization, and other reinforcement-based
procedures known to facilitate the acquisition of new skills
in individuals with and without disabilities (Miltenberger,
2011). The IBI curriculum integrates complex sequences of
programs from basic attending or vocalizing skills, up to
complex verbal, social, and problem-solving skills (Lovaas,
2002).

Over 20 independent trials have been conducted which
jointly suggest that IBI has moderate to large effects on
daily living skills, cognitive functioning, language, and
social behavior (Foxx, 2008; Remington et al., 2007; Virués-
Ortega, 2010). The field of IBI has shown a considerable
growth as suggested by the increasing number of service
providers and certified professionals (Shook & Johnston,
2011).

Parents of children undergoing IBI and other evidence-
based interventions frequently want to know whether their
child will be able to attend school without special support,
what areas of behavioral functioning – whether motor,
social or cognitive – are likely to improve as a consequence
of treatment, and what intervention intensity and duration

may be optimal for their child. Until recently, outcome
research had been of little assistance to respond to these
and other questions pertaining to the longitudinal
progression of children undergoing treatment.

While the evidence available strongly suggests that some
individuals benefit significantly from IBI and other
approaches to treatment, participant and intervention
characteristics associated with greater intervention effects
are not well understood. The wider literature of treatment
outcomes in ASD has examined a range of mediating and
moderating factors that could, potentially, be established
as clinically valuable predictors. These include pre-
intervention IQ, treatment duration and intensity, family
characteristics, age at intervention onset, social initiation
skills, and structural dismorphologies of the central nervous
system. The scant literature available on these factors
have been reviewed by Rogers and Vismara (2008) who
concluded that “The current intervention research focus on
main effects models provides little information about who
does well in which treatments and why” (pp. 28-29).

Age, pre-intervention functioning, and intervention
intensity have been examined in the narrower literature of
IBI outcome predictors. Studies that have examined the
role of age at the onset of IBI have shown that the earlier
the intervention, the greater the intervention effect. For
instance, Granpeesheh, Dixon, Tarbox, Kaplan, and Wilke
(2009) found that children below seven years at treatment
onset mastered more behavioral objectives every month
than children who started IBI intervention above that age.

The studies that have examined pre-intervention
functioning as a predictor of treatment outcome have not
always been consistent in their findings. Perry et al. (2008)
examined progress of children with ASD that received IBI
services by comparing standardized assessments at the
beginning and end of the service. Children were classified
as having either higher, intermediate, or lower functioning
at intake based on their Vineland Adaptive Behavior
Composite score. The higher functioning group made
substantial gains (∼20 IQ increments) relative to the other
two groups. By contrast, Ben-Itzchak, Lahat, Burgin, and
Zachor (2008) reported that pre-intervention IQ (normal,
borderline, low) did not predict the IQ gains after a year of
IBI in a group of 81 young children with ASD and
developmental disabilities.

More evidence has been accrued on the effects of
intervention intensity. However, findings remain
inconsistent. Taking IQ as a prototypical outcome (Table 1),
Makrygianni and Reed (2010) in a correlational study did
not find any effects of intensity – similar results were found
by Sheinkopf and Siegel (1998). Virués-Ortega (2010)

análisis multinivel se examinaron posibles predictores longitudinales incluyendo sexo, edad,
intensidad y duración de la intervención, tiempo total de intervención y nivel de funcionamien-
to previo a la intervención. Los resultados indicaron que el tiempo total de intervención, el
funcionamiento previo y la edad causaban los mayores incrementos en bondad de ajuste de los
modelos longitudinales. El análisis longitudinal es una estrategia analítica prometedora en la
identificación de predictores fiables de la efectividad de la intervención conductual intensiva.
© 2012 Asociación Española de Psicología Conductual. Publicado por Elsevier España, S.L.
Todos los derechos reservados.

Prediction of treatment outcomes and longitudinal analysis in children 93

reported no effects of intensity on IQ in a pooled analysis
of 19 experimental IBI studies. Finally, Reed, Osborne, and
Corness (2007) established a moderate effect of intensity
in a small trial on IBI using the Psychoeducational Profile as
outcome. In summary, treatment intensity has not been
established as a consistent predictor of IBI intervention
effects.

Longitudinal modeling of intervention outcomes may
help to establish intervention predictors more firmly than
traditional pre-post assessments. Longitudinal analyses
are able to fit the mathematical functions followed by
outcome trajectories of individual clients over a period of
time. By doing so longitudinal analysis maximizes the
statistical power of regression models aiming at meaningful
outcome predictors. For instance, if IBI effects were to
follow a non-linear progression, rather than a linear
trajectory, it may be possible to establish the role of a
particular predictor more accurately through longitudinal
multi-level analyses suited to specific non-linear
mathematical functions (Singer & Willett, 2003).
Furthermore, predictors identified based on time-series
spanning the treatment duration, as opposed to pre-post
assessments, may strengthen the clinical utility of the
predictor. For example, pre-intervention functioning could
be a strong predictor of treatment outcomes during the
first year of treatment, but not during the second.

IBI operates through a package of systematic teaching
strategies which are expected to provide the individual
with an increasing set of cognitive and behavioral resources
that will in turn offset, to various extents, the behavioral
excesses and deficits that are characteristic of ASD and
other developmental disabilities. Being a training-based
and goal-directed approach to intervention, IBI may lead to
some degree of behavioral gains for as long as the
intervention is in place. Longitudinal analysis of IBI may
help to identify distinct treatment gain itineraries across
subjects and tie those to specific predictors. For instance,
it may be possible that individuals starting at a higher pre-
intervention level of functioning benefit more from IBI but
reach an asymptote (ceiling) sooner than individuals that
start at a lower level of functioning. The longitudinal
predictors of IBI effects shall be greatly informative, albeit,
they have been rarely explored in the literature. There are
several longitudinal analyses that feature patterns of
change in individuals with ASD (Dietz, Swinkels, Buitelaar,
van Daalen & van Engeland, 2007; Jonsdottir et al., 2007;
Magiati, Moss, Charman, & Howlin, 2011). Nonetheless,
these analyses are constrained by the number of longitudinal

assessments (three or less); the number of treatment
outcomes (e.g., Dietz et al. only reported IQ); and the data
analysis strategy (e.g., no multilevel analyses).

This article describes growth patterns of motor, cognitive,
verbal, daily-living, and social skills in a sample of children
with ASD admitted into a home-based IBI program managed
by trained behavior analysts and delivering 20 to 40 weekly
hours of intervention. We used the children’s performance
in standardized assessments conducted periodically to
longitudinally create curves charting the rates and
asymptotes of various behavioral repertoires. Subsequent
analyses were conducted to test the impact of several
personal and intervention-related predictors on the
longitudinal growth of IBI outcomes. The present analysis
may help to enhance the prognostic information available
to families and clinicians by determining the extent to
which specific client- and treatment-related variables more
closely predict treatment outcome over the duration of the
intervention.

Method

Participants

Twenty-four children diagnosed with ASD (Age: Mean =
50.05 months, SD = 28.3; Gender: 21 boys and 3 girls)
admitted to the IBI program of Fundación Planeta
Imaginario (Barcelona, Spain) participated in the study.
An a priori power analysis indicated that a total sample
size of 15 was required to detect large effects (Cohen
effect size = 1). Therefore, our sample would suffice to
identify moderate to large effect sizes. A priori power
analysis assumptions were based on the pooled effect
size of 20 trials on IBI using IQ reported by Virués-Ortega
(2010) (Pooled effect size = 1.19). Participants were
recruited consecutively and were not excluded based on
their age or pre-intervention functioning at the time of
referral. All participants received a diagnosis of ASD from
an external medical consultant based on the diagnostic
criteria of the Diagnostic and Statistical Manual of Mental
Disorders, 4th edition text revised. Diagnosis was
supported by standardized assessments of autism
including either the Autism Diagnostic Interview-Revised
(ADI-R) or the Autism Diagnostic Observation Schedule-
Generic (ADOS-G) (Le Couteur, Haden, Hammal, &
McConachie, 2008). Further personal characteristics are
presented in Table 2.

Table 1 Effect of treatment intensity on IQ in intensive behavioral intervention outcome studies.

Study Sample Intensity range Analysis Effect
sizea (h/week) size

Makrygianni & Reed (2010) 86 15-30 Correlational (Pearson r) .22
Sheinkopf & Siegel (1998) 11 21-32 Correlational (Pearson r) −.06
Virués-Ortega (2010) 340 12-45 Meta-regression .01

Note. Effects reported as Cohen d effect sizes. a Sample size of the intervention group.
*All effect sizes were non-significant, p > .05.

94 J. Virues-Ortega et al.

Instruments

Fine and gross motor, cognitive, language, self-care and
social skills were assessed by means of the Early Learning
Accomplishment Profile (E-LAP; Glover, Priminger, &
Sanford, 1988; Peisner-Feinberg & Hardin, 2001) and the
Learning Accomplishment Profile-Diagnostic, 3rd edition,
(LAP-D; Hardin, Peisner-Feinberg, & Weeks, 2005). The
E-LAP and LAP-D scores are developmental age values
expressed in months. The score range is 0 to 36 for the
E-LAP and 36 to 72 for the LAP-D. If a participant achieved
the upper limit of the score range of E-LAP, the assessment
would be repeated with the LAP-D, which would then
continue to be used as the means of standardized assessment
every 6-month period until treatment was discontinued. In
order to control for potential ceiling effects in our data, if
a participant reached the LAP-D ceiling, assessment could
be repeated one additional time to inform maintenance
(provided that the individual would continue to receive
services through the program for the next six-month
period).

Both the E-LAP and the LAP-D have a high level of
inter-rater reliability, internal consistency, and
convergent validity with IQ (Fleming, 2000; Hardin et al.,
2005; Long, Blackman, Farrell, Smolkin, & Conaway,
2005; Peisner-Feinberg & Hardin, 2001). The test-retest
reliability of both instruments is reportedly excellent,
ranging between .93 and .99 (Peisner-Feinberg & Hardin,
2001, Hardin et al., 2005). Practice effects were unlikely,
as exposure to materials and tasks during the assessment
was minimal (few trials); and prompting, reinforcement,
and correction strategies were not present during the
assessment. The Spanish version of the E-LAP and the
LAP-D materials were used in the present study. The
LAP-D was validated in a representative sample of
Spanish-speaking children (Hardin et al., 2005). No
Spanish validation of the E-LAP is currently available.

Nonetheless, test scoring is performance-based – there
are no standard scores.

Both instruments have been used frequently as
standardized assessments in intervention studies with
individuals with ASD (e.g., Ganz, Simpson & Corbin-
Newsome, 2008). Moreover, the construct validity of E-LAP
and LAP-D is supported by items screening all diagnostic
areas of ASD (e.g., “initiates on play activities,” “responds
correctly when asked to show a toy,” “inflexible and rigid
in behavior”), items informing non-pathognomonic clinical
features of autism (e.g., motor functioning), and items
covering developmentally relevant skills (e.g., matching
skills). In summary, the E-LAP and LAP-D were considered
adequate for the present analysis due to their likely
resilience to practice effects; excellent stability; excellent
convergent validity with intellectual assessment measures;
and relevance to the clinical, adaptive, and behavioral
features of ASD.

Procedure

Participants were admitted consecutively to an IBI program
within the period May 2006 through January 2011. This
program was an official international replication site of the
UCLA Young Autism Project model and affiliated with the
Lovaas Institute (2011). At the onset of intervention,
participants received an average of 31.87 weekly hours (SD
= 10.11, range 15 -47.30) of home-based systematic
teaching following the UCLA young autism model of service
delivery and curriculum (Lovaas, 2002). Average treatment
duration was 21.87 months (SD = 14.38, range 5.33-58.57).
In keeping with all IBI bonafide programs, in addition to the
hours of formal intervention, incidental teaching and
practice goals were operating during most waking hours
(parents and caregivers acted as active co-therapists).
One-to-one teaching was delivered by trained tutors that
were supervised on a weekly basis by licensed psychologists

Table 2 Characteristics of the study sample.

Pre-test Post-test
(N=24) (N= 24)

Age in months, M±SD 51.91±27.31 69.46±27.26
Gender (male:female) 23:1
Ethnicity (% Caucasian) 100%
Social class,a % high 100%
IQ,b M±SD 74.50±13.98 91.50±16.86
Skills mastered in selected areas,c M±SD
Attending (max. 19) 13.04±4.34 19.16±3.05
Imitation (max. 27) 7.84±8.41 19.92±7.24
Matching (max. 13) 6.02±7.48 13.08±6.08
Basic labeling (max. 13) 12.44±5.33 31.21±19.88
Independent play (max. 15) 3.76±4.76 11.72±6.00
Interaction with peers/adults (max. 25) 2.28±3.82 11.60±9.06

Note. aEstimated by parental education and professional background. bWechsler Preschool and Primary Scale of Intelligence, 3rd ed.;
Bailey Scales of Infant Development, and Merrill-Palmer Scales of Mental Tests. cNumber of skills mastered by area (Lovaas Institute
Midwest, 2010).
M = mean; SD = stardard deviation.

Prediction of treatment outcomes and longitudinal analysis in children 95

with a background in behavior analysis. Parents received
weekly or bi-weekly progress updates, and supervision and
specific routines that required their involvement in order
to ensure the consistency of the interventions across
contexts and caregivers. Intervention was individualized
and comprehensive; and targeted motor, behavioral, daily-
living, verbal, cognitive, and social skills. Goals were
informed by a standardized curriculum composed of over
850 skills organized in 45 broad clinical areas (e.g., reading,
self-control skills). These goals are informed by
developmental sequences of typically developing children
(Luiselli et al., 2008) and include skills that are instrumental
for the acquisition of more complex repertoires (e.g.,
matching skills, imitation). Teaching sessions were delivered
via one-to-one teaching with gradual transition to group
activities and natural contexts. Transition to natural social
contexts was emphasized after mastery in one-to-one
teaching format. Decision-making in terms of hour allocation
and treatment discontinuation weighted a number of
factors including availability of school support, progress
achieved, family priorities, and treatment costs. Typically,
individuals that showed a persistent asymptote in their
learning achievements or that became independent at
school were assigned a reduced number of hours in
preparation of service discontinuation (for details on the

IBI curriculum see Lovaas, 2002). The current program was
in line with the guidelines for responsible conduct published
by the Behavior Analyst Certification Board (2010).

All participants underwent standardized assessments
with the E-LAP or the LAP-D prior to the intervention and
approximately every six months into the program (average
data points per participant 3.8, range 2-6). The selection,
administration, and correction of instruments followed the
guidelines by Jurado and Pueyo (2012).The research
assistants conducting the standardized assessments were
not involved in the administration of treatment and were
not familiar with the hypotheses of the study.

Data analysis

Figure 1 shows the individual growth trajectories of
participants for the eight E-LAP and LAP-D outcomes. Visual
inspection of the data plots over time suggests that
trajectories accelerated away from the start point shortly
after the intervention commenced while progression
decelerated as the individual approached a personal or
scale ceiling. Therefore, individual trajectories did not
follow a linear progression but rather an exponential
negative growth. Exponential negative trajectories are
composed formally of a negatively accelerated curve,

Figure 1 Trajectories of Early Learning Accomplishment Profile and Learning Accomplishment Profile-Diagnostic scores over time.
Fitted exponential negative curves (solid black line) were obtained for individuals above (dotted grey lines) and below (solid grey
lines) the median of pre-intervention functioning at baseline in each domain.

Gross Motor Fine Motor Pre-writing Cognitive

Receptive

80

80

60

60

40

40

20

20

0

0

0 20 40 60 0 20 40 60 0 20 40 60 0 20 40 60

Expressive Self-care Social

Intervention Time (Months)

Sc
al

e
sc

or
es

Sc
al

e
sc

or
es

96 J. Virues-Ortega et al.

ending in an upper asymptote. According to the formal
attributes of the data we selected a multilevel regression
model based on the following exponential negative
function:

Yij = αi – (αi – π0i) e –π TIMEij

Where αi represents the upper asymptote, π0i represents
the lower end of the trajectory, and π1i represents the
slope of the curve. Figure 2 illustrates different exponential
negative patterns of change over time for various parameter
values.

Multilevel models provide two distinct levels of analysis:
level-1 and level-2. The structural parts of the level-1
submodel contain two level-1 parameters and one within-
person variance component (εij). The first parameter, known
as intercept (π0i), represents the initial status of an
individual i in the population. The second parameter, known
as slope (π1i), represents the rate of change for the
individual i in the population by unit of time. Therefore,
level-1 establishes individual change overtime. By contrast,
the parameters at level-2 do not represent individual
variation, but average level of the outcome in the
population. Specifically, the parameters at level-2 represent
the average outcome level in the population corresponding
to the intercept and slope values at level-1. At level-2, the

pattern of change is not examined in terms of time, as is
the case at level-1, but rather, in terms of a predictor. In
summary, there are four parameters at level-2: γ00 is the
population average of level-1 intercept with level-2
predictor value of 0, γ01 is the population average difference
in level-1 intercept for a 1-unit variation in the predictor,
γ10 is the population average of the level-1 slope when the
predictor equals 0, and finally, γ11 is the population average
difference when the predictor equals 1. γ00 and γ10 are
baseline parameters while γ01 and γ11 estimate the association
of the predictor with the initial status and the rate of
change of the longitudinal progression, respectively. The
model also provides a residual variance value for the
intercept (σ02), the slope (σ12), and the covariance among
these two (σ01). For multilevel models incorporating two
predictors we will also report γ12 and γ12, which represents
the population’s average variation in the outcome level for
a one-unit increment in the predictors 1 and 2 (level-1),
respectively (for more details in multilevel analysis refer to
Singer & Willet, 2003). The estimation of the predictor
coefficients at level-2 is presented formally below:

π0i = ϒ00 + ϒ01 (PREDICTORi – PREDICTOR) + ξ0i
π0i = ϒ10 + ϒ11 (PREDICTORi – PREDICTOR) + ξ1i

According to this model, individual growth parameters
(π0i, π1i) across children will be a function of population
average values (γ00, γ10), and population variance components
(ξ0i, ξ1i) represented by residual variances (σ02, σ12) and
covariance (σ01).

We estimated a series of multilevel models using different
sets of predictors in order to select models that would
maximize goodness-of-fit for a given outcome when
compared with an unconditional baseline model (model
with no predictors). This was accomplished in two sequential
multilevel analyses. In the first sets of models we examined
the impact of time-based predictors (intervention duration
in weeks, total hours of intervention – weekly hours of
interventions multiplied by weeks of intervention – and age
in months). We would then select the model incorporating
the single time-based predictor with best goodness-of-fit
for each of the eight outcomes under analysis. Subsequently,
we calculated a new set of two-predictor models
incorporating the predictor previously selected and a
specific personal factor that, when added, resulted in
further increases in goodness-of-fit. The personal factors
examined for each of the eight outcomes were age (if not
selected in the preceding step), gender, and pre-intervention
functioning. Two levels of pre-intervention functioning
were established using the median value at baseline as cut-
off point. The rationale for selecting these predictors is
twofold: a) they are all common individual/treatment
characteristics readily accessible to the clinician, and b)
they have been examined in previous IBI studies although
not in the context of a longitudinal analysis. Longitudinal
predictors that changed overtime (intervention duration,
total intervention duration, age) were re-calculated each
time an individual was assessed.

The Akaike information criterion (AIC) and the Bayesian
information criterion (BIC) were computed as goodness-of-
fit parameters for all one- and two-predictor models. Lower
AIC and BIC values are indicative of better fitting. The best

100

75

50

25

0

E(Y)

Time

0 2 4 6 8 10

α = 100

π1 = .3

π1 = .2

π1 = .1

π0 = 15

Figure 2 Exponential patterns of change based on different
parameter values.

Prediction of treatment outcomes and longitudinal analysis in children 97

fitting two-predictor model was selected for each outcome
and was fully reported. All analyses were conducted with
STATA version 11 (STATA Corporation, College Station, TX)
and its GLAMM program for multi-level analysis. A .05 level
of significance was used throughout. Results have been
reported according to the guidelines by Hartley (2012).

By comparing the goodness-of-fit of one- and two-
predictor models with an unconditional model, we aimed
to establish which factors would better explain the
longitudinal variation in our data. This analysis will help to
determine prominent trajectories of intervention outcomes
based on specific predictors. This strategy also serves the
purpose of suggesting causality in the absence of a control
group, similar to the way in which dose-response relations
inform causation (see a discussion relevant to this point in
Arjas and Parner, 2004). Namely, the causation inference
would be supported if intervention intensity (e.g., total
intervention hours at each time of assessment) is indeed
superior in its ability to increase the fit of the model
relative to an arbitrary time-dependent predictor
(individuals’ age).

Results

The examination of the goodness-of-fit parameters of
multilevel regression models showed that one-predictor
and two-predictor models had a superior fit than
unconditional models for every domain of the E-LAP and
the LAP-D. AIC and BIC goodness-of-fit parameters of all
models are reported in Table 3. Total intervention time

(hours per week multiplied by weeks of intervention) was
the single predictor with the highest favorable impact on
goodness-of-fit for all E-LAP and LAP-D outcomes. Other
time-based predictors including individuals’ age and
intervention duration in months had a positive impact in
the model’s fit, but did so to a lesser extent than total
intervention time in all eight outcomes.

Further improvements in goodness-of-fit were achieved
in two-predictor models. Keeping total intervention time as
the first factor, we examined the fit of regression models
incorporating age, gender, or pre-intervention level as a
second predictor. Age was the second most efficient
predictor in terms of improving fit of the regression models
for gross motor function, receptive language, self-care,
and social behavior; while pre-intervention level was the
second most efficient predictor for regression models using
fine motor function, prewriting, cognitive, and expressive
language (Table 3). The regression models of domains
assessing motor, daily living, and social skills (gross motor
function, fine motor function, self-care and social behavior)
achieved better fitting than regression models of language-
related domains (prewriting, receptive language, expressive
language, cognitive).

Table 4 presents the best fitting two-predictor multilevel
model for each E-LAP and LAP-D outcome. Both predictors
were statistically significant (p < .001) for every outcome.
Rate of change attributable to total intervention time in
hours (γ11) ranged from .004 to .009 (outcome average
increase by predictor unit). Coefficient magnitudes for age
in months (γ12) as a predictor ranged from .391 to .514.
Finally, coefficients for the dichotomous variable pre-

Table 3 Goodness-of-fit parameters of all one- and two-predictor multilevel models of change for Early Learning
Accomplishment Profile and Learning Accomplishment Profile-Diagnostic scores.

Goodness-of-fit (AIC, BIC)

GMF FMF PWR COG RLG ELG SFC SBH

Unconditional model 761.56 774.54 761.31 774.29 809.53 822.50 782.89 795.87
800.47 813.44 761.91 774.88 755.07 768.05 747.29 760.26
One-predictor models
Intervention duration 715.86 721.17 779.29 746.27 763.16 732.19 706.67 706.42
731.43 736.70 794.86 761.84 778.73 747.77 722.24 721.99
Total intervention time 693.33 691.12 744.67 720.03 737.42 712.48 677.15 688.53
708.59 706.38 759.93 735.29 752.68 727.74 692.41 703.79
Age 716.29 731.43 776.85 761.27 769.47 740.37 708.01 721.45
731.86 747 792.42 776.84 785.03 755.94 723.58 737.02
Two-predictor models
Age 664.01 673.20 727.92 710.50 724.67 702.71 651.35 676.02
681.81 691 745.72 728.30 742.47 720.52 669.16 693.82
Gender 691.93 690.19 745.08 719.15 735.78 711.83 675.18 687.74
709.73 707.99 762.88 736.95 753.59 729.63 692.99 705.55
Pre-intervention level 675.54 661.39 715.56 705.10 726.15 698.89 661.74 676.27
693.35 679.20 733.36 722.90 743.96 716.70 679.55 694.07

Note. AIC = Akaike information criterion; BIC = Bayesian information criterion; COG = Cognitive; ELG = Expressive language; FMF = Fine
motor function; GMF = Gross motor function; PWR = Prewriting; RLG = Receptive language; SBH = Social behavior; SFC = Self-care.
Intercept constant; slope is established as the intervention duration in months. Level-2 and level-3 best fitting models by outcome are
highlighted.

98 J. Virues-Ortega et al.

intervention level (γ12) ranged from 22.971 to 35.669. Figure
1 portrays fitted curves based on an exponential negative
growth of subsamples above and below the median value of
pre-intervention level for each of the eight standardized
outcomes.

Discussion

Multilevel regression analyses based on an exponential
negative growth trajectory indicated that total intervention
duration in hours was the single predictor with the highest
contribution to the model fit for all outcomes when
compared with unconditional models. This finding suggests
that a subtle characteristic of the intervention – a
combination of both treatment intensity (weekly hours)
and treatment duration (total weeks of treatment) –
optimizes the fitting of individual trajectories to a specific
mathematical function for the duration of the intervention
and across a range of standardized outcomes. Improvements
in model fitting caused by duration alone did not improve
goodness-of-fit to the extent achieved by total intervention
time as a single predictor (Table 3). Therefore, our data
suggest that both intensity and duration, as represented by
total intervention time, remained important factors of
intervention gains regardless of pre-intervention functioning
or age. Finally, total intervention time remained significant
(p < .001) in all final two-predictor multilevel models (Table
4). When used in one-predictor models, pre-intervention
functioning was inferior to total intervention time in terms
of improving goodness-of-fit for all outcomes.

We tested the impact of pre-intervention functioning in
the goodness-of-fit of multilevel models incorporating two
predictors. Including pre-intervention level as a second
predictor, improved goodness-of-fit for all outcomes in the
two-predictor models (Table 3). For four of the eight
standardized outcomes examined (fine motor, pre-writing,
cognitive, expressive language), pre-intervention level was

the personal characteristic (above age and gender) that
generated the greatest improvement in model fit. Pre-
intervention level was a significant factor (p < .001) in the
final two-predictor models for fine motor, pre-writing,
cognitive and expressive language domains (Table 4).
Interestingly, these outcomes involved more complex
cognitive abilities relative to the remainder of E-LAP and
LAP-D outcomes (e.g., fine vs. gross motor; expressive vs.
receptive language; cognitive vs. self-care).

Our results suggest that individuals starting intervention
at a lower level in a given outcome were more likely to
follow an asymptotical growth as opposed to individuals
that initiated treatment with a higher level of performance
(cf. fitted curves on Fig. 1). The visual inspection of the
individual longitudinal trajectories in our sample suggests
that pre-intervention level is a plausible predictor of
individuals’ performance over the course of the intervention
to the extent that a bimodal pattern seems obvious in most
of the outcomes (e.g., Cognitive, Social). Bimodal
trajectories in our dataset are consistent with the distinction
between most and least positive responders to IBI discussed
by Remington et al. (2007). The visual examination of
individual trajectories on Figure 1 suggests that the pre-
intervention median is an acceptable cut-off point as
attested by the predictors significance and fit gains in
models that incorporated this factor. A more sophisticated
strategy to determine the cut-off point would have required
asymmetrical assignment of participants above and below
the cut-off points, which may have harmed statistical
power and increase the potential for type II error. Therefore,
future analyses would benefit from samples sizes larger
than ours.

Learning processes have been found to accommodate
well to exponential negative or logistic patterns of change
(e.g., Hicklin, 1976). The possibility remains, however, that
non-linear patterns of growth found in the present study
may have been caused by measurement-dependent factors,
like inadequate scaling assumptions or excessive ceiling

Table 4 Multilevel models for Early Learning Accomplishment Profile and Learning Accomplishment Profile-Diagnostic scores
change over the duration of intensive behavioral intervention.

Goodness-of-fit (AIC, BIC)

GMF FMF PWR COG RLG ELG SFC SBH

Fixed effects
Intercept (γ00) 7.44 13.91** 10.97* 8.87 −3.00 5.46 -4.45 .52
Intervention, hours (γ11) .00** .01** .01** .01** .01** .01** .01** .01**

Age, months (γ12) .51** – – – .51** – .57** .39**

Pre-intervention levela (γ12) – 28.43** 35.67** 24.27** – 22.97** – –
Variance components
Level-1: Within-person (σε2) 25.32 23.32 47.64 32.91 47.79 28.39 16.54 18.90
Level-2: Intercept (σ02) 89.91 60.71 158.79 123.89 204.10 122.62 155.74 159.19
Level-2: Slope (σ12) .07 .19 .25 .33 4.37 .40 .10 .32
Level-2: Covariance (σ01) .46 2.37 -4.16 2.76 .11 .98 2.81 3.92

Note. COG = Cognitive; ELG = Expressive language; FMF = Fine motor function; GMF = Gross motor function; PWR = Prewriting; RLG =
Receptive language; SBH = Social behavior; SFC = Self-care. Goodness-of-fit parameters and domain abbreviations in Table 3. *p < .01;
**p < .001. aPre-intervention levels above and below the median at pre-test.

Prediction of treatment outcomes and longitudinal analysis in children 99

effects in the psychometric instrument used to establish
treatment outcomes. These potential shortcomings,
however, may have had little impact on the validity of the
predictors, which is independent from the specific shape of
the longitudinal growth.

The contributions of our study are primarily methodological
and to a lesser extent practical. As discussed in our
introduction, the literature on the effect of intensity and
other predictors on the outcome of IBI have yielded
inconsistent results. This inconsistency may be explained,
at least to some extent, by non-linear variations of the
predictor and the outcome overtime. Therefore, longitudinal
studies may enhance our ability to examine outcome
predictors with sufficient statistical power. Our results
provide evidence in this direction being the first study to
use this methodology in the context of IBI intervention.

In terms of the applied relevance of our findings, future
longitudinal studies expanding the present analysis could
eventually provide the basis for evidence-informed clinical
decision-making. Namely, clinicians could combine various
predictors available at the beginning of the intervention
(e.g., pre-intervention functioning in an specific area, age,
expected treatment intensity and duration) to estimate the
progress of the client over the next years, which could in
turn inform the decision-making of family, caregivers and
health decision-makers in terms of treatment planning and
resource allocation.

In summary, the present analysis helps to identify the
general features of the longitudinal progression of children
with autism undergoing IBI. Our results suggest that
increased intervention time, lower age at intervention
onset, and higher pre-intervention functioning might be
associated with greater IBI outcomes for intervention
programs of up to four years in duration. The present study
provides the methodological basis for predictor identification
in the longitudinal analysis of IBI.

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Two Factor Model of ASD Symptoms

One of the key factors in determining whether an individual has Autism Spectrum Disorder (ASD) is in their social and communication skills. Individuals who are diagnosed with ASD have delayed joint attention, eye gazing, and other social interactions such as pointing (Swain et al., 2014).

Joint attention is an important social skill to master because it is a building block for developing theory of mind which, helps us to understand other’s perspectives. Korhonen et al. (2014) found that individuals with autism have impaired joint attention. However, some did not show impairment in joint attention, which lead to evidence that suggests there are different trajectories for joint attention. One suggestion as to why Korhonen et al. (2014) found mixed results, is that there is evidence that joint attention may not be directly linked to individuals with ASD since they were unable to find a difference in joint attention between ASD and developmentally delayed (DD) individuals. Another suggestion for the mixed results, is individual interest in the task vary. Research has found that while individualized studies are beneficial in detecting personal potential and abilities, it would be difficult to generalize the study in order to further research to ASD as a whole (Korhonen et al., 2014). In addition to joint attention, atypical gaze shifts is a distinguishing factor in individuals with ASD. Swain et al. (2014) found the main difference between typically developing (TD) and ASD individuals in the first 12 months of life is in gaze shifts. Individuals that were diagnosed with ASD earlier had lower scores on positive affect, joint attention, and gaze shifts, however those diagnosed later differed from typically developing (TD) only in gaze shifts. It is not until 24 months that later onset ASD individuals significantly differ from their TD peers, by displaying lower positive affect and gestures (Swain et al., 2014). These findings may lead to other ASD trajectories.

Another defining characteristic of ASD is the excess of restrictive patterns of interest and repetitive motor movements. These patterns and movements often impaired the individual from completing daily tasks. Like joint attention and gaze shifts, these repetitive movements and patterns of interest have different trajectories (Joseph et al., 2013). Joseph et al. (2013) found that individuals with high cognitive functioning ASD engage in more distinct and specific interests and less in repetitive motor movements than individuals with lower cognitive functioning ASD. Another finding showed that at the age of two, repetitive motor and play patterns were more common than compulsion. By the age of four all these behaviors increased however, repetitive use of specific objects was found to be less frequent in older children than younger children. This finding suggests that the ritualistic behaviors and motor movements may present themselves differently based on the age of the individual (Joseph et al., 2013).

Joseph et al. (2013), Korhornen et al. (2014), and Swain et al. (2014) all defined key characteristics of an ASD individual and explains the different trajectories of each characteristic. The difficulty with the trajectories is that it is specific to each individual, some symptoms may worsen while others remain stable. It is also difficult to generalize finding with small sample sizes (Joseph et al., 2013).

Discussion Questions:

1. Korhonen et al. (2014) did not use preference-based stimuli to look for joint attention and did not separate high- from low-functioning ASD individuals. Do you think that there could be a difference in level of motivation from each group? If so, how do you think this could change the results?

2. Swain et al. (2014) found that early and late onset of ASD did not differ in their social skills scores at the age of 12 months. If we know that their social skills do not differ then, is there another factor that would allow diagnosis of late onset ASD to be diagnosed at an earlier point in development?

3. Joseph et al. (2013) explains that it is difficult to assess the trajectories of ASD with a small sample size however, how do you think that their findings still help advance the research on ASD?

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