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Enhancing the Validity of a Quality of Life Measure for Autistic People


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Accurate measurement of quality of life (QoL) is important for evaluation of autism services and trials of interventions. We undertook psychometric validation of the World Health Organisation measure—WHOQoL-BREF, examined construct validity of the WHO Disabilities module and developed nine additional autism-specific items (ASQoL) from extensive consultation with the autism community. The sample of 309 autistic people was recruited from the Adult Autism Spectrum Cohort-UK. The WHOQoL-BREF had good psychometric properties, including criterion, convergent, divergent and discriminant validity. The WHO Disabilities module showed adequate construct validity and reliability. The ASQoL items form a unitary factor of QoL, with one global item. Future studies can use the WHO measures alongside the ASQoL items to measure QoL of autistic people. Electronic supplementary material The online version of this article (10.1007/s10803-017-3402-z) contains supplementary material, which is available to authorized users.
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Journal of Autism and Developmental Disorders (2018) 48:1596–1611
1 3
Enhancing theValidity ofaQuality ofLife Measure forAutistic People
HelenMcConachie1,5 · DavidMason1· JeremyR.Parr2· DeborahGarland3· ColinWilson4· JacquiRodgers2
Published online: 29 November 2017
© The Author(s) 2017. This article is an open access publication
Accurate measurement of quality of life (QoL) is important for evaluation of autism services and trials of interventions.
We undertook psychometric validation of the World Health Organisation measure—WHOQoL-BREF, examined construct
validity of the WHO Disabilities module and developed nine additional autism-specific items (ASQoL) from extensive
consultation with the autism community. The sample of 309 autistic people was recruited from the Adult Autism Spectrum
Cohort-UK. The WHOQoL-BREF had good psychometric properties, including criterion, convergent, divergent and discri-
minant validity. The WHO Disabilities module showed adequate construct validity and reliability. The ASQoL items form
a unitary factor of QoL, with one global item. Future studies can use the WHO measures alongside the ASQoL items to
measure QoL of autistic people.
Keywords Autism· Quality of life· Public mental health· Measurement properties
Measuring quality of life (QoL) of autistic people has been
an under-researched area (Burgess and Gutstein 2007) but
is gaining more empirical attention as awareness of the need
to understand the lives of autistic adults increases (Chiang
and Wineman 2014; Howlin and Magiati 2017; van Heijst
and Geurts 2015). QoL is conceptualised as a multi-faceted
construct that taps into different domains of life experience
(Felce and Perry 1995; Harper 1998). Most studies have
demonstrated that the QoL of autistic people is significantly
lower than in general population samples (Jennes-Coussens
etal. 2006; Kamp-Becker etal. 2010; Kamio etal. 2013)
with a few exceptions (Hong etal. 2016; Moss etal. 2017).
It is important to validate existing measures and explore
their psychometric properties with autistic people (Ikeda
etal. 2014; Feldhaus 2015) so that the conclusions drawn
from analyses are not called into question (Cottenceau
etal. 2012). The measures used in studies of QoL of autis-
tic people have not been specifically validated with autistic
people (Ayres etal. 2017). Some have been validated for
use with related populations (e.g. the comprehensive qual-
ity of life scale (Cummins 1997), which has parallel ver-
sions for the general population and those with intellectual
disability). Mason and colleagues examined the validity
of the WHOQoL (BREF) with a large sample (n = 370)
of autistic adults and concluded that the original factor
structure, as defined by the WHO, was adequate for use
with autistic people (Mason etal. submitted-b). However,
three items had loaded differently from those reported in
the original structure (‘how well are you able to concen-
trate?’, ‘are you able to accept your bodily appearance?’,
and ‘how well are you able to get around?’) and a mental
health item did not load onto any factor (‘how often do you
have negative feelings such as blue mood, despair, anxi-
ety, depression?’). One explanation for this is that items
were interpreted differently by autistic people from the
original meaning; for example, the question about physical
Electronic supplementary material The online version of this
article ( contains
supplementary material, which is available to authorized users.
* Helen McConachie
1 Institute ofHealth andSociety, Newcastle University,
NewcastleuponTyne, UK
2 Institute ofNeuroscience, Newcastle University,
NewcastleuponTyne, UK
3 National Autistic Society Resource Centre,
NewcastleuponTyne, UK
4 Autism Advocate, Sunderland, UK
5 Institute ofHealth andSociety, Newcastle University, Sir
James Spence Institute level 3, Royal Victoria Infirmary,
NewcastleuponTyneNE14LP, UK
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1597Journal of Autism and Developmental Disorders (2018) 48:1596–1611
1 3
mobility was interpreted in terms of ease of getting around
in the environment rather than referring to physical restric-
tions (Mason etal. submitted-b).
A lack of established measurement validity is a prob-
lem for QoL research with autistic people for three rea-
sons. Firstly, it calls into question judgements about
how to improve QoL for autistic people that are based
on research using tools lacking established validity. For
example, a recent study found that all WHOQoL-BREF
domains are negatively predicted by having a mental
health diagnosis and that some domains are positively
predicted by receiving support and being in a relationship
(Mason etal. submitted-a). If these, and other predictors
(e.g. perceived informal support, Renty and Roeyers 2006;
early diagnosis, Kamio etal. 2013; perceived stress, Hong
etal. 2016, and social outcome, Moss etal. 2017) are used
to inform targets for interventions for autistic people, the
appropriateness of the targets is called into question if the
outcome measure used in identifying the target is not suf-
ficiently valid. Thus, a reliable and valid measure of QoL
is required to measure progress and outcomes in inter-
vention and treatment studies. Secondly, lack of a valid
measure undermines comparisons of QoL with the general
population (Jennes-Coussens etal. 2006; Kamp-Becker
etal. 2010; Kamio etal. 2013; Mason etal. submitted-a)
or other groups, for example people with Attention Deficit
Hyperactivity Disorder, Disruptive Behaviour, or Affec-
tive Disorder (Barneveld etal. 2014). As such, the often
reported lower QoL reported by autistic people may not
actually reflect worse QoL but may reflect an unsuitable
QoL measure. Finally, aspects of QoL may be interpreted
differently by autistic people. Mason etal. (submitted-b)
report that the mental health item of the WHOQoL-BREF
was commented on by autistic people as conflating dif-
ferent feelings making it very difficult to answer. Simi-
larly, an item about concentration (‘how well are you able
to concentrate?’) was frequently interpreted in terms of
the impact of sensory aspects of the environment (lights,
noise, etc). These findings suggest that measurement of
QoL may be subtly different for autistic people compared
to the general population.
The WHOQoL suite of measures includes an additional
module of items for people with intellectual or physical
disability, the WHO Disabilities module (Power and Green
2010). This measure may have utility for capturing some
important facets of QoL for autistic adults that are not
addressed in the WHOQoL-BREF. It includes questions
capturing perceptions of autonomy, discrimination and
inclusion (Power and Green 2010); however, we expected
that there would be some additional aspects of QoL sali-
ent to autistic people that are not captured by either WHO
QoL measure such as sensory issues, e.g. hypersensitivity
to sound, that are now a part of the diagnostic criteria for
autism (American Psychiatric Association 2013).
Aims oftheStudy
In order to enhance the validity of measurement of quality
of life with autistic people, the present study had two linked
objectives. The first was, with the participation of autistic
adults, to develop autism-specific items to be used in con-
junction with the WHOQoL-BREF and WHO Disabilities
module to measure QoL. The second was to establish the
convergent, divergent, discriminant, and construct validity;
internal consistency; and test–retest reliability of the WHO-
QoL-BREF, WHO Disabilities module and autism-specific
QoL items with autistic adults. The paper is structured in
two stages for the separate objectives.
Methods: Objective 1—Autism‑Specic Item
Summary ofProcess
A flow diagram showing the item development process
can be found in Fig.1. Four discussion groups were con-
ducted with autistic people in the North East of England
(Mason etal. submitted-b) considering the items included
in the WHOQoL-BREF and WHO Disabilities module (see
below). From the themes coded from the transcripts, items
were developed to capture autism-relevant aspects of QoL
not already covered. A Delphi survey and cognitive inter-
views were used to further refine the items, and autistic peo-
ple and autism researchers were invited to comment on the
draft autism-specific items. The final set of additional items
was agreed by the authors.
Consultation with autistic people: Twenty autistic people
were contacted via the National Autistic Society in the
North East of England, and via a drama group for people
with intellectual disability (Males = 13, Females = 7; mean
age = 28.0 years, range 19–45). They attended one of four
discussion groups which took place during summer 2016,
led by a researcher, and two members of the autism commu-
nity. As a group, consultees completed a systematic sorting
task based on Q-sort methodology (Stephenson1953) with
the questions in the WHOQoL-BREF and Disabilities mod-
ule. As well as the physical distribution of sorted questions
into a pyramid shape of boxes according to importance, the
process produced an ongoing ‘think-out-loud’ discussion.
Second, consultees were asked to write down areas of QoL
they thought were not covered by the WHOQoL-BREF or
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1598 Journal of Autism and Developmental Disorders (2018) 48:1596–1611
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Disabilities items. Finally, consultees were shown the full
WHOQoL-BREF/DIS questionnaire on paper, and the group
discussed positive and negative views of the measure. Each
person was thanked for their time and received a shopping
Consultation with Autism Researchers: On two occa-
sions, at autism research meetings, researchers (n = 8 and
Fig. 1 Flowchart for the item
development stage of the study
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1599Journal of Autism and Developmental Disorders (2018) 48:1596–1611
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n = 10) were presented with the draft autism-specific items
and asked to comment on them. Researchers scored items on
their importance and clarity of wording (see Delphi survey
below for scoring scale). Researchers were also asked to
review the terminology used for the draft items, highlight
any concepts that may be difficult for autistic people to inter-
pret and suggest alternative wording.
Research Participants
Cognitive interviews: 15 autistic interviewees (9 men, 6
women; mean age 35.1 years, range 18 to 55) were recruited
from the North East of England via the Adult Autism Spec-
trum Cohort-UK study (ASC-UK,
adultautismspectrum/). This is a research programme about
the life experiences of autistic adults and their relatives/car-
ers. Each interviewee was thanked for their consultation and
time with a shopping voucher.
Delphi survey: For the Delphi survey first round, 192
invitations were sent out to autistic participants via ASC-
UK. These individuals had all previously completed the
WHOQoL-BREF. Participants who could not give informed
consent were also invited to take part, and were represented
by a relative or carer authorised to act on their behalf (20
consented). There were 139 responders (72%; mean age
44.7 years, SD = 14.8). For the second round of the Delphi
survey, 341 invitations were sent out and there were 235
responders (69%; mean age 42.8 years, SD = 14.2).
WHOQoL‑BREF (Harper 1998)
This is a 26 item measure that comprises two global ques-
tions and 4 QoL domains: Physical (7-items, e.g. ‘How well
are you able to get around?’), Psychological (6-items, e.g.
‘To what extent do you feel your life to be meaningful?’),
Social (3-items, e.g. ‘How satisfied are you with the sup-
port you get from your friends?’), and Environment (8-items,
e.g. ‘How satisfied are you with your transport’). A higher
score indicates a greater (better) subjective QoL. Partici-
pants complete the measure based on the two weeks prior to
administration. Cronbach’s alpha demonstrates acceptable
to good internal consistency for each domain: Physical QoL
(0.82), Psychological QoL (0.81), Social QoL (0.68), and for
Environment QoL (0.80) (Skevington etal. 2004).
WHO Disabilities Module (Power andGreen 2010)
The Disabilities module was designed to be administered
to people with physical or intellectual disabilities alongside
either the WHOQoL-100 or WHOQoL-BREF. It has one
global question (‘Does your disability have a negative (bad)
effect on your day-to-day life?’). The other 12 items can
be summed to give an overall QoL score, or scored in 3
domains: Discrimination (3-items, e.g. ‘Do you need some-
one to stand up for you when you have problems?’), Auton-
omy (3-items, e.g. ‘Do you feel in control of your life?’),
and Inclusion (6-items, e.g. ‘Do you feel that your dreams,
hopes, and wishes will happen?’). A higher score indicates
greater subjective QoL. Participants complete the measure
based on the 2 weeks prior to administration. Internal con-
sistency has been shown to be good (Cronbach’s alpha = 0.85
for physical disability, and 0.80 for intellectual disability
(Power and Green 2010)).
Creating theAutism‑Specific QoL Items (ASQoL)
In order to address the first objective (to develop some
autism-specific items to be used in conjunction with the
WHOQoL-BREF and Disabilities module), transcripts of
each discussion group were examined for quotes that sug-
gested either a nuanced understanding of a WHOQoL ques-
tion, a direct suggestion for a new QoL item from a par-
ticipant, or discussion that implied a missing aspect of QoL
particular to the experiences of autism people. The tran-
scripts were read through repeatedly by Authors 1 and 2;
twelve themes were extracted and refined by all the discus-
sion group leaders (see Supplementary material TableS1).
Agreement for the coding of themes (i.e. relative frequency
of application of codes throughout one transcript) of Authors
1 and 2 was calculated as 93.4%. On the basis of the themes,
the authors drafted 11 potential items in similar format to
the WHOQoL-BREF and Disabilities module questions. The
items included concepts about barriers to accessing services,
friendships, sources of support, and sensory issues.
Cognitive Interview Schedule
Cognitive interviews were used to examine the understand-
ing and appropriateness of the proposed autism-specific
items. A semi-structured interview schedule consisting of
standardised prompts was created to explore: (i) compre-
hension of the question, (ii) retrieval of relevant informa-
tion, (iii) a judgement about retrieved information, and (iv)
a response to the question that is intelligible (Boeije and
Willis 2013). The last of these (iv) was assessed by asking
the participant to complete the survey question. For example,
the prompts for one item (‘Do sensory issues in the environ-
ment make it difficult to do things you want to do?’) were: (i)
‘How easy is the question to understand?’, (ii) ‘What does
the question mean to you?’, (iii) ‘The question asks about
sensory issues, what did you think about when you read the
question?’ (iv) ‘How did you arrive at the answer you gave?’,
and (v) ‘Is there anything else unclear about the question?’.
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1600 Journal of Autism and Developmental Disorders (2018) 48:1596–1611
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Delphi Survey About Proposed Autism‑Specific
Two rounds were designed for the Delphi survey, following
methods used in research operationalising QoL for those
with multiple disabilities (Petry etal. 2007). Participants
completed the measures online (about 75%) or on paper. In
round 1, the participant read the proposed QoL item and was
asked, ‘How important is this question?’ responding with a
five point Likert scale (not important, a little, moderately,
very, and extremely important) and then ‘Is this question
clearly worded?’ with a yes or no response. Results were
collated; those items that fell below the threshold for con-
sensus were presented in round 2. In addition, all comments
were read through separately by Authors 1, 2 and 5, and then
discussed together to inform further editing of the wording
of items. In round 2, the participant was presented with the
item from round 1, a short paragraph of text explaining why
the item had been changed, and the amended version of the
item. Participants then answered the questions about the
item’s importance and clarity once more. For both rounds,
there was free text space for comments about each item. A
threshold of 80% consensus (i.e. 80% of participants rated
the item as very or extremely important, and clear) was used
for round 1, and 75% consensus for round 2 (Hasson etal.
Recruitment from ASC-UK: Participants in ASC-UK who
had completed both the ASC-UK registration question-
naire (78 items including demographic information and the
individual’s health and life situation) and the WHOQoL-
BREF were contacted about the study. Participants living
in the North East of England could consent to take part in
the cognitive interview, Delphi survey, and/or psychomet-
ric validation portions of the study; participants living in
the rest of England and Wales could consent to take part in
the Delphi survey and/or psychometric validation. Potential
participants were contacted by letter or e-mail along with a
detailed information sheet, an abbreviated information sheet,
and a consent form indicating the separate parts of the study.
Cognitive interview: Following receipt of the consent
form, Author 2 made contact with the participant and
arranged the interview at a time and place convenient to
them. Seven interviews took place within research prem-
ises, seven took place at the participant’s home, and one in
a quiet and private section of public space. Each interview
was audio recorded and transcribed. The semi-structured
interview schedule described above was followed for each
participant. Cognitive interviews were used iteratively
(Boeije and Willis 2013), with the proposed items being
modified for the last 8 participants in light of revisions
made following the first Delphi survey round.
Delphi survey: Following consent, participants received
round 1 of the Delphi survey in the format of their choos-
ing—either electronically (via Qualtrics), or in paper for-
mat. Additionally each participant was sent a list of all
the items in the WHOQoL-BREF and Disabilities mod-
ule in order to put the new ASQoL items into context.
Participants who had completed round 1, and participants
who returned their consent form subsequently, were sent
round 2 of the survey. After each round, items that were
above the threshold consensus were retained (some small
changes to the wording were made if the cognitive inter-
views or survey free text comments suggested improve-
ment was needed). Questions that did not meet the crite-
ria were given more consideration by the research team,
including autistic community representatives.
Results: Objective 1—Autism‑Specic Item
In Table1 the final nine autism-specific (ASQoL) items
are listed. Three of the items are negatively phrased, hence
are reverse-scored. One of the items was intended as a
‘global’ QoL item about autistic identity.
In round 1 of the Delphi survey, items 2 and 7 (Table1)
were retained without change (importance consensus 80
and 82%; clarity consensus 96 and 98% respectively) (see
Supplementary material TableS2). One item was dis-
carded: ‘Do other people’s stereotyped expectations of
autism have a negative impact on you?’ being rated by
only 66% of respondents as ‘very important’ or ‘extremely
important’. Furthermore, some participants commented
that stereotypes may not be a negative issue if other peo-
ple are unaware of an individual’s autism diagnosis. For
example, “It is not relevant to me as I have not disclosed
my autism to anyone except medical professionals”. The
remaining eight items were modified based on the com-
ments received and sent out to participants for round 2.
After round 2 of the survey, one further item was dis-
carded due to a low importance rating (49%): ‘Do you
feel able to help other people as much as you would like
to?’. In addition, some participants noted the difference
between having the capacity to help and having the oppor-
tunity to do so. They also commented on a lack of clarity
regarding what ‘help’ might entail; for example, “Help can
be interpersonal, involving personal interaction, or task-
based (‘doing things for people’). Autistics can be good at
the latter while being bad at the former”.
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1601Journal of Autism and Developmental Disorders (2018) 48:1596–1611
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Methods: Objective 2—Psychometric
Summary ofProcedure
We aimed to assess:
1. the convergent, divergent, discriminant and criterion
validity of the WHOQoL-BREF measure;
2. the construct validity, internal consistency, reliability
and stability of the WHOQoL-BREF;
3. the factor structure, internal consistency, and reliability
of the WHO Disabilities module;
4. the validity, internal consistency, and reliability of the
new ASQoL items.
Five hundred forty-four participants from ASC-UK were
invited into the validation study. Of these, 426 participants
consented to take part (78.3%) and a total of 309/426 com-
pleted the validation study (72.5%, 153 females (49.5%),
149 males (48.2%), and 5 (1.6%) who reported ‘other’ as
their gender); two participants did not report gender. 31 par-
ticipants (10.0%) reported they had help filling in the meas-
ures; 14 autistic people (4.5%) were represented by a relative
or carer who completed the measures on their behalf; 260
participants (84.2%) reported they had no help completing
the measures, and 4 participants (1.3%) did not answer the
When participants join the ASC-UK they provide infor-
mation about what diagnosis they have, and who made the
diagnosis. The mean age at diagnosis for the present sample
was 37.35years (SD = 16.10). The reported diagnoses were
not verified by the research team. However, participants
completed the Social Responsiveness Scale 2 (SRS2), a
measure of autism severity (Constantino and Gruber 2012).
A score of 52 is considered a cut-off for the presence of
autism; the mean SRS2 total score for the sample was 110.35
(SD = 26.81).
WHOQoL-BREF and WHO Disabilities module, ASQoL
items: see descriptions above.
As the WHOQoL-BREF is scored as 4 domains (Physi-
cal, Psychological, Social, and Environment), a range of
theoretically related secondary measures were included in
the study for the assessment of validity.
Hospital Anxiety andDepression Scale (HADS) (Zigmond
andSnaith 1983)
This is a 14-item measure of self-reported anxiety (7-items)
and depression (7-items). Each item is scored between 0
and 3 with a higher score indicating more severe anxiety
or depression. Depression and anxiety scores range from
0 to 21. Internal consistency is good for anxiety (anxiety,
alpha = 0.80; depression, alpha = 0.76 (Mykletun etal. 2001)
and for a young adult autistic sample: anxiety, alpha = 0.83;
depression, alpha = 0.65 (Uljarevic etal. 2017)). For both
Table 1 Finalised ASQoL items
after two rounds of the Delphi
survey and consultations
Items and scoring can be freely downloaded from
a Indicates item is to be reverse scored; G indicates a global QoL item
1. Do you have enough support from others to make important decisions?
For example, picking a course to study, finding a job, deciding where to live, planning for getting older
2. Can you ‘be yourself’ around your friends/people you know well?
For example, you don’t have to put on an ‘act’
3. How secure do you feel about your financial situation?
That is, that your current sources of income will continue (e.g. benefits, salary, pension etc.)
4. Do you have enough support in your life, if or when you need it, to help you deal with problems?
For example, someone who knows you well and will give advice about social and other problems
5. Are you satisfied with your current friendships?
(i.e. whether you have several, few, or no friends)
a6. Do you feel there are barriers when accessing health services?
For example, staff do not allow you time to answer, or you cannot see the same GP
a7. Do sensory issues in the environment make it difficult to do things you want to do?
For example, supermarket too noisy, public transport too busy, etc
a8. Do you feel there are barriers to your needs being met in ‘official’ situations (e.g. at the benefit’s office,
at work, with your landlord, etc.)?
For example, how other people communicate with you, or share information; feel unable to disclose your
9G. Are you at ease (OK) with ‘Autism’ as an aspect of your identity?
Here, ‘Autism’ means any of the words that refer to the Autism Spectrum
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1602 Journal of Autism and Developmental Disorders (2018) 48:1596–1611
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anxiety and depression scales, scores of 0–7 indicate normal
range, 8–10 is classified as ‘borderline’ suggesting the pres-
ence of depression/anxiety, and 11 or above indicates prob-
able presence of depression/anxiety (Crawford etal. 2001;
Snaith 2003). Participants complete the measure based on
‘how you have been feeling’ in the week prior to completion.
Craig Hospital Inventory ofEnvironmental Factors‑Short
Form (CHIEF‑SF) (Ephraim etal. 2006)
This 12 item measure comprises 5 subscales: policies, physi-
cal/structural, work/school, attitudes/support, and services/
assistance designed to measure barriers faced by people
with disabilities. Items are scored 0–4 (0, never; 1, less than
monthly; 2, monthly; 3, weekly; and 4, daily). A higher
score indicates a greater impact of environmental barriers.
A ‘not applicable’ option is included for those not in work or
school. Internal consistency is excellent (alpha = 0.92; (Liao
etal. 2012)). Participants base their answers on their views
from the year prior to completion.
Interpersonal Support Evaluation List‑12 (ISEL‑12) (Merz
etal. 2014)
This measure contains 12 items which assess the perceived
availability of social support in the general population.
Each item is rated on a 4 point scale ranging from ‘defi-
nitely false’ to ‘definitely true’. Items are totalled to give
a score between 0 and 36, with a higher score indicating
higher perceived availability of social support. The measure
has 3 subscales (each with 4 items; range of scores from 0 to
12): appraisal, belonging, and tangible. Internal consistency
has been found to be acceptable or good for subscales (Cron-
bach’s alpha = 0.71 for the appraisal subscale; alpha = 0.76
for the belonging subscale; and alpha = 0.60 for the tangible
subscale); however, the internal consistency for the over-
all measure has been found to be acceptable (alpha > 0.70)
(Merz etal. 2014).
Comprehensive Quality ofLife Questionnaire—Adult
Version (ComQoL‑A5) (Cummins 1997)
This measure includes two subjective QoL scales with one
question per scale for each of the 7 QoL domains. Partici-
pants respond for each domain on a 5 point Likert scale
(could not be more important, very, somewhat, slightly, and
not at all important) and a 7 point Likert scale (delighted,
pleased, mostly satisfied, mixed, mostly dissatisfied,
unhappy, and terrible). The subjective QoL domains are:
material well-being, health, productivity, intimacy, safety,
community, and emotional well-being. Internal consistency
for the satisfaction scale (alpha = 0.81) and the importance
scale (alpha = 0.69) have been reported (Cummins 1997) for
the general population.
Following consent, participants were sent the seven meas-
ures electronically (via Qualtrics) or via post as they pre-
ferred. Participants were asked to complete and return
measures within 1 month. They received a brief descrip-
tion of each measure and information on the total number
of items, and were invited to contact Author 2 if they had
difficulties completing measures. The final question asked
whether the participant had received assistance filling in
the measures. Demographic data were available through
the ASC-UK registration questionnaire (which records age,
gender, mental health diagnoses, educational qualifications,
etc). Participants were also asked to update their data about
mental health diagnoses, hence the data reported on this are
One month later, participants who had completed the
measures online were re-sent three of the measures (the
WHOQoL-BREF, WHO Disabilities module, and ASQoL
items) in order to examine test–retest reliability.
A favourable ethical opinion for this study was granted
by Wales REC 6 (reference—16/WA/0295) and by
South Central—Oxford C REC (for including adults that
are represented by a relative or carer; reference—16/
SC/0598). A favourable ethical opinion for the ASC-UK
study was granted by Wales Research Ethics Committee 5
Hypotheses andAnalysis
Aim 1: To assess convergent and divergent validity, the spe-
cific hypotheses about relationships between the WHOQoL-
BREF domains and secondary measures were: the HADS
anxiety and depression scores will show the strongest
correlations with the Psychological domain; the ISEL-12
score will correlate most strongly with the Social domain;
the CHIEF-SF will correlate most strongly with the Envi-
ronment domain. To assess discriminant validity, it was
hypothesised that there would be a relationship between
the HADS (cut-offs for presence of anxiety and depression)
and lower scores on each domain of the WHOQoL-BREF.
To assess criterion validity, it was hypothesised that each
WHOQoL-BREF domain would correlate significantly with
the ComQoL satisfaction subscale (and not necessarily with
the importance subscale since WHOQoL-BREF measures
Aim 2: To assess the construct validity of the WHOQoL-
BREF a confirmatory factor analysis was performed. Inter-
nal consistency was assessed with Cronbach’s alpha. Stabil-
ity of scores was assessed at around a 12 month interval.
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1603Journal of Autism and Developmental Disorders (2018) 48:1596–1611
1 3
Aim 3: For the Disabilities module, an exploratory factor
analysis was conducted to examine construct validity of the
measure with autistic people.
Aim le 4: For the ASQoL items, an exploratory factor
analysis was conducted to examine construct validity. Fur-
ther validity analyses examined the correlations between the
ASQoL total and global item scores, and WHOQoL-BREF
For each WHO-related QoL measure, internal consist-
ency was assessed using Cronbach’s alpha, and test–retest
reliability via a second administration at approximately a 1
month interval.
Missing Data
Missing data percentages for the items in: WHOQoL-
BREF ranged from 0.3 to 3.2%; WHO Disabilities module
ranged from 0.6 to 3.9%; ASQOL ranged from 1.0 to 2.3%;
ComQoL ranged from 0.6 to 1.3%; CHIEF-SF ranged from
1.0 to 4.9%; HADS ranged from 1.0 to 2.3%; and ISEL-12
ranged from 1.0 to 2.6%. The WHOQoL-BREF data clean-
ing process was applied to the data set. Subsequently, miss-
ing data were imputed for each measure using estimation
maximisation as this is a robust method of imputation which
outperforms mean substitution (Myers 2011). Participants
that had more than 20% missing data were excluded (n = 3,
leaving a sample of 306 for analysis).
Results: Objective 2—Psychometric
Responders (n = 309) and non-responders (n = 117) were
compared on the following data from ASC-UK registra-
tion: age, gender, Social Responsiveness Scale total (SRS2,
a self-report measure of autism severity (Constantino and
Gruber 2012)), and education level attained (highest for-
mal qualification reported). T-tests were computed to assess
group differences for age and SRS2 total and chi squared
tests to assess differences in gender (male and female only)
and qualifications (none and basic school leaver; advanced
school leaver; degree level). Responders and non-responders
did not differ on age (t(424) = − 0.728, p = 0.467, Cohen’s
d = 0.08), gender (χ2(1, n = 417) = 0.268, p = 0.605, Cram-
er’s V = 0.25), SRS2 total score (t(350) = 0.113, p = 0.910,
Cohen’s d = 0.01), or education qualifications (χ2(6,
n = 417) = 0.964, p = 0.617, Cramer’s V = 0.05) (Table2).
A greater proportion of respondents preferred to com-
plete the measures electronically (n = 231, 75%) rather than
on paper (n = 75, 25%). It was expected that there might
be demographic differences between those who completed
online or on paper, and also those requiring help/not in com-
pleting the measures. Those who completed the measures
online did not differ from those who completed paper ver-
sions on age (t(307) = 0.275, p = 0.784, Cohen’s d = 0.04)
or SRS2 total (t(258) = 0.572, p = 0.568, Cohen’s d = 0.08).
However, they did differ from online responders on qualifi-
cations attained (χ2(6, n = 309) = 15.183, p < 0.001; Cramer’s
V = 0.225); those with lower level qualifications were more
likely to complete the measures on paper. Those respond-
ing electronically were less likely to have help (χ2(1,
n = 304) = 28.018, p < 0.001; Cramer’s V = 0.316). Relatives
or carers acting on behalf of a participant lacking capacity
to respond themselves (n = 14) were more likely to complete
measures electronically.
Descriptive Statistics
Table3 shows the descriptive statistics for the WHOQoL-
BREF and WHO Disabilities module transformed scores
(ranging from 0 to 100); a higher score indicates better sub-
jective QoL. For the ASQoL items the mean transformed
score was 52.88 (SD=12.32). Table3 also shows compari-
sons with normative data for the WHOQoL-BREF (Skeving-
ton and McCrate 2012) and Disabilities module data (Power
and Green 2010)1 and calculation of Cohen’s d effect size
between the present sample and normative data for each
domain. The effect sizes are large (except for Discrimina-
tion) and indicate that autistic people report lower quality
of life. Table3 also shows the proportion (and number) of
participants within one standard deviation of normative data,
more than one standard deviation below normative data, and
more than one standard deviation above normative data.
With the exception of the Environment and Discrimina-
tion domains, more than half of autistic people report their
QoL to be more than one standard deviation below relevant
norms. (Raw scores and Cronbach’s alpha are presented for
the secondary measures CHIEF-SF, HADS, and ISEL-12,
and transformed scores (0–100) for the ComQoL in Sup-
plementary material TableS3.)
1 The normative data presented for the Disabilities module are mean
scores and standard deviations for people with physical disabilities
(Power and Green 2010). Mean domain scores were computed by tak-
ing the mean of each item belonging to that domain. Standard devia-
tions were computed by first finding pooled variance using follow-
ing steps: multiplying the sample size of the item by the variance of
that item; multiplying the sample size of each item by the difference
between the item and domain mean the item belongs to; summing the
previous two steps together for each item in the domain; and dividing
the result by the total sample size of each item in the domain minus
one. The square root of the pooled variance was then calculated to
yield a standard deviation for each domain.
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1604 Journal of Autism and Developmental Disorders (2018) 48:1596–1611
1 3
Psychometric Properties
Construct Validity—WHOQoL‑BREF
An exploratory analysis of the factor structure of the WHO-
QoL-BREF suggested that the four domain model (Mason
etal. submitted-b) was acceptable. Therefore, we conducted
a confirmatory factor analysis with a new sample. Partici-
pants who had taken part in both the present study and the
earlier study (Mason etal. submitted-b) were excluded from
the CFA. This left 120 participants; this sample was aug-
mented with a second sample of more recently recruited
participants from the ASC-UK cohort study who had com-
pleted the WHOQoL-BREF, resulting in a final sample of
N = 328 for the CFA, with a mean age of 38.49 (SD = 13.71;
142 males, 151 females, and 32 participants who recorded a
gender of ‘other’ or did not report gender).
A CFA was conducted using 24 items (items 3–26; the two
global items were excluded) to test the WHO factor structure
(Harper 1998). Maximum Likelihood estimation was used
and the overall fit was acceptable (CFI = 0.830, GFI = 0.852,
RMSEA = 0.077, PCLOSE = 0.001, χ2 = 725.15, df = 246,
p < 0.001). Modification indices were used to improve fit
by covarying the error terms of items, starting with the
highest modification index. After 4 iterations, the model fit
had improved (CFI = 0.902, GFI = 0.886, RMSEA = 0.059,
PCLOSE = 0.006, χ2 = 519.90, df = 242, p < 0.001) (see Sup-
plementary material Fig. S1.) Thus the structural validity of
the WHO factor structure is acceptable for use with autistic
Internal Consistency
Internal consistency was excellent for the overall WHOQoL-
BREF measure (alpha = 0.93) comparable to UK population
data (Skevington and McCrate 2012). Internal consistency
was good for the physical (0.87), psychological (0.84), and
environment (0.84) domains and acceptable for the Social
domain (0.68).
Convergent andDivergent Validity—WHOQoL‑BREF
Pearson’s correlation coefficients were computed between
WHOQoL-BREF domains and secondary measures for the
hypotheses specified above; all tests were two-tailed (see
Table4). In addition, each correlation coefficient was trans-
formed into a z-score to assess the comparative strength
of correlations. The asymptotic covariance was estimated
between each pair of correlation coefficients yielding an
overall z-score and p value (Lee and Preacher 2013).
Table 2 Characteristics and demographic details of the validation sample (n = 309)
Age Range Mean age Standard
Age (years) 18–76 42.96 13.78
Male 18–76 45.71 14.92
Female 20–71 40.80 12.15
Age (by group) nMean age Standard
16–25 42 22.69 1.83
26–40 96 32.86 3.93
41–60 141 50.69 5.62
61+ 30 66.40 4.12
Demographics N%
Educational qualifications
None 21 6.8
Basic school leaver 56 18.1
Advanced school leaver 85 27.5
Bachelor’s degree 58 18.8
Post graduate degree 60 19.4
Other/not reported 29 9.4
Current mental health condition diagnosis
Yes 232 75.1
No 62 20.0
Not reported 15 4.9
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1605Journal of Autism and Developmental Disorders (2018) 48:1596–1611
1 3
As hypothesised, the HADS Depression subscale was
most strongly correlated with the Psychological domain,
and the strength of correlation was significantly greater
than that with the Physical domain (z = 3.985), Social
domain (z = 7.502) and Environment domain (z = 5.306, all
p < 0.001). The HADS Anxiety subscale was most strongly
Table 3 Means and standard deviations for the present sample and normative data, with effect sizes. Proportions of the participants within,
above, and below one standard deviation of normative values for the WHOQoL-BREF and WHO Disabilities module
*Indicates a significant difference between the present sample and normative data, all p < 0.001
a Comparison values are taken from Skevington and McCrate (2012)
b Domain scores were computed by pooling item level mean, standard deviation, and sample size data taken from Power and Green (2010) before
calculating proportions
Data source (n) Physical Psychological Social Environment
Validation data (306) 54.00 (22.74)* 43.89 (20.13)* 41.77 (22.82)* 56.19 (19.49)*
Normative (1324–1328) 76.49 (16.19)* 67.82 (15.56)* 70.52 (20.67)* 68.20 (13.81)*
Cohen’s d 1.14 1.33 1.32 0.71
Discrimination Autonomy Inclusion Total
Disabilities module
Validation data (306) 45.15 (24.81) 58.82 (23.02)* 38.98 (21.94)* 45.49 (19.49)*
Normative (2561–2598) 43.67 (10.19) 66.25 (4.12)* 58.42 (9.90)* 56.88 (8.58)*
Cohen’s d 0.08 0.45 1.14 0.76
Proportion, % (n) Physical Psychological Social Environment
>1 SD below norms 56.5 (173) 68.3 (209) 54.2 (166) 43.8 (134)
Within 1 SD of norms 37.9 (116) 29.7 (91) 44.8 (137) 48.4 (148)
>1 SD above norms 5.6 (17) 2.0 (6) 1.0 (3) 7.8 (24)
Discrimination Autonomy Inclusion Total
Disabilities moduleb
>1 SD below norms 40.2 (123) 52.9 (162) 65.4 (200) 58.2 (178)
Within 1 SD of norms 22.9 (70) 14.4 (44) 23.9 (73) 24.8 (76)
>1 SD above norms 36.9 (113) 32.7 (100) 10.8 (33) 17.0 (52)
Table 4 Correlations between
the WHOQoL-BREF domains
and measures used to assess
Correlations in bold are those hypothesised to be strongest
HADS Hospital Anxiety and Depression Scale, CHIEF-SF Craig Hospital Inventory of Environmental Fac-
tors—Short Form, ISEL-12 Interpersonal Support Evaluation List-12, COMQOL Comprehensive Quality
of Life questionnaire—Adult version
*p < 0.05; **p < 0.01; ***p < 0.001
Physical Psychological Social Environmental
Psychological QoL 0.670***
Social QoL 0.326*** 0.537***
Environment QoL 0.713*** 0.669*** 0.418***
HADS depression − 0.635*** 0.756*** − 0.467*** − 0.590***
HADS anxiety − 0.580*** 0.600*** − 0.305*** − 0.539***
CHIEF-SF − 0.670*** − 0.465*** − 0.265*** 0.668***
ISEL-12 0.315*** 0.455*** 0.538*** 0.500***
COMQOL importance − 0.012 0.215*** 0.115* 0.029
COMQOL satisfaction 0.682*** 0.792*** 0.590*** 0.724***
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1606 Journal of Autism and Developmental Disorders (2018) 48:1596–1611
1 3
correlated with the Psychological domain; however, the
strength of correlation was significantly greater only than
that with the Social domain (z = 6.365, p < 0.001). As
hypothesised, the ISEL-12 total score was most strongly
correlated with the Social domain; however, the strength
of correlation was significantly greater only than that with
the Physical domain (z = 3.898, p < 0.001). Also as hypoth-
esised, the CHIEF-SF was most strongly correlated with
the Environment domain. That correlation was signifi-
cantly stronger than for the CHIEF-SF with the Psycho-
logical domain (z = 5.629, p < 0.001) and the Social domain
(z = 9.063, p < 0.001).
Criterion andDiscriminant Validity—WHOQoL‑BREF
In terms of criterion validity, the ComQoL satisfaction sub-
scale was, as hypothesised, significantly correlated with all
WHOQoL-BREF QoL domains.
For assessing discriminant validity, WHOQoL-BREF
domain scores were related to presence or absence of depres-
sion and anxiety. 3 × 4 MANOVAs were computed using the
HADS cut-off values (normal range, borderline, presence).
Level of severity of Depression had an overall significant
effect on QoL domains (Wilk’s λ = 0.496, F(8,600) = 31.54,
p < 0.001). Level of severity was a main effect on each
domain of the WHOQoL-BREF (all p < 0.001) (see Table5).
Post hoc analyses showed that for Physical, Psychological,
and Environment QoL each increase in HADS Depression
severity resulted in significantly lower QoL. For Social QoL
those with a categorisation of borderline depression had sig-
nificantly lower QoL than those categorised as normal range
but the difference between those categorised as borderline
and those with probable presence of depression was non-
significant (p = 0.243). Similarly, level of severity of Anxiety
had an overall significant effect on QoL domains (Wilk’s
λ = 0.691, F(8,600) = 15.24, p < 0.001). Level of severity
was a main effect on each domain of the WHOQoL-BREF
(Physical, Psychological, and Environment, p < 0.001; Social
QoL, p = 0.001). Post hoc analyses showed that for Physi-
cal, Psychological, and Environment QoL each increase
in HADS severity resulted in significantly lower QoL. For
Social QoL those with a categorisation of presence of anxi-
ety had significantly lower QoL than those categorised as
normal range, but the difference between those categorised
as borderline and those with probable presence of anxiety
was non-significant (p = 0.61).
Construct Validity: Exploratory Factor Analysis
Disabilities Module andASQoL Items
Internal consistency for the Disabilities module was good
(alpha = 0.89). Internal consistency for the Autonomy (0.78)
and Inclusion (0.86) domains was good and was acceptable
for the Discrimination domain (0.69).
Minimum rank factor analysis (MRFA) was used to exam-
ine the underlying factor structure of the Disabilities module
and the ASQoL items (Lorenzo-Seva and Ferrando 2006).
Polychoric correlations were used as this has been shown to
outperform Pearson’s correlations when using ordinal data
(Baglin 2014). Oblique rotation was selected to identify
each factor model because each domain of the Disabilities
module correlated significantly and it was anticipated that
any separate factors arising from the ASQoL items would
also be significantly correlated. Due to space restrictions
only results for the final model of each factor analysis are
outlined below (see Supplementary material TableS4 for
factor loadings.)
Table 5 Means and standard
deviations for each QoL domain
score related to HADS subscale
score cut-offs. MANOVA effect
sizes and post hoc comparisons
are included
For post hoc comparisons: for each domain score each matching superscript letter indicates significant dif-
ference for each pair of values labelled with the same letter
*Indicates all pairings significantly different
Partial eta squared = 0.01 for a small effect, 0.06 for a medium effect, and 0.13 for a large effect
WHOQoL-BREF domains
Physical QoL Psychological QoL Social QoL Environment QoL
HADS depression
Normal range 66.78 (19.92)* 57.62 (16.47)* 53.07 (19.96)a,b 65.63 (17.38)*
Borderline 53.25 (16.34)* 42.90 (12.42)* 36.17 (21.67)a55.13 (15.85)*
Presence 37.94 (18.87)* 26.73 (13.79)* 30.49 (20.13)b44.61 (17.66)*
Partial eta squared 0.32 0.46 0.21 0.23
HADS anxiety
Normal range 76.88 (14.97)* 65.10 (17.20)* 51.24 (19.58)a71.46 (15.32)*
Borderline 61.31 (20.39)* 48.80 (16.46)* 45.67 (22.11) 62.95 (15.45)*
Presence 46.40 (20.54)* 37.40 (17.84)* 38.36 (23.01)a50.55 (18.92)*
Partial eta squared 0.25 0.25 0.05 0.17
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1607Journal of Autism and Developmental Disorders (2018) 48:1596–1611
1 3
Disabilities Module
Both Bartlett’s statistic (1809.3, p < 0.001) and the Kai-
ser–Meyer–Olkin (KMO) test (0.85) suggested good
adequacy for the polychoric correlation matrix. For the
three domain structure given by Power and Green (2010)
a three factor solution emerged as the most robust and
conceptually coherent. However, in this model three ques-
tions related to inclusion loaded with the three items of
the discrimination scale. Both models explained a large
proportion of the variance in the data: the one factor
model yielded an explained common variance of 59.76%
and this was 81.60% for the three factor model. Factor
loadings were all strong, above 0.58 and 0.46 for the one
factor model and three factor model respectively. The
internal consistency of the one factor model was excel-
lent (Cronbach’s alpha = 0.89). Internal consistency for
each factor in this exploratory factor analysis was good
to excellent: autonomy (0.78), inclusion (3 items; 0.80),
and the combined items from discrimination and inclu-
sion (0.83).
ASQoL Items
A one factor solution emerged as the most robust and
coherent solution. Both Bartlett’s statistic (878.3,
p < 0.001) and the Kaiser–Meyer–Olkin (KMO) test (0.82)
suggested good adequacy for the polychoric correlation
matrix. This model yielded an explained common vari-
ance of 66.6%. Item 9 (Are you at ease (OK) with ‘Autism’
as an aspect of your identity?) as expected did not load
with the other items (loading = 0.18) and all other load-
ings were high (> 0.46). This suggested that item 9 can
be conceptualised as a global QoL item. A second factor
analysis was conducted on the eight items (excluding item
9) and this did not substantially change the results. Internal
consistency for the eight items was excellent (Cronbach’s
alpha = 0.82).
To evidence construct validity, the total ASQoL score
(8 items) was used to predict the global QoL item. A
regression analysis with total score as the dependent vari-
able and the global item as the independent variable was
significant (R2 = 0.02, p < 0.01). Total score was a signifi-
cant predictor of global ASQoL standardised (β = 0.15,
p = 0.007).
Both the total score of the eight ASQoL items, and the
global item, were correlated with the WHOQoL-BREF
domains. The total score was significantly correlated with
the Physical domain (r(306) = 0.67, p < 0.001), Psycho-
logical domain (r(306) = 0.67, p < 0.001), Social domain
(r(306) = 0.53, p < 0.001), and Environment domain
(r(306) = 0.79, p < 0.001).
Test–Retest Reliability
One hundred forty-one participants (46%) completed ques-
tionnaires at an interval of 3–5 weeks. Domain scores were
calculated for each measure and intraclass correlation coeffi-
cient (ICC) estimates (along with 95% confidence intervals)
were calculated. ICCs were based on a mean-rating (k = 2),
absolute agreement, two-way mixed-effects model (Koo and
Li 2016). The WHOQoL-BREF test–retest coefficients were:
Physical domain ICC = 0.63 [0.47, 0.74], p < 0.001; Psycho-
logical domain ICC = 0.69 [0.54, 0.79], p < 0.001; Social
domain ICC = 0.74 [0.64, 0.82], p < 0.001; and Environment
domain ICC = 0.79 [0.71, 0.85], p < 0.001. The Disabilities
module test–retest coefficients were: Total score ICC = 0.71
[0.61, 0.80], p < 0.001; Discrimination ICC = 0.74 [0.64,
0.82], p < 0.001; Autonomy ICC = 0.83 [0.76, 0.88],
p < 0.001; and Inclusion ICC = 0.77 [0.68, 0.83], p < 0.001.
The ASQoL test–retest coefficient for the total score was
ICC = 0.76 [0.67, 0.83], p < 0.001.
As participants had initially completed the WHOQoL-
BREF on joining ASC-UK, we estimated the stability of
domain scores at about a 1year interval; the gap between
times of completion of the questionnaire was 8–21months
(mean = 13.30 months, SD = 3.3). In case having mental
health difficulties might affect stability of QoL, the ICCs
were calculated separately for 56 participants who did not
report a mental health condition diagnosis at either time
point, and 181 participants who did report a mental health
condition at both time points. ICCs were, respectively:
Physical domain ICC = 0.78 [0.62, 0.87] and 0.76 [0.68,
0.82]; Psychological domain ICC = 0.86 [0.76, 0.92] and
0.77 [0.69, 0.83]; Social domain ICC = 0.84 [0.73, 0.91]
and 0.64 [0.52, 0.74]; and Environment domain ICC = 0.81
[0.74, 0.86] and 0.84 [0.73, 0.91]. All ICCs were significant
at p < 0.001. Generally longer term stability of QoL scores
was stronger than for 1 month test–retest reliability; Social
QoL was less stable for those with ongoing mental health
This study, involving a large cohort of autistic people across
the adult age and ability range, is the first to present detailed
validation of a measure of quality of life for this population.
The appropriateness, reliability and structural validity of
the internationally accepted WHOQoL-BREF along with
the WHO Disabilities module have been demonstrated, as
well as detailed comparison of WHOQoL-BREF domains
with theoretically related measures to establish criterion,
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1608 Journal of Autism and Developmental Disorders (2018) 48:1596–1611
1 3
convergent, divergent and discriminant validity. Thus the
findings of previous studies of autistic people that have uti-
lised the WHOQoL-BREF can be generally accepted, that
is, that quality of life of autistic people is significantly lower
than for the general population.
An additional autism-specific module of nine items
(ASQoL) was developed following extensive consultation
with the autism community, covering issues such as sen-
sory overload, friendships, barriers to accessing services,
and identity as an autistic person. The items mostly relate to
social factors (formal and informal acceptance and support),
which may help to explain why the internal consistency of
the WHOQoL-BREF Social domain is lower than for the
other domains with this population. The structural valid-
ity and internal consistency of one global item and a total
ASQoL score have been established. The ASQoL items are
ready to be used by researchers (see Table1 key), along-
side the WHOQoL-BREF and WHO Disabilities module,
to measure the QoL of autistic adults; this is an important
step forward when considering evaluation of intervention
trials and studies of the effectiveness of healthcare provision.
This is the first study to use the WHO Disabilities module
(Power and Green 2010) with autistic people. It was notable
that autistic people reported markedly lower inclusion in
society than had physically disabled people in the normative
study. The Disabilities module factor structure was partially
replicated, though there were some different item loadings
as three Inclusion items merged with Discrimination. One
theme which arose from consultation with autistic people
was experiencing a lack of respect and the problem of other
people’s lack of knowledge about autism, which could con-
tribute to both exclusion and perceived discrimination. A
one factor solution emerged as optimal from the analysis,
with excellent internal consistency.
One month test–retest reliability for most of the QoL
domains was at the border of published criteria for moder-
ate/good reliability (Koo and Li 2016); does this reflect a
variable phenomenon or a not very reliable measure? As
participants are asked to answer for the past 2 weeks, this
may indicate that the measure taps into the current ‘state’
for autistic people rather than a more consistent ‘trait’.
Indeed, particularly for the WHOQoL-BREF, some items
capture what may be inherently variable facets of life over
short spaces of time (e.g., sleep, concentration, safety). It is
also the case that autistic people may not easily summarise
information in order to generalise, given a particular style
of ‘autistic sense-making’ (De Jaegher 2013); they are more
likely to focus on detail or on what happened most recently.
It is clear from the existing literature about autistic people’s
lives (Elichaoff 2015; Hirvikoski and Blomqvist 2015; Bar-
giela etal. 2016; Hickey etal. 2017) that they encounter
frequent and multiple stressors, and thus their perceived QoL
(particularly physical and psychological) may indeed go up
and down within a month’s interval. Stability over an aver-
age of 1 year was better for the WHOQoL-BREF domains,
which suggests that the measure does have potential sensitiv-
ity as an outcome measure for evaluation of interventions.
However, changes in QoL scores over a short period should
perhaps be interpreted with caution. Certainly, the measure
showed excellent discriminant validity in relation to whether
autistic people have mental health difficulties.
The consultation with the autism community was mul-
tifaceted and thorough in developing the suggested new
autism-specific module of items. A number of the research
team’s expectations were confounded; for example, it was
anticipated that ‘special interests’ would be mentioned as
an important contributor to quality of life of autistic peo-
ple (Jordan and Caldwell-Harris 2012), but that was not the
case, neither in the discussion groups nor in the individual
interviews where people talked freely within the activ-
ity structures. Furthermore, the global question introduc-
ing the WHO Disabilities module is ‘Does your disability
have a negative (bad) effect on your day-to-day life?’; it was
anticipated that autism described as a ‘condition’ would
be preferred by participants, rather than ‘disability’, but
no such comment was made during the study (Kenny etal.
2016). The ASQoL items will merit further study in other
samples, in particular to explore more fully their face valid-
ity. There may also be important issues for autistic people
(stress, adversity) not currently addressed which would merit
future revision. For example, in the current study a number
of participants talked about their roles as carers for others;
however, the draft question ‘Do you feel able to help other
people as much as you would like to?’ was judged not suf-
ficiently important to retain from the Delphi survey, even
though many comments were supportive regarding acknowl-
edging autistic people’s positive contributions, not solely
their problems.
Strengths andLimitations
The study has a number of strengths including the large and
varied sample of participants, with representation of people
who needed help to complete questionnaires or for whom
another person responded (15%). The sample was about half
women, who are more usually under-represented in autism
studies (Loomes etal. 2017). In future studies with the ASC-
UK cohort, it would be useful to explore the reasons why
women may be more willing to volunteer to take part in
studies of their life-experiences, and also to examine the
equivalence of completion by paper or online format, as has
been demonstrated for other questionnaires in both disabled
and general populations (Weigold etal. 2013; Bagby etal.
2014; Bishop etal. 2010).
The confirmatory factor analysis of the WHOQoL-BREF
data was carried out with an independent sample of autistic
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1609Journal of Autism and Developmental Disorders (2018) 48:1596–1611
1 3
people, building on a previous exploratory analysis (Mason
etal. submitted-b), providing evidence that the measure is
robust for use with autistic people. One limitation is that
the diagnoses of the participants were not verified by the
research team; however, the mean SRS2 score is approxi-
mately double the cut-off for autism on the measure. Fur-
thermore, the level of functioning and IQ could be important
characteristics to consider when validating measures. In the
present sample, a large proportion had achieved advanced
school leaver qualifications or higher (approximately 66%)
and this suggests a cognitively able sample. As such, it
would be useful for future studies looking at the validity
of the WHOQoL-BREF with autistic people to assess in
greater detail diagnoses, functioning, and cognitive ability
to explore the generalisability of findings.
The consultation process with autistic people was exten-
sive; nevertheless it did not start from an open-ended
enquiry about what matters for people in terms of quality of
life, which might have elicited additional themes, nor has the
consultation as yet involved autistic people from countries
other than the UK. The eight ASQoL items accounted for
just 2% of the variance in the global item. Although a signifi-
cant predictor it does suggest that these items capture only
a small aspect of ‘autistic identity’. Future work into other
identity domains (McDonald 2017) and the ASQoL would
expand understanding of autistic identity further. Through
data sharing and use with other large samples, the adequacy
and strengths of the ASQoL module can be tested in future.
The WHOQoL measures along with the newly developed
ASQoL show promise for use with autistic people in clini-
cal settings to elucidate some of the challenging issues they
may be experiencing in their lives. The new items pick up on
concerns such as sensory overload, lack of financial security
and barriers to accessing healthcare that may affect a broad
range of people but which are particularly salient for autistic
people. Quality of life is clearly reduced by depression and
anxiety but accurate measurement in studies will require use
of an additional measure for mental health, as there is only
one such question in the WHOQoL-BREF (Mason etal. sub-
mitted-b). Given the poor performance of the mental health
item (0.18 loading in the CFA), further consultation could
explore whether to include mental health items that are rel-
evant to autistic people in the ASQoL, or instead whether
a separate mental health screener is preferable. Mental
health issues are very common in autistic people (Lever and
Geurts 2016; Russell etal. 2016) and yet intervention and
support services with staff knowledgeable about autism are
still insufficient. Future research will need to establish the
sensitivity of the combined measures in evaluation of new
services and interventions (Bishop-Fitzpatrick etal. 2014)
but the current study to validate a quality of life measure
with autistic adults is an important step forward.
Acknowledgments The authors are grateful to the very many autistic
people who contributed to the study both as advisors and as partici-
pants. The study was funded by the Shirley Foundation and Research
Autism, and the ASC-UK study from which the sample was drawn was
funded by Autistica. We are grateful to WHO for permission to use the
WHOQoL-BREF and WHOQoL-DIS measures. Use of these is by
licence from the WHO QoL office (
publications/whoqol/en/) and contacting The
ASQoL items are available from Newcastle University website (http://
Author Contributions HM, JP, DG and JR designed and ran the study,
oversaw data analysis, and edited the manuscript. DM, DG and CW
ran the discussion groups, and contributed to qualitative analyses. DM
conducted the interviews, designed the Delphi survey, conducted statis-
tical analysis and drafted the manuscript. JP facilitated recruitment of
participants through the Autism Spectrum Cohort UK. DG facilitated
recruitment to the discussion groups. All authors commented on and
approved the final manuscript.
Funding Research Autism, Shirley Foundation and Autistica.
Compliance with Ethical Standards
Conflict of interest The authors have no conflicts of interest to declare.
Open Access This article is distributed under the terms of the Creative
Commons Attribution 4.0 International License (http://creativecom-, which permits unrestricted use, distribu-
tion, and reproduction in any medium, provided you give appropriate
credit to the original author(s) and the source, provide a link to the
Creative Commons license, and indicate if changes were made.
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Supplementary resource (1)

... First, using existing measures that are not validated in autistic adults may result in inaccurate findings, as they may not reflect the unique way that autistic people experience loneliness (Jones, 2022;. Further, this would mean that rates of loneliness between autistic and non-autistic people cannot be accurately compared (McConachie et al., 2018). ...
... Second, autistic adults may interpret items on questionnaires differently from the general population, for whom existing measures were intended McConachie et al., 2018). For example, on the widely-used UCLA Loneliness Scale (Russell, 1996), respondents are asked "how often do you feel close to people?", which autistic adults may interpret literally (Mason et al., 2019). ...
... The researchers then tested the reliability and validity of the WHOQoL-BREF, WHO Disabilities module, and ASQoL items. To assess the psychometric properties of the WHOQoL-BREF and validity/reliability of the12 Although the study byMason et al. (2022) was published after the study byMcConachie et al. (2018), the findings of the former informed the latter. ...
Loneliness is a universal feeling that people might feel when there is a gap between the ideal and actual states of their social relationships. Historically, it has been thought that autistic people do not have a desire for social connection and instead show a preference for aloneness. However, recent research, coupled with first-hand accounts of autistic individuals, has shown that not only do autistic people experience loneliness, but they may be particularly vulnerable to it (e.g., due to the challenges they experience in social environments and/or due to a lack of supportive environments in which to cultivate social relationships). To date, there has been limited research on loneliness in autistic adults. In this thesis, I used both quantitative and qualitative methods to further our current understanding of loneliness in autistic adults, with a focus on examining the measures used to assess loneliness in autistic adults, as well as autistic people’s lived experiences of loneliness. In Chapter One, I introduce my motivation for this research as a neurodivergent individual and provide an overview of research into both autism and loneliness. In Chapter Two, I use a systematic review to synthesise the current evidence base on loneliness in autistic adults, and to identify gaps in research that can guide subsequent work. In Chapter Three, I use mixed-methods to examine if, and how accurately, existing measures of loneliness capture the experiences of autistic adults. In Chapter Four, I use qualitative methods to explore the unique experience of loneliness in autistic adults. In Chapter Five, I use mixed- methods to investigate experiences of loneliness in autistic adults before, and during the early stages of, the COVID-19 pandemic. In Chapter Six, I discuss the contributions of my research to knowledge on autistic adults’ experiences of loneliness, outline future directions for such work, highlight the strengths and limitations of my research, and present my personal reflections.
... However, low well-being and poor QoL outcomes are frequently reported for autistic people, particularly in adulthood (e.g., Ayres et al., 2017;Mason et al., 2018;Lai et al., 2019;Lawson et al., 2020). Poor mental health is also known to have adverse effects on cognitive abilities, social isolation, and QoL (e.g., McClintock et al., 2010;and see Lai et al., 2019;Mason et al., 2019), whereas increased facilitation of social integration is linked to higher QoL and fewer anxiety and depression symptoms (Lever and Geurts, 2016;McConachie et al., 2018;Mason et al., 2018) but this is not well understood in older autistic adults (Mason et al., 2019). Therefore, accounting for individual differences is an important consideration for future autism ageing studies. ...
... In the present study, reliability across the mental health and quality of life measures used was good (>0.85) to excellent (0.94). These findings concur with previous reports of sustained difficulties related to mental health and QoL (see Roestorf and Bowler, 2016;Roestorf, 2018;Yarar Zivrali et al., in press, for cross-sectional comparisons with non-autistic groups; and see, e.g., Gotham et al., 2015;Van Heijst and Geurts, 2015;Lever and Geurts, 2016;McConachie et al., 2018). Given that these difficulties still remained significant at T2, the findings raise important issues about the mental health and well-being needs of autistic adults in the context of ageing. ...
Background: Poor mental health is known to adversely affect functional abilities, social isolation and quality of life (QoL). It is, therefore, crucial to consider the long-term impacts of mental health conditions as autistic adults grow older. Objectives: To explore, in a group of community-based autistic adults, the extent of: (i) autistic traits, co-occurring physical and mental health conditions; (ii) age-related differences in those conditions, and changes over time; and (iii) their impact on everyday living and QoL. Method: 68 autistic adults (aged 19-80 years) participated in the first study (T1); 49 participants from T1 took part in a follow-up at T2 (mean retest interval 2.4 years). Standardised self-report measures of autistic traits, mental health and QoL were completed at both time points. Results: Over two-thirds (71%) of autistic adult participants experienced at least one co-occurring condition, and over a third (37%) met the criteria for three or more co-occurring conditions. Mental and physical health difficulties were related to autistic traits and difficulties in everyday life and were consistent predictors of poor QoL at T1 and T2. Conclusion: Mental health difficulties in autism persisted into older age and did not improve over time. These findings have important implications for mental health provision for autistic adults in older age. Pre-print article
... Efforts to reconceptualize the measurement of flourishing for, and by, autistic adults are underway (Rodogno et al., 2016;Silverman, 2019), and may further extend to children. QoL measurement development projects offer researchers models for engaging autistic stakeholders, such as caregivers of autistic children and autistic adults (Eapen et al., 2014;McConachie et al., 2018). Evidence of measurement bias in the present study directs attention to how caregivers understand and interpret behaviors as indicators of flourishing. ...
Flourishing is a positive health indicator that aligns with strengths-based perspectives and measures within autism research. Flourishing indicators were recently included in the National Survey of Children's Health (NSCH) and have been used to evidence disparities in flourishing experienced by autistic children compared to non-autistic peers. Yet, little has been done to examine the utility of standard flourishing items for this population. This study examined the NSCH caregiver-reported flourishing items for measurement item bias. A cross-sectional, representative sample of autistic and non-autistic US children aged 6-17 years (n = 41,691) was drawn from the 2018-2019 NSCH public dataset. A confirmatory factor analysis using a multiple indicators and multiple causes model (MIMIC-CFA) was conducted to (1) test for differential item functioning (DIF; i.e., measurement bias); and (2) estimate latent mean group differences after controlling for DIF. Findings supported a 3-factor (social competence, school motivation, and behavioral control), 10-item model structure consistent with past literature, yet measurement bias was evident for 6 of the 10 items. Persistent group differences, after accounting for DIF and covariates, indicates that caregivers of autistic children perceive their children are experiencing meaningfully lower flourishing outcomes compared to caregivers of non-autistic children. However, evidence of measurement bias for items related to the social competence dimension calls into question the applicability of this measure for autistic children. Further interpretation of group differences and use of this measure should be approached with caution.
During the COVID‐19 pandemic, applied behavior analysis services for many autistic individuals were transitioned to telehealth. The current study assessed caregiver‐reported quality of life (QoL) and social validity for families of autistic children receiving only telehealth services (n = 96) or a combination of telehealth and in‐person services (n = 173). Barriers to the telehealth experience were analyzed via an ANOVA, and the impact of funding source was analyzed using an independent samples t‐test. Caregivers reported benefit across QoL and social validity items, with scores ranging from 3.31 to 4.44 (1 = least benefit, 5 = most benefit). While many caregivers reported no barriers regarding technology (44.61%), childcare (69.52%), and employment (64.68%), the presence of those barriers significantly impacted QoL and social validity scores. Funding source was not found to have a significant impact. Overall, caregivers found value in their child's telehealth services. Clinicians have an obligation to mitigate barriers to ensure the success of the intervention.
It is often suggested that supporting autistic people to identify and use their strengths will lead to positive outcomes. However, little research has explored if this is true. To date, no research has explored whether autistic people already have knowledge of and use their strengths, nor whether increased strengths knowledge and use is linked to good outcomes, such as a better quality of life, well-being and improved mental health. Comparing large samples of autistic and non-autistic people, this study tested these unanswered questions. We found that autistic and non-autistic people reported similar strengths, but autistic people reported less knowledge and use of their strengths compared to non-autistic people. Importantly however, autistic people who reported using their strengths often had better quality of life, well-being and mental health than autistic people who reported using their strengths less frequently. We, therefore, propose that supporting autistic people to use their strengths more often may be a valuable way to boost well-being in this population.
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Gangguan Spektrum Autisme merupakan gangguan perkembangan yang ditandai dengan hambatan komunikasi dan interaksi sosial. Gangguan ini berlangsung sepanjang usia hingga dewasa. Indikator keberhasilan layanan intervensi pada individu dengan disabilitas dapat diketahui melalui kualitas hidup. Penelitian sebelumnya menemukan kualitas hidup individu autistik dewasa cenderung lebih rendah. Salah satu upaya meningkatkan kualitas hidup individu autistik dewasa melalui identifikasi faktor-faktor prediktor kualitas hidup. Namun, di Indonesia belum ada penelitian terkait konteks tersebut. Penelitian ini bertujuan untuk mengetahui kualitas hidup individu autistik dewasa di Indonesia dan peranan dukungan sosial. Metode penelitian menggunakan pendekatan kuantitatif. Kualitas hidup diukur dengan kuesioner WHOQoL-BREF, Disability Module dan ASQoL. Dukungan sosial diukur dengan ISEL-12. Jumlah partisipan 31 orang yang berusia 18-30 tahun tersebar di beberapa daerah di Indonesia. Hasil penelitian menunjukkan dukungan sosial berkorelasi positif dengan kualitas hidup. Hal ini menunjukkan meningkatnya dukungan sosial sejalan dengan meningkatnya kualitas hidup pada individu autistik dewasa. Sumber dukungan sosial yang dirasakan paling menguntungkan bagi individu autistik dewasa berasal dari keluarga, teman, dan profesional (terapis, guru, psikolog). Bantuan yang diberikan mencakup bantuan informasi, bantuan emosional, dan bantuan langsung. Upaya meningkatkan kesejahteraan hidup bagi individu autistik dewasa memerlukan kerjasama dari berbagai elemen mulai dari pembuat kebijakan hingga masyarakat. Temuan ini menambah wawasan pengetahuan mengenai gambaran kehidupan individu autistik dewasa di Indonesia untuk pengembangan program-program selanjutnya.
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This study describes social, mental health, and quality of life outcomes in early adulthood, and examines childhood predictors in the Special Needs and Autism Project (SNAP), a longitudinal population-based cohort. Young autistic adults face variable but often substantial challenges across many areas of life. Prediction of outcomes is important to set expectations and could lead to the development of targeted early intervention. Autistic children were enrolled at age 12 and parents reported outcomes 11 years later when their children were age 23 (n = 121). Thirty six percent of autistic adults were in competitive employment or education and 54% had frequent contact with friends. Only 5% of autistic adults were living independently, and 37% required overnight care. Moderate or severe anxiety and depression symptoms were found for 11% and 12% of young adults, respectively. Subjective quality of life was similar to UK averages except for social relationships. Using childhood IQ, autism traits and adaptive functioning meaningful predictions can be made of living situation, employment and education and physical health. Prediction was poor for friendships, mental health outcomes and other aspects of quality of life. Our results suggest that although young autistic adults face challenges across normative, social outcomes, they may be faring better in regard to mental health or quality of life. Childhood IQ, autism traits and adaptive functioning are most useful for predicting outcomes. After accounting for these factors, childhood measurements of behavioral and emotional problems and language offered little improvement in prediction of adult outcomes.
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As a group, individuals with autism spectrum disorder (ASD) have significant deficits in adaptive and leisure skills, which can result from the inherent characteristics of autism in addition to restricted access to opportunities for participation in adaptive community-based activities throughout the lifespan. Adaptive and leisure skills training are imperative for facilitating successful community participation, enhancing vocational skills, and increasing independence. Applied behavior analysis is a scientific evidence-based approach to improving socially significant behaviors including adaptive and leisure skills. Although there is a rich source of literature on teaching adaptive skills to young learners with ASD, there is a scarcity of research teaching adaptive skills during one’s transition into adulthood and throughout the lifespan with individuals with ASD. The current chapter provides a thorough overview of the existing literature, benefits, barriers, and importance for adaptive leisure behavior skills training. (The authors of this chapter acknowledge the ongoing discourse related to the use of person-first versus identity-first terminology and language pertaining to a diagnosis of autism. Any variation in the use of terminology throughout this chapter pertaining to a diagnosis of autism (e.g., person with autism, autistic, autism spectrum disorder, etc.) is not indicative of any particular bias either for or against any one perspective on this matter. The authors of this chapter adamantly support the rights of all individuals to decide their preferred language in communicating about their identity/disability).
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Introduction This study aims to investigate self-perceived quality of life, daily functioning, and the use of compensatory strategies in emerging adults with autism ¹ . Methods and analysis Participants will be recruited from the Lillehammer Neurodevelopmental 10-year follow-up study (LINEUP), with the aim of 15 individual in-depth interviews. Subsequently, two focus groups with clinicians will be invited to reflect on the themes found in the individual interviews. All interviews will be recorded and analyzed using reflexive thematic analysis. Ethics and dissemination The study is approved by the Regional Committee for Medical Research Ethics in South-East Norway. The findings will be disseminated to academic and clinical audiences through journal articles and conference presentations. To reach the broader autistic and autism communities, the findings will be shared with the Autism Society at national and local meetings, in their membership magazine, and on their social media channel.
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Autism spectrum disorder is associated with co-existing conditions that may adversely affect an individual’s quality of life. No systematic review of quality of life of adults on the autism spectrum has been conducted. Our objectives were as follows: (1) review the evidence about quality of life for adults on the autism spectrum; (2) critically appraise current practice in assessing quality of life of adults on the autism spectrum. We searched bibliographic databases and other literature to identify studies using a direct measure of quality of life of adults on the autism spectrum. Hand searching of reference lists, citation searching and personal communication with field experts were also undertaken. In total, 827 studies were identified; 14 were included. Only one quality of life measure designed for use with the general autism spectrum population was identified. Quality of life of adults on the autism spectrum is lower than that of typically developing adults, when measured with tools designed for the general population. There are no comprehensive autism spectrum disorder–specific quality of life measurement tools validated for use with representative samples of adults on the autism spectrum. There is a pressing need to develop robust measures of quality of life of autistic adults.
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Individuals on the autism spectrum face stigma that can influence identity development. Previous research on the 22-item Autism Spectrum Identity Scale (ASIS) reported a four-factor structure with strong split-sample cross-validation and good internal consistency. This study reports the discriminative and criterion validity of the ASIS with other measures. Adults (n = 1139) who have, or identify with, an autism spectrum diagnosis took a nationally distributed online survey that also included demographic questions and measures for stigma, self-esteem, and quality of life (QoL). All four ASIS factors discriminated from measures of stigma and self-esteem. The ASIS also showed good criterion validity with the factors of Positive Difference and Changeability demonstrating widespread relationships with subjective quality of life in the expected directions.
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The WHO Quality of Life-Brief questionnaire was used to assess quality of life (QoL) among 52 adults with autism (mean age 49 years) followed-up since childhood. Overall, assessments of QOL were more positive than measures of objective social outcome (jobs, independence, relationships etc.) but correlations between caregiver and self-reports were low. Informant ratings indicated few correlations between current QoL and any child or adult factors. On self-report ratings, QoL was significantly negatively correlated with severity of repetitive behaviours in childhood; higher QoL was positively associated with better adult social outcomes. However, only a minority of adults (n = 22) could provide self-report data and findings highlight the need to develop valid measures for assessing the well-being of adults with autism. Electronic supplementary material The online version of this article (doi:10.1007/s10803-017-3105-5) contains supplementary material, which is available to authorized users.
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When faced with child-related challenges associated with autism spectrum disorder, positive and negative social exchanges may be critical to parents’ psychological well-being. This study examined the types and sources of positive and negative social exchanges reported by mothers and fathers of children with autism spectrum disorder and their association with parental depressive symptoms in 176 families of children (5–12 years; 85% male) with autism spectrum disorder. One-way repeated measure multivariate analyses of variance and multilevel modeling were used. Results indicated that informational was the most frequent type, and one’s spouse was the primary source, of both positive and negative social exchanges. Fathers reported fewer positive, and also fewer negative, social exchanges with family, friends, and health professionals than mothers. Positive and negative social exchanges with one’s spouse were most strongly associated with depressive symptoms. Findings have implications for interventions designed to foster optimal outcomes in families of children with autism spectrum disorder.
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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.
Background: Measurement of the Quality of Life (QoL) of autistic adults is receiving increasing empirical attention. The World Health Organisation (WHO) QoL measure (WHOQoL-BREF) has been utilised in several studies. Autistic adults report significantly lower QoL compared to neurotypical adults across several domains. However, no studies have investigated the suitability of WHOQoL-BREF as a tool to measure the QoL of autistic adults. Methods: This study explored the validity and reliability of WHOQoL-BREF with a mixed methods approach. Quantitatively, structural validity was explored by an exploratory factor analysis of WHOQoL-BREF data from 352 autistic adults aged 18-80 years. Qualitatively, four discussion groups (n=20 autistic people) were conducted to explore the face validity of the items of WHOQoL-BREF. Results: The five factor structure was conceptually similar to the WHO formulation of QoL, with adequate to good internal consistency of domains; however, some items loaded in an unexpected way. The reasons for these unexpected loadings were explored in the transcripts from the discussion groups. Conclusions: The findings suggest that the WHOQoL-BREF has acceptable validity and reliability for use with autistic adults; however caution is needed when interpreting data from the social domain and some other items
Objective: To derive the first systematically calculated estimate of the relative proportion of boys and girls with autism spectrum disorder (ASD) through a meta-analysis of prevalence studies conducted since the introduction of the DSM-IV and the International Classification of Diseases, Tenth Revision. Method: Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. The Medline, Embase, and PsycINFO databases were searched, and study quality was rated using a risk-of-bias tool. Random-effects meta-analysis was used. The pooled outcome measurement was the male-to-female odds ratio (MFOR), namely the odds of being male in the group with ASD compared with the non-ASD group. In effect, this is the ASD male-to-female ratio, controlling for the male-to-female ratio among participants without ASD. Results: Fifty-four studies were analyzed, with 13,784,284 participants, of whom 53,712 had ASD (43,972 boys and 9,740 girls). The overall pooled MFOR was 4.20 (95% CI 3.84-4.60), but there was very substantial between-study variability (I2 = 90.9%). High-quality studies had a lower MFOR (3.32; 95% CI 2.88-3.84). Studies that screened the general population to identify participants regardless of whether they already had an ASD diagnosis showed a lower MFOR (3.25; 95% CI 2.93-3.62) than studies that only ascertained participants with a pre-existing ASD diagnosis (MFOR 4.56; 95% CI 4.10-5.07). Conclusion: Of children meeting criteria for ASD, the true male-to-female ratio is not 4:1, as is often assumed; rather, it is closer to 3:1. There appears to be a diagnostic gender bias, meaning that girls who meet criteria for ASD are at disproportionate risk of not receiving a clinical diagnosis.
Lay summary: Research on the validity of measurement of anxiety and depression in ASD is currently lacking. The aim of this study was to explore the properties of the Hospital Anxiety and Depression Scale (HADS) in a sample of 151 young people with ASD. Participants completed HADS and a range of mental health and well-being measures. Encouragingly, our findings suggest that HADS provides a reliable and valid assessment of anxiety and depression in ASD.
Background: augmenting validated paper versions of existing outcome measures with an equivalent online version may offer substantial research advantages (cost, rapidity and reliability). However, equivalence of online and paper questionnaires cannot be assumed, nor can acceptability to respondents. The aim was to test whether online and written versions of the Roland Morris Disability Questionnaire (RMDQ), a standard measure of functional disability in back pain, are equivalent at both group and individual levels to establish whether they can be used interchangeably. Methods: this is a within-participants equivalence study. 167 participants with back pain fully completed both the paper and online versions of the RMDQ in random order. Participants were recruited from a chiropractic clinic and patient support groups in Southern England. Limits of equivalence were pre-defined as 0.5 RMDQ points, the Bland-Altman range was calculated, and participants' comments were examined using content analysis. Results: the mean score difference was 0.03 (SD = 1.43), with the 95% Confidence Interval falling entirely within our limits of equivalence (-0.19 to 0.25). The Bland-Altman range was -2.77 to 2.83 RMDQ points. Participants identified unique advantages and disadvantages associated with each version of the RMDQ. Conclusions: the group and individual level data suggest that online and paper versions of the RMDQ are equivalent and can be used interchangeably. The Bland-Altman range appears to reflect the known measurement properties of the RMDQ. Furthermore, participants' comments confirmed the potential value to be had from offering them the choice of completing the RMDQ online or on paper