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RESEARCH
thebmj
BMJ
2023;383:e076733 | doi: 10.1136/bmj-2023-076733 1
Autism intervention meta-analysis of early childhood studies
(Project AIM): updated systematic review and secondary
analysis
Micheal Sandbank,1 Kristen Bottema-Beutel,2 Shannon Crowley LaPoint,3 Jacob I Feldman,4,5
D Jonah Barrett,6 Nicolette Caldwell,7 Kacie Dunham,4,8 Jenna Crank,9 Suzanne Albarran,10
Tiany Woynaroski4,5,8,11,12
ABSTRACT
OBJECTIVE
To summarize the breadth and quality of evidence
supporting commonly recommended early childhood
autism interventions and their estimated eects on
developmental outcomes.
DESIGN
Updated systematic review and meta-analysis (autism
intervention meta-analysis; Project AIM).
DATA SOURCES
A search was conducted in November 2021 (updating
a search done in November 2017) of the following
databases and registers: Academic Search Complete,
CINAHL Plus with full text, Education Source,
Educational Administration Abstracts, ERIC, Medline,
ProQuest Dissertations and Theses, PsycINFO,
Psychology and Behavioral Sciences Collection, and
SocINDEX with full text, Trials, and ClinicalTrials.gov.
ELIGIBILITY CRITERIA FOR SELECTING STUDIES
Any controlled group study testing the eects of any
non-pharmacological intervention on any outcome in
young autistic children younger than 8 years.
REVIEW METHODS
Newly identied studies were integrated into the
previous dataset and were coded for participant,
intervention, and outcome characteristics.
Interventions were categorized by type of approach
(such as behavioral, developmental, naturalistic
developmental behavioral intervention, and
technology based), and outcomes were categorized
by domain (such as social communication, adaptive
behavior, play, and language). Risks of bias were
evaluated following guidance from Cochrane. Eects
were estimated for all intervention and outcome types
with sucient contributing data, stratied by risk of
bias, using robust variance estimation to account
for intercorrelation of eects within studies and
subgroups.
RESULTS
The search yielded 289 reports of 252 studies,
representing 13 304 participants and eects for 3291
outcomes. When contributing eects were restricted
to those from randomized controlled trials, signicant
summary eects were estimated for behavioral
interventions on social emotional or challenging
behavior outcomes (Hedges’ g=0.58, 95% condence
interval 0.11 to 1.06; P=0.02), developmental
interventions on social communication (0.28, 0.12
to 0.44; P=0.003); naturalistic developmental
behavioral interventions on adaptive behavior (0.23,
0.02 to 0.43; P=0.03), language (0.16, 0.01 to 0.31;
P=0.04), play (0.19, 0.02 to 0.36; P=0.03), social
communication (0.35, 0.23 to 0.47; P<0.001), and
measures of diagnostic characteristics of autism
(0.38, 0.17 to 0.59; P=0.002); and technology based
interventions on social communication (0.33, 0.02
to 0.64; P=0.04) and social emotional or challenging
behavior outcomes (0.57, 0.04 to 1.09; P=0.04).
When eects were further restricted to exclude
caregiver or teacher report outcomes, signicant
eects were estimated only for developmental
interventions on social communication (0.31, 0.13
to 0.49; P=0.003) and naturalistic developmental
behavioral interventions on social communication
(0.36, 0.23 to 0.49; P<0.001) and measures of
diagnostic characteristics of autism (0.44, 0.20 to
0.68; P=0.002). When eects were then restricted to
exclude those at high risk of detection bias, only one
signicant summary eect was estimated—naturalistic
developmental behavioral interventions on measures
of diagnostic characteristics of autism (0.30, 0.03 to
0.57; P=0.03). Adverse events were poorly monitored,
but possibly common.
CONCLUSION
The available evidence on interventions to support
young autistic children has approximately doubled in
four years. Some evidence from randomized controlled
trials shows that behavioral interventions improve
caregiver perception of challenging behavior and child
social emotional functioning, and that technology
based interventions support proximal improvements
in specic social communication and social emotional
skills. Evidence also shows that developmental
interventions improve social communication
in interactions with caregivers, and naturalistic
For numbered aliations see
end of the article
Correspondence to: M Sandbank
micheal_sandbank@med.unc.edu
(or @michealsandbank.bsky.
social on Bluesky;
ORCID 0000-0002-6562-8267)
Additional material is published
online only. To view please visit
the journal online.
Cite this as: BMJ ;:e
http://dx.doi.org/10.1136/
bmj-2023-076733
Accepted: 29 September 2023
WHAT IS ALREADY KNOWN ON THIS TOPIC
Many dierent types of early childhood interventions are recommended and
oered to support generalized development in young autistic children
Previous research is mixed in quality and conclusions about the eectiveness of
these interventions
WHAT THIS STUDY ADDS
The evidence available has approximately doubled in only four years
Some evidence from high quality studies supports the eectiveness of specic
early childhood interventions for improving certain outcomes
Researchers have inadequately monitored and reported adverse events, eects,
and harms (for any intervention type), therefore physicians should guide families
to watch children closely when starting an intervention
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developmental behavioral interventions improve
core challenges associated with autism, particularly
diculties with social communication. However,
potential benets of these interventions cannot be
weighed against the potential for adverse eects
owing to inadequate monitoring and reporting.
Introduction
Autism is a relatively common diagnosis, with
current estimates suggesting approximately 1-4%
of the population is aected.1-3 Early childhood
interventions are often strongly recommended for
young autistic children to promote skill gain in
areas that might contribute to positive long term
outcomes.4 5 Pediatricians and other physicians
are often the first line of care directing families of
autistic children to early childhood interventions to
support their development. Therefore, physicians
should be familiar with the available interventions
and the landscape of evidence supporting them to
make practice recommendations. However, the types
of early childhood interventions recommended for
this population vary widely in terms of approach and
intensity, and current best practice guidelines dier
across countries. For example, in the United States,
the most commonly recommended treatment is early
intensive behavioral intervention, an approach that
incorporates operant conditioning, targets functional
skills, and is characterized by a recommended intensity
of 20-40 hours per week.6 In contrast, the National
Institute for Health and Care Excellence in England
concluded that only two intervention approaches have
sucient evidence to support their use. These relatively
low intensity interventions are pediatric autism
communication therapy, and joint attention, symbolic
play, engagement and regulation (JASPER), which
target early social communication in the context of
natural interactions.7 Previous attempts to synthesize
Unpublished data identified through emails to 187
investigators associated with 167 preregistered trials
Full text articles excluded
Average age of participants greater than 8 years
No confirmed autism diagnosis
Biological or pharmacological intervention
Does not include a comparison group
Not an RCT or QED
Insufficient information reported to allow for effect size calculation
Other
207
65
19
68
98
43
234
734
Records excluded from title and abstract screening
Full text records screened for possible eligibility
5335
Duplicate records removed
1092
Full text records assessed for eligibility
1
220
872
Reports included in updated search
139
Reports included in original search
150
Reports included in current dataset analyzed in current paper
289
Dissertations8Peer reviewed reports130 Unpublished dataset1
Study samples reflected in current dataset
252
RCT173 QED79
Records identified through database searching
6427
Fig | PRISMA (preferred reporting items for systematic review and meta-analysis) diagram. QED=quasi-experimental design study;
RCT=randomized controlled trial
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BMJ
2023;383:e076733 | doi: 10.1136/bmj-2023-076733 3
intervention evidence to generate consistent clinical
guidelines have been hindered by a number of factors:
heterogenous intervention approaches that prevented
aggregation of evidence; low standards of evidence
for designating practices as evidence based; limited
evaluation of intervention outcomes; overreliance on
vote counting over quantitative synthesis; and a rapidly
transforming evidence base. Consequently, doctors,
clinicians, and families have to navigate confusing and
often conflicting guidance on which supports are the
most likely to be ecacious for autistic children.
Project AIM: initial scope and findings
We conducted Project AIM (autism intervention
meta-analysis), a scoping systematic review and
meta-analysis of controlled group studies of any non-
pharmacological intervention designed to support
any outcome in young autistic children.8 The initial
search identified 139 studies of common intervention
approaches (parsed by type) on various outcomes
(categorized by domain) in young autistic children.
This quantitative synthesis of evidence from group
design intervention studies allowed comparison of
the overall quality and findings of evidence according
to intervention approach. The results of Project AIM
were selected by the Interagency Autism Coordinating
Committee of the US Department of Health and Human
Services as an important advance in autism research.9
Subsequently, the findings have been incorporated
into clinical guidelines5 10 and continue to shape
intervention recommendations for young autistic
children.
The initial Project AIM investigation and subsequent
secondary analyses documented gaps in study
quality, within specific intervention types and across
the literature as a whole. Notable gaps included an
overrepresentation of quasi-experimental studies (ie,
in which participants were not randomly assigned
to treatment groups), overreliance on outcomes
measured by unmasked assessors and proxy reports,
and inadequate monitoring of adverse events and
harms.8 11 When intervention eects were estimated
from all available evidence, regardless of quality,
several intervention approaches were estimated to
have positive and statistically significant eects on
a variety of outcomes. However, when study quality
was taken into account and eects were restricted
to those immune to these risks of bias (ie, selection
bias, detection bias, placebo-by-proxy bias), no
intervention approach was estimated to have positive
and statistically significant intervention eects on any
outcome. Although adverse events, eects, and harms
were inadequately monitored, many studies reported
information indicating they occurred (such as reasons
for attrition that should have been reported as adverse
events, or statistically significant negative eects on a
measured outcome, which would qualify as a harm).11
Our initial report also found that intervention
eects were larger on proximal outcomes that were
specifically targeted in the intervention compared
with outcomes indicating more distal developmental
improvement; and on outcomes that were measured in
contexts identical or similar to those of the intervention
compared with those generalized to other contexts.12
Behavioral interventions
Percentage
Risk of bias
Design type
(RCT v QED)
Incomplete outcome
data (attrition bias)
Masking of assessors and
coders (detection bias)
Parent-teacher report
Boundedness
Distality
High Some concerns
Low
0
40
60
100
80
20
Developmental interventions
Percentage
0
40
60
100
80
20
NDBI interventions
Percentage
0
40
60
100
80
20
Technology based interventions
Percentage
0
40
60
100
80
20
Fig | Summary of risk of bias ratings for studies of behavioral, developmental,
naturalistic developmental behavioral intervention (NDBI), and technology based
intervention types. QED=quasi-experimental design study; RCT=randomized controlled
trial
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These findings show that conclusions drawn about
intervention eectiveness, in addition to being
compromised by quality concerns, are dependent
on researcher measurement decisions. Interventions
shown to have proximal impacts in specific contexts
are often designated as eective, with little attention
given to the limited scope of change quantified by
these eects. These designations are then repeated in
systematic reviews and meta-analyses, and eventually
shape clinical guidelines until most interventions
that have been designated as evidence based and
recommended for clinical use are those that have been
shown to eect circumscribed and specific change,
rather than generalized developmental gains. Although
physicians guiding families to clinical supports
are often led to believe that a substantial evidence
base endorses the eectiveness of interventions for
enabling broad developmental improvements, our
work showed that there is little evidence that this
occurs. Families might then believe that their child’s
lack of improvement when participating in these
interventions is indicative of the complexity of their
child’s condition, rather than the inadequacy of the
interventions available.
Behavioral
Social communication
Social emotional or challenging behavior
Developmental
Social communication
NDBI
Adaptive
Cognitive
Diagnostic characteristics of autism
Language
Play
Restricted and repetitive behaviors
Social communication
Technology based
Language
Social communication
Social emotional or challenging behavior
0.54 (-0.24 to 1.32)
0.58 (0.11 to 1.06)
0.28 (0.12 to 0.44)
0.23 (0.02 to 0.43)
0.18 (-0.02 to 0.38)
0.38 (0.17 to 0.59)
0.16 (0.01 to 0.31)
0.19 (0.02 to 0.36)
-0.01 (-0.32 to 0.31)
0.35 (0.23 to 0.47)
0.21 (-0.13 to 0.55)
0.33 (0.02 to 0.64)
0.57 (0.04 to 1.09)
-1.0 -0.5 0.5 1.00 1.5
Intervention and
outcome type
Hedges’ g
(95% CI)
Hedges’ g
(95% CI)
9
10
14
11
13
17
26
8
7
32
9
17
8
Studies
84
57
123
31
48
46
138
65
20
322
29
84
53
Effect
sizes
0
Summary estimate for RCTs
Fig | Forest plot of summary estimates for each outcome type by intervention type when only outcomes from randomized controlled trials (RCTs) are
included. % CI=% condence interval; NDBI=naturalistic developmental behavioral intervention
Table | Estimates of variance and summary eects by intervention and outcome type with increasingly restrictive quality thresholds
All RCTS RCTS without caregiver report RCTs with low detection bias
Intervention or outcome τg (% CI) P τg (% CI) P τg (% CI) P
Behavioral
Social communication 0.873 0.54 (–0.24 to 1.32) 0.15 — — — — — —
Social emotional or challenging
behavior
0.334 0.58 (0.11 to 1.06) 0.02 — — — — — —
Developmental
Social communication 0.028 0.28 (0.12 to 0.44) 0.003 0.037 0.31 (0.13 to 0.49) 0.003 — — —
NDBI
Adaptive 0.011 0.23 (0.02 to 0.43) 0.03 — — —. — — —
Cognitive 0.028 0.18 (–0.02 to 0.38) 0.07 0.029 0.19 (–0.02 to 0.39) 0.07 0.000 0.17 (–0.02 to 0.37) 0.07
Diagnostic characteristics of autism 0.086 0.38 (0.17 to 0.59) 0.002 0.095 0.44 (0.20 to 0.68) 0.002 0.037 0.30 (0.03 to 0.57) 0.03
Language 0.065 0.16 (0.01 to 0.31) 0.04 0.075 0.13 (–0.04 to 0.30) 0.13 0.048 0.06 (–0.13 to 0.25) 0.49
Play 0.000 0.19 (0.02 to 0.36) 0.03 — — —. — — —
Restricted and repetitive behaviors 0.029 –0.01 (–0.32 to 0.31) 0.96 — — —. — — —
Social communication 0.047 0.35 (0.23 to 0.47) <.001 0.046 0.36 (0.23 to 0.49) <.001 0.009 0.11 (–0.03 to 0.26) 0.11
Technology based
Language 0.107 0.21 (–0.13 to 0.55) 0.18 0.131 0.26 (–0.14 to 0.66) 0.16 — — —
Social communication 0.263 0.33 (0.02 to 0.64) 0.04 0.013 0.20 (–0.01 to 0.41) 0.06 — — —
Social emotional or challenging
behavior
0.279 0.57 (0.04 to 1.09) 0.04 0.466 0.64 (–0.07 to 1.36) 0.07 — — —
95% CI=95% condence interval; NDBI=naturalistic developmental behavioral intervention; RCT=randomized controlled trial.
Summary estimates with <5 degrees of freedom were dropped from results and reflected as —.
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Need for an updated meta-analysis
Although the initial Project AIM report was published
in January 2020, the search was completed in
November 2017. Therefore, studies published
after the search date were not included in quality
evaluations and summary eect estimation. During
the decade before the search, there were considerable
increases in funding and the rate of publication of
autism related research.13 Two thirds of the studies
and randomized controlled trials included in Project
AIM were published in the five years preceding the
search date. Therefore, it was reasonable to assume
that a substantial number of studies, including many
randomized controlled trials, had been published
since the original search was conducted. The rapidly
expanding evidence base suggests that an updated
review would ensure the conclusions of Project AIM
reflect the most recent evidence on interventions
for young autistic children and provide guidance
to medical professionals, specialist clinicians, and
families.
Research questions
In the current report, we sought to answer the following
questions with an updated dataset that integrated
studies published since 2017 (inclusive of all research
conducted from 1975 to 2021).
1. What is the overall quality of evidence supporting
each intervention type in terms of use of random
assignment, limited reliance on proxy report, use
of masked assessors, and minimal attrition? What
percentage of eects reflect generalized or distal
outcomes?
2. What percentage of studies monitored and
reported or showed evidence of adverse events, adverse
eects, or harms?
3. When all available evidence is considered from
randomized controlled trials, what intervention
types are estimated to have positive and statistically
significant eects on targeted outcomes?
4. What intervention types are estimated to have
positive and statistically significant eects on targeted
outcomes when evidence from randomized controlled
trials is further restricted to outcomes measured
directly (ie, excluding caregiver or teacher report
outcomes) and by masked assessors?
5. Are intervention eects on proximal outcomes
significantly larger than intervention eects on distal
outcomes? Are intervention eects on context bound
outcomes significantly larger than intervention eects
on generalized outcomes?
Method
Search
Search terms and databases
We completed an updated search on 17 November 2021
that replicated the initial Project AIM search in terms
and databases, but was limited to studies published
after 1 November 2017 (the date of our previous
search). Searched databases included Academic Search
Complete, CINAHL Plus with full text, Education
Source, Educational Administration Abstracts, ERIC,
Medline, Proquest Dissertations and Theses, PsycINFO,
Psychology and Behavioral Sciences Collection, and
SocINDEX with full text. Search terms are listed in the
supplementary materials. This search yielded 6427
records that were then screened. In addition to searching
databases that index dissertations and theses, we
sought unpublished data by searching the journal Trials
for published protocols and ClinicalTrials.gov with the
search term “autism” to identify potentially relevant
registered but unpublished clinical trials. Potentially
relevant trials (n=168) were identified and we emailed
researchers associated with those trials (n=187) with
a request to share information that would allow their
inclusion in the updated meta-analysis.
Screening process
All identified records were double screened at the
abstract level by two of 13 independent screeners
Developmental
Social communication
NDBI
Cognitive
Diagnostic characteristics of autism
Language
Social communication
Technology based
Language
Social communication
Social emotional or challenging behavior
0.31 (0.13 to 0.49)
0.19 (-0.02 to 0.39)
0.44 (0.20 to 0.68)
0.13 (-0.04 to 0.30)
0.36 (0.23 to 0.49)
0.26 (-0.14 to 0.66)
0.20 (-0.01 to 0.41)
0.64 (-0.07 to 1.36)
-1.0 -0.5 0.5 1.00 1.5
Intervention and
outcome type
Hedges’ g
(95% CI)
Summary estimate for RCTs without
caregiver-teacher report outcomes
Hedges’ g
(95% CI)
13
13
14
22
26
8
13
7
Studies
100
48
30
101
231
21
44
51
Effect
sizes
Fig | Forest plot of summary estimates for each outcome type by intervention type when only outcomes from randomized controlled trials (RCTs) not
derived from caregiver or teacher reports are included. %CI=% condence interval; NDBI=naturalistic developmental behavioral intervention
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using the web application abstrackr.14 Any record
flagged as potentially eligible by at least one screener
was then examined at the full text level. Studies were
considered eligible if they were published in English
between November 2017 and November 2021; they
were experimental (ie, a randomized controlled trial)
or quasi-experimental design group studies that
included an intervention and a control or comparison
group; they reported a simple majority of participants
had autism; they included participant samples with
an average age <8 years (<96 months); and they had
not already been included in the previous Project AIM
meta-analysis. Some studies that were eligible for
inclusion reported insucient data to allow extraction
of appropriate eect sizes. For each of these studies,
the first author contacted the corresponding author by
email to request information that would allow eect
size calculation.
Coding procedures
All studies were independently double coded by
the first author and by one of a team of five trained
reliability coders. All discrepancies were identified
and resolved through discussion before final codes
were entered. Coding procedures were nearly identical
to those used in the original Project AIM, but are briefly
described here. The coding manual can be accessed
through an online data repository.15 This update was
not registered but replicated procedures used in the
previous meta-analysis.
Participant characteristics
When reported, we extracted the following participant
sample characteristics from reports: chronological age
in months; language age in months (expressive was
given preference, but receptive and total language ages
were also extracted in the absence of expressive); and
proportion of the sample reported as male.
Intervention characteristics
Interventions were coded for type, setting, implementer,
and cumulative intensity (total amount of intervention
provided to participants in hours across the duration of
the study). Approaches were categorized as belonging
to one of nine possible types using the categorization
system derived for the original Project AIM: animal
assisted; behavioral; cognitive behavioral therapy;
developmental; naturalistic developmental behavioral
intervention (NDBI); TEACCH (formerly treatment of
autistic and related communications handicapped
children); technology based; sensory based; or
other. Sensory based interventions were then further
coded as sensory integration therapy, other sensory
based interventions, or music therapy to ensure that
interventions were grouped according to a consistent
theory of change. We provide a non-exhaustive list of
examples for each intervention type below. In the rare
event that coders were unable to agree on intervention
type, the first author contacted the corresponding
author of the study and asked for their input.
Animal assisted therapy—Interventions that were
mediated entirely through the presence of an animal or
that were characterized primarily by interaction with
an animal were categorized as animal assisted therapy.
Examples include equine assisted therapy and use of
service dogs.
Behavioral—Interventions were categorized as
traditional behavioral interventions if they relied
heavily on operant principles of learning and
corresponding techniques (eg, were primarily
adult led, used explicit instruction and prompting,
provided explicit reinforcement with tangible
rewards). Examples include early intensive behavioral
intervention, the picture exchange communication
system, and discrete trial training.
Cognitive behavioral therapy—Interventions
described as cognitive behavioral therapy, which
focuses on identifying and changing thinking patterns
in an eort to change behavior, were categorized as
such.
Developmental—Interventions were categorized as
developmental if they were primarily child led, stressed
the relational or transactional and social underpinnings
of development, and taught skills according to a
developmental sequence with the goal of allowing
developmental cascades by repairing breakdowns in
relational cycles. Examples include pediatric autism
communication therapy and Hanen models.
Music therapy—Interventions were categorized as
music therapy if they were explicitly described as such
or incorporated music and rhythm based experiences
toward therapeutic ends.
NDBI
Cognitive
Diagnostic characteristics of autism
Language
Social communication
0.17 (-0.02 to 0.37)
0.30 (0.03 to 0.57)
0.06 (-0.13 to 0.25)
0.11 (-0.03 to 0.26)
-1.0 -0.5 0.5 1.00 1.5
Intervention and
outcome type
Hedges’ g
(95% CI)
Summary estimate for RCTs
with low detection bias
Hedges’ g
(95% CI)
10
9
15
15
Studies
42
19
58
77
Effect
sizes
Fig | Forest plot of summary estimates for each outcome type by intervention type when only outcomes from randomized controlled trials (RCTs)
and low risk of detection bias are included. % CI=% condence interval; NDBI=naturalistic developmental behavioral intervention
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Naturalistic developmental behavioral intervention—
Interventions were categorized as NDBIs if they were
one of several named interventions listed in the
consensus paper by Schreibman and colleagues.16
These interventions are motivated by developmental
and behavioral theories of learning and characterized
by child initiated interactions that give way to reciprocal
social routines that are maintained through shared
control by the adult and child. NDBIs are naturalistic
in that they take place in environments and within
routines that are already likely to occur in the child’s
life (eg, play in the home or community), and rely on
natural antecedents and rewards. Intervention targets
tend to center on early social communication skills
that are thought to serve as a foundation for further
developmental cascades. Examples include the early
start Denver model, JASPER, and pivotal response
teaching.
Sensory integration therapy—Interventions
were described as sensory integration therapy
if they were explicitly described as such or were
characterized by structured exposure to several
types of sensory opportunities (ie, tactile, vestibular,
proprioceptive).17 18 This category included Ayres
sensory integration and more general sensory
integration therapy.
Sensory based interventions—Interventions were
categorized as sensory based interventions if they
incorporated targeted exposure to sensory stimuli
with the goal of enhancing processing of sensory
stimuli and theoretically related outcomes, but did
not include several types of sensory opportunities.
Examples include auditory integration, touch therapy,
and massage.
TEACCH—Interventions were categorized as
TEACCH if they were explicitly named as such. TEACCH
is characterized by heavy reliance on predictable
environments, structured work systems, and routines
(eg, visual and picture activity schedules).
Technology based interventions—Interventions
were categorized as technology based if technological
mediation was described as the main change agent
of the intervention. Technologies included electronic
devices such as computers, iPads, or robots.
Other—Interventions that could not be adequately
categorized in the previous categories were coded
as other for type. These studies were excluded from
summary eect estimation, but included in moderator
analyses.
Outcome characteristics
Outcomes were coded for domain, proximity, and
boundedness.
Domain—Dependent variable names were extracted
for each outcome, and outcomes were coded as
representing diagnostic characteristics of autism (ie,
social communication; restricted or repetitive patterns
of behaviors, interests, or activities; sensory; or
overall autism features—ie, total scores on diagnostic
assessments) or related domains (ie, brain imaging,
academic, adaptive, cognitive, language, motor, play,
sleep, social emotional or challenging behavior).
Further details about outcome domain coding are
provided in the previous report.8 A non-exhaustive
list of example measures and associated metrics
represented in each domain category for which
we estimated summary eects is provided in the
supplementary materials.
Proximity—Outcomes derived from measures of
skills and developmental achievements directly taught
or modeled in the intervention were coded as proximal,
while those derived from measures of broader skill sets
and developmental milestones (across the targeted
domain or in a distinct untargeted domain) were coded
as distal.
Boundedness—To code outcome boundedness,
coders considered the context of outcome
measurement and context of the intervention across
the four dimensions of materials, setting, interaction
partner, and interaction style to determine the degree
to which they matched. Outcomes measured in
contexts that were the same or highly similar (diering
on only one dimension) to that of the intervention
context were coded as context bound. Outcomes
measured in contexts that diered across two or more
dimensions from the intervention context were coded
as generalized.
Risks of bias
Studies and outcomes were coded for standard risks of
bias following guidance from Cochrane (ie, selection
bias, detection bias, performance bias, attrition
bias)19 and reliance on caregiver or teacher report to
index outcomes. We coded this additional outcome
characteristic because it indicates the risk of a specific
type of detection bias known as placebo-by-proxy bias,
which is introduced when assessors are not only aware
of participant group assignment, but also personally
invested in the outcome.20
Adverse events, eects, and harms
Adverse events refer to any unfavorable outcomes
that occur during or subsequent to participating in
an intervention, but that might or might not have
been caused by the intervention. Adverse eects
refer to unfavorable outcomes that can be reasonably
attributed to the intervention. Harms refer to
sustained deterioration during or after participation
in an intervention.21 Using procedures similar to
those outlined by Bottema-Beutel and colleagues,11
two independent coders searched full text copies
of each article for the following terms: “adverse
events,” “adverse eects,” “harm,” “side eect,” and
“complication,” and coded whether adverse events
were reported, whether the adverse events were
described in such a way that they could reasonably
be considered adverse eects, the number of adverse
events reported, and whether the authors described
adverse event monitoring procedures. Direct quotes
describing the adverse events and monitoring
procedures were copied and pasted verbatim into the
coding spreadsheet.
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Eect size information
For each reported outcome, postintervention means,
standard deviations, and sample sizes were extracted
for the intervention and counterfactual groups.
These values were used to calculate and code the
standardized mean dierence (d) representing each
intervention eect and corresponding variance.
In the few cases in which reported outcomes were
dichotomous, frequencies were extracted to estimate
d and its variance. All calculations of d were derived
using the Campbell Collaboration Practical Meta-
Analysis Eect Size Calculator.22 Standardized mean
dierence eect sizes were then converted to Hedges’
g to correct for small sample sizes using R statistical
computing software (R Core Team, 2022). Eect sizes
were reflected for outcomes for which lower scores
were considered adaptive, so that directionality of
eect size was consistent across all eects.
Reliability
Primary and reliability coding sheets were
independently sent to a separate coding auditor who
identified discrepancies using a program created
in-house for this purpose. Initial codes were stored
in a separate location for reliability analyses before
discrepancy discussions. Reliability was calculated
in R23 with the irr package.24 Reliability was indexed
using unweighted κ for categorical variables,25 where
values over 0.6 reflect substantial agreement and
values over 0.8 reflect near perfect agreement; and
two way absolute intraclass correlation coecients for
continuous variables,26 where values over 0.75 reflect
strong agreement and values over 0.9 reflect excellent
agreement. κ values ranged from 0.62 to 0.9, and the
average κ across all included categorical variables
was 0.75. Intraclass correlation coecients ranged
from 0.68 to 0.99, and the average value across all
continuous variables was 0.90.
Analyses
All analyses were conducted in R.23 Given that this
meta-analysis involved a complex data structure,
wherein multiple eect sizes were extracted within
overlapping participant samples and these eect
sizes were then categorized according to intervention
and outcome type, eect sizes were analyzed
using robust variance estimation meta-analysis to
account for the dependence structure of the data.
Specifically, subgroup correlated eects working
models27 were used to aggregate eect sizes based
on type of outcome (outcome characteristics)
within each type of intervention (intervention
characteristics) using the clubSandwich28 and
metafor packages.29 Summary eect sizes for each
outcome and intervention type were only retained
in the final models when the degrees of freedom (df)
were greater than five.30 For moderation analyses,
putative categorical moderators (ie, proximal v distal
outcome, generalized v context bound outcome) were
added to the final model used to estimate summary
eects for all studies.
To assess possible publication bias across the extant
literature, we conducted Egger’s regression test with
cluster robust variance estimation methods (ie, the
Egger MLMA with standard error approach) using the
rma.mv function in the metafor package with the SCE
covariance matrix obtained from the clubSandwich
package.31 Egger’s regression test was conducted for
each presented model, and within interventions for
outcome types with df≥5 in the null models.
Patient and public involvement
Coauthors Jacob I Feldman and Tiany Woynaroski
are parents of autistic individuals and they worked
on the research question, analyses, and drafting of
the manuscript. Although members of the public were
not directly involved in this review because of funding
limitations and lack of researcher training to engage
the public, the focus of this work is aligned with the
research priorities of autistic people, which include
rigorous evaluations of interventions designed to
support development, health, and wellbeing.32
Results
Descriptives of included study samples,
interventions, and outcomes
Figure 1 presents the PRISMA (preferred reporting
items for systematic review and meta-analysis)
diagram detailing the search process. From the 6427
records retrieved in the updated search, we included
139 eligible reports. Reports identified in the updated
search were combined with reports from the original
search to yield a dataset of 289, reporting on 252
separate study samples (173 randomized controlled
trials, 79 quasi-experimental design studies) that
included a total of 13 304 participants and 3291
outcomes. The mean age of participant samples
was 56.11 months (range 18.9-95.2, standard
deviation 19.08). The mean language age equivalent
in months for samples where it was reported was
22.36 (standard deviation 12.73). The average
percentage of samples reported as male was 82.57
(standard deviation 11.27). There were 10 studies
(10 reports) of animal assisted interventions, 4
studies (4 reports) of cognitive behavioral therapy, 48
studies (51 reports) of behavioral interventions, 19
studies (24 reports) of developmental interventions,
6 studies (8 reports) of music therapy, 57 studies
(75 reports) of NDBI, 6 studies (6 reports) of sensory
integration therapy, 6 studies (7 reports) of other
sensory based interventions, 9 studies (9 reports)
of TEACCH, 30 studies (33 reports) of technology
based interventions, and 65 studies (72 reports) of
interventions categorized as other. (When summed
across intervention type, the total numbers of studies
and reports slightly exceed the overall totals of 252
study samples and 289 reports because a small
number of studies reported eects for two separate
intervention approaches tested against a control. For
these studies, a single report or study is represented
in two separate intervention categories.) Data and
analytic code are available in an online repository.33
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Risk of bias and outcome characteristics
Figure 2 presents the percentage of outcomes for each
risk of bias rating (ie, low risk of bias, high risk of bias,
unclear) for all risk of bias indicators by intervention
type. Only studies included in the generation of
eect sizes are summarized in terms of risk of bias.
Studies of animal assisted interventions, cognitive
behavioral therapy, music therapy, sensory integration
therapy, and other sensory based interventions are
not described here because there were too few studies
of these intervention types to reliably estimate the
summary eects of these approaches.30 Overall,
173 of 252 studies were randomized controlled
trials. Nearly two thirds of all outcomes (65.19%)
were at risk of detection bias, and nearly half of all
outcomes (45.51%) were derived from caregiver or
teacher reports. Attrition bias was coded as high for
15.67% of outcomes. In most studies, the nature of
the interventions prevented the possibility of fully
masking participants to the intervention received.
Therefore, we coded most outcomes (97.54%) as being
at risk of performance bias, and we do not report risk
of performance bias by intervention type because there
is little variation. Most outcomes (80.56%) were coded
as being generalized from the intervention context;
the remainder were coded as context bound. Similarly,
most outcomes (72.7%) were coded as being distal
from the intervention targets; the remainder were
coded as proximal.
Summary eects
We present eects stratified by risk of bias, first
including all eects from randomized controlled
trials, and subsequently restricting eects to those
from randomized controlled trials and outcomes
that were directly assessed (excluding caregiver or
teacher reports), and then to eects from randomized
controlled trials and outcomes subject to low risk of
detection bias. Table 1 presents summary (Hedges’
g), heterogeneity (𝜏2), and significance estimates (P)
for each of these analyses. Figures 3, 4, and 5 present
forest plots of summary eect estimates associated
with decreasing risks of bias and increasing levels of
certainty. Summary estimates that include eects from
quasi-experimental design studies, which are subject
to high risk of selection bias, are provided in the
supplementary materials (see figure S1).
Estimated eects from randomized controlled trials
Figure 3 reflects summary eect sizes derived from
outcomes extracted only from randomized controlled
trials, according to intervention and outcome type.
Statistically significant eects were estimated for
behavioral interventions on social emotional or
challenging behavior (g=0.58, 95% confidence
interval 0.11 to 1.06); for developmental interventions
on social communication outcomes (0.28, 0.12 to
0.44); for NDBIs on adaptive (0.23, 0.02 to 0.43),
language (0.16, 0.01 to 0.31), play (0.19, 0.02 to
0.36), and social communication outcomes (0.35, 0.23
to 0.47), and measures of diagnostic characteristics
of autism (0.38, 0.17 to 0.59); for technology based
interventions on social communication outcomes
(0.33, 0.02 to 0.64) and social emotional or challenging
behavior (0.57, 0.04 to 1.09). There were not enough
controlled studies of animal assisted interventions,
cognitive behavioral therapy, music therapy, sensory
integration, or sensory based interventions to generate
summary estimates of their eects on any outcome for
young children. Additionally, there were not enough
randomized controlled trials of TEACCH to generate
summary eects for any outcome.
Estimated eects from randomized controlled
trials excluding outcomes from caregiver or teacher
reports
Figure 4 shows summary eects estimated exclusively
from outcomes that were extracted from randomized
controlled trials and that were not derived from
caregiver or teacher reports (ie, were not at risk of
placebo-by-proxy bias). Statistically significant eects
were estimated for developmental interventions
on social communication outcomes (g=0.31, 95%
confidence interval 0.13 to 0.49) and for NDBIs on
measures of diagnostic characteristics of autism (0.44,
0.20 to 0.68) and social communication outcomes
(0.36, 0.23 to 0.49).
Estimated eects from randomized controlled
trials excluding all outcomes subject to high risk of
detection bias
Figure 5 shows summary eects estimated exclusively
from outcomes that were extracted from randomized
controlled trials where assessors were naive to
group assignment. Summary eect estimation was
possible for NDBIs only on measures of diagnostic
characteristics of autism (g=0.30, 95% confidence
interval 0.03 to 0.57), and cognitive (0.17, −0.02 to
0.37), language (0.06, −0.13 to 0.025), and social
communication outcomes (0.11, −0.03 to 0.26). Only
the eect on measures of diagnostic characteristics of
autism was statistically significant.
Moderator analyses of proximity and boundedness
Meta-regression analyses across the entire dataset
suggest that summary eects were significantly
smaller for distal outcomes compared with proximal
outcomes (B=−0.15, P=0.002). Additionally, eect
sizes coded as generalized were significantly smaller
than those coded as context bound (B=−0.27,
P<0.001).
Publication bias
Results from the Egger multilevel meta-analysis test31
indicated that, across all outcomes analyzed in our
models, there was evidence for funnel plot asymmetry
(B=2.98, P<0.001). There was still evidence of funnel
plot asymmetry when we restricted outcomes to only
those from randomized controlled trials (B=5.86,
P=0.01), only those from randomized controlled trials
that did not include caregiver or teacher outcomes
(B=8.18, P<0.001), and only those from randomized
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controlled trials that were at low risk of detection bias
(B=2.26, P=0.03).
Looking within interventions by outcome type (see
table S1 and figures S1-S8), limited evidence was
found for small study or publication bias within each
outcome type with a sucient number of eect sizes
and clusters. After correcting for multiple comparisons
with a Benjamini-Yekutieli false discovery rate
correction,34 no outcome type within any intervention
was identified with a statistically significant result for
the Egger multilevel meta-analysis test. Therefore, the
evidence indicates that small sample or publication
bias influenced the results, but the extent to which
publication bias could have influenced the results for
individual intervention types is not clear.
Adverse events, eects, and harms
When reports across both search periods were
considered together, 10% mentioned adverse events,
and of these, 66% reported that no adverse events
occurred, 34% reported that adverse events occurred,
and 17% reported that adverse eects occurred.
Additionally, only 28% of articles that mentioned
adverse events reported monitoring procedures for
determining if adverse events or eects occurred.
None of the reports in both search periods mentioned
harms, or indicated any intention to monitor harms
after the end of the intervention. The number of
reported adverse events across studies ranged from 0
to 67. Interestingly, three quarters (76%) of the studies
that mentioned adverse events, but did not describe
any monitoring procedures, reported that no adverse
events occurred. In contrast, only half (50%) of studies
that described at least some adverse event monitoring
procedures reported that no adverse events occurred.
Therefore, it is possible that the frequency with which
adverse events are reported to occur is dependent
upon the robustness and transparency of procedures
for monitoring them. Descriptions of adverse events
extracted from studies that reported this information
are provided in the supplementary materials.
Examples of adverse events that could be attributed
to the intervention were intense child aggression
and serious adverse eects on parent mental health
after participating in a parent mediated intervention
(see Bottema-Beutel and colleagues11 for additional
examples of adverse events extracted from studies
during the initial search period).
Discussion
The purpose of this systematic review and meta-
analysis was to provide an updated summary of
evidence on interventions designed for young autistic
children that includes more recently published studies.
In only four years after our initial search for Project
AIM, the available evidence has doubled, including
the number of randomized controlled trials (from 87
in our original report to 173 in the current report).
Three quarters of all controlled group design tests of
interventions and 80% of all randomized controlled
trials were published in the past decade, which means
that even intervention guidelines based on relatively
recent evidence reviews35 36 no longer reflect most
available evidence from controlled group design
studies.
Findings by intervention type
As in the previous meta-analysis, we estimated the
eects for behavioral interventions, developmental
interventions, NDBIs, and technology based
interventions. However, there were still not enough
studies to reliably estimate the summary eects for
animal assisted interventions or cognitive behavioral
therapy (although cognitive behavioral therapy
is typically recommended for older populations).
Because we restricted summary eect estimation to
those from randomized controlled trials, we were also
unable to estimate the summary eects of TEACCH on
any outcome (though see supplementary figure 1 for
estimates of summary eects when quasi-experimental
studies were included). We further divided the
previous sensory based intervention category into
three groups (music therapy, sensory integration
therapy, other sensory based interventions) to ensure
that intervention categories had consistent theories of
change. We found that these categories also had too
few controlled studies to reliably estimate summary
eects.
Behavioral interventions
In the US, traditional behavioral interventions are
the most frequently recommended intervention
approach for autistic children.37 When evidence from
randomized controlled trials is considered, it appears
that behavioral interventions might have moderate
positive eects on social emotional or challenging
behavior outcomes. These estimates are mainly driven
by eects from unmasked caregiver or teacher report
measures. However, this finding diers from our
previous report, in which not enough randomized
controlled trials of behavioral interventions had been
conducted to allow summary estimation of any eects.
Several randomized controlled trials were published in
the four years after the original search date; however,
this increase was mostly due to randomized tests
of focused behavioral interventions (eg, functional
communication training, prevent teach reinforce,
predictive parenting), and not by randomized tests
of early intensive behavioral intervention or other
comprehensive behavioral approaches. There was
only one randomized controlled trial of early intensive
behavioral intervention added to our current sample,
which tested randomized comparisons of this
treatment and the early start Denver model (an NDBI)
delivered at various intensities (and found that neither
intervention nor intensity was associated with superior
eects on measured outcomes).38 More studies that
compare robust delivery of two dierent but commonly
recommended comprehensive interventions are
needed. Given that behavioral interventions are
routinely recommended for this population, it is vital
that more controlled tests with unbiased measures are
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conducted, and that support recommendations are
altered to reflect the current evidence base.
Developmental interventions
As in our original meta-analysis, estimates from
randomized controlled trials suggest developmental
interventions have positive and statistically significant
eects on social communication, even when outcomes
at risk of placebo-by-proxy bias are excluded. However,
because developmental researchers frequently relied
on observational measures of social communication
derived from interactions with unmasked caregivers
to indicate developmental intervention eects,
our confidence in this estimate is limited by the
risk of detection bias. Therefore, we recommend
that intervention researchers estimate social
communication eects (or eects on any domain)
using assessments that use masked assessors, and
not only masked coders; this can be done even when
deriving variables from communication samples by
using a masked clinician who is skilled at facilitating
natural and responsive interactions.
Naturalistic developmental behavioral interventions
Despite only being named as a category in 2015,16
NDBIs are now the most frequently studied
intervention approach for this population. Summary
eect estimates from randomized controlled trials
suggest that NDBIs might improve adaptive behavior,
language, play, social communication, and measures
of diagnostic characteristics of autism, though our
confidence in these estimates is limited by reliance
on measures that are subject to high detection bias.
In contrast to our previous meta-analysis, we found
a statistically significant positive eect of NDBIs on
measures of the diagnostic characteristics of autism,
even when eects were restricted to outcomes from
randomized controlled trials with low risk of detection
bias. This was the only intervention eect we were
able to estimate when accounting for all risks of bias
considered. Given that diagnostic measures index core
features of autism (ie, repetitive patterns of behaviors,
interests, or activities, and social communication
challenges), and that our other estimates suggest
NDBIs have null eects on repetitive patterns of
behaviors, interests, or activities but positive eects
on social communication outcomes, it is likely that
the positive and statistically significant estimates
of NDBIs on overall measures of the diagnostic
characteristics of autism were driven by improvements
in social communication. We were unable to estimate
summary eects of NDBIs on outcomes categorized as
social communication alone when outcomes at high
risk of detection bias were excluded because social
communication outcomes were frequently measured
using interactions with unmasked assessors (eg,
caregivers) in studies of NDBIs. Most of these eects
were excluded when detection bias was considered,
and diagnostic measures of autism remained the
only masked assessments indicating improvements
in social communication in these studies. Therefore,
we conclude that there is relatively strong evidence
that NDBIs can have positive eects on core
features associated with autism, specifically social
communication dierences.
Using measures of diagnostic characteristics of
autism (eg, Autism Diagnostic Observation Schedule,
Childhood Autism Rating Scale) to index intervention
outcomes could be problematic because interpreting
“improved” scores on such measures as evidence of a
positive intervention eect implies that the goal of an
intervention is to make a child “less autistic.” Although
these measures indicate diculties associated
with autism diagnosis, they also capture neutral or
even beneficial aspects of autism (such as special
interests). If interventions are only intended to support
improvements in social communication, it is important
that researchers use high quality masked assessments
of this specific construct to index intervention eects,
such as the Communication and Symbolic Behavior
Scales39 or the Early Social Communication Scales40
as administered and coded by people who are naive to
children’s group assignment.
Technology based interventions
The number of studies of technology based
interventions nearly tripled (from 10 to 30 studies)
in the four years between the original and updated
searches. However, most of the high quality evidence
supporting the potential benefits of technology
based interventions reflects eects on proximal,
circumscribed outcomes. Technology based
interventions could have broad appeal because
they tend to be new and motivating, use predictable
formats, and have the potential to increase access for
those who might otherwise have diculty accessing
intervention and supports. For these reasons,
intervention developers might wish to integrate
technological supports into more established
intervention approaches to help the development of
specific skills.
Other interventions
Categories of intervention approaches that were too
sparsely represented in the literature to allow summary
eect estimation for autistic children between birth
and 8 years included animal assisted therapy, cognitive
behavioral therapy, music therapy, sensory integration
therapy, other sensory based interventions, and
TEACCH. Given that these interventions are frequently
prescribed for and used by this population, there is a
need for more rigorous research evaluating the ecacy
of such approaches. In the meantime, physicians
guiding families toward interventions should bear in
mind that the current evidence base is limited, and
they should keep up to date with emerging literature.
Proximity and boundedness
As expected, we found evidence that intervention
eects are greater on proximal than distal outcomes,
and for context bound than generalized outcomes,
replicating the findings of our previous work.8 41 These
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findings are consistent with the conclusions of a recent
review of early interventions for children with or at
high likelihood of receiving an autism diagnosis.42
Therefore, researchers who measure outcomes that
are not designed to show sustained developmental
change are likely to observe stronger eects and to
draw more positive conclusions about intervention
ecacy than is warranted. Intervention is often
recommended for autistic children in early childhood
at high intensities and for long durations based on the
assumption that this approach is necessary to support
generalized developmental improvements. If the goal
of evidence summaries is to determine whether early
childhood interventions support such improvements,
controlled group design studies that use measures well
equipped to tap broad developmental change oer
the most relevant evidence. Therefore, in evaluating
evidence and guiding families toward early childhood
interventions, it is important that physicians and other
clinicians consider not only the quality of evidence, but
also the scope of change reflected by the outcomes.12
Current trends and future research
The doubling of randomized controlled trials in
only four years suggests that it is possible for autism
researchers to conduct randomized controlled trials,
and that randomized tests of interventions should
be a minimum standard for establishing intervention
ecacy. Consequently, physicians should refrain from
drawing firm conclusions when available evidence is
largely quasi-experimental in nature, and researchers
should continue to treat random assignment as a
crucial component of experimental design.
Outcomes derived from caregiver or teacher report
measures comprised a substantial subset (nearly half)
of all outcomes, and the percentage of these report
measures even increased between the original and
updated samples. Physicians evaluating evidence from
randomized controlled trials of specific intervention
approaches should keep this in mind and exercise
caution in interpreting results that emphasize
statistically significant eects on outcomes measured by
proxy reports and unmasked assessments with minimal
reporting of eects on more stringent outcome measures.
Robust intervention evaluations require that
researchers assess, interpret, and report adverse
events, adverse eects, and harms.43 However,
these remain infrequently monitored or reported in
the autism intervention literature. Despite this lack
of monitoring, however, there is some evidence to
suggest that adverse events and adverse eects might
be relatively common.11 One promising finding from
our updated review is the increased frequency of
describing adverse event monitoring procedures:
44% of studies that mentioned adverse events
provided at least some description of their approach to
monitoring procedures. Moving forward, researchers
should develop definitions of adverse events and
procedures for monitoring them that are tailored
by intervention type, and shared across research
groups. These measures could include procedures for
active monitoring for adverse events that might occur
within the intervention sessions (eg, child distress,
injury from intervention equipment, aggression), and
additional procedures for active monitoring of events
that could occur outside of the intervention sessions
(eg, sleep disturbance, changes in eating habits,
anxiety, parental distress). Because anecdotal reports
and qualitative evidence suggest autistic adults might
have experienced long term harms from participation
in specific interventions,44 and because they have
expressed that research about potential intervention
harms is among their top research priorities,43 45
researchers should make concerted eorts to follow
participants over longer periods of time to document
the potential for sustained negative impacts of
interventions. Until such information is explored by
researchers, families and practitioners will continue
to have little basis on which to weigh the potentially
positive eects of interventions against the potential
for negative impacts.
Strengths and limitations
This updated meta-analysis has several strengths.
Our search of published and unpublished literature
ensured that we identified and retrieved as many
potentially eligible studies as possible. Our double
screening and double coding process and high
reliability suggest our data reliably reflect attributes
of included studies and outcomes. Additionally, our
statistical methods ensured that we estimated summary
eects as precisely as possible, while accounting for
the intercorrelated structure of the data and retaining
power in subgroup analyses.27 Our coding process also
accounted for study and outcome level risks of bias,
and outcome characteristics that are often ignored (ie,
boundedness and proximity), ensuring appropriate
caution in interpreting results.
Our investigation also has a number of limitations.
Although our procedures closely followed those used
in the previous meta-analysis, this update was not
registered. Even though we reliably categorized most
interventions and our intervention categories have
proved useful to other reviews,10 approximately a
quarter of studies featured interventions that were
categorized as other because they did not cluster
together in terms of their theory of change and were
not reflected in the current summary estimates.
However, eect sizes from these studies were
reflected in moderator analyses (ie, for proximity
and boundedness) and can be included in future
meta-regressions that investigate whether specific
participant or intervention characteristics are
associated with stronger eects. There were a range
of studies in this catch-all category which describe
interventions that might deserve further attention
given that they support improvements in domains
that are often regarded as impairments by autistic
people46 and implicated in important developmental
processes, but rarely targeted by intervention. These
range from specialized intervention approaches
that support sleep47 and management of eating
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aversions48 to academic programs that support
reading acquisition.49
Clinical recommendations
Authors of other prominent autism evidence reviews
based their clinical recommendations on rubrics
specifying dierent thresholds of study quality.35 50
For example, the National Clearinghouse on Autism
Evidence and Practice designated practices as evidence
based if they were supported by two peer reviewed
quasi-experimental or randomized controlled group
studies conducted by at least two research groups;
five single case design studies conducted by at least
three research groups and featuring a minimum of
20 participants in total; or a combination of one
controlled group study and three single case design
studies conducted by at least two research groups.50
In contrast, the National Autism Center designated
interventions as established if they were supported
by two quasi-experimental or randomized controlled
group studies or four single case design studies with
at least 12 participants and consistent eects.35
Because we recognize that the designation of a single
threshold as sucient for drawing conclusions is
somewhat arbitrary and varies by reviewer, we elected
to transparently present summary eects associated
with increasingly lower risks of bias (and higher levels
of certainty). Consistent with our previous findings
which were derived using a similar analytic approach,
we observed that because contributing eects were
increasingly excluded based on risk of bias, fewer
summary eects could be estimated, and associated
confidence intervals were wider. Consequently, as
quality thresholds increased, fewer summary eects
could be synthesized with a high degree of confidence
(df≥5), and crossed the threshold for statistical
significance, even though the magnitude of summary
eect estimates was often similar. To some extent, this
observation is a function of our analytic approach.
Raising quality standards will often reduce the sample
of studies and eects that meet such standards, and
so reduce power to detect a true eect that might be
present. By presenting estimates associated with
increasing levels of confidence, we hoped to show
which clinical recommendations might be supported
by some evidence (ie, evidence from randomized
controlled trials but including eects at risk of
detection and placebo-by-proxy bias), and which
might be supported by the best available evidence (ie,
evidence from randomized controlled trials excluding
eects at high risk of detection bias).
Given that there are few intervention approaches at
present with the best available evidence supporting
their ecacy for improving developmental outcomes,
what intervention recommendations should medical
professionals make? The Australian government
recently updated their national guideline for
supporting autistic children and their families, and
integrated a framework for making ethical support
recommendations.10 51 The framework suggests that
supports should be plausible (have a clear mechanism
of eectiveness and be supported by the best available
evidence), practical (feasible to deliver in local
conditions), desirable (consistent with child wants and
needs, and family priorities), and defensible (benefits
outweigh eort and opportunity costs, and will be
viewed positively by the child later in life). Drawing on
this framework, we recommend that physicians guide
families toward interventions with the most robust
evidence supporting their ecacy for improving the
intended outcomes, provided that the supports can
be oered in a way that integrates and strengthens
child wellbeing and family routines rather than
disrupting them. For example, interventions provided
within the home, embedded in daily routines, and
focused on strengthening caregiver capacities to
support development are less likely to disrupt child
and family wellbeing than interventions provided
at high intensities in clinics directly to the child by
clinicians. Clinicians should ensure that they have
adequate systems for monitoring whether the selected
intervention promotes progress in terms of acquiring
specific skills in specific contexts, and also in the
broader, generalized development of these children.
Finally, it cannot be assumed that interventions and
supports are harmless, so physicians should advise
families to monitor for indicators of negative eects
and child or family distress.
Conclusion
Studies investigating interventions for young autistic
children have proliferated at an astonishing rate, but
corresponding improvements in study quality have
not kept pace. Some high quality evidence exists,
which suggests that NDBIs can improve core features
associated with autism. However, it is not clear if such
outcomes are desirable for autistic people given that
measures of core features of autism are not restricted
to impairments that need to be addressed to positively
influence autistic development. Interventions tend
to have larger eects on small and specific changes
in specific contexts, and smaller eects on distal
and generalized developmental improvement. We
are unable to weigh the potential benefits of any
intervention against the potential for unintended
negative consequences because most researchers
are not adequately monitoring and reporting adverse
events.
AUTHOR AFFILIATIONS
1Division of Occupational Science and Occupational Therapy,
Department of Health Sciences, University of North Carolina at
Chapel Hill, Chapel Hill, NC, USA
2Lynch School of Education and Human Development, Boston
College, Chestnut Hill, MA, USA
3TEACCH Autism Program, University of North Carolina at Chapel
Hill, Chapel Hill, NC, USA
4Department of Hearing and Speech Sciences, Vanderbilt University
Medical Center, Nashville, TN, USA
5Frist Center for Autism and Innovation, Vanderbilt University,
Nashville, TN, USA
6University of Alabama at Birmingham, Birmingham, AL, USA
7Department of Curriculum and Instruction, University of Arkansas,
Fayetteville, AR, USA
8Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
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9Austin, TX, USA
10Department of Special Education, University of Texas at Austin,
Austin, TX, USA
11Vanderbilt Kennedy Center, Nashville, TN, USA
12Department of Communication Sciences and Disorders, John A
Burns School of Medicine, University of Hawaii at Manoa, Honolulu,
HI, USA
The authors acknowledge the helpful eorts of Elton Wells, who
created in-house soware that automated agreement checks for this
investigation.
Contributors: MS conceptualized the research project, oversaw all
aspects of the work, was responsible for conducting the search, full text
screening, data extraction, and manuscript draing, and acts as the
primary guarantor of the published ndings. KB-B conceptualized the
research project, assisted with title and abstract screening, conducted
adverse event coding, and participated in manuscript draing. SCLP
assisted with title and abstract screening, served as a key reliability
coder, summarized risk of bias analyses, and edited the draed
manuscript. JIF assisted with title and abstract screening, conducted
primary data analyses, assembled results tables and gures, edited the
draed manuscript, and acts as the secondary guarantor of published
ndings. DJB assisted with title and abstract screening, served as a key
reliability coder, and edited the draed manuscript. NC assisted with
reliability coding and edited the draed manuscript. KD assisted with
reliability coding and edited the draed manuscript. JC assisted with
title and abstract screening, audited agreement between coders, and
edited the draed manuscript. SA assisted with reliability coding and
edited the draed manuscript. TW conceptualized the research project
and edited the draed manuscript. The corresponding author attests
that all listed authors meet authorship criteria and that no others
meeting the criteria have been omitted.
Funding: Research reported in this publication was supported in
part by the National Center for Advancing Translational Sciences of
the National Institutes of Health under award number TL1TR002244
(principal investigator Hartmann) and the National Institute on
Deafness and other Communication Disorders of the National
Institutes of Health under award number F31DC020129 (principal
investigator KD). Aliated institutions and funders had no role in
study design; collection, analysis, or interpretation of data; nor the
writing of or decision to submit this report for publication.
Competing interests: All authors have completed the ICMJE uniform
disclosure form at www.icmje.org/disclosure-of-interest/ and declare:
support from the National Center for Advancing Translational Sciences
of the National Institutes of Health, and the National Institute on
Deafness and other Communication Disorders of the National Institutes
of Health for the submitted work. MS has received fees for presenting
research ndings in invited talks from Children’s Healthcare of Atlanta
and the New Jersey Autism Center of Excellence, and from law rms
representing the National Disability Insurance Scheme of Australia
for providing expert evidence on the ecacy of early childhood
interventions in court hearings. In the past three years, she taught
courses in a program that was accredited by the Behavior Analyst
Certication Board on both behavioral and NDBI early childhood
interventions. KB-B has previously received fees for consulting with
school districts on intervention practices for autistic children and
teaches courses on autism interventions in her role as an associate
professor of special education. She has also accepted speaker fees to
discuss her work on research quality, adverse events, and researcher
conflicts of interest as they pertain to autism intervention research.
She also receives royalties for a coedited book titled Clinical Guide
to Early Interventions for Children with Autism published by Springer.
SCLP was formerly aliated with an entity that trained students to
become board certied behavior analysts and provided early intensive
behavioral intervention. She is currently employed by the TEACCH
Autism Program and served as an interventionist on an intervention
developed at TEACCH for autistic transition age youth. JIF has been
paid to provide adaptive horseback riding lessons (an animal assisted
therapy). He is employed in a department that teaches students
to provide early communication therapies. NC is a board certied
behavior analyst at the doctoral level (BCBA-D) and is the current
president elect of the Arkansas Association for Behavior Analysis. She
teaches courses in a university program accredited by the behavior
analyst certication board and formerly provided quality assurance
and consultation services for the Arkansas Medicaid waiver program
which provides behavioral based services for children with autism
aged 0-8. SA is a board certied behavior analyst who directly provides
services to autistic children, adolescents, and adults. She is co-owner
of a clinical practice that receives direct payment for behavior analytic
services through contracts with local school districts, private and public
insurance payors, and Texas Medicaid waiver programs. Susanne is
an instructor for coursework that is approved by the behavior analyst
certication board, and she serves as a practicum and eld supervisor
for master’s level students in pursuit of advanced degrees in the eld of
behavior analysis. KD is a PhD candidate in a department that teaches
students to provide early communication therapies, including some
evaluated as part of this meta-analysis. JC was previously employed as
an early intervention therapist, and was paid to provide behavioral and
NDBI type therapies to children. TW is the parent of an autistic child;
has previously been paid to provide traditional behavioral, naturalistic
developmental behavioral, and developmental interventions to
young children on the autism spectrum; has received grant funding
from internal and external agencies, including the National Institutes
of Health and the Vanderbilt Institute for Clinical and Translational
Research, to study the ecacy of various interventions geared toward
young children with autism (though not to support this specic work);
and is employed by the Department of Hearing and Speech Sciences
at Vanderbilt University Medical Center, which oers intervention
services (which include the types of interventions evaluated in this
meta-analysis) for autistic children through their outpatient clinics and
trains clinical students in the provision of treatments delivered over the
course of early childhood. All other authors have no conflicts of interest
to declare.
Ethical approval: Ethical approval was not required for this work.
Data sharing: The coding manual that guided primary data extraction,
and the full dataset are available at https://osf.io/kr2cd/
The lead author (the manuscript’s guarantor) arms that the
manuscript is an honest, accurate, and transparent account of the
study being reported; that no important aspects of the study have
been omitted; and that any discrepancies from the study as planned
(and, if relevant, registered) have been explained.
Dissemination to participants and related patient and public
communities: The results of this work will be disseminated to the
public via press releases, presentations at conferences oriented
towards clinicians that serve young autistic children, and plain
language summaries posted on websites and social media. The lead
investigators are currently seeking funding to support development of
a website that will provide the public with open access to the dataset,
plain language summaries of ndings and links to published papers,
and data visualizations that intuitively characterize the data and
ndings for a lay audience. These ndings will also inform a future
clinical trial that includes robust patient involvement in evaluation of
the intervention acceptability and tolerability.
Provenance and peer review: Not commissioned; externally peer-
reviewed.
This is an Open Access article distributed in accordance with the
Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license,
which permits others to distribute, remix, adapt, build upon this work
non-commercially, and license their derivative works on dierent
terms, provided the original work is properly cited and the use is non-
commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
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Web appendix: Supplementary materials
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