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www.thelancet.com/digital-health Published online February 24, 2020 https://doi.org/10.1016/S2589-7500(20)30017-0
1
Articles
Lancet Digital Health 2020
Published Online
February 24, 2020
https://doi.org/10.1016/
S2589-7500(20)30017-0
See Online/Comment
https://doi.org/10.1016/
S2589-7500(20)30058-3
Psychiatry and Behavioral
Sciences, Duke University
Medical Center, Durham, NC,
USA (Prof S H Kollins PhD,
Prof R S E Keefe PhD); Duke
Clinical Research Institute,
Durham, NC, USA
(Prof S H Kollins); Akili
Interactive Labs, Boston, MA,
USA (D J DeLoss PhD,
E Cañadas PhD, J Lutz PhD);
Department of Psychiatry,
Virginia Commonwealth
University, Richmond, VA, USA
(Prof R L Findling MD); VeraSci,
Durham, NC, USA
(Prof R S E Keefe); Department
of Pediatrics, University of
Cincinnati College of Medicine,
Cincinnati, OH, USA
(Prof J N Epstein PhD); Meridien
Research & Lake Erie College of
Osteopathic Medicine,
Bradenton, FL, USA
(A J Cutler MD); and Psychiatry
and Neuroscience and
Physiology, SUNY Upstate
Medical University,
Syracuse, NY, USA
(Prof S V Faraone PhD)
Correspondence to:
Dr Scott Kollins, Psychiatry and
Behavioral Sciences, Duke
University Medical Center,
Durham, NC 27710, USA
scott.kollins@duke.edu
A novel digital intervention for actively reducing severity of
paediatric ADHD (STARS-ADHD): a randomised controlled trial
Scott H Kollins, Denton J DeLoss, Elena Cañadas, Jacqueline Lutz, Robert L Findling, Richard S E Keefe, Jeffery N Epstein, Andrew J Cutler,
Stephen V Faraone
Summary
Background Attention-deficit hyperactivity disorder (ADHD) is a common paediatric neurodevelopmental disorder with
substantial eect on families and society. Alternatives to traditional care, including novel digital therapeutics, have
shown promise to remediate cognitive deficits associated with this disorder and may address barriers to standard
therapies, such as pharmacological interventions and behavioural therapy. AKL-T01 is an investigational digital
therapeutic designed to target attention and cognitive control delivered through a video game-like interface via at-home
play for 25 min per day, 5 days per week for 4 weeks. This study aimed to assess whether AKL-T01 improved attentional
performance in paediatric patients with ADHD.
Methods The Software Treatment for Actively Reducing Severity of ADHD (STARS-ADHD) was a randomised, double-
blind, parallel-group, controlled trial of paediatric patients (aged 8–12 years, without disorder-related medications) with
confirmed ADHD and Test of Variables of Attention (TOVA) Attention Performance Index (API) scores of −1·8 and
below done by 20 research institutions in the USA. Patients were randomly assigned 1:1 to AKL-T01 or a digital control
intervention. The primary outcome was mean change in TOVA API from pre-intervention to post-intervention. Safety,
tolerability, and compliance were also assessed. Analyses were done in the intention-to-treat population. This trial is
registered with ClinicalTrials.gov, NCT02674633 and is completed.
Findings Between July 15, 2016, and Nov 30, 2017, 857 patients were evaluated and 348 were randomly assigned to
receive AKL-T01 or control. Among patients who received AKL-T01 (n=180 [52%]; mean [SD] age, 9·7 [1·3] years) or
control (n=168 [48%]; mean [SD] age, 9·6 [1·3] years), the non-parametric estimate of the population median change
from baseline TOVA API was 0·88 (95% CI 0·24–1·49; p=0·0060). The mean (SD) change from baseline on the
TOVA API was 0·93 (3·15) in the AKL-T01 group and 0·03 (3·16) in the control group. There were no serious adverse
events or discontinuations. Treatment-related adverse events were mild and included frustration (5 [3%] of 180)
and headache (3 [2%] of 180). Patient compliance was a mean of 83 (83%) of 100 expected sessions played
(SD, 29·2 sessions).
Interpretation Although future research is needed for this digital intervention, this study provides evidence that
AKL-T01 might be used to improve objectively measured inattention in paediatric patients with ADHD, while
presenting minimal adverse events.
Funding Sponsored by Akili Interactive Labs.
Copyright © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.
Introduction
Attention-deficit hyperactivity disorder (ADHD) is a
neurodevelopmental disorder of persistent impaired
attention, hyperactivity, and impulsivity that negatively
aects daily functioning and quality of life. ADHD is
one of the most commonly diagnosed paediatric mental
health disorders, with a prevalence estimated to be 5%
worldwide,1 and exerts a substantial burden on families
and society.2
Front-line intervention for ADHD includes pharmaco-
logical and non-pharmacological interventions, which
have shown short-term ecacy.3–5 Existing treatments
have side-eects that limit their acceptability,6 are only
eective when administered, and may not be as eective
for reducing daily impairments versus ADHD symptoms.7
Pharmacotherapy may not be suitable for some patients
due to caregiver preferences or concerns about abuse,
misuse, and diversion. Barriers to access also limit the
use of behavioural interventions, given a shortage of
properly trained paediatric mental health specialists8 and
variability in insurance coverage for such services.9,10
Indeed, studies in both the USA and the UK have found
that most children with paediatric mental health needs do
not have proper access to services.11,12
Digital therapeutics for ADHD may address these
limitations with improved access, minimal side-eects,
and low potential for abuse. Numerous studies and
meta-analyses on digital interventions targeting specific
cognitive functions have attempted to assess the
magnitude of ecacy for children and adolescents with
ADHD. In general, the quality of the studies is low, and
many do not include a control group.3 Reported eect
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sizes are generally small but dier widely with respect to
interventions and study designs.13 In addition, a recent
meta-review concluded that a range of digital inter-
ventions, including working memory training and
neurofeedback, could not be recommended for treatment
of ADHD owing to inconsistent findings and generally
minimal eects on outcomes provided by independent
unmasked observers.14 Nevertheless, meta-analyses
conclude that interventions more broadly targeting
cognitive functions generally show larger eects.
ADHD has numerous well characterised but hetero-
geneous neurobiological substrates underlying cognitive
impairments that can serve as targets for intervention
development.15 For example, impairments related to
attention and cognitive control are associated with lower
activation of frontal, frontoparietal, and ventral attention
networks.16 Research on video games and game-based
interventions that may alter brain structures and
function, suggest that targeted digital interventions—
based on current models of cognitive function and which
leverage video game design to engage patients over
time—are promising.17 Anguera and colleagues18
described a game-like intervention developed to engage
the cognitive control and attention systems in older
adults. The digital intervention improved cognitive
performance and these changes were associated with
functional electroen cephalogram (EEG) changes in the
prefrontal cortex.This intervention specifically targeted
the management of cognitive interference, which occurs
when two or more tasks compete for cognitive and
attentional resources. The cognitive control and attention
systems evaluated in these studies are similar to the
deficits observed in paediatric patients with ADHD.18,19
This overlap informed the development of a novel digital
therapeutic, AKL-T01 (Akili Interactive Labs, Boston, MA,
USA), which was developed to engage paediatric users
through video game graphics and reward loops and to
use real-time adaptive mechanisms that continuously
personalise intervention diculty on the basis of the
user’s ability and progression. Specifically, AKL-T01
targets attentional control to manage competing tasks
and to eciently (flexibly) shift attention between tasks.
Further, divided and selective attention systems are
required to process several tasks simultaneously. In an
Research in context
Evidence before this study
We searched PubMed with the search terms “ADHD,” “cognitive/
digital training/therapeutic,” “children/pediatric,” and “clinical
trial” between Jan 1, 2010, and July 31, 2019. We found that
few digital interventions were available before 2010. We also
examined review articles and meta-analyses between
Jan 1, 2010, and July 31, 2019. Although we did not limit the
search to English language publications, we were not able to
review non-English language publications, however, no relevant
trial seemed to be available in a non-English language journal.
Several digitally based interventions for attention deficit
hyperactivity disorder (ADHD) were identified. Many trials
focused on training working memory, and fewer on targeted
attention and cognitive control specifically. Further, many of the
studies contained methodological limitations, including
inadequate control conditions or masking, or both, no random
assignment to intervention conditions, small sample size, and no
safety or adverse event assessments. Many studies used outcome
measures similar to the training tasks and did not use US Food
and Drug Administration-approved cognitive outcomes or those
commonly used in clinical settings. Indeed, meta-analyses on
cognitive training for children with ADHD confirm that most
current studies have inadequate methodology and cannot
definitively evaluate the efficacy and clinical relevance of such
treatments. The most comprehensive review of these studies to
date concluded that digital interventions cannot be
recommended on the basis of the current body of evidence.
Added value of this study
In this randomised controlled trial, AKL-T01 (an investigational
digital therapeutic) increased attentional functioning in an
objective measure of attention to a significant degree in
paediatric patients with ADHD, as well as patient-reported and
parent-reported attentional functioning. Across several
secondary outcomes, including parent and clinician ratings of
ADHD symptoms and functional impairment, AKL-T01
significantly improved outcomes from pre-intervention to
post-intervention, but not to a significantly greater degree
than the control condition. This trial represents one of a small
number of randomised controlled trials for digital
interventions for paediatric patients with ADHD. The methods
were modelled after randomised controlled trials for other
treatment modalities (ie, pharmaceutical trials) and represent
a model approach for evaluating the effects of a digital
intervention.
Implications of all the available evidence
This study shows that a digital intervention can significantly
increase attentional functioning of children with ADHD. Future
trials are warranted to examine the durability and time course
of this novel intervention, as well as the appropriate dose that
might provide optimal benefit. In addition, studies to better
characterise the clinical significance of objective attention
measures versus subjective symptom ratings are needed. These
findings have implications for clinical practice, as AKL-T01 is a
safe and easy-to-access intervention that could address various
intervention needs for paediatric patients with ADHD and
without comorbid conditions (ie, attention deficits), but
cannot replace current standard of care.
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unmasked, proof-of-concept study, a prototype version of
AKL-T01 showed improvements in attention, inhibition,
and working memory in paediatric patients with ADHD
but not in patients without ADHD.20
The primary objective of the present trial was to
evaluate the ecacy and tolerability of AKL-T01 in
paediatric patients with ADHD.
Methods
Study design
STARS-ADHD was a randomised, double-blind, parallel-
group, controlled trial done at 20 research institutions
(appendix p 3) in the USA from July 15, 2016, to
Nov 30, 2017. During the screening phase (days −28 to −7),
patients were evaluated for eligibility. Children treated
with medication for ADHD discontinued medication to
at least 3 days before baseline. At baseline (day 0),
additional eligibility criteria were assessed. The study
was done in accordance with the International
Conference on Harmonisation Regulations, and was
approved by each site’s institutional review board
(Copernicus Group [14 sites], Duke University Health
System, Cincinnati Children’s Hospital Medical Center,
University of California Davis, University of California
San Francisco, Johns Hopkins Medical Center, and
Western Institutional Review Board).
Participants
Eligible patients were aged 8–12 years with a confirmed
diagnosis of ADHD as per the Diagnostic and Statistical
Manual of Mental Disorders (5th edn) criteria and
confirmed via the Mini International Neuropsychiatric
Interview for Children and Adolescents Kid Screen at
screening. Other key inclusion criteria included baseline
scores on the ADHD Rating Scale-IV (ADHD-RS-IV) of
28 and above, the Test of Variables of Attention (TOVA)
Attention Performance Index (API) −1·8 and below,
indicating cognitive deficits in the attention domain, and
a baseline intelligence quotient of 80 and above. Key
exclusion criteria were significant comorbid psychiatric
diagnoses and use of ADHD medications that could
not be discontinued. Parents provided written informed
consent with patient assent at screening–baseline.
Complete inclusion and exclusion criteria are described
in the appendix (p 2).
Children who recently used or were currently using
stimulants were eligible, provided they were not optimally
managed and willing to washout between 7 and 3 days
before baseline. This group was of particular interest
because parents and children would have recent experience
with some form of pharmacological intervention. As such,
randomisation was stratified by medication status at
screening (see below).
To minimise bias, parents and patients were informed
that the study was evaluating the eect of two dierent
investigational interventions for ADHD. Previous market
research with expert interviews in a sample of 59 children
and parents was done to evaluate expectation of benefit
of both interventions. The results suggested parents
had a similar expectation of benefit from both AKL-T01
and our control condition (see appendix p 4). Parents and
patients were discouraged from discussing their
randomised intervention with anyone other than an
unmasked study coordinator. Investigators and other
masked site sta were not permitted access to source
documents or case report forms.
Randomisation and masking
Eligible patients were randomly assigned 1:1 to receive
AKL-T01 or a control. The randomisation scheme was
generated by Duke Clinical Research Institute statistics
by means of validated computer software-generated
pseudorandom numbers. Randomisation was stratified
by stimulant medication status at screening. Each site
had unmasked sta who enrolled patients through
the clinical data management system, obtained the
randomised intervention, and trained patients on the
assigned device. Devices were provided to the unmasked
site sta by Akili (Akili Interactive Labs, Boston, MA,
USA) along with the list that linked the device serial
number to intervention; Akili was masked as to which
patient received which device until after database lock.
Parents, patients, and investigators completing outcome
measure assessments were masked to intervention
allocation (appendix p 4).
Procedures
Eligible patients were instructed to use their randomised
intervention for about 10 min while the unmasked
coordinator monitored the session to ensure patients
could follow the rules of their assigned intervention.
Study interventions were administered by means of
an iPad mini 2 tablet (Apple, USA). iPads either had
AKL-T01 or the control preloaded, and patients accessed
their randomised intervention with a unique username
and password.
AKL-T01 is an investigational digital therapeutic that
uses a proprietary algorithm designed to improve
attention and related cognitive control processes, by
training inter ference management at an adaptive and
personalised high degree of diculty. Interference is
instantiated through a video game-like interface displaying
two tasks that are to be done in parallel (multitasking): a
perceptual discrimination targeting task in which users
respond to the instructed stimulus targets and ignore the
stimulus distractors (similar to a Go–No-Go task) and a
sensory motor navigation task in which users continuously
adjust their location to interact with or avoid positional
targets. Performance in each task is assessed during
single and interference (multitask) conditions. On the
basis of the individual’s performance, the interference
training is adapted in real time, by means of a stair-
casing algorithm methodology. This tailors the training
specifically to each individual’s performance level to
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achieve a consistent and optimal challenge at a predefined
level of diculty that is challenging but also tolerable.
Further, on the basis of dierence between single-task and
interference performance, the user advances by reducing
interference costs (closes the performance gap between
interference and single-task conditions, at a specified
level). Progress is signalled by earning rewards and
unlocking new environments. As the user proceeds
through the intervention and the dierent environments,
periodic recalibration occurs to maintain an optimal
diculty level. AKL-T01 is presented in the video.
The control was designed to match AKL-T01 on
expectancy, engagement, and time on task in the form of a
challenging and engaging digital word game, targeting
cognitive domains not targeted by the AKL-T01 intervention
and not primarily associated with ADHD.21 The user was
instructed to find and connect letters on a grid to spell
words; points are awarded on the basis of number of words
formed, word length, and the use of unusual letters. There
is progression in diculty to maintain engagement and
expectation of benefit from patients and their caregivers.
During the intervention period (days 1–28), patients
were instructed to use AKL-T01 or the control at home
for 5 sessions per day (total time on task about 25 min),
5 days per week, for 4 weeks or the control for
25 min per day, 5 days per week, for 4 weeks. Compliance
was monitored electronically by unmasked study co-
ordinators, who notified parents by email if the intervention
was not administered over a 48-h period. AKL-T01 and the
control also generated automatic reminders. Additional
information regarding the protocol may be found in the
appendix (pp 6–7). The post-intervention visit was scheduled
on day 28. Patients were reassessed for attentional
functioning, ADHD symptoms, and impairment.
Outcomes
The primary outcome measure was the mean change in
the TOVA API from pre-intervention to post-intervention.
The TOVA is a validated, computerised, continuous
performance test that objectively measures attention and
inhibitory control, normalised by age and sex.22 TOVA
has been cleared by the US Food and Drug Administration
(FDA) to facilitate assessment of attention deficits and to
evaluate the eects of interventions in ADHD.
TOVA presents targets and non-targets as squares that
either appear at the top or bottom of the screen. The task
takes 21·6 min and consists of two halves: the first half
has a target-to-non-target ratio of 1:3·5 (similar to
sustained attention tests); the second half has a target-to-
non-target ratio of 3·5:1, thus requiring more inhibitory
control. TOVA calculates a wide range of outcome
measures that assess processes known to be disrupted in
patients with ADHD, such as response time variability
(attention consistency), ex-Gaussian tau (attentional
lapses), and response time (processing speed).22,23 The
TOVA API is a composite score of the sum of three
scores: reaction time (RT) mean Half-1 (highly infrequent
targets), RT variability total (both halves), and d-prime
Half-2 (highly frequent targets).22,23
Secondary ecacy endpoints were between-group
comparisons of pre-intervention and post-intervention
change in scores on the Impairment Rating Scale (IRS),
ADHD-RS-IV (Total [ADHD-RS-T], Inattentive [ADHD-
R-I], Hyperactive/Impulsive [ADHD-RS-H] subscales),
Clinical Global Impressions—Improvement (CGI-I),
and the Behavior Rating Inventory of Executive Function
(BRIEF; Parent Inhibit, Working Memory subscales, and
Metacognition Index [post hoc]). Descriptions of each of
the measures are provided in the appendix (pp 4–5).
We further analysed patient-reported and parent-
reported perceived benefits related to attention improve-
ments in real life (post hoc), assessed during a structured
exit questionnaire asking whether the intervention
helped their or their child’s attention in real life, with yes
or no responses.
During the intervention period, caregivers spontaneously
reported adverse events by phone to masked investigators;
any adverse events spontaneously reported during study
visits were captured. Details about use, performance, and
compliance with intervention were automatically recorded
by the study devices and uploaded to central servers when
the iPads were connected to wireless internet.
The proportions of responders at the end of treatment
phase for primary and secondary endpoints were
prespecified on the basis of previous studies,20,24 and
clinical meaningfulness for these analyses was defined as:
API improvement greater than 1·4 points, and post-test
API score 0 or more (normative range), ADHD-RS
improvement of 2 points or more, CGI-I post-score of 1
(very much improved) or 2 or less (very much or much
improved), and any improvement in IRS. Additional
post-hoc responder definitions were also examined:
ADHD-RS total score change 30% or more, and
percentage of participants who scored in the normative
range for TOVA standard score measures (>85, per the
TOVA Clinical Manual).22 In post-hoc analyses, inter-
vention eects in the subgroup of children who washed
out of ADHD medication after screening (n=20) and
children who had discontinued medication before but
within 30 days of screening (n=45) were explored.
Statistical analysis
A prespecified analysis plan governed all analyses, unless
identified as post hoc. Power analyses determined that a
sample size of 150 patients per intervention group would
be sucient to detect an eect size of 0·40 with 90% or
more power on a two-tailed, between-patients t test and α
criterion of 0·05.
A single interim analysis for sample-size re-estimation
was prespecified to occur after half of the initial sample
size was collected, and done by separate unmasked
statistical sta in order to minimise any bias in the conduct
of the study. If the conditional power of the interim sample
indicated the need for a larger final sample, the estimated
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5
sample size required would have been communicated to
the sponsor and investigators, up to a maximum of
1000 total participants. The α criterion for the final analysis
of the primary outcome was reduced by means of the
O’Brien-Fleming alpha-spending method to 0·0412. Since
the interim analysis only looked at the primary outcome,
and analysis of the secondary outcomes was gated by
success of the primary outcome, no α adjustment for the
secondary outcomes was necessary. The interim analysis
was done at n=75/75 (AKL-T01/control).
All outcomes were analysed in all randomly assigned
patients by means of an intention-to-treat methodology.
Safety data are presented for the population of patients
that received AKL-T01 or control for at-home intervention.
For the safety analysis, patients who received an
intervention inconsistent with their original randomly
assigned group (n=1 for AKL-T01 and n=1 for control)
were recategorised to the intervention received.
The primary endpoint (change in TOVA API) was
analysed by means of a Wilcoxon rank-sum test owing to
evidence of non-normality. The α criterion for the final
analysis was adjusted for the interim analysis sample-size
re-estimation by means of the O’Brien-Fleming alpha-
spending function, and the interim and post-interim
cohorts were tested separately, with the results combined
via the Cui-Hung-Wang method.25 If the combined p value
was less than the adjusted criterion, the primary endpoint
was considered successful. The α criterion for the primary
endpoint significance was 0·041.
Key secondary endpoints (IRS, ADHD-RS, CGI-I, and
BRIEF-Parent) were analysed by Wilcoxon rank-sum tests
due to evidence of non-normality. The between-group
dierence for the primary endpoint was calculated by
means of the Hodges-Lehmann estimate of location shift
to coincide with the use of a Wilcoxon test. Type I error
was controlled with a resampling bootstrap method to
adjust p values due to correlated endpoints. Secondary
endpoints were not adjusted for sample-size re-estimation.
If the adjusted p value was less than 0·050, the endpoint
was considered significant. Responder analyses used χ²
tests to compare AKL-T01 and control for the primary and
secondary endpoints. To summarise findings across a
range of outcomes, odds ratios and CIs were calculated to
compare the ecacy of AKL-T01 versus control.
Prespecified sensitivity analyses of the primary and key
secondary endpoints were designed to assess the eects
of: cohort, site, age, sex, missing data (if >10% of outcome
data were missing), and parent expectancy (secondary
endpoints only). For the primary and key secondary
endpoints, post-hoc non-parametric analyses for within-
group changes (pre-intervention vs post-intervention)
were done by means of the Wilcoxon signed-rank test
(SAS version 9.4).
Descriptive statistics summarised patient demo-
graphics, protocol deviations, intervention compliance,
intervention-related adverse events, and qualitative survey
results. Compliance with intervention was defined as the
percentage of instructed sessions use completed during
the intervention period or the percentage of instructed
use time for the control (as the control did not follow a
five-sessions-a-day format).
Post-hoc t tests for between-group and within-group
changes were done on the primary and key secondary
endpoints to assess sensitivity of results to statistical
methodology. All such tests were in agreement with
corresponding non-parametric Wilcoxon tests with
respect to significance for all results reported.
A post-hoc Fisher’s exact test was done to compare the
percentage of patients–parents in the two groups who
indicated real-life improvements related to attention on
the exit questionnaire (yes or no response).
Protocol amendments
Three versions of the protocol were used throughout the
study. Under the first version (version 1.0, dated
Nov 17, 2015), no participants were enrolled. Under the
second version (version 1.01, dated April 6, 2016),
43 participants were enrolled. Under the third version
(version 1.02, dated July 25, 2016), 305 participants were
enrolled. Details regarding all of the changes made for
each of these amendments are found in the appendix
(pp 6–7). One exclusion criterion was added to version 1.01
that disallowed participation for children who had
previously been in a study with AKL-T01. Version 1.01
also added a requirement that participants be able to spell
at least two words during the EVO: Words assessment at
baseline. Version 1.02 added a requirement that sites
document a discussion with participants and caregivers
regarding intended and unintended use of the devices.
Figure 1: Trial profile
Detailed information on inclusion and exclusion criteria can be found in the appendix (p 2).
AKL-T01=an investigational digital therapeutic.
168 allocated to control
167 received allocated intervention
1 received incorrect allocation
160 included in intention-to-treat-analysis
2 lost to follow-up
2 withdrawn by parent
4 excluded owing to invalid test
857 patients assessed for eligibility
348 randomly assigned
509 excluded
482 did not meet inclusion criteria
32 met exclusion criteria
4 enrolled late
180 allocated to AKL-T01
179 received allocated intervention
1 received incorrect allocation
169 included in intention-to-treat-analysis
3 lost to follow-up
1 withdrawn by parent
1 investigator decision
6 excluded owing to invalid test
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Other changes included more details on device
management and inventory procedures, updates to the
statistical analysis section, requirements that unmasking
events be captured in the study database, and a
requirement that the same clinician rater complete
assessments at pre-study and post-study timepoints.
These changes are not expected to have any eect on
study outcomes. VeraSci Trials (formerly NeuroCog
Trials) reviewed data for select cognitive and clinical
measures to determine quality and consistency within
and between measures. This trial is registered with
ClinicalTrials.gov, NCT02674633.
Role of the funding source
The funder had a role in study conception and design,
confirming data and statistical analyses, and conducting
the study. All authors had full access to all the data in the
study and were involved in data interpretation and
writing of the report. The corresponding author had final
responsibility for the decision to submit for publication.
Results
Of 857 children screened for eligibility, 348 patients were
randomly assigned to receive AKL-T01 (n=180) or control
(n=168) between July 15, 2016, and Nov 30, 2017 (figure 1
and appendix p 3). Demographic and clinical character-
istics at baseline are shown in table 1.
The mean number of sessions completed by patients
in the AKL-T01 group was 83·2 out of 100 sessions
(83% instructed use; SD=29·2 sessions). Patients in the
control group used their intervention 480·7 min of
500 min (96% instructed use).
There was a significant dierence between intervention
groups on the primary ecacy endpoint (adjusted
p=0·0060); non-parametric estimate of the population
median change (Hodges-Lehmann estimate) was 0·88
(95% CI 0·24–1·49). The mean (SD) change from baseline
on the TOVA API was 0·93 (3·15) in the AKL-T01 group
and 0·03 (3·16) in the control group (figure 2). There were
no intervention-group dierences for secondary measures:
IRS, ADHD-RS, ADHD-RS-I, ADHD-RS-H, BRIEF-
Parent Inhibit and Working Memory and Metacognition
(post hoc) from pre-intervention to post-intervention or
mean CGI-I score at post-intervention (appendix pp 4–5).
Sensitivity analyses showed no evidence that site,
baseline TOVA API, age, or sex attenuated the inter-
vention eect. Because missing data did not exceed the
prespecified limit, sensitivity analyses for missing data
were not done. Sensitivity to parent expectancy was not
evaluated owing to lack of significant dierences between
groups on the secondary endpoints.
In post-hoc within-group analyses, change in TOVA
API score from pre-intervention to post-intervention
significantly improved with AKL-T01 (p<0·0001) but not
with control (p=0·67). Both AKL-T01 and patients in the
control group showed significant within-group improve-
ments in all secondary endpoints (appendix pp 4–5).
AKL-T01
(n=180)
Control
(n=168)
Age, years 9·7 (1·3) 9·6 (1·3)
Male 125 (69%) 123 (73%)
Female 55 (31%) 45 (27%)
Baseline score
Test of Variables of Attention—Attention
Performance Index*
−5·1 (3·0) −4·9 (3·1)
Impairment Rating Scale 5·5 (1·1) 5·5 (1·2)
ADHD-Rating Scale 39·0 (6·8) 38·3 (6·6)
ADHD-Rating Scale—Inattentive 21·9 (3·5) 21·6 (3·7)
ADHD-Rating Scale—Hyperactivity 17·1 (6·0) 16·7 (5·4)
Clinical Global Impressions—Severity† 4·5 (0·7) 4·6 (0·6)
Data are n (%) or mean (SD). AKL-T01=an investigational digital therapeutic.
*n=179 for AKL-T01. †Assessed only at baseline.
Table 1: Baseline characteristics
Figure 2: Primary endpoint: TOVA API mean (SE) change pre-intervention to
post-intervention in the intention-to-treat population
*Adjusted p<0·050; prespecified Wilcoxon rank-sum test. Triangle represents
median change, pre-intervention to post-intervention.
AKL-T01 (n=169) Active control (n=160)
–0·25
0
0·25
0·50
0·75
1·00
1·25
1·50
Improvement
Mean (SE) change in TOVA API
*
AKL-T01 Control χ² test p
Test of Variables of Attention—Attention
Performance Index (type A: improvement
>1·4 points)
79/169 (47%) 51/160 (32%) 7·60 0·0058
Attention Performance Index (type B:
post-intervention score ≥0)
18/170 (11%) 7/160 (4%) 4·54 0·033
ADHD-Rating Scale (improvement ≥2 points from
pre-intervention to post-intervention)
128/173 (74%) 119/164 (73%) 0·088 0·77
ADHD-Rating Scale (≥30% reduction)* 42/173 (24%) 31/164 (19%) 1·43 0·23
Impairment Rating Scale 82/171 (48%) 60/161 (37%) 3·87 0·049
Clinical Global Impressions (≤2 at post-
intervention)
29/175 (17%) 26/164 (16%) 0·032 0·86
Clinical Global Impressions (1 at post-intervention) 1/175 (1%) 1/164 (1%) 0·0021 0·96
Data are n/N (%) unless otherwise indicated. AKL-T01=an investigational digital therapeutic. *Post-hoc analysis.
ADHD=Attention-deficit hyperactivity disorder. AKL-T01=an investigational digital therapeutic.
Table 2: Clinical responder analysis intention-to-treat population
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Additional exploratory post-hoc analyses were done
to better interpret the change in objective measures
of attention. The Standard Score transformations of
TOVA components related to attention were analysed for
between-group dierences: significant between-group
eects in favour of AKL-T01 were found for RT mean
Half-1 (p<0·0003), RT variability total (p=0·019), and
ex-Gaussian tau (p=0·0014).
Responder analyses showed that AKL-T01 resulted
in TOVA API score improvements of greater than
1·4 points in 79 (47%) of 169 patients versus 51 (32%)
of 160 controls (p=0·0058; table 2). AKL-T01 versus
control was also associated with the movement of more
patients into the normative ranges across dierent
measures of attention on TOVA: API of 0 and above in
18 (11%) of 170 versus 7 (4%) of 160, RT mean Half-1 in
32% versus 16%, and RT variability total in 22% versus
13% (p values API p=0·033, RT mean Half-1 p=0·0043,
RT variability total p=0·030). Overall, AKL-T01 versus
controls moved significantly more patients into the
normative range in at least one objective measure of
attention (36% vs 21%, p=0·0027). IRS responder rates
were significantly higher after AKL-T01 versus control
(82 [48%] of 171 vs 60 [37%] of 161, p=0·049). Remaining
responder comparisons did not dierentiate between
groups.
The percentage of patients reporting an improvement
in attention on the exit questionnaire for AKL-T01 versus
control (126 [73%] of 172 vs 107 [66%] of 162) was not
significant (χ²(1)=2·054, p=0·15). However, the percentage
of parents reporting improvements in their child’s
attention was significantly higher for AKL-T01 versus
control (97 [56%] of 173 vs 71 [44%] of 162, χ²(1)=5·015,
p=0·025). Comparisons of the ecacy of AKL-T01 versus
control across a range of outcomes are summarised in
figure 3.
In post-hoc analyses of patients who discontinued
stimulant medication, within 30 to 3 days before the start
of the study (washout group), AKL-T01 significantly
dierentiated from control on most secondary ecacy
endpoints including ADHD-RS (p=0·0092), ADHD-RS-I
(p=0·0083), and CGI-I (p=0·012). The dierence between
medication washout groups on the IRS was not significant
(p=0·065; figure 4).
The proportion of patients reporting any intervention-
related adverse events was 12 (7%) of 180 with AKL-T01
and 3 (2%) of 168 with control (table 3). There were no
serious intervention-related AEs or discontinuations due
to AEs in either group. The most common intervention-
related AEs associated with AKL-T01 were frustration
(5 [3%] of 180) and headache (3 [2%] of 180).
Discussion
In this randomised controlled clinical trial of a digital
intervention for ADHD, the active intervention AKL-T01
significantly improved performance on the primary
outcome measure—an objective measure of attention
(TOVA API) in paediatric patients with ADHD
compared with the control condition. Across a range of
secondary outcomes, including parent and clinician
ratings of ADHD symptoms and functional impairment,
the eects of AKL-T01 from pre-intervention to post-
intervention were not dierent from the control
condition. Additional attention-related measures from
TOVA, including mean reaction time during infrequent
target stimuli, and response variability (ie, total RT
variability and ex-Gaussian tau) showed significantly
greater improvements in the AKL-T01 group. Globally,
parent-reported improvement of attention, as assessed
by the exit survey, was higher in the AKL-T01 group
compared with controls. A prespecified subgroup of
patients who washed out of medications showed
significant between-group eects on several secondary
endpoints, including ADHD symptoms.
Both interventions were very well tolerated; only
12 (7%) of 180 and 3 (2%) of 168 patients in the AKL-T01
and control groups had intervention-related AEs,
respectively. All AEs associated with AKL-T01 were
classified as mild or moderate in severity and resolved
after study discontinuation.
The current study findings of improved attention (via
TOVA API) following treatment with AKL-T01 are
consistent with benefits reported in previous uncon-
trolled studies.18,20 As a digital therapeutic, AKL-T01 could
Figure 3: STARS-ADHD intention-to-treat responder forest plot
Odds ratio of 1·0 indicates that participants do not respond more to AKL-T01 than control. CIs in which the lower
bound does not cross 1·0 are significant. API=Attention Performance Index. CGI-I= Clinical Global Impressions-
Improvement. IRS=Impairment Rating Scale. RT mean H1=Reaction Time Mean during First Half of the TOVA.
RS Inattentive=ADHD Rating Scale—Inattentive. RS Hyperactive= ADHD Rating Scale—Hyperactivity.
ADHD=Attention-deficit hyperactivity disorder. TOVA=Test of Variables of Attention. AKL-T01=an investigational
digital therapeutic.
0 0·5 1·0
Intention-to-treat odds ratio (95% CI)
5·0
CGI-I ≤2
RS Hyperactive ≥30% improvement
RS Total ≥30% improvement
IRS ≥1 point improvement
Child report improved attention
Parent report improved attention
RS Inattentive ≥30% improvement
TOVA RT variability normative range
(attentional consistency)
TOVA RT mean H1 normative range
(selective and sustained attention)
TOVA API normative range
(attention composite)
TOVA API >1·4
(attention composite)
Responder type
Favours control Favours AKL-T01
Objective attention
Subjective attention
Impairments and symptoms
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theoretically address several challenges faced by existing
interventions. First, its risk–benefit profile is favourable,
as only 12 (7%) of 180 patients assigned to AKL-T01
had AEs, compared with rates of 40–60% in trials
of commonly used stimulant medications.26 Therefore,
AKL-T01 could be added to standard of care without
substantial additional safety concerns. Second, the digital
nature of this intervention could reduce barriers to access
that are inherent in other forms of behavioural or non-
pharmacological interventions.27 Digital interventions
have been cited as possible ways to improve otherwise
poor access to mental health services.14
The primary outcome measure for this trial—the TOVA
API—diers from most pharmacological ecacy trials for
ADHD, which typically use parent-rated or clinician-rated
symptom measures. The selection of the TOVA was based
on several factors. First, because AKL-T01 was designed
specifically to target cognitive control and attention, we
sought an outcome that would most precisely and validly
index these processes. The TOVA is an FDA-cleared
device23 for the objective assessment of attention and
inhibitory control as part of an ADHD diagnosis or for
monitoring intervention outcomes and has been widely
used in both clinical practice and research studies. Second,
the TOVA measures cognitive functions that are relevant
to the clinical presentation of ADHD,28 and attention
performance metrics such as RT mean, RT variability, and
ex-Gaussian tau are well characterised indicators of
attention-relevant cognitive processes, and are associated
with clinically relevant outcomes including academic
behaviour29 and inattention and social problems.30
Finally, the TOVA setting has been described as
mimicking “one component of the classroom situation in
which children are required to remain seated and engaged
in a tedious, repetitive task,”31 suggesting ecological
validity of the TOVA test for real-world settings in which
children with ADHD often struggle.31 Traditional,
symptom-based measures were included as secondary
measures. We also selected the IRS as a targeted measure
of ADHD-related impairment because, as noted
previously, attentional processes are specifically linked to
relevant clinical outcomes and symptom-based measures
(eg, ADHD-RS) and do not always correlate highly with
measures of functional impairment.5 For example, in a
paediatric stimulant medication trial, greater than 40% of
patients who showed a positive response on the primary
outcome measure (>30% reduction on the ADHD-RS)
failed to show significant functional improvement on a
validated measure of impairment.7 It has also been
reported that across four large-scale ADHD research
samples, the average correlation between symptoms and
Figure 4: Medication washout subpopulation, subjective measures
ADHD=attention-deficit hyperactivity disorder. BRIEF=Behavior Rating Inventory
of Executive Function.
AKL-T01 (n=31) Control (n=34)
2·75
3·00
3·25
3·75
4·25
4·00
3·50
p=0·012
Clinical Global Impressions—Improvement
AKL-T01 (n=29) Control (n=34)
–1·2
–0·8
–0·4
0p=0·065
Improvement
Impairment Rating Score overall
Improvement Improvement Improvement
AKL-T01 (n=31) Control (n=31)
–7·5
–2·5
–5·0
0p=0·022
BRIEF metacognition (t-score)
AKL-T01 (n=30) Control (n=34)
–6·0
–2·0
–4·0
0p=0·0083
ADHD Rating Scale inattentive Total
AKL-T01 (n=30) Control (n=34)
–12·0
–4·0
–8·0
0p=0·0092
Improvement
ADHD Rating Scale Total
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9
impairment accounted for less than 10% of variance.31 In
the current trial, despite there being few group dierences
on the ADHD-RS, significantly more children in the
AKL-T01 group were responders on the IRS, suggesting
that this intervention, like other non-pharmacological
interventions, may dierentially aect impairment versus
symptoms.
In post-hoc analysis, children with a recent history
of pharmacological intervention exhibited significant
AKL-T01-related improvements in a range of symptom-
based outcomes, including ADHD-RS. This finding
could be related to biological factors associated with a
recent pharmacological intervention, or psychological
factors such as parents being more attuned to symptom
changes in children recently treated with medication.
Further studies are warranted to explore the potential
eects of AKL-T01 in this important subgroup.
In the current study, there were no dierences between
AKL-T01 and the control condition on secondary
measures, and several factors might explain these
findings. First, it is possible that parent or clinician-
reported outcomes (ie, ADHD-RS) are not sensitive to the
eects of AKL-T01. In other words, the shown eects of
the intervention on attentional processes may not be
as readily observable by parents and clinicians. The
clinical implications of this possibility will be important
to explore in future studies. Second, expectations of
ecacy have been shown to moderate intervention eects
in general, and also for digital interventions.32 In our
study, parents of patients in both groups believed that
their child received a novel intervention for ADHD; thus,
the expectation of intervention eect can be assumed
for both interventions, and may partially explain
improvements in both groups. This design feature is
dierent from most pharmacological studies in which
patients and their caregivers are aware of a non-active,
placebo condition. Finally, specific mechanisms common
to AKL-T01 and the control condition may have resulted
in improvements in both groups. Both interventions
required continued perseverance, sometimes in the face
of failure, and may have trained coping and reappraisal
skills or even increased the sense of self-ecacy and
mastery.33 Thus any intervention that requires the patient
to engage in a regular, structured setting that may include
repeated failure or repetitiveness can be seen as a
potential intervention for ADHD.
The current study has several important limitations.
First, the inclusion criteria required that patients have a
TOVA API up to −1·8, thus showing an objective baseline
deficit in attentional function. This resulted in a
substantial number of patients with a clinical ADHD
diagnosis being excluded from the trial. Second, children
could not be taking medication for ADHD during the trial
and could not have significant psychiatric comorbidity.
Therefore, it is unclear if these findings will generalise to
the broader population of patients with ADHD who have
comorbid conditions or patients taking medication.
Third, the study evaluated a 28-day intervention period
with approximately 25-min daily sessions; it is unclear if
the benefits in attentional functioning might have been
observed with a dierent regimen. The current study
represents a single intervention of 1-month duration,
which is quite short. Additional studies with longer
intervention periods are needed. An ongoing study
(ClinicalTrials.gov identifier: NCT03649074) is examining
longer intervention periods (repeat intervention for a total
of 2 months) and durability of eects 1 month after the
intervention. In addition, that study is investigating
whether the intervention has eects in children currently
treated with stimulant medication, which will help
address questions of generalisability. Fourth, power
analyses were calculated for our primary outcome to
power our trial, but no power calculations were done for
any of our secondary outcomes or post-hoc analyses.
Fifth, the study did not collect data (eg, EEG) that would
oer mechanistic explanation for the findings. The
foundational study from which the intervention was
developed reported that eects of the AKL-T01 prototype
were mediated by EEG changes. Since EEG data were not
collected in this study, conclusions cannot be drawn
about the neural mechanisms that might underlie
intervention eects. Given these limitations, the transfer
of benefit of the AKL-T01 intervention to real-world
settings and the full clinical meaningfulness of the
findings, as well as the mechanisms underlying these
eects, should be explored in further studies.
Despite these limitations, the current trial had several
features that strengthen confidence in the results.
Diagnostic methods modelled after pharmacological
randomised controlled trials were used to establish
eligibility for the study. Considerable steps were taken to
minimise potential biases or dierences in the expectation
of benefit between AKL-T01 and control. These included
having masked raters and clear procedures for minimising
discussion between parents and study sta about inter-
vention assignment, and instructing parents and children
in both groups who believed that they were receiving an
investigational intervention for ADHD.
AKL-T01 (n=180) Active control
(n=168)
Patients experiencing intervention-
emergent adverse events
12 (7%) 3 (2%)
Frustration 5 (3%) 0
Headache 3 (2%) 2 (1%)
Emotional reaction 2 (1%) 1 (1%)
Dizziness 1 (1%) 0
Nausea 1 (1%) 0
Aggression 1 (1%) 0
Data are n (%). AKL-T01=an investigational digital therapeutic.
Table 3: Summary of intervention-emergent adverse events (intention-
to-treat population)
Articles
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The STARS-ADHD trial represented a randomised
controlled trial for the evaluation of a digital intervention
to improve objectively measured attention in children with
ADHD. It showed that compared to the control condition,
AKL-T01 significantly improved objective measures of
attention, as measured by the TOVA. AKL-T01 also showed
eects that were not dierent from the control condition,
including the ADHD-RS. The intervention was well
tolerated, and this risk–benefit ratio suggests that AKL-T01
could be a novel addition to the range of intervention
options for ADHD. The digital nature of the intervention
could help to increase access for populations who might
not otherwise be able to find non-pharma cological
interventions. Various additional questions remain to be
answered regarding the full clinical meaning fulness of the
findings, the eect of dierent dosing schedules, and
which patients might benefit the most from this type of
intervention. Given these limitations, the results of the
current trial are not sucient to suggest that AKL-T01
should be used as an alternative to established and
recommended treatments for ADHD.
Contributors
SHK, RLF, RSEK, AJC, and SVF had a role in the concept and design.
SHK, DJD, EC, JL, RLF, RSEK, JNE, and AJC had a role in acquisition,
analysis, or interpretation of data. SHK, DJD, EC, JL, RSEK, RLF, and
JNE drafted the manuscript. SHK, DJD, EC, JL, RLF, RSEK, JNE, AJC,
and SVF critically revised the manuscript. DJD and RSEK did the critical
analysis. SHK and RSEK obtained funding. DJD, EC, and RSEK
provided administrative, technical and material support. SHK, DJD,
RSEK, and JNE supervised.
Declaration of interests
SHK is a consultant, principal investigator and owns stock options for
Akili Interactive Labs and received research support or consulting fees
from Arbor, Bose, Ironshore, Jazz, KemPharm, Neos, Otsuka, Rhodes,
Shire, Sunovion, and Tris. JL, DJD, and EC are employed by Akili
Interactive Labs and may own stock options. EC is a patent holder
(WO/2018/027080) for Processor Implemented Systems and Methods for
Measuring Cognitive Abilities. RLF receives or has received research
support, acted as a consultant or served on a speaker’s bureau for Acadia,
Aevi, Akili, Alcobra, Allergan, Amerex, American Academy of Child &
Adolescent Psychiatry, American Psychiatric Press, Arbor, Bracket,
Daiichi-Sankyo, Epharma SolutionfMRIs, Forest, Genentech, Insys,
Ironshore, KemPharm, Luminopia, Lundbeck, Merck, the US National
Institutes of Health, Neurim, Noven, Nuvelution, Otsuka, Patient-
Centered Outcomes Research Institute, Pfizer, Physicians Postgraduate
Press, Receptor Life Sciences, Roche, Sage, Shire, Sunovion, Supernus
Pharmaceuticals, Syneurx, Teva, TouchPoint, Tris, and Validus. RSEK is a
consultant for Akili Interactive Labs and has received research support or
consulting fees from Aeglea, Akebia, Akili Interactive Labs, Alkermes,
Allergan, ArmaGen, Astellas, Avanir, AviNeuro/ChemRar, Axovant, Blood
Alcohol Content Testing Battery, Biogen, Boehringer Ingelheim, Cerecor,
CoMentis, Critical Path Institute, Forum Pharmaceuticals, Gammon
Howard & Zeszotarski, Global Medical Education, GW Pharmaceuticals,
Intracellular Therapeutics, Janssen, Kempharm, Lundbeck, Lysogene,
Matrics Battery, MedScape, Mentis Cura, Merck, Merrakris Therapeutics,
Minerva Neurosciences, Mitsubishi, Montana State University, Monteris,
Moscow Research Institute of Psychiatry, National Institute of Mental
Health, Neuralstem, Neuronix, Novartis, NY State Oce of Mental
Health, Orygen, Otsuka, Paradigm Testing, Percept Solutions, Pfizer,
Pharm-Olam, Regenix Bio, Reviva, Roche, Sangamo, Sanofi, SOBI,
Sengenix, Six Degrees Medical, Sunovion, Takeda, Targacept, Teague
Rotenstreich Stanaland Fox & Holt, Thrombosis Research Institute,
University of Moscow, University of Southern California, University of
Texas Southwest Medical Center, Virtual Reality Functional Capacity
Assessment Tool, VeraSci, WebMD, and Wilson Therapeutics and is
owner of VeraSci, which provided support for this trial. JNE is a
consultant for Akili Interactive Labs and receives grant support, research
support, or royalties from Akili Interactive Labs, the American Academy
of Pediatrics, American Board of Pediatrics, IXICO, Multi-Health
Systems, and mehealth for ADHD. AJC has received research support,
honoraria, or consulting fees from Akili Interactive Labs, Arbor,
Ironshore, Neos, Otsuka, Purdue Canada, Shire, Sunovion, Supernus,
and Trisand is a member of the Neuroscience Education Institute Board.
SVF reports income, potential income, travel expenses, continuing
education support, or research support from Akili Interactive Labs,
Arbor, Enzymotec, Genomind, Ironshore, Otsuka, Shire–Takeda,
Sunovion, and Supernus and a US patent (US20130217707 A1) for the use
of sodium–hydrogen exchange inhibitors in the treatment of ADHD.
Data sharing
The STARS-ADHD Investigators agree to share de-identified individual
participant data, the study protocol, and the statistical analysis plan with
academic researchers 6 months after publication, and following
completion of a Data Use Agreement. Proposals should be directed to
medinfo@akiliinteractive.com.
Acknowledgments
Duke Clinical Research Institute conducted the data and statistical
analyses. Writing and data analysis support, under the direction of the
authors, was provided by Titiimaea Alailima, an employee of Akili
Interactive Labs, Boston, MA, USA, Jerey Bower, and Norma Palma,
former employees of Akili Interactive Labs in accordance with Good
Publication Practice guidelines. Editorial support, under the direction of
the authors, was provided by Peloton Advantage, an OPEN Health
company, Parsippany, NJ, with funding by Akili Interactive Labs, in
accordance with Good Publication Practice guidelines. The views and
opinions expressed within this manuscript are those of all the authors
and do not necessarily represent those of the sponsor.
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