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A novel digital intervention for actively reducing severity of paediatric ADHD (STARS-ADHD): a randomised controlled trial

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Background Attention-deficit hyperactivity disorder (ADHD) is a common paediatric neurodevelopmental disorder with substantial effect 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.
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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 eect 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
aects 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 ecacy.3–5 Existing treatments
have side-eects that limit their acceptability,6 are only
eective when administered, and may not be as eective
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-eects,
and low potential for abuse. Numerous studies and
meta-analyses on digital interventions targeting specific
cognitive functions have attempted to assess the
magnitude of ecacy 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 eect
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sizes are generally small but dier 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 eects on outcomes provided by independent
unmasked observers.14 Nevertheless, meta-analyses
conclude that interventions more broadly targeting
cognitive functions generally show larger eects.
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 diculty on the basis of the
user’s ability and progression. Specifically, AKL-T01
targets attentional control to manage competing tasks
and to eciently (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 ecacy 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 eect of two dierent
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 diculty. 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 diculty that is challenging but also tolerable.
Further, on the basis of dierence 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 dierent environments,
periodic recalibration occurs to maintain an optimal
diculty 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 diculty 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 eects 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 ecacy 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 eects 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 sucient to detect an eect 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
dierence 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 ecacy of AKL-T01 versus control.
Prespecified sensitivity analyses of the primary and key
secondary endpoints were designed to assess the eects
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 eect 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 dierence between intervention
groups on the primary ecacy 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 dierences 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 eect. 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 dierences 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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7
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 dierences: significant between-group
eects 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 dierent
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 dierentiate 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 ecacy 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
dierentiated from control on most secondary ecacy
endpoints including ADHD-RS (p=0·0092), ADHD-RS-I
(p=0·0083), and CGI-I (p=0·012). The dierence 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 eects of AKL-T01 from pre-intervention to post-
intervention were not dierent 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 eects 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 ScaleInattentive. RS Hyperactive= ADHD Rating ScaleHyperactivity.
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—diers from most pharmacological ecacy 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 dierences
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 dierentially aect 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
eects of AKL-T01 in this important subgroup.
In the current study, there were no dierences 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
eects of AKL-T01. In other words, the shown eects 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
ecacy have been shown to moderate intervention eects
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 eect can be assumed
for both interventions, and may partially explain
improvements in both groups. This design feature is
dierent 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-ecacy 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 dierent 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 eects 1 month after the
intervention. In addition, that study is investigating
whether the intervention has eects 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
oer mechanistic explanation for the findings. The
foundational study from which the intervention was
developed reported that eects 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 eects. 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
eects, 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 dierences 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
eects that were not dierent 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 eect of dierent 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 sucient 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 Oce 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, Jerey 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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... Studies found that gamebased digital therapeutic devices, such as the Food and Drug Administration-approved EndeavorRx, support positive outcomes when used to treat ADHD among children aged 8 to 12 years without the need for pharmacological intervention [41]. Kollins et al [42] found that daily game-like interaction with a digital therapeutic program (AKL-T01) improved inattention in children and young people diagnosed with ADHD. Similarly, Fried et al [39] found that the use of a digital mindfulness application showed a marked reduction in inattentiveness and anxiety for children aged 6 to 12 years with a history of ADHD. ...
... Additionally, results from a survey administered during the 4-week pilot study found a correlation between sleep patterns and both stress levels and attention span [39]. These findings could help to inform appropriate management plans and highlight the potential for the continual development of game-like digital interventions designed to exercise memory training and neurofeedback [41,42]. These innovative applications not only provide clinicians with attention assessments by measuring key stimuli and response variabilities but also supply users with continual intervention by sharpening perseverance and self-efficacy skills [42]. ...
... These findings could help to inform appropriate management plans and highlight the potential for the continual development of game-like digital interventions designed to exercise memory training and neurofeedback [41,42]. These innovative applications not only provide clinicians with attention assessments by measuring key stimuli and response variabilities but also supply users with continual intervention by sharpening perseverance and self-efficacy skills [42]. ...
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This study presents a narrative review of the use of digital health technologies (DHTs) and artificial intelligence to screen and mitigate risks and mental health consequences associated with ACEs among children and youth. Several databases were searched for studies published from August 2017 to August 2022. Selected studies (1) explored the relationship between digital health interventions and mitigation of negative health outcomes associated with mental health in childhood and adolescence and (2) examined prevention of ACE occurrence associated with mental illness in childhood and adolescence. A total of 18 search papers were selected, according to our inclusion and exclusion criteria, to evaluate and identify means by which existing digital solutions may be useful in mitigating the mental health consequences associated with the occurrence of ACEs in childhood and adolescence and preventing ACE occurrence due to mental health consequences. We also highlighted a few knowledge gaps or barriers to DHT implementation and usability. Findings from the search suggest that the incorporation of DHTs, if implemented successfully, has the potential to improve the quality of related care provisions for the management of mental health consequences of adverse or traumatic events in childhood, including posttraumatic stress disorder, suicidal behavior or ideation, anxiety or depression, and attention-deficit/hyperactivity disorder. The use of DHTs, machine learning tools, natural learning processing, and artificial intelligence can positively help in mitigating ACEs and associated risk factors. Under proper legal regulations, security, privacy, and confidentiality assurances, digital technologies could also assist in promoting positive childhood experiences in children and young adults, bolstering resilience, and providing reliable public health resources to serve populations in need.
... According to previous studies involving children with ADHD, digital therapies have reported evidence of improvement in ADHD-related impairments [36,37]. Specifically, according to the results of AKL-T01 conducted under conditions similar to this study, when home-based treatment using digital therapeutic interventions was performed over four weeks, five days a week in pediatric ADHD patients, improvement in the TOVA (Test of Variables of Attention) and API (Attention Performance Index) scores was observed across 20 research institutions in the United States [38]. The API score, as indicated in previous research, represents objective attention scores in children with ADHD. ...
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Previous research has investigated non-pharmacological digital therapeutic interventions to improve compliance and reduce side effects in attention-deficit/hyperactivity disorder (ADHD) medication treatments for children. This study focuses on validating the effects of game-based intervention content for enhancing working memory and concentration. It tracks quantitative changes to evaluate improvements in concentration and working memory when digital game-based content is used as adjunct therapy alongside medication for children with ADHD. Thirty children participated; one group received digital therapeutic intervention based on game content alongside medication (experimental) and the other group received conventional treatments (control). The study results show that children with ADHD in the experimental group, who use digital game-based content, exhibit a reduction of 8.13 ± 6.71 points in the K-ARS total score at the fourth week compared to baseline, while the control group shows a reduction of 7.14 ± 8.73 points. Inattention decreases by 36.84% in the experimental group and 28.56% in the control group, while hyperactivity–impulsivity decreases by 50.71% in the experimental group and 34.00% in the control group. All the results are analyzed using a paired t-test between baseline and the fourth week. Significant decreases in the K-CBCL total problem behavior score and internalizing and externalizing behaviors are consistently observed at 28 days compared with baseline. The FAIR attention–concentration test results show significant differences between the experimental and control groups in the Q-percentile and Q-standard scores, with repeated measures ANOVA results showing p = 0.006 and p = 0.007, respectively. Digital content was shown to influence digital therapeutic intervention—a non-pharmacological treatment for ADHD.
... Neudecker et al. 25 reported significant improvements in executive functions, inhibition, parent-reported psychological difficulties, and motor skills, following a home-based exergaming intervention in a sample of 51 children with ADHD (ages [8][9][10][11][12]. Preliminary positive findings suggest that such interventions may help reduce inattentive symptoms although results regarding impulsivity are mixed [26][27][28][29] . Recently, several scoping and systematic reviews have synthesized the evidence on the use of technological cognitive intervention systems in children and adolescents with ADHD 27,30,31 . ...
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Attention-deficit/hyperactivity disorder (ADHD) presents with symptoms like impulsiveness, inattention, and hyperactivity, often affecting children’s academic and social functioning. Non-pharmacological interventions, such as digital cognitive therapy, are emerging as complementary treatments for ADHD. The randomized controlled trial explored the impact of an AI-driven digital cognitive program on impulsiveness, inattentiveness, and neurophysiological markers in 41 children aged 8–12 with ADHD. Participants received either 12 weeks of AI-driven therapy or a placebo intervention. Assessments were conducted pre- and post-intervention and magnetoencephalography (MEG) analyzed brain activity. Results showed significant reductions in impulsiveness and inattentiveness scores in the treatment group, associated with normalized MEG spectral profiles, indicating neuromaturation. Notably, improvements in inhibitory control correlated with spectral profile normalization in the parieto-temporal cortex. Improvements in inhibitory control, linked to normalized spectral profiles, suggest AI-driven digital cognitive therapy can reduce impulsiveness in ADHD children by enhancing neurophysiological efficiency. This emphasizes personalized, technology-driven ADHD treatment, using neurophysiological markers for assessing efficacy.
... It is also possible to drive a raft to avoid the frozen peaks SHI LIU along the riverbank. With video games as the main carrier, patients only needed 25 minutes of play per day to have a significant increase in attention after four weeks [22]. Then they studied the patient's condition with the help of medication. ...
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Attention Deficit Hyperactive Disorder (ADHD) is a neurodevelopmental disorder characterized by inability to concentrate for a long time, hyperactivity and emotional impulsivity. It usually develops in childhood. The pathogenesis is unclear. Brain Computer Interface (BCI) refers to a direct communication pathway between the brain and an external device, enabling information exchange between the two. This paper will explore the possibility of treating ADHD through brain-computer interface methods through the examples of existing inter-brain interfaces for the treatment of neurological diseases in ADHD, such as neurofeedback, transcranial magnetic stimulation, and digital therapy. All three of these modalities are treatments or diagnostic methods based on brain-computer interface technology. Then this paper analyzes the possible problems of these treatments through some data and ethical issues. The paper also proposes relevant solutions. Through the research in this paper, it can provide a new research direction for the treatment of ADHD in the future. Create a broader prospect for the treatment of ADHD.
... Besides potentially negative consequences, playing games has also been argued [65] and demonstrated to have positive effects for cognitive [66,67], social behaviour [68], motor skills [69], and physical activity [70]. Next to demonstrable positive effects, video games have positive social implications and facilitate beneficial social learning. ...
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Si de nombreux jeux vidéo sont vendus moyennant un certain montant, le modèle d’affaire des jeux gratuits ( free-to-play ) s’est rapidement imposé au cours de la dernière décennie. Or ce modèle, qui est basé sur la collecte de données personnelles, les microtransactions et le profilage publicitaire, implique des ventes d’items et un temps de connexion le plus étendu possible afin d’accroître les profits. Pour ce faire, différentes stratégies sont utilisées, dont des stratégies dites « persuasives » qui influencent les joueurs et joueuses à demeurer connectés, à dépenser et à revenir fréquemment sur le jeu gratuit. Parmi ces stratégies, les mécaniques de jeux de hasard et d’argent (JHA) sont reconnues pour leur force persuasive et leur pouvoir addictif. Elles sont pourtant de plus en plus présentes au sein des jeux mobiles pour les adultes, mais également ceux pour les enfants. Afin de documenter le phénomène, 249 jeux mobiles gratuits pour enfants ont été analysés pour évaluer la prévalence des mécaniques persuasives et de JHA, leurs formes d’actualisation et leurs types d’intégration dans l’expérience vidéoludique des enfants. Nos résultats démontrent une « gamblification » des jeux mobiles gratuits pour enfants et un conditionnement des comportements qui passe par une normalisation des mécaniques persuasives et de JHA auprès de ce jeune public. La convergence des jeux vidéo avec les JHA se confirme à nouveau avec une focalisation sur des jeux pour un très jeune public. L’article se conclut en prenant acte de ce sérieux enjeu de santé publique en lien avec le bien-être des enfants.
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Background For decades, researchers have been searching for objective laboratory markers to measure and define attention deficit and hyperactivity disorder. However, in clinical practice, the most commonly used tools are still psychological scales, which are neither objective nor laboratory-based. This reliance on scales may stem from the fact that they are currently the only available method in this field for collecting daily-life data, and such data plays an irreplaceable role in defining mental disorders. Fortunately, wearable devices now offer the possibility of collecting objective daily-life data. In our study, we aim to monitor children’s activity synchrony (AcSyn) and activity volume (AcVo) using accelerometers in a school setting to examine the correlation between these daily-life markers and the symptoms related to attention deficit and hyperactivity disorder. Methods This study included an entire class of children of 1st grade (n = 39). Children were required to wear the accelerometer on their wrist to record their activities during school time for 3 weeks using 1-second epoch, based on which we calculated AcSyn and AcVo, and conducted correlation analysis with Attention Deficit and Hyperactivity Disorder Rating Scale. Results In-class AcSyn was significantly correlated with teacher-reported inattention score (r=-0·480, P = 0·001), but not hyperactivity/impulsivity score. In-class/recess AcVo is significantly related to parent-reported hyperactivity/impulsivity score (r = 0·448-0·482, P ≤ 0·002), but not inattention score. Conclusions AcSyn and AcVo are potential markers to measure children’s attention/hyperactivity/impulsivity performance in daily-life. Moreover, when combined with event labels and analyzed on micro or macro time scales, AcSyn and AcVo have the potential to provide profound insights.
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This mini-review examines the available papers about virtual reality (VR) as a tool for the diagnosis or therapy of neurodevelopmental disorders, focusing on Attention Deficit Hyperactivity Disorder (ADHD), Autism Spectrum Disorder (ASD), and Specific Learning Disorders (SLD). Through a search on literature, we selected 62 studies published between 1998 and 2024. After exclusion criteria, our synoptic table includes 32 studies on ADHD (17 were on diagnostic evaluation and 15 were on therapeutic interventions), 2 on pure ASD, and 2 on pure SLD. These cover a total of 8,139 participants with ADHD (ages 3–19), 458 with ASD (ages 4–19), and 162 with SLD (ages 7–11). Results show that VR offers high ecological validity and enables improvements in cognitive and social skills. Specifically, in individuals with ADHD, VR showed benefits in attention and executive function, with optimal results when combined with pharmacological treatments. For ASD kids, VR proved effective in enhancing social skills and emotional regulation through personalized virtual scenarios. However, the literature on SLD remains limited, suggesting an evolving area of research. Despite limitations related to small sample sizes and technology costs, VR presents a promising outlook for clinical intervention in neuro-developmental disorders, supporting enhanced skills in a safe and controlled environment. We conclude that both immersive and non-immersive VR represents a valuable supplement to traditional therapies, allowing for personalized approaches.
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Neurocognitive impairment (NCI) is present in around 40% of people with HIV and substantially affects everyday life, adherence to combined antiretroviral therapy (cART) and overall life expectancy. Suboptimal therapy regimen, opportunistic infections, substance abuse and highly prevalent psychiatric co-morbidities contribute to NCI in people with HIV. In this review, we highlight the need for efficacious treatment of HIV-related NCI through pharmacological approaches and cognitive neurorehabilitation, discussing recent randomized controlled trials in this domain. We also discuss the benefits of a thorough and interdisciplinary diagnostic work-up between specialists in neurology, psychiatry, neuropsychology and infectious diseases, helping to disentangle the various factors contributing to cognitive complaints and deficits in people with HIV. While the advent of cART has contributed to slowing the progression of cognitive deficits in people with HIV and reducing the prevalence of HIV-associated dementia, NCI persists at a significant rate. Adjuvant stimulating or neuroprotective pharmacological agents have shown some potential benefits. Despite promising outcomes, studies on cognitive neurorehabilitation of HIV-related NCI remain sparse and limited in terms of methodological aspects. The access to cognitive neurorehabilitation is also restricted, in particular at the global scale. Novel technology bears a significant potential for restoring cognitive function in people with HIV, affording high degrees of standardization and personalization, along with opportunities for telerehabilitation. Entertaining serious video game environments with immersive graphics can further promote patient motivation, training adherence and impact on everyday life, as indicated by a growing body of evidence, including in seropositive children and older individuals in Africa. Upon validation of technology-assisted cognitive neurorehabilitation for HIV-related NCI in large-scale randomized controlled trials with state-of-the-art methodology, these approaches will promote socio-professional reintegration and quality of life of people with HIV.
Chapter
Executive functions are critical for adaptive responses in new and complex contexts. However, research indicates the presence of deficits in executive functions neurodiverse children. In this chapter, we synthesize evidence on the current digital technologies employed in training executive functions in neurodiverse children, such as autistic individuals, intellectual disabled children, dyslexic children, and attention deficit hyperactivity disorder children. The chapter concludes considering some of the limitations in computerized trainings of executive functions, as well as some future directions of research on this topic.
Chapter
In this chapter, we summarize available digital interventions utilized for assessment and therapy of ADHD. During the COVID-19 pandemic the use of digital tools in clinical psychology became prominent due to restrictions of in-person therapy. Since multiple interventions have been introduced over the last decade, we aim to provide an overview of different web- and PC-based (telemedicine, cognitive and behavioral interventions, neurofeedback) and mobile Health interventions as well as wearables/sensors and virtual reality-based tools. For each topic, we have focused on the therapeutic concept, its implementation and efficacy.
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Background The benefits and safety of medications for attention-deficit hyperactivity disorder (ADHD) remain controversial, and guidelines are inconsistent on which medications are preferred across different age groups. We aimed to estimate the comparative efficacy and tolerability of oral medications for ADHD in children, adolescents, and adults. Methods We did a literature search for published and unpublished double-blind randomised controlled trials comparing amphetamines (including lisdexamfetamine), atomoxetine, bupropion, clonidine, guanfacine, methylphenidate, and modafinil with each other or placebo. We systematically contacted study authors and drug manufacturers for additional information. Primary outcomes were efficacy (change in severity of ADHD core symptoms based on teachers' and clinicians' ratings) and tolerability (proportion of patients who dropped out of studies because of side-effects) at timepoints closest to 12 weeks, 26 weeks, and 52 weeks. We estimated summary odds ratios (ORs) and standardised mean differences (SMDs) using pairwise and network meta-analysis with random effects. We assessed the risk of bias of individual studies with the Cochrane risk of bias tool and confidence of estimates with the Grading of Recommendations Assessment, Development, and Evaluation approach for network meta-analyses. This study is registered with PROSPERO, number CRD42014008976. Findings 133 double-blind randomised controlled trials (81 in children and adolescents, 51 in adults, and one in both) were included. The analysis of efficacy closest to 12 weeks was based on 10 068 children and adolescents and 8131 adults; the analysis of tolerability was based on 11 018 children and adolescents and 5362 adults. The confidence of estimates varied from high or moderate (for some comparisons) to low or very low (for most indirect comparisons). For ADHD core symptoms rated by clinicians in children and adolescents closest to 12 weeks, all included drugs were superior to placebo (eg, SMD −1·02, 95% CI −1·19 to −0·85 for amphetamines, −0·78, −0·93 to −0·62 for methylphenidate, −0·56, −0·66 to −0·45 for atomoxetine). By contrast, for available comparisons based on teachers' ratings, only methylphenidate (SMD −0·82, 95% CI −1·16 to −0·48) and modafinil (−0·76, −1·15 to −0·37) were more efficacious than placebo. In adults (clinicians' ratings), amphetamines (SMD −0·79, 95% CI −0·99 to −0·58), methylphenidate (−0·49, −0·64 to −0·35), bupropion (−0·46, −0·85 to −0·07), and atomoxetine (−0·45, −0·58 to −0·32), but not modafinil (0·16, −0·28 to 0·59), were better than placebo. With respect to tolerability, amphetamines were inferior to placebo in both children and adolescents (odds ratio [OR] 2·30, 95% CI 1·36–3·89) and adults (3·26, 1·54–6·92); guanfacine was inferior to placebo in children and adolescents only (2·64, 1·20–5·81); and atomoxetine (2·33, 1·28–4·25), methylphenidate (2·39, 1·40–4·08), and modafinil (4·01, 1·42–11·33) were less well tolerated than placebo in adults only. In head-to-head comparisons, only differences in efficacy (clinicians' ratings) were found, favouring amphetamines over modafinil, atomoxetine, and methylphenidate in both children and adolescents (SMDs −0·46 to −0·24) and adults (−0·94 to −0·29). We did not find sufficient data for the 26-week and 52-week timepoints. Interpretation Our findings represent the most comprehensive available evidence base to inform patients, families, clinicians, guideline developers, and policymakers on the choice of ADHD medications across age groups. Taking into account both efficacy and safety, evidence from this meta-analysis supports methylphenidate in children and adolescents, and amphetamines in adults, as preferred first-choice medications for the short-term treatment of ADHD. New research should be funded urgently to assess long-term effects of these drugs. Funding Stichting Eunethydis (European Network for Hyperkinetic Disorders), and the UK National Institute for Health Research Oxford Health Biomedical Research Centre.
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Objective: To evaluate the relationship between symptom and functional improvement and remission in children and adolescents with attention-deficit/hyperactivity disorder (ADHD) enrolled in an 11-week open-label dose-optimization phase of an methylphenidate extended release (MPH-MLR) pivotal study. Methods: Assessments included the Weiss Functional Impairment Rating Scale-Parent (WFIRS-P) and ADHD Rating Scale, Fourth Edition (ADHD-RS-IV). Definitions included the following: symptom improvement (≥30% decrease in ADHD-RS-IV total score); symptom remission (ADHD-RS-IV total score ≤18); functional improvement (decrease in WFIRS-P total score ≥0.25 [minimally important difference]); and functional remission (WFIRS-P total score ≤0.65). Results: Two hundred children completed the open-label phase. At initial assessment, functional impairment was evident across all WFIRS-P domains and similar between children and adolescents. Those who were treatment naive had more functional impairment (WFIRS-P total: 0.82 vs. 0.70, p = 0.02). Significant improvements in all WFIRS-P domains were noted at open-label end (p < 0.001), with the largest improvement in Learning. At open-label end, 94% of children and adolescents demonstrated symptom improvement, of which 57% also showed functional improvement, and 75% of children and adolescents showed symptom remission, of which 81% also showed functional remission. Conclusions: Children and adolescents treated with MPH-MLR showed moderate-to-large improvement in functioning during 3 months of treatment, both overall and in specific domains. However, a significant number of those who would be considered symptomatic responders failed to show improvement in functioning or continue to have significant functional impairment. Treatment with MPH-MLR showed that both symptomatic and functional remission are achievable goals. Identification of children and adolescents who have been successfully treated for their symptoms, but continue to suffer functional impairment, will allow us to offer additional targeted treatment interventions over and above medication to address residual difficulties.
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Objective Pharmacological and behavioral therapies have limited impact on the distinct neurocognitive impairments associated with ADHD, and existing cognitive training programs have shown limited efficacy. This proof-of-concept study assessed treatment acceptability and explored outcomes for a novel digital treatment targeting cognitive processes implicated in ADHD. Method Participants included 40 children with ADHD and 40 children without ADHD. Following psychiatric screening, ADHD ratings, and baseline neuropsychological measures, participants completed 28-days of at-home treatment. Neuropsychological assessment was repeated at end-of-study along with treatment satisfaction measures. Results Eighty-four percent of treatment sessions were completed and ratings showed strong intervention appeal. Significant improvements were observed on a computerized attention task for the ADHD group and a highly impaired ADHD High Severity subgroup. There was no change for the non-ADHD group. Spatial working memory also improved for the ADHD group and the ADHD High Severity subgroup. Conclusion Findings provide preliminary support that this treatment may improve attention, working memory, and inhibition in children with ADHD. Future research requires larger-scale randomized controlled trials that also evaluate treatment impact on functional impairments. Trial registration ClinicalTrials.gov NCT01943539
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Background Attention deficit hyperactivity disorder (ADHD) is one of the most commonly diagnosed psychiatric disorders in childhood. A wide variety of treatments have been used for the management of ADHD. We aimed to compare the efficacy and safety of pharmacological, psychological and complementary and alternative medicine interventions for the treatment of ADHD in children and adolescents. Methods and findings We performed a systematic review with network meta-analyses. Randomised controlled trials (≥ 3 weeks follow-up) were identified from published and unpublished sources through searches in PubMed and the Cochrane Library (up to April 7, 2016). Interventions of interest were pharmacological (stimulants, non-stimulants, antidepressants, antipsychotics, and other unlicensed drugs), psychological (behavioural, cognitive training and neurofeedback) and complementary and alternative medicine (dietary therapy, fatty acids, amino acids, minerals, herbal therapy, homeopathy, and physical activity). The primary outcomes were efficacy (treatment response) and acceptability (all-cause discontinuation). Secondary outcomes included discontinuation due to adverse events (tolerability), as well as serious adverse events and specific adverse events. Random-effects Bayesian network meta-analyses were conducted to obtain estimates as odds ratios (ORs) with 95% credibility intervals. We analysed interventions by class and individually. 190 randomised trials (52 different interventions grouped in 32 therapeutic classes) that enrolled 26114 participants with ADHD were included in complex networks. At the class level, behavioural therapy (alone or in combination with stimulants), stimulants, and non-stimulant seemed significantly more efficacious than placebo. Behavioural therapy in combination with stimulants seemed superior to stimulants or non-stimulants. Stimulants seemed superior to behavioural therapy, cognitive training and non-stimulants. Behavioural therapy, stimulants and their combination showed the best profile of acceptability. Stimulants and non-stimulants seemed well tolerated. Among medications, methylphenidate, amphetamine, atomoxetine, guanfacine and clonidine seemed significantly more efficacious than placebo. Methylphenidate and amphetamine seemed more efficacious than atomoxetine and guanfacine. Methylphenidate and clonidine seemed better accepted than placebo and atomoxetine. Most of the efficacious pharmacological treatments were associated with harms (anorexia, weight loss and insomnia), but an increased risk of serious adverse events was not observed. There is lack of evidence for cognitive training, neurofeedback, antidepressants, antipsychotics, dietary therapy, fatty acids, and other complementary and alternative medicine. Overall findings were limited by the clinical and methodological heterogeneity, small sample sizes of trials, short-term follow-up, and the absence of high-quality evidence; consequently, results should be interpreted with caution. Conclusions Clinical differences may exist between the pharmacological and non-pharmacological treatment used for the management of ADHD. Uncertainties about therapies and the balance between benefits, costs and potential harms should be considered before starting treatment. There is an urgent need for high-quality randomised trials of the multiple treatments for ADHD in children and adolescents. PROSPERO, number CRD42014015008.
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Background Poor behavioural inhibition is the central impairment in attention deficit hyperactivity disorder (ADHD). At present, there is no reliable objective measure to detect ADHD. A proper pinpointing evaluation for ADHD depends mainly on the history from parents, family members as well as teachers and schoolmates, by means of questionnaires and conduct rating scales. Objective The aim of this study was to detect continuous performance task (CPT) (test of variants of attention) changes in children suffering from ADHD compared with normal children. Patients and methods CPT, Conners' parent rating scale and Wechsler intelligence scale were done for two groups of children each containing 39 children, a group of ADHD children and the other a normal control group. Results We found a significant difference between the mean total IQ score among the ADHD patients group compared with control group. Comparing both groups revealed statistically significant increase in omission, commission and reaction time among patients. A significant negative correlation was found between age on one side and IQ, hyperactivity and psychosomatic subscales, hyperactivity and total DSM-IV scores on the other hand and between commission and opposition, restlessness subscales and ADHD index and also between reaction time and restlessness and emotional index. There was a significant positive correlation between omission and hyperactivity and anxiety subscales, restlessness and emotional indices and DSM-IV hyperactive and total scores. In addition, there was a significant positive correlation between perfectionism and commission and also between reaction time and inattention and social problems subscales. Conclusion CPT can have a substantial role in objective identification of ADHD. © 2016 The Egyptian Journal of Neurology, Psychiatry and Neurosurgery.
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Objective: The purpose of this study was to investigate the clinical effect and safety of a broad spectrum, 36 ingredient micronutrient (vitamins and minerals) in treating children with attention-deficit/hyperactivity disorder (ADHD). Methods: This open-label, on-off-on-off (reversal design) study followed 14 participants (8-12 years of age) with ADHD, diagnosed using standardized instruments, for 6 months with no dropouts. Following baseline assessment, including hematology and biochemistry screening, participants began an 8 week treatment phase with micronutrients titrated up to maximum dose (15 capsules/day). Treatment was withdrawn for 4 weeks, reinstated for a further 8 weeks, and then withdrawn for 4 weeks. Primary outcomes included the Conners' Parent Rating Scale, the Clinical Global Impressions Scale (CGI), and the Strengths and Difficulties Questionnaire - Parent version (SDQ). Secondary outcomes were mood and global functioning. Results: Modified Brinley plots revealed a reduction in ADHD symptoms, improved mood, and improved overall functioning during intervention phases, and deterioration in ADHD symptoms, mood, and overall functioning during the withdrawal phases. Reliable change analyses, Cohen's d and percent superiority effect sizes, 95% confidence intervals and t tests confirmed clinically and statistically significant change between the intervention and withdrawal phases, with large effect sizes observed pre- to post-exposure of micronutrients (d = 1.2-2.2) on ADHD symptoms during intervention phases. Seventy-one percent of participants showed at least a 30% decrease in ADHD symptoms by the end of the second treatment phase, and 79% were identified as "much improved" or "very much improved" at the end of the second phase (5 months) based on the clinician-rated CGI when considering functioning generally. The SDQ showed that these benefits occurred across other areas of functioning including emotional symptoms, conduct problems, and prosocial behaviours. The children's self-reports confirmed the improvements. Excellent adherence to treatment occurred throughout, side effects were mild and transitory, and no safety issues were identified through blood analyses. Conclusions: This study demonstrates the clinical benefit, feasibility, and safety of broad-spectrum micronutrients in the treatment of childhood ADHD. Replications utilizing double-blind placebo-controlled studies are warranted. Trial is registered with the Australia and New Zealand Clinical Trial Registry: ACTRN12612000645853.
Article
Attention-deficit/hyperactivity disorder (ADHD) is one of the most common psychiatric conditions; it affects millions of youth in the United States. The disorder is a significant public health challenge because it independently increases risk for a wide range of adverse social and health outcomes, including substance use and other psychiatric conditions, obesity, educational underattainment, incarceration, and even premature mortality.¹ The cost of illness for ADHD has been estimated to be in the tens of billions of dollars annually in the United States alone.
Article
The purpose of this study is to estimate the national prevalence of parent-reported attention deficit/hyperactivity disorder (ADHD) diagnosis and treatment among U.S. children 2–17 years of age using the 2016 National Survey of Children’s Health (NSCH). The NSCH is a nationally representative, cross-sectional survey of parents regarding their children’s health that underwent a redesign before the 2016 data collection. It included indicators of lifetime receipt of an ADHD diagnosis by a health care provider, whether the child currently had ADHD, and receipt of medication and behavioral treatment for ADHD. Weighted prevalence estimates were calculated overall and by demographic and clinical subgroups (n = 45,736). In 2016, an estimated 6.1 million U.S. children 2–17 years of age (9.4%) had ever received an ADHD diagnosis. Of these, 5.4 million currently had ADHD, which was 89.4% of children ever diagnosed with ADHD and 8.4% of all U.S. children 2–17 years of age. Of children with current ADHD, almost two thirds (62.0%) were taking medication and slightly less than half (46.7%) had received behavioral treatment for ADHD in the past year; nearly one fourth (23.0%) had received neither treatment. Similar to estimates from previous surveys, there is a large population of U.S. children and adolescents who have been diagnosed with ADHD by a health care provider. Many, but not all, of these children received treatment that appears to be consistent with professional guidelines, though the survey questions are limited in detail about specific treatment types received. The redesigned NSCH can be used to annually monitor diagnosis and treatment patterns for this highly prevalent and high-impact neurodevelopmental disorder.
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Objectives: To determine the adjusted incremental total costs (direct and indirect) for patients (aged 3-17 years) with attention-deficit/hyperactivity disorder (ADHD) and the differences in the adjusted incremental direct expenditures with respect to age groups (preschoolers, 0-5 years; children, 6-11 years; and adolescents, 12-17 years). Methods: The 2011 Medical Expenditure Panel Survey was used as the data source. The ADHD cohort consisted of patients aged 0 to 17 years with a diagnosis of ADHD, whereas the non-ADHD cohort consisted of subjects in the same age range without a diagnosis of ADHD. The annual incremental total cost of ADHD is composed of the incremental direct expenditures and indirect costs. A two-part model with a logistic regression (first part) and a generalized linear model (second part) was used to estimate the incremental costs of ADHD while controlling for patient characteristics and access-to-care variables. Results: The 2011 Medical Expenditure Panel Survey database included 9108 individuals aged 0 to 17 years, with 458 (5.0%) having an ADHD diagnosis. The ADHD cohort was 4.90 times more likely (95% confidence interval [CI] 2.97-8.08; P < 0.001) than the non-ADHD cohort to have an expenditure of at least 1,andamongthosewithpositiveexpenditures,theADHDcohorthad58.41, and among those with positive expenditures, the ADHD cohort had 58.4% higher expenditures than the non-ADHD cohort (P < 0.001). The estimated adjusted annual total incremental cost of ADHD was 949.24 (95% CI 593.30593.30-1305.18; P < 0.001). The adjusted annual incremental total direct expenditure for ADHD was higher among preschoolers (989.34;95989.34; 95% CI 402.70-1575.98;P=0.001)thanamongadolescents(1575.98; P = 0.001) than among adolescents (894.94; 95% CI 428.16428.16-1361.71; P < 0.001) or children (682.71;95682.71; 95% CI 347.94-$1017.48; P < 0.001). Conclusions: Early diagnosis and use of evidence-based treatments may address the substantial burden of ADHD.
Article
Background: Digital health interventions (DHIs), including computer-assisted therapy, smartphone apps and wearable technologies, are heralded as having enormous potential to improve uptake and accessibility, efficiency, clinical effectiveness and personalisation of mental health interventions. It is generally assumed that DHIs will be preferred by children and young people (CYP) given their ubiquitous digital activity. However, it remains uncertain whether: DHIs for CYP are clinically and cost-effective, CYP prefer DHIs to traditional services, DHIs widen access and how they should be evaluated and adopted by mental health services. This review evaluates the evidence-base for DHIs and considers the key research questions and approaches to evaluation and implementation. Methods: We conducted a meta-review of scoping, narrative, systematic or meta-analytical reviews investigating the effectiveness of DHIs for mental health problems in CYP. We also updated a systematic review of randomised controlled trials (RCTs) of DHIs for CYP published in the last 3 years. Results: Twenty-one reviews were included in the meta-review. The findings provide some support for the clinical benefit of DHIs, particularly computerised cognitive behavioural therapy (cCBT), for depression and anxiety in adolescents and young adults. The systematic review identified 30 new RCTs evaluating DHIs for attention deficit/hyperactivity disorder (ADHD), autism, anxiety, depression, psychosis, eating disorders and PTSD. The benefits of DHIs in managing ADHD, autism, psychosis and eating disorders are uncertain, and evidence is lacking regarding the cost-effectiveness of DHIs. Conclusions: Key methodological limitations make it difficult to draw definitive conclusions from existing clinical trials of DHIs. Issues include variable uptake and engagement with DHIs, lack of an agreed typology/taxonomy for DHIs, small sample sizes, lack of blinded outcome assessment, combining different comparators, short-term follow-up and poor specification of the level of human support. Research and practice recommendations are presented that address the key research questions and methodological issues for the evaluation and clinical implementation of DHIs for CYP.