What are the implications and impacts of current digital health interventions for children and adolescents with diagnoses ADHD?
ajamisam
Abstract
Attention deficit hyperactivity disorder (ADHD) has become increasingly relevant in
the wake of a technology centred and stimulation rich society. ADHD is a
neurodevelopment condition which has a multifaceted effect on behaviour, work
performance and sleep. Most cases of ADHD are diagnosed at a young age. The
treatment for ADHD is typically either medication or various modes of therapy,
however many of these aim to only ameliorate the symptoms and not the underlying
condition. In the evolving landscape of ADHD treatment, digital health interventions
(DHIs) have received notable interest for their potential to address the diverse range
of symptoms exhibited by children and adolescents with ADHD, and to deliver this
remotely. This review aims to examine the evidence and literature to identify patterns
amongst interventions which might indicate features which facilitate success. Our
findings show that DHIs hold promise in improving attention, executive functions, and
ADHD symptoms. A number of insights relating to outcome measures were
uncovered, highlighting the need for standardised outcome measures which align with
the diverse ADHD care ecosystem, and offer a more accurate representation of
treatment outcomes. Whilst we faced challenged in meta-analysis of the studies,
reviews such as this play a crucial role in advancing the digital health space, and
elucidating the need for validation and evidence for interventions. As is concluded by
this review, clinicians, innovators and researchers all have a role to play in improving
the standards of evidence, in the hopes of providing safer, more user-centric and
accessible interventions.
Attachments
800-2060.pdf
Steps
Data extraction
Studies collected throughout the search process will be processed through Microsoft Excel and Rayyan (a free web based systematic review software), whereby any duplicates will be removed, and studies will be sorted to either be included or excluded based on the previously defined criteria.
Data will be extracted by hand and will be programmed into Microsoft Excel for synthesis. Data point and variables extracted from studies will the following:
Study characteristics : authors, year of publication, country, study design, sample size, study period, setting, patient recruitment.
Patient characteristics : number of patients randomised into each arm, age, gender, and status of condition; medication, coexisting neurodevelopmental issues (e.g. autism spectrum disorder, etc).
Intervention and control groups : intervention name, domain, administration route and duration of administration.
Outcomes : outcome name, type (binary/continuous), measure, and time points.
Two independent reviewers will perform data extraction, and any discrepancies will be resolved through consensus.
Risk of bias assessment
The quality and risk of bias of included studies will be assessed using appropriate tools, such as-
Cochrane Risk of Bias tool (v1) for RCTs (Higgins et al., 2011).
Newcastle-Ottawa scale for non-randomized designs.
Strategy for data synthesis
For continuous outcomes effect size measures for continuous outcomes depend on the consistency
of the measurement tool and scale. Standardized Mean Difference (SMD) will be used for data
combination. Effect sizes include a 95% confidence interval. Data with a consistent direction of effect are included.
For studies with binary Outcomes a Risk Ratio (RR) will be used. Effect sizes are presented with a 95% confidence interval.
This review will use a random-effects model due to the high probability of heterogeneity in the RCTs
and other studies that will be included in this review.
Analysis of subgroups or subsets
Sub-group analysis will be carried out, and studies will be pooled and grouped based on the following factors:
- Studies Active controls (e.g. other active intervention)
- Studies with no intervention in the control group
- ADHD on medication
- ADHD not on medication
-
12 years of age
- 8-12 years of age
- Intervention domain
- Intervention setting
- Study duration
Sub group analysis will pool studies who’s interventions are in the same domain, and thus outcomes
will be compared.
