# Digital health technologies for adults with ADHD: a scoping review

## TL;DR

This paper reviews digital health technologies for adults with ADHD, exploring how they can improve diagnosis and treatment.

## Contribution

The study categorizes DHTs for ADHD and identifies opportunities to improve their integration into clinical practice.

## Key findings

- 133 studies were identified, with DHTs categorized into treatment, clinical management, and diagnostic roles.
- Most DHTs focus on cognitive therapy, machine learning for diagnosis, and neurofeedback.
- The paper highlights gaps in evidence and suggests frameworks for better clinical integration.

## Abstract

Adult Attention-Deficit/Hyperactivity Disorder (ADHD) is associated with negative long-term outcomes including accident and injury, impairment in social and occupational functioning, and a high rate of mental health comorbidities. Access to suitable healthcare remains challenging due to diagnostic delays, variable treatment responses, and difficulties transitioning out of pediatric support structures. Digital health technologies (DHTs) hold the potential to address these challenges.

We conducted a scoping review to identify DHTs developed specifically for adults with ADHD, categorize them by their intended role within the health and social care system and by their core technological features, examine their methodological trends, and examine the quality of evidence by conducting a Risk of Bias analysis.

A systematic search across databases, up to December 2025, identified 133 eligible studies. 63 were categorized as Treat a Specific Condition, most frequently using web/app-based cognitive therapy or psychoeducation (n = 26), cognitive training programs (n = 13), transcranial stimulation (n = 12), and neurofeedback (n = 9). 36 were categorized as Drive Clinical Management, with technologies mostly supporting diagnostic decision-making through machine-learning analysis of participant features, such as data from continuous performance tasks (n = 11), neuroimaging (n = 11), and virtual reality (n = 5). 19 papers were classified as Diagnose a Specific Condition and used similar machine-learning classification, yet do not situate the DHT as a support tool that complements the traditional clinical assessment pathway.

Through our analysis, we identify various opportunities to strengthen the evidence base. This includes clarifying clinical integration points for diagnostic DHTs, ensuring technologies support adherence by incorporating lived experience, and developing remote monitoring technologies that demonstrate value to both clinicians and patients. Key questions remain on how DHTs can be translated into clinical practice, and we highlight various implementation-oriented frameworks which can guide development by encouraging multidisciplinary research that ensures the broader health and care system is considered alongside isolated measures of preliminary efficacy.

https://osf.io/tk3pm.