Neurodivergence

When companies talk about “personalized” health tech and neurotechnology, they usually mean more data, not more kinds of brains. Most systems are still designed around an imagined neurotypical user: someone who can tolerate constant notifications, parse dense interfaces, sit still for long periods, and communicate in conventional ways. For many neurodivergent people—autistic folks, people with ADHD, learning disabilities, intellectual disabilities, and others—that picture just doesn’t fit.

A growing body of research shows that neurodivergent populations are both underrepresented in the design and testing of health technologies and poorly served by how these tools currently work. Here’s what the literature actually says.

Excluded at step zero: who even gets into the studies?

One of the clearest examples comes from digital mental health. A review by Sheehan and Hassiotis (2017) looked at digital mental health tools for people with intellectual disabilities and found that this group is routinely excluded from mainstream e-mental health trials and that the evidence base tailored to them is “small and methodologically weak."

In practice, that means:

• Many commercial mental health apps are validated without including people with cognitive or communication differences.

• Interfaces often assume high reading level, fast information processing, and comfort with abstract scales and metaphors, which can be especially challenging for some autistic or intellectually disabled users.

Legally, we’re not doing much better. Hutson and Hutson (2023) argue that existing digital-inclusion and disability law still leans on generic “reasonable accommodations,” without fully grappling with autistic sensory and cognitive needs in online environments. In other words, the law mostly assumes that making a website slightly more accessible is enough—rather than requiring that systems be built from the start to accommodate very different ways of perceiving and processing the world.

Wearables and remote monitoring: “compliance” vs. how ADHD actually works

Wearables and smartphone-based monitoring are often marketed as perfect tools for people with ADHD: they remind you, track you, and nudge you. Reality is more complicated.

In a CHI 2020 study, Cibrian and colleagues worked with children with ADHD and their caregivers to design wearable-based self-regulation tools. They found multiple “tensions and design challenges”: devices could feel stigmatizing, buzzing reminders could be distracting rather than helpful, and rigid expectations about when and how kids wore devices often collided with the very executive-function challenges the device was supposed to support (Cibrian et al., 2020).

Similarly, an interview study on remote measurement technologies (RMT) for ADHD—using wearables and smartphone tasks over 10 weeks—reported barriers including forgetting to charge devices, difficulty maintaining routines, and concerns about data burden and fatigue (Denyer et al., 2023). While participants saw potential value in objective data, the systems were not designed around ADHD-typical patterns of time management, motivation, and attention.

Taken together, these studies show a common theme: “adherence problems” are often framed as user failure, when they’re actually design failure—interfaces and protocols that implicitly assume neurotypical executive function.

Neurotechnology & XR: autistic people as test subjects, not co-designers

You’d think cutting-edge neurotechnology and immersive systems would be leading the way on inclusion. Not yet.

A systematic review by Newbutt et al. (2024) examined how autistic people have been involved in the design of extended reality (XR) technologies (VR, AR, etc.). They found that most studies involved autistic participants only as end-users in late-stage testing, not as collaborators in deciding what should be built or how it should work. Co-design and participatory methods were the exception, not the norm.

This matters because XR can easily amplify sensory overload—think bright visuals, sudden sounds, and complex environments. Without autistic people shaping the design, systems may unintentionally recreate the very barriers they’re meant to reduce.

Zooming out to AI-driven neurotech, a recent scoping review by Xu and colleagues (2024) looked at 117 studies on inclusive and adaptive human–AI systems for neurodivergent users. They found:

• Growing interest in AI tools for autism, ADHD, and dyslexia.

• But fragmented evidence, very few long-term evaluations, and

• Most systems did not sufficiently involve neurodivergent users in co-design, nor did they consistently address sensory and cognitive heterogeneity.

So even in domains that talk a lot about “personalization,” the people whose brains are allegedly being “personalized to” often have little say in how these systems are built.

“Inclusive design” is still mostly theory for neurodivergent users

Autistic researcher Jessica Dark (2024) argues that neurodivergent people have historically been treated as research objects rather than research partners. She proposes eight principles of neuro-inclusion, emphasizing shared power, accessible communication, and flexibility in methods so that autistic participants can genuinely shape research and outcomes.

In parallel, Hutson and Hutson (2023) show that legal and policy frameworks around digital inclusion rarely reflect this neuro-inclusive perspective, especially for autistic people navigating complex online systems.The tension is clear:

We have theory and principles for neuro-inclusive design—but they’ve only partially filtered into mainstream health tech and neurotechnology practice.

There are promising counterexamples. There's the need for flexible protocols, alternative communication channels, and a shift away from deficit-based assumptions. But these projects are still relatively niche compared to the huge volume of neurotech being developed without deep neurodivergent input.

What “underrepresentation” actually looks like in practice

If we stitch these findings together, underrepresentation of neurodivergent people in health tech and neurotechnology shows up in at least four layers:

1. Who’s in the data and trials?

   • Intellectual disability and some forms of neurodivergence are often excluded from mainstream digital mental health trials (Sheehan & Hassiotis, 2017).

   • AI and neurotech systems for neurodivergent users are still mostly validated on small, non-diverse samples, with limited long-term follow-up (Xu et al., 2024).

2. Who gets to shape the design?

   • Autistic participants are typically consulted late in XR/neurotech projects, if at all, and rarely have power over research questions or design decisions (Newbutt et al., 2024; Dark, 2024).

3. What assumptions are baked into the interface?

   • Wearables for ADHD often assume punctual charging, routine usage, and responsiveness to frequent prompts—behaviors that directly clash with core ADHD challenges (Cibrian et al., 2020; Denyer et al., 2023).

   • Many digital environments assume tolerance of sensory load and rapid cognitive processing, which can be inaccessible or even harmful for autistic users (Hutson & Hutson, 2023; Newbutt et al., 2024).

4. How “inclusion” is defined at all.

   • Policy and legal frameworks still mostly treat accessibility as an add-on, not a fundamental design constraint tailored to neurodivergent cognition (Hutson & Hutson, 2023).

   • Research culture is only beginning to see neurodivergent people as knowledge-holders whose lived experience should drive innovation (Dark, 2024).

Where the field needs to go

The literature doesn’t say “neurotechnology is bad for neurodivergent people.” It says something more subtle but more damning:

Neurotechnology and digital health have mostly been designed for the “median mind”—and then retrofitted, if at all, for everyone else.

A more just direction, based on current research, would include:

• Routine co-design and co-leadership by neurodivergent people at every stage of tech development, not just user testing.

• Protocols and interfaces that assume non-linear attention, sensory differences, and alternative communication styles as default cases, not edge cases.

• Evaluation metrics that prioritize lived usability and autonomy, not just symptom scores or “engagement” measured as time on device.

• Legal and regulatory frameworks that explicitly require neuro-inclusive standards for health tech and neurotechnology, extending beyond physical disability models.

There are already seeds of this shift: participatory wearable projects for ADHD, co-designed XR systems with autistic workers, neuro-inclusive AI guidelines, and BCI maker experiences created with neurodivergent youth. But the literature makes it clear that these are still the exception, not the rule.

If neurotechnology is going to live up to its promise, it can’t just measure and modulate brains—it has to respect the full diversity of how brains work.

References

Cibrian, F. L., Lakes, K. D., Tavakoulnia, A., Guzman, K., Schuck, S., & Hayes, G. R. (2020). Supporting self-regulation of children with ADHD using wearables: Tensions and design challenges. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–13.

Dark, J. (2024). Eight principles of neuro-inclusion: An autistic perspective on innovating inclusive research methods. Frontiers in Psychology, 15, 1326536.

Denyer, H., Deng, Q., Adanijo, A., Asherson, P., Bilbow, A., Folarin, A., Groom, M. J., Hollis, C., Wykes, T., Dobson, R. J. B., Kuntsi, J., & Simblett, S. (2023). Barriers to and facilitators of using remote measurement technology in the long-term monitoring of individuals with ADHD: Interview study. JMIR Formative Research, 7, e44126.

Hutson, J., & Hutson, P. (2023). Digital inclusion for people with autism spectrum disorders: Review of the current legal models and doctrinal concepts. Journal of Digital Technologies and Law, 1(4), 851–879.

Newbutt, N., Glaser, N., Francois, M. S., Schmidt, M., & Cobb, S. (2024). How are autistic people involved in the design of extended reality technologies? A systematic literature review. Journal of Autism and Developmental Disorders, 54(11), 4232–4258.

Sheehan, R., & Hassiotis, A. (2017). Digital mental health and intellectual disabilities: State of the evidence and future directions. Evidence-Based Mental Health, 20(4), 107–111.

Xu, Z., Liu, F., Xia, G., Duan, Y., & Yu, L. (2024). A scoping review of inclusive and adaptive human–AI interaction design for neurodivergent users. Disability and Rehabilitation: Assistive Technology, 1–19.