Opinion: My Journey into Improving Parkinson’s Disease Measurement Standards

7-13-2026 Update: I updated the research paper and made some minor changes:

1. I cut the repeated headings. Some sections had the title, but the first line just repeated the title in sentence form. That’s gone now.
2. I added better bridges between sections so it doesn’t feel like three separate chunks; the Qualification Gap now flows into the Evidence Continuum, which flows into the Evidence Ecosystem.
3. In sections 4 and 5, I made it clearer that AI is doing analysis and helping connect patterns across data, not spitting out diagnoses on its own.

The updated document can be downloaded at the end of the article. I am also including a NotebookLM mini-podcast that you can listen to here.

A few weeks ago, I wrote a story called The Evolution of Parkinson’s Progression Measurement: Reimagining the MDS-UPDRS about a need and desire to see the MDS-UPDRS updated. I saw my wife go through it, and it’s certainly clever and well thought out. The scale walks a clinician through four parts: non-motor symptoms, motor symptoms in daily life, a hands-on motor exam, and motor complications like dyskinesia, scoring each item from normal to severe. It has helped an untold number of patients and doctors, giving them a shared, standardized way to track a disease that looks different in every patient. What it isn’t, though, is precise, or at least not as precise as it should be.

The past decade brought a shift from single-purpose research tools to integrated platforms capable of continuous, passive monitoring outside the clinic.  It’s been 18 years since the revision that came out in 2008, and that was a revision of sorts. But it helps standardize how researchers and clinicians assess Parkinson’s disease and provide care for every patient they see. That mattered. It still matters. But time, technology, and medicine haven’t stood still. Although the MDS-UPDRS was published, researchers had already started exploring electronic ways to measure Parkinson’s disease. Before smartphones and wearables became common, studies used accelerometers, actigraphy watches, force plates, computerized gait analysis, digitizing tablets for handwriting and spiral drawing, computerized finger-tapping tests, electromyography (EMG), voice analysis software, dexterity tests like the Purdue Pegboard, and imaging like PET and SPECT scans. These tools showed promise. But they weren’t validated or standardized enough, and they weren’t ready for regular clinical use. So they stayed research tools instead of replacing clinician-rated exams. Still, they laid the groundwork for the digital biomarkers we have today.

Most of them, though, lived in the clinic, not the home. Force plates, gait analysis systems, EMG, and the Purdue Pegboard all needed lab equipment and, usually, a technician to run them and read the results. PET and SPECT scans required a hospital’s imaging equipment. Digitizing tablets and finger-tapping tests needed their own hardware and a controlled setting, too.

Actigraphy watches were the outlier. The whole point of actigraphy was to get data outside the clinic. Patients wore them for days or weeks at home. That’s part of why they became an early bridge to the kind of continuous monitoring wearables do now. Voice analysis was a partial exception, too, since a recording device was never tied to a lab. But most studies back then still had patients come in and record under controlled conditions instead of sending them home with the software.

So why didn’t the MDS-UPDRS keep up? A few reasons, complacency chief among them. There was no reason to change it; the tests worked, and everyone was happy with how things were. There was also little interest from the people who oversee the standards, namely the Movement Disorder Society, in pushing things further. When the standards came out in 2008, they set the course for the MDS-UPDRS for years to come. But the iPhone announcement a year earlier and released just months prior to 2008, had already set a different course in digital medicine, one very few people saw coming.

The past decade changed that. We went from single-purpose research tools to full platforms that can monitor patients quietly and continuously, outside the clinic. Smartphone apps now catch tremor, bradykinesia, and gait through sensors already built into the phone. Wearables track motor changes across a whole day instead of a fifteen-minute visit. The FDA has taken notice too, qualifying several digital tools through its Clinical Outcome Assessment pathway, something the earlier accelerometers and tablets never reached, but that progress has happened around the MDS-UPDRS, not in it. What’s changed isn’t just the sensors. It’s everything around them: the data standards, the validation studies, the regulatory paths that finally let these tools move from research curiosity to real clinical use.

We know research and development of software and hardware takes a lot of time and money, and then there’s medical research and standards which have to be developed on top of that, so the pathway isn’t easy, but the committee that sets the standards that all Parkinson’s patients and doctors all follow… has been silent on the topic.  That silence sent me looking for answers myself; I wanted to understand the process so I’d have a little better idea of why the MDS-UPDRS has sat still while the grass grows around it. So, to better my education and to understand how researchers and clinicians work to develop and produce a lot of the research I write about, I wrote a PDF proof based on the article I mentioned at the beginning of the article. Even with artificial intelligence helping me, it still took several hours to get through it. It was a learning experience that made me appreciate the work these doctors go through to put something like that together and write revisions if needed to publish it. My research paper is called “BEYOND THE MDS-UPDRS: A Conceptual Framework for the Next Generation of Parkinson’s Disease Assessment”

The manuscript is built around five interconnected concepts. Individually, each addresses a different challenge in modern Parkinson’s disease assessment. Together, they form a single framework describing how clinical measurement can evolve while preserving the strengths of established clinician-rated assessment.

1. The Qualification Gap

The Qualification Gap describes the interval between the creation of a promising new measurement technology and the point at which sufficient scientific evidence exists for it to be used routinely in clinical care, research, or regulatory decision-making. Many digital biomarkers and objective measurement tools demonstrate impressive technical performance, but they often require additional analytical validation, clinical validation, longitudinal evidence, independent replication, and regulatory qualification before they can be confidently incorporated into practice. The concept emphasizes that innovation alone is not enough—scientific confidence must be earned through evidence.

2. The Evidence Continuum

The Evidence Continuum explains how clinical measurements gradually gain scientific credibility. Rather than viewing technologies as simply “validated” or “experimental,” it presents evidence as progressing along a continuum from discovery and proof-of-concept through analytical validation, clinical validation, longitudinal evaluation, independent replication, and ultimately qualification for a specific context of use. This concept recognizes that different technologies mature at different rates and that confidence develops progressively rather than appearing all at once.

3. The Parkinson’s Disease Evidence Ecosystem

The Evidence Ecosystem is the central concept of the manuscript. It proposes that Parkinson’s disease assessment should be viewed as an integrated system in which multiple forms of validated evidence contribute complementary information. Clinician-rated assessment remains the interpretive foundation, while digital biomarkers, biological biomarkers, imaging, objective physiological measurements, and patient-reported outcomes each contribute unique insights into different dimensions of the disease. Instead of competing with one another, these evidence domains work together to improve clinical understanding and reduce uncertainty.

4. The Hybrid Assessment Framework

The Hybrid Assessment Framework translates the Evidence Ecosystem into a practical clinical application. It argues that future assessment strategies should integrate complementary forms of validated evidence according to the scientific or clinical question being addressed. Rather than replacing the MDS-UPDRS, clinician-rated examination continues to serve as the foundation while objective technologies are added when they provide meaningful additional information. The framework promotes evidence-informed decision-making rather than technology-driven substitution.

5. Adaptive Measurement

The Adaptive Measurement concept describes how Parkinson’s disease assessment should evolve over time. It recognizes that clinical measurement is not static and that new technologies will continue to emerge. However, new measurements should only be incorporated after they have demonstrated appropriate scientific maturity. Adaptive Measurement therefore combines stability with flexibility: the MDS-UPDRS and other established instruments provide continuity, while validated evidence domains are progressively integrated as scientific confidence grows.

How the concepts connect

The five concepts are intended to function as a single framework rather than independent ideas: The Qualification Gap explains why promising technologies cannot immediately enter clinical practice. The Evidence Continuum describes how those technologies accumulate the evidence needed to gain scientific trust. The Evidence Ecosystem shows how multiple validated evidence domains complement one another instead of competing. The Hybrid Assessment Framework demonstrates how those complementary evidence domains can be applied in clinical care and research. The Adaptive Measurement provides the long-term vision, allowing Parkinson’s disease assessment to evolve as new evidence matures while preserving the continuity and rigor of established clinician-rated assessment.

Together, these concepts shift the discussion away from finding a single “best” measurement and toward building an integrated, evidence-based system that more accurately reflects the multidimensional nature of Parkinson’s disease. 

It sounded good, I like it, and I really thought I had something amazing, too. What I learned from the experience, there’s a lot that goes into the MDS-UPDRS, and it’s not something you can change overnight.

If that’s the case, is somebody doing something about it… yet?

Still, I’m not backing down on what I think it should take for devices and equipment to qualify under the new MDS-UPDRS standard. Here’s what I wrote in my article: “The promise of digital biomarkers depends on a question researchers haven’t fully answered yet: whose data are we actually comparing? A smartwatch worn in a clinic in Boston captures motion data under different conditions than those of one used in a trial site in rural Kentucky. Hardware matters more than the field has acknowledged. When researchers deploy webcams to capture facial expression or movement, the gap between a 720p consumer camera and a 4K clinical system isn’t just a question of image quality; it’s a question of what the algorithm ever gets to see. The same logic applies to microphones used for voice analysis and to the sensors embedded in wearables. Much like the smartphone, the computational power of the processor will help drive the technology behind these devices, making them even more advanced. But without agreed-upon minimum standards across devices and study sites, the data collected today may not be comparable to the data collected tomorrow or anywhere else.” 

If you’d rather listen than read, the NotebookLM mini-podcast about the research paper is available here

The updated research paper can be downloaded here for your review. Let me know what you think of it for a first-time try at research.