BIRS + Personalized Digital Health

BIRS

I just learned about a recent workshop at the Banff International Research Station for Mathematical Innovation and Discovery (BIRS) in Banff, Canada. The topic was quite relevant to what we’re trying to do at Stats-of-1: Workshop on the use of Wearable and Implantable Devices in Health Research

I’m super bummed I didn’t know about the event sooner! And I’m in awe of the lectures, organizers, and presenters. The latter included my former postdoc colleague Professor Jessilyn Dunn—and even my dissertation co-advisor, the excellent Professor Amy Herring. (Amy is a master at longitudinal data analysis and missing data, and I’m ever grateful to have been her student!)

The BIRS Workshop website describes the event as:

The use of wearable and implantable devices in health research and clinical practice is exploding due to rapid advances in sensor technology, widespread user adoption, and data richness. Indeed, technological advances in sensor technology have far outstripped the development of new analytical methods that could transform this high-volume, high complexity data into clinically useful information. This workshop will bring together statisticians, mathematicians, clinicians and data scientists to tackle emerging and existing problems in health research. Specifically, the overarching goal is to present the real-world clinical questions, existing state-of-the-art methods and build scientifically-aware analytical groups dedicated to answering the most important scientific problems in health applications of wearable and implantable computing. This workshop is a timely event to capitalize on this momentum.

Anyway, definitely check out the lectures. They’re available on the BIRS website here, and are linked to this Tweet.

Personalized Digital Health

All this got me thinking, maybe I’ve been too broad and abstract in defining the scope and mission of this blog. I realized that “n-of-1” may be seen as too niche or fringe of a term within the statistics community—and really, I was (and am) most interested in health applications (i.e., not so much in more abstract “recurring cycles”, at least for now).

What I really want to do with this blog is to bring methodologists together, to unify methods for individual-focused health using data collected on wearables, sensors, implantables, and other devices. While I envisioned growing this field from the vantage point of n-of-1 studies, I soon realized that I wanted to incorporate many other analytical aspects of using time-rich, longitudinally dense health data collected from devices. (See my post, “Research-of-1: My Esametric Vision”.)

This, of course, is almost exactly the BIRS Workshop description’s stated goal: “to present the real-world clinical questions, existing state-of-the-art methods and build scientifically-aware analytical groups dedicated to answering the most important scientific problems in health applications of wearable and implantable computing”.

But what would I have been able to offer at BIRS, aside from perhaps proposing to label these “analytical groups dedicated to answering the most important scientific problems in health applications of wearable and implantable computing” as esametricians, and this rising amalgamation of approaches as esametry? All the presenters were academics, as was the large majority of participants—and I was not. (Impostor Syndrome much, you say? Shush!)

The two-part answer is that:

I may reach out to the Workshop organizers or participants at some point. It would be freaking amazing if some of them would want to co-host or lead a workshop or meetup to help digital health practitioners, self-researchers, and self-trackers understand and apply their methods to their own health data (e.g., folks from Quantified Self, Open Humans, and Sage Bionetworks).

Pivot

After all this, I realized that I should change how I present Stats-of-1, from a blog focused on “n-of-1” to one focused on “personalized digital health”. I’m hoping this shift in messaging will attract more smart folks to contribute their methodological know-how—and even better, to show non-academic researchers how to apply these innovative methods to their own very real, sometimes maddeningly idiosyncratic chronic illnesses and conditions.