Feedback Loops and Fit-Tech Framework

With so many product launches and so much investment activity in the digital health & fitness arena it helps to have a framework for understanding the meaning of it all.  This post is the first in a series in which I’ll share the frameworks I use, with a few notes on some of the companies I see doing really interesting work across the entire ecosystem.

I always start by considering any new product or technology with a behavioral emphasis.  At the end of the day, it’s consumer/patient/athlete behavior that really matters when we talk about improving health, reducing costs, or improving performance.  I have another framework to look at things from a technology stack perspective which I’ll share in the next installment.

I’ve run this simple diagram by countless people, inside and outside the fitness-tech arena, and everyone seems to get it right away, so it’s been useful for me.  It’s simple, and maybe obvious, but I always find it worth stating.

Feedback_Loop

This picture is informed more by my background as a coach, athlete, and lifestyle design enthusiast than by any market study or tech trends.    The Action step is where the rubber meets the road, but it’s hard, complicated, and can take a long time to move the needle.  It’s also influenced by many factors beyond the fitness or health care arena.

A lot has been written about wearables, so I won’t say much about the data collection step.  As I’ve mentioned in previous posts, it’s really not the things that are interesting but the data they provide and the services they enable.

When I consider Expertise + Analytics box I’m including machines & people.   Some users are curious and enthusiastic enough to dig into physiology and learn how to interpret their own data, but that’s a fraction of the fraction of people who actually keep using their wearables (the percentage may be high, or maybe not), so the emphasis here needs to be on professional service providers of one sort or another.   IBM’s Watson and health focused providers like Vivametrica, and even more niche companies analyzing specific data sets like power data from bicycles are all contributors to the more algorithmic, machine based contribution the to this arena.

Finally, motivation is critical and many social, financial, mental health and other factors play into this.  It’s surely the hardest nut to crack and it’s also where a lot of the personalization of health care comes into play.   Some of the most interesting apps focus in this area so I’ll devote a whole post to this soon.  The opportunity for fitness clubs and other activity-related social groups to contribute here and leverage the technologies already on the market is substantial, and I believe still vastly underutilized.

As always, I welcome your comments and feedback via email or the comments section.  Follow me on Twitter or LinkedIn to make sure you get all the updates to the blog and subsequent articles in the series.

 

 

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