The current complexity of the wearables industry is O(N^2)
Ok, let me untangle the title of this post up front. The Wearables user experience gets very complex (very quickly) when athletes use more than one device or platform. Stick with me for 5 minutes (maybe 10) and this will make more sense.
The Roadmap
This is the first of a series of posts on the state and [predicted] future of the wearables industry. Ok, I agree, that reads like a lofty statement but really my intention is to simply share [my] views, as a coach, athlete and product leader. The goal of these posts is to show where we are today and how things could unfold in the future. Here’s the roadmap of posts:
The current state (this post)
The need for better sensor prioritization and fusion
New health & fitness metrics we can expect in the future
The role of AI in coaching and training
Some Background
Over the past 15+ years, I have had the incredible opportunity to coach youth, junior and elite athletes while leading product (or parts of product) for brands like WHOOP, Android Health (Google), and Supersapiens. This and the next set of posts will be my attempt to share some observations on the current state, and future opportunities for the wearables market. I will largely draw on my experiences as a product leader, coach and what I am seeing with our PCG athletes.
I should also mention that I am going to reference platforms like Google, WHOOP, Supersapiens and others that I have a personal and [past] professional connection to. This is on purpose. I am using these platforms as examples, (recognizing there are others I could also be highlighting,) since I use these in my daily life and end up recommending most of these to the PCG athletes and coaches. This bias is largely based on my view that they are category leaders - which is why I chose to join their teams in the first place. However, to state the obvious, this does not mean these are the only or best platforms. You can easily replace these brands, within a given category, with your go-to product. Hopefully, my points and suggestions will be just as relevant. With that setup in place, let’s get to it…
The case for complexity
If you are like me, and like most of our PCG athletes, you spend a lot of your time “swivel chairing” between a bunch of wearable platforms and downstream platforms to get key insights into your performance, fitness and health. In Computer Science, we refer to this swivel chairing action as “context switching”. Just like the act of moving between wearable platforms, the time between each switch is generally considered a waist of time from the user’s perspective, just the unloading and reloading of the process or thread state. If that’s an unhelpful reference for you, keep reading and I’ll clear things up by the end of this post.
The devices and platforms I use to track and analyze my training
I was recently lecturing at a local college and shared the above slide as a way to describe the wearables and platforms that I use along with the key features & metrics that each support. The TLDR (or summary), is that navigating between all these different platforms is incredibly time consuming, complex and undoubtably leaves important insights into my (and our athletes’ performance) undiscovered.
I recognize that I may sit at one end of the spectrum when it comes to the shear number of devices being used, but I can assure you I am not alone. Here’s a quick summary of why I use each of these:
Devices
WHOOP: as a result of a clever charging design, this is the only device that I use nearly 24x7 (I tend to take off during swim training). The primary value I get from WHOOP is recovery tracking.
Supersapiens: fueling before and after specific (generally long or intense) training sessions is key to executing not only the current session but also being prepared for the next session. Using a CGM has helped me learn about how I fuel and how this impacts my performance.
Wahoo: I use the Wahoo Roam as my bike computer, the Rival as my running watch and the Tickr X HR chest strap as my way to capture heart rate during a training session. Wahoo has created some very specific user interfaces that I have really come to appreciate.
Stryd: I do a lot of my runs on trails where GPS is less then great. Using Stryd, I can get accurate distance and pace in any condition and I get a more accurate power reading (really critical for power duration curve analysis).
Garmin: I ride with the Garmin Vector 3 pedals and love them. After suffering a really bad back injury years ago, I invested in dual-sided power meters to better measure and adjust the distribution of power across my good and my not-so-good leg.
Pixel Watch: While I often jump between iOS and Android (which drives my family and AT&T crazy), I generally spend most of my time these days on Android. As such, I love the pixel watch for my day-to-day smart watch usage. Also, my go-to swim tracking app is Swim.com, which, has the most accurate wrist-based swim tracking algorithms in the market (and yes, I am biased but this is definitely true for me).
Platforms
HumanGo: HumanGo is an AI-based training platform that creates adaptive training plans for athletes while giving coaches (like our PCG coaches) the ability to control every aspect. At PCG, we are experimenting with HumanGo with a few individual athletes and for group-based coaching. Much more on this in future posts.
TrainingPeaks: TP remains our go-to platform for athlete programming. They are the platform in the endurance training space.
WKO+: WKO+ is the Tableau of endurance athlete analysis. Not the sexiest software you’ll ever see but absolutely necessary for deep diving on power duration curves, zones, energy zones. etc.
Strava: I don’t need to really say anything here….we all know that if it’s not on Strava, it didn’t happen! In all seriousness, Strava, for me, is the one place that I ensure every activity is captured all the time. It is the platform that is the single source of truth for all of my activities. Even when I have had gaps in formal training and TP was not in the picture, all activities were still sent to Strava. For this reason and in addition to the social features, I use some of the basic “progress” features for analysis.
INSCYD: This is a relatively new platform that is being used by PCG to establish athlete’s metabolic profiles. You can check out some of the key metrics we establish and track here.
Fitbit: I use Fitbit on and off, correlated to how often I am using my Pixel watch for extended period of times. Fitbit is not my go-to platform but I am optimistic about Google’s direction.
On any give day I am using the vast majority of the above platforms; going after something specific such as my recovery score from WHOOP while ignoring other metrics such as the heart rate it captured from my run (since I got this from my WAHOO Rival and Tickr X). Switching between each platform having to dig out the “signal” from the rest of the noise is the “context switch” I referred to at outset of this post. This does’t even begin to address an entire host of other user friction points such as:
Profile Management: think, every time my threshold or training zones change, I need to update it across multiple platforms. To make matters more challenging, not every platform has or allows for the same definition zones.
Metrics: the same metric (e.g. HR, power, HRV, etc) can be captured across multiple devices. Which do you choose? How are users supposed to assess and understand quality?
Downstream [derived] metrics: often, downstream metrics can be negatively impacted by redundant or low quality upstream metrics. For example, the other day I went for a run and forgot my Tickr X. The HR captured from my wrist-based device was completely off. This was a zone 2 run and it had my peak HR at 195! BTW, my peak HR is around 175. With a new peak HR and [high] average HR, TrainingPeaks prompted me to update my new HR zones. If had accepted this, my training zones would have been wildly incorrect and my zones for one platform would now be out of sync with the others (back to the profile management problem).
It’s not all doom and gloom though. Apple users have been enjoying the ability to view data from multiple data sources in a single view via the Fitness and Health apps for a while now. And just today, Google announced at the Mobile World Congress 2024, that Fitbit will start to show HealthConnect data (from third party sources) along side data captured from Fitbit trackers and the Pixel watch. These are positive steps to help reduce the user friction but still outsources a lot of the complexity to the user.
So why does all of this matter?
For this post, my goal was to illustrate that the wearable ecosystem is highly fragmented with many devices/sensors and platforms generating unique AND overlapping metrics. Essentially, the more devices you use to gain activity-specific or best-in-class insights, the more complex your life becomes.
Fun (maybe?) fact and why I chose the title of this post: In computer science, we measure algorithmic complexity using Big O notation. Since adding a new device / sensor to your life can, in a worst case, duplicate the metrics being captured which leads to all kinds of downstream complexity(see above list), I argue that the current state of the wearables industry is O(n^2) - really complex! Meaning, adding another device, sensor, platform can more than double the complexity for the user. Peaked your interest on Big O? Check this out.
While using multiple devices can create a lot of user complexity and ultimately can degrade the reliability of downstream derivative health and fitness metrics, I believe we will continue to live in, and benefit from, a multi-device world. Therefore, to address this complexity, I believe there are three key gaps that need to be addressed:
Sensor prioritization and fusion
Introduction of new health/fitness metrics (and correlations)
AI and digital coaching
Also, for those that have made it this far, I want to acknowledge platforms like Apple’s HealthKit, Google’s Health Connect and non-OEM platforms like Terra do play an important role in reducing some of this friction. However, I will try to demonstrate that these platforms are [currently] solving for a different problem and have not tackled the challenge users face with navigating a multi-device/platform ecosystem.
