How IoT and AI can help us make health decisions based on data not opinion
What’s data got to do with staying fit? If anything, intuitively one would think that working with data is something that is prone to make people whose job revolves around this less, rather than more, fit: a sedentary work routine does not help much there.
It certainly worked that way for Ethan Agarwal. After putting on some weight in business school, Agarwal was thrust into a fast-paced, highly demanding job at McKinsey that required a lot of travel, making his schedule unpredictable. Agarwal says that when evaluating existing options for guidance in getting back in shape and returning to a healthy lifestyle, he came up empty.
This is where it starts getting interesting, because Agarwal decided to do something about it. Since Agarwal did not have the time to join a regular fitness program, and did not stay long enough in one place to commit to one even if he did find the time, he decided what he needed was motivation and guidance delivered through an app, thus Aaptiv was born.
Aaptiv’s value proposition is built around a premise overlooked by others, and the way it uses data to gain insights and grow its business is interesting. It’s not that there are no other fitness apps around, and many of those are built on the same core premise R12; motivating and guiding their users by enabling them to access sessions delivered by professional trainers.
The difference is in the approach: Aaptiv uses audio, rather than video, to deliver sessions to its users, and it relies heavily on analytics to run its business. Chris Fischer, CTO and VP, Product at Aaptiv, says audio is the right experience to maximize engagement with a cardio-based workout:
If you run on a treadmill or outdoors, the idea of staring at a video can be more distracting than useful. It’s not a practical option for many people. Plus, the focus on audio gives our members a perfect blending of trainers’ descriptions and motivation with high-quality music.
Having instruction match with the music helps push people further than they may have thought possible. People point to the syncing of the beats of the sound with the flow of their exercises. It’s that piece that our members highlight most.
Fischer did not elaborate on the exact way the company is able to produce audio content with this kind of synchronization, but he did mention there is a significant amount of metadata it collects and uses for every class: workout category, specific moves performed, how long it lasts, musical genre, tempo, and more.
Although analyzing audio itself for example was not discussed, a mixed data and metadata approach could be applied there. In any case all of that happens in the cloud, as Aaptiv does not operate any physical data centers.
Back to basics
But is this working? Good question — how about some data to answer it? Fischer describes Aaptiv as a data-driven tech company, and says analytics are key to all of the decisions they make:
Success hinges on our understanding both qualitatively through speaking with users and quantitatively from the search and behavioral patterns we recognize. For instance, we’ve learned that most members, regardless of experience level, want to be given more direction on how to work out.
We help them realize their goals by leading them to the right classes that will drive them in the right direction. It’s not simply which category of training they prefer or which equipment they’re most comfortable with. We keep up with overall behavioral trends they show us, collectively.
For instance, many Aaptiv members are coming back to the same trainer over and over again. In doing this, they are simulating the personal connection they’ve felt standing side by side with a personal trainer cheering them on and at times also correcting their form.
Sixty one percent of users turn to the same trainer for multiple classes in their first four completed workouts using Aaptiv, and that number skyrockets to 81 percent when users complete their first five workouts.
One thought would be to attribute this to the fact that users like a certain type of training offered by specific trainers. Fischer however says most of their trainers teach classes across several categories, so his belief is that users tend to stick with trainers because of their coaching styles and personalities: “Trust is built, and the guidance individuals receive on a regular basis keeps them coming back.”
Data-driven or not, some things do not change apparently. Another one of those is the seasonal peaks in signups. Fischer says they have grown more than 10x in active paying members since the summer of 2016, but still expect a larger bump in new signups early in the year and in the summer: “Those are times that traditionally people refocus on and reinvest themselves in their fitness goals.”
In the end, tech and data is all fine and well, but it all comes down to the basics, as confirmed by Fischer:
Integration with other technological solutions and platforms is something we talk about a lot. This month, we rolled out Aaptiv for the Apple Watch. Longer-term, we also hope to recommend increasingly personalized programs to help our members achieve their goals.
Over time, we can anticipate exactly where members are or will be struggling during a workout, and then give them the right advice or routine to stick with it. A long-term commitment to improvement, however you define it, is the best path toward fulfilling your goals.
If you make it a prominent part of your schedule and your life – turning fitness into a habit – it will soon stop feeling like a daunting prospect and emerge as something worth taking both pride and pleasure in.
NOTE: An earlier version of this article which erroneously attributed some of the quotes to Aaptiv CEO Ethan Agarwal was corrected to attribute them to Aaptiv CTO and VP Product, Chris Fischer
Bigdata and data center