This is the first in a three-part series about tracking one’s overall fitness. By examining the current set of solutions, we identify gaps that need to be filled by creative and talented developers.
We’ve all seen endless reports on the growing health problems in America related to obesity, poor eating habits, and a lack of exercise. And with the advent of wearables, we now have a tool that can help encourage users to modify their behavior. It is becoming increasingly common to monitor one’s activity during waking hours, and in some cases, while asleep. Of course, this is opt-in technology, but the incentives it provides have the potential to change Americans’ behavior for the better.
We can distill the solution to our increasing problem with general fitness to just four words: eat less, move more. In a nutshell, it’s pretty simple. But we forget — we get busy at work and forget to stretch our muscles and move around a bit, then we plop down on the couch at night and watch TV or surf the internet a little too much. Food-wise, we eat just a few more bites past the point of satiation, get tempted into that desert, or eat a full-size restaurant portion knowing that the serving is oversized to begin with.
These tendencies are normal, frequently subconscious, and seemingly harmless on their own, but they add up over time. Even 100 extra calories over our suggested limit per day will add about one pound a month, or 12 pounds a year — that’s only two cookies, half of a popsicle, or one-fourth of an order of fries. Just undoing that takes at least 2,000 extra steps, or about one mile.
Enter wearables. They monitor steps, nudge us to get up and move if we sit too long, and are even beginning to offer deeper insight like recognizing when we’re running, not walking, or if we’ve gone for a bike ride. But what if they did even more?
The crucial next iterations of wearable technology will need to include better and more pervasive recognition of activities in real time, not just after a user has finished. By measuring a user’s heart rate during exercise, they get a better indication of effort and fitness level. Post-workout heart rate recovery is also an important indicator of fitness — correlating resting heart rate with exercise patterns can help show the wearer that their efforts are making them healthier (or not).
There are some very good apps already available for running and cycling, but other activities are not as easily identified. The apps that do already offer tracking for a variety of activities often require a lot of manual input. The goal for developers should be to make that much, much easier and as automatic as possible. And to do that, you need context.
Platforms exist today that allow you to geofence to a location. If a user’s in a gym, for example, that knowledge can give your app the context it needs to evaluate their workout. Scrape the gym’s website for class schedules, and there is even more information you can factor in. Did the accelerometer and heart rate sensor information suggest that the user may have been kickboxing at a time when a kickboxing class was offered?
It all boils down to data intelligence. The enormous amount of raw data available through wearables can help make contextual sense of the motion, but it needs to be distilled into smaller sets of data to reduce storage and transmission bandwidth. Ultimately, this intelligence will help users more thoroughly understand and optimize their workouts with minimal effort, providing for a more engaging and beneficial experience and a greater incentive to download fitness apps.
So how can you help successfully integrate this data into your app? Tune into my next blog post, The Future of Fitness, Taking the Next Steps, Part 2.