QS Mobile Cardio Startup Princeton University

Hey guys!
I’m working with a startup team in an accellerator program at Princeton University this summer, and I could really use your input to gague what is important for our service.

Our team is making a service that works with major fitness sensors to take in raw data and provide a deeper analysis than the proprietary app currently does. Our main goals are improved energy, focus, motivation, and general fitness.

This week we’re working on a pricing model, so what do you guys think of a free app with a $40 annual premium fee for the personally tailored services (like workout recovery monitoring)?

Any responses would be really helpful for us.

Feel free to PM me, and we can chat!

Without seeing the product, I can’t tell if I’d pay $40, or if you’d have to pay me $40 to use it :slight_smile:

Generally speaking, I’d be reluctant to sign up for an annual plan, unless I had already been using the service for several months, and switching to the annual plan saved me some money.

I’m with Eric; because of my work with QS I encounter plans like this very often - more than once per week. The common idea is to take data from multiple sources and analyze it to provide deeper benefits. Having seen projects come and go, I’ve been forced to be skeptical, so as a user I would need to really be convinced before I signed up for any paid service. The twist here, I think is “raw data.” I take this to mean that you are going to access the device via the firmware. I’ve seen some hacked projects like this but nothing as a paid service.

Right, we’re currently working with Basis, which is a watch sensor, and Zephyr, which is a chest strap. We have access to their SDK, so we’re pulling in the gritty data from the devices and using them for our analysis. We’re handwriting the algorithms based on our research, which is why the prototype development is slow.

I’ll post the app mockup when we get it ready (sometime in the next two days), but until then, here’s our landing page:

If any of you guys are free to talk over PM or gchat, etc for five minutes, we could really use your input!

[quote=“griv1012, post:4, topic:672”]
Right, we’re currently working with Basis, which is a watch sensor, and Zephyr, which is a chest strap.[/quote]

I’m currently in the market for a heart rate monitor, but based on what I’ve heard, neither the Basis nor the Zephyr are accurate or reliable enough to give correct HRV readings. Do you plan to support the Polar H7, too?

I’d love to hear more about how this service works, other than “trust our handcrafted algorithms”.

What exactly is the value you provide to (problem you fix for) the user? Stats/machine learning algorithms that analyze data across multiple devices/users is of course very interesting. But a solution in search of a problem does not a business make.

How does the problem you fix relate to the time scale of the device? For example, a HR monitor gives you a measure of exertion during exercise, the problem it solves is “When I am working out, how do I motivate myself to work harder?”. A scale is also a QS device but it won’t help you with that problem because your bodyweight doesn’t (significantly) change during a workout. So pooling data from those two devices together doesn’t make sense w.r.t. the workout motivation problem, even if you can do it mathematically.

Check out this blog post about the topic.


Heart rate monitoring isn’t just for feedback during workouts; it’s also useful for per-day measurements such as the resting heart rate–which some people use to plan their workouts (and avoid overtraining).

Point taken. It is a device that can address problems across multiple time scales. My point remains that the problems one homes to solve with such devices can be characterized by time scale.