This post was written more than four years ago. The world changes fast, and the information, conclusions, or attributions may or may not still be accurate. Check the sources and links, and email me if you have any questions.

Love or hate hackathons – there are many merits and demerits – they’re a good way to meet other technologists when you’re new to a city, and building out an app in the course of hours or days is a great learning experience.

This weekend foursquare held their second hackathon event, and over 500 developers participated from around the world.  I worked on an app called “fourhealth” with John in foursquare’s San Francisco office, and I’m pretty happy with where we ended up with about 15 hours of work.

When you visit a restaurant, café, or airport, take out your mobile device and check-in with foursquare. You’ll get to review tips and photos left by others, you’ll earn mostly meaningless points, and you can see where friends have gone. I find it useful for planning trips and finding good food in new cities.

Could your check-ins also be an opportunity to help you make better, healthier choices? That was the idea behind this app. Rarely do social media and health meet, and our hypothesis was that they need to meet much more often.

When signing up for the app, it gathers some additional information from users — most importantly, their weight and height to generate body mass index. BMI is an imperfect heuristic proxy for a measurement of body fat and is not a measurement of overall health, but it’s the most practicable measurement that can be used without asking pages of questions or reviewing someone’s medical records.

Combining the data of a user’s foursquare check-ins with their BMI, conclusions can be drawn that people with nominal or high BMIs tend to eat at certain places, and that data could be used to guide users to a healthier venue. Tracked over time, it could be an interesting experiment.

fourhealth_foursquare_hackathon_san_francisco

So, how it works:

  1. Login to fourhealth using foursquare
  2. Every time you check-in to a restaurant, we’ll store that data
  3. As more people use the app and if you check into a restaurant that unhealthy people frequent, we’ll combine our data with foursquare’s venue search API to find a healthier alternative.

If you check-in at a restaurant where the average BMI is high, you’ll receive a push notification or text message suggesting you go somewhere healthier. The wording would have to be tested to see what drives the most action but isn’t offensive, but it could be something like:

  • “Organic Café is 600 meters away.”
  • “There’s a healthier restaurant just 600 meters away: Organic Café.”
  • “Unhealthy people eat here. Try Organic Café instead, just 600 meters away.”

Privacy is really important. Certainly, nobody wants their height, weight, or BMI data being shared, so any calculations would have to require multiple check-ins from multiple people to ensure accuracy and add a level of obscurity against divulging a user’s data.

Another idea of data to track is subjective feeling. An hour after you eat, we could send you a text message saying “In one word, how do you feel after eating at Monty’s Terrible Diner?”  We’ll store that data for visualization purposes (think, a tag cloud of ‘bloated’ and ‘sick’), and we’ll also use that information for guidance purposes (“fourhealthers say this diner makes them feel bloated and sick”).  And, if you ever visit that venue again, we’ll remind you via text about how you felt the last time (“The last time you ate at Monty’s Terrible Diner, you felt bloated.”)

Subjective feeling has been used in really interesting ways by We Feel Fine, and it has a real value in filling that grey area that can’t be defined by any linear scale.

The big picture of social media and health has a lot of opportunity. I’d be really interested in combining foursquare’s push API and collecting information about user modes of transportation. I’d predict that healthier people walk or bike between venues, versus drive a car, and that can vary by metropolitan area and other demographics.

This area is ripe for some serious health interventions and definitely holds a place in some transformative healthcare changes, and large datasets generated by ideas like this are invaluable on their own.

I think the rule of building apps that you’d actually want to use holds true here. Although I’m not sure if we’ll continue to work on the app (most hackathon projects see death), I could see myself using something like this on a daily basis to help me find healthier food options while traveling, and to push me to walk, bike, or drive a bit further to get healthier food instead of going to a drive-thru.