We developed an app for athletes and runners to socialize with like minded people near them. Targeted audience of the app is mainly the runners and sports community, so the app can match you to similar runners in your local area. With the help of a smart algorithm it can match you up with other runners in your area based on your skills, goals and workout style. This app can be considered as a “one stop place” for all matters on running, coaching, club/race events and interactive chats between runners and people passionate about sports.
To develop a social networking mobile app for sports people with the help of some smart matching algorithms and fitness trackers, we were asked to create a social networking medium by utilizing the data from multiple fitness trackers used by the users. One of the core functionalities is to develop a user matching algorithm based on the user location, running style, running route, running schedule etc., which helps in finding the best companion runners.
A mobile application for Android and iOS has to be developed with the following as main considerations:
We discuss to ensure that we have the exact idea of what is required
There's regular interaction with the client to ensure things are on track
Begins according to the needs of our client
The final output will be a perfect match to our clients requirement
The most challenging part here was the data collection and management from the multiple trackers. We have integrated almost a dozen of popular fitness trackers into the app with the help of SDKs and provided APIs. Some of the trackers didn’t have any data exporting mechanism, hence we utilized some third party solutions.
Next challenge we faced was keeping the user’s data on our database updated all the time. We could reduce the workload of our servers by utilizing the webhooks provided by some of the third party trackers. On the other hand we had to rely on the Cron jobs that communicate with the tracker servers.
Based on the data we collected from trackers, we came up with an advanced yet smart algorithm to help find most appropriate runners. We utilized the user’s running schedules, routes, running pace, running time, running distance and other similar parameters for finding the best matches.
Like any other social media apps, we too had the chat feature. For integrating chat functionality we opted for our own chat solutions. We set up a dedicated server for running Openfire which uses the XMPP protocol for instant messages. Our chat feature also has every feature included in a chat application like user presence, read receipts, chat histories and etc.
By using the app, the community could arouse interest of its young members into sports. They could create a strong sports community and host events for their members. The App has inspired many users to find new people, socialize better and create a larger sports network. Even though the app development was quite challenging, the application indeed proved the efforts made by the entire team.