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What is a Waddle?
Data Controlling
Growing your Wadle

Inspiration - Group Number 75 (Vim Support Group)

In our digital world, there are a multitude of applications that exist to help folks manage their mental health. Therapy, meditation, emergency contact -- there seems to be a platform for every niche and need, providing help to people all around with world with nothing more than internet access. We noticed, however, that there was a real lack of mental health apps that focused more on reflection and the ability to use trends in mood and emotions to not only support one's self, but to be able to more easily reach out to others for support when needed. This is where Wadle comes in.

What it does

Wadle enables you and your peers to connect and support one another's mental health. Through the power of daily 60 second questionnaires, data and statistics, resources and tips, you and your peers gain access to a library of mental health-related information. Through the cloud and Google's Natural Language Processing, your peers can learn about ways to support you. You can even choose how much information your peers get! Privacy, collaboration and support-building is the main priority for Wadle.

How we built it

Our Software is designed with a micro-service architecture with a frontend built entirely on React.jsx, our router (or API Gateway) built on a Python server, and 2 microservices built to meet our application's requirements. The microservice architecture enables greater modularity and a long-term, scalable solution to build out the features of our application. Although it was our first time implementing this model, it enabled a great amount of collaboration.

The front end is built upon a solid foundation of the React-Redux-Javascript project.

Overall, our current iteration is a designed and functional application that is built for the web, accessible using any modern device.

The API Gateway is built on Python with FastAPI conducting the routing. The routing redirects requests to 2 other microservices. Our API Gateway also conducts important authentication using JSON Web Tokens.

Our first microservice is built in Python for the Machine-Learning component of our application. We utilize user-created data and Natural Language Processing to understand and provide feedback to users.

Our second microservice is built on Golang. This microservice acts as our database connector, conducting all the relevant SQL queries with our external database (PostgreSQL).

Technologies Used (Our Stack)

WadleStack

In only 24 hours, we were tasked with creating a web application to support individual mental or physical health.

Stack	Technology Used
Frontend	React, Figma, Javascript, SCSS
Backend	Golang, Python, Fibre, FastAPI
Cloud/Hosting	OAuth, Google Cloud, Google NLP, Heroku
Database	Google Firestore, PostgreSQL
Querying	SQL

Challenges we ran into

When developing Wadle we opted to utilize a more complex backend which included

Designing a complex data model
First time implementing a Software Architecture (Microservices) in a hackathon
Developing a full-stack application with ML, data analysis, consideration of accessibility in the design, and modularity of a software.

Accomplishments that we're proud of

Creating an Accessible Design
Implementing most designs into code through styling
Building a functioning Microservice architecture
Building an app that affects us directly as students struggling with Mental Health!
Conducting user interviews and necessary research through our rigorous planning

What we learned

Through the process of developing Wadle for CalgaryHacks 2022 we learned many skills that will help our team as we work on other projects in the future.

Prioritization of features
Git and project management skills in a fast-paced team environment
Greater collaboration for data analysis and modelling
Importance of planning and user research prior to developing an application

Business Viability

Revenue Streams

Integrating Wadle into professional services within the health industry.
Sponsored Health datasets, API's and other resources
Premium content, while keeping the core app free and accessible

Obtaining Users

Working with Health Professionals
Target certain demographics & adapt to environments such as universities, workplaces and other communities

Lowering Expenses

Deploying & building a web app allows for extensive A/B testing of users with respect to new features
Scalable backend technologies provide throughput to scale the technology needed.
Minimizing "additional features" by having the focus on peer-relationship building

Competitive Advantage

Less monetization, no mandatory costs or insurance
Rather than receiving "anonymous" support, we help you build your relationship with the people around you
Not presenting an overwhelming amount of data like other applications
Ability to export and share data with healthcare professionals and parties (not questionnaire results).
Changing privacy settings per peer
Approachable, modern design

What's next for Wadle

As we further develop Wadle we hope to improve on some current implementations and develop additional features:

Integrate all backend functions into a single database with redundancy
Identify a growth target and plan
Dockerize and Grow
Utilize applications like voice flow to answer questionnaires orally

Built With

Submitted to

CalgaryHacks 2022
- Winner (Tier 1) First Place

Created by

I worked on the front end, primarily creating the Redux we used to store and retrieve data states, as well as creating the API calls used by the front end to render accurate and up-to-date information. Additionally I provided assistance with the preliminary design of the UI/UX design, and create appropriate questions to be presented to users.

Brooke Kindleman
I developed the Python backend. My contributions mainly consisted of handling authentication, creating routes to take requests from the frontend, and integrating the Google Cloud Natural Language AI.

Jonathan Breidfjord
I was responsible for styling and implementing the general structure for the Frontend. One of the biggest challenges for me was developing custom graphs to align with the mockups. I also assisted with identifying the mechanisms utilized for machine learning.

Robert Brown
I was responsible for the Golang Microservice and set up all endpoints. I setup the Database and all relevent queries using SQL. I also designed the entire design framework on Figma and created mockups for our product. I finally helped with frontend integration with the microservice.

Risat Haque