MoodWatch helps users be mindful of their mental well-being. Users record numerical ratings of their moods for events and days. To keep users active, a daily email reminder is sent to those who have not logged that day. These records are displayed in an interactive chart to help users gain perspective by adjusting the time scale and viewing statistical. A special user account is available to healthcare professionals who provide patients with a second set of eyes. These accounts allow access to information users have agreed to share. Physicians get a high level view of their patient's logs, as well as the ability to prescribe medication and contact other professionals who share the same patient to discuss treatment.
PostgreSQL, SQLAlchemy, Python, Bcrypt, Pandas, Cron, Flask, Flask-Mail, Jinja, Javascript, Jquery, AJAX, Bootstrap, Chart.js
Users are able to keep track of their moods by numerically rating days and events. Physicians and their patients also get to look back at changes in their medications. User resources are stored in a PostgreSQL database which are mapped to Python classes using Flask-SQLAlchemy ORM.
Using the chart.js library, users' mood logs are visualized on a line chart. The Python Pandas library has been used to do statistical analysis such as moving average and standard deviation. The mood charts have interactive features such as changing the time window and visibility of different datasets. This is accomplished with a combination of AJAX requests for specific datasets and Jquery event listeners plus DOM element manipulation.
Users are reminded to stay active in logging moods through daily email reminders. A cron job is scheduled to run in the later part of each day, running a Python script that queries the database for inactive users and send email reminders with the Flas-Mail extension.
This was started as a project during my time as a software engineering fellow at Hackbright Academy. Students were given four weeks to create a working web app that could be presented to partner comapnies and recruiters.
I will continue to improve existing features, as well as build new features to explore new technologies. Some of my planned changes include:
- Exploring ways to improve interactivity with the mood charts
- Implement more complex data analysis to provide more insight and possibly predictions on user moods
- Learning about how health tech companies follow HIPAA regliations and applying it to my own app
- Implementing APIs
- Twilio API: Text message alerts
- OpenFDA API: Get more information on medications to help users make informed decisions
- BetterDoctorAPI/ YelpAPI + GoogleMaps API: Help users find nearby/ reputable healthcare professionals