## 💡Inspiration

* 2020 US Census survey showed that adults were 3x more likely to screen positive for depression or anxiety in 2020 vs 2019
* A 2019 review of 18 papers summarized that wearable data could help identify depression, and coupled with behavioral therapy can help improve mental health
* 1 in 5 americans owns wearables now, and this adoption is projected to grow 18% every year
* Pattrn aims to turn activity and mood data into actionable insights for better mental health.

## 🤔 What it does

* Digests activity monitor data and produces bullet point actionable summary on health status
* Allows users to set goals on health metrics, and provide daily, weekly, month review against goals
* Based on user mood rating and memo entry, deduce activities that correlates with good and bad days
[![Screen-Shot-2022-10-16-at-1-09-40-PM.jpg](https://i.postimg.cc/MZhjdqRw/Screen-Shot-2022-10-16-at-1-09-40-PM.jpg)](https://postimg.cc/bd9JvX3V)

[![Fire-Shot-Capture-060-Pattrn-localhost.png](https://i.postimg.cc/zBQpx6wQ/Fire-Shot-Capture-060-Pattrn-localhost.png)](https://postimg.cc/bDQQJ6B0)

## 🦾 How we built it

* Frontend: ReactJS
* Backend: Flask, Google Cloud App Engine, Intersystems FHIR, Cockroach Labs DB, Cohere

## 👨🏻‍🤝‍👨🏽 Challenges / Accomplishments

* Ideating and validating took up a big chunk of this 24 hour hack
* Continuous integration and deployment, and Github collaboration for 4 developers in this short hack
* Each team member pushing ourselves to try something we have never tried before

## 🛠 Hack for Health

* Pattrn currently is able to summarize actionable steps for users to take towards a healthy lifestyle
* Apart from health goal setting and reviewing, pattrn also analyses what activities have historically correlated with "good" and "bad" days

## 🛠 Intersystems Tech Prize

* We paginated a GET and POST request
* Generated synthetic data and pushed it in 2 different time resolution (Date, Minutes)
* Endpoints used: Patient, Observation, Goals, Allergy Intolerance
* Optimized API calls in pushing payloads through bundle request

## 🛠 Cockroach Labs Tech Prize

* Spawned a serverless Cockroach Lab instance
* Saved user credentials
* Stored key mapping for FHIR user base
* Stored sentiment data from user daily text input

## 🛠 Most Creative Use of GitHub

* Implemented CICD, protected master branch, pull request checks

## 🛠 Cohere Prize

* Used sentiment analysis toolkit to parse user text input, model human languages and classify sentiments with timestamp related to user text input
* Framework designed to implement a continuous learning pipeline for the future

## 🛠 Google Cloud Prize

* App Engine to host the React app and Flask observer and linked to Compute Engine
* Hosted Cockroach Lab virtual machine

## What's next for Pattrn

* Continue working on improving sentiment analysis on user’s health journal entry
* Better understand pattern between user health metrics and daily activities and events
* Provide personalized recommendations on steps to improve mental health
* Provide real time feedback eg. haptic when stressful episode are predicted

Temporary login credentials:
Username: [norcal2@hacks.edu](mailto:norcal2@hacks.edu)
Password: norcal
## Inspiration

We had a chat with the engineers at the Jump booth and brainstormed some ideas, particularly in regards to building an explorer for Wormhole cross-chain events. Additionally, since wormhole has a limit for the amount of value allowed to be bridged in a given time (as an additional protection after the hack earlier this year), we wanted to create an estimator for the likelihood a transaction would succeed. This is important especially for cross-chain arbitrage since transactions that are not sucessfully approved could become irreversibly locked up in wormhole for 24 hours.

## What it does

Our project aims to provide greater insight into underlying Wormhole events and help users decide estimate the likihood of their transaction succeeding. Using a spy relay and the public Guardian APIs, we created a system that displays both a real-time tally of Guardian signatures and an success estimator for transactions that takes in chainid and notional value. The real-time graph displays a rolling-window live tally of the Guardians that have signed the past 1000 VAAs. This way, users can easily visualize network reliability and see that the guardians with little to no recent transactions are probably offline. Additionally, by inputting a notional amount and chain id, users can also estimate if their transaction will be successful based on guardian availablity and single transaction notional amount.

## How we built it

We started by building an MVP for the transaction success estimator and concurrently worked on getting guardian data feeds directly from the gossip network. We ran a spy relay in a Digital Ocean droplet, which we used to provide a live VAA broadcast feed to our backend via RPC. Our backend then provides the frontend, which we built in React, with a REST endpoint to query the latest VAA signature counts on a rolling window basis. The information required for our transaction success estimator was pulled in from the public endpoints from available guardians.

## Challenges we ran into

Cross-chain Wormhole transactions must be signed off by 13 of 19 "guardians" to be considered valid. Thus, we needed information from all 19 guardians to reliably predict whether or not cross-chain transactions will fail. However, only 7 of the 19 guardians provide public APIs, so the rest needed to be collected directly from the Gossip network that the guardians broadcast to. This posed a significant challenge for us, since the current "spy" relay implemtation supports only listening for VAA (Verified Action Approvals) and not the Heartbeat events that we were interested in. We compromised by using only the publically available APIs for the transaction success estimator and supplementing it with live feeds from the VAA spy relay.

## Future

We managed to get something useful(maybe?) out there for everyone using the wormhole bridge. Also, we managed to sneak in another hack overnight and in while we ran into issues with both the lack of documentation on the wormhole + guardian code.

In the future, we plan on adding additional functionality to the network if we see people use the mvp. There were things like mempool monitoring and gossip networks that could be done if there was more time.
## Inspiration

---

Through extensive research into health management applications and a comprehensive competitor analysis, it became evident that these applications predominantly emphasize physical health and are often overly generalized, lacking broad applicability. Subsequent discussions between our development team and colleagues underscored the vulnerability of developers to an unhealthy work environment.

In light of these findings, we have undertaken the initiative to introduce a service that is holistic in its approach, encompassing three key dimensions of health: physical, mental, and overall well-being. Our objective is to offer developers constructive guidance and recommendations to enhance their health and well-being.

This initiative represents a tailored and enhanced iteration of wearable health applications, akin to industry-leading solutions such as Apple Health and Google Fit.

## What it does

---

This application leverages data from the Terra API to furnish users with an accurate assessment of their current physical, mental, and overall well-being status. Moreover, it offers actionable recommendations designed to be seamlessly integrated into developers' everyday lives, with the overarching aim of inspiring and motivating users to adopt and sustain a healthier lifestyle.

## How we built it

---

Node.js with the express library was used to build the Backend; React, Figma, HTML, and CSS were used to build the front end and design UIUX.

While MongoDB had not been completely integrated into the code at this stage, the application took a proactive approach by segregating data collection into two distinct components: one housing the user's raw data and the other storing the analyzed final status. This bifurcation was a deliberate measure aimed at safeguarding user privacy while still furnishing managers with the requisite information for effective team management.

## Challenges we ran into

---

Within a constrained timeframe, the necessity to complete a product development project posed a significant challenge for our team. This was further exacerbated by the need to utilize tools and technologies with which we had limited prior experience, thereby compounding the time constraint issue.

## Accomplishments that we're proud of

---

What we take the utmost pride in is the exceptional design of the service, a testament to the remarkable work of Atmiya and Maxwell. Equally, the project's value is derived from the successful utilization of previously unexplored skills for Min and Omar, underscoring our ability to tackle new challenges and achieve the project's completion.

## What we learned

---

Through the hack Harvard, we could learn about the following

-the power of peer mentorship

-how teamwork can boost productivity

-the way to understand new skills in a short time period

## What's next for DevHealth

---

Regrettably, due to the constraints of time, we were unable to realize all the desired functions we had planned for. Nevertheless, we remain hopeful for the opportunity to further develop this service. Our envisioned future enhancements include:

Building a web application tailored for team managers to streamline their interaction with the service.

Creating an iOS application to enhance accessibility and provide timely notifications to users.

Developing a watchOS application to gather a broader spectrum of data not typically collected by mainstream wearables like the Apple Watch and Fitbit, among others.