Pediatric Apple Watch Study Application

This repository contains the Pediatric Apple Watch Study Application application. The Pediatric Apple Watch Study Application is using the Spezi ecosystem and builds on top of the Stanford Spezi Template Application.

Application Structure

The Spezi Template Application uses a modularized structure using the Spezi modules enabled by the Swift Package Manager.

The application uses the FHIR standard to provide a shared standard to encode data exchanged between different modules.

Note

Do you want to learn more about the Stanford Spezi Template Application and how to use, extend, and modify this application? Check out the Stanford Spezi Template Application documentation

Build and Run the Application

You can build and run the application using Xcode by opening up the PAWS.xcodeproj.

Generate Invitation Codes

When signing in to the application for the first time, you will be required to enter a valid invitation code before a user account is created. Use the upload_codes.py script to generate new codes and upload them to a specified Firebase instance or a local file.

export FIRESTORE_EMULATOR_HOST="localhost:8080"
export GCLOUD_PROJECT=<project_id>

python -m scripts.upload_codes --outfile=<local_path> \
--count=<number_of_codes> --length=<code_length> \
--service_account=<service_account_key_file> [--dry]

Generate Screenshots

PAWS uses Fastlane Snapshots to automatically screenshot specific screens in the app during UI tests. To generate new screenshots, you will likewise need to set the proper environment variables for you shell session.

export FIRESTORE_EMULATOR_HOST="localhost:8080"
export GCLOUD_PROJECT=<project_id>

firebase emulators:start --import=./firebase

Then, run fastlane snapshot. By default, results will end up in the .screenshots folder, overwriting previous files.

Note

Snapshot will run UI tests and concurrently take screenshots on multiple device simulators. As such, multiple new PAWS accounts will be created, possibly in rapid succession, using the same hard-coded testing invitation codes.

The current workaround for simultaneous account registrations during fastlane snapshot is to continually reset invitation codes to an unused state in Firestore by running a designated Python script on repeat (in a shell session with the same environment variables).

for i in {1..360}; do python -m scripts.upload_codes; sleep 10; done

ECG Data Pipeline

Pipeline Structure

The Spezi ECG Data Pipeline adopts a modular structure, comprising several Python modules and a notebook for interactive data visualization and analysis:

• firebase_access.py: Manages access to Firebase for data storage and retrieval.
• data_preparation.py: Prepares and processes raw ECG data.
• utils.py: Provides utility functions for data processing.
• visualization.py: Contains functions for data visualization.
• ECGDataPipelineTemplate.ipynb: An interactive notebook for analyzing and reviewing ECG data.

Notebook Setup Instructions

You can open and run the ECGDataPipelineTemplate.ipynb notebook in, e.g., Google Colab. Once the notebook is open, execute the following cell to clone the Spezi ECG Data Analysis Pipeline repository and navigate into the cloned directory:

# Clone GitHub repository for Spezi ECG Data Pipeline
git clone https://github.com/StanfordBDHG/PediatricAppleWatchStudy.git
cd PediatricAppleWatchStudy/ECGDataPipeline

Remember to upload the serviceAccountKey_file.json to the workspace directory to enable Firebase access. This file is necessary for authentication and should be securely handled.

Use the Interactive ECG Reviewing Tool

To start reviewing ECG data, execute the cells in your notebook.

This interactive tool allows you to plot ECG data, add diagnoses, evaluate the trace quality, and add notes.

Contributing

Contributions to this project are welcome. Please make sure to read the contribution guidelines and the contributor covenant code of conduct first.

License

This project is licensed under the MIT License. See Licenses for more information.

Our Research

For more information, check out our website at biodesigndigitalhealth.stanford.edu.