Towards Migraine-Event Prediction Using Continuous Long-Term Biometric Sensor Data.

Area of Science:

• Neurology
• Biomedical Engineering
• Digital Health

Background:

• Migraine is a prevalent chronic neurological disorder causing significant personal and societal burden.
• Existing data collection methods for migraine research can be limited in continuous coverage and detail.

Purpose of the Study:

• To develop and evaluate a mobile application for enhanced data collection using the Empatica E4 biometric sensor.
• To lay the groundwork for a future migraine event prediction system.

Main Methods:

• Implementation of a mobile app integrated with the Empatica E4 biometric sensor.
• An initial user study involving three participants wearing the E4 device for eight days.
• Qualitative interviews to gather user experience feedback on the app and device.

Main Results:

• User feedback indicated a need for more frequent reminders and clearer explanations of collected data.
• Participants requested improvements in device connectivity and app interface design.
• Suggestions included enhanced diagnostic tools, statistics, and support for additional sensors.

Conclusions:

• The developed mobile app shows potential for improving continuous biometric data collection for migraine research.
• User-centered design feedback is crucial for optimizing mobile health solutions in chronic disease management.
• Further development is needed to address user-reported issues and enhance the app's functionality for migraine prediction.