Context-awareness, Physiological Monitoring, and Machine Learning in Migraine and Cluster Headache (COPIMAC)

Ghent University Hospital (UZ)

Status

Completed

Conditions

Migraine

Cluster Headache

Treatments

Procedure: Wearable sensor detection

Study type

Observational

Funder types

Other

Identifiers

NCT04949204

BC-07403

Details and patient eligibility

About

This is an observational, longitudinal cohort pilot study measuring physiological signals through wearable sensors combined with machine learning algorithms to detect behaviour, stress and headaches in patients with migraine and cluster headache.

Full description

Migraine and cluster headache are among the most disabling headache conditions in humans. Both conditions have different phases such as the prodromal/premonitory phase, the aura phase, the headache phase and the postdromal phase. This pilot study examines the potential of wearable sensors combined with machine learning algorithms to investigate the biological changes over time in both conditions. Behaviour such as movement, sleep and activity will be monitored. Stress levels will be estimated through the wearable data combined with input from the patients. Participants will use a wearable device around the wrist and self-developed headache application for 21 days.

Enrollment

19 patients

Sex

All

Ages

18 to 65 years old

Volunteers

No Healthy Volunteers

Inclusion criteria

migraine or cluster headache diagnosis based on ICHD 3 criteria
at least 2 attacks each month
onset of headache syndrome before the age of 50
if multiple headache syndromes coexist: attacks are clearly distinguishable
participant has smartphone that he/she wants to apply for this research

Exclusion criteria

chronic migraine patients
history of alcohol or illicit drug abuse
significant medical comorbidity deemed by the investigator to interfere with the study
use of betablockers
participating in other academic or commercial trials