Predicting Migraine Attacks Based on Environmental and Behavioral Changes as Detected From the Smartphone

NCT ID: NCT03762902

Last Updated: 2019-09-13

Basic Information

• Recruitment Status: TERMINATED
• Total Enrollment: 10 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2019-04-30
• Study Completion Date: 2019-09-01

Brief Summary

This study is conducted at the Henry Ford Health System with Lifegraph's behavioral monitoring technology, to examine the relation between migraine attacks and behavioral and environmental changes as detected from the smartphone sensors. The investigators hypothesize that Lifegraph's technology can predict the occurrence of migraine attacks with high precision.

Detailed Description

Migraine attacks can damage quality of life and lead to missed work days if not treated in time. These attacks last for about 4-72 hours, accompanied by headache and other symptoms. The time window for early intervention, which can potentially reduce the severity of an attack, lasts 2-48 hours before symptoms are starting to appear (10 hours on average). This time window is defined in the literature as the prodromal phase, when intervention during this phase can allow early treatment to improve the patient's condition and reduce the intensity and duration of the attack.

Migraine attacks and the prodromal phase can be characterized by one or more behavioral or environmental symptoms, either causal or resultant. Some of them can be passively measured by the smartphone usage, such as changes in sleep, physical activity and weather.

Lifegraph's smartphone application runs in the background of the subjects' personal smartphone, collects data passively and automatically, while rigorously maintaining privacy and with no effect to the daily use. Proprietary machine-learning algorithms analyze the collected data and turn it into behavioral channels, such as activity, sleep and mobility. The technology learns the personal routine of each user and detects changes in his/her behavioral patterns that can indicate an upcoming migraine.

Eligible subjects will meet a neurologist, sign an informed consent, fill an initial questionnaire and install the Lifegraph application on their smartphone. The application requires a one-time registration process.

During the study, subjects will self-report migraine attacks they experience through the smartphone application. Each report will include start time, end time and pain intensity. Data will be analyzed during the study in order to learn each subject's behavior and his/her migraine attacks. Subjects will be blinded to the app's migraine predictions to avoid expectancy bias.

Conditions

Migraine Disorders; Headache Disorders; Headache Disorders, Primary; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Eligibility Criteria

Inclusion Criteria

• Individuals who suffer from episodic migraine with 4-14 days of migraine per month (ICHD-3 patients).
• Individuals who possess a smartphone - Android version 5.0 and above or iOS version 10.0 and above.

Exclusion Criteria

• Individuals who are unable to sign the consent form.
• Pregnant women.
• Individuals suffering from headaches that do not meet the IHS migraine criteria or don't have moderate to severe chronic pain of VAS chronic grade 4 and above.

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 75 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Henry Ford Health System

OTHER

Sponsor Role: collaborator

Lifegraph Ltd.

INDUSTRY

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Ashhar Ali, DO

Role: PRINCIPAL_INVESTIGATOR

Senior Staff Physician, Department of Neurology

Locations

Henry Ford Health System Main Campus

Detroit, Michigan, United States

Henry Ford Health System

West Bloomfield, Michigan, United States

Countries

United States

Other Identifiers

LifegraphMigrainePrediction

Identifier Type: -

Identifier Source: org_study_id