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

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details. Identifier: NCT03762902
Recruitment Status : Recruiting
First Posted : December 4, 2018
Last Update Posted : May 15, 2019
Henry Ford Health System
Information provided by (Responsible Party):
Lifegraph Ltd.

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.

Condition or disease
Migraine Disorders Headache Disorders Headache Disorders, Primary Brain Diseases Central Nervous System Diseases Nervous System Diseases

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.

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Study Type : Observational
Estimated Enrollment : 50 participants
Observational Model: Cohort
Time Perspective: Prospective
Official Title: The Relation Between Analyzed Sensory Data of the Smartphone and Migraine Attacks, Recorded by Individuals Who Suffer From Episodic Migraine
Actual Study Start Date : April 30, 2019
Estimated Primary Completion Date : September 30, 2019
Estimated Study Completion Date : November 30, 2019

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Migraine

Primary Outcome Measures :
  1. Assessing Lifegraph's predictive ability of migraine attacks before subjects report they experience an attack. [ Time Frame: 3 months ]
    Lifegraph has created a scalable and dynamic platform to accommodate different conditions, different types of patients with different types of data, concurrently. This platform converts the raw sensor data accumulating in Lifegraph's servers into behavioral and environmental features that have been found to be informative and helpful in generating insights relevant to migraines. The features are fed into machine learning algorithms that search for early signs of change, that may indicate an oncoming attack. These algorithms may be divided into population-based and personalized models. The study will develop a separate predictive model for each subject to predict the probability of experiencing a migraine attack during a particular interval (e.g. the next 12, 24, or 48 hours). Higher precision values of prediction will represent a better outcome. The precision is expected to be 50-70%, depends on the time passed since first installing the app and the number of reported migraine attacks

Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.

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Ages Eligible for Study:   18 Years to 75 Years   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
Individuals who suffer from episodic migraine

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.

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its identifier (NCT number): NCT03762902

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Contact: Ashhar Ali, DO (313) 916-1906
Contact: Jackie Reuther (313) 916-1906

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United States, Michigan
Henry Ford Health System Main Campus Recruiting
Detroit, Michigan, United States, 48202
Contact: Jackie Reuther    313-916-1906   
Contact: Theresa Holmes    (313) 916-4578   
Principal Investigator: Ashhar Ali, DO         
Henry Ford Health System Recruiting
West Bloomfield, Michigan, United States, 48322
Contact: Jackie Reuther    313-916-1906   
Contact: Theresa Holmes    (313) 916-4578   
Principal Investigator: Ashhar Ali, DO         
Sponsors and Collaborators
Lifegraph Ltd.
Henry Ford Health System
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Principal Investigator: Ashhar Ali, DO Senior Staff Physician, Department of Neurology

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Responsible Party: Lifegraph Ltd. Identifier: NCT03762902     History of Changes
Other Study ID Numbers: LifegraphMigrainePrediction
First Posted: December 4, 2018    Key Record Dates
Last Update Posted: May 15, 2019
Last Verified: May 2019
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No

Keywords provided by Lifegraph Ltd.:
Machine Learning
Data Science
Henry Ford Health System

Additional relevant MeSH terms:
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Migraine Disorders
Nervous System Diseases
Brain Diseases
Central Nervous System Diseases
Headache Disorders
Headache Disorders, Primary
Pathologic Processes
Neurologic Manifestations
Signs and Symptoms