Migraine DisordersMigraine AlertNCT02910921
Individualized Prediction of Migraine Attacks Using a Mobile Phone App and Fitbit
An observational study of Migraine Disorders and Headache Disorders, Primary, run by Second Opinion Health at 2 sites in United States.
Trial summary
This trial is collaboration between Mayo Clinic, Second Opinion Health (Simon Bloch, simon@somobilehealth.com 408-981-3814) and Allergan. Mayo Clinic investigators are conducting the clinical trial, Second Opinion Health is providing the software for use in the trial (Migraine Alert app for data collection, analysis… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Migraine Disorders, Headache Disorders, Primary, Headache Disorders
- How many people
- 19 participants
- Where
- 2 sitesUnited States
- Study dates
- Nov 2016to 31 Jul 2018
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: People with episodic migraines
Who may be able to join
- Subjects fulfilling ICHD-3beta criteria for migraine with average of 5 - 10 migraine attacks per month and up to 12 headache days per month
- Males of females 18 years of age or older
- Subject report of weather being one of the triggers
- Subject has an iPhone
- Subject is willing to wear a Fitbit device for the duration of the study
- Subject has an active Facebook account or is willing to create one
Who cannot join
- Children younger than 18 years of age
- Subjects with headaches other than migraine or probable migraine
- Inability to provide informed consent
- Not willing to maintain a daily diary
- Current participation in another clinical trial
Trial locations and contacts
2 sites in 1 country.
United States2 sites
University of Southern California
Los Angeles, California, 90033
Mayo Clinic Arizona
Scottsdale, Arizona, 852589
About this study
Official title: Individualized Prediction of Migraine Attacks Using a Mobile Phone App
This trial is collaboration between Mayo Clinic, Second Opinion Health (Simon Bloch, simon@somobilehealth.com 408-981-3814) and Allergan. Mayo Clinic investigators are conducting the clinical trial, Second Opinion Health is providing the software for use in the trial (Migraine Alert app for data collection, analysis and machine learning algorithms), and Allergan is providing funding.
The investigators hypothesize that the use of a mobile phone app and Fitbit wearable to collect daily headache diary data, exposure/trigger data and physiologic data will predict the occurrence of migraine attacks with high accuracy. The objective of the trial is to assess the ability to use daily exposure/trigger and symptom data, as well as physiologic data (collected by Fitbit) to create individual predictive migraine models to accurately predict migraine attacks in individual patients via a mobile phone app.
Read the detailed description
Eliminating migraine attacks before they start is of an enormous importance to migraine sufferers. But figuring out the onset of an attack before it actually starts remains a major challenge for the medical community.
The widespread use of mobile smartphones, the availability of wearable devices that measure health information, and advances in multivariate pattern analysis via machine learning algorithms allow for development of individual predictive models that can determine the likelihood of an individual patient developing a migraine on a given day. Such models are based upon objectively measured biometric parameters (e.g. activity, sleep), objectively measured environmental conditions (e.g. weather parameters), exposures to possible migraine triggers, and patient reported symptoms. Using machine-learning algorithms to explore this large dataset that is collected for each patient, the optimal combination of factors that most accurately predict the likelihood of a migraine attack is determined.
Prediction of individual migraine attacks would have substantial positive impacts for patients with migraine. Accurate prediction of a migraine attack would give the migraineur a greater sense of control over their condition, a sense of control that is often lacking in patients with migraine. Most importantly, if individual migraine attacks could be predicted with high accuracy, treatment of that inevitable migraine attack before development of symptoms could prevent the attack altogether.
Eligible subjects will enter a baseline phase during which subjects will wear a Fitbit device and record data into the daily headache diary using the mobile phone app. This phase will be of variable duration for each subject to a maximum of 75 days. It is during the baseline phase that the individualized predictive model for a migraine attack is developed and optimized.
During the second phase (75 days), the accuracy of the predictive model will be tested. The probability of developing a migraine will be calculated and the accuracy of the prediction will be tested against the patient reported incidence of migraine attacks within the mobile phone app. Subjects will be blinded to the app's migraine attack predictions to avoid expectancy bias.
Migraine prediction suffers from 'the curse of dimensionality' (machine learning parlance). Too many factors affect outcomes, but the outcomes (positive migraine attacks) are few and far in between. To develop an accurate machine learning model using traditional approaches requires a long and impractical time duration. Migraine Alert has effectively addressed these using proprietary algorithms and techniques that generate individual models using fewer migraines. Covariate analysis is performed for each individual using features derived from the raw data. Individual models may differ from one other in the specific feature they use and/or the importance attached to them in the model. Proprietary techniques are used to create these individual models and to monitor their pre-validation and post-validation accuracy and recall. Concept drift as evidenced by any degradation in accuracy or recall is monitored in the prediction phase and model is retrained as necessary.
- Conditions
- Migraine Disorders, Headache Disorders, Primary, Headache Disorders, Brain Diseases, Central Nervous System Diseases, Nervous System Diseases
- Keywords
- Migraine, Prediction, Forecast, App, Fitbit, Individual, Algorithm, Machine Learning, Mayo Clinic, Allergan, Second Opinion
- Medical subject headings
- Migraine Disorders, Headache Disorders, Primary, Headache Disorders, Brain Diseases, Central Nervous System Diseases, Nervous System Diseases, Alzheimer Disease
What is being measured
The primary outcome is the main question the study is designed to answer.
AUC of individual prediction models using cross validation data on environmental and physiological variables.
The study will develop a separate predictive model for each participant that will forecast probability of experiencing a migraine attack during a particular interval. The outcome measures performance of this model using the Area Under the Curve (AUC) metric. AUC measures how often the algorithm predicts a higher probability for a migraine over non-migraine. This measure is attractive because it is independent of the quantization threshold, which is required for other metrices such as precision/recall. In the baseline phase, 30% of the data will be randomly selected for cross validation and will not used for training the model. Once the model is trained, the AUC of the model is measured on the cross validation data as the outcome of this phase. The data will include various measurements of weather such as temperature, pressure, humidity, wind and physiological measurements such as sleep duration and quality and activity level measured through a wearable Fitbit device.
Measured: 10 weeks
AUC of individual prediction models using post prediction data on environmental and physiological variables.
The metric and the type of the data is the same as in Outcome 1. The only difference is that the data is obtained from the user after the model is trained. The user is not shown the prediction to avoid expectancy bias.
Measured: 10 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Sep 2016 (estimated)
- Study startNov 2016
- Primary completionJul 2018
- Study completion31 Jul 2018
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Migraine DisordersStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 19 participants (actual)
Sponsor and investigators
- Lead sponsor
- Second Opinion Health
- Sponsor type
- Industry (pharma, biotech, devices)
- Collaborators
- Mayo Clinic, University of Southern California, Allergan
- Responsible party
- Sponsor
- Investigators
- Rashmi Halker Singh, MD, Principal investigator, Mayo ClinicSoma Sahai-Srivastava, MD, Principal investigator, University of Southern California
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT02910921
- Sponsor's protocol ID
- 16-005803
- Acronym
- Migraine Alert
- Status last verified
- Aug 2018
- First submitted
- 13 Sep 2016
- Last updated
- 6 Aug 2018
- Results posted
- No
- Data monitoring committee
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 6 Aug 2018. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Individualized Prediction of Migraine Attacks Using a Mobile Phone App
- ClinicalTrials.gov ID
- NCT02910921
- Acronym / sponsor ID
- Migraine Alert / 16-005803
- Conditions
- Migraine Disorders, Headache Disorders, Primary, Headache Disorders, Brain Diseases, Central Nervous System Diseases, Nervous System Diseases
- Keywords
- Migraine, Prediction, Forecast, App, Fitbit, Individual, Algorithm, Machine Learning
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 19 participants (actual)
- Lead sponsor
- Second Opinion HealthIndustry (pharma, biotech, devices)
- Collaborators
- Mayo Clinic, University of Southern California, Allergan
- Study start
- Nov 2016 (actual)
- Primary completion
- Jul 2018 (actual)
- Study completion
- 31 Jul 2018 (actual)
- First posted
- 22 Sep 2016 (estimated)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This trial is collaboration between Mayo Clinic, Second Opinion Health (Simon Bloch, simon@somobilehealth.com 408-981-3814) and Allergan. Mayo Clinic investigators are conducting the clinical trial, Second Opinion Health is providing the software for use in the trial (Migraine Alert app for data collection, analysis and machine learning algorithms), and Allergan is providing funding.
The investigators hypothesize that the use of a mobile phone app and Fitbit wearable to collect daily headache diary data, exposure/trigger data and physiologic data will predict the occurrence of migraine attacks with high accuracy. The objective of the trial is to assess the ability to use daily exposure/trigger and symptom data, as well as physiologic data (collected by Fitbit) to create individual predictive migraine models to accurately predict migraine attacks in individual patients via a mobile phone app.
Detailed description
Eliminating migraine attacks before they start is of an enormous importance to migraine sufferers. But figuring out the onset of an attack before it actually starts remains a major challenge for the medical community.
The widespread use of mobile smartphones, the availability of wearable devices that measure health information, and advances in multivariate pattern analysis via machine learning algorithms allow for development of individual predictive models that can determine the likelihood of an individual patient developing a migraine on a given day. Such models are based upon objectively measured biometric parameters (e.g. activity, sleep), objectively measured environmental conditions (e.g. weather parameters), exposures to possible migraine triggers, and patient reported symptoms. Using machine-learning algorithms to explore this large dataset that is collected for each patient, the optimal combination of factors that most accurately predict the likelihood of a migraine attack is determined.
Prediction of individual migraine attacks would have substantial positive impacts for patients with migraine. Accurate prediction of a migraine attack would give the migraineur a greater sense of control over their condition, a sense of control that is often lacking in patients with migraine. Most importantly, if individual migraine attacks could be predicted with high accuracy, treatment of that inevitable migraine attack before development of symptoms could prevent the attack altogether.
Eligible subjects will enter a baseline phase during which subjects will wear a Fitbit device and record data into the daily headache diary using the mobile phone app. This phase will be of variable duration for each subject to a maximum of 75 days. It is during the baseline phase that the individualized predictive model for a migraine attack is developed and optimized.
During the second phase (75 days), the accuracy of the predictive model will be tested. The probability of developing a migraine will be calculated and the accuracy of the prediction will be tested against the patient reported incidence of migraine attacks within the mobile phone app. Subjects will be blinded to the app's migraine attack predictions to avoid expectancy bias.
Migraine prediction suffers from 'the curse of dimensionality' (machine learning parlance). Too many factors affect outcomes, but the outcomes (positive migraine attacks) are few and far in between. To develop an accurate machine learning model using traditional approaches requires a long and impractical time duration. Migraine Alert has effectively addressed these using proprietary algorithms and techniques that generate individual models using fewer migraines. Covariate analysis is performed for each individual using features derived from the raw data. Individual models may differ from one other in the specific feature they use and/or the importance attached to them in the model. Proprietary techniques are used to create these individual models and to monitor their pre-validation and post-validation accuracy and recall. Concept drift as evidenced by any degradation in accuracy or recall is monitored in the prediction phase and model is retrained as necessary.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- People with episodic migraines
Key inclusion criteria (6 listed)
- Subjects fulfilling ICHD-3beta criteria for migraine with average of 5 - 10 migraine attacks per month and up to 12 headache days per month
- Males of females 18 years of age or older
- Subject report of weather being one of the triggers
- Subject has an iPhone
- Subject is willing to wear a Fitbit device for the duration of the study
Key exclusion criteria (5 listed)
- Children younger than 18 years of age
- Subjects with headaches other than migraine or probable migraine
- Inability to provide informed consent
- Not willing to maintain a daily diary
- Current participation in another clinical trial
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
AUC of individual prediction models using cross validation data on environmental and physiological variables.
Time frame: 10 weeks
The study will develop a separate predictive model for each participant that will forecast probability of experiencing a migraine attack during a particular interval. The outcome measures performance of this model using the Area Under the Curve (AUC) metric. AUC measures how often the algorithm predicts a higher probability for a migraine over non-migraine. This measure is attractive because it is independent of the quantization threshold, which is required for other metrices such as precision/recall. In the baseline phase, 30% of the data will be randomly selected for cross validation and will not used for training the model. Once the model is trained, the AUC of the model is measured on the cross validation data as the outcome of this phase. The data will include various measurements of weather such as temperature, pressure, humidity, wind and physiological measurements such as sleep duration and quality and activity level measured through a wearable Fitbit device.
AUC of individual prediction models using post prediction data on environmental and physiological variables.
Time frame: 10 weeks
The metric and the type of the data is the same as in Outcome 1. The only difference is that the data is obtained from the user after the model is trained. The user is not shown the prediction to avoid expectancy bias.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Aug 2018.
- MigraineLink provided by the sponsor.
- Migraine HeadachesLink provided by the sponsor.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Rashmi Halker Singh, MD principal investigator
Mayo Clinic
Soma Sahai-Srivastava, MD principal investigator
University of Southern California
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Migraine Disorders and Headache Disorders, Primary, run by Second Opinion Health at 2 sites in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 6 Aug 2018. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
AUC of individual prediction models using cross validation data on environmental and physiological variables. (time frame: 10 weeks). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 6 Aug 2018.