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.

Individualized Prediction of Migraine Attacks Using a Mobile Phone App and Fitbit

Migraine Disorders

Headache Disorders, Primary

Headache Disorders

Brain Diseases

Central Nervous System Diseases

Nervous System Diseases

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.

Individualized Prediction of Migraine Attacks Using a Mobile Phone App

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.

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.

Individualized Prediction of Migraine Attacks Using a Mobile Phone App and Fitbit

About this trial

Study ID

Status

Type

Placebo

Accepting

Not accepting

Trial Timing

What is this trial about?

What are the participation requirements?