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

Overview

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.

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.