Individualized Prediction of Migraine Attacks Using a Mobile Phone App
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 19
- 试验地点
- 2
- 主要终点
- AUC of individual prediction models using post prediction data on environmental and physiological variables.
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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
排除标准
- •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
结局指标
主要结局
AUC of individual prediction models using post prediction data on environmental and physiological variables.
时间窗: 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.
AUC of individual prediction models using cross validation data on environmental and physiological variables.
时间窗: 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.
次要结局
未报告次要终点