The Sponsor decided to cancel the study due to commercial considerations

This is a prospective, observational study. The participant will be required to approve his/her participation in the study by completing the electronic consent form. Data collected within the first eight weeks (weeks 1-8) will be used to develop the prediction models (either personal or population/group models). The developed algorithm will be freezer and tested against the data collected during weeks 1-8 of a different cohort population

Migraine treatment may be acute (abortive) or preventive (prophylactic), with patients often requiring both. The objectives of acute treatment are to treat attacks early; to achieve quick pain relief; to minimize or eliminate adverse events; to restore function; to decrease recurrence and the need for rescue treatment; and to reduce medical resource use. Preventive treatment is aimed to lower the frequency, intensity, and duration of the attacks.

In general, a prodrome is an early sign or symptom (or set of signs and symptoms) that often indicates the onset of a disease before more diagnostically specific signs and symptoms develop. Prodromal or premonitory symptoms occur in about 60% of those with migraines, with an onset that can range from two hours to two days before the start of pain or the aura. These symptoms may include a wide variety of phenomena including altered mood, irritability, depression or euphoria, fatigue, craving for certain food(s), stiff muscles (especially in the neck), constipation or diarrhea, and sensitivity to smells or noise. This may occur in those with either migraine with aura or migraine without aura. Neuroimaging indicates the limbic system and hypothalamus as the origin of prodromal symptoms in migraine.

Robust prediction of migraine attacks could lead to pre-emptive treatment, provide patients with opportunities to plan for impending attacks, reduce interictal anxiety, and improve self-efficacy among other benefits.

This study aims to develop and evaluate the effectiveness of a migraine prediction tool based on early prodromal (pre-migraine) symptoms, that may enhance the acute treatment of migraine

Prior enrollment, Informed consent must be obtained from each participant before any protocol-related activities are performed. Participants will receive a daily notification (through an app notification) with a link to the daily questionnaire. The app may collect other data items, related to the specific location of every participant, such as weather, air quality, etc.

The primary outcome measure is the presence of headache attack occurring over the 24-hour period recorded following a daily diary entry.

During the development phase, 100% of the data will be used for developing and training the prediction model. Once the model will be ready, it will be "freezer" and used over a different cohort population to validate the prediction model. The AUC of the prediction model will be assessed to validate the accuracy (Specificity vs. Sensitivity) of the prediction model.

The study will include the development of a general predictive model for the study population, with adjustments to individual participants, forecasting the probability of experiencing a headache and/or aura phase of a migraine attack during a particular interval. The outcome measure performance of this model using the Area Under the Curve (AUC) metric. The AUC model measures how often the algorithm predicts a higher probability for a migraine over non-migraine.

I

Inclusion Criteria:

• Subject age 18-75 years.
• Subject has migraine diagnosis for over 6 months prior to enrolling to the study.
• Subject has 4-24 headache days per month.
• Subject has a Nerivio user account, or willing to create a Nerivio user account to participate in the study.
• Subject is willing to sign an Informed Consent Form.

Exclusion Criteria:

• NA

The population consists of migraineurs who have already used Nerivio and its mobile application prior to joining the study and those who have not, with a target ratio of 50:50 (and up to 30:70 or 70:30) between these two subject types