Digital phenotyping for migraine: A game-changer for research and management

Migraine is a complex neurobiological disorder characterized by diverse phenotypes and unpredictable therapy outcomes. Digital phenotyping (DP), defined as the real-time collection of behavioral and physiological data in natural environments to identify individual phenotypes, represents a promising approach with the potential to enhance clinicians' ability to identify migraine subtypes. Additionally, DP offers new insights into the intricate neurobiological and behavioral patterns, as well as environmental influences, associated with each phase of a migraine attack, including potential triggers, pre-attack symptoms, the characteristics of an attack and response to treatment. Moreover, a DP-based approach has the potential to revolutionize migraine research and clinical trials by enabling more personalized, patient-centred diagnostics and tailored acute and preventive treatments. Despite the limited literature available and the heterogeneity of study designs, migraine DP may lay the groundwork for future digital twin models and the discovery of digital biomarkers for diagnosis, therapy optimization and outcome evaluation. Furthermore, DP could serve as a predictive marker for migraine attacks, empowering patients to monitor their condition and adopt a proactive approach to treatment. Integrating DP into migraine studies could also contribute to the development of an updated international migraine classification that incorporates neurobiological and psychosocial factors alongside clinical symptomatology. To fully realize its potential in migraine research and care, experts should prioritize collaboration with artificial intelligence (AI) specialists, data scientists and medical engineers. Establishing a multidisciplinary ecosystem will be essential to developing robust and clinically meaningful DP tools for migraine research. This review aims to show the current landscape of both active and passive DP methodologies, which leverage smartphones, wearable biosensors and AI-driven analytics to capture real-time physiological, cognitive and environmental data, at the same time as pointing to the future ahead of migraine DP.