Background: The purpose of this narrative review is to examine the literature investigating a causal relationship between stress and migraine and evaluate its implications for managing migraine.Methods: PubMed, PsycINFO and CINAHL were searched from 1988 to August 2021, identifying 2223 records evaluating the relationship between stress and migraine. Records were systematically screened. All potentially relevant records were thematically categorized into six mechanistic groups. Within each group the most recent reports providing new insights were cited.Results: First, studies have demonstrated an association of uncertain causality between high stress loads from stressful life events, daily hassles or other sources, and the incidence of new-onset migraine. Second, major stressful life events seem to precede the transformation from episodic to chronic migraine. Third, there is some evidence for changes in levels of stress as a risk factor for migraine attacks. Research also suggests there may be a reversed causality or that stress-trigger patterns are too individually heterogeneous for any generalized causality. Fourth, migraine symptom burden seems to increase in a setting of stress, partially driven by psychiatric comorbidity. Fifth, stress may induce sensitization and altered cortical excitability, partially explaining attack triggering, development of chronic migraine, and increased symptom burden including interictal symptom burden such as allodynia, photophobia or anxiety. Finally, behavioral interventions and forecasting models including stress variables seem to be useful in managing migraine.Conclusion: The exact causal relationships in which stress causes incidence, chronification, migraine attacks, or increased burden of migraine remains unclear. Several individuals benefit from stress-oriented therapies, and such therapies should be offered as an adjuvant to conventional treatment and to those with a preference. Further understanding the relationship between stress, migraine and effective therapeutic options is likely to be improved by characterizing individual patterns of stress and migraine, and may in turn improve therapeutics.

Introduction: Triggers, premonitory symptoms and physiological changes occur in the preictal migraine phase and may be used in models for forecasting attacks. Machine learning is a promising option for such predictive analytics. The objective of this study was to explore the utility of machine learning to forecast migraine attacks based on preictal headache diary entries and simple physiological measurements. Methods: In a prospective development and usability study 18 patients with migraine completed 388 headache diary entries and self-administered app-based biofeedback sessions wirelessly measuring heart rate, peripheral skin temperature and muscle tension. Several standard machine learning architectures were constructed to forecast headache the subsequent day. Models were scored with area under the receiver operating characteristics curve. Results: Two-hundred-and-ninety-five days were included in the predictive modelling. The top performing model, based on random forest classification, achieved an area under the receiver operating characteristics curve of 0.62 in a hold-out partition of the dataset. Discussion: In this study we demonstrate the utility of using mobile health apps and wearables combined with machine learning to forecast headache. We argue that high-dimensional modelling may greatly improve forecasting and discuss important considerations for future design of forecasting models using machine learning and mobile health data.

Background and methods: In this narrative review, we introduce key artificial intelligence (AI) and machine learning (ML) concepts, aimed at headache clinicians and researchers. Thereafter, we thoroughly review the use of AI in headache, based on a comprehensive literature search across PubMed, Embase and IEEExplore. Finally, we discuss limitations, as well as ethical and political perspectives. Results: We identified six main research topics. First, natural language processing can be used to effectively extract and systematize unstructured headache research data, such as from electronic health records. Second, the most common application of ML is for classification of headache disorders, typically based on clinical record data, or neuroimaging data, with accuracies ranging from around 60% to well over 90%. Third, ML is used for prediction of headache disease trajectories. Fourth, ML shows promise in forecasting of headaches using self-reported data such as triggers and premonitory symptoms, data from wearable sensors and external data. Fifth and sixth, ML can be used for prediction of treatment responses and inference of treatment effects, respectively, aiming to optimize and individualize headache management. Conclusions: The potential uses of AI and ML in headache are broad, but, at present, many studies suffer from poor reporting and lack out-of-sample evaluation, and most models are not validated in a clinical setting.

Background: Biofeedback is effective in treating migraines. It is believed to have a beneficial effect on autonomous nervous system activity and render individuals resilient to stressors that may trigger a migraine. However, widespread use of biofeedback is hampered by the need for a trained therapist and specialized equipment. Emerging digital health technology, including smartphones and wearables (mHealth), enables new ways of administering biofeedback. Currently, mHealth interventions for migraine appear feasible, but development processes and usability testing remain insufficient. Objective: The objective of this study was to evaluate and improve the feasibility and usability of an mHealth biofeedback treatment app for adults with migraine. Methods: In a prospective development and usability study, 18 adults with migraine completed a 4-week testing period of self-administered therapist-independent biofeedback treatment consisting of a smartphone app connected to wearable sensors (Cerebri, Nordic Brain Tech AS). The app included biofeedback training, instructions for self-delivery, and a headache diary. Two wearable sensors were used to measure surface electromyographic voltage at the trapezius muscle and peripheral skin temperature and heart rate at the right second fingertip. Participants were instructed to complete a daily headache diary entry and biofeedback session of 10 minutes duration. The testing period was preceded by a preusability expectation interview and succeeded by a postusability experience interview. In addition, an evaluation questionnaire was completed at weeks 2 and 4. Adherence was calculated as the proportion of 10-minute sessions completed within the first 28 days of treatment. Usability and feasibility were analyzed and summarized quantitatively and qualitatively. Results: A total of 391 biofeedback sessions were completed with a median of 25 (IQR 17-28) per participant. The mean adherence rate was 0.76 (SD 0.26). The evaluation questionnaire revealed that functionality and design had the highest scores, whereas engagement and biofeedback were lower. Qualitative preexpectation analysis revealed that participants expected to become better familiar with physical signals and gain more understanding of their migraine attacks and noted that the app should be simple and understandable. Postusability analysis indicated that participants had an overall positive user experience with some suggestions for improvement regarding the design of the wearables and app content. The intervention was safe and tolerable. One case of prespecified adverse events was recorded in which a patient developed a skin rash from the sticky surface electromyography electrodes. Conclusions: The app underwent a rigorous development process that indicated an overall positive user experience, good usability, and high adherence rate. This study highlights the value of usability testing in the development of mHealth apps.

Introduction: Compression of the duodenum and left renal vein between the aorta and superior mesenteric artery usually leads to symptoms of proximal bowel obstruction or hematuria and, more rarely, nonspecific mild headaches. Case: A young woman presented with new daily persistent headache refractory to numerous pharmacological treatments, onabotulinumtoxinA, nerve blocks, and occipital nerve stimulation. Following several years of daily severe headache, worsening abdominal pain and intolerance for food intake led to the discovery of aortomesenteric compression. Surgical treatment gave prompt improvement in gastric symptoms but also essentially resolved the headache. Conclusion: This is the first description of new daily persistent headache in association with aortomesenteric compression as well as marked improvement of headache following aortomesenteric decompression. In patients with new daily persistent headache and orthostatic symptoms one may consider a differential diagnosis of Nutcracker syndrome, especially in patients with comorbid hypermobility syndromes, hematuria or gastric symptoms.

Objective: The objective of this study was to develop and investigate the usability of a biofeedback treatment smartphone app for adolescent migraine sufferers. Background: Biofeedback is effective in treating pediatric migraine. However, biofeedback is not widely used due to the necessity of a trained therapist and specialized equipment. Emerging digital technology, including smartphones and wearables, enables new ways of administering biofeedback. Methods: In a prospective open‐label development and usability study, 10 adolescent migraine sufferers used a newly developed biofeedback app with wearable sensors that measured their muscle tension, finger temperature, and heart rate. Three iterative rounds of usability testing, including a 2‐week home testing period, were completed. A biofeedback algorithm, combining and optimizing the 3 physiological modalities, and several algorithms for sham‐treatment were created. Usability was evaluated statistically and summarized thematically. Results: Five of ten participants completed all 3 rounds of usability testing. A total of 72 biofeedback sessions were completed. Usability scoring was consistently high, with median scores ranging from 3.5 to 4.5 on a 5‐point scale. The biofeedback optimization algorithm correlated excellently to the raw physiological measurements ( r = 0.85, P < .001). The intervention was safe and tolerable. Conclusion: We developed an app for young migraine sufferers to receive therapist‐independent biofeedback. The app underwent a rigorous development process as well as usability and feasibility testing. It is now ready for clinical trials.