Religious belief can be used as a pain coping strategy. Our purpose was to evaluate the relationship between headache and religious activity using prospective data from a large population-based study.

Background: Many antihypertensive drugs are also used as migraine prophylactics, but the relationship between blood pressure and headache is not been well understood. The objective of the present study was to explore the association between blood pressure and headache prevalence, and the effect of antihypertensive medication on this relationship, using both cross-sectional and prospective data from a large population.

The main objective of this study was to investigate the angiotensin converting enzyme (ACE) genotype as a possible risk factor for migraine (both with and without aura) compared to controls. We also wanted to examine whether a clinical response to an ACE inhibitor, lisinopril, or an angiotensin II receptor blocker, candesartan, in migraine prophylaxis was related to ACE genotype.

Cluster headache (CH) is regarded as a chronobiological disorder. The hypothalamic biological clock may thus be involved in the pathophysiology, but few studies have actually investigated this in CH patients. A variable number tandem repeat (VNTR) polymorphism of the PER3 clock gene has been associated to preferred daily rhythm (chronotype) in several studies. We aimed to study the distribution of PER3 VNTR polymorphisms and chronotypes in a CH population. We used blood samples from a biobank of CH patients for genetic tests, and invited all tested patients to complete the Horne-Ostberg Morningness-eveningness Questionnaire (MEQ), the Pittsburgh sleep quality Index (PSQI) and the Shift Work Index. Genotypes were compared to a previously tested population of 432 healthy students. One hundred forty nine patients were genotyped, and we found no difference in PER3 VNTR polymorphisms between patients and controls. Seventy-four patients completed the MEQ (54 men, 20 women, mean age 52.3 years ± 13.4), and chronotypes were as follows: 12 % morning-, 37 % intermediate-, and 51 % evening types. Compared with a previous Danish study of CH patients and controls, there were no difference in chronotype distribution. Sixty percent of patients were defined as bad sleepers (PSQI >5), and 51 % of patients currently employed were shift workers. No association between CH, PER3 VNTR polymorphism and chronotype was found in this study.

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.