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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.

Mutations in the CACNA1A gene show a wide range of neurological phenotypes including hemiplegic migraine, ataxia, mental retardation and epilepsy. In some cases, hemiplegic migraine attacks can be triggered by minor head trauma and culminate in encephalopathy and cerebral oedema. A 37-year-old male without a family history of complex migraine experienced hemiplegic migraine attacks from childhood. The attacks were usually triggered by minor head trauma, and on several occasions complicated with encephalopathy and cerebral oedema. Genetic testing of the proband and unaffected parents revealed a de novo heterozygous nucleotide missense mutation in exon 25 of the CACNA1A gene (c.4055G>A, p.R1352Q). The R1352Q CACNA1A variant shares the phenotype with other described CACNA1A mutations and highlights the interesting association of trauma as a precipitant for hemiplegic migraine. Subjects with early-onset sporadic hemiplegic migraine triggered by minor head injury or associated with seizures, ataxia or episodes of encephalopathy should be screened for mutations. These patients should also be advised to avoid activities that may result in head trauma, and anticonvulsants should be considered as prophylactic migraine therapy.

IntroductionThe management of short-lasting unilateral neuralgiform headache attacks with conjunctival injection and tearing (SUNCT) and short-lasting unilateral neuralgiform headache attacks with cranial autonomic symptoms (SUNA) remains challenging in view of the paucity of data and evidence-based treatment recommendations are missing.MethodsIn this single-centre, non-randomised, prospective open-label study, we evaluated and compared the efficacy of oral and parenteral treatments for SUNCT and SUNA in a real-world setting. Additionally, single-arm meta-analyses of the available reports of SUNCT and SUNA treatments were conducted.ResultsThe study cohort comprised 161 patients. Most patients responded to lamotrigine (56%), followed by oxcarbazepine (46%), duloxetine (30%), carbamazepine (26%), topiramate (25%), pregabalin and gabapentin (10%). Mexiletine and lacosamide were effective in a meaningful proportion of patients but poorly tolerated. Intravenous lidocaine given for 7–10 days led to improvement in 90% of patients, whereas only 27% of patients responded to a greater occipital nerve block. No statistically significant differences in responders were observed between SUNCT and SUNA. In the meta-analysis of the pooled data, topiramate was found to be significantly more effective in SUNCT than SUNA patients. However, a higher proportion of SUNA than SUNCT was considered refractory to medications at the time of the topiramate trial, possibly explaining this isolated difference.ConclusionsWe propose a treatment algorithm for SUNCT and SUNA for clinical practice. The response to sodium channel blockers indicates a therapeutic overlap with trigeminal neuralgia, suggesting that sodium channels dysfunction may be a key pathophysiological hallmark in these disorders. Furthermore, the therapeutic similarities between SUNCT and SUNA further support the hypothesis that these conditions are variants of the same disorder.

A previously healthy boy presented to the healthcare services on several occasions with pain in his knee, ankle and foot. Clinical examinations and additional diagnostic testing initially revealed no red flags, but after 5 months, the boy was diagnosed with a serious and unexpected condition.

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.

Responsive to treatment individually, chronic migraine remains strikingly resistant collectively, incurring the second-highest population burden of disability worldwide. A heterogeneity of responsiveness, requiring prolonged—currently heuristic—individual evaluation of available treatments, may reflect a diversity of causal mechanisms, or the failure to identify the most important, single causal factor. Distinguishing between these possibilities, now possible through the application of complex modelling to large-scale data, is critical to determine the optimal approach to identify new interventions in migraine and making the best use of existing ones. Examining a richly phenotyped cohort of 1446 consecutive unselected patients with chronic migraine, here we use causal multitask Gaussian process models to estimate individual treatment effects across 10 classes of preventatives. Such modelling enables us to quantify the accessibility of heterogeneous responsiveness to high-dimensional modelling, to infer the likely scale of the underlying causal diversity. We calculate the treatment effects in the overall population, and the conditional treatment effects among those modelled to respond and compare the true response rates between these two groups. Identifying a difference in response rates between the groups supports a diversity of causal mechanisms. Moreover, we propose a data-driven machine prescription policy, estimating the time-to-response when sequentially trialling preventatives by individualized treatment effects and comparing it to expert guideline sequences. All model performances are quantified out-of-sample. We identify significantly higher true response rates among individuals modelled to respond, compared with the overall population (mean difference of 0.034; 95% confidence interval 0.003–0.065; P = 0.033), supporting significant heterogeneity of responsiveness and diverse causal mechanisms. The machine prescription policy yields an estimated 35% reduction in time-to-response (3.750 months; 95% confidence interval 3.507–3.993; P

Responsive to treatment individually, chronic migraine remains strikingly resistant collectively, incurring the second-highest population burden of disability worldwide. A heterogeneity of responsiveness, requiring prolonged—currently heuristic— individual evaluation of available treatments, may reflect a diversity of causal mechanisms, or the failure to identify the most important, single causal factor. Distinguishing between these possibilities, now possible through the application of complex modelling to large-scale data, is critical to determining the optimal approach to identifying new interventions in migraine and making the best use of existing ones.Examining a richly phenotyped cohort of 1446 consecutive unselected patients with chronic migraine, here we use causal multitask Gaussian process models to estimate individual treatment effects across ten classes of preventatives. Such modelling enables us to quantify the accessibility of heterogeneous responsiveness to high-dimensional modelling, to infer the likely scale of the underlying causal diversity. We calculate the treatment effects in the overall population, and the conditional treatment effects among those modelled to respond and compare the true response rates between these two groups. Identifying a difference in response rates between the groups supports a diversity of causal mechanisms. Moreover, we propose a data-driven machine prescription policy, estimating the time-to-response when sequentially trialing preventatives by individualized treatment effects and compare it to expert guideline sequences. All model performances are quantified out-of-sample.We identify significantly higher true response rates among individuals modelled to respond, compared to the overall population (mean difference of 0.034; 95% CI 0.003 to 0.065; p=0.033), supporting significant heterogeneity of responsiveness and diverse causal mechanisms. The machine prescription policy yields an estimated 35% reduction in time-to-response (3.750 months; 95% CI 3.507 to 3.993; pWe conclude that the highly distributed mode of causation in chronic migraine necessitates high-dimensional modelling for optimal management. Machine prescription should be considered an essential clinical decision-support tool in the future management of chronic migraine.

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.

Based on few clinical trials, flunarizine is considered a first-line prophylactic treatment for migraine in several guidelines. In this meta-analysis, we examined the pooled evidence for its effectiveness, tolerability, and safety. Prospective randomized controlled trials of flunarizine as a prophylaxis against migraine were identified from a systematic literature search, and risk of bias was assessed for all included studies. Reduction in mean attack frequency was estimated by calculating the mean difference (MD), and a series of secondary outcomes –including adverse events (AEs) – were also analyzed. The database search yielded 879 unique records. Twentyfive studies were included in data synthesis. We scored 31/175 risk of bias items as “high”, with attrition as the most frequent bias. A pooled analysis estimated that flunarizine reduces the headache frequency by 0.4 attacks per 4 weeks compared with placebo (5 trials, 249 participants: MD 20.44; 95% confidence interval 20.61 to 2 0.26). Analysis also revealed that the effectiveness of flunarizine prophylaxis is comparable with that of propranolol (7 trials, 1151 participants, MD 20.08; 95% confidence interval 20.34 to 0.18). Flunarizine also seems to be effective in children. The most frequent AEs were sedation and weight increase. Meta-analyses were robust and homogenous, although several of the included trials potentially suffered from high risk of bias. Unfortunately, reporting of AEs was inconsistent and limited. In conclusion, pooled analysis of data from partially outdated trials shows that 10- mg flunarizine per day is effective and well tolerated in treating episodic migraine – supporting current guideline recommendations.

ObjectiveTo compare the clinical characteristics and treatment responses of primary new daily persis- tent headache (NDPH) to transformed chronic daily headache (T-CDH, encompassing chronic migraine and chronic tension-type headache).MethodsAnalysis of prospectively collected clinical data in consecutive patients seen in a headache clinic between 2007 and 2019.ResultsInclusion criteria were met by 366 patients with NDPH and 696 with T-CDH. There was a lower female preponderance in NDPH than T-CDH (62.6% vs. 73.3%, p Nausea, vomiting, photophobia, phonophobia, motion sensitivity, vertigo, and cranial autonomic symptoms were all less common in NDPH than T-CDH (all pAcute treatments were less effective in NDPH, and medication overuse was less common (16% vs. 42%, pConclusionsWhilst there is overlap in the phenotype and response to treatment of NDPH and T-CDH; NDPH appears to be less symptomatically diverse, and less responsive to treatment.

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.

Objective To investigate the effect size, safety, and tolerability of a therapist‐independent biofeedback treatment app among adolescent with migraine. Materials and Methods This was a prospective, 3:1 ratio randomized, sham‐controlled, double‐blind, pilot study with 16 adolescents diagnosed with migraine randomized to eight weeks of biofeedback treatment (n = 12) or sham biofeedback (n = 4), carried out at two university hospitals in Norway. The prespecified and primary objective of the study was to observe changes in outcomes within the active treatment group. The sham control group was included in a minor ratio primarily to evaluate its feasibility. The primary outcome was change in headache frequency. A modified intention to treat analysis was performed, including participants completing at least seven biofeedback sessions in weeks 1–4 (n = 12 vs. n = 4) and weeks 5–8 (n = 7 vs. n = 2). Results Adherence was poor with 40% (136/336) of planned biofeedback sessions completed during weeks 5–8. Within the biofeedback group, a not statistically significant reduction in headache frequency was observed at weeks 1–4 (2.92 days/month, 95% CI −1.00 to 6.84, p = .145) and weeks 5–8 (1.85 days/month, 95% CI −2.01 to 5.72, p = .395). The biofeedback group experienced a median of one fewer headache days/month versus sham that did not reach significance (95% CI −4.0 to 9.0, p = .760). Conclusions We observed a small reduction in headache frequency in the active treatment group. Findings were likely undermined by low adherence and underpowered analyses but indicate that a therapist‐independent biofeedback treatment app has the potential to be an effective, tolerable, and inexpensive treatment option., Biofeedback is an effective migraine treatment; however, a completely therapist‐independent smartphone administered treatment for children and adolescents has not been investigated. In this randomized controlled trial, we show that smartphone biofeedback treatment has the potential to be an effective in pediatric migraine, reducing migraine frequency by approximately 30%.

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.

Purpose of Review This review presents a critical appraisal of the treatment strategies for short-lasting unilateral neuralgiform headache attacks (SUNHA), paroxysmal hemicrania (PH), and hemicrania continua (HC). We assess the available, though sparse, evidence on both medical and surgical treatments. In addition, we present estimated pooled analyses of the most common treatments and emphasize recent promising findings. Recent Findings The majority of literature available on the treatment of these rare trigeminal autonomic cephalalgias are small open-label observational studies and case reports. Pooled analyses reveal that lamotrigine for SUNHA and indomethacin for PH and HC are the preventative treatments of choice. Second-line choices include topiramate, gabapentin, and carbamazepine for SUNHA; verapamil for PH; and cyclooxygenase-2 inhibitors and gabapentin for HC. Parenteral lidocaine is highly effective as a transitional treatment for SUNHA. Novel therapeutic strategies such as non-invasive neurostimulation, targeted nerve and ganglion blockades, and invasive neurostimulation, including implanted occipital nerve stimulators and deep brain stimulation, appears to be promising options. Summary At present, lamotrigine as a prophylactic and parenteral lidocaine as transitional treatment remain the therapies of choice for SUNHA. While, by definition, both PH and CH respond exquisitely to indomethacin, evidence for other prophylactics is less convincing. Evidence for the novel emerging therapies is limited, though promising.

Purpose of Review This article reviews the recent research and development of electronic health (eHealth) and, in particular, mobile health (mHealth) strategies to deliver behavioral treatment for migraine. Prospects for future development and research of mobile health in migraine are suggested. Recent Findings Advances in digital technology and mobile technology have led to an era where electronic and mobile approaches are applied to several aspects of healthcare. Electronic behavioral interventions for migraine seem to be acceptable and feasible, but efficacy measures are uncertain. Clinical trials on mHealth-based classical behavioral therapies, such as relaxation, biofeedback, and cognitive behavioral therapy are missing in the literature. Within mHealth, headache diaries are the most researched and scientifically developed. Still, there is a gap between commercially available apps and scientifically validated and developed apps. Summary Digital technology and mobile health has not yet lived out its potential in behavioral migraine therapy. Application of proper usability and functionality designs towards the right market, together with appraisal of medical and technological recommendations, may facilitate rapid development of eHealth and mHealth, while also establishing scientific evidence. This is a post-peer-review, pre-copyedit version of an article published in [Current Pain and Headache Reports] Locked until 31.7.2019 due to copyright restrictions. The final authenticated version is available online at: https://doi.org/10.1007/s11916-018-0718-0

BACKGROUND The use of wearables and mobile phone apps in medicine is gaining attention. Biofeedback has the potential to exploit the recent advances in mobile health (mHealth) for the treatment of headaches. OBJECTIVES The aim of this study was to assess the validity of selected wireless wearable health monitoring sensors (WHMS) for measuring surface electromyography (SEMG) and peripheral skin temperature in combination with a mobile phone app. This proof of concept will form the basis for developing innovative mHealth delivery of biofeedback treatment among young persons with primary headache. METHODS Sensors fulfilling the following predefined criteria were identified: wireless, small size, low weight, low cost, and simple to use. These sensors were connected to an app and used by 20 healthy volunteers. Validity was assessed through the agreement with simultaneous control measurements made with stationary neurophysiological equipment. The main variables were (1) trapezius muscle tension during different degrees of voluntary contraction and (2) voluntary increase in finger temperature. Data were statistically analyzed using Bland-Altman plots, intraclass correlation coefficient (ICC), and concordance correlation coefficient (CCC). RESULTS The app was programmed to receive data from the wireless sensors, process them, and feed them back to the user through a simple interface. Excellent agreement was found for the temperature sensor regarding increase in temperature (CCC .90; 95% CI 0.83-0.97). Excellent to fair agreement was found for the SEMG sensor. The ICC for the average of 3 repetitions during 4 different target levels ranged from .58 to .81. The wireless sensor showed consistency in muscle tension change during moderate muscle activity. Electrocardiography artifacts were avoided through right-sided use of the SEMG sensors. Participants evaluated the setup as usable and tolerable. CONCLUSIONS This study confirmed the validity of wireless WHMS connected to a mobile phone for monitoring neurophysiological parameters of relevance for biofeedback therapy.

Background: The use of wearables and mobile phone apps in medicine is gaining attention. Biofeedback has the potential to exploit the recent advances in mobile health (mHealth) for the treatment of headaches. Objectives: The aim of this study was to assess the validity of selected wireless wearable health monitoring sensors (WHMS) for measuring surface electromyography (SEMG) and peripheral skin temperature in combination with a mobile phone app. This proof of concept will form the basis for developing innovative mHealth delivery of biofeedback treatment among young persons with primary headache. Methods: Sensors fulfilling the following predefined criteria were identified: wireless, small size, low weight, low cost, and simple to use. These sensors were connected to an app and used by 20 healthy volunteers. Validity was assessed through the agreement with simultaneous control measurements made with stationary neurophysiological equipment. The main variables were (1) trapezius muscle tension during different degrees of voluntary contraction and (2) voluntary increase in finger temperature. Data were statistically analyzed using Bland-Altman plots, intraclass correlation coefficient (ICC), and concordance correlation coefficient (CCC). Results: The app was programmed to receive data from the wireless sensors, process them, and feed them back to the user through a simple interface. Excellent agreement was found for the temperature sensor regarding increase in temperature (CCC .90; 95% CI 0.83-0.97). Excellent to fair agreement was found for the SEMG sensor. The ICC for the average of 3 repetitions during 4 different target levels ranged from .58 to .81. The wireless sensor showed consistency in muscle tension change during moderate muscle activity. Electrocardiography artifacts were avoided through right-sided use of the SEMG sensors. Participants evaluated the setup as usable and tolerable. Conclusions: This study confirmed the validity of wireless WHMS connected to a mobile phone for monitoring neurophysiological parameters of relevance for biofeedback therapy.