Validation of an algorithm for automated classification of migraine and tension-type headache attacks in an electronic headache diary

Association Between Electronic Diary-Rated Sleep, Mood, Energy, and Stress With Incident Headache in a Community-Based Sample

BACKGROUND AND OBJECTIVES

The aim of this study was to examine the diurnal links between average and changes in average levels of prospectively rated mood, sleep, energy, and stress as predictors of incident headache in a community-based sample.

METHODS

This observational study included structured clinical diagnostic assessment of both headache syndromes and mental disorders and electronic diaries that were administered 4 times per day for 2 weeks yielding a total of 4,974 assessments. The chief outcomes were incident morning (am) and later-day (pm) headaches. Generalized linear mixed-effects models were used to evaluate the average and lagged values of predictors including subjectively rated mood, anxiety, energy, stress, and sleep quality and objectively measured sleep duration and efficiency on incident am and pm headaches.

RESULTS

The sample included 477 participants (61% female), aged 7 through 84 years. After adjusting for demographic and clinical covariates and emotional states, incident am headache was associated with lower average (ß = -0.206*; confidence intervals: -0.397 to -0.017) and a decrease in average sleep quality on the prior day (ß = -0.172*; confidence interval: -0.305, -0.039). Average stress and changes in subjective energy levels on the prior day were associated with incident headaches but with different valence for am (decrease) (ß = -0.145* confidence interval: -0.286, -0.005) and pm (increase) (ß = 0.157*; confidence interval: 0.032, 0.281) headache. Mood and anxiety disorders were not significantly associated with incident headache after controlling for history of a diagnosis of migraine.

DISCUSSION

Both persistent and acute changes in arousal states manifest by subjective sleep quality and energy are salient precursors of incident headaches. Whereas poorer sleep quality and decreased energy on the prior day were associated with incident morning headache, an increase in energy and greater average stress were associated with headache onsets later in the day. Different patterns of predictors of morning and later-day incident headache highlight the role of circadian rhythms in the manifestations of headache. These findings may provide insight into the pathophysiologic processes underlying migraine and inform clinical intervention and prevention. Tracking these systems in real time with mobile technology provides a valuable ancillary tool to traditional clinical assessments.

Validation of a Digital Headache Calendar at a Tertiary Referral Center

BACKGROUND

Headache calendars are essential tools in monitoring changes in headache frequency and type. They are used in clinical practice for long-term monitoring, but their validation remains limited. The aim of this study was to validate the use of a digital calendar in monitoring single migraine and tension-type headache attacks.

METHODS

From July 2022 to February 2023, patients diagnosed with migraine and tension-type headache were enrolled. The validation of the digital calendar involved the comparison of self-reported single headache attacks in the digital calendar with the diagnostic headache diary based on the ICHD-3 criteria for migraine and tension-type headache. Sensitivity and specificity were calculated to assess reliability, and the level of agreement was calculated using kappa statistics.

RESULTS

This study included 30 patients (87% women) diagnosed with migraine and tension-type headache. The level of agreement in the classification of a single migraine attack revealed a sensitivity of 82% and a specificity of 78%, representing a substantial level of agreement (κ = 0.60). The classification of a single tension-type headache attack revealed a sensitivity of 84% and a specificity of 72%, with a moderate level of agreement (κ = 0.54).

CONCLUSIONS

The digital calendar proves effective in monitoring single headache attacks in patients with migraine and tension-type headache. In clinical practice, we recommend using the digital calendar as a monitoring tool for headache patients, as they can accurately identify true migraine and tension-type headache days.

Feasibility and Usability of a Mobile App-Based Interactive Care Plan for Migraine in a Community Neurology Practice: Development and Pilot Implementation Study

BACKGROUND

Migraine is a common and major cause of disability, poor quality of life, and high health care use. Access to evidence-based migraine care is limited and projected to worsen. Novel mobile health app-based tools may effectively deliver migraine patient education to support self-management, facilitate remote monitoring and treatment, and improve access to care. The risk that such an intervention may increase the care team workload is a potential implementation barrier.

OBJECTIVE

This study aims to describe a novel electronic health record-integrated mobile app-based Migraine Interactive Care Plan (MICP) and evaluate its feasibility, usability, and impact on care teams in a community neurology practice.

METHODS

Consecutive enrollees between September 1, 2020, and February 16, 2022, were assessed in a single-arm observational study of usability, defined by 74.3% (127/171) completing ≥1 assigned task. Task response rates, rate and type of care team escalations, and patient-reported outcomes were summarized. Patients were prospectively recruited and randomly assigned to routine care with or without the MICP from September 1, 2020, to September 1, 2021. Feasibility was defined by equal to or fewer downstream face-to-face visits, telephone contacts, and electronic messages in the MICP cohort. The Wilcoxon rank-sum test was used to compare continuous variables, and the chi-square test was used for categorical variables for those with at least 3 months of follow-up.

RESULTS

A total of 171 patients were enrolled, and of these, 127 (74.3%) patients completed ≥1 MICP-assigned task. Mean escalations per patient per month was 0.9 (SD 0.37; range 0-1.7). Patient-confirmed understanding of the educational materials ranged from 26.6% (45/169) to 56.2% (95/169). Initial mean headache days per week was 4.54 (SD 2.06) days and declined to 2.86 (SD 1.87) days at week 26. The percentage of patients reporting favorable satisfaction increased from a baseline of 35% (20/57) to 83% (15/18; response rate of 42/136, 30.9% to 28/68, 41%) over the first 6 months. A total of 121 patients with MICP were compared with 62 patients in the control group. No differences were observed in the rate of telephone contacts or electronic messages. Fewer face-to-face visits were observed in the MICP cohort (13/121, 10.7%) compared with controls (26/62, 42%; P<.001).

CONCLUSIONS

We describe the successful implementation of an electronic health record-integrated mobile app-based care plan for migraine in a community neurology practice. We observed fewer downstream face-to-face visits without increasing telephone calls, medication refills, or electronic messages. Our findings suggest that the MICP has the potential to improve patient access without increasing care team workload and the need for patient input from diverse populations to improve and sustain patient engagement. Additional studies are needed to assess its impact in primary care.

Machine learning prediction will be part of future treatment of depression

Machine learning (ML) is changing the way that medicine is practiced. While already clinically utilised in diagnostic radiology and outcome prediction in intensive care unit, ML approaches in psychiatry remain nascent. Implementing ML algorithms in psychiatry, particularly in the treatment of depression, is significantly more challenging than other areas of medicine in part because of the less demarcated disease nosology and greater variability in practice. Given the current exiguous capacity of clinicians to predict patient and treatment outcomes in depression, there is a significantly greater need for better predictive capability. Early studies have shown promising results. ML predictions were significantly better than chance within the sequenced treatment alternatives to relieve depression (STAR*D) trial (accuracy 64.6%, p < 0.0001) and combining medications to enhance depression outcomes (COMED) randomised Controlled Trial (RCT) (accuracy 59.6%, p = 0.043), with similar results found in larger scale, retrospective studies. The greater flexibility and dimensionality of ML approaches has been demonstrated in studies incorporating diverse input variables including electroencephalography scans, achieving 88% accuracy for treatment response, and cognitive test scores, achieving up to 72% accuracy for treatment response. The predicting response to depression treatment (PReDicT) trial tested ML informed prescribing of antidepressants against standard therapy and found there was both better outcomes for anxiety and functional endpoints despite the algorithm only having a balanced accuracy of 57.5%. Impeding the progress of ML algorithms in psychiatry are pragmatic hurdles, including accuracy, expense, acceptability and comprehensibility, and ethical hurdles, including medicolegal liability, clinical autonomy and data privacy. Notwithstanding impediments, it is clear that ML prediction algorithms will be part of depression treatment in the future and clinicians should be prepared for their arrival.

Investigating the effects of weather on headache occurrence using a smartphone application and artificial intelligence: A retrospective observational cross-sectional study

OBJECTIVE

To investigate the relationship between weather and headache occurrence using big data from an electronic headache diary smartphone application with recent statistical and deep learning (DL)-based methods.

BACKGROUND

The relationship between weather and headache occurrence remains unknown.

METHODS

From a database of 1 million users, data from 4375 users with 336,951 hourly headache events and weather data from December 2020 to November 2021 were analyzed. We developed statistical and DL-based models to predict the number of hourly headache occurrences mainly from weather factors. Temporal validation was performed using data from December 2019 to November 2020. Apart from the user dataset used in this model development, the physician-diagnosed headache prevalence was gathered.

RESULTS

Of the 40,617 respondents, 15,127/40,617 (37.2%) users experienced physician-diagnosed migraine, and 2458/40,617 (6.1%) users had physician-diagnosed non-migraine headaches. The mean (standard deviation) age of the 4375 filtered users was 34 (11.2) years, and 89.2% were female (3902/4375). Lower barometric pressure (p < 0.001, gain = 3.9), higher humidity (p < 0.001, gain = 7.1), more rainfall (p < 0.001, gain = 3.1), a significant decrease in barometric pressure 6 h before (p < 0.001, gain = 11.7), higher barometric pressure at 6:00 a.m. on the day (p < 0.001, gain = 4.6), lower barometric pressure on the next day (p < 0.001, gain = 6.7), and raw time-series barometric type I (remaining low around headache attack, p < 0.001, gain = 10.1) and type II (decreasing around headache attack, p < 0.001, gain = 10.1) changes over 6 days, were significantly associated with headache occurrences in both the statistical and DL-based models. For temporal validation, the root mean squared error (RMSE) was 13.4, and the determination coefficient (R² ) was 52.9% for the statistical model. The RMSE was 10.2, and the R² was 53.7% for the DL-based model.

CONCLUSIONS

Using big data, we found that low barometric pressure, barometric pressure changes, higher humidity, and rainfall were associated with an increased number of headache occurrences.

Diagnostic accuracy of an artificial intelligence online engine in migraine: A multi‐center study

Objective

This study assesses the concordance in migraine diagnosis between an online, self‐administered, Computer‐based, Diagnostic Engine (CDE) and semi‐structured interview (SSI) by a headache specialist, both using International Classification of Headache Disorders, 3rd edition (ICHD‐3) criteria.

Background

Delay in accurate diagnosis is a major barrier to headache care. Accurate computer‐based algorithms may help reduce the need for SSI‐based encounters to arrive at correct ICHD‐3 diagnosis.

Methods

Between March 2018 and August 2019, adult participants were recruited from three academic headache centers and the community via advertising to our cross‐sectional study. Participants completed two evaluations: phone interview conducted by headache specialists using the SSI and a web‐based expert questionnaire and analytics, CDE. Participants were randomly assigned to either the SSI followed by the web‐based questionnaire or the web‐based questionnaire followed by the SSI. Participants completed protocols a few minutes apart. The concordance in migraine/probable migraine (M/PM) diagnosis between SSI and CDE was measured using Cohen’s kappa statistics. The diagnostic accuracy of CDE was assessed using the SSI as reference standard.

Results

Of the 276 participants consented, 212 completed both SSI and CDE (study completion rate = 77%; median age = 32 years [interquartile range: 28–40], female:male ratio = 3:1). Concordance in M/PM diagnosis between SSI and CDE was: κ = 0.83 (95% confidence interval [CI]: 0.75–0.91). CDE diagnostic accuracy: sensitivity = 90.1% (118/131), 95% CI: 83.6%–94.6%; specificity = 95.8% (68/71), 95% CI: 88.1%–99.1%. Positive and negative predictive values = 97.0% (95% CI: 91.3%–99.0%) and 86.6% (95% CI: 79.3%–91.5%), respectively, using identified migraine prevalence of 60%. Assuming a general migraine population prevalence of 10%, positive and negative predictive values were 70.3% (95% CI: 43.9%–87.8%) and 98.9% (95% CI: 98.1%–99.3%), respectively.

Conclusion

The SSI and CDE have excellent concordance in diagnosing M/PM. Positive CDE helps rule in M/PM, through high specificity and positive likelihood ratio. A negative CDE helps rule out M/PM through high sensitivity and low negative likelihood ratio. CDE that mimics SSI logic is a valid tool for migraine diagnosis.

Study Design and Protocol of a Randomized Controlled Trial of the Efficacy of a Smartphone-Based Therapy of Migraine (SMARTGEM)

Background

Digitalization and electronic health (eHealth) offer new treatment approaches for patients with migraine. Current smartphone applications (apps) for migraine patients include a wide spectrum of functions ranging from digital headache diaries to app-based headache treatment by, among others, analysis of the possible triggers, behavioral therapy approaches and prophylactic non-drug treatment methods with relaxation therapy or endurance sport. Additional possibilities arise through the use of modern, location-independent communication methods, such as online consultations. However, there is currently insufficient evidence regarding the benefits and/or risks of these electronic tools for patients. To date, only few randomized controlled trials have assessed eHealth applications.

Methods

SMARTGEM is a randomized controlled trial assessing whether the provision of a new digital integrated form of care consisting of the migraine app M-sense in combination with a communication platform (with online consultations and medically moderated patient forum) leads to a reduction in headache frequency in migraine patients, improving quality of life, reducing medical costs and work absenteeism (DRKS-ID: DRKS00016328).

Discussion

SMARTGEM constitutes a new integrated approach for migraine treatment, which aims to offer an effective, location-independent, time-saving and cost-saving treatment. The design of the study is an example of how to gather high quality evidence in eHealth. Results are expected to provide insightful information on the efficacy of the use of electronic health technology in improving the quality of life in patients suffering from migraine and reducing resource consumption.

Screening for Medication Overuse Headache Can Reduce Patients' Suffering From Chronic Daily Headache: A Case Report

Headache is one of the major global health problems and an economic burden on the population. Common causes of chronic daily headaches are migraine and tension-type headaches, respectively. Medication overuse headache (MOH) is one of the common secondary causes of chronic daily headaches. It appears if the original chronic headache was not treated properly and the patient excessively used over-the-counter medicines as an abortive medication. It can be diagnosed easily if the clinician asks for a detailed history and finds out if the patient fulfills the criteria of MOH. The management requires patient education and withdrawal of the medication use, which can be done successfully most of the time in an outpatient clinic. General practitioners are the initial encounter with this type of patient, so they must screen for this type of headache and establish management to reduce the patient's suffering and burden on other health care facilities.

Development and validation of a web-based headache diagnosis questionnaire

Information technology advances may help in conducting epidemiological studies using web-based surveys. Questionnaire-based headache diagnosis should be validated against the doctor’s diagnosis. This study aimed to develop and validate a web-based diagnostic questionnaire for migraine, probable migraine (PM), and tension-type headache (TTH). We constructed a seven-item questionnaire for diagnosing migraine, PM, and TTH. A web-based survey was conducted among adults aged 20–59 years; migraine, PM, and TTH were diagnosed based on the responses. Validation interview was performed via telephone by a neurologist within 1 month after the web-based interview. Finally, 256 participants completed both web-based survey and validation interview. Of them, 121 (47.3%), 65 (25.4%), 61 (23.8%), and 9 (3.5%) were diagnosed with migraine, PM, TTH, and unclassified headache (UH), respectively in the web-based survey, whereas 119 (46.5%), 60 (23.4%), 74 (28.9%), 2 (0.8%), and 1 (0.4%) were diagnosed with migraine, PM, TTH, UH, and primary stabbing headache, respectively in the validation interview. The best agreement was found in migraine (sensitivity: 92.6%; specificity: 94.8%; kappa coefficient: 0.875), followed by TTH (sensitivity: 78.4%; specificity: 98.4%; kappa coefficient: 0.809). PM showed the least agreement (sensitivity: 85.0%; specificity: 92.9%; kappa coefficient: 0.757). In conclusion, our questionnaire is valid in identifying these headache disorders.

mBrain: towards the continuous follow-up and headache classification of primary headache disorder patients

Background

The diagnosis of headache disorders relies on the correct classification of individual headache attacks. Currently, this is mainly done by clinicians in a clinical setting, which is dependent on subjective self-reported input from patients. Existing classification apps also rely on self-reported information and lack validation. Therefore, the exploratory mBrain study investigates moving to continuous, semi-autonomous and objective follow-up and classification based on both self-reported and objective physiological and contextual data.

Methods

The data collection set-up of the observational, longitudinal mBrain study involved physiological data from the Empatica E4 wearable, data-driven machine learning (ML) algorithms detecting activity, stress and sleep events from the wearables’ data modalities, and a custom-made application to interact with these events and keep a diary of contextual and headache-specific data. A knowledge-based classification system for individual headache attacks was designed, focusing on migraine, cluster headache (CH) and tension-type headache (TTH) attacks, by using the classification criteria of ICHD-3. To show how headache and physiological data can be linked, a basic knowledge-based system for headache trigger detection is presented.

Results

In two waves, 14 migraine and 4 CH patients participated (mean duration 22.3 days). 133 headache attacks were registered (98 by migraine, 35 by CH patients). Strictly applying ICHD-3 criteria leads to 8/98 migraine without aura and 0/35 CH classifications. Adapted versions yield 28/98 migraine without aura and 17/35 CH classifications, with 12/18 participants having mostly diagnosis classifications when episodic TTH classifications (57/98 and 32/35) are ignored.

Conclusions

Strictly applying the ICHD-3 criteria on individual attacks does not yield good classification results. Adapted versions yield better results, with the mostly classified phenotype (migraine without aura vs. CH) matching the diagnosis for 12/18 patients. The absolute number of migraine without aura and CH classifications is, however, rather low. Example cases can be identified where activity and stress events explain patient-reported headache triggers. Continuous improvement of the data collection protocol, ML algorithms, and headache classification criteria (including the investigation of integrating physiological data), will further improve future headache follow-up, classification and trigger detection.

Trial registration This trial was retrospectively registered with number NCT04949204 on 24 June 2021 at www.clinicaltrials.gov.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-01813-w.

Tele-healthcare in migraine medicine: from diagnosis to monitoring treatment outcomes

INTRODUCTION

: Primary headaches represent a huge cost in terms of decreased productivity and migraine occupies the first position among disabilities in working population. Migraine has a high incidence, disproportionate to the available primary care centres. In most cases, migraine can be managed through the simple and accurate collection of clinical history, which makes it an ideal candidate for tele-healthcare.

AREAS COVERED

: In this narrative review we retrace the most important scientific evidence regarding use of tele-healthcare in headache medicine. Over the last few years, it has proved to be a valid and useful tool for the management of migraine. Furthermore, current pandemic has imposed a drastic change in the way of thinking and setting up medicine, forcing clinicians and patients to a huge expansion of telemedicine.

EXPERT OPINION

: We should permanently insert the culture of telemedicine in the headache care not only in academies and scientific societies, but extend it to specialized hospitals for the treatment of headaches. Only by broadening the old book-based strategy, we will be able to open the door to the multidimensional culture of headache medicine. Experts of excellence centres should set an example and pave the way for the rest of the clinicians.

Computerized migraine diagnostic tools: a systematic review

BACKGROUND

Computerized migraine diagnostic tools have been developed and validated since 1960. We conducted a systematic review to summarize and critically appraise the quality of all published studies involving computerized migraine diagnostic tools.

METHODS

We performed a systematic literature search using PubMed, Web of Science, Scopus, snowballing, and citation searching. Cutoff date for search was 1 June 2021. Published articles in English that evaluated a computerized/automated migraine diagnostic tool were included. The following summarized each study: publication year, digital tool name, development basis, sample size, sensitivity, specificity, reference diagnosis, strength, and limitations. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool was applied to evaluate the quality of included studies in terms of risk of bias and concern of applicability.

RESULTS

A total of 41 studies (median sample size: 288 participants, median age = 43 years; 77% women) were included. Most (60%) tools were developed based on International Classification of Headache Disorders criteria, half were self-administered, and 82% were evaluated using face-to-face interviews as reference diagnosis. Some of the automated algorithms and machine learning programs involved case-based reasoning, deep learning, classifier ensemble, ant-colony, artificial immune, random forest, white and black box combinations, and hybrid fuzzy expert systems. The median diagnostic accuracy was concordance = 89% [interquartile range (IQR) = 76-93%; range = 45-100%], sensitivity = 87% (IQR = 80-95%; range = 14-100%), and specificity = 90% (IQR = 77-96%; range = 65-100%). Lack of random patient sampling was observed in 95% of studies. Case-control designs were avoided in all studies. Most (76%) reference tests exhibited low risk of bias and low concern of applicability. Patient flow and timing showed low risk of bias in 83%.

CONCLUSION

Different computerized and automated migraine diagnostic tools are available with varying accuracies. Random patient sampling, head-to-head comparison among tools, and generalizability to other headache diagnoses may improve their utility.

A decision support system for primary headache developed through machine learning

BACKGROUND

Primary headache is a disorder with a high incidence and low diagnostic accuracy; the incidence of migraine and tension-type headache ranks first among primary headaches. Artificial intelligence (AI) decision support systems have shown great potential in the medical field. Therefore, we attempt to use machine learning to build a clinical decision-making system for primary headaches.

METHODS

The demographic data and headache characteristics of 173 patients were collected by questionnaires. Decision tree, random forest, gradient boosting algorithm and support vector machine (SVM) models were used to construct a discriminant model and a confusion matrix was used to calculate the evaluation indicators of the models. Furthermore, we have carried out feature selection through univariate statistical analysis and machine learning.

RESULTS

In the models, the accuracy, F1 score were calculated through the confusion matrix. The logistic regression model has the best discrimination effect, with the accuracy reaching 0.84 and the area under the ROC curve also being the largest at 0.90. Furthermore, we identified the most important factors for distinguishing the two disorders through statistical analysis and machine learning: nausea/vomiting and photophobia/phonophobia. These two factors represent potential independent factors for the identification of migraines and tension-type headaches, with the accuracy reaching 0.74 and the area under the ROC curve being at 0.74.

CONCLUSIONS

Applying machine learning to the decision-making system for primary headaches can achieve a high diagnostic accuracy. Among them, the discrimination effect obtained by the integrated algorithm is significantly better than that of a single learner. Second, nausea/vomiting, photophobia/phonophobia may be the most important factors for distinguishing migraine from tension-type headaches.

Determining the Evolution of Headache Among Regular Users of a Daily Electronic Diary via a Smartphone App: Observational Study

Background

Smartphone-based apps represent a major development in health care management. Specifically in headache care, the use of electronic headache diaries via apps has become increasingly popular. In contrast to the soaring volume of available data, scientific use of these data resources is sparse.

Objective

In this analysis, we aimed to assess changes in headache and migraine frequency, headache and migraine intensity, and use of acute medication among people who showed daily use of the headache diary as implemented in the freely available basic version of the German commercial app, M-sense.

Methods

The basic version of M-sense comprises an electronic headache diary, documentation of lifestyle factors with a possible impact on headaches, and evaluation of headache patterns. This analysis included all M-sense users who had entered data into the app on a daily basis for at least 7 months.

Results

We analyzed data from 1545 users. Mean MHD decreased from 9.42 (SD 5.81) at baseline to 6.39 (SD 5.09) after 6 months (P<.001; 95% CI 2.80-3.25). MMD, AMD, and migraine intensity were also significantly reduced. Similar results were found in 985 users with episodic migraine and in 126 users with chronic migraine.

Conclusions

Among regular users of an electronic headache diary, headache and migraine frequency, in addition to other headache characteristics, improved over time. The use of an electronic headache diary may support standard headache care.

Primary headaches during the COVID-19 lockdown in Germany: analysis of data from 2325 patients using an electronic headache diary

BACKGROUND

Lockdown measures due to the COVID-19 pandemic have led to lifestyle changes, which in turn may have an impact on the course of headache disorders. We aimed to assess changes in primary headache characteristics and lifestyle factors during the COVID-19 lockdown in Germany using digital documentation in the mobile application (app) M-sense.

MAIN BODY

We analyzed data of smartphone users, who entered daily data in the app in the 28-day period before lockdown (baseline) and in the first 28 days of lockdown (observation period). This analysis included the change of monthly headache days (MHD) in the observation period compared to baseline. We also assessed changes in monthly migraine days (MMD), the use of acute medication, and pain intensity. In addition, we looked into the changes in sleep duration, sleep quality, energy level, mood, stress, and activity level. Outcomes were compared using paired t-tests. The analysis included data from 2325 app users. They reported 7.01 ± SD 5.64 MHD during baseline and 6.89 ± 5.47 MHD during lockdown without significant changes (p > 0.999). MMD, headache and migraine intensity neither showed any significant changes. Days with acute medication use were reduced from 4.50 ± 3.88 in the baseline to 4.27 ± 3.81 in the observation period (p < 0.001). The app users reported reduced stress levels, longer sleep duration, reduced activity levels, along with a better mood, and an improved energy level during the first lockdown month (p ≤ 0.001). In an extension analysis of users who continued to use M-sense every day for 3 months after initiation of lockdown, we compared the baseline and the subsequent months using repeated-measures ANOVA. In these 539 users, headache frequency did not change significantly neither (6.11 ± 5.10 MHD before lockdown vs. 6.07 ± 5.17 MHD in the third lockdown month, p = 0.688 in the ANOVA). Migraine frequency, headache and migraine intensity, and acute medication use were also not different during the entire observation period.

CONCLUSION

Despite slight changes in factors that contribute to the generation of headache, COVID-19-related lockdown measures did not seem to be associated with primary headache frequency and intensity over the course of 3 months.

E-diary use in clinical headache practice: A prospective observational study

Aim

To determine whether our E-diary can be used to diagnose migraine and provide more reliable migraine-related frequency numbers compared to patients’ self-reported estimates.

Methods

We introduced a self-developed E-diary including automated algorithms differentiating headache and migraine days, indicating whether a patient has migraine. Reliability of the E-diary diagnosis in combination with two previously validated E-questionnaires was compared to a physician’s diagnosis as gold standard in headache patients referred to the Leiden Headache Clinic (n = 596). In a subset of patients with migraine (n = 484), self-estimated migraine-related frequencies were compared to diary-based results.

Results

The first migraine screening approach including an E-headache questionnaire, and the E-diary revealed a sensitivity of 98% and specificity of 17%. In the second approach, an E-migraine questionnaire was added, resulting in a sensitivity of 79% and specificity of 69%. Mean self-estimated monthly migraine days, non-migrainous headache days and days with acute medication use were different from E-diary-based results (absolute mean difference ± standard deviation respectively 4.7 ± 5.0, 6.2 ± 6.6 and 4.3 ± 4.8).

Conclusion

The E-diary including algorithms differentiating headache and migraine days showed usefulness in diagnosing migraine. The use emphasised the need for E-diaries to obtain reliable information, as patients do not reliably recall numbers of migraine days and acute medication intake. Adding E-diaries will be helpful in future headache telemedicine.

[Digitalization in headache therapy]

Hintergrund

Die Digitalisierung bietet Unterstützung und innovative Ansätze in der Diagnostik und Therapie von Kopfschmerzen. Mit dem Digitale-Versorgung-Gesetz gibt es nun erstmals die Möglichkeit, diese auch in die Regelversorgung einzubinden. Allerdings fällt eine Beurteilung der vielfältigen Angebote derzeit schwer; einheitliche Qualitätsstandards und aussagekräftige Studien zur Bewertung der Effektivität und Sicherheit fehlen.

Ziel der Arbeit

Übersicht über aktuelle Ansätze der Digitalisierung in der Kopfschmerzbehandlung und Konkretisierung an zwei Beispielen (App M‑sense und Kopfschmerzregister der Deutschen Migräne- und Kopfschmerzgesellschaft DMKG).

Material und Methoden

Literaturrecherche, Produktinformationen und Darstellung durch die Projektverantwortlichen.

Ergebnisse

Die meisten digitalen Angebote im Kopfschmerzbereich sind aktuell Kopfschmerzkalender, meist als Smartphone-App. Es gibt jedoch auch vielversprechende Erweiterungen (z. B. Triggeranalyse) und neue Ansätze wie digitale Anleitungen zu Entspannung und Ausdauersport, Chatbots für die Patienten sowie Unterstützung der Ärzte durch strukturierte Erhebung von Patientendaten und deren Verarbeitung zu Diagnosezwecken.

Schlussfolgerung

Verschiedene Ansätze der Digitalisierung könnten zukünftig Behandler und Patient effektiv in der Kopfschmerzbehandlung und Therapiebegleitung unterstützen. Allerdings sind qualitativ hochwertige Studien notwendig, um ihren Nutzen und ihre Effektivität zu evaluieren.