Abstract
Background
Migraine is a major source of disability in Korea, yet many patients remain underdiagnosed or undertreated. Traditional epidemiological studies are limited by recall bias and incomplete patient-reported outcomes. App-based headache diaries offer real-time, repeated measures of symptoms and treatment, enabling large-scale assessment to better understand migraine burden and treatment response. This study aimed to evaluate the large-scale implementation of a nationally distributed smartphone “Headache Diary” mobile application and describe migraine burden, acute treatment response, and functional impairment in real-world practice in Korea.
Methods
We conducted a retrospective, non-interventional analysis using self-reported data from the nationwide “Headache Diary” mobile application, developed by the Korean Headache Society. We included users who submitted at least one headache report between December 2020 and January 2025. Migraine/probable migraine were identified using an algorithm based on medication use and ICHD-3 criteria, while the remaining participants were classified into a non-migraine group (including tension-type headache or unclassifiable headache disorders). Acute-treatment failure was defined as pain relief reported after 2 hours or no effect. We also analyzed clinical characteristics, acute treatment patterns, and functional impairment associated with headache attacks.
Results
Of 23,713 users, 17,913 (75.5%) had migraine or probable migraine. Migraine patients experienced more severe headache intensity (55.5% vs. 13.7%), higher rates of premonitory symptoms (67.7% vs. 29.5%), more frequent identification of triggers (89.9% vs. 61.2%), and greater use of acute medications (73.1% vs. 23.8%) compared to non-migraine users. Among 211,302 headache attacks analyzed, 155,894 attacks were migraine, and 96,742 attacks were available for acute treatment effect assessment. Of the 86,301 triptan-treated attacks, 40.3% resulted in acute treatment failure. Adolescents (10–19 years) showed the highest failure rate (62.1%). Notably, attacks with premonitory symptoms (odds ratio [OR] 0.91, 95% CI 0.89–0.94), aura (OR 0.79, 95% CI 0.75–0.83), or potential triggers (OR 0.93, 95% CI 0.91–0.96) had lower odds of treatment failure. Functional impairment was reported in 36.6% of attacks, most as decreased work or study efficiency (21.0%) and reduced household activity efficiency (17.2%).
Conclusions
Nationwide, app-based data revealed that migraine patients in Korea frequently experience severe attacks, premonitory symptoms, and triggers, with substantial rates of acute treatment failure and functional impairment. These findings highlight the need for earlier recognition and timely intervention and suggest that real-time digital platforms can provide valuable insights for patient-centered migraine management.
Clinical trial number
Not applicable.
Supplementary Information
The online version contains supplementary material available at 10.1186/s10194-026-02370-7.
Graphical Abstract
Background
Migraine is one of the most prevalent and debilitating neurological disorders worldwide, affecting over one billion people. Recent Global Burden of Disease (GBD) estimates highlight the scale of headache-related disability: in 2023, headache disorders affected 2.9 billion people globally (age-standardized prevalence 34.6%) and generated an age-standardized YLD (Year(s) Lived with Disability) rate of 541.9 per 100,000 population. Migraine accounts for most headache-attributed disability, with an age-standardized YLD rate of 487.5 per 100,000 population in 2023 [1]. Despite advances in diagnostic criteria and therapeutic strategies, under-recognition, delayed diagnosis, and suboptimal treatment remain widespread, contributing to a considerable personal and societal burden [2–6]. Although traditional studies using retrospective surveys or claims data have clarified the epidemiology of migraine, results are often limited by recall bias, incomplete detail, and poor capture of patient-reported outcomes [7, 8]. In contrast, mobile health technologies offer a unique opportunity to obtain large-scale, real-time data, patterns and outcomes of treatment, and daily functional impact directly from patients. In particular, headache diary applications offer accessible means to collect real time data, enabling researchers and physicians to better understand patient experiences and treatment outcomes [9]. Studies using app-based data, such as the Migraine Buddy platform, have demonstrated the feasibility of capturing real-world burden of headache, treatment experiences across the world, and patient-centered treatment options [10, 11].
In Korea, migraine remains underdiagnosed and undertreated. Previous national data showed an average diagnostic delay of 10 years, and only one-quarter of individuals had consulted a physician for their headaches [12, 13]. Moreover, even among individuals who seek medical care for migraine, long-term adherence to treatment remains low [14], suggesting gaps in both patient awareness and continuity of care. To address these gaps, the Korean Headache Society developed a nationwide smartphone-based “Headache Diary” application to assist patients in systematically tracking headache attacks, associated features, medication use, and functional impact. However, no comprehensive data have been reported using the real-world data derived from this digital platform.
By using this digital platform-based dataset, this study aimed to evaluated the feasibility of large-scale real-world data collection and characterize the comprehensive migraine burden in Korea. Specifically, we investigated the proportion of migraine and clinical characteristics of migraine presentations and analyzed patterns of acute treatment use, treatment responses, and self-reported disability.
Methods
Study design and data source
We conducted a retrospective, non-interventional study using self-reported data from the ‘Headache Diary’ mobile application, which was developed and launched in 2020 by the Korean Headache Society. The Headache Diary has been widely disseminated across clinical settings in Korea and is freely accessible to individuals experiencing headaches. To ensure broad accessibility for personal headache management, the application was distributed via the Google Play Store (Android) and Apple App Store (iOS). Participants were recruited through a variety of channels, including public media outreach (newspapers and online news), digital platforms such as YouTube, and official announcements on the Korean Headache Society website. Additionally, headache specialists directly introduced the application to patients during routine outpatient consultations. Participation was entirely voluntary; individuals chose to download the app and self-record their data without any direct incentives, targeting the Korean-speaking population. As the platform was primarily designed as a clinical tool for patient self-monitoring rather than a dedicated research instrument, no formal screening or inclusion procedures were applied at the point of download. App users can record structured headache data—location, quality, intensity, attack duration, potential triggers, premonitory symptoms, associated symptoms and postdromes, acute medication use and response, and daily impairment. Users can then monitor episodes longitudinally to track attack frequency, identify patterns, and evaluate treatment effect. The app automatically captures the weather conditions at the time of each recorded attack. To ensure participant privacy, all data recorded in the application were stored in a secure cloud-based system with anonymized patient identifiers. No personally identifiable information was collected or accessed during the study. In compliance with the Personal Information Protection Act of Korea, the research team only utilized de-identified datasets for analysis. This study was approved by the Institutional Review Board of Sungkyunkwan University (No. 2024–09-083). Due to the retrospective nature of the study, the need to obtain informed consent was waived.
Study population
The base cohort comprised all individuals with headaches who submitted at least one report in the Headache Diary app between December 1, 2020, and January 17, 2025. From the base cohort, we then identified patients with migraine using an algorithm based on medication history and diagnostic criteria adapted from the International Classification of Headache Disorders, 3rd edition (ICHD-3) (Fig. 1) [15]. First, all individuals who reported at least one intake of a triptan or ergotamine within the application were classified as migraine cases. Since triptans and ergotamine are strictly regulated as prescriptiononly medications in Korea requiring a formal physician’s diagnosis, individuals reporting their intake of these medications were regarded as migraine cases. Among individuals without use of triptan or ergotamine, we classified those with migraine with or without aura according to ICHD-3 criteria (Supplemental Table 1). For those who did not meet the criteria for migraine, the algorithm further assessed for probable migraine (ICHD-3 section 1.3) to identify individuals who fulfilled all but one of the required ICHD-3 criteria from B through D (i.e., missing either the required duration, at least two headache characteristics, or at least on associated symptom) (Supplemental Table 1). This stepwise, algorithmic approach ensured a standardized and clinically meaningful classification of headache types within the study population. The enrollment year was defined as the year that includes the individual’s first headache-report date.
Fig. 1.
Algorithm to identify patients with migraine
Units of analysis
We made two units for data analysis depending on the study objectives. For the first objective, which was to identify the proportion of migraine among all headaches, the unit of analysis was the individual in the base cohort (i.e., individuals with any headache). The index date was defined as the start date of the headache in the patient’s first recorded report. For the second objective—to estimate acute treatment responses and the number of days with functional impairment due to migraine—the unit of analysis was headache attack among individuals with migraine. In this context, the index date was defined as the start date of the headache for each attack. Headache attacks were defined using an algorithm that incorporated explicit headache duration, medication use, and inferred durations from subsequent diary reports. To ensure the reliability of the analyzed data and minimize the potential inclusion of accidental or non-meaningful entries, single-entry records were excluded only in cases where the attack duration could not be determined (e.g., no recorded duration, no medication use, and no subsequent report within 24 hours) (Fig. 2).
Fig. 2.
Algorithm to define headache attacks and their durations
Data collection
We collected the information on demographics (birth year, age, and sex), the date of the first headache report on the app, headache start and termination date and time (hours and minutes) for each episode, and weather conditions (barometric pressure, humidity, and temperature) of each attack. Clinical characteristics of headaches were also obtained, including headache intensity (rated on a 1–10 scale, mild for 1–3, moderate for 4–6, and severe for 7–10), location of pain (categorized into 14 anatomical regions according to anterior, lateral, posterior, and cervical distribution), headache quality (throbbing, tightening, exploding, or sharp), premonitory symptoms (feeling of impending pain, stiffness or tightness in the back of the neck, yawning, fatigue, mood changes, appetite changes, photophobia, phonophobia, or osmophobia), and aura (symptoms lasting between 5 and 60 minutes, gradual onset over more than 5 minutes, visual disturbances, zigzag lines [fortification patterns], unilateral symptoms, language difficulties or slurred speech, and numbness or tingling in parts of the body). Additional information was also collected regarding the headache associated symptoms (indigestion, nausea, vomiting, dizziness, worsening with movement, photophobia, phonophobia, osmophobia, stiffness or tightness in the back of the neck, tearing or redness of the eyes, and runny nose or nasal congestion), and headache triggers (stress, fatigue, lack of sleep, napping or late sleeping, weekends, fasting, overeating, indigestion, light, sound, smell, cold, exercise, alcohol, and menstruation). The daily impairment due to headache was also recorded and categorized as absence from work or school, decreased work or study efficiency, inability to perform household activities, decreased efficiency in household activities, or inability to participate in leisure activities.
Statistical analysis
We compared the characteristics of participants with migraine and those with non-migraine headaches in the base cohort. We also described the distribution of characteristics across individual headache attacks. Continuous variables are presented as mean ± standard deviation (SD) or median with interquartile range (IQR), while categorical variables are reported as frequency with percentage.
Headache attack durations were primarily analyzed based on user-reported data. In cases where users recorded the onset of a headache but failed to provide an end time, the duration was imputed using a predefined logic. For subsequent attacks reported within 24 hours, the duration was calculated as the time interval between the onset of the index attack and the start of the following entry. If the next attack was reported after 24 hours, we performed a simple imputation using the most frequent reported duration (mode) in the dataset (Fig. 2).
Acute treatment success was defined as the pain freedom or relief reported within 2 hours, whereas acute treatment failure was defined as not being pain freedom or relief within 2 hours, including reports of “no effect”. We estimated the risk of acute treatment failure based on the onset time of treatment efficacy among participants with migraine. We also estimated the risk of acute treatment failure stratified by age, sex, and several clinical characteristics including headache intensity, triggers, aura, premonitory symptoms, and type of treatment. Crude and adjusted odds ratios (OR) with 95% confidence intervals (CI) were calculated using univariate and multivariate logistic regression, respectively. To account for potential within-person correlation across repeated attacks, we conducted a sensitivity analysis using generalized estimating equations (GEE) with a logit link and binomial distribution. Lastly, we quantified the number of headache attacks with functional impairment among all migraine attacks, stratified by type of daily activity affected. In addition, to enhance the objectivity of functional impairment, we compared headache intensity and duration between attacks with and without reduced work efficiency—defined as the presence of either decreased work/study efficiency or decreased efficiency in household activities—using two-sample t tests.
Results
Characteristics of headache attacks
Of the 215,891 reports submitted via the Headache Diary app, a total of 211,302 reports were classified as headache attacks following the algorithm in the Fig. 2. The characteristics of headache attacks are described in the Table 1. Of the 211,302 analyzed attacks, the headache duration was directly reported by users in 93.3% (n = 197,228) of cases, and median duration was 7.6 [3.0–14.9] days. The median headache intensity was 5.0 (3.0–6.0), with 24.8% classified as severe. Nearly half of attacks (48.7%) were preceded by premonitory symptoms, most commonly feeling of impending pain (35.8%) followed by stiffness/tightness in the back of the neck. Individuals became aware of premonitory symptoms a median of 1 (0.5–2) hour before headache starts. Associated symptoms were present in 76.6% of attacks with stiffness/tightness in the back of the neck in nearly half of the cases. Acute medications were used in 62.9% of all attacks, among which triptans accounted for 61.1% (Table 1).
Table 1.
Characteristics of headache attacks
| Characteristics | Headache Attacks (N = 211,302) |
|---|---|
| Demographic characteristics | |
| Female sex, n (%) | 180,750 (85.5) |
| Environmental characteristics | |
| Barometric pressure, mean ± SD, hPa | 1015.8 ± 21.6 |
| Humidity, mean ± SD, % | 64.7 ± 21.0 |
| Temperature, mean ± SD, °C | 15.0 ± 10.8 |
| Clinical characteristics | |
| Duration of attacks, hours | |
| Mean ± SD | 11.6 ± 18.8 |
| Median (IQR) | 7.6 (3.0–14.9) |
| Headache intensity, scores | |
| Mean ± SD | 4.7 ± 2.3 |
| Median (IQR) | 5.0 (3.0–6.0) |
| 1–3 (mild) | 69,465 (32.9) |
| 4–6 (moderate) | 89,439 (42.3) |
| 7–10 (severe) | 52,398 (24.8) |
| Unilateral pain, n (%) | 68,469 (32.4) |
| Quality of headache, n (%) | |
| Throbbing | 104,560 (49.5) |
| Tightening | 42,450 (20.1) |
| Exploding | 9,290 (4.4) |
| Sharp | 14,202 (6.7) |
| Premonitory symptoms, n (%) | 102,801 (48.7) |
| Awareness of premonitory symptoms before headache start, median (IQR), hours | 1.0 (0.5–2.0) |
| Feeling of Impending Pain | 75,542 (35.8) |
| Stiffness/Tightness in the Back of the Neck | 59,390 (28.1) |
| Yawning | 20,678 (9.8) |
| Fatigue | 44,293 (21.0) |
| Mood Changes | 7,038 (3.3) |
| Appetite Changes | 16,526 (7.8) |
| Photo-, phono- or osmophobia | 16,095 (7.6) |
| Associated symptoms, n (%) | 161,949 (76.6) |
| Indigestion | 55,320 (26.2) |
| Nausea | 74,218 (35.1) |
| Vomiting | 13,741 (6.5) |
| Dizziness | 66,873 (31.6) |
| Worsened by Movement | 49,880 (23.6) |
| Photophobia | 49,650 (23.5) |
| Phonophobia | 46,266 (21.9) |
| Osmophobia | 33,710 (16.0) |
| Stiffness/Tightness in the Back of the Neck | 101,835 (48.2) |
| Tearing/Redness of the Eyes | 20,337 (9.6) |
| Runny Nose/Nasal Congestion | 22,984 (10.9) |
| Acute medication use, n (%) | |
| Any medication | 132,842 (62.9) |
| Triptans | 81,134 (38.4) |
| Ergotamine | 4,638 (2.2) |
| Unknown/Other medications | 65,597 (31.0) |
Triptans included in sumatriptan, zolmitriptan, naratriptan, almotriptan, and frovatriptan
Other medications refer to agents other than triptans or ergotamine
Comparison of headache characteristics between individuals with migraine and individuals without migraine
Of 23,713 users of the Headache Diary app, 17,913 were classified as having migraine or probable migraine and the remaining 5,800 users were classified as having tension-type or unspecified headache (Fig. 1, Table 2). Compared to individuals without migraine, individuals with migraine were more likely to experience severe headache intensity (55.5% vs. 13.7%), report premonitory symptoms (67.7% vs. 29.5%), identify potential triggers (89.9% vs. 61.2%), and use acute medications (73.1% vs. 23.8%). Individuals with migraine had more days with functional impairment due to headache than those without migraine, both per patient (median 3.0 vs. 1.0 days) and per month (median 1.1 vs. 0.9 days) (Table 2).
Table 2.
Baseline characteristics of patients with migraine and non-migraine headaches
| Characteristics | All patients N = 23,713 |
Migraine, n (%) n = 17,913 (75.5) |
Non-migraine, n (%) n = 5,800 (24.5) |
|---|---|---|---|
| Demographic characteristics* | |||
| Enrollment, n (%), years | |||
| 2020 (December~) | 244 (1.0) | 201 (1.1) | 43 (0.7) |
| 2021 | 10,864 (45.8) | 7,646 (42.7) | 3,218 (55.5) |
| 2022 | 5,396 (22.8) | 4,311 (24.1) | 1,085 (18.7) |
| 2023 | 3,529 (14.9) | 2,812 (15.7) | 717 (12.4) |
| 2024 | 3,576 (15.1) | 2,870 (16.0) | 706 (12.2) |
| 2025 (~January) | 104 (0.4) | 73 (0.4) | 31 (0.5) |
| Age group, n (%) | |||
| 10s | 1,572 (6.6) | 1,148 (6.4) | 424 (7.3) |
| 20s | 6,755 (28.5) | 4,818 (26.9) | 1,937 (33.4) |
| 30s | 7,042 (29.7) | 5,565 (31.1) | 1,477 (25.5) |
| 40s | 4,859 (20.5) | 3,856 (21.5) | 1,003 (17.3) |
| 50s | 2,249 (9.5) | 1,713 (9.6) | 536 (9.2) |
| 60s+ | 525 (2.2) | 333 (1.9) | 192 (3.3) |
| Unknown | 711 (3.0) | 480 (2.7) | 231 (4.0) |
| Female sex, n (%) | 20,334 (85.8) | 15,440 (86.2) | 4,894 (84.4) |
| Headache characteristics† | |||
| Frequency of headache attacks per patient | |||
| Mean ± SD | 8.9 ± 29.8 | 11.5 ± 33.8 | 0.9 ± 4.7 |
| Median (IQR) | 2.0 (1.0–5.0) | 3.0 (1.0–8.0) | 0.0 (0.0–1.0) |
| Frequency of headache attacks, times per month‡ | |||
| Mean ± SD | 3.5 ± 4.0 | 3.5 ± 4.0 | 4.0 ± 5.9 |
| Median (IQR) | 2.4 (1.1–4.5) | 2.4 (1.1–4.5) | 2.1 (1.3–4.7) |
| Severe intensity, n (%)§ | 10,733 (45.3) | 9,941 (55.5) | 792 (13.7) |
| Premonitory symptoms, n (%) | 13,833 (58.3) | 12,122 (67.7) | 1,711 (29.5) |
| Aura, n (%) | 3,490 (14.7) | 3,490 (19.5) | - |
| Potential triggers, n (%) | 19,655 (82.9) | 16,103 (89.9) | 3,552 (61.2) |
| Acute medication, n (%) | 14,472 (61.0) | 13,091 (73.1) | 1,381 (23.8) |
| Triptans, n (%) | 5,903 (24.9) | 5,903 (33.0) | - |
| Ergotamine, n (%) | 550 (2.3) | 550 (3.1) | - |
| Unknown/Other medications, n (%) | 11,827 (49.9) | 10,446 (58.3) | 1,381 (23.8) |
| Disability due to headache | |||
| Duration with functional impairment per patient | |||
| Mean ± SD | 7.4 ± 26.6 | 8.4 ± 28.6 | 1.5 ± 1.0 |
| Median (IQR) | 2.0 (1.0–5.0) | 3.0 (1.0–6.0) | 1.0 (1.0–2.0) |
| Duration with functional impairment, days per month‡ | |||
| Mean ± SD | 2.4 ± 4.1 | 2.4 ± 4.1 | 1.7 ± 2.2 |
| Median (IQR) | 1.1 (0.4–2.9) | 1.1 (0.4–2.9) | 0.9 (0.6–1.7) |
The unit of analysis is the patient. The index date was defined as the enrollment date (i.e., the date of the first reported headache attack) in the mobile application
Measured in the index date
Measured during the entire enrollment period in each patient
Average of total times or days per patient per month. Restricted to patients with an enrollment period of 30 days or longer (n = 6740; migraine n = 6659, non-migraine n = 81)
7–10 scores
Acute treatment effectiveness, predictors of failure, and daily functional impact
Of 155,894 migraine attacks, information on acute treatment effect was available for 96,742 attacks (Table 3). Among 86,301 (89.2%) attacks treated with triptans, 38.2% achieved acute treatment success within 1 hour and 21.6% within 2 hours, while 40.3% were classified as treatment failures (Table 3). The proportion of acute treatment failure varied by demographic and clinical subgroups (Table 4). Individuals with 10–19 years showed the highest failure rate (62.1%; OR 1.73, 95% CI 1.55–1.94). Treatment failure was more frequent in moderate-to-severe attacks compared with mild attacks. Notably, the likelihood of treatment failure was lower in attacks with premonitory symptoms (OR 0.91, 95% CI 0.89–0.94), aura (OR 0.79, 95% CI 0.75–0.83), or potential triggers (OR 0.93, 95% CI 0.91–0.96). After adjustment for clinically relevant covariates, the risk of acute treatment failure in the adolescent group (10–19 years) decreased significantly (adjusted OR 0.53, 95% CI 0.45–0.62). For the remaining variables, the directions and strengths remained largely consistent with the patterns observed in the crude analysis.
Table 3.
Distribution of acute treatment responses in migraine attacks
| Type of Medication | Migraine Attacks, N |
Acute Treatment Success | Acute Treatment Failure | ||
|---|---|---|---|---|---|
| Pain freedom or relief within 1 hour | Pain freedom or relief within 2 hours | Pain freedom or relief within 4 hours | No Effect | ||
| Any medication use | 96,742 | 33,189 (34.3) | 18,796 (19.4) | 43,711 (45.2) | 1,046 (1.1) |
| Triptans | 86,301 | 32,928 (38.2) | 18,608 (21.6) | 33,804 (39.2) | 961 (1.1) |
Table 4.
Distribution of acute treatment responses according to Demographic and clinical characteristics
| Characteristics | Headache Attacks, N |
Acute Treatment Success | Acute Treatment Failure | Odds ratio (95% CI) | |||
|---|---|---|---|---|---|---|---|
| Pain freedom or relief within 1 hour | Pain freedom or relief within 2 hours | Pain freedom or relief within 4 hours | No Effect | Crude | Adjusted | ||
| Age group | |||||||
| 10s | 1,317 | 305 (23.2) | 194 (14.7) | 797 (60.5) | 21 (1.6) | 1.73 (1.55–1.94) | 0.53 (0.45–0.62) |
| 20s | 13,074 | 4,455 (34.1) | 2,982 (22.8) | 5,515 (42.2) | 122 (0.9) | 0.80 (0.77–0.84) | 0.75 (0.71–0.78) |
| 30s | 29,341 | 9,233 (31.5) | 6,367 (21.7) | 13,300 (45.3) | 441 (1.5) | 0.93 (0.90–0.96) | 1.00 (0.97–1.03) |
| 40s | 31,407 | 10,754 (34.2) | 5,383 (17.1) | 14,990 (47.7) | 280 (0.9) | (reference) | (reference) |
| 50s | 17,647 | 7,443 (42.2) | 2,832 (16.0) | 7,220 (40.9) | 152 (0.9) | 0.76 (0.73–0.79) | 0.87 (0.84–0.91) |
| 60s+ | 2,532 | 653 (25.8) | 650 (25.7) | 1,214 (47.9) | 15 (0.6) | 1.00 (0.92–1.08) | 1.02 (0.94–1.11) |
| Sex | |||||||
| Male | 13,410 | 4,118 (30.7) | 2,938 (21.9) | 6,270 (46.8) | 84 (0.6) | (reference) | (reference) |
| Female | 83,612 | 29,085 (34.8) | 15,858 (19.0) | 37,704 (45.1) | 965 (1.2) | 0.96 (0.92–1.00) | 0.92 (0.89–0.96) |
| Headache intensity | |||||||
| Mild | 26,267 | 9,163 (34.9) | 5,233 (19.9) | 11,755 (44.8) | 116 (0.4) | (reference) | (reference) |
| Moderate | 42,342 | 14,173 (33.5) | 8,258 (19.5) | 19,514 (46.1) | 397 (0.9) | 1.08 (1.04–1.11) | 1.07 (1.04–1.11) |
| Severe | 28,133 | 9,853 (35.0) | 5,305 (18.9) | 12,442 (44.2) | 533 (1.9) | 1.04 (1.00–1.07) | 0.98 (0.94–1.02) |
| Premonitory symptoms | 54,461 | 18,689 (34.3) | 11,115 (20.4) | 24,052 (44.2) | 605 (1.1) | 0.91 (0.89–0.94) | 0.97 (0.94–0.99) |
| Aura | 7,002 | 2,917 (41.7) | 1,226 (17.5) | 2,758 (39.4) | 101 (1.4) | 0.79 (0.75–0.83) | 0.78 (0.74–0.83) |
| Potential triggers | 69,841 | 24,480 (35.1) | 13,390 (19.2) | 31,152 (44.6) | 819 (1.2) | 0.93 (0.91–0.96) | 0.96 (0.93–0.99) |
| Type of treatment | |||||||
| Triptans | 86,301 | 32,928 (38.2) | 18,608 (21.6) | 33,804 (39.2) | 961 (1.1) | (reference) | (reference) |
| Other than triptans | 10,441 | 261 (2.5) | 188 (1.8) | 9907 (94.9) | 85 (0.8) |
32.99 (29.98–36.30) |
47.87 (39.23–58.41) |
Among 211,302 headache attacks, 36.6% were associated with functional impairment in daily activities (Table 5). The most commonly affected activity was decreased work or study efficiency (21.0%), followed by decreased efficiency in household activities (17.2%). Less frequently reported impairments included inability to perform household activities (5.3%), inability to participate in leisure activities (4.9%), and absence from work or school (1.6%). Headache attacks with reduced work/study efficiency (presenteeism) showed higher mean intensity (5.3±2.1 vs. 4.5 ± 2.3, p < 0.01) and an extended mean duration (13.1±22.3 vs. 11.0 ± 16.6, p < 0.01) than those without such impairment (Supplemental Table 3).
Table 5.
The number of days with functional impairment due to migraine by type of daily activities (total headache attacks, n = 211302)
| Type of Daily Activities | Attacks with Functional Impairment, n (%) |
|---|---|
| Any limitations of daily activities | 75,558 (36.6) |
| Absence from Work/School | 3,385 (1.6) |
| Decreased Work/Study Efficiency | 43,375 (21.0) |
| Unable to Perform Household Activities | 10,990 (5.3) |
| Decreased Efficiency in Household Activities | 35,500 (17.2) |
| Unable to Participate in Leisure Activities | 10,011 (4.9) |
Discussion
This nationwide, app-based study of over 23,000 users and 210,000 headache attacks demonstrated several key findings. Compared with individuals without migraine, individuals with migraine were more likely to experience severe intensity, premonitory symptoms, potential triggers, and acute medication use. Despite frequent triptan use, about 40% of treated attacks resulted in treatment failure, particularly among adolescents and in moderate-to-severe attacks, whereas attacks with premonitory symptoms, aura, or triggers showed lower failure rates. More than one-third of attacks were linked to functional impairment, most commonly reduced work, study, or household efficiency, highlighting the daily burden of migraine.
Clinical characteristics and symptom recognition
Among these findings, the high prevalence of severe headache intensity and premonitory symptoms in individuals with migraine is particularly noteworthy. More than half of the individuals with migraine reported attacks of severe intensity, which is consistent with the substantial clinical burden documented in previous studies [3, 16]. The high prevalence of premonitory symptoms (67.7%) and triggers (89.9%) in our study aligns with previous real-time diary and app-based studies, where patients frequently recognized early symptoms via electronic diaries [17], or showed similar pooled estimated relative frequency (66%, 95% CI 45–82) in clinic-based studies [18]. Likewise, app-based diary studies have documented that more than 70% of migraine patients reported triggers as precipitating migraine attacks in real-world settings [9, 19]. These findings underscore the heterogeneity of migraine attacks and highlight the value of patient-reported, real-time data in capturing features that are often underrepresented. Importantly, the ability to document premonitory symptoms and triggers in real time may inform strategies for earlier recognition and more timely initiation of acute therapy.
Treatment response and potential determinants
In our study, even though triptans were used in nearly 90% of migraine attacks, approximately 40% of triptan-treated attacks resulted in treatment failure. While this figure appears lower than the 56% failure rate from the American Migraine Prevalence and Prevention (AMPP) study [20], direct comparison is limited due to differing efficacy endpoint (2-hour pain relief in our study vs. pain freedom in AMPP study). We identified age-related differences in treatment response: individuals in teens (10–19 years) showed the highest likelihood of failure, whereas middle-aged patients exhibited lower failure rates. Several factors might contribute to this disparity. Locally, the less frequent use of triptans in teens (15.2%; Supplemental Table 2) might be due to Korean regulatory restrictions, where most triptans (except almotriptan for those aged 15 or older) are not approved for patients under 18, severely limiting acute treatment options in this age group (10–19 years). Beyond these institutional constraints, our study demonstrated that migraine attacks were more likely to be treated successfully when accompanied by premonitory symptoms, aura, or potential triggers. The TEMPO study demonstrated that early triptan dosing yielded higher 2-hour pain freedom rates compared to late dosing (52.8% vs. 30.2%) [21], and recent evidence suggests that gepants administered during the prodromal phase can effectively prevent moderate-to-severe headaches [22]. However, our findings revealed that adolescents (10–19 years) reported significantly lower frequencies of premonitory symptoms and potential triggers compared with other age groups (34% vs. 49–66%; Supplemental Table 4). This discrepancy might suggest a recognition delay in younger patients. Adolescents often fail to identify a migraine until pain intensity becomes severe [23] and report fewer, less consistent prodromes due to a still-maturing nervous system [24, 25]. This recognition delay inherently leads to treatment delay, causing patients to miss the optimal window for early dosing. Psychiatric comorbidities, such as anxiety and depression, might be associated with increased headache profiles; higher baseline levels of these conditions are associated with increased pain intensity, frequency, and duration in adolescent migraine patients [26]. In addition, lower adherence to treatment or recommendations including poor lifestyle modification [27] and discrepancies between self-reported medication intake and objective measures [28] can further compromise clinical outcomes in this population. Adolescents exhibit more robust central sensitization than adults with allodynia rate reaching up to 96.6% [29]. This heightened physiological sensitivity might be associated with diminished responsiveness to acute treatment. These findings raise the hypothesis that attacks with premonitory symptoms, aura, or triggers may be associated with earlier treatment initiation and better outcomes. However, as our dataset does not record the exact interval from headache onset to medication intake, this interpretation remains speculative. Alternative explanations also warrant consideration. Individuals identifying warning signs may possess superior self-management skills, utilizing non-pharmacological strategies such as lifestyle or stress management to complement drug efficacy [30]. Furthermore, headache subtypes with prominent premonitory features or aura might reflect a distinct biological profile; for instance, higher polygenic risk scores have been linked specifically to better triptan responsiveness [31]. Our observations may thus reflect an inherent biological predisposition toward better treatment response in certain subgroups rather than a direct causal relationship.
Functional impairment and productivity loss
Our results showed that 36.6% of migraine attacks were associated with functional impairment, most often manifesting as reduced efficiency, while complete absence from work or school was relatively uncommon. This underscores presenteeism as a key contributor to migraine-related disability. A study conducted in Japan showed that presenteeism accounted for a significantly higher economic loss ($375.4 USD/year/person) than absenteeism ($238.3 USD/year/person) [32]. In addition, a workplace-based study found that approximately 89% of migraine-related productivity loss was attributable to presenteeism [33]. Together, these findings highlight that the hidden burden of reduced productivity, although less visible than missed workdays, represents the dominant component of migraine-related disability and should be a major focus of future clinical management and health policy.
Strengths and limitations
Our study has several distinct advantages compared to previous digital health research. While most existing headache diary studies have focused on symptom prevalence [34, 35] or trigger identification [9, 17], we evaluated real-world acute treatment response at the attack level using a large-scale, nationwide dataset. By analyzing outcomes from 96,742 treated attacks, we provide robust insights into the effectiveness of real-world migraine management that are often missing from traditional epidemiological surveys. Furthermore, as the majority of large-scale app-based studies have been conducted in Western settings, this research provides essential region-specific evidence from South Korea. This contributes to a more global understanding of migraine burden and highlights how local clinical practices and regulatory environments may influence treatment outcomes.
This study has several limitations. First, potential selection bias and limited generalizability may have been introduced due to the study’s reliance on a voluntary, smartphone-based platform. Because users were predominantly young to middle-aged adults who are more familiar with digital tools, the findings may not be generalizable to older populations who are less likely to utilize mobile applications. Furthermore, the study population may overrepresent individuals with a higher headache burden, higher motivation, or greater health awareness, potentially leading to an overestimation of headache intensity and treatment failure rates. Second, the classification algorithm for migraine and probable migraine used in this study has not been formally validated against a clinical gold standard, such as a neurologist’s diagnosis based on a structured clinical interview. This may introduce misclassification bias, including both false-positive and false-negative identification of cases. While we utilized key diagnostic elements from the ICHD-3 criteria to reflect real-world reporting patterns, the algorithm may not fully capture the clinical complexity typically assessed during in-person evaluations. In particular, the application did not systematically screen for ‘red flags’ that are essential for identifying secondary headache disorders. Consequently, some cases of secondary headaches or tension-type headaches may have been inadvertently misclassified as migraine. Future research incorporating clinician-confirmed validation or validation sub-studies is warranted to further assess the diagnostic performance of this digital tool. Third, although the application records information prospectively and in real time, recall bias and underreporting cannot be entirely excluded, particularly if patients failed to log every attack. Fourth, the absence of data on preventive medication use, psychiatric comorbidities, and socioeconomic status represents a significant limitation. The high treatment failure rates observed in this study may be attributed to inadequate preventive management or undertreated psychological factors. Fifth, the temporal sequence between reported factors (e.g., triggers, premonitory symptoms) and headache onset could not be fully determined, precluding causal inference. Sixth, although this study showed the association of premonitory symptoms, aura, or potential triggers with better treatment outcome, we cannot establish a causal relationship because the application used in this study does not record information on the timing of medication intake relative to headache onset. Seventh, because the app was freely accessible without clinical oversight, some users may have provided incomplete, inconsistent, or non-clinically meaningful data. To mitigate this, we applied predefined data-quality filters during attack construction (including exclusion of single-entry records with insufficient duration information) and used an ICHD-3–based stepwise algorithm for headache classification. Nevertheless, the possibility of non-serious or inaccurate entries—particularly among users without a formal diagnosis or clinical guidance—remains a limitation. Similarly, non-expert users might misinterpret or misreport complex neurological symptoms, such as aura, without professional guidance. The evaluation of acute treatment effectiveness is inherently subjective and may be influenced by individual patient expectations rather than standardized clinical assessments. Eighth, the definition of acute treatment success in this study was based on pain freedom or relief within 2 hours rather than the IHS-standard endpoint of pain freedom [36]. This choice was made to improve the usability of the application for the general population in a real-world setting, as reporting the time to perceived treatment effect is often more straightforward for general users. However, using pain relief may lead to an overestimation of treatment efficacy compared to studies using stricter criteria. Consequently, caution is required when comparing our findings directly with results from controlled clinical trials that utilize pain freedom as a primary endpoint. Finally, although large-scale digital data collection requires rigorous oversight, we prioritized privacy through the use of anonymized identifiers in strict compliance with Korea’s Personal Information Protection Act. We believe this framework effectively balances the utility of real-world data with the ethical necessity of participant protection.
Conclusion
Utilizing a nationwide, app-based headache diary, we demonstrate that nearly 40% of triptan-treated attacks resulted in failure, with the rate reaching a peak of 62.1% in adolescents. More than one-third of attacks caused functional impairment, mainly reduced efficiency (presenteeism). These findings highlight the need for optimized treatment strategies across all age groups, with a particular focus on the adolescent population. We anticipate that this platform-based headache diary will facilitate early intervention via premonitory symptom recognition and serve as a framework for national monitoring and policy-making regarding productivity loss. Future app-based studies should incorporate preventive therapy, comorbidities, long-term outcomes, and more detailed self-reported data to better guide patient-centered migraine management.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Acknowledgements
Not applicable.
Abbreviations
- AMPP
American Migraine Prevalence and Prevention study
- CI
Confidence Interval
- DALY
Disability-Adjusted Life Year(s)
- GBD
Global Burden of Disease (Study)
- ICHD-3
International Classification of Headache Disorders, 3rd edition
- IQR
Interquartile Range
- IRB
Institutional Review Board
- OR
Odds Ratio
- SD
Standard Deviation
- TEMPO
Early dosing and efficacy of triptans in acute migraine treatment study
- YLD
Year(s) Lived with Disability
Author contributions
BK, MKC, HKP, JYS were responsible for study design and data interpretation. JYS and YN performed data analysis. BK, HKP, MKC, JYS, YN, HYG, YJS, and MTL made contributions to manuscript writing and review. All authors read and approved the final manuscript.
Funding
This study is sponsored by Pfizer Pharmaceuticals Korea Limited.
Data availability
The datasets generated and/or analysed during the current study were obtained from the Headache Diary application operated by the Korean Headache Society. The results of this study are proprietary to Pfizer Pharmaceuticals Korea Limited and cannot be shared publicly due to company policy and data ownership restrictions.
Declarations
Ethics approval and consent to participate
This study was approved by the Institutional Review Boards of the following institutions:
Eulji Medical Center Seoul (EMCS IRB approval number: 2024–09-017), Inje University Ilsan Paik Hospital (ISPAIK IRB approval number: 2024–09-018), Severance Hospital, Yonsei University College of Medicine (IRB approval number: 4–2024–1085), Sungkyunkwan University (SKKU IRB approval number: 2024–09-083) Due to the retrospective nature of the study, the requirement for informed consent was waived by all participating IRBs. This study was conducted in accordance with the Declaration of Helsinki.
Consent for publication
Not applicable
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets generated and/or analysed during the current study were obtained from the Headache Diary application operated by the Korean Headache Society. The results of this study are proprietary to Pfizer Pharmaceuticals Korea Limited and cannot be shared publicly due to company policy and data ownership restrictions.