This study uses a noninterventional, retrospective analysis of self-reported data collected via the Migraine Insight mobile app (Multimedia Appendix 1). A collaboration was established with representatives from Migraine Insight to access and extract the relevant dataset. Participants voluntarily subscribed to the app, which offers both free and paid versions with varying features. All users provided informed consent in an “Opt in” manner discussed in the Apps privacy statement of self-reported deidentified data to third parties for research purposes.

An anonymized dataset containing entries from approximately 10,000 US-based users was extracted, covering a 3-year period from November 2018 to November 2021. To ensure data reliability, users who interacted with the app only once were excluded from the analysis. This exclusion was necessary because meaningful pattern recognition—especially temporal associations between triggers and migraine onset—requires multiple data points per participant. Single-use entries do not provide sufficient longitudinal information to evaluate consistent trigger-event relationships.

The dataset included user-reported migraine occurrences and associated potential triggers. Data were analyzed using Microsoft Excel. The variables examined included dietary items, environmental conditions, physiological states, and exposure to electronics. The reported food items were standardized into common categories to address variation in spelling and terminology.

Food-related triggers were classified into high-risk and low-risk groups based on the frequency with which they were reported in association with migraine events. High-risk food items were defined as those with a prevalence of ≥2% in the dataset, while low-risk items were those with <2% prevalence. The 2% threshold was chosen to balance inclusivity with relevance, highlighting commonly reported foods that could meaningfully impact population-level analyses. High-risk foods identified included coffee, tea, chocolate, wine, and cheese. Low-risk foods included meat, food additives, fruits, nuts, carbohydrates, soft beverages, beer, and high-sodium foods. The frequencies of reported high-risk foods and corresponding migraine onsets were further analyzed, with duplicates from the same user excluded to prevent skewed results from repetitive entries.

The investigations of this study were compliant with ethical practices established by the Nuremburg Code. Participants of the Migraine Insight tracking app provided consent for the utilization of data when agreeing to terms of use. To ensure the protection of user privacy, all identifying information was removed from the dataset prior to analysis. None of the data points, contained names, contact information, GPS coordinates, or other personally identifiable information were preserved. The anonymization process was designed to align with best practices in digital health research and data ethics, minimizing the risk of reidentification. Given the retrospective nature of the study and the use of fully anonymized, pre-existing data, the project was reviewed by the institutional review board at St. George’s University and was deemed to meet the criteria for exemption from full review. The ethical considerations in this study reflect a commitment to participant confidentiality, data security, and the responsible use of mobile health technologies in clinical research.

This study identifies high-risk environmental, dietary, and behavioral factors preceding migraine onset by using data from the Migraine Insight app. The patterns observed align with previous research highlighting certain foods and environmental conditions as potential migraine triggers. High-risk items, including chocolate, tea, coffee, wine, and cheese, emerged as significant contributors, corroborating findings by Martin and Vij [10,11], who identified chocolate, caffeine, and alcohol as common dietary triggers. Similarly, data from Migraine Insight users support research by Nowaczewska et al [12], emphasizing the role of chocolate and aged cheese in migraine risk.

Among dietary triggers, tea and coffee were the second and third most frequently reported triggers (Table 2). There were 127 reports of tea triggering migraines from 45 users, 9 of whom also reported coffee as a trigger. In total, 123 users reported migraines triggered by coffee, with 266 occurrences. Tea consumption led to migraines in 59.4% (75/127) of the users who reported its intake, while coffee triggered migraines in 52.2% (139/266) of the users. One possible explanation for tea’s higher likelihood of inducing migraines is its greater tannin content compared to coffee. Tannins, a type of flavonoid phenol, are found in various dietary sources [13]. A study analyzing tannin levels in both beverages found that roasted coffee beans contain 18 (SD 1.7) mg·g⁻¹ of tannins, whereas tea contains nearly twice that amount at 37 (SD 2.6) mg·g⁻¹ [14]. Tannins are also present in high concentrations in chocolate, wine, and dairy—all of which were frequently reported as migraine triggers [15].

Beyond dietary influences, nondietary factors such as weather, light, and stress have been widely recognized as migraine triggers. Stress, in particular, is commonly cited, with a study of over 1200 participants reporting that 80% identified stress as a migraine trigger [16-18]. Similarly, an analysis of 1027 individuals who met the International Headache Society's migraine criteria found that 53.2% reported weather changes as a precipitating factor [19]. Another study identified weather as the second most frequently perceived trigger after stress [18]. Light exposure also emerged as a significant factor, particularly among migraineurs with aura, where 75% identified light as a trigger compared to 35% of those without aura [20].

The use of a mobile tracking app for data collection represents a novel and valuable approach in migraine research. Unlike traditional paper-based diaries, which are prone to recall bias, real-time data logging offers a more accurate and comprehensive view of potential triggers. This method, as demonstrated in our study, aligns with existing literature and enhances the reliability of self-reported data [7,21]. Furthermore, the impact of COVID-19 lockdowns on stress-induced migraine attacks, as studied by Kato et al [9], underscores the value of leveraging unbiased datasets in migraine research.

While this study provides meaningful insights into the dietary, environmental, and behavioral factors preceding migraines, certain limitations must be acknowledged. The reliance on self-reported data introduces the potential for recall bias, as participants may not accurately remember or report their exposures. Additionally, the self-selecting nature of the participant pool may lead to selection bias, limiting the generalizability of the findings. Although mobile apps provide real-time data collection that reduces retrospective inaccuracies, they also introduce challenges such as inconsistent user engagement and variations in reporting accuracy. Traditional methods such as structured clinical assessments or controlled dietary studies may offer more standardized data but lack the real-world applicability and convenience of mobile tracking. Future research should aim to balance these methodologies by incorporating objective biomarkers or clinician-verified reports alongside mobile app data. Expanding the participant base to encompass more diverse populations and employing more controlled methodologies will further strengthen the validity of these findings. Addressing these gaps will contribute to a deeper understanding of migraine pathophysiology and improved management strategies.

This study highlights the potential of mobile health apps such as Migraine Insight to identify high-risk dietary, environmental, and behavioral factors associated with migraine onset. The findings validate existing literature by confirming that commonly cited triggers—especially chocolate, tea, and coffee—remain prominent among real-world users. Notably, chocolate demonstrated a statistically significant association with migraine episodes, reinforcing its relevance in dietary counseling for migraine prevention.

Beyond individual triggers, this research underscores the utility of app-based, real-time data collection as a reliable alternative to traditional migraine diaries. The ability to capture consistent, personalized data offers new opportunities for understanding complex, multifactorial patterns in migraine occurrence. While limitations related to self-reporting, recall bias, and user engagement remain, the insights gained from large-scale, user-generated datasets provide a strong foundation for further investigation.

Future research should aim to integrate app-based tools with clinical assessments and objective measures such as biomarkers to enhance validity. Expanding participation to include more diverse populations and exploring longitudinal outcomes will further strengthen the evidence base. Ultimately, mobile tracking technologies hold significant promise in advancing personalized migraine management and improving the quality of life for individuals living with these conditions.