Evidence map›Paper›PMID 41409398›Full record

ArticleJournal of pain research2025

Episodic Migraine Pain Curves: Real-Time Smartphone-Based Analysis and Clinical Implications.

Ana Beatriz Gago-Veiga, Alicia Gonzalez-Martinez, Sonia Quintas, Alba Vieira, Javier Gálvez-Goicuría, Ancor Sanz-García, Jose L Ayala, Monica Sobrado, Jose Vivancos, Josué Pagan

Abstract read
In one paragraph

Article in Journal of pain research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Ana Beatriz Gago-VeigaNeurology Department, Hospital Universitario de La Princesa & Instituto de Investigación Sanitaria Princesa (IIS-Princesa), Madrid, Spain.ORCID 0000-0002-0038-3406
Alicia Gonzalez-MartinezNeurology Department, Hospital Universitario de La Princesa & Instituto de Investigación Sanitaria Princesa (IIS-Princesa), Madrid, Spain.ORCID 0000-0002-1228-1503
Sonia QuintasNeurology Department, Hospital Universitario de La Princesa & Instituto de Investigación Sanitaria Princesa (IIS-Princesa), Madrid, Spain.ORCID 0000-0003-0622-8228
Alba VieiraNeurology Department, Hospital Universitario de La Princesa & Instituto de Investigación Sanitaria Princesa (IIS-Princesa), Madrid, Spain.ORCID 0000-0002-7211-4862
Javier Gálvez-GoicuríaBrainguard SL, Pozuelo de Alarcón, Spain.ORCID 0000-0001-6565-3705
Ancor Sanz-GarcíaUnidad de Análisis de datos, Instituto de Investigación Sanitaria (IIS-Princesa), Hospital Universitario de la Princesa, Madrid, Spain.ORCID 0000-0002-5024-5108
Jose L AyalaComputer Architecture and Automation Department, Universidad Complutense de Madrid, Madrid, Spain.ORCID 0000-0001-7236-5330
Monica SobradoNeurology Department, Hospital Universitario de La Princesa & Instituto de Investigación Sanitaria Princesa (IIS-Princesa), Madrid, Spain.
Jose VivancosNeurology Department, Hospital Universitario de La Princesa & Instituto de Investigación Sanitaria Princesa (IIS-Princesa), Madrid, Spain.
Josué PaganElectronic Engineering Department, Universidad Politécnica de Madrid, Madrid, Spain.ORCID 0000-0002-8357-7950

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Migraine involves a wide range of symptoms, with pain being one of the most prominent and disabling. While the ICHD-3 provides a well-established classification framework, exploring pain dynamics such as onset, duration, and intensity may offer additional insights to support the development of more personalized treatment strategies. Patients and Methods: A previous study categorized episodic migraine patients based on pain curve dynamics (onset, duration, and intensity). This study analyzes socio-demographic and clinical characteristics across the previously identified subgroups. Patients met ICHD-3 criteria and used a smartphone app for real-time data collection on migraine parameters, including onset, pain duration and intensity, symptoms, triggers, and treatment responses. The aim was to identify differences across subgroups in these variables. Results: The study included 51 participants, mostly women, with a mean age of 39.1 years. Four distinct migraine patterns emerged based on pain dynamics: Type 1 (High intensity), Type 2 (Acute onset and intense), Type 3 (Prolonged and intense), and Type 4 (Low intensity). Significant associations were found between curve types and demographic factors such as sex, aura presence, and cardiovascular risk. Although food-related triggers were common across groups, their distribution did not significantly differ. However, prolonged migraines posed unique challenges regarding treatment timing and effectiveness. Conclusion: Pain curve-based classification reveals clinically relevant migraine subtypes. Significant differences were observed across curve types in sex distribution, aura prevalence, associated symptoms such as nausea and phonophobia, and treatment response profiles. This approach, supported by real-time data, may advance personalized migraine management.

Indexed as

cluster analysismigraine disordersmobile applicationspain measurementtreatment outcome

Identifiers

PMID41409398
PMCPMC12705317

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.