ArticleBMC research notes2025
Pain detection using biometric information acquired by a wristwatch wearable device: a pilot study of spontaneous menstrual pain in healthy females.
Article in BMC research notes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- AI applications across the cancer pain care Continuum: applications and future directions.Frontiers in pain research (Lausanne, Switzerland) · 2026Review
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
objectivePain is subjective, and self-reporting pain might be challenging. Studies conducted to detect pain using biological signals and real-time self-reports pain are limited. We evaluated the feasibility of collecting pain data on healthy females' menstrual pain and conducted preliminary analysis.
resultsFive healthy adult females participated. They wore two wristwatch devices (Silmee and Fitbit) and a Holter ECG (electrocardiogram) during menstruation to record the pain intensity and timing. Subsequently, we analyzed the correlation between heart and pulse rates and assessed pre- and post-pain biometric differences. We collected sixty pain records from five participants. The correlation coefficients between heart rate and pulse rate ranged from 0.79 to 0.95 with Holter ECG vs. Fitbit and 0.32 to 0.74 with Holter ECG vs. Silmee. Analysis revealed significant changes in motion frequency post-pain (p = 0.04). For abdominal pain with a numerical rating scale score of ≥ 4 (n = 13), motion frequency (p < 0.001) and pulse rate (p = 0.02) showed significant differences post-pain compared to baseline values. Healthy females could wear the wristwatch device in daily life and report pain in real time. Wristwatch devices can effectively collect biological data to detect moderate pain by focusing on acceleration and pulse rate.
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