Pain detection using biometric information acquired by a wristwatch wearable device: a pilot study of spontaneous menstrual pain in healthy females
Abstract Objective Pain 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 preliminar...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-01-01
|
| Series: | BMC Research Notes |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13104-025-07098-2 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832586059633917952 |
|---|---|
| author | Hideyuki Hirayama Shiori Yoshida Konosuke Sasaki Emi Yuda Yutaka Yoshida Mitsunori Miyashita |
| author_facet | Hideyuki Hirayama Shiori Yoshida Konosuke Sasaki Emi Yuda Yutaka Yoshida Mitsunori Miyashita |
| author_sort | Hideyuki Hirayama |
| collection | DOAJ |
| description | Abstract Objective Pain 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. Results Five 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. |
| format | Article |
| id | doaj-art-62eecdaea0fd4db9925b79e94026caaf |
| institution | Kabale University |
| issn | 1756-0500 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Research Notes |
| spelling | doaj-art-62eecdaea0fd4db9925b79e94026caaf2025-01-26T12:13:21ZengBMCBMC Research Notes1756-05002025-01-011811610.1186/s13104-025-07098-2Pain detection using biometric information acquired by a wristwatch wearable device: a pilot study of spontaneous menstrual pain in healthy femalesHideyuki Hirayama0Shiori Yoshida1Konosuke Sasaki2Emi Yuda3Yutaka Yoshida4Mitsunori Miyashita5Department of Palliative Nursing, Tohoku University Graduate School of MedicineDepartment of Oncology Nursing, Tohoku University Graduate School of MedicineDepartment of Oncology Nursing, Tohoku University Graduate School of MedicineTohoku University Graduate School of Information SciencesTohoku University Graduate School of Information SciencesDepartment of Palliative Nursing, Tohoku University Graduate School of MedicineAbstract Objective Pain 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. Results Five 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.https://doi.org/10.1186/s13104-025-07098-2PainWearable deviceBiometric informationMenstruation |
| spellingShingle | Hideyuki Hirayama Shiori Yoshida Konosuke Sasaki Emi Yuda Yutaka Yoshida Mitsunori Miyashita Pain detection using biometric information acquired by a wristwatch wearable device: a pilot study of spontaneous menstrual pain in healthy females BMC Research Notes Pain Wearable device Biometric information Menstruation |
| title | Pain detection using biometric information acquired by a wristwatch wearable device: a pilot study of spontaneous menstrual pain in healthy females |
| title_full | Pain detection using biometric information acquired by a wristwatch wearable device: a pilot study of spontaneous menstrual pain in healthy females |
| title_fullStr | Pain detection using biometric information acquired by a wristwatch wearable device: a pilot study of spontaneous menstrual pain in healthy females |
| title_full_unstemmed | Pain detection using biometric information acquired by a wristwatch wearable device: a pilot study of spontaneous menstrual pain in healthy females |
| title_short | Pain detection using biometric information acquired by a wristwatch wearable device: a pilot study of spontaneous menstrual pain in healthy females |
| title_sort | pain detection using biometric information acquired by a wristwatch wearable device a pilot study of spontaneous menstrual pain in healthy females |
| topic | Pain Wearable device Biometric information Menstruation |
| url | https://doi.org/10.1186/s13104-025-07098-2 |
| work_keys_str_mv | AT hideyukihirayama paindetectionusingbiometricinformationacquiredbyawristwatchwearabledeviceapilotstudyofspontaneousmenstrualpaininhealthyfemales AT shioriyoshida paindetectionusingbiometricinformationacquiredbyawristwatchwearabledeviceapilotstudyofspontaneousmenstrualpaininhealthyfemales AT konosukesasaki paindetectionusingbiometricinformationacquiredbyawristwatchwearabledeviceapilotstudyofspontaneousmenstrualpaininhealthyfemales AT emiyuda paindetectionusingbiometricinformationacquiredbyawristwatchwearabledeviceapilotstudyofspontaneousmenstrualpaininhealthyfemales AT yutakayoshida paindetectionusingbiometricinformationacquiredbyawristwatchwearabledeviceapilotstudyofspontaneousmenstrualpaininhealthyfemales AT mitsunorimiyashita paindetectionusingbiometricinformationacquiredbyawristwatchwearabledeviceapilotstudyofspontaneousmenstrualpaininhealthyfemales |