JMIR Publications

ABSTRACT

Background:

Despite the substantial burden imposed by premenstrual syndrome (PMS) on women’s quality of life, clinical diagnosis remains dependent on subjective self-assessment. The emergence of wearable technology enables continuous collection of physiological metrics that may serve as objective indicators of PMS symptom severity.

Objective:

This study evaluated the feasibility of using Fitbit-derived heart rate (HR) and autonomic indices, along with interstitial glucose data obtained from FreeStyle Libre, as objective digital biomarkers for PMS diagnosis and monitoring.

Methods:

This prospective, longitudinal observational study enrolled 122 women aged 18-60 years in Japan. Physiological data, including HR, HR variability, and interstitial glucose levels, were collected using Fitbit Inspire 3 and FreeStyle Libre devices over 14 weeks. PMS severity was assessed using the Menstrual Distress Questionnaire (MDQ).

Results:

Participants with severe PMS symptoms exhibited higher autonomic nervous system (ANS) markers such as root mean square of successive differences of RR intervals (RMSSD) and the standard deviation of normal-to-normal intervals and lower sleep HRs during the luteal phase compared with those who had milder symptoms (eg, sleep RMSSD: P=.002; sleep mean HR: P=.007). Furthermore, beginning 3 days before menstruation, participants with severe PMS showed a decline in ANS markers accompanied by an upward trend in HR, whereas those with mild symptoms exhibited the opposite pattern (eg, sleep RMSSD: P=.002; sleep mean HR: P=.005).

Conclusions:

Sleep ANS markers and HRs serve as objective measures for assessing PMS symptoms. Continuous monitoring using wearable devices offers a promising, noninvasive method for objective PMS diagnosis and personalized health management. Clinical Trial: UMIN Clinical Trials Registry UMIN000051467