Characterization of the Autonomic Nervous System Activity in Females Classified According to Mood Scores During the Follicular Phase

TL;DR

This study demonstrates that mood scores during the follicular phase can classify women into PMS and non-PMS groups and track autonomic nervous system activity changes using a simple pulse wave device.

Contribution

It introduces a straightforward method to classify PMS status and monitor autonomic activity with a simple device based on mood scores during the menstrual cycle.

Findings

Participants with low mood showed reduced parasympathetic activity.

Mood-based classification correlates with autonomic nervous system changes.

Feasibility of using pulse wave measurements for PMS monitoring.

Abstract

Many sexually mature females suffer from premenstrual syndrome (PMS), but effective coping methods for PMS are limited due to the complexity of symptoms and unclear pathogenesis. Awareness has shown promise in alleviating PMS symptoms but faces challenges in long-term recording and consistency. Our research goal is to establish a convenient and simple method to make individual female aware of their own psychological, and autonomic conditions. In previous research, we demonstrated that participants could be classified into non-PMS and PMS groups based on mood scores obtained during the follicular phase. However, the properties of neurophysiological activity in the participants classified by mood scores have not been elucidated. This study aimed to classify participants based on their scores on a mood questionnaire during the follicular phase and to evaluate their autonomic nervous system (ANS) activity using a simple device that measures pulse waves from the earlobe. Participants were grouped into Cluster I (high positive mood) and Cluster II (low mood). Cluster II participants showed reduced parasympathetic nervous system activity from the follicular to the menstrual phase, indicating potential PMS symptoms. The study demonstrates the feasibility of using mood scores to classify individuals into PMS and non-PMS groups and monitor ANS changes across menstrual phases. Despite limitations such as sample size and device variability, the findings highlight a promising avenue for convenient PMS self-monitoring.