Tracking of menstrual cycles and prediction of the fertile window via measurements of basal body temperature and heart rate as well as machine-learning algorithms.

ABSTRACT:

Background

Fertility awareness and menses prediction are important for improving fecundability and health management. Previous studies have used physiological parameters, such as basal body temperature (BBT) and heart rate (HR), to predict the fertile window and menses. However, their accuracy is far from satisfactory. Additionally, few researchers have examined irregular menstruators. Thus, we aimed to develop fertile window and menstruation prediction algorithms for both regular and irregular menstruators.

Methods

This was a prospective observational cohort study conducted at the International Peace Maternity and Child Health Hospital in Shanghai, China. Participants were recruited from August 2020 to November 2020 and followed up for at least four menstrual cycles. Partic

SUBMITTER: Yu JL 

PROVIDER: S-EPMC9375297 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature