A survey assessing the impact of symptoms related to the menstrual cycle and perceptions of workplace productivity: considerations for employer-sponsored menstrual health programs.

Background: Hormonal-related symptoms experienced during natural or contraceptive-driven menstrual cycles have implications on work-related productivity; however, employer-sponsored menstrual health resources are widely unavailable. Actionable research-based evidence is needed to develop menstrual health programs that proactively help working females mitigate their hormonal-related symptoms and optimize their hormone profiles and work-related performance. This study sought to evaluate the prevalence and severity of hormonal-related symptoms and assess the directional impact of hormonal-related symptoms on work-related productivity across cyclical hormone phases.

Absolute Reliability of Gait Parameters Acquired With Markerless Motion Capture in Living Domains.

: To examine the between-day absolute reliability of gait parameters acquired with Theia3D markerless motion capture for use in biomechanical and clinical settings. : Twenty-one (7 M,14 F) participants aged between 18 and 73 years were recruited in community locations to perform two walking tasks: self-selected and fastest-comfortable walking speed. Participants walked along a designated walkway on two separate days.

Feasibility of Markerless Motion Capture for Three-Dimensional Gait Assessment in Community Settings.

Three-dimensional (3D) kinematic analysis of gait holds potential as a digital biomarker to identify neuropathologies, monitor disease progression, and provide a high-resolution outcome measure to monitor neurorehabilitation efficacy by characterizing the mechanisms underlying gait impairments. There is a need for 3D motion capture technologies accessible to community, clinical, and rehabilitation settings. Image-based markerless motion capture (MLMC) using neural network-based deep learning algorithms shows promise as an accessible technology in these settings.