The ongoing COVID-19 pandemic highlighted a significant interplay between cardiovascular disease (CVD), COVID-19 related inflammatory status, and depression. Cardiovascular (CV) injury is responsible for a substantial percentage of COVID-19 deaths while COVID-19 social restrictions emerged as a non-negligible risk factor for CVD as well as a variety of mental health issues, and in particular, depression. Inflammation seems to be a shared condition between these two disorders. Gender represents a potential modifying factor both in CVD and depression, as well as in COVID-19 short- and long-term outcomes, particularly in cases involving long-term COVID complications. Results from emerging studies indicate that COVID-19 pandemic affected male and female populations in different ways. Women seem to experience less severe short-term complications but suffer worse long-term COVID complications, including depression, reduced physical activity, and deteriorating lifestyle habits, all of which may impact CV risk. Here, we summarize the current state of knowledge about the interplay between COVID-19, depression, and CV risk in women.

Ambulatory diffuse optical tomography (aDOT) is based on near-infrared spectroscopy (NIRS) and enables three-dimensional imaging of regional hemodynamics and oxygen consumption during a person’s normal activities. Although NIRS has been previously used for muscle assessment, it has been notably limited in terms of the number of channels measured, the extent to which subjects can be ambulatory, and/or the ability to simultaneously acquire synchronized auxiliary data such as electromyography (EMG) or electrocardiography (ECG). We describe the development of a prototype aDOT system, called NINscan-M, capable of ambulatory tomographic imaging as well as simultaneous auxiliary multimodal physiological monitoring. Powered by four AA size batteries and weighing 577 g, the NINscan-M prototype can synchronously record 64-channel NIRS imaging data, eight channels of EMG, ECG, or other analog signals, plus force, acceleration, rotation, and temperature for 24+ h at up to 250 Hz. We describe the system’s design, characterization, and performance characteristics. We also describe examples of isometric, cycle ergometer, and free-running ambulatory exercise to demonstrate tomographic imaging at 25 Hz. NINscan-M represents a multiuse tool for muscle physiology studies as well as clinical muscle assessment.

The brain is a central component of cognitive and physical human performance. Measures, including functional brain activation, cerebral perfusion, cerebral oxygenation, evoked electrical responses, and resting hemodynamic and electrical activity are all related to, or can predict, health status or performance decrements. However, measuring brain physiology typically requires large, stationary machines that are not suitable for mobile or self-monitoring. Moreover, when individuals are ambulatory, systemic physiological fluctuations-e.g., in heart rate, blood pressure, skin perfusion, and more-can interfere with noninvasive brain measurements. In efforts to address the physiological monitoring and performance assessment needs for astronauts during spaceflight, we have developed easy-to-use, wearable prototypes, such as NINscan, for near-infrared scanning, which can collect synchronized multimodal physiology data, including hemodynamic deep-tissue imaging (including brain and muscles), electroencephalography, electrocardiography, electromyography, electrooculography, accelerometry, gyroscopy, pressure, respiration, and temperature measurements. Given their self-contained and portable nature, these devices can be deployed in a much broader range of settings-including austere environments-thereby, enabling a wider range of novel medical and research physiology applications. We review these, including high-altitude assessments, self-deployable multimodal e.g., (polysomnographic) recordings in remote or low-resource environments, fluid shifts in variable-gravity, or spaceflight analog environments, intracranial brain motion during high-impact sports, and long-duration monitoring for clinical symptom-capture in various clinical conditions. In addition to further enhancing sensitivity and miniaturization, advanced computational algorithms could help support real-time feedback and alerts regarding performance and health.