Wearable technology in the sports medicine clinic to guide the return-to-play and performance protocols of athletes following a COVID-19 diagnosis

The coronavirus disease 2019 (COVID-19) pandemic has enabled the adoption of digital health platforms for self-monitoring and diagnosis. Notably, the pandemic has had profound effects on athletes and their ability to train and compete. Sporting organizations worldwide have reported a significant increase in injuries manifesting from changes in training regimens and match schedules resulting from extended quarantines. While current literature focuses on the use of wearable technology to monitor athlete workloads to guide training, there is a lack of literature suggesting how such technology can mediate the return to sport processes of athletes infected with COVID-19. This paper bridges this gap by providing recommendations to guide team physicians and athletic trainers on the utility of wearable technology for improving the well-being of athletes who may be asymptomatic, symptomatic, or tested negative but have had to quarantine due to a close exposure. We start by describing the physiologic changes that occur in athletes infected with COVID-19 with extended deconditioning from a musculoskeletal, psychological, cardiopulmonary, and thermoregulatory standpoint and review the evidence on how these athletes may safely return to play. We highlight opportunities for wearable technology to aid in the return-to-play process by offering a list of key parameters pertinent to the athlete affected by COVID-19. This paper provides the athletic community with a greater understanding of how wearable technology can be implemented in the rehabilitation process of these athletes and spurs opportunities for further innovations in wearables, digital health, and sports medicine to reduce injury burden in athletes of all ages. © The Author(s) 2023

Neuromonitoring in neonatal critical care part II: extremely premature infants and critically ill neonates

Abstract: Neonatal intensive care has expanded from cardiorespiratory care to a holistic approach emphasizing brain health. To best understand and monitor brain function and physiology in the neonatal intensive care unit (NICU), the most commonly used tools are amplitude-integrated EEG, full multichannel continuous EEG, and near-infrared spectroscopy. Each of these modalities has unique characteristics and functions. While some of these tools have been the subject of expert consensus statements or guidelines, there is no overarching agreement on the optimal approach to neuromonitoring in the NICU. This work reviews current evidence to assist decision making for the best utilization of these neuromonitoring tools to promote neuroprotective care in extremely premature infants and in critically ill neonates. Neuromonitoring approaches in neonatal encephalopathy and neonates with possible seizures are discussed separately in the companion paper. Impact: For extremely premature infants, NIRS monitoring has a potential role in individualized brain-oriented care, and selective use of aEEG and cEEG can assist in seizure detection and prognostication.For critically ill neonates, NIRS can monitor cerebral perfusion, oxygen delivery, and extraction associated with disease processes as well as respiratory and hypodynamic management. Selective use of aEEG and cEEG is important in those with a high risk of seizures and brain injury.Continuous multimodal monitoring as well as monitoring of sleep, sleep–wake cycling, and autonomic nervous system have a promising role in neonatal neurocritical care

Comparing the Relative Strengths of EEG and Low-Cost Physiological Devices in Modeling Attention Allocation in Semiautonomous Vehicles

As semiautonomous driving systems are becoming prevalent in late model vehicles, it is important to understand how such systems affect driver attention. This study investigated whether measures from low-cost devices monitoring peripheral physiological state were comparable to standard EEG in predicting lapses in attention to system failures. Twenty-five participants were equipped with a low-fidelity eye-tracker and heart rate monitor and with a high-fidelity NuAmps 32-channel quick-gel EEG system and asked to detect the presence of potential system failure while engaged in a fully autonomous lane changing driving task. To encourage participant attention to the road and to assess engagement in the lane changing task, participants were required to: (a) answer questions about that task; and (b) keep a running count of the type and number of billboards presented throughout the driving task. Linear mixed effects analyses were conducted to model the latency of responses reaction time (RT) to automation signals using the physiological metrics and time period. Alpha-band activity at the midline parietal region in conjunction with heart rate variability (HRV) was important in modeling RT over time. Results suggest that current low-fidelity technologies are not sensitive enough by themselves to reliably model RT to critical signals. However, that HRV interacted with EEG to significantly model RT points to the importance of further developing heart rate metrics for use in environments where it is not practical to use EEG

Effect of pulsed electromagnetic fields (pemfs) on muscle activity, tissue oxygenation and vo2 during exercise.

PEMF are a medical and non-invasive therapy successfully used for clinical treatments of bone disease, due to the piezoelectric effect that improve bone mass and density, by the stimulation of osteoblastogenesis, with modulation of calcium storages and mineral metabolism. PEMF enhance tissue oxygenation, microcirculation and angiogenesis, in rats and cells erythrocytes, in cells-free assay. Such responses could be caused by a modulation of nitric oxide signal and interaction between PEMF and Ca2+/NO/cGMP/PKG signal. PEMF improve blood flow velocity of smallest vein without changing their diameter. PEMF therapy helpful in patients with diabetes, due to increased microcirculation trough enhance capillary blood velocity and diameter. We investigated the influence of stimulation on muscular activity, tissue oxygenation and pulmonary VO2, during exercise, on different intensity, as heavy or moderate, different subjects, as a athlete or sedentary, and different sport activity, as a cycling or weightlifting. In athletes, we observed a tendency for a greater change and a faster kinetic of HHb concentration. PEMF increased the velocity and the quantity of muscle O2 available, leading to accelerate the HHb kinetics. Stimulation induced a bulk muscle O2 availability and a greater muscle O2 extraction, leading to a reduced time delay of the HHb slow component. Stimulation increased the amplitude of muscle activity under different conditions, likely caused by the effect of PEMF on contraction mechanism of muscular fibers, by the change of membrane permeability and Ca2+ channel conduction. In athletes, we observed an increase of overall activity during warm-up. In sedentary people, stimulation increased the magnitude of muscle activity during moderate constant-load exercise and warm-up. In athletes and weightlifters, stimulation caused an increase of blood lactate concentration during exercise, confirming a possible influence of stimulation on muscle activity and on glycolytic metabolism of type-II muscular fibers

Ballistocardiography : physically-based modeling to bridge physiology and technology

The ballistocardiogram (BCG) captures the motion of the center of mass (CoM) of the human body resulting from the blood motion within the circulatory system. The BCG signal reflects the status of the cardiovascular system as a whole and, for this reason, it offers a more holistic evaluation of cardiovascular performance than traditional markers, such as electrocardiography or echocardiography. In addition, the acquisition of BCG signals is not invasive, can be performed with several devices -such as accelerometers, chairs, hydraulic system- and does not require body contact. However, the utilization of the BCG as a clinical diagnosis tool and monitoring method is currently hindered by the absence of standardized methods to link the motion of the CoM of the human body, which constitutes the physiological BCG (pBCG), with the BCG signal acquired with sensing devices, which constitute the measured BCG (mBCG). To address this issue, in the first part of the present work we provide a formal definition of pBCG and mBCG, which will be then utilized to (i) define the physical connection between the mBCG obtained with two sensing devices, i.e. the suspended bed and the load cell system, and the pBCG signal and (ii) reconstruct the individual CoM motion. In the second part of the thesis, we focus on the synergistic combination between the physiology behind the BCG signal and the physics of the sensing devices, which may lead to novel clinical applications. In particular, we propose a cuff-less method for absolute pulse pressure assessment via the synergistic integration of two components, namely (i) theoretical simulations of cardiovascular physiology by means of a mathematical closed-loop model of the cardiovascular system, and (ii) synchronous ECG, SCG and BCG data acquired in our laboratory. Then, we present an evolutionary algorithm aimed at individualizing the closed-loop model of the cardiovascular system, with which we will also provide an estimate of the arterial pressure. Finally, in the last part of the thesis, we draw the conclusion of this study, showing how the integration of the mathematical modeling alongside with clinical studies can improve the understanding of the BCG signal and actively contributing to the development of new clinical monitoring solution.Includes bibliographical references (pages 80-84)

Enhancing brain/neural-machine interfaces for upper limb motor restoration in chronic stroke and cervical spinal cord injury

Operation of assistive exoskeletons based on voluntary control of sensorimotor rhythms (SMR, 8-12 Hz) enables intuitive control of finger or arm movements in severe paralysis after chronic stroke or cervical spinal cord injury (SCI). To improve reliability of such systems outside the laboratory, in particular when brain activity is recorded non-invasively with scalp electroencephalography (EEG), a hybrid EEG/electrooculography (EOG) brain/neural-machine interface (B/NMI) was recently introduced. Besides providing assistance, recent studies indicate that repeated use of such systems can trigger neural recovery. However, important prerequisites have to achieved before broader use in clinical settings or everyday life environments is feasible. Current B/NMI systems predominantly restore hand function, but do not allow simultaneous control of more proximal joints for whole-arm motor coordination as required for most stroke survivors suffering from paralysis in the entire upper limb. Besides paralysis, cognitive impairments including post-stroke fatigue due to the brain lesion reduce the capacity to maintain effortful B/NMI control over a longer period of time. This impedes the applicability in daily life assistance and might even limits the efficacy of neurorehabilitation training. In contrast to stroke survivors, tetraplegics due to cervical SCI lack motor function in both hands. Given that most activities of daily living (ADL) involve bimanual manipulation, e.g., to open the lid of a bottle, bilateral exoskeleton control is required but was not shown yet in tetraplegics. To further enhance B/NMI systems, we first investigated whether B/NMI whole-arm exoskeleton control in hemiplegia after chronic stroke is feasible and safe. In contrast to simple grasping, control of more complex tasks involving the entire upper limb was not feasible with established B/NMIs because high- dimensionality of such multiple joint systems exceeds the bandwidth of these interfaces. Thus, we blended B/NMI control with vision-guidance to receive a semiautonomous whole-arm exoskeleton control. Such setup allowed to divide ADL tasks into a sequence of EEG/EOG-triggered sub-tasks reducing complexity for the user. While, for instance, a drinking task was resolved into EOG-induced reaching, lifting and placing back the cup, grasping and releasing movements were based on intuitive SMR control. Feasibility of such shared vision-guided B/NMI control was assumed when executions were initialized within 3 s (fluent control) and a minimum of 75 % of subtasks were executed within that time (reliable control). We showed feasibility in healthy subjects as well as stroke survivors without report of any side effects documenting safe use. Similarly, feasibility and safety of bilateral B/NMI control after cervical SCI was evaluated. To enable bilateral B/NMI control, established EEG-based grasping and EOG-based releasing or stop commands were complemented with a novel EOG command allowing to switch laterality by performing prolonged horizontal eye movements (>1 s) to the left or to the right. Study results with healthy subjects and tetraplegics document fluent initialization of grasping motions below 3 s as well as safe use as unintended grasping could be stopped before a full motion was conducted. Superiority of novel bilateral control was documented by a higher accuracy of up to 22 % in tetraplegics compared to a bilateral control without prolonged EOG command. Lastly, as reliable B/NMI control is cognitively demanding, e.g., by imagining or attempting the desired movements, we investigated whether heart rate variability (HRV) can be used as biomarker to predict declining control performance, which is often reported in stroke survivors due to their cognitive impairments. Referring to the close brain-heart connection, we showed in healthy subjects that a decline in HRV is specific as well as predictive to a decline in B/NMI control performance within a single training session. The predictive link was revealed by a Granger-causality analysis. In conclusion, we could demonstrate important enhancements in B/NMI control paradigms including complex whole-arm exoskeleton control as well as individual performance monitoring within a training session based on HRV. Both achievements contribute to broaden the use as a standard therapy in stroke neurorehabilitation. Especially the predictive characteristic of HRV paves the way for adaptive B/NMI control paradigms to account for individual differences among impaired stroke survivors. Moreover, we also showed feasibility and safety of a novel implementation for bilateral B/NMI control, which is necessary for reliable operation of two hand-exoskeletons for bimanual ADLs after SCI

Determining Clinically Relevant Measures for Evaluating Recovery in Collegiate Athletes using Heart Rate Variability and RESTQ-Sport.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

Mutual information measures applied to EEG signals for sleepiness characterization

Excessive daytime sleepiness (EDS) is one of the main symptoms of several sleep related disorders with a great impact on the patient lives. While many studies have been carried out in order to assess daytime sleepiness, the automatic EDS detection still remains an open problem. In this work, a novel approach to this issue based on non-linear dynamical analysis of EEG signal was proposed. Multichannel EEG signals were recorded during five maintenance of wakefulness (MWT) and multiple sleep latency (MSLT) tests alternated throughout the day from patients suffering from sleep disordered breathing. A group of 20 patients with excessive daytime sleepiness (EDS) was compared with a group of 20 patients without daytime sleepiness (WDS), by analyzing 60-s EEG windows in waking state. Measures obtained from cross-mutual information function (CMIF) and auto-mutual-information function (AMIF) were calculated in the EEG. These functions permitted a quantification of the complexity properties of the EEG signal and the non-linear couplings between different zones of the scalp. Statistical differences between EDS and WDS groups were found in ß band during MSLT events (. p-value<0.0001). WDS group presented more complexity than EDS in the occipital zone, while a stronger nonlinear coupling between occipital and frontal zones was detected in EDS patients than in WDS. The AMIF and CMIF measures yielded sensitivity and specificity above 80% and AUC of ROC above 0.85 in classifying EDS and WDS patients.Peer ReviewedPostprint (author's final draft

Wearable Technology and Analytics as a Complementary Toolkit to Optimize Workload and to Reduce Injury Burden

Wearable sensors enable the real-time and non-invasive monitoring of biomechanical, physiological, or biochemical parameters pertinent to the performance of athletes. Sports medicine researchers compile datasets involving a multitude of parameters that can often be time consuming to analyze in order to create value in an expeditious and accurate manner. Machine learning and artificial intelligence models may aid in the clinical decision-making process for sports scientists, team physicians, and athletic trainers in translating the data acquired from wearable sensors to accurately and efficiently make decisions regarding the health, safety, and performance of athletes. This narrative review discusses the application of commercial sensors utilized by sports teams today and the emergence of descriptive analytics to monitor the internal and external workload, hydration status, sleep, cardiovascular health, and return-to-sport status of athletes. This review is written for those who are interested in the application of wearable sensor data and data science to enhance performance and reduce injury burden in athletes of all ages