The aim of the study herein reported was to review mobile health (mHealth) technologies and explore their use to monitor and mitigate the effects of the COVID-19 pandemic. Methods: A Task Force was assembled by recruiting individuals with expertise in electronic Patient-Reported Outcomes (ePRO), wearable sensors, and digital contact tracing technologies. Its members collected and discussed available information and summarized it in a series of reports. Results: The Task Force identified technologies that could be deployed in response to the COVID-19 pandemic and would likely be suitable for future pandemics. Criteria for their evaluation were agreed upon and applied to these systems. Conclusions: mHealth technologies are viable options to monitor COVID-19 patients and be used to predict symptom escalation for earlier intervention. These technologies could also be utilized to monitor individuals who are presumed noninfected and enable prediction of exposure to SARS-CoV-2, thus facilitating the prioritization of diagnostic testing.

Telemedicine has evolved over the past 50 years with video consultations and tele-health (TH) mobile apps now widely used to support care in the management of chronic conditions, but are infrequently utilized in acute conditions such as emergencies. In the wake of the COVID-19 pandemic, demand is growing for video consultations as they minimize health provider-patient interactions and thereby risk of infection. Advanced applications such as tele-ultrasound (TUS) have not yet gained a foothold despite having achieved technical maturity and availability of software offering from numerous companies for TUS for their respective portable ultrasound devices. However, ultrasound is indispensable for triage in emergencies and also offers distinct advantages in the diagnosis of COVID-19 pneumonia for certain patient populations such as pregnant women, children, and immobilized patients. Additionally, recent work suggests lung ultrasound can accurately risk stratify patients for likely infection when immediate PCR testing is not available and has prognostic utility for positive patients regarding need for admission and ICU treatment. Though currently underutilized, a wider implementation of TUS in TH applications and processes may be an important stepping-stone for telemedicine. The addition of ultrasound to TH may allow it to cross the barrier from being an application mainly used for primary care and chronic conditions to an indispensable tool used in emergency care, disaster situations, remote areas, and low-income countries where it is difficult to obtain high quality diagnostic imaging. The objective of this review is to provide an overview of the current state of telemedicine, insights into current and future use scenarios, its practical application as well as current TUS uses and their potential value with an overview of currently available portable and handheld ultrasound devices. In the wake of the COVID-19 pandemic we point out an unmet need and use case of TUS as a supportive tool for health care providers and organizations in the management of affected patients.

BACKGROUND:Differentiating the etiology of acute febrile respiratory illness in children is a challenge in lowincome, malaria-endemic settings because the main pathogens responsible (viruses, bacteria, and malaria parasites) overlap in clinical presentation and frequently occur together as mixed infections. The critical task is to rapidly identify bacterial pneumonia to enable appropriate antibiotic treatment, ideally at point of care. Current diagnostic tests are insufficient and there is a need for the discovery and development of new tools. Here we report the identification of a unique biomarker signature that can be identified in blood samples. METHODS:Blood samples from 195 pediatric Mozambican patients with clinical pneumonia were analyzed with an aptamer-based high dynamic range assay to quantify ~1200 proteins. For discovery of new biomarkers, we identified a training set of patient samples in which the underlying etiology of the pneumonia was established as bacterial, viral or malaria. Proteins whose abundances varied significantly between patients with verified etiologies (FDR<0.01) formed the basis for predictive diagnostic models that were created using machine learning techniques (Random Forest, Elastic Net). These models were validated on a dedicated test set of samples. RESULTS:219 proteins had significantly different abundances between bacterial and viral infections, and 151 differed between bacterial infections and a mixed pool of viral and malaria infections. Predictive diagnostic models achieved >90% sensitivity and >80% specificity, regardless of whether one or two pathogen classes were present. Bacterial pneumonia was strongly associated with markers of neutrophil activity, in particular neutrophil degranulation. Degranulation markers included HP, LCN2, LTF, MPO, MMP8, PGLYRP1, RETN, SERPINA1, S100A9, and SLPI. CONCLUSION:Blood protein signatures highly associated with neutrophil biology reliably differentiated bacterial pneumonia from other causes. With appropriate technology, these markers could provide the basis for a rapid diagnostic for field-based triage for antibiotic treatment of pediatric pneumonia.

BACKGROUND Differential etiologies of pediatric acute febrile respiratory illness pose challenges for all populations globally but especially in malaria-endemic settings because the pathogens responsible overlap in clinical presentation and frequently occur together. Rapid identification of bacterial pneumonia with high quality diagnostic tools would enable appropriate, point of care antibiotic treatment. Current diagnostics are insufficient, and the discovery and development of new tools is needed. We report a unique biomarker signature identified in blood samples to accomplish this. METHODS Blood samples from 195 pediatric Mozambican patients with clinical pneumonia were analyzed with an aptamer-based, high dynamic range, quantitative assay (~1200 proteins). We identified new biomarkers using a training set of samples from patients with established bacterial, viral, or malarial pneumonia. Proteins with significantly variable abundance across etiologies (FDR&lt;0.01) formed the basis for predictive diagnostic models derived from machine learning techniques (Random Forest, Elastic Net). Validation on a dedicated test set of samples was performed. RESULTS Significantly different abundances between bacterial and viral infections (219 proteins) and bacterial infections and mixed (viral and malaria) infections (151 proteins) were found. Predictive models achieved &gt;90% sensitivity and &gt;80% specificity, regardless of number of pathogen classes. Bacterial pneumonia was strongly associated with neutrophil markers, in particular degranulation including HP, LCN2, LTF, MPO, MMP8, PGLYRP1, RETN, SERPINA1, S100A9, and SLPI. CONCLUSION Blood protein signatures highly associated with neutrophil biology reliably differentiated bacterial pneumonia from other causes. With appropriate technology, these markers could provide the basis for a rapid diagnostic for field-based triage for antibiotic treatment of pediatric pneumonia.