Detection of COVID-19 from voice, cough and breathing patterns: Dataset and preliminary results.

COVID-19 heavily affects breathing and voice and causes symptoms that make patients' voices distinctive, creating recognizable audio signatures. Initial studies have already suggested the potential of using voice as a screening solution. In this article we present a dataset of voice, cough and breathing audio recordings collected from individuals infected by SARS-CoV-2 virus, as well as non-infected subjects via large scale crowdsourced campaign. We describe preliminary results for detection of COVID-19 from cough patterns using standard acoustic features sets, wavelet scattering features and deep audio embeddings extracted from low-level feature representations (VGGish and OpenL3). Our models achieve accuracy of 88.52%, sensitivity of 88.75% and specificity of 90.87%, confirming the applicability of audio signatures to identify COVID-19 symptoms. We furthermore provide an in-depth analysis of the most informative acoustic features and try to elucidate the mechanisms that alter the acoustic characteristics of coughs of people with COVID-19.

Digital Biomarkers for Neuromuscular Disorders: A Systematic Scoping Review.

Biomarkers play a vital role in clinical care. They enable early diagnosis and treatment by identifying a patient's condition and disease course and act as an outcome measure that accurately evaluates the efficacy of a new treatment or drug. Due to the rapid development of digital technologies, digital biomarkers are expected to grow tremendously. In the era of change, this scoping review was conducted to see which digital biomarkers are progressing in neuromuscular disorders, a diverse and broad-range disease group among the neurological diseases, to discover available evidence for their feasibility and reliability. Thus, a total of 10 studies were examined: 9 observational studies and 1 animal study. Of the observational studies, studies were conducted with amyotrophic lateral sclerosis (ALS), Duchenne muscular dystrophy (DMD), and spinal muscular atrophy (SMA) patients. Non-peer reviewed poster presentations were not considered, as the articles may lead to erroneous results. The only animal study included in the present review investigated the mice model of ALS for detecting rest disturbances using a non-invasive digital biomarker.

Impact of mobile health and medical applications on clinical practice in gastroenterology.

Mobile health apps (MHAs) and medical apps (MAs) are becoming increasingly popular as digital interventions in a wide range of health-related applications in almost all sectors of healthcare. The surge in demand for digital medical solutions has been accelerated by the need for new diagnostic and therapeutic methods in the current coronavirus disease 2019 pandemic. This also applies to clinical practice in gastroenterology, which has, in many respects, undergone a recent digital transformation with numerous consequences that will impact patients and health care professionals in the near future. MHAs and MAs are considered to have great potential, especially for chronic diseases, as they can support the self-management of patients in many ways. Despite the great potential associated with the application of MHAs and MAs in gastroenterology and health care in general, there are numerous challenges to be met in the future, including both the ethical and legal aspects of applying this technology. The aim of this article is to provide an overview of the current status of MHA and MA use in the field of gastroenterology, describe the future perspectives in this field and point out some of the challenges that need to be addressed.

Patient electronic communication data in clinical care: what is known and what is needed.

The novel coronavirus (COVID-19) and physical distancing guidelines around the world have resulted in unprecedented changes to normal routine and increased smartphone use to maintain social relationships and support. Reports of depressive and anxiety symptom are on the rise, contributing to suffering among people-especially adolescents and young adults-with pre-existing mental health conditions. Psychiatric care has shifted primarily to telehealth limiting the important patient nonverbal communication that has been part of in-person clinical sessions. Supplementing clinical care with patient electronic communication (EC) data may provide valuable information and influence treatment decision making. Research in the impact of patient EC data on managing psychiatric symptoms is in its infancy. This review aims to identify how patient EC has been used in clinical care and its benefits in psychiatry and research. We discuss smartphone applications used to gather different types of EC data, how data have been integrated into clinical care, and implications for clinical care and research.