Remote Short Sessions of Heart Rate Variability Biofeedback Monitored With Wearable Technology: Open-Label Prospective Feasibility Study.

Background: Heart rate variability (HRV) biofeedback is often performed with structured education, laboratory-based assessments, and practice sessions. It has been shown to improve psychological and physiological function across populations. However, a means to remotely use and monitor this approach would allow for wider use of this technique.

Development of the ehive Digital Health App: Protocol for a Centralized Research Platform.

Background: The increasing use of smartphones, wearables, and connected devices has enabled the increasing application of digital technologies for research. Remote digital study platforms comprise a patient-interfacing digital application that enables multimodal data collection from a mobile app and connected sources. They offer an opportunity to recruit at scale, acquire data longitudinally at a high frequency, and engage study participants at any time of the day in any place.

A machine learning approach to determine resilience utilizing wearable device data: analysis of an observational cohort.

Objective: To assess whether an individual's degree of psychological resilience can be determined from physiological metrics passively collected from a wearable device.

Materials And Methods: Data were analyzed in this secondary analysis of the Warrior Watch Study dataset, a prospective cohort of healthcare workers enrolled across 7 hospitals in New York City. Subjects wore an Apple Watch for the duration of their participation.

StudyU: A Platform for Designing and Conducting Innovative Digital N-of-1 Trials.

N-of-1 trials are the gold standard study design to evaluate individual treatment effects and derive personalized treatment strategies. Digital tools have the potential to initiate a new era of N-of-1 trials in terms of scale and scope, but fully functional platforms are not yet available. Here, we present the open source StudyU platform, which includes the StudyU Designer and StudyU app.

Evaluation of a machine learning approach utilizing wearable data for prediction of SARS-CoV-2 infection in healthcare workers.

Objective: To determine whether a machine learning model can detect SARS-CoV-2 infection from physiological metrics collected from wearable devices.

Materials And Methods: Health care workers from 7 hospitals were enrolled and prospectively followed in a multicenter observational study. Subjects downloaded a custom smart phone app and wore Apple Watches for the duration of the study period.

Factors Associated With Longitudinal Psychological and Physiological Stress in Health Care Workers During the COVID-19 Pandemic: Observational Study Using Apple Watch Data.

Background: The COVID-19 pandemic has resulted in a high degree of psychological distress among health care workers (HCWs). There is a need to characterize which HCWs are at an increased risk of developing psychological effects from the pandemic. Given the differences in the response of individuals to stress, an analysis of both the perceived and physiological consequences of stressors can provide a comprehensive evaluation of its impact.

A Resilience-Building App to Support the Mental Health of Health Care Workers in the COVID-19 Era: Design Process, Distribution, and Evaluation.

Background: The COVID-19 pandemic has resulted in increased strain on health care systems and negative psychological effects on health care workers (HCWs). This is anticipated to result in long-term negative mental health effects on the population, with HCWs representing a particularly vulnerable group. The scope of the COVID-19 pandemic necessitates the development of a scalable mental health platform to provide services to large numbers of at-risk or affected individuals.

Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study.

Background: Changes in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with infection and observed prior to its clinical identification.

Objective: We performed an evaluation of HRV collected by a wearable device to identify and predict COVID-19 and its related symptoms.

Methods: Health care workers in the Mount Sinai Health System were prospectively followed in an ongoing observational study using the custom Warrior Watch Study app, which was downloaded to their smartphones.

The asthma mobile health study, smartphone data collected using ResearchKit.

Widespread adoption of smart mobile platforms coupled with a growing ecosystem of sensors including passive location tracking and the ability to leverage external data sources create an opportunity to generate an unprecedented depth of data on individuals. Mobile health technologies could be utilized for chronic disease management as well as research to advance our understanding of common diseases, such as asthma. We conducted a prospective observational asthma study to assess the feasibility of this type of approach, clinical characteristics of cohorts recruited via a mobile platform, the validity of data collected, user retention patterns, and user data sharing preferences.

Impacts of incorporating personal genome sequencing into graduate genomics education: a longitudinal study over three course years.

Background: To address the need for more effective genomics training, beginning in 2012 the Icahn School of Medicine at Mount Sinai has offered a unique laboratory-style graduate genomics course, "Practical Analysis of Your Personal Genome" (PAPG), in which students optionally sequence and analyze their own whole genome. We hypothesized that incorporating personal genome sequencing (PGS) into the course pedagogy could improve educational outcomes by increasing student motivation and engagement. Here we extend our initial study of the pilot PAPG cohort with a report on student attitudes towards genome sequencing, decision-making, psychological wellbeing, genomics knowledge and pedagogical engagement across three course years.

Identification of a novel somatic mutation in a -mutated corticotroph adenoma.

Cushing's disease (CD) is caused by pituitary corticotroph adenomas that secrete excess adrenocorticotropic hormone (ACTH). In these tumors, somatic mutations in the gene have been identified as recurrent and pathogenic and are the sole known molecular driver for CD. Although other somatic mutations were reported in these studies, their contribution to the pathogenesis of CD remains unexplored.

The Asthma Mobile Health Study, a large-scale clinical observational study using ResearchKit.

The feasibility of using mobile health applications to conduct observational clinical studies requires rigorous validation. Here, we report initial findings from the Asthma Mobile Health Study, a research study, including recruitment, consent, and enrollment, conducted entirely remotely by smartphone. We achieved secure bidirectional data flow between investigators and 7,593 participants from across the United States, including many with severe asthma.

METHODS FOR CLUSTERING TIME SERIES DATA ACQUIRED FROM MOBILE HEALTH APPS.

In our recent Asthma Mobile Health Study (AMHS), thousands of asthma patients across the country contributed medical data through the iPhone Asthma Health App on a daily basis for an extended period of time. The collected data included daily self-reported asthma symptoms, symptom triggers, and real time geographic location information. The AMHS is just one of many studies occurring in the context of now many thousands of mobile health apps aimed at improving wellness and better managing chronic disease conditions, leveraging the passive and active collection of data from mobile, handheld smart devices.

Development and clinical application of an integrative genomic approach to personalized cancer therapy.

Background: Personalized therapy provides the best outcome of cancer care and its implementation in the clinic has been greatly facilitated by recent convergence of enormous progress in basic cancer research, rapid advancement of new tumor profiling technologies, and an expanding compendium of targeted cancer therapeutics.

Methods: We developed a personalized cancer therapy (PCT) program in a clinical setting, using an integrative genomics approach to fully characterize the complexity of each tumor. We carried out whole exome sequencing (WES) and single-nucleotide polymorphism (SNP) microarray genotyping on DNA from tumor and patient-matched normal specimens, as well as RNA sequencing (RNA-Seq) on available frozen specimens, to identify somatic (tumor-specific) mutations, copy number alterations (CNAs), gene expression changes, gene fusions, and also germline variants.

Development and preliminary evaluation of an online educational video about whole-genome sequencing for research participants, patients, and the general public.

Background: As whole-genome sequencing (WGS) increases in availability, WGS educational aids are needed for research participants, patients, and the general public. Our aim was therefore to develop an accessible and scalable WGS educational aid.

Methods: We engaged multiple stakeholders in an iterative process over a 1-year period culminating in the production of a novel 10-minute WGS educational animated video, "Whole Genome Sequencing and You" (https://goo.

Preparing the next generation of genomicists: a laboratory-style course in medical genomics.

The growing gap between the demand for genome sequencing and the supply of trained genomics professionals is creating an acute need to develop more effective genomics education. In response we developed "Practical Analysis of Your Personal Genome", a novel laboratory-style medical genomics course in which students have the opportunity to obtain and analyze their own whole genome. This report describes our motivations for and the content of a "practical" genomics course that incorporates personal genome sequencing and the lessons we learned during the first three iterations of this course.

How do students react to analyzing their own genomes in a whole-genome sequencing course?: outcomes of a longitudinal cohort study.

Purpose: Health-care professionals need to be trained to work with whole-genome sequencing (WGS) in their practice. Our aim was to explore how students responded to a novel genome analysis course that included the option to analyze their own genomes.

Methods: This was an observational cohort study.

Informed decision-making among students analyzing their personal genomes on a whole genome sequencing course: a longitudinal cohort study.

Background: Multiple laboratories now offer clinical whole genome sequencing (WGS). We anticipate WGS becoming routinely used in research and clinical practice. Many institutions are exploring how best to educate geneticists and other professionals about WGS.

Willingness to participate in genomics research and desire for personal results among underrepresented minority patients: a structured interview study.

Patients from traditionally underrepresented communities need to be involved in discussions around genomics research including attitudes towards participation and receiving personal results. Structured interviews, including open-ended and closed-ended questions, were conducted with 205 patients in an inner-city hospital outpatient clinic: 48 % of participants self-identified as Black or African American, 29 % Hispanic, 10 % White; 49 % had an annual household income of <$20,000. When the potential for personal results to be returned was not mentioned, 82 % of participants were willing to participate in genomics research.