The COVID-19 pandemic has highlighted how the healthcare system could be overwhelmed. Telehealth stands out to be an effective solution, where patients can be monitored remotely without packing hospitals and exposing healthcare givers to the deadly virus. This article presents our Intel award winning solution for diagnosing COVID-19 related symptoms and similar contagious diseases. Our solution realizes an Internet of Things system with multimodal physiological sensing capabilities. The sensor nodes are integrated in a wearable shirt (vest) to enable continuous monitoring in a noninvasive manner; the data are collected and analyzed using advanced machine learning techniques at a gateway for remote access by a healthcare provider. Our system can be used by both patients and quarantined individuals. The article presents an overview of the system and briefly describes some novel techniques for increased resource efficiency and assessment fidelity. Preliminary results are provided and the roadmap for full clinical trials is discussed.

Using a statistical model of keystroke and linguistic features, a novel assessment approach leverages ordinary text-typing activities to monitor for signs of early cognitive decline in older adults. Early detection could allow for appropriate interventions and effective treatment.

Future healthcare systems will rely heavily on clinical decision support systems (CDSS) to improve the decision-making processes of clinicians. To explore the design of future CDSS, we developed a research-focused CDSS for the management of patients in the intensive care unit that leverages Internet of Things (IoT) devices capable of collecting streaming physiologic data from ventilators and other medical devices. We then created machine learning (ML) models that could analyze the collected physiologic data to determine if the ventilator was delivering potentially harmful therapy and if a deadly respiratory condition, acute respiratory distress syndrome (ARDS), was present. We also present work to aggregate these models into a mobile application that can provide responsive, real-time alerts of changes in ventilation to providers. As illustrated in the recent COVID-19 pandemic, being able to accurately predict ARDS in newly infected patients can assist in prioritizing care. We show that CDSS may be used to analyze physiologic data for clinical event recognition and automated diagnosis, and we also highlight future research avenues for hospital CDSS.

Interventions to improve medication adherence have had limited success and can require significant human resources to implement. Research focused on improving medication adherence has undergone a paradigm shift, of late, with a shift towards developing personalized, theory-driven interventions. The current research integrates foundational and translational science to implement a mechanisms-focused, context-aware approach. Increasing adoption of mobile and wearable sensing systems presents new opportunities for understanding how medication-taking behaviors unfold in natural settings, especially in populations who have difficulty adhering to medications. When combined with survey and ecological momentary assessment data, these mobile and wearable sensing systems can directly capture the context of medication adherence , including personal, behavioral, and environmental factors. The purpose of this paper is to present a new transdisciplinary research framework in medication adherence, highlight critical advances in this rapidly-evolving research field, and outline potential future directions for both research and clinical applications.

In a Philadelphia neighbourhood where opioid overdoses are frequent, neighbors used a smartphone app to request and give help for a victim of suspected overdose. A one-year study demonstrated the feasibility of this approach, which empowered the local community to save lives and even respond to overdoses faster than emergency medical services.

As the digitalization of mental health systems progresses, the ethical and social debate on the use of these mental health technologies has seldom been explored among end-users. This article explores how service users (e.g., patients and users of mental health services) and peer support specialists understand and perceive issues of privacy, confidentiality, and security of digital mental health interventions. Semi-structured qualitative interviews were conducted among service users (n = 17) and peer support specialists (n = 15) from a convenience sample at an urban community mental health center in the United States. We identified technology ownership and use, lack of technology literacy including limited understanding of privacy, confidentiality, and security as the main barriers to engagement among service users. Peers demonstrated a high level of technology engagement, literacy of digital mental health tools, and a more comprehensive awareness of digital mental health ethics. We recommend peer support specialists as a potential resource to facilitate the ethical engagement of digital mental health interventions for service users. Finally, engaging potential end-users in the development cycle of digital mental health support platforms and increased privacy regulations may lead the field to a better understanding of effective uses of technology for people with mental health conditions. This study contributes to the ongoing debate of digital mental health ethics, data justice, and digital mental health by providing a first-hand experience of digital ethics from end-users' perspectives.

Social isolation is a common problem faced by individuals with serious mental illness (SMI), and current intervention approaches have limited effectiveness. This paper presents a blended intervention approach, called mobile Social Interaction Therapy by Exposure (mSITE), to address social isolation in individuals with serious mental illness. The approach combines brief in-person cognitive-behavioral therapy (CBT) with context-triggered mobile CBT interventions that are personalized using mobile sensing data. Our approach targets social behavior and is the first context-aware intervention for improving social outcomes in serious mental illness.