Growth in the development of health and fitness mobile apps amid COVID-19 pandemic.

Objectives: Technology in the form of mobile apps has played an essential role in facilitating, tracking, and maintaining health and fitness activities during the pandemic. When countries opted for partial or complete lockdowns to contain the spread of the coronavirus disease 2019 virus, it led to people working on their health and fitness-related activities from their homes, just as they continued working from home. This paper aims to quantify the impact of coronavirus disease 2019 on the development of health and fitness mobile apps.

Pandemic information support lifecycle: Evidence from the evolution of mobile apps during COVID-19.

Information sharing and consumption play an important role during a pandemic in managing constrained resources and devising effective plans to minimize a pandemic's impact. The type of support extended by information also changes as a pandemic evolves. In this paper, we present a novel framework to understand the different types of information support needed during a pandemic crisis.

Examining multimorbidity differences across racial groups: a network analysis of electronic medical records.

Health disparities across ethnic or racial groups are typically examined through single behavior at a time. The syndemics and multimorbidity health disparities have not been well examined by race. In this study, we study health disparities by identifying the networks of multimorbidities among individuals from seven population groups based on race, including White, African American, Asian, Hispanic, Native American, Bi- or Multi-racial and Pacific Islander.

BIARAM: A process for analyzing correlated brain regions using association rule mining.

Background And Objective: Because examining correlated (vs. individual) brain activity is a superior method for locating neural correlates of a stimulus, using a network approach for analyzing brain activity is gaining interest. In this study, we propose and illustrate the use of association rule mining (ARM) to analyze brain regions that are activated simultaneously.

Examining health disparities by gender: A multimorbidity network analysis of electronic medical record.

Problem: Multimorbidity health disparities have not been well examined by gender. Co-occurring diseases may be mutually deleterious, co-occurring independently, or co-occurring from a common antecedent. Diseases linked by a common antecedent may be caused by biological, behavioral, social, or environmental factors.

RFID-based information visibility for hospital operations: exploring its positive effects using discrete event simulation.

Long queues and wait times often occur at hospitals and affect smooth delivery of health services. To improve hospital operations, prior studies have developed scheduling techniques to minimize patient wait times. However, these studies lack in demonstrating how such techniques respond to real-time information needs of hospitals and efficiently manage wait times.

Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks.

Understanding the circumstances under which drivers and passengers are more likely to be killed or more severely injured in an automobile accident can help improve the overall driving safety situation. Factors that affect the risk of increased injury of occupants in the event of an automotive accident include demographic or behavioral characteristics of the person, environmental factors and roadway conditions at the time of the accident occurrence, technical characteristics of the vehicle itself, among others. This study uses a series of artificial neural networks to model the potentially non-linear relationships between the injury severity levels and crash-related factors.