Data from the All of Us research program reinforces existence of activity inequality.

Large-scale and detailed analyses of activity in the United States (US) remain limited. In this work, we leveraged the comprehensive wearable, demographic, and survey data from the All of Us Research Program, the largest and most diverse population health study in the US to date, to apply and extend the previous global findings on activity inequality within the context of the US. We found that daily steps differed by sex at birth, age, body characteristics, geography, and built environment.

Tree-based classification model for Long-COVID infection prediction with age stratification using data from the National COVID Cohort Collaborative.

Objectives: We propose and validate a domain knowledge-driven classification model for diagnosing post-acute sequelae of SARS-CoV-2 infection (PASC), also known as Long COVID, using Electronic Health Records (EHRs) data.

Materials And Methods: We developed a robust model that incorporates features strongly indicative of PASC or associated with the severity of COVID-19 symptoms as identified in our literature review. The XGBoost tree-based architecture was chosen for its ability to handle class-imbalanced data and its potential for high interpretability.

The Importance of Data Quality Control in Using Fitbit Device Data From the Research Program.

Wearable digital health technologies (DHTs) have become increasingly popular in recent years, enabling more capabilities to assess behaviors and physiology in free-living conditions. The Research Program (AoURP), a National Institutes of Health initiative that collects health-related information from participants in the United States, has expanded its data collection to include DHT data from Fitbit devices. This offers researchers an unprecedented opportunity to examine a large cohort of DHT data alongside biospecimens and electronic health records.

A Systematic Review of Time Series Classification Techniques Used in Biomedical Applications.

Digital clinical measures collected via various digital sensing technologies such as smartphones, smartwatches, wearables, and ingestible and implantable sensors are increasingly used by individuals and clinicians to capture the health outcomes or behavioral and physiological characteristics of individuals. Time series classification (TSC) is very commonly used for modeling digital clinical measures. While deep learning models for TSC are very common and powerful, there exist some fundamental challenges.

A method for intelligent allocation of diagnostic testing by leveraging data from commercial wearable devices: a case study on COVID-19.

Mass surveillance testing can help control outbreaks of infectious diseases such as COVID-19. However, diagnostic test shortages are prevalent globally and continue to occur in the US with the onset of new COVID-19 variants and emerging diseases like monkeypox, demonstrating an unprecedented need for improving our current methods for mass surveillance testing. By targeting surveillance testing toward individuals who are most likely to be infected and, thus, increasing the testing positivity rate (i.

The Development of a Thin-Filmed Noninvasive Tissue Perfusion Sensor to Quantify Capillary Pressure Occlusion of Explanted Organs.

A new thin-filmed perfusion sensor was developed using a heat flux gauge, thin-film thermocouple, and a heating element. This sensor, termed "CHFT+," is an enhancement of the previously established combined heat flux-temperature (CHFT) sensor technology predominately used to quantify the severity of burns [1]. The CHFT+ sensor was uniquely designed to measure tissue perfusion on explanted organs destined for transplantation, but could be functionalized and used in a wide variety of other biomedical applications.