Publications by authors named "Y Sri Ranjan"
Background: The use of digital biomarkers through remote patient monitoring offers valuable and timely insights into a patient's condition, including aspects such as disease progression and treatment response. This serves as a complementary resource to traditional health care settings leveraging mobile technology to improve scale and lower latency, cost, and burden.
Objective: Smartphones with embedded and connected sensors have immense potential for improving health care through various apps and mobile health (mHealth) platforms.
Article Synopsis
- The study focused on the emergence and understanding of long COVID through the use of digital health technologies, particularly wearable devices, to collect objective data and self-reported symptoms.
- It involved a large-scale longitudinal study where participants, diagnosed with COVID-19, were compared to controls to evaluate the prevalence and severity of long COVID symptoms over a 12-week period.
- The findings highlighted significant changes in resting heart rate and identified potential sociodemographic and health factors associated with the risk of developing long COVID.
Article Synopsis
- * The RADAR-IoT framework is an open-source IoT gateway that connects various devices, processes data on-device, and integrates with cloud-based health platforms for real-time analysis.
- * By combining static IoT sensors with wearable devices, RADAR-IoT offers a comprehensive view of health and environment, making it useful in areas like infection control and monitoring chronic conditions, despite some existing limitations.
Background: Changes in sleep and circadian function are leading candidate markers for the detection of relapse in Major Depressive Disorder (MDD). Consumer-grade wearable devices may enable remote and real-time examination of dynamic changes in sleep. Fitbit data from individuals with recurrent MDD were used to describe the longitudinal effects of sleep duration, quality, and regularity on subsequent depression relapse and severity.
View Article and Find Full Text PDFBackground: Previous mobile health (mHealth) studies have revealed significant links between depression and circadian rhythm features measured via wearables. However, the comprehensive impact of seasonal variations was not fully considered in these studies, potentially biasing interpretations in real-world settings.
Objective: This study aims to explore the associations between depression severity and wearable-measured circadian rhythms while accounting for seasonal impacts.