Recent Advancements in Wearable Hydration-Monitoring Technologies: Scoping Review of Sensors, Trends, and Future Directions.

Background: Monitoring hydration is crucial for maintaining health and preventing dehydration. Despite the potential of wearable devices for continuous hydration monitoring, health research hasn't fully explored this application, and clear design guidelines are absent. This scoping review aimed to address this gap by analyzing current research trends and assessing the potential impact of wearable technologies for hydration monitoring.

A Positionally Encoded Transformer for Monitoring Health Contexts of Hajj Pilgrims from Wearable Sensor Data.

Monitoring the health of individuals during physically demanding tasks, such as the Hajj pilgrimage, requires robust methods for real-time detection of health-relevant contexts, including physical tiredness, emotional mood, and activity type. This paper introduces a positionally encoded Transformer model designed to detect these contexts from time-series data collected via wearable sensors. The model leverages Long Short-Term Memory (LSTM) for feature extraction and Transformer layers for context classification, utilizing positional encoding to capture the sequential dependencies within the sensor data.

Detection of Short-Form Video Addiction with Wearable Sensors Via Temporally-Coherent Domain Adaptation.

Short-form Video Addiction (SVA), a novel digital addiction of the modern world, proliferates among young adults and is not formally diagnosable. SVA detection from resulting bio-signals is crucial to prevent its adverse impacts. Existing formal methods involve large and expensive neuro-imaging devices in laboratory setups that are intrusive and not feasible to use in daily life.

Enhancing Wearable Sensor Data Classification Through Novel Modified- Recurrent Plot-Based Image Representation and Mixup Augmentation.

Deep learning advancements have revolutionized scalable classification in many domains including computer vision, healthcare and Natural Language Processing (NLP). However, when it comes to classification and domain adaptation based on wearables, it suffers from persistent underperformance, largely due to the scarcity of pre-trained deep learning models that are abundantly available for computer vision and NLP. This is primarily because wearable sensor data need sensor-specific preprocessing, architectural modification, and extensive data collection.

Community-Wide Monitoring of Lead in Drinking Water Distribution Systems Using Hand-Held Voltammetric Sensors and Geographic Information Systems.

Lead (Pb) contamination in drinking water remains a critical public health concern, particularly for children, due to lead pipes and plumbing in many water systems. Conventional Pb detection methods, such as ICP-MS and AAS, are costly, time-intensive, and require specialized personnel. In this study, we developed and utilized a portable voltammetric Pb detection system, the E-Tongue, which features a mercury-free, gold nanostar-modified screen-printed carbon electrode, and nontoxic buffer reagents (0.

Enhancing Wearable based Real-Time Glucose Monitoring via Phasic Image Representation Learning based Deep Learning.

In the U.S., over a third of adults are pre-diabetic, with 80% unaware of their status.

HydroTrack: Spectroscopic Analysis Prototype Enabling Real-Time Hydration Monitoring in Wearables.

In the rapidly growing field of wearable technology, optical devices are emerging as a significant innovation, offering non-invasive methods for analyzing skin and underlying tissue properties. Despite their promise, progress has been slowed by a lack of specialized prototypes and advanced analysis techniques. Addressing this gap, our study introduces, HydroTrack, an 18-channel spectroscopy sensor, ingeniously embedded in a smart-watch.

Effects of Antidepressants on COVID-19 Outcomes: Retrospective Study on Large-Scale Electronic Health Record Data.

Background: Antidepressants exert an anticholinergic effect in varying degrees, and various classes of antidepressants can produce a different effect on immune function. While the early use of antidepressants has a notional effect on COVID-19 outcomes, the relationship between the risk of COVID-19 severity and the use of antidepressants has not been properly investigated previously owing to the high costs involved with clinical trials. Large-scale observational data and recent advancements in statistical analysis provide ample opportunity to virtualize a clinical trial to discover the detrimental effects of the early use of antidepressants.

Estimating Heterogeneous Causal Effect of Polysubstance Usage on Drug Overdose from Large-Scale Electronic Health Record.

Drug overdose has become a public health crisis in the United States with devastating consequences. However, most of the drug overdose incidences are the consequence of recitative polysubstance usage over a defined period of time which can be happened by either the intentional usage of required drug with other drugs or by accident. Thus, predicting the effects of polysubstance usage is extremely important for clinicians to decide which combination of drugs should be prescribed.

Activity-Aware Deep Cognitive Fatigue Assessment using Wearables.

Cognitive fatigue is a common problem among workers which has become an increasing global problem. While existing multi-modal wearable sensors-aided automatic cognitive fatigue monitoring tools have focused on physical and physiological sensors (ECG, PPG, Actigraphy) analytic on specific group of people (say gamers, athletes, construction workers), activity-awareness is utmost importance due to its different responses on physiology in different person. In this paper, we propose a novel framework, Activity-Aware Recurrent Neural Network (AcRoNN), that can generalize individual activity recognition and improve cognitive fatigue estimation significantly.

Nursing Perspectives on the Impacts of COVID-19: Social Media Content Analysis.

Background: Nurses are at the forefront of the COVID-19 pandemic. During the pandemic, nurses have faced an elevated risk of exposure and have experienced the hazards related to a novel virus. While being heralded as lifesaving heroes on the front lines of the pandemic, nurses have experienced more physical, mental, and psychosocial problems as a consequence of the COVID-19 outbreak.