Continuous Data Collection Research Articles

Toward High-Throughput Surface-Sensitive X-Ray Scattering Studies of Electrochemical Interfaces

Lithium-ion batteries (LIBs) are widely recognised as essential parts of portable electronics, electric vehicles, and grid storage. As such, LIBs have considerable potential to contribute to our modern society, especially as a fundamental component in the development of a sustainable energy system [1]. Despite their success, many fundamental questions in the context of LIBs still puzzle researchers today. These involve interfaces and interphases [2]. For example, the structure and speciation of the potential- and surface-dependent electric double layer (EDL) (especially in advanced concentrated electrolytes) is currently not well understood. Relatedly, basic knowledge of multiple properties of the solid electrolyte interphase (SEI), a passivation layer formed on anode surfaces by electrolyte reduction products, needs to be unravelled to predict electrolyte behaviour as well as cell kinetics and lifetime [3]. To understand the structural properties of these solid-liquid interfaces and interphases, X-ray reflectivity (XRR) is a powerful method that employs model electrodes such as single crystals or thin films [4,5]. XRR provides surface sensitivity with sub-Ångström resolution, is non-destructive, allows probing buried nanoscale layers, and, importantly, can be carried out in operando modality [3]. One of the main challenges of XRR is that it is typically employed for a single electrochemical cell at a given time, and a single experiment takes on the order of several hours up to one day. In this scenario, the experiments are not photon-limited and continuous data collection is not necessary to obtain meaningful results because changes are slow towards later stages of the experiment (even though initial processes occur relatively fast). To overcome this limitation, we designed a high-throughput setup and corresponding experimental workflow, in which up to ten electrochemical cells can be measured via XRR and other surface-sensitive X-ray scattering methods (e.g., GISAXS and GIWAXS) quasi-simultaneously in an interleave fashion. We will present our novel experimental setup and first results.Literature:[1] G. Zubi, R. Dufo-López, M. Carvalho, & G. Pasaoglu,Renewable and Sustainable Energy Reviews, 89, 292-308, 2018.[2] K. Xu, Journal of Power Sources, 559, 232652, 2023.[3] C. Cao, H.-G. Steinrück, Encyclopedia of Solid-Liquid Interfaces, 391–416, Elsevier, 2024.[4] T. T. Fister, X. Hu, J. Esbenshade, ... & P. Fenter, Chemistry of Materials, 28(1), 47-54, 2016.[5] C. Cao, H.-G. Steinrück, B. Shyam, K. H. Stone, & M. F. Toney, Nano Letters, 16(12), 7394-7401, 2016.

(Invited) 3D Printing for Tissue Scaffolds with Tunable Mechanical Durability and Sensing Capabilities

Pelvic organ prolapse (POP) is a prevalent condition among senior women, characterized by the descent or herniation of pelvic organs into the vaginal canal. It is a widespread issue, affecting a significant portion of the elderly female population, and yet, effective medical solutions for this condition remain elusive. This can be attributed to the complex nature of POP, where multiple factors, including tissue weakening and structural changes, contribute to its development. Despite the significant impact on women's quality of life, the absence of comprehensive treatment options underscores the urgency for innovative approaches to address this pressing health concern. In this context, 3D printing emerges as a valuable rapid prototyping tool, offering unique capabilities for designing and fabricating smart sensors and actuators. Its precision and versatility enable the creation of intricate devices that can be customized to specific medical applications. Moreover, 3D printing allows for the integration of sensors directly into biomedical devices, facilitating seamless monitoring and feedback mechanisms. Leveraging this technology, the research endeavors to harness the potential of 3D printing to develop advanced thermoelectric devices that can be seamlessly embedded within POP tissue scaffolds. These innovative devices will serve a dual purpose—detecting the health status of the tissue scaffolds and assessing their mechanical durability. In the realm of embedded medical devices, self-powered sensors hold particular significance due to their wireless powering capabilities and in-situ monitoring capabilities of human health. These sensors can operate without the need for external power sources or frequent battery replacements, making them ideal for long-term implantation within the human body. By tapping into the body's own energy sources or ambient environmental factors, self-powered sensors ensure continuous data collection and transmission, enhancing the efficiency and reliability of healthcare monitoring systems. Consequently, this talk aims to demonstrate the feasibility of 3D-printed thermoelectric devices as self-powered sensors, integrated into POP tissue scaffolds. These innovative devices will play a pivotal role in monitoring the tissue scaffold's condition, ensuring its mechanical integrity, and ultimately contributing to improved healthcare outcomes for women affected by POP.

Status and Trends of the Digital Healthcare Industry.

This review presents a comprehensive overview of the rapidly evolving digital healthcare industry, aiming to provide a broad understanding of the recent landscape and directions for the future of digital healthcare. This review examines the key trends in sectors of the digital healthcare industry, which can be divided into four main categories: digital hardware, software solutions, platforms, and enablers. We discuss electroceuticals, wearables, standalone medical software, non-medical health management services, telehealth, decentralized clinical trials, and infrastructural systems such as health data systems. The review covers both global and domestic perspectives, addressing definitions, significance, revenue trends, major companies, regulations, and socioenvironmental factors. Diverse growth patterns are evident across digital healthcare sectors. The applications of electroceuticals are expanding. Wearables are becoming more ubiquitous, facilitating continuous health monitoring and data collection. Artificial intelligence in standalone medical software is demonstrating clinical efficacy, with regulatory frameworks adapting to support commercialization. Non-medical health management services are expanding their scope to address chronic conditions under professional guidance. Telemedicine and decentralized clinical trials are gaining traction, driven by the need for flexible healthcare solutions post-pandemic. Efforts to build robust digital infrastructure with health data are underway, supported by data banks and data aggregation platforms. Advancements in digital healthcare create a dynamic, transformative landscape, integrating, complementing, and offering alternatives to traditional paradigms. This evolution is driven by continuous innovation, increased stakeholder participation, regulatory adaptations promoting commercialization, and supportive initiatives. Ongoing discussions about optimal digital technology integration and effective healthcare strategy implementation are essential for progress.

PENGEMBANGAN PERANGKAT MULTI SENSOR UNTUK PRECISION FARMING MONITORING MEDIA TANAM

The development of a multi-sensor device and the implementation of a data transmission system to a database for predicting plant suitability parameters are the focus of this paper. This technology enables accurate and continuous data collection for agricultural condition monitoring. The study utilizes a DHT22 sensor for air temperature and humidity, a pH sensor for soil pH, a soil moisture sensor for soil moisture, a rain gauge sensor for rainfall, and a raindrop sensor for rain detection. The data from these sensors are managed by an ESP32 microcontroller and transmitted to a Firebase database. The multi-sensor device was functionally tested and compared with valid reference sensor readings. The results show that all the multi-sensor devices and the recording system in the Firebase database functioned well. The average measurement error for the DHT22 temperature sensor was 1.15°C, with 2.9% for humidity, 0.72% for the pH sensor, and the soil moisture sensor showed 100% accuracy. This system can be applied as a parameter for predicting the suitability of plant types with soil and environmental conditions.

Pengaruh Pendidikan Terhadap Tingkat Kesehatan Masyarakat Kecamatan Suragala Kabupaten Lombok Timur Menggunakan Algoritma Random Forest

SDGs are a global concern of the international community, including the Indonesian nation. There are many activities carried out by the international community to address this SDGs issue. Starting from real actions in the field to a continuous data collection process. In Indonesia, data collection has been carried out repeatedly, but the follow-up to each data collection is felt to be lacking, therefore Hamzanwadi University, using the Gemilang Village Thematic KKN application, has also played an active role in the data collection process. However, up to now, information about data collection results and follow-up actions has not been provided properly, in fact none of the data collection results have been processed to obtain clear and measurable information. Through internal higher education research carried out by Hamzanwadi University, an attempt was made to carry out research starting with the SDGs data processing process, namely education and health, to provide knowledge and information about whether or not there is an influence of education on the health of the people of Suralaga District, East Lombok Regency, so that if the influence If education plays a significant role, the Suralaga District Government can take concrete steps to improve it in the future. From the results of data processing it can be concluded that the influence of education on the level of public health is very high, using 90% training data and 10% testing data can achieve an accuracy of 88.61%, which means 700 data show a positive trend of education on the level of public health

Occurrence of the invasive pink salmon (Oncorhynchus gorbuscha, Walbaum 1792) in Greenland 2020 and 2021 as revealed using citizen science, snorkeling, and environmental DNA metabarcoding of fishes in the Kapisillit River

The occurrence of the invasive pink salmon ( Oncorhynchus gorbuscha, Walbaum 1792) in Greenland was initially described in 2019 using social media to collect data on their presence. In this study we continue data collection through social media and initiate a monitoring program of the Kapisillit River in Southwest Greenland using snorkeling and environmental DNA (eDNA) metabarcoding in 2020 and 2021. The Kapisillit River is the only freshwater system in Greenland, where the red-listed Atlantic salmon ( Salmo salar, Linnaeus 1758) is known to spawn. This genetically unique population of Atlantic salmon has been found to decline, wherefore there is general conservation concern that the occurrence of pink salmon at some point can become an additional stressor to the “Kapisillit salmon”. In 2021, pink salmon were present near all larger populated areas in Greenland and likely more abundant than in 2019. From visual observations and using eDNA, the presence of pink salmon was documented in the Kapisillit River in 2021. From the number of individuals observed combined with the spatial distribution of eDNA detections in the river, we suggest that the pink salmon invasion in the Kapisillit River is at an early stage.

Applications of Fog Computing in Healthcare.

Fog computing is a decentralized computing infrastructure that processes data at or near its source, reducing latency and bandwidth usage. This technology is gaining traction in healthcare due to its potential to enhance real-time data processing and decision-making capabilities in critical medical scenarios. A systematic review of existing literature on fog computing in healthcare was conducted. The review included searches in major databases such as PubMed, IEEE Xplore, Scopus, and Google Scholar. The search terms used were "fog computing in healthcare," "real-time diagnostics and fog computing," "continuous patient monitoring fog computing," "predictive analytics fog computing," "interoperability in fog computing healthcare," "scalability issues fog computing healthcare," and "security challenges fog computing healthcare." Articles published between 2010 and 2023 were considered. Inclusion criteria encompassed peer-reviewed articles, conference papers, and review articles focusing on the applications of fog computing in healthcare. Exclusion criteria were articles not available in English, those not related to healthcare applications, and those lacking empirical data. Data extraction focused on the applications of fog computing in real-time diagnostics, continuous monitoring, predictive analytics, and the identified challenges of interoperability, scalability, and security. Fog computing significantly enhances diagnostic capabilities by facilitating real-time data analysis, crucial for urgent diagnostics such as stroke detection, by processing data closer to its source. It also improves monitoring during surgeries by enabling real-time processing of vital signs and physiological parameters, thereby enhancing patient safety. In chronic disease management, continuous data collection and analysis through wearable devices allow for proactive disease management and timely adjustments to treatment plans. Additionally, fog computing supports telemedicine by enabling real-time communication between remote specialists and patients, thereby improving access to specialist care in underserved regions. Fog computing offers transformative potential in healthcare, improving diagnostic precision, patient monitoring, and personalized treatment. Addressing the challenges of interoperability, scalability, and security will be crucial for fully realizing the benefits of fog computing in healthcare, leading to a more connected and efficient healthcare environment.

Implementation of a Standardized Red Blood Cell Transfusion Policy in a Level IV Neonatal Intensive Care Unit: A Quality Improvement Project.

Within the neonatal intensive care unit (NICU), infants frequently receive packed red blood cell (PRBC) transfusions. Although medically necessary, potential negative long- and short-term outcomes exist following PRBC transfusions in very low birth-weight (VLBW) infants (<1500g). Synthesis of the literature demonstrates that the use of a restrictive PRBC transfusion policy can lead to a decreased number of transfusions administered with no increase in long-term neurodevelopmental outcomes. Blood transfusions have also been linked to the diagnosis of necrotizing enterocolitis (NEC) or intraventricular hemorrhage (IVH) in VLBW infants. For this quality improvement project, a restrictive PRBC transfusion policy was implemented in a level IV NICU to promote consistent care and evaluate changes in PRBC administration. The data were collected both pre- and post-policy implementation including: the number of blood transfusions, diagnosis of NEC, and diagnosis of IVH among infants <1500g. The data showed no significant change in the number of PRBC transfusions administered. Likewise, few infants were diagnosed with NEC or IVH during this same time period with minimal change between pre- and post-policy implementation data. Following policy implementation, there was a significant improvement in communication among providers regarding transfusion ordering and the inclusion of hematocrit thresholds in daily progress notes. This unintended outcome has helped to promote sustainability and enhance patient care within the NICU where this policy was implemented. Continued data collection may be beneficial in indicating whether a standardized PRBC transfusion policy will impact the administration of transfusions and diagnosis of NEC or IVH.

Assessment of factors and mechanism contributing to groundwater depressurisation due to longwall mining

Assessment of mining impact on groundwater is one of critical considerations for longwall extension and sustainability, however usually constrained by limited data availability, hydrogeological variation, and the complex coupled hydro-mechanical behaviour. This paper aims to determine the factors and mechanism of groundwater depressurisation and identify knowledge gaps and methodological limitations for improving groundwater impact assessment. Analysis of dewatering cases in Australian, Chinese, and US coalfields demonstrates that piezometric drawdown can further lead to surface hydrology degradation, while the hydraulic responses vary with longwall parameters and geological conditions. Statistical interpretation of 422 height of fracturing datasets indicates that the groundwater impact positively correlates to panel geometry and depth of cover, and more pronounced in panel interaction and top coal caving cases. In situ stress, rock competency, clay mineral infillings, fault, valley topography, and surface–subsurface water interaction are geological and hydrogeological factors influencing groundwater hydraulics and long-term recovery. The dewatering mechanism involves permeability enhancement and extensive flow through fracture networks, where interconnected fractures provide steep hydraulic gradients and smooth flow pathways draining the overlying water to goaf of lower heads. Future research should improve fracture network identification and interconnectivity quantification, accompanied by description of fluid flow dynamics in the high fracture frequency and large fracture aperture context. The paper recommends a research framework to address the knowledge gaps with continuous data collection and field-scale numerical modelling as key technical support. The paper consolidates the understanding of longwall mining impacting mine hydrology and provides viewpoints that facilitate an improved assessment of groundwater depressurisation.

Risk assessment and prediction of nosocomial infections based on surveillance data using machine learning methods

BackgroundNosocomial infections with heavy disease burden are becoming a major threat to the health care system around the world. Through long-term, systematic, continuous data collection and analysis, Nosocomial infection surveillance (NIS) systems are constructed in each hospital; while these data are only used as real-time surveillance but fail to realize the prediction and early warning function. Study is to screen effective predictors from the routine NIS data, through integrating the multiple risk factors and Machine learning (ML) methods, and eventually realize the trend prediction and risk threshold of Incidence of Nosocomial infection (INI).MethodsWe selected two representative hospitals in southern and northern China, and collected NIS data from 2014 to 2021. Thirty-nine factors including hospital operation volume, nosocomial infection, antibacterial drug use and outdoor temperature data, etc. Five ML methods were used to fit the INI prediction model respectively, and to evaluate and compare their performance.ResultsCompared with other models, Random Forest showed the best performance (5-fold AUC = 0.983) in both hospitals, followed by Support Vector Machine. Among all the factors, 12 indicators were significantly different between high-risk and low-risk groups for INI (P < 0.05). After screening the effective predictors through importance analysis, prediction model of the time trend was successfully constructed (R2 = 0.473 and 0.780, BIC = -1.537 and -0.731).ConclusionsThe number of surgeries, antibiotics use density, critical disease rate and unreasonable prescription rate and other key indicators could be fitted to be the threshold predictions of INI and quantitative early warning.

Fuzzy Logic Controller-based Intelligent Irrigation System Using Solar Radiation Data

Solar radiation is a critical factor influencing agricultural productivity and water resource management. Irrigation systems play a pivotal role in maintaining crop health and yield, and optimizing their operation requires accurate solar radiation data. This abstract explores the significance of solar radiation data in enhancing the efficiency of irrigation systems. Irrigating agricultural fields using an intelligent information system plays a crucial role. This study introduces an irrigation control system employing closed-loop control to use the available water resources efficiently. Continuous data collection from field sensors was done and transmitted to a central station in wireless mode. The data was then retrieved and processed in a computer-based or microcontroller-based solution model, enhancing system autonomy, reliability, and cost-effectiveness. Weather conditions are translated into fuzzy set values. The pump, water outlet valves, and sprinklers are set into motion according to control commands. This research simplifies the need for manual labor and reduces water wastage.

Toward Indirect Real-Time Prediction of Bridge Vibration Responses Under Traffic Flow Through a Population of Connected Sensing Vehicles

Condition monitoring of bridge structures as the lifelines of smart cities is high of importance. While indirect vehicle scanning techniques have shown promising results and have less cost compared to mounting fixed sensors on the bridge, they have limitations in predicting response under traffic flow since the crossing time of one vehicle is very short. This paper presents a novel crowsensing-based framework for predicting bridge acceleration responses and identifying the modal characteristics. This method utilizes smartphone data from a diverse population of sensing vehicles as they traverse the bridge to predict bridge acceleration response at various virtual sensing locations. Subsequently, the predicted acceleration is used to identify the mode shapes and natural frequencies of the bridge. The principal innovation in this practical and cost-effective monitoring solution is the utilization of a randomly selected set of sensing vehicles at each timestamp. These selections may differ from one timestamp to the next, reflecting the real-world conditions where certain vehicles may intermittently lose or disconnect their internet connectivity. By consistently updating the set of sensing agents while other vehicles cross the bridge, the proposed framework overcomes the data length limitations of conventional vehicle-based methods by leveraging multiple vehicles and continuous data collection. Comprehensive numerical studies are conducted to evaluate the performance of the method. In the numerical investigations, a three-span bridge is subjected to the continuous passing of a large number of half-car vehicle models with random speeds and initial locations. Vehicle-bridge interaction is considered in the analysis. Utilizing a single randomly selected sensing agent at each timestamp, the results demonstrate the effectiveness of the framework in predicting bridge acceleration response with a relative error of less than 5%. Additionally, the method achieves an accuracy level of 95% in identifying the bridge's initial three mode shapes.

Smart Home Automation

The Smart Home Concept responds to the increasing need for integrating smart appliances and systems within residential environments. It includes a growing array of devices, services, and applications designed to simplify daily tasks and enhance the quality of life. Utilizing various technologies and standards, numerous device suppliers offer a wide range of solutions, including meters, actuators, sensors, and micro systems, which are integrated into the home environment. This advanced system incorporates sensors, artificial intelligence, and machine learning algorithms to develop an intelligent, responsive, and personalized living space. Continuous sensor data collection on environmental conditions and user behaviors allows AI to autonomously manage various home functions. The system emphasizes interoperability and standardization to ensure compatibility with a wide range of devices. Improvements in natural language processing and voice recognition further enhance human-machine interactions. This comprehensive approach aims to optimize energy efficiency, bolster security, and streamline daily activities, providing residents with a more intuitive and adaptable smart home experience in the evolving field of home automation.

Expediting Discharges on the Inpatient Medicine Teaching Services

Introduction: Delayed discharges can adversely affect patient flow throughout the hospital. Late afternoon discharges may lead to high hospital occupancy, admission bottlenecks, ER overcrowding, and disrupted ICU transfers. Discharge by noon (DBN) is key to enhancing hospital capacity, bed utilization, and patient satisfaction and outcomes. Last year, DBN for patients returning home with or without home care services (HHC) was chosen as one of three annual priorities for our hospital medicine division. This was implemented in October 2023 but did not gain momentum among the medical resident teaching services (MTS), paving the way for our QI project. Model for Improvement: Baseline data on the percentage of MTS DBN to home and HHC were available from November 2023 to January 2024. We surveyed our residents/faculty and conducted a root-cause analysis. The main barriers to DBN included a lack of planning and motivation, delayed communication with case management, and prolonged rounding. Aims Statement: Our goal was to increase the median percentage of MTS discharge orders placed by noon to home and HHC from 22% to 35% within 3 months. Measures: Our primary outcome measure was the percentage of MTS DBN to home and HHC. Weekly EMR-generated reports were collected. The outcome measure was then calculated and uploaded weekly on a run chart for analysis. Qualitative process measures included assessing if residents were planning discharges and documenting the number of discharges completed before noon. Changes: Our interventions targeted the lack of planning and motivation. These included introducing the expedited initiative and its importance to residents/faculty through conferences. In addition, a process was created where potential next-day discharges were discussed at afternoon sign-out and a tracking chart of DBN orders was designed for each team room. Tests of Change: Our pre-intervention median percentage of MTS DMB to home and HHC was 22% and improved to 40% post-intervention. While we observed a variation in discharge practice based on the rounding attending, we had notably sustained an upward shift in practice from our baseline when data was plotted on a run chart. We are currently continuing data collection to track sustainability and variability among rounding physicians. Conclusion: We attained an upward shift in the percentage of MTS DBN to home and HHC post-intervention. Although an upward shift has been achieved, we are looking to sustain and further improve it. In future PDSA cycles, we will look at individualizing feedback and optimizing interdisciplinary rounds. We plan on further investigating balancing measures such as bed availability, discharge timing for rehabilitation destinations, and patient and staff satisfaction.

Optimisation of Remote Monitoring Programmes in Heart Failure: Evaluation of Patient Drop-Out Behaviour and Healthcare Professionals' Perspectives.

Heart failure (HF) is a growing epidemic, affecting millions of people worldwide, and is a major cause of mortality, morbidity, and impaired quality of life. Traditional cardiac rehabilitation is a valuable approach to the physical and quality-of-life recovery of patients with cardiovascular disease. The innovative approach of remote monitoring through telemedicine offers a solution based on modern technologies, enabling continuous collection of health data outside the hospital environment. Remote monitoring devices present challenges that could adversely affect patient adherence, resulting in the risk of dropout. By applying a cognitive-behavioral model, we aim to identify the antecedents of dropout behavior among patients adhering to traditional cardiac rehabilitation programs and remote monitoring in order to improve the latter. Our study was conducted from October 2023 to January 2024. In the first stage, we used data from literature consultation. Subsequently, data were collected from the direct experience of 49 health workers related to both remote monitoring and traditional treatment, recruited from the authors' workplace. Results indicate that patients with cardiovascular disease tend to abandon remote monitoring programs more frequently than traditional cardiac rehabilitation therapies. It is critical to design approaches that take these barriers into account to improve adherence and patient satisfaction. This analysis identified specific antecedents to address, helping to improve current monitoring models. This is crucial to promote care continuity and to achieve self-management by patients in the future.

Wearable Devices in Colorectal Surgery: A Scoping Review.

Wearable devices are increasingly utilised to monitor patients perioperatively, allowing for continuous data collection and early complication detection. There is considerable variability in the types and usage settings of wearables, particularly within colorectal surgery. To address this, a scoping review was conducted to investigate current utilisation of wearable devices in colorectal surgery. A systematic search across MEDLINE and Embase was conducted following PRISMA Scoping Review guidelines. Results were synthesised narratively, categorised by perioperative phase (preoperative; postoperative; combination), and supplemented with descriptive statistics and tables. Out of 1525 studies initially identified, 20 were included, reporting data on 10 different wearable devices. Use of wearable devices varied across settings with those used preoperatively tending to focus on baseline physical status or prehabilitation, while postoperative use centred around monitoring and identification of complications. Wearable devices can enhance perioperative monitoring, enable proactive interventions, and promote personalised care for improved patient outcomes in colorectal surgery.

Thermoregulatory differences in sex and time-of-day within naturally ventilated office environments

Thermal comfort research lacks continuous data collection on thermoregulation and adaptive behavior in real-world environments. This paper presents the findings of a field study that aimed at understanding the temporal and individual differences between humans’ thermoregulation and adaptation within a dynamic thermal environment. The dataset contains a 6-week long summer field study of 2022 observing nine participants in a naturally ventilated office without a cooling system. The data collection comprises a total of 43 measurement days (22,776 data points at 60s frequency). The results indicate that the overall trend in clothing insulation deviates from standard assumptions (0.5 clo), especially above air temperatures of 26 °C. The mean clothing insulation was 0.29 clo ±0.08, overall ranging from 0.20 to 0.73 clo. The data showed significant sex differences in tonic levels of electrodermal activity, the low-frequency to high-frequency heart rate variability ratio, and clothing insulation. Other heart rate variability data, like the root mean square of successive differences and beats per minute, as well as the distal skin temperature of the wrist, show significant sex differences and time-of-day variations in morning-to-afternoon comparisons. The findings indicate a nuanced relationship between air temperature and thermoregulatory responses, highlighting the need for future research with larger, more diverse participant cohorts and enhanced environmental monitoring to strengthen the validity and generalizability of the findings.

Proactive health behavior in middle-aged and older adult females with urinary incontinence: A grounded theory study.

To develop a conceptual framework for proactive health behavior among middle-aged and older adult females with urinary incontinence. Qualitative grounded theory study. There is a growing body of research emphasizing the pivotal significance of proactive health behavior. Proactive health behavior can empower patients to actively manage their illnesses and facilitate disease recovery. Clearly defining patients' relevant beliefs and assumptions regarding proactive health behavior can effectively promote their adoption. However, there is currently a lack of relevant research in this area. We conducted in-depth interviews with middle-aged and older adult females with urinary incontinence (n = 17) and nursing caregivers (n = 9). We used theoretical sampling, whlie conducting continuous comparative analysisi and data collection. The study has yielded a substantive theory to facilitate healthcare professionals' comprehension of proactive health behavior in middle-aged and older adult females with urinary incontinence. The foundation for middle-aged and older adult females to adopt proactive health behavior is having a certain level of health literacy regarding their conditions. Patients' internal motivation to engage in proactive health behavior includes a sense of health responsibility and health demands. Additionally, external support received by patients can also facilitate their adoption of proactive health behavior. The proactive health behavior practices of middle-aged and older adult females mainly include proactive medical care behavior and establishing a healthy lifestyle. The conceptual framework established in this study offers theoretical support for middle-aged and older adult females with urinary incontinence to adopt proactive health behavior. It provides a basis for future exploration of proactive health behavior among this demographic and informs the development of more effective health interventions and support measures tailored to their needs. The study specifically elucidates the mechanisms and manifestations of proactive health behavior adopted by middle-aged and older adult females with urinary incontinence, laying the foundation for clarifying the level of proactive health among patients and implementing corresponding intervention measures. Additionally, it can also serve as a reference for related research on other diseases.

Exploring Digital Biomarkers for Depression Using Mobile Technology

Introduction &amp; BackgroundWith the advent of ubiquitous sensors and mobile technologies, wearables and smartphones offer a cost-effective means for monitoring mental health conditions, particularly depression. These devices enable the continuous collection of behavioral data, providing novel insights into the daily manifestations of depressive symptoms. Objectives &amp; ApproachThe present study summarizes findings from our five recent investigations that explored the relationships between depression severity and digital biomarkers captured by wearables and smartphones. These studies analyzed data from RADAR-MDD, a multinational mobile health program, involving 623 participants and tracked for up to two years. Participants' depression severity was measured biweekly using the PHQ-8 questionnaire conducted via smartphones. Concurrently, participants’ Fitbit and smartphone data were also collected. Given the longitudinal nature and repeated measurements for each participant, multilevel modeling techniques were employed to analyze the data. Relevance to Digital FootprintsOur approach involved extracting features from passive data that reflect various aspects of daily behavior—such as sleep quality, social interaction, physical activity, and walking patterns—akin to digital footprints. ResultsWe found several significant links between depression severity and various behavioral biomarkers: elevated depression levels were associated with diminished sleep quality (assessed through Fitbit metrics), reduced sociability (approximated by Bluetooth), decreased levels of physical activity (quantified by step counts and GPS data), a slower cadence of daily walking (captured by smartphone accelerometers), and disturbances in circadian rhythms (analyzed across various data streams). Conclusions &amp; ImplicationsLeveraging digital biomarkers for assessing and continuously monitoring depression introduces a new paradigm in early detection and development of customized intervention strategies. Findings from these studies not only enhance our comprehension of depression in real-world settings but also underscore the potential of mobile technologies in the prevention and management of mental health issues.

Wearable Devices for Respiratory Monitoring

AbstractRespiratory diseases are currently monitored through traditional pulmonary function tests, such as spirometry. However, the restrictions of these procedures, particularly in the context of the COVID‐19 pandemic, have underscored the need for alternative approaches to respiratory health assessment. Wearable devices have emerged as a promising solution, providing continuous data collection, and overcoming the limitations posed by conventional methods. This review explores the multifaceted field of wearable devices for respiratory monitoring, presenting the most common sensing technologies applied to pulmonary ventilation, their constituent materials, fabrication techniques, and diverse morphologies to enhance sensor performance. The role of machine learning algorithms and ethical data sharing is highlighted, contributing to the forthcoming patient‐centered healthcare landscape. Ultimately, the importance of validation and calibration protocols for wearable devices is underlined. In anticipation of evolving healthcare needs, this in‐depth study addresses the current challenges in wearable respiratory monitoring while laying a robust foundation for a personalized, connected, and ethically sound future for respiratory care.