Recent Trends of Bioanalytical Sensors with Smart Health Monitoring Systems: From Materials to Applications.

Smart biosensors attract significant interest due to real-time monitoring of user health status, where bioanalytical electronic devices designed to detect various activities and biomarkers in the human body have potential applications in physical sign monitoring and health care. Bioelectronics can be well integrated by output signals with wireless communication modules for transferring data to portable devices used as smart biosensors in performing real-time diagnosis and analysis. In this review, the scientific keys of biosensing devices and the current trends in the field of smart biosensors, (functional materials, technological approaches, sensing mechanisms, main roles, potential applications and challenges in health monitoring) will be summarized. Recent advances in the design and manufacturing of bioanalytical sensors with smarter capabilities and enhanced reliability indicate a forthcoming expansion of these smart devices from laboratory to clinical analysis. Therefore, a general description of functional materials and technological approaches used in bioelectronics will be presented after the sections of scientific keys to bioanalytical sensors. A careful introduction to the established systems of smart monitoring and prediction analysis using bioelectronics, regarding the integration of machine-learning-based basic algorithms, will be discussed. Afterward, applications and challenges in development using these smart bioelectronics in biological, clinical, and medical diagnostics will also be analyzed. Finally, the review will conclude with outlooks of smart biosensing devices assisted by machine learning algorithms, wireless communications, or smartphone-based systems on current trends and challenges for future works in wearable health monitoring.

A Decentralized Peer-to-Peer Remote Health Monitoring System.

Within the Internet of Things (IoT) and blockchain research, there is a growing interest in decentralizing health monitoring systems, to provide improved privacy to patients, without relying on trusted third parties for handling patients' sensitive health data. With public blockchain deployments being severely limited in their scalability, and inherently having latency in transaction processing, there is room for researching and developing new techniques to leverage the security features of blockchains within healthcare applications. This paper presents a solution for patients to share their biomedical data with their doctors without their data being handled by trusted third party entities. The solution is built on the Ethereum blockchain as a medium for negotiating and record-keeping, along with Tor for delivering data from patients to doctors. To highlight the applicability of the solution in various health monitoring scenarios, we have considered three use-cases, namely cardiac monitoring, sleep apnoea testing, and EEG following epileptic seizures. Following the discussion about the use cases, the paper outlines a security analysis performed on the proposed solution, based on multiple attack scenarios. Finally, the paper presents and discusses a performance evaluation in terms of data delivery time in comparison to existing centralized and decentralized solutions.

RADAR-Base: Open Source Mobile Health Platform for Collecting, Monitoring, and Analyzing Data Using Sensors, Wearables, and Mobile Devices.

BACKGROUND: With a wide range of use cases in both research and clinical domains, collecting continuous mobile health (mHealth) streaming data from multiple sources in a secure, highly scalable, and extensible platform is of high interest to the open source mHealth community. The European Union Innovative Medicines Initiative Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) program is an exemplary project with the requirements to support the collection of high-resolution data at scale; as such, the Remote Assessment of Disease and Relapse (RADAR)-base platform is designed to meet these needs and additionally facilitate a new generation of mHealth projects in this nascent field. OBJECTIVE: Wide-bandwidth networks, smartphone penetrance, and wearable sensors offer new possibilities for collecting near-real-time high-resolution datasets from large numbers of participants. The aim of this study was to build a platform that would cater for large-scale data collection for remote monitoring initiatives. Key criteria are around scalability, extensibility, security, and privacy. METHODS: RADAR-base is developed as a modular application; the backend is built on a backbone of the highly successful Confluent/Apache Kafka framework for streaming data. To facilitate scaling and ease of deployment, we use Docker containers to package the components of the platform. RADAR-base provides 2 main mobile apps for data collection, a Passive App and an Active App. Other third-Party Apps and sensors are easily integrated into the platform. Management user interfaces to support data collection and enrolment are also provided. RESULTS: General principles of the platform components and design of RADAR-base are presented here, with examples of the types of data currently being collected from devices used in RADAR-CNS projects: Multiple Sclerosis, Epilepsy, and Depression cohorts. CONCLUSIONS: RADAR-base is a fully functional, remote data collection platform built around Confluent/Apache Kafka and provides off-the-shelf components for projects interested in collecting mHealth datasets at scale.

Physiology and technology for the ICU in vivo.

This paper discusses the physiological and technological concepts that might form the future of critical care medicine. Initially, we discuss the need for a personalized approach and introduce the concept of personalized physiological medicine (PPM), including (1) assessment of frailty and physiological reserve, (2) continuous assessment of organ function, (3) assessment of the microcirculation and parenchymal cells, and (4) integration of organ and cell function for continuous therapeutic feedback control. To understand the cellular basis of organ failure, we discuss the processes that lead to cell death, including necrosis, necroptosis, autophagy, mitophagy, and cellular senescence. In vivo technology is used to monitor these processes. To this end, we discuss new materials for developing in vivo biosensors and drug delivery systems. Such in vivo biosensors will define the diagnostic platform of the future ICU in vivo interacting with theragnostic drugs. In addition to pharmacological therapeutic options, placement and control of artificial organs to support or replace failing organs will be central in the ICU in vivo of the future. Remote monitoring and control of these biosensors and artificial organs will be made using adaptive physiological mathematical modeling of the critically ill patient. The current state of these developments is discussed.

Assessment of Shoulder Range of Motion Using a Wireless Inertial Motion Capture Device-A Validation Study.

(1) Background: Measuring joint range of motion has traditionally occurred with a universal goniometer or expensive laboratory based kinematic analysis systems. Technological advances in wearable inertial measurement units (IMU) enables limb motion to be measured with a small portable electronic device. This paper aims to validate an IMU, the 'Biokin', for measuring shoulder range of motion in healthy adults; (2) Methods: Thirty participants completed four shoulder movements (forward flexion, abduction, and internal and external rotation) on each shoulder. Each movement was assessed with a goniometer and the IMU by two testers independently. The extent of agreement between each tester's goniometer and IMU measurements was assessed with intra-class correlation coefficients (ICC) and Bland-Altman 95% limits of agreement (LOA). Secondary analysis compared agreement between tester's goniometer or IMU measurements (inter-rater reliability) using ICC's and LOA; (3) Results: Goniometer and IMU measurements for all movements showed high levels of agreement when taken by the same tester; ICCs > 0.90 and LOAs < ±5 degrees. Inter-rater reliability was lower; ICCs ranged between 0.71 to 0.89 and LOAs were outside a prior defined acceptable LOAs (i.e., > ±5 degrees); (4) Conclusions: The current study provides preliminary evidence of the concurrent validity of the Biokin IMU for assessing shoulder movements, but only when a single tester took measurements. Further testing of the Biokin's psychometric properties is required before it can be confidently used in routine clinical practice and research settings.

Digital diabetes: Perspectives for diabetes prevention, management and research.

Digital medicine, digital research and artificial intelligence (AI) have the power to transform the field of diabetes with continuous and no-burden remote monitoring of patients' symptoms, physiological data, behaviours, and social and environmental contexts through the use of wearables, sensors and smartphone technologies. Moreover, data generated online and by digital technologies - which the authors suggest be grouped under the term 'digitosome' - constitute, through the quantity and variety of information they represent, a powerful potential for identifying new digital markers and patterns of risk that, ultimately, when combined with clinical data, can improve diabetes management and quality of life, and also prevent diabetes-related complications. Moving from a world in which patients are characterized by only a few recent measurements of fasting glucose levels and glycated haemoglobin to a world where patients, healthcare professionals and research scientists can consider various key parameters at thousands of time points simultaneously will profoundly change the way diabetes is prevented, managed and characterized in patients living with diabetes, as well as how it is scientifically researched. Indeed, the present review looks at how the digitization of diabetes can impact all fields of diabetes - its prevention, management, technology and research - and how it can complement, but not replace, what is usually done in traditional clinical settings. Such a profound shift is a genuine game changer that should be embraced by all, as it can provide solid research results transferable to patients, improve general health literacy, and provide tools to facilitate the everyday decision-making process by both healthcare professionals and patients living with diabetes.

Can We Improve Physical Health Monitoring for Patients Taking Antipsychotics on a Mental Health Inpatient Unit?

BACKGROUND: Patients with severe mental illness are at risk of medical complications, including cardiovascular disease, metabolic syndrome, and diabetes. Given this vulnerability, combined with metabolic risks of antipsychotics, physical health monitoring is critical. Inpatient admission is an opportunity to screen for medical comorbidities. Our objective was to improve the rates of physical health monitoring on an inpatient psychiatry unit through implementation of an electronic standardized order set. METHODS: Using a clinical audit tool, we completed a baseline retrospective audit (96 eligible charts) of patients aged 18 to 100 years, discharged between January and March 2012, prescribed an antipsychotic for 3 or more days. We then developed and implemented a standard electronic admission order set and provided training to inpatient clinical staff. We completed a second chart audit of patients discharged between January and March 2016 (190 eligible charts) to measure improvement in physical health monitoring and intervention rates for abnormal results. RESULTS: In the 2012 audit, thyroid-stimulating hormone (TSH), blood pressure, blood glucose, fasting lipids, electrocardiogram (ECG), and height/weight were measured in 71%, 92%, 31%, 36%, 51%, and 75% of patients, respectively. In the 2016 audit, TSH, blood pressure, blood glucose, fasting lipids, ECG, and height/weight were measured in 86%, 96%, 96%, 64%, 87%, and 71% of patients, respectively. There were statistically significant improvements (P < 0.05) in monitoring rates for blood glucose, lipids, ECG, and TSH. Intervention rates for abnormal blood glucose and/or lipids (feedback to family doctor and/or patient, consultation to hospitalist, endocrinology, and/or dietician) did not change between 2012 and 2016. CONCLUSIONS: Electronic standardized order set can be used as a tool to improve screening for physical health comorbidity in patients with severe mental illness receiving antipsychotic medications.

Advancing Continuous Predictive Analytics Monitoring: Moving from Implementation to Clinical Action in a Learning Health System.

In the intensive care unit, clinicians monitor a diverse array of data inputs to detect early signs of impending clinical demise or improvement. Continuous predictive analytics monitoring synthesizes data from a variety of inputs into a risk estimate that clinicians can observe in a streaming environment. For this to be useful, clinicians must engage with the data in a way that makes sense for their clinical workflow in the context of a learning health system (LHS). This article describes the processes needed to evoke clinical action after initiation of continuous predictive analytics monitoring in an LHS.

[The use of telemedicine in dialysis and new professional practices]. / Usage de la télémédecine en dialyse et nouvelles pratiques professionnelles.

Since 2006, a remote patient monitoring scheme using digital pens has enabled patients with chronic kidney disease to be monitored remotely in their own home. The implementation of this project was accompanied by a technical and economic study. Today, this scheme has evolved to integrate therapeutic patient education programmes and their evaluation.

"Next-generation psychiatric assessment: Using smartphone sensors to monitor behavior and mental health": Correction to Ben-Zeev et al. (2015).

UNLABELLED: Reports an error in "Next-generation psychiatric assessment: Using smartphone sensors to monitor behavior and mental health" by Dror Ben-Zeev, Emily A. Scherer, Rui Wang, Haiyi Xie and Andrew T. Campbell (Psychiatric Rehabilitation Journal, 2015[Sep], Vol 38[3], 218-226). Model fit statistics in Table 1 are reported as a row for Model 2, but not for Model 1, due to a production error. Model 1 fit statistics should appear as a row with the following information: 2LL 1490.0, AIC 1498.0 & BIC 1505.3. (The following abstract of the original article appeared in record 2015-14736-001.) OBJECTIVE: Optimal mental health care is dependent upon sensitive and early detection of mental health problems. We have introduced a state-of-the-art method for the current study for remote behavioral monitoring that transports assessment out of the clinic and into the environments in which individuals negotiate their daily lives. The objective of this study was to examine whether the information captured with multimodal smartphone sensors can serve as behavioral markers for one's mental health. We hypothesized that (a) unobtrusively collected smartphone sensor data would be associated with individuals' daily levels of stress, and (b) sensor data would be associated with changes in depression, stress, and subjective loneliness over time. METHOD: A total of 47 young adults (age range: 19-30 years) were recruited for the study. Individuals were enrolled as a single cohort and participated in the study over a 10-week period. Participants were provided with smartphones embedded with a range of sensors and software that enabled continuous tracking of their geospatial activity (using the Global Positioning System and wireless fidelity), kinesthetic activity (using multiaxial accelerometers), sleep duration (modeled using device-usage data, accelerometer inferences, ambient sound features, and ambient light levels), and time spent proximal to human speech (i.e., speech duration using microphone and speech detection algorithms). Participants completed daily ratings of stress, as well as pre- and postmeasures of depression (Patient Health Questionnaire-9; Spitzer, Kroenke, & Williams, 1999), stress (Perceived Stress Scale; Cohen et al., 1983), and loneliness (Revised UCLA Loneliness Scale; Russell, Peplau, & Cutrona, 1980). RESULTS: Mixed-effects linear modeling showed that sensor-derived geospatial activity (p < .05), sleep duration (p < .05), and variability in geospatial activity (p < .05), were associated with daily stress levels. Penalized functional regression showed associations between changes in depression and sensor-derived speech duration (p < .05), geospatial activity (p < .05), and sleep duration (p < .05). Changes in loneliness were associated with sensor-derived kinesthetic activity (p < .01). CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: Smartphones can be harnessed as instruments for unobtrusive monitoring of several behavioral indicators of mental health. Creative leveraging of smartphone sensing could provide novel opportunities for close-to-invisible psychiatric assessment at a scale and efficiency that far exceeds what is currently feasible with existing assessment technologies. (PsycINFO Database Record

To test or not to test? An updated evidence-based assessment of the value of screening and monitoring tests when using systemic biologic agents to treat psoriasis and psoriatic arthritis.

BACKGROUND: Safety profiles of systemic biologic agents for the treatment of psoriasis and psoriatic arthritis (PsA) encompass a wide spectrum of adverse events. To date, no uniform evidence-based guidelines exist regarding screening and monitoring patients who are undergoing biologic therapy. OBJECTIVE: We sought to identify studies evaluating screening and monitoring tests in the treatment of psoriasis and PsA with systemic biologic agents, and to propose evidence-based practical guidelines. METHODS: The MEDLINE database was searched to identify data on risks associated with adalimumab, etanercept, infliximab, and ustekinumab. Articles were reviewed and graded according to methods developed by the US Preventative Services Task Force. RESULTS: Evidence was strongest (grade B) for tuberculosis screening. Interferon-gamma release assay was preferable to tuberculin skin testing. Among known hepatitis B virus carriers, the evidence grade was C for monitoring liver function tests and viral load. LIMITATIONS: This study was limited by the lack of high-quality controlled trials evaluating screening and monitoring tests in patients treated with biologic agents. CONCLUSIONS: Baseline tuberculosis testing remains the only screening test with strong evidence to support its practice. Other screening and monitoring tests commonly performed in patients who are taking biologic agents are supported only in certain clinical settings or lack evidence to support or recommend against their practice.

[Risk management of a patient on the basis of remote health monitoring: current situation and prospects].

Review is dedicated to the problem of remote monitoring of health status. Existing approaches to the organization of an outdoor monitoring of a patient using telemedicine technologies are reviewed and analyzed. A new approach to risk management of a patient which meets the requirements of pediatric hospital is provided.

Assessment of the feasibility of an ultra-low power, wireless digital patch for the continuous ambulatory monitoring of vital signs.

BACKGROUND AND OBJECTIVES: Vital signs are usually recorded at 4-8 h intervals in hospital patients, and deterioration between measurements can have serious consequences. The primary study objective was to assess agreement between a new ultra-low power, wireless and wearable surveillance system for continuous ambulatory monitoring of vital signs and a widely used clinical vital signs monitor. The secondary objective was to examine the system's ability to automatically identify and reject invalid physiological data. SETTING: Single hospital centre. PARTICIPANTS: Heart and respiratory rate were recorded over 2 h in 20 patients undergoing elective surgery and a second group of 41 patients with comorbid conditions, in the general ward. OUTCOME MEASURES: Primary outcome measures were limits of agreement and bias. The secondary outcome measure was proportion of data rejected. RESULTS: The digital patch provided reliable heart rate values in the majority of patients (about 80%) with normal sinus rhythm, and in the presence of abnormal ECG recordings (excluding aperiodic arrhythmias such as atrial fibrillation). The mean difference between systems was less than ±1 bpm in all patient groups studied. Although respiratory data were more frequently rejected as invalid because of the high sensitivity of impedance pneumography to motion artefacts, valid rates were reported for 50% of recordings with a mean difference of less than ±1 brpm compared with the bedside monitor. Correlation between systems was statistically significant (p<0.0001) for heart and respiratory rate, apart from respiratory rate in patients with atrial fibrillation (p=0.02). CONCLUSIONS: Overall agreement between digital patch and clinical monitor was satisfactory, as was the efficacy of the system for automatic rejection of invalid data. Wireless monitoring technologies, such as the one tested, may offer clinical value when implemented as part of wider hospital systems that integrate and support existing clinical protocols and workflows.