Smart wearable body sensors for patient self-assessment and monitoring

A Novel Unsupervised Machine Learning Approach to Assess Postural Dynamics in Euthymic Bipolar Disorder

Bipolar disorder (BD) is a mood disorder with different phases alternating between euthymia, manic or hypomanic episodes, and depressive episodes. While motor abnormalities are commonly seen during depressive or manic episodes, not much attention has been paid to postural abnormalities during periods of euthymia and their association with illness burden. We collected 24-hour posture data in 32 euthymic participants diagnosed with BD using a shirt-based wearable. We extracted a set of nine time-domain features, and performed unsupervised participant clustering. We investigated the association between posture variables and 12 clinical characteristics of illness burden. Based on their postural dynamics during the daytime, evening, or nighttime, participants clustered in three clusters. Higher illness burden was associated with lower postural variability, in particular during daytime. Participants who exhibited a mostly upright sitting/standing posture during the night with frequent nighttime postural transitions had the highest number of lifetime depressive episodes. Euthymic participants with BD exhibit postural abnormalities that are associated with illness burden, especially with the number of depressive episodes. Our results contribute to understanding the role of illness burden on posture changes and sleep consolidation in periods of euthymia.

Apple Watch 6 vs. Galaxy Watch 4: A Validity Study of Step-Count Estimation in Daily Activities

The purpose of this study was to examine the validity of two wearable smartwatches (the Apple Watch 6 (AW) and the Galaxy Watch 4 (GW)) and smartphone applications (Apple Health for iPhone mobiles and Samsung Health for Android mobiles) for estimating step counts in daily life. A total of 104 healthy adults (36 AW, 25 GW, and 43 smartphone application users) were engaged in daily activities for 24 h while wearing an ActivPAL accelerometer on the thigh and a smartwatch on the wrist. The validities of the smartwatch and smartphone estimates of step counts were evaluated relative to criterion values obtained from an ActivPAL accelerometer. The strongest relationship between the ActivPAL accelerometer and the devices was found for the AW (r = 0.99, p < 0.001), followed by the GW (r = 0.82, p < 0.001), and the smartphone applications (r = 0.93, p < 0.001). For overall group comparisons, the MAPE (Mean Absolute Percentage Error) values (computed as the average absolute value of the group-level errors) were 6.4%, 10.5%, and 29.6% for the AW, GW, and smartphone applications, respectively. The results of the present study indicate that the AW and GW showed strong validity in measuring steps, while the smartphone applications did not provide reliable step counts in free-living conditions.

Research on Monitoring Assistive Devices for Rehabilitation of Movement Disorders through Multi-Sensor Analysis Combined with Deep Learning

This study aims to integrate a convolutional neural network (CNN) and the Random Forest Model into a rehabilitation assessment device to provide a comprehensive gait analysis in the evaluation of movement disorders to help physicians evaluate rehabilitation progress by distinguishing gait characteristics under different walking modes. Equipped with accelerometers and six-axis force sensors, the device monitors body symmetry and upper limb strength during rehabilitation. Data were collected from normal and abnormal walking groups. A knee joint limiter was applied to subjects to simulate different levels of movement disorders. Features were extracted from the collected data and analyzed using a CNN. The overall performance was scored with Random Forest Model weights. Significant differences in average acceleration values between the moderately abnormal (MA) and severely abnormal (SA) groups (without vehicle assistance) were observed (p < 0.05), whereas no significant differences were found between the MA with vehicle assistance (MA-V) and SA with vehicle assistance (SA-V) groups (p > 0.05). Force sensor data showed good concentration in the normal walking group and more scatter in the SA-V group. The CNN and Random Forest Model accurately recognized gait conditions, achieving average accuracies of 88.4% and 92.3%, respectively, proving that the method mentioned above provides more accurate gait evaluations for patients with movement disorders.

Experiences With Mobile Health-Enabled Ambulatory Monitoring Among Stroke Survivors: A Qualitative Study

Inquiring into the experiences of stroke survivors toward ambulatory monitoring is crucial for optimizing user adoption, design, implementation, and sustainability of ambulatory monitoring in the stroke population. This study was aimed to identify facilitators and barriers for ambulatory monitoring among stroke survivors, as well as their suggestions for development and implementation of ambulatory monitoring. We conducted individual semi-structured interviews with 40 stroke survivors who received ambulatory monitoring. The interviews were analyzed using thematic content analysis. Six themes about facilitators associated with ambulatory monitoring emerged: (1) user support, (2) technological features, (3) convenience, (4) personal strategies, (5)social influence, and (6)time commitment. Three themes about barriers to using ambulatory monitoring emerged: (1) personal factors, (2) functionality, (3) study design. Three themes about suggestions emerged: (1) personalization, (2) functionality, and (3) interactive feedback. As mobile health technology is becoming more popular, the findings of this study provide timely implications and practical considerations for ambulatory monitoring in the stroke population.

Detecting Eating Episodes From Wrist Motion Using Daily Pattern Analysis

This paper presents new methods to detect eating from wrist motion. Our main novelty is that we analyze a full day of wrist motion data as a single sample so that the detection of eating occurrences can benefit from diurnal context. We develop a two-stage framework to facilitate a feasible full-day analysis. The first-stage model calculates local probabilities of eating P(Ew) within windows of data, and the second-stage model calculates enhanced probabilities of eating P(Ed) by treating all P(Ew) within a single day as one sample. The framework also incorporates an augmentation technique, which involves the iterative retraining of the first-stage model. This allows us to generate a sufficient number of day-length samples from datasets of limited size. We test our methods on the publicly available Clemson All-Day (CAD) dataset and FreeFIC dataset, and find that the inclusion of day-length analysis substantially improves accuracy in detecting eating episodes. We also benchmark our results against several state-of-the-art methods. Our approach achieved an eating episode true positive rate (TPR) of 89% with 1.4 false positives per true positive (FP/TP), and a time weighted accuracy of 84%, which are the highest accuracies reported on the CAD dataset. Our results show that the daily pattern classifier substantially improves meal detections and in particular reduces transient false detections that tend to occur when relying on shorter windows to look for individual ingestion or consumption events.

The Role and Importance of Using Sensor-Based Devices in Medical Rehabilitation: A Literature Review on the New Therapeutic Approaches

Due to the growth of sensor technology, more affordable integrated circuits, and connectivity technologies, the usage of wearable equipment and sensing devices for monitoring physical activities, whether for wellness, sports monitoring, or medical rehabilitation, has exploded. The current literature review was performed between October 2022 and February 2023 using PubMed, Web of Science, and Scopus in accordance with P.R.I.S.M.A. criteria. The screening phase resulted in the exclusion of 69 articles that did not fit the themes developed in all subchapters of the study, 41 articles that dealt exclusively with rehabilitation and orthopaedics, 28 articles whose abstracts were not visible, and 10 articles that dealt exclusively with other sensor-based devices and not medical ones; the inclusion phase resulted in the inclusion of 111 articles. Patients who utilise sensor-based devices have several advantages due to rehabilitating a missing component, which marks the accomplishment of a fundamental goal within the rehabilitation program. As technology moves faster and faster forward, the field of medical rehabilitation has to adapt to the time we live in by using technology and intelligent devices. This means changing every part of rehabilitation and finding the most valuable and helpful gadgets that can be used to regain lost functions, keep people healthy, or prevent diseases.

Experience and insights from a prolonged electrocardiographic monitorization with a wearable system after an embolic stroke of unknown source

BACKGROUND

Embolic cerebrovascular events that remain of unknown etiology after a thorough diagnostic evaluation, are known as Embolic Strokes of Undetermined Source (ESUS). Subclinical atrial fibrillation (AF) represents a significant underlying cause of ESUS. Our aims were to examine the overall diagnostic yield of a prolonged cardiac monitoring wearable system (PCMw) after an ESUS to detect AF and factors associated with it, including the time frame from the ESUS event to PCMw initiation. Additionally, to evaluate the frequency of unexpected arrhythmic events (UAE) and their prognostic implications.

METHODS

We retrospectively analyzed 200 ECG recordings (3-leads, 30 days duration) by means of a PCMw in patients with an ESUS to detect AF lasting longer than 30 s, between 2017 and 2021. UAE were defined as arrhythmia events that were not correlated to the main reason of prolonged cardiac monitoring.

RESULTS

AF was detected in 21 patients (10.5%). Patients with AF had more left atrial enlargement (OR = 4.22 [1.59-6.85]; p = .01) and atrial arrythmias in the initial 24-h Holter during hospitalization (OR = 5.73 [2.03-16.49]; p = .001). The detection of AF was significatively higher if the PCMw was worn within the first 30 days after the ESUS compared to beyond 30 days (17% vs. 10.3%; p = .002). Fifty three patients (26.5%) had UAE during PCMw. In six of them these findings led to targeted treatment.

CONCLUSION

PCMw represents a feasible non-invasive device that could reliably detect subclinical AF episodes after an ESUS. Diagnostic yield was significatively higher when used within the first 30 days after the event, especially in selected patients. UAE were common, but did not impact prognosis.

Wearable sensor platforms for real-time monitoring and early warning of metabolic disorders in humans

Nowadays, the prevalence of metabolic syndromes (MSs) has attracted increasing concerns as it is closely related to overweight and obesity, physical inactivity and overconsumption of energy, making the diagnosis and real-time monitoring of the physiological range essential and necessary for avoiding illness due to defects in the human body such as higher risk of cardiovascular disease, diabetes, stroke and diseases related to artery walls. However, the current sensing techniques are inconvenient and do not continuously monitor the health status of humans. Alternatively, the use of recent wearable device technology is a preferable method for the prevention of these diseases. This can enable the monitoring of the health status of humans in different health domains, including environment and structure. The use wearable devices with the purpose of facilitating rapid treatment and real-time monitoring can decrease the prevalence of MS and long-time monitor the health status of patients. This review highlights the recent advances in wearable sensors toward continuous monitoring of blood pressure and blood glucose, and further details the monitoring of abnormal obesity, triglycerides and HDL. We also discuss the challenges and future prospective of monitoring MS in humans.

Current state of the art and future directions for implantable sensors in medical technology: Clinical needs and engineering challenges

Implantable sensors have revolutionized the way we monitor biophysical and biochemical parameters by enabling real-time closed-loop intervention or therapy. These technologies align with the new era of healthcare known as healthcare 5.0, which encompasses smart disease control and detection, virtual care, intelligent health management, smart monitoring, and decision-making. This review explores the diverse biomedical applications of implantable temperature, mechanical, electrophysiological, optical, and electrochemical sensors. We delve into the engineering principles that serve as the foundation for their development. We also address the challenges faced by researchers and designers in bridging the gap between implantable sensor research and their clinical adoption by emphasizing the importance of careful consideration of clinical requirements and engineering challenges. We highlight the need for future research to explore issues such as long-term performance, biocompatibility, and power sources, as well as the potential for implantable sensors to transform healthcare across multiple disciplines. It is evident that implantable sensors have immense potential in the field of medical technology. However, the gap between research and clinical adoption remains wide, and there are still major obstacles to overcome before they can become a widely adopted part of medical practice.

Telemedicine in Primary Practice in the Age of the COVID-19 Pandemic-Review

Background and Objectives: In the era of the COVID-19 pandemic, telemedicine, so far underestimated, has gained in value. Currently, telemedicine is not only a telephone or chat consultation, but also the possibility of the remote recording of signals (such as ECG, saturation, and heart rate) or even remote auscultation of the lungs. The objective of this review article is to present a potential role for, and disseminate knowledge of, telemedicine during the COVID-19 pandemic. Material and Methods: In order to analyze the research material in accordance with PRISMA guidelines, a systematic search of the ScienceDirect, Web of Science, and PubMed databases was conducted. Out of the total number of 363 papers identified, 22 original articles were subjected to analysis. Results: This article presents the possibilities of remote patient registration, which contributes to an improvement in remote diagnostics and diagnoses. Conclusions: Telemedicine is, although not always and not by everyone, an accepted form of providing medical services. It cannot replace direct patient-doctor contact, but it can undoubtedly contribute to accelerating diagnoses and improving their quality at a distance.

Reliability analysis of body sensor networks with correlated isolation groups

Body sensor networks (BSNs) are playing a crucial role in tackling arising challenges during the COVID-19 pandemic. This work contributes by modeling and analyzing the BSN reliability considering the effects of correlated functional dependence (FDEP) and random isolation time behavior. Particularly, the FDEP exists in BSNs where a relay is utilized to assist the communication between some biosensors and the sink device. When the relay malfunctions, the dependent biosensors may communicate directly with the sink for a limited, uncertain time. These biosensors then become isolated from the rest of the BSN when their remaining power depletes to the level insufficient to support the direct communication. Moreover, multiple biosensors sharing the same relay and a biosensor communicating with the sink via several alternative relays create correlations among different FDEP groups. In addition, the competition in the time domain exists between the local failure of the relay and the propagated failures of dependent biosensors. Both the correlation and competition complicate the reliability modeling and analysis of BSNs. This work proposes a combinatorial and analytical methodology to address both effects in the BSN reliability analysis. The proposed method is demonstrated using a detailed case study and verified using a continuous-time Markov chain method.

5G Technology in Healthcare and Wearable Devices: A Review

Wearable devices with 5G technology are currently more ingrained in our daily lives, and they will now be a part of our bodies too. The requirement for personal health monitoring and preventive disease is increasing due to the predictable dramatic increase in the number of aging people. Technologies with 5G in wearables and healthcare can intensely reduce the cost of diagnosing and preventing diseases and saving patient lives. This paper reviewed the benefits of 5G technologies, which are implemented in healthcare and wearable devices such as patient health monitoring using 5G, continuous monitoring of chronic diseases using 5G, management of preventing infectious diseases using 5G, robotic surgery using 5G, and 5G with future of wearables. It has the potential to have a direct effect on clinical decision making. This technology could improve patient rehabilitation outside of hospitals and monitor human physical activity continuously. This paper draws the conclusion that the widespread adoption of 5G technology by healthcare systems enables sick people to access specialists who would be unavailable and receive correct care more conveniently.

Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review

The prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient's autonomy.

Opportunities for Improving Motor Assessment and Rehabilitation After Stroke by Leveraging Video-Based Pose Estimation

ABSTRACT

Stroke is a leading cause of long-term disability in adults in the United States. As the healthcare system moves further into an era of digital medicine and remote monitoring, technology continues to play an increasingly important role in post-stroke care. In this Analysis and Perspective article, opportunities for using human pose estimation-an emerging technology that uses artificial intelligence to track human movement kinematics from simple videos recorded using household devices (e.g., smartphones, tablets)-to improve motor assessment and rehabilitation after stroke are discussed. The focus is on the potential of two key applications: (1) improving access to quantitative, objective motor assessment and (2) advancing telerehabilitation for persons post-stroke.

Comparison of Slate Safety Wearable Device to Ingestible Pill and Wearable Heart Rate Monitor

BACKGROUND

With the increase in concern for deaths and illness related to the increase in temperature globally, there is a growing need for real-time monitoring of workers for heat stress indicators. The purpose of this study was to determine the usability of the Slate Safety (SS) wearable physiological monitoring system.

METHODS

Twenty nurses performed a common task in a moderate or hot environment while wearing the SS device, the Polar 10 monitor, and having taken the e-Celsius ingestible pill. Data from each device was compared for correlation and accuracy.

RESULTS

High correlation was determined between the SS wearable device and the Polar 10 system (0.926) and the ingestible pill (0.595). The SS was comfortable to wear and easily monitored multiple participants from a distance.

CONCLUSIONS

The Slate Safety wearable device demonstrated accuracy in measuring core temperature and heart rate while not restricting the motion of the worker, and provided a remote monitoring platform for physiological parameters.

Regulatory Qualification of a Cross-Disease Digital Measure: Benefits and Challenges from the Perspective of IMI Consortium IDEA-FAST

Background

Innovative Medicines Initiative (IMI) consortium IDEA-FAST is developing novel digital measures of fatigue, sleep quality, and impact of sleep disturbances for neurodegenerative diseases and immune-mediated inflammatory diseases. In 2022, the consortium met with the European Medicines Agency (EMA) to receive advice on its plans for regulatory qualification of the measures. This viewpoint reviews the IDEA-FAST perspective on developing digital measures for multiple diseases and the advice provided by the EMA.

Summary

The EMA considered a cross-disease measure an interesting and arguably feasible concept. Developers should account for the need for a strong rationale that the clinical features to be measured are similar across diseases. In addition, they may expect increased complexity of study design, challenges when managing differences within and between disease populations, and the need for validation in both heterogeneous and homogeneous populations.

Key Messages

EMA highlighted the challenges teams may encounter when developing a cross-disease measure, though benefits potentially include reduced resources for the technology developer and health authority, faster access to innovation across different therapeutic fields, and feasibility of cross-disease comparisons. The insights included here can be used by project teams to guide them in the development of cross-disease digital measures intended for regulatory qualification.

Blockchain Enabled Anonymous Privacy-Preserving Authentication Scheme for Internet of Health Things

The Internet of Health Things (IoHT) has emerged as an attractive networking paradigm in wireless communications, integrated devices and embedded system technologies. In the IoHT, real-time health data are collected through smart healthcare sensors and, in recent years, the IoHT has started to have an important role in the Internet of Things technology. Although the IoHT provides comfort in health monitoring, it also imposes security challenges in maintaining patient data confidentiality and privacy. To overcome such security issues, in this paper, a novel blockchain-based privacy-preserving authentication scheme is proposed as an approach for achieving efficient authentication of the patient without the involvement of a trusted entity. Moreover, a secure handover authentication mechanism that ensures avoiding the patient re-authentication in multi-doctor communication scenarios and revoking the possible malicious misbehavior of medical professionals in the IoHT communication with the patient is developed. The performance of the proposed authentication and handover scheme is analyzed concerning the existing state-of-the-art authentication schemes. The results of the performance analyses reveal that the proposed authentication scheme is resistant to different types of security attacks. Moreover, the results of analyses show that the proposed authentication scheme outperforms similar state-of-the-art authentication schemes in terms of having lower computational, communication and storage costs. Therefore, the novel authentication and handover scheme has proven practical applicability and represents a valuable contribution to improving the security of communication in IoHT networks.

Patterning Wettability for Open-Surface Fluidic Manipulation: Fundamentals and Applications

Effective manipulation of liquids on open surfaces without external energy input is indispensable for the advancement of point-of-care diagnostic devices. Open-surface microfluidics has the potential to benefit health care, especially in the developing world. This review highlights the prospects for harnessing capillary forces on surface-microfluidic platforms, chiefly by inducing smooth gradients or sharp steps of wettability on substrates, to elicit passive liquid transport and higher-order fluidic manipulations without off-the-chip energy sources. A broad spectrum of the recent progress in the emerging field of passive surface microfluidics is highlighted, and its promise for developing facile, low-cost, easy-to-operate microfluidic devices is discussed in light of recent applications, not only in the domain of biomedical microfluidics but also in the general areas of energy and water conservation.

Smart Wearable Systems for the Remote Monitoring of Selected Vascular Disorders of the Lower Extremity: A Systematic Review

This systematic review aims at providing an overview of the state of the art regarding smart wearable systems (SWS) applications to monitor the status of patients suffering from vascular disorders of the lower extremity. Peer-reviewed literature has been analyzed to identify employed data collection methods, system characteristics, and functionalities, and research challenges and limitations to be addressed. The Medline (PubMed) and SCOPUS databases were considered to search for publications describing SWS for remote or continuous monitoring of patients suffering from intermittent claudication, venous ulcers, and diabetic foot ulcers. Publications were first screened based on whether they describe an SWS applicable to the three selected vascular disorders of the lower extremity, including data processing and output to users. Information extracted from publications included targeted disease, clinical parameters to be measured and wearable devices used; system outputs to the user; system characteristics, including capabilities of remote or continuous monitoring or functionalities resulting from advanced data analyses, such as coaching, recommendations, or alerts; challenges and limitations reported; and research outputs. A total of 128 publications were considered in the full-text analysis, and 54 were finally included after eligibility criteria assessment by four independent reviewers. Our results were structured and discussed according to three main topics consisting of data collection, system functionalities, and limitations and challenges.

eHealth: A Survey of Architectures, Developments in mHealth, Security Concerns and Solutions

The ramifications of the COVID-19 pandemic have contributed in part to a recent upsurge in the study and development of eHealth systems. Although it is almost impossible to cover all aspects of eHealth in a single discussion, three critical areas have gained traction. These include the need for acceptable eHealth architectures, the development of mobile health (mHealth) technologies, and the need to address eHealth system security concerns. Existing survey articles lack a synthesis of the most recent advancements in the development of architectures, mHealth solutions, and innovative security measures, which are essential components of effective eHealth systems. Consequently, the present article aims at providing an encompassing survey of these three aspects towards the development of successful and efficient eHealth systems. Firstly, we discuss the most recent innovations in eHealth architectures, such as blockchain-, Internet of Things (IoT)-, and cloud-based architectures, focusing on their respective benefits and drawbacks while also providing an overview of how they might be implemented and used. Concerning mHealth and security, we focus on key developments in both areas while discussing other critical topics of importance for eHealth systems. We close with a discussion of the important research challenges and potential future directions as they pertain to architecture, mHealth, and security concerns. This survey gives a comprehensive overview, including the merits and limitations of several possible technologies for the development of eHealth systems. This endeavor offers researchers and developers a quick snapshot of the information necessary during the design and decision-making phases of the eHealth system development lifecycle. Furthermore, we conclude that building a unified architecture for eHealth systems would require combining several existing designs. It also points out that there are still a number of problems to be solved, so more research and investment are needed to develop and deploy functional eHealth systems.

Decision support system for the differentiation of schizophrenia and mood disorders using multiple deep learning models on wearable devices data

In the modern world, with so much inherent stress, mental health disorders (MHDs) are becoming more common in every country around the globe, causing a significant burden on society and patients' families. MHDs come in many forms with various severities of symptoms and differing periods of suffering, and as a result it is difficult to differentiate between them and simple to confuse them with each other. Therefore, we propose a support system that employs deep learning (DL) with wearable device data to provide physicians with an objective reference resource by which to make differential diagnoses and plan treatment. We conducted experiments on open datasets containing activity motion signal data from wearable devices to identify schizophrenia and mood disorders (bipolar and unipolar), the datasets being named Psykose and Depresjon. The results showed that, in both workflow approaches, the proposed framework performed well in comparison with the traditional machine learning (ML) and DL methods. We concluded that applying DL models using activity motion signal data from wearable devices represents a prospective objective support system for MHD differentiation with a good performance.

Advantage of Vital Sign Monitoring Using a Wireless Wearable Device for Predicting Septic Shock in Febrile Patients in the Emergency Department: A Machine Learning-Based Analysis

Intermittent manual measurement of vital signs may not rapidly predict sepsis development in febrile patients admitted to the emergency department (ED). We aimed to evaluate the predictive performance of a wireless monitoring device that continuously measures heart rate (HR) and respiratory rate (RR) and a machine learning analysis in febrile but stable patients in the ED. We analysed 468 patients (age, ≥18 years; training set, n = 277; validation set, n = 93; test set, n = 98) having fever (temperature >38 °C) and admitted to the isolation care unit of the ED. The AUROC of the fragmented model with device data was 0.858 (95% confidence interval [CI], 0.809−0.908), and that with manual data was 0.841 (95% CI, 0.789−0.893). The AUROC of the accumulated model with device data was 0.861 (95% CI, 0.811−0.910), and that with manual data was 0.853 (95% CI, 0.803−0.903). Fragmented and accumulated models with device data detected clinical deterioration in febrile patients at risk of septic shock 9 h and 5 h 30 min earlier, respectively, than those with manual data. Continuous vital sign monitoring using a wearable device could accurately predict clinical deterioration and reduce the time to recognise potential clinical deterioration in stable ED patients with fever.

Detection of Sleep Paralysis by using IoT Based Device and Its Relationship Between Sleep Paralysis And Sleep Quality

When a person wakes up in the middle of the night, they are paralyzed. Despite the fact that most episodes are associated with extreme terror and some might cause clinically significant suffering, little is understood about the experience. This study will analyze existing research on the relationship between sleep paralyses and sleep in general. Many studies have connected poor sleep quality to an increased risk of sleep paralysis. Awake yet unable to act, sleep paralysis occurs. This might happen between awake and sleeping. The problem is approached in three steps: Data collection, data storage, calculation and machine learning prediction of sleep paralysis. The data came from the Smart Device. The dataset has several (independent) and dependent variables (Outcome). This device has been put to the test. Each exam has its own set of features and predicted outcomes. To assess the system's validity, we executed a posture recognition accuracy test. The device was hidden on top of the bed. The controller is in charge of measurement and data collection. Experiments were conducted by collecting pressure data from a patient lying down. The person acted out his sleeping positions on a mat for a while. Machine learning has been used to predict sleep paralysis. By comparing sleep postures to the outcome, we were able to show the link between sleep qualities and sleep paralysis. Machine learning approaches have been used to predict sleep paralysis. Comparing sleeping positions with the results showed the link between sleep quality and sleep paralysis. Sleep paralysis correlates with poor sleep quality. The Random Forest model has the highest accuracy of 91.9 percent in predicting sleep paralysis in the given dataset. SVM with Linear Kernel was 80.49 percent accurate, RBF was 42.68 percent, and Polynomial was 47.56 percent. The accuracy of logistic regression was 76.83 percent. KNN had a dismal performance of 60.98%. Decision Trees and Gradient Boosting both fared well at 85.37 percent.

Next generation smartwatches to estimate whole body composition using bioimpedance analysis: accuracy and precision in a diverse multiethnic sample

BACKGROUND

Novel advancements in wearable technologies include continuous measurement of body composition via smart watches. The accuracy and stability of devices are unknown.

OBJECTIVES

This study evaluated smart watches with integrated bioimpedance (BIA) sensors for their ability to measure and monitor change in body composition.

DESIGN

Participants recruited across body mass indexes received duplicate body composition measures using two wearable smart watch (W-BIA) models in sitting and standing positions and multiple versions of each watch were used to evaluate inter- and intra-model precision. Duplicate laboratory-grade octapolar bioimpedance (8-BIA) and criterion dual-energy X-ray absorptiometry (DXA) scans were acquired to compare estimates between the watches and laboratory methods. Test-retest precision and least significant changes assessed the ability to monitor change in body composition.

RESULTS

Of 109 participants recruited, 75 subjects completed the full manufacturer-recommended protocol. No significant differences were observed between W-BIA watches in position or between watch models. Significant fat-free mass (FFM) differences (p < 0.05) were observed between both W-BIA and 8-BIA when compared to DXA, though the systematic biases to the criterion were correctable. No significant difference was observed between the W-BIA and the laboratory-grade BIA technology for FFM (55.3 ± 14.5 kg for W-BIA versus 56.0 ± 13.8 kg for 8-BIA, p > 0.05, CCC = 0.97). FFM was less precise on the watches than DXA (CV = 0.7%, RMSE = 0.4 kg versus CV = 1.3%, RMSE = 0.7 kg for W-BIA), requiring more repeat measures to equal the same confidence in body composition change over time as DXA.

CONCLUSIONS

After systematic correction, smart watch BIA devices are capable of stable, reliable and accurate body composition with precision comparable but lower than laboratory measures. These devices allow for measurement in environments not accessible to laboratory systems such as the home, training centers, and geographically remote locations.

Key Stakeholder Barriers and Facilitators to Implementing Remote Monitoring Technologies: Protocol for a Mixed Methods Analysis

Background

The implementation of novel digital solutions within the National Health Service has historically been challenging. Since the start of the COVID-19 pandemic, there has been a greater push for digitization and for operating remote monitoring solutions. However, the implementation and widespread adoption of this type of innovation have been poorly studied.

Objective

We aim to investigate key stakeholder barriers and facilitators to implementing remote monitoring solutions to identify factors that could affect successful adoption.

Methods

A mixed methods approach will be implemented. Semistructured interviews will be conducted with high-level stakeholders from industry and academia and health care providers who have played an instrumental role in, and have prior experience with, implementing digital solutions, alongside the use of an adapted version of the Technology Acceptance Model questionnaire.

Results

Enrollment is currently underway, having started in February 2022. It is anticipated to end in July 2022, with data analysis scheduled to commence in August 2022.

Conclusions

The results of our study may highlight key barriers and facilitators to implementing digital remote monitoring solutions, thereby allowing for improved widespread adoption within the National Health Service in the future.

Trial Registration

ClinicalTrials.gov NCT05321004; https://clinicaltrials.gov/ct2/show/NCT05321004

Conflicting Aims and Values in the Application of Smart Sensors in Geriatric Rehabilitation: Ethical Analysis

Background

Smart sensors have been developed as diagnostic tools for rehabilitation to cover an increasing number of geriatric patients. They promise to enable an objective assessment of complex movement patterns.

Objective

This research aimed to identify and analyze the conflicting ethical values associated with smart sensors in geriatric rehabilitation and provide ethical guidance on the best use of smart sensors to all stakeholders, including technology developers, health professionals, patients, and health authorities.

Methods

On the basis of a systematic literature search of the scientific databases PubMed and ScienceDirect, we conducted a qualitative document analysis to identify evidence-based practical implications of ethical relevance. We included 33 articles in the analysis. The practical implications were extracted inductively. Finally, we carried out an ethical analysis based on the 4 principles of biomedical ethics: autonomy, beneficence, nonmaleficence, and justice. The results are reported in categories based on these 4 principles.

Results

We identified 8 conflicting aims for using smart sensors. Gains in autonomy come at the cost of patient privacy. Smart sensors at home increase the independence of patients but may reduce social interactions. Independent measurements performed by patients may result in lower diagnostic accuracy. Although smart sensors could provide cost-effective and high-quality diagnostics for most patients, minorities could end up with suboptimal treatment owing to their underrepresentation in training data and studies. This could lead to algorithmic biases that would not be recognized by medical professionals when treating patients.

Conclusions

The application of smart sensors has the potential to improve the rehabilitation of geriatric patients in several ways. It is important that patients do not have to choose between autonomy and privacy and are well informed about the insights that can be gained from the data. Smart sensors should support and not replace interactions with medical professionals. Patients and medical professionals should be educated about the correct application and the limitations of smart sensors. Smart sensors should include an adequate representation of minorities in their training data and should be covered by health insurance to guarantee fair access.

DyNeuMo Mk-1: Design and pilot validation of an investigational motion-adaptive neurostimulator with integrated chronotherapy

There is growing interest in using adaptive neuromodulation to provide a more personalized therapy experience that might improve patient outcomes. Current implant technology, however, can be limited in its adaptive algorithm capability. To enable exploration of adaptive algorithms with chronic implants, we designed and validated the 'Picostim DyNeuMo Mk-1' (DyNeuMo Mk-1 for short), a fully-implantable, adaptive research stimulator that titrates stimulation based on circadian rhythms (e.g. sleep, wake) and the patient's movement state (e.g. posture, activity, shock, free-fall). The design leverages off-the-shelf consumer technology that provides inertial sensing with low-power, high reliability, and relatively modest cost. The DyNeuMo Mk-1 system was designed, manufactured and verified using ISO 13485 design controls, including ISO 14971 risk management techniques to ensure patient safety, while enabling novel algorithms. The system was validated for an intended use case in movement disorders under an emergency-device authorization from the Medicines and Healthcare Products Regulatory Agency (MHRA). The algorithm configurability and expanded stimulation parameter space allows for a number of applications to be explored in both central and peripheral applications. Intended applications include adaptive stimulation for movement disorders, synchronizing stimulation with circadian patterns, and reacting to transient inertial events such as posture changes, general activity, and walking. With appropriate design controls in place, first-in-human research trials are now being prepared to explore the utility of automated motion-adaptive algorithms.

Feasibility of wireless continuous monitoring of vital signs without using alarms on a general surgical ward: A mixed methods study

Background

Wireless continuous vital sign monitoring by wearable devices have recently become available for patients on general wards to promote timely detection of clinical deterioration. Many continuous monitoring systems use conventional threshold alarm settings to alert nurses in case of deviating vital signs. However, frequent false alarms often lead to alarm fatigue and inefficiencies in the workplace. The aim of this study was to determine the feasibility of continuous vital sign monitoring without the use of alarms, thereby exclusively relying on interval trend monitoring.

Methods

This explanatory sequential mixed methods study was conducted at an abdominal surgical ward of a tertiary teaching hospital. Heart rate and respiratory rate of patients were measured every minute by a wearable sensor. Trends were visualized and assessed six times per day by nurses and once a day by doctors during morning rounds. Instead of using alarms we focused exclusively on regular vital sign trend analysis by nurses and doctors. Primary outcome was feasibility in terms of acceptability by professionals, assessed by the Usefulness, Satisfaction and Ease of Use questionnaire and further explored in two focus groups, as well as fidelity.

Results

A total of 56 patients were monitored and in 80.5% (n = 536) of nurses’ work shifts the trends assessments were documented. All deviating trends (n = 17) were recognized in time. Professionals (N = 46) considered continuous monitoring satisfying (4.8±1.0 on a 1–7 Likert-scale) and were willing to use the technology. Although insight into vital sign trends allowed faster anticipation and action upon changed patient status, professionals were neutral about usefulness (4.4±1.0). They found continuous monitoring easy to use (4.7±0.8) and easy to learn (5.3±1.0) but indicated the need for gaining practical experience. Nurses considered the use of alarms for deviating vital signs unnecessary, when trends were regularly assessed and reported.

Conclusion

We demonstrated that continuous vital signs trend monitoring without using alarms was feasible in the general ward setting, thereby avoiding unnecessary alarms and preventing alarm fatigue. When monitoring in a general ward setting, the standard use of alarms may therefore be reconsidered.

Use, and acceptability, of digital health technologies in musculoskeletal physical therapy: A survey of physical therapists and patients

Objectives

Determine (a) frequency of digital health use to obtain/record clinical information (pre‐COVID‐19); (b) willingness to use digital technologies among physical therapists and patients with musculoskeletal conditions.

Methods

102 physical therapists, and 103 patients were recruited in Australia. An electronic survey ascertained (a) demographic/clinical characteristics, (b) frequency of methods to obtain and record clinical information; (c) willingness to use digital technologies to support musculoskeletal care.

Results

Physical therapists mostly used non‐digital methods to obtain subjective (e.g., face‐to‐face questioning, n = 98; 96.1%) and objective information (e.g., visual estimation, n = 95; 93.1%). The top three digital health technologies most frequently used by therapists: photo‐based image capture (n = 19; 18.6%), accessing information logged/tracked by patients into a mobile app (n = 14; 13.7%), and electronic systems to capture subjective information that the patient fills in (n = 13; 12.7%). The top three technologies used by patients: activity trackers (n = 27; 26.2%), logging/tracking health information on mobile apps or websites (n = 12; 11.7%), and entering information on a computer (n = 12; 7.8%). Physical therapists were most willing to use technologies for: receiving diagnostic imaging results (n = 99; 97.1%), scheduling appointments (n = 92; 90.2%) and capturing diagnostic results (n = 92; 90.2%). Patients were most willing to use technologies for receiving notifications about health test results (n = 91; 88.4%), looking up health information (n = 83; 80.6%) and receiving personalised alerts/reminders (n = 80; 77.7%).

Conclusions

Physical therapists and patients infrequently use digital health technologies to support musculoskeletal care, but expressed some willingness to consider using them for select functions.

The Use of Wearable Pulse Oximeters in the Prompt Detection of Hypoxemia and During Movement: Diagnostic Accuracy Study

Background

Commercially available wearable (ambulatory) pulse oximeters have been recommended as a method for managing patients at risk of physiological deterioration, such as active patients with COVID-19 disease receiving care in hospital isolation rooms; however, their reliability in usual hospital settings is not known.

Objective

We report the performance of wearable pulse oximeters in a simulated clinical setting when challenged by motion and low levels of arterial blood oxygen saturation (SaO₂).

Methods

The performance of 1 wrist-worn (Wavelet) and 3 finger-worn (CheckMe O2+, AP-20, and WristOx2 3150) wearable, wireless transmission–mode pulse oximeters was evaluated. For this, 7 motion tasks were performed: at rest, sit-to-stand, tapping, rubbing, drinking, turning pages, and using a tablet. Hypoxia exposure followed, in which inspired gases were adjusted to achieve decreasing SaO₂ levels at 100%, 95%, 90%, 87%, 85%, 83%, and 80%. Peripheral oxygen saturation (SpO₂) estimates were compared with simultaneous SaO₂ samples to calculate the root-mean-square error (RMSE). The area under the receiver operating characteristic curve was used to analyze the detection of hypoxemia (ie, SaO₂<90%).

Results

SpO₂ estimates matching 215 SaO₂ samples in both study phases, from 33 participants, were analyzed. Tapping, rubbing, turning pages, and using a tablet degraded SpO₂ estimation (RMSE>4% for at least 1 device). All finger-worn pulse oximeters detected hypoxemia, with an overall sensitivity of ≥0.87 and specificity of ≥0.80, comparable to that of the Philips MX450 pulse oximeter.

Conclusions

The SpO₂ accuracy of wearable finger-worn pulse oximeters was within that required by the International Organization for Standardization guidelines. Performance was degraded by motion, but all pulse oximeters could detect hypoxemia. Our findings support the use of wearable, wireless transmission–mode pulse oximeters to detect the onset of clinical deterioration in hospital settings.

Trial Registration

ISRCTN Registry 61535692; http://www.isrctn.com/ISRCTN61535692

International Registered Report Identifier (IRRID)

RR2-10.1136/bmjopen-2019-034404

Remote Monitoring of Chronic Critically Ill Patients after Hospital Discharge: A Systematic Review

Background: Over the past few decades, critical care has seen many advancements. These advancements resulted in a considerable increase in the prevalence of chronically critically ill patients requiring prolonged medical care, which led to a massive increase in healthcare utilization. Methods: We performed a search for suitable articles using PubMed and Google Scholar from the inception of these databases to 15 May 2021. Results: Thirty-four articles were included in the review and analyzed. We described the following characteristics and problems with chronic critically ill patient management: the patient population, remote monitoring, the monitoring of physiological parameters in chronic critically ill patients, the anatomical location of sensors, the barriers to implementation, and the main technology-related issues. The main challenges in the management of these patients are (1) the shortage of caretakers, (2) the periodicity of vital function monitoring (e.g., episodic measuring of blood pressure leads to missing important critical events such as hypertension, hypotension, and hypoxia), and (3) failure to catch and manage critical physiological events at the right time, which can result in poor outcomes. Conclusions: The prevalence of critically ill patients is expected to grow. Technical solutions can greatly assist medical personnel and caregivers. Wearable devices can be used to monitor blood pressure, heart rate, pulse, respiratory rate, blood oxygen saturation, metabolism, and central nervous system function. The most important points that should be addressed in future studies are the performance of the remote monitoring systems, safety, clinical and economic outcomes, as well as the acceptance of the devices by patients, caretakers, and healthcare professionals.

Top-Down Detection of Eating Episodes by Analyzing Large Windows of Wrist Motion Using a Convolutional Neural Network

In this work, we describe a new method to detect periods of eating by tracking wrist motion during everyday life. Eating uses hand-to-mouth gestures for ingestion, each of which lasts a few seconds. Previous works have detected these gestures individually and then aggregated them to identify meals. The novelty of our approach is that we analyze a much longer window (0.5–15 min) using a convolutional neural network. Longer windows can contain other gestures related to eating, such as cutting or manipulating food, preparing foods for consumption, and resting between ingestion events. The context of these other gestures can improve the detection of periods of eating. We test our methods on the public Clemson all-day dataset, which consists of 354 recordings containing 1063 eating episodes. We found that accuracy at detecting eating increased by 15% in ≥4 min windows compared to ≤15 s windows. Using a 6 min window, we detected 89% of eating episodes, with 1.7 false positives for every true positive (FP/TP). These are the best results achieved to date on this dataset.

Reconfigurable Architectures with High-Frequency Noise Suppression for Wearable ECG Devices

Recent advances in electronics and microelectronics have aided the development of low-cost devices that are widely used as well-being or preventive monitoring devices by many people. Remote health monitoring, which includes wearable sensors, actuators, and modern communication and information systems, offers effective programs that allow people to live peacefully in their own homes while also being protected in some way. High-frequency noise, power-line interface, and baseline drift are prevalent during the data-acquisition system of an ECG signal, and they can limit signal understanding. They (noises) must be isolated in order to provide an appropriate diagnostic of the patient. When removing high-frequency components (noise) from an ECG signal with an FIR filter, the critical path delay increases considerably as the filter's duration increases. To reduce high-frequency noise, simple moving average filters with pipelining and look-ahead transformation techniques are extensively used in this study. With the use of pipelining and look-ahead techniques, the only objective is to increase the clock speed of the designs. The moving average filters (conventional and proposed) were created on an Altera Cyclone IV FPGA EP4CE115F29C7 chip using the Quartus II software v13.1 tool. Finally, performance metrics such logic elements, clock speed, and power consumption were compared and studied thoroughly. The recursive pipelined 8-tap MA filter with look-ahead approach outperforms the other designs (685.48 MHz) in this investigation.

Oxygen saturation measurements using novel diffused reflectance with hyperspectral imaging: Towards facile COVID-19 diagnosis

Oxygen saturation level plays a vital role in screening, diagnosis, and therapeutic assessment of disease's assortment. There is an urgent need to design and implement early detection devices and applications for the COVID-19 pandemic; this study reports on the development of customized, highly sensitive, non-invasive, non-contact diffused reflectance system coupled with hyperspectral imaging for mapping subcutaneous blood circulation depending on its oxygen saturation level. The forearm of 15 healthy adult male volunteers with age range of (20-38 years) were illuminated via a polychromatic light source of a spectrum range 400-980 nm. Each patient had been scanned five times to calculate the mean spectroscopic reflectance images using hyperspectral camera. The customized signal processing algorithm includes normalization and moving average filter for noise removal. Afterward, employing K-means clustering for image segmentation to assess the accuracy of blood oxygen saturation (SpO₂) levels. The reliability of the developed diffused reflectance system was verified with the ground truth technique, a standard pulse oximeter. Non-invasive, non-contact diffused reflectance spectrum demonstrated maximum signal variation at 610 nm according to SpO₂ level. Statistical analysis (mean, standard deviation) of diffused reflectance hyperspectral images at 610 nm offered precise calibrated measurements to the standard pulse oximeter. Diffused reflectance associated with hyperspectral imaging is a prospective technique to assist with phlebotomy and vascular approach. Additionally, it could permit future surgical or pharmacological intercessions that titrate or limit ischemic injury continuously. Furthermore, this technique could offer a fast reliable indication of SpO₂ levels for COVID-19 diagnosis.

Internet of Things use case applications for COVID-19

The Internet of Things (IoT) is a technology built upon various physical objects equipped with different types of sensors, which are connected together using communication methods. These devices have been applied to several domains, especially healthcare. In addition to the numerous benefits that IoT has demonstrated in healthcare, this technology is being adopted for combating the recent COVID-19 pandemic. The key role of IoT in COVID-19 could be classified into five major tasks: Monitoring, Diagnosing, Tracing, Disinfecting, and Vaccinating. This chapter reviews the state-of-art applications of IoT based on these tasks in order to better mitigate this virus. Additionally, potential areas for applying IoT systems to fight against COVID-19 or even future pandemics will be demonstrated.

Wearable E-Textile and CNT Sensor Wireless Measurement System for Real-Time Penile Erection Monitoring

Erection measurements are the most important indicator of male urological disease diagnosis, treatment, and results. Rigiscan has been used widely in studies and diagnoses for nocturnal penile tumescence for evaluating erectile dysfunction by measuring the number and timing of erectile dysfunctions during sleep. However, this device has limitations such as the weight and bulk of the device and has been questioned for its role as a standard for ED Erectile Dysfunction (ED) diagnosis. In this study, we propose a real-time wearable monitoring system that can quantitatively measure the length and circumference of the penis using electronic textiles (E-textile) and carbon nanotube (CNT) sensors. The E-textile sensor is used to measure the length, circumference, and gradient with portability, convenience, and comfort. Sensors were created by coating CNTs on latex for flexibility. The CNT-based latex condom-type sensor in our proposed system shows the length, circumference, and curvature measurements with changes in resistance, and the E-textile performance shows a 1.44% error rate and a cavity radius of 110 to 300. The results of this conceptual study are for supplementary sensor development with a combination of new technologies with alternatives or existing methods for measuring erection function.

Accurate Estimation of Heart and Respiration Rates Based on an Optical Fiber Sensor Using Adaptive Regulations and Statistical Classifications Spectrum Analysis

The aim of this work is to present a method for accurately estimating heart and respiration rates under different actual conditions based on a mattress which was integrated with an optical fiber sensor. During the estimation, a ballistocardiogram (BCG) signal, which was obtained from the optical fiber sensor, was used for extracting the heart rate and the respiration rate. However, due to the detrimental effects of the differential detector, self-interference, and variation of installation status of the sensor, the ballistocardiogram (BCG) signal was difficult to detect. In order to resolve the potential concerns of individual differences and body interferences, adaptive regulations and statistical classifications spectrum analysis were used in this paper. Experiments were carried out to quantify heart and respiration rates of healthy volunteers under different breathing and posture conditions. From the experimental results, it could be concluded that (1) the heart rates of 40-150 beats per minute (bpm) and respiration rates of 10-20 breaths per minute (bpm) were measured for individual differences; (2) for the same individuals under four different posture contacts, the mean errors of heart rates were separately 1.60 ± 0.98 bpm, 1.94 ± 0.83 bpm, 1.24 ± 0.59 bpm, and 1.06 ± 0.62 bpm, in contrast, the mean errors of the polar beat device were 1.09 ± 0.96 bpm, 1.44 ± 0.99 bpm, and 1.78 ± 0.94 bpm. Furthermore, the experimental results were validated by conventional counterparts which used skin-contacting electrodes as their measurements. It was reported that the heart rate was 0.26 ± 2.80 bpm in 95% confidence intervals (± 1.96SD) in comparison with Philips sure-signs VM6 medical monitor, and the respiration rate was 0.41 ± 1.49 bpm in 95% confidence intervals (± 1.96SD) in comparison with ECG-derived respiratory (EDR) measurements for respiration rates. It was indicated that the developed system using adaptive regulations and statistical classifications spectrum analysis performed better and could easily be used under complex environments.

Aligning Patient's Ideas of a Good Life with Medically Indicated Therapies in Geriatric Rehabilitation Using Smart Sensors

New technologies such as smart sensors improve rehabilitation processes and thereby increase older adults’ capabilities to participate in social life, leading to direct physical and mental health benefits. Wearable smart sensors for home use have the additional advantage of monitoring day-to-day activities and thereby identifying rehabilitation progress and needs. However, identifying and selecting rehabilitation priorities is ethically challenging because physicians, therapists, and caregivers may impose their own personal values leading to paternalism. Therefore, we develop a discussion template consisting of a series of adaptable questions for the patient–physician encounter based on the capability approach. The goal is to improve geriatric rehabilitation and thereby increase participation in social life and well-being. To achieve this goal, we first analyzed what is considered important for participation on basis of the capability approach, human rights, and ethics of care. Second, we conducted an ethical analysis of each of the four identified dimensions of participation: political, economic, socio-cultural, and care. To improve compliance with rehabilitation measures, health professionals must align rehabilitation measures in an open dialogue with the patient’s aspiration for participation in each dimension. A discussion template based on the capability approach allows for a proactive approach in patient information and stimulates a critical assessment of treatment alternatives while reducing the risk of imposing personal values.

A Perspective Roadmap for IoMT-Based Early Detection and Care of the Neural Disorder, Dementia

The Internet of Medical Things (IoMT) has emerged as one of the most important key applications of IoT. IoMT makes the diagnosis and care more convenient and reliable with proven results. The paper presents the technology, open issues, and challenges of IoMT-based systems. It explores the various types of sensors and smart equipment based on IoMT and used for diagnosis and patient care. A comprehensive survey of early detection and postdetection care of the neural disorder dementia is conducted. The paper also presents a postdiagnosis dementia care model named “Demencare.” This model incorporates eight sensors capable of tracking the daily routine of dementia patient. The patients can be monitored locally by an edge computing device kept at their premises. The medical experts may also monitor the patients' status for any deviation from normal behavior. IoMT enables better postdiagnosis care for neural disorders, like dementia and Alzheimer's. The patient's behavior and vital parameters are always available despite the remote location of the patients. The data of the patients may be classified, and new insights may be obtained to tackle patients in a better manner.

Mobile health technology for remote home monitoring after surgery: a meta-analysis

BACKGROUND

Mobile health (mHealth) technology has been proposed as a method of improving post-discharge surveillance. Little is known about how mHealth has been used to track patients after surgery and whether its use is associated with differences in postoperative recovery.

METHODS

Three databases (PubMed, MEDLINE and the Cochrane Central Registry of Controlled Trials) were searched to identify studies published between January 1999 and February 2021. Mobile health was defined as any smartphone or tablet computer capable of electronically capturing health-related patient information and transmitting these data to the clinical team. Comparable outcomes were pooled via meta-analysis with additional studies compiled via narrative review. The quality of each study was assessed based on Grading of Recommendations Assessment, Development, and Evaluation (GRADE) criteria.

RESULTS

Forty-five articles met inclusion criteria. While the majority of devices were designed to capture general health information, others were specifically adapted to the expected outcomes or potential complications of the index procedure. Exposure to mHealth was associated with fewer emergency department visits (odds ratio 0.42, 95 per cent c.i. 0.23 to 0.79) and readmissions (odds ratio 0.47, 95 per cent c.i. 0.29 to 0.77) as well as accelerated improvements in quality of life after surgery. There were limited data on other postoperative outcomes.

CONCLUSION

Remote home monitoring via mHealth is feasible, adaptable, and may even promote more effective postoperative care. Given the rapid expansion of mHealth, physicians and policymakers need to understand these technologies better so that they can be integrated into high-quality clinical care.

Feasibility and reliability of continuously monitoring alcohol use among female adolescents and young adults

INTRODUCTION AND AIMS

Transdermal alcohol sensors allow objective, continuous monitoring and have potential to expand current research on adolescent and young adult alcohol use. The purpose of this manuscript is to evaluate the feasibility and reliability of transdermal alcohol sensor use among female adolescents as compared to female young adults.

DESIGN AND METHODS

This trial included 59 female adolescents and young adults aged 14-24 years who reported drinking during the previous month. All participants were asked to wear a Giner Wrist Transdermal Alcohol Sensor (WrisTAS)-7 over a 1 month prospective study. Participants came to the research lab weekly to complete a detailed self-report of behaviours, including day of drinking events, amounts and types of alcohol use and length of drinking events. Estimates of blood alcohol concentration (eBAC) were computed from self-report data using the Matthew and Miller, NHTSA and Zhang equations. Daily transdermal alcohol concentration (TAC) peaks and calculated eBAC peak data were analysed with paired-samples t-tests and repeated measures correlations for validity comparisons.

RESULTS

All participants (100%, n = 59) completed the trial, however, two participants were removed due to greater than 50% of missing transdermal alcohol sensor data. Of the 57 participants, the data included 1,722 days of continuous alcohol monitoring. Missing data was recorded more frequently among female adolescents at about (11.78%) as compared to female young adults (8.59%; χ²  = -18.40, P < 0.001). Participant self-report of drinking occurred with greater frequency (374 events) than detected by the WrisTAS transdermal alcohol sensors (243 events). On days when self-report and sensor data indicated a drinking event, participants' eBAC was moderately correlated with TAC, after accounting for repeated measures.

DISCUSSION AND CONCLUSIONS

This study finds that transdermal alcohol sensors are moderately reliable when sensor data is paired with self-report. This objective data collection method may improve the ability to collect alcohol curves among adolescents.

Mobile 5P-Medicine Approach for Cardiovascular Patients

Medicine is heading towards personalized care based on individual situations and conditions. With smartphones and increasingly miniaturized wearable devices, the sensors available on these devices can perform long-term continuous monitoring of several user health-related parameters, making them a powerful tool for a new medicine approach for these patients. Our proposed system, described in this article, aims to develop innovative solutions based on artificial intelligence techniques to empower patients with cardiovascular disease. These solutions will realize a novel 5P (Predictive, Preventive, Participatory, Personalized, and Precision) medicine approach by providing patients with personalized plans for treatment and increasing their ability for self-monitoring. Such capabilities will be derived by learning algorithms from physiological data and behavioral information, collected using wearables and smart devices worn by patients with health conditions. Further, developing an innovative system of smart algorithms will also focus on providing monitoring techniques, predicting extreme events, generating alarms with varying health parameters, and offering opportunities to maintain active engagement of patients in the healthcare process by promoting the adoption of healthy behaviors and well-being outcomes. The multiple features of this future system will increase the quality of life for cardiovascular diseases patients and provide seamless contact with a healthcare professional.

The impact of wearable continuous vital sign monitoring on deterioration detection and clinical outcomes in hospitalised patients: a systematic review and meta-analysis

BACKGROUND

Timely recognition of the deteriorating inpatient remains challenging. Wearable monitoring systems (WMS) may augment current monitoring practices. However, there are many barriers to implementation in the hospital environment, and evidence describing the clinical impact of WMS on deterioration detection and patient outcome remains unclear.

OBJECTIVE

To assess the impact of vital-sign monitoring on detection of deterioration and related clinical outcomes in hospitalised patients using WMS, in comparison with standard care.

METHODS

A systematic search was conducted in August 2020 using MEDLINE, Embase, CINAHL, Cochrane Database of Systematic Reviews, CENTRAL, Health Technology Assessment databases and grey literature. Studies comparing the use of WMS against standard care for deterioration detection and related clinical outcomes in hospitalised patients were included. Deterioration related outcomes (primary) included unplanned intensive care admissions, rapid response team or cardiac arrest activation, total and major complications rate. Other clinical outcomes (secondary) included in-hospital mortality and hospital length of stay. Exploratory outcomes included alerting system parameters and clinical trial registry information.

RESULTS

Of 8706 citations, 10 studies with different designs met the inclusion criteria, of which 7 were included in the meta-analyses. Overall study quality was moderate. The meta-analysis indicated that the WMS, when compared with standard care, was not associated with significant reductions in intensive care transfers (risk ratio, RR 0.87; 95% confidence interval, CI 0.66-1.15), rapid response or cardiac arrest team activation (RR 0.84; 95% CI 0.69-1.01), total (RR 0.77; 95% CI 0.44-1.32) and major (RR 0.55; 95% CI 0.24-1.30) complications prevalence. There was also no statistically significant association with reduced mortality (RR 0.48; 95% CI 0.18-1.29) and hospital length of stay (mean difference, MD - 0.09; 95% CI - 0.43 to 0.44).

CONCLUSION

This systematic review indicates that there is no current evidence that implementation of WMS impacts early deterioration detection and associated clinical outcomes, as differing design/quality of available studies and diversity of outcome measures make it difficult to reach a definite conclusion. Our narrative findings suggested that alarms should be adjusted to minimise false alarms and promote rapid clinical action in response to deterioration. PROSPERO Registration number: CRD42020188633 .

Improving Healthy Aging by Monitoring Patients' Lifestyle through a Wearable Device: Results of a Feasibility Study

Population aging is related to a huge growth in healthcare and welfare costs. Therefore, wearable devices could be strategic for minimizing years of disability in old age and monitoring patients’ lifestyles and health. The purpose of this study was to assess the feasibility of using smart devices to monitor patients’ physical activity in a primary care setting. To assess the acceptance of this novel technology from the point of view of both patients and healthcare professionals, two questionnaires (one paper-based and one ex-novo developed) were administered to 11 patients with type 2 diabetes mellitus and a non-compliant behavior towards the therapeutic indications of their general practitioner (GP). Seven participants would continue to use a wearable activity tracker to monitor their health. We observed that 75% of patients reported a device’s characteristics satisfaction level of over 80% of the total score assigned to this dimension. No differences were observed in the questionnaire’s scores between the two professionals categories (GPs and nurses). Three dimensions (equipment characteristics, subjective norm, perceived risks, perceived ease-of-use and facilitating conditions) correlated > 0.5 with the device’s acceptability level. Some weak correlations were observed between healthcare professionals’ perception and patients’ parameters, particularly between the dimensions of collaboration and web interface ease-of-use and patients’ median number of steps and hours of sleep. In conclusion, despite the limited number of subjects involved, a good acceptance level towards these non-medical devices was observed, according to both patients’ and healthcare professionals’ impressions.

Sensor-based platforms for remote management of chronic diseases in developing regions: A qualitative approach examining the perspectives of healthcare professionals

The continuous monitoring of chronic diseases serves as one of the cornerstones in the efforts to improve the quality of life of patients and maintain the healthcare services provided to them. This study aims to provide an in-depth understanding of the perspectives of healthcare professionals on using sensor-based networks (SBN) used for remote and continuous monitoring of patients with chronic illness in Kosovo, a developing country. A qualitative research method was used to interview 26 healthcare professionals. The study results demonstrate the positive attitudes of participants to using SBN, and considers their concerns on the impact of these platforms on the patient's life, the number of visits in the medical centre, data privacy concerning interactions between patients and their medical personnel and the costs of the platform. Further to that, the study makes an important contribution to knowledge by identifying the challenges and drawbacks of these platforms and provides recommendations for system designers.

BandPass: A Bluetooth-Enabled Remote Monitoring Device for Sarcopenia

As the United States population ages, managing pathologies that largely affect older adults, including sarcopenia (i.e., loss of muscle mass and strength) represents a significant and growing clinical challenge. In addition to increased rates of sarcopenia with age, its incidence and impact increase after acute illness, increasing the risk of functional decline, institutionalization, or death. Resistance-based exercises promote muscle regeneration and strength and are an advised therapy for such patients. Yet, such therapeutic exercises are normally conducted either under direct clinical oversight or unsupervised by patients at home, where compliance rates are low. The presented device, BandPass, aims to create an integrated force data detection and acquisition system for monitoring and transmitting at-home exercise force data to patients and clinicians. A potentiometer-based sensor was integrated to a resistance exercise band through the use of custom designed electronics, which incorporated Bluetooth Low Energy (BLE) for wireless transmission to a mobile 'app'. A protocol for calibrating the device was developed using a range of loads and validated in static benchtop and dynamic testing. Data from a pilot study with 7 older adults was also collected and analyzed to test the device. BandPass is 94% accurate with a coefficient of variation (CoV) of 4.9% and sensitivity of 150g. The pilot study recorded 147 exercises, allowing for analysis on patients' exercise performances. BandPass was successfully able to measure force continuously over time during exercises, measure longitudinal compliance with exercises, and quantify force continuously over time. A mobile health (mHealth) force-sensing system allows for the remote monitoring of prescribed in-home resistance exercise band programs for at-risk older adults, bridging the gap between clinicians and patients.

Protocol for a systematic review assessing ambulatory vital sign monitoring impact on deterioration detection and related clinical outcomes in hospitalised patients

INTRODUCTION

Ambulatory monitoring systems (AMS) can facilitate early detection of clinical deterioration, and have the potential to improve hospitalised patient outcomes. The objective of this systematic review is to assess the impact of vital signs monitoring on detection of deterioration and related outcomes in hospitalised patients using AMS, in comparison with standard care.

METHODS AND ANALYSIS

A systematic search was conducted on 27 August 2020 in MEDLINE, Embase, CINAHL, Cochrane Database of Systematic Reviews, CENTRAL and Health Technology Assessment databases, as well as grey literature. Search results will be reviewed in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis checklist for systematic reviews. Studies comparing the use of ambulatory monitoring devices against standard care for deterioration detection and related clinical outcomes in hospitalised patients will be included and further clinical and other outcomes will also be explored. Deterioration-related outcomes may include (but not limited to) unplanned intensive care admissions, rapid response team activation and unscheduled emergency interventions, as defined by the included studies. Two reviewers will independently extract study data and assess the quality and risk of bias of included studies. Where possible, a meta-analysis will be conducted and quantitative results presented. Alternatively, a narrative synthesis will be reported.

ETHICS AND DISSEMINATION

Ethical approval is not required for this study as no primary data will be collected. This study is part of our virtual High Dependency Unit project and will be disseminated through peer-reviewed publications, public and scientific conference presentations.

PROSPERO REGISTRATION NUMBER

CRD42020188633.

Reliability of heart rate and respiration rate measurements with a wireless accelerometer in postbariatric recovery

Recognition of early signs of deterioration in postoperative course could be improved by continuous monitoring of vital parameters. Wearable sensors could enable this by wireless transmission of vital signs. A novel accelerometer-based device, called Healthdot, has been designed to be worn on the skin to measure the two key vital parameters respiration rate (RespR) and heart rate (HeartR). The goal of this study is to assess the reliability of heart rate and respiration rate measured by the Healthdot in comparison to the gold standard, the bedside patient monitor, during the postoperative period in bariatric patients. Data were collected in a consecutive group of 30 patients who agreed to wear the device after their primary bariatric procedure. Directly after surgery, a Healthdot was attached on the patients’ left lower rib. Vital signs measured by the accelerometer based Healthdot were compared to vital signs collected with the gold standard patient monitor for the period that the patient stayed at the post-anesthesia care unit. Over all patients, a total of 22 hours of vital signs obtained by the Healthdot were recorded simultaneously with the bedside patient monitor data. 87.5% of the data met the pre-defined bias of 5 beats per minute for HeartR and 92.3% of the data met the pre-defined bias of 5 respirations per minute for RespR. The Healthdot can be used to accurately derive heart rate and respiration rate in postbariatric patients. Wireless continuous monitoring of key vital signs has the potential to contribute to earlier recognition of complications in postoperative patients. Future studies should focus on the ability to detect patient deterioration in low-care environments and at home after discharge from the hospital.