A Systematic Review of Wearable Patient Monitoring Systems - Current Challenges and Opportunities for Clinical Adoption

Mitigating data quality challenges in ambulatory wrist-worn wearable monitoring through analytical and practical approaches

Chronic disease management and follow-up are vital for realizing sustained patient well-being and optimal health outcomes. Recent advancements in wearable technologies, particularly wrist-worn devices, offer promising solutions for longitudinal patient monitoring, replacing subjective, intermittent self-reporting with objective, continuous monitoring. However, collecting and analyzing data from wearables presents several challenges, such as data entry errors, non-wear periods, missing data, and wearable artifacts. In this work, we explore these data analysis challenges using two real-world datasets (mBrain21 and ETRI lifelog2020). We introduce practical countermeasures, including participant compliance visualizations, interaction-triggered questionnaires to assess personal bias, and an optimized pipeline for detecting non-wear periods. Additionally, we propose a visualization-oriented approach to validate processing pipelines using scalable tools such as tsflex and Plotly-Resampler. Lastly, we present a bootstrapping methodology to evaluate the variability of wearable-derived features in the presence of partially missing data segments. Prioritizing transparency and reproducibility, we provide open access to our detailed code examples, facilitating adaptation in future wearable research. In conclusion, our contributions provide actionable approaches for improving wearable data collection and analysis.

An Infrastructure Framework for Remote Patient Monitoring Interventions and Research

Remote patient monitoring (RPM) enables clinicians to maintain and adjust their patients' plan of care by using remotely gathered data, such as vital signs, to proactively make medical decisions about a patient's care. RPM interventions have been touted as a means to improve patient care and well-being while reducing costs and resource needs within the health care ecosystem. However, multiple interworking components must be successfully implemented for an RPM intervention to yield the desired outcomes, and the design and key driver of each component can vary depending on the medical context. This viewpoint and perspective paper presents a 4-component RPM infrastructure framework based on a synthesis of existing literature and practice related to RPM. Specifically, these components are identified and considered: (1) data collection, (2) data transmission and storage, (3) data analysis, and (4) information presentation. Interaction points to consider between components include transmission, interoperability, accessibility, workflow integration, and transparency. Within each of the 4 components, questions affecting research and practice emerge that can affect the outcomes of RPM interventions. This framework provides a holistic perspective of the technologies involved in RPM interventions and how these core elements interact to provide an appropriate infrastructure for deploying RPM in health systems. Further, it provides a common vocabulary to compare and contrast RPM solutions across health contexts and may stimulate new research and intervention opportunities.

Pro-Con Debate: Universal Versus Selective Continuous Monitoring of Postoperative Patients

In this Pro-Con commentary article, we discuss use of continuous physiologic monitoring for clinical deterioration, specifically respiratory depression in the postoperative population. The Pro position advocates for 24/7 continuous surveillance monitoring of all patients starting in the postanesthesia care unit until discharge from the hospital. The strongest arguments for universal monitoring relate to inadequate assessment and algorithms for patient risk. We argue that the need for hospitalization in and of itself is a sufficient predictor of an individual's risk for unexpected respiratory deterioration. In addition, general care units carry the added risk that even the most severe respiratory events will not be recognized in a timely fashion, largely due to higher patient to nurse staffing ratios and limited intermittent vital signs assessments (e.g., every 4 hours). Continuous monitoring configured properly using a "surveillance model" can adequately detect patients' respiratory deterioration while minimizing alarm fatigue and the costs of the surveillance systems. The Con position advocates for a mixed approach of time-limited continuous pulse oximetry monitoring for all patients receiving opioids, with additional remote pulse oximetry monitoring for patients identified as having a high risk of respiratory depression. Alarm fatigue, clinical resource limitations, and cost are the strongest arguments for selective monitoring, which is a more targeted approach. The proponents of the con position acknowledge that postoperative respiratory monitoring is certainly indicated for all patients, but not all patients need the same level of monitoring. The analysis and discussion of each point of view describes who, when, where, and how continuous monitoring should be implemented. Consideration of various system-level factors are addressed, including clinical resource availability, alarm design, system costs, patient and staff acceptance, risk-assessment algorithms, and respiratory event detection. Literature is reviewed, findings are described, and recommendations for design of monitoring systems and implementation of monitoring are described for the pro and con positions.

Attributes, Methods, and Frameworks Used to Evaluate Wearables and Their Companion mHealth Apps: Scoping Review

BACKGROUND

Wearable devices, mobile technologies, and their combination have been accepted into clinical use to better assess the physical fitness and quality of life of patients and as preventive measures. Usability is pivotal for overcoming constraints and gaining users' acceptance of technology such as wearables and their companion mobile health (mHealth) apps. However, owing to limitations in design and evaluation, interactive wearables and mHealth apps have often been restricted from their full potential.

OBJECTIVE

This study aims to identify studies that have incorporated wearable devices and determine their frequency of use in conjunction with mHealth apps or their combination. Specifically, this study aims to understand the attributes and evaluation techniques used to evaluate usability in the health care domain for these technologies and their combinations.

METHODS

We conducted an extensive search across 4 electronic databases, spanning the last 30 years up to December 2021. Studies including the keywords "wearable devices," "mobile apps," "mHealth apps," "physiological data," "usability," "user experience," and "user evaluation" were considered for inclusion. A team of 5 reviewers screened the collected publications and charted the features based on the research questions. Subsequently, we categorized these characteristics following existing usability and wearable taxonomies. We applied a methodological framework for scoping reviews and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist.

RESULTS

A total of 382 reports were identified from the search strategy, and 68 articles were included. Most of the studies (57/68, 84%) involved the simultaneous use of wearables and connected mobile apps. Wrist-worn commercial consumer devices such as wristbands were the most prevalent, accounting for 66% (45/68) of the wearables identified in our review. Approximately half of the data from the medical domain (32/68, 47%) focused on studies involving participants with chronic illnesses or disorders. Overall, 29 usability attributes were identified, and 5 attributes were frequently used for evaluation: satisfaction (34/68, 50%), ease of use (27/68, 40%), user experience (16/68, 24%), perceived usefulness (18/68, 26%), and effectiveness (15/68, 22%). Only 10% (7/68) of the studies used a user- or human-centered design paradigm for usability evaluation.

CONCLUSIONS

Our scoping review identified the types and categories of wearable devices and mHealth apps, their frequency of use in studies, and their implementation in the medical context. In addition, we examined the usability evaluation of these technologies: methods, attributes, and frameworks. Within the array of available wearables and mHealth apps, health care providers encounter the challenge of selecting devices and companion apps that are effective, user-friendly, and compatible with user interactions. The current gap in usability and user experience in health care research limits our understanding of the strengths and limitations of wearable technologies and their companion apps. Additional research is necessary to overcome these limitations.

Continuously monitoring the human machine? - A cross-sectional study to assess the acceptance of wearables in Germany

Background: Wearables have the potential to transform healthcare by enabling early detection and monitoring of chronic diseases. This study aimed to assess wearables' acceptance, usage, and reasons for non-use. Methods: Anonymous questionnaires were used to collect data in Germany on wearable ownership, usage behaviour, acceptance of health monitoring, and willingness to share data. Results: Out of 643 respondents, 550 participants provided wearable acceptance data. The average age was 36.6 years, with 51.3% female and 39.6% residing in rural areas. Overall, 33.8% reported wearing a wearable, primarily smartwatches or fitness wristbands. Men (63.3%) and women (57.8%) expressed willingness to wear a sensor for health monitoring, and 61.5% were open to sharing data with healthcare providers. Concerns included data security, privacy, and perceived lack of need. Conclusion: The study highlights the acceptance and potential of wearables, particularly for health monitoring and data sharing with healthcare providers. Addressing data security and privacy concerns could enhance the adoption of innovative wearables, such as implants, for early detection and monitoring of chronic diseases.

Ethical and regulatory challenges of AI technologies in healthcare: A narrative review

Over the past decade, there has been a notable surge in AI-driven research, specifically geared toward enhancing crucial clinical processes and outcomes. The potential of AI-powered decision support systems to streamline clinical workflows, assist in diagnostics, and enable personalized treatment is increasingly evident. Nevertheless, the introduction of these cutting-edge solutions poses substantial challenges in clinical and care environments, necessitating a thorough exploration of ethical, legal, and regulatory considerations. A robust governance framework is imperative to foster the acceptance and successful implementation of AI in healthcare. This article delves deep into the critical ethical and regulatory concerns entangled with the deployment of AI systems in clinical practice. It not only provides a comprehensive overview of the role of AI technologies but also offers an insightful perspective on the ethical and regulatory challenges, making a pioneering contribution to the field. This research aims to address the current challenges in digital healthcare by presenting valuable recommendations for all stakeholders eager to advance the development and implementation of innovative AI systems.

Recent Advances of Nanogenerator Technology for Cardiovascular Sensing and Monitoring

Cardiovascular sensing and monitoring is a widely used function in cardiovascular devices. Nowadays, achieving desired flexibility, wearability and implantability becomes a major design goal for the advancement of this family of devices. As an emerging technology, nanogenerator (NG) offers an intriguing promise for replacing the battery, an essential obstacle toward tissue-like soft electronics. This article reviews most recent advancements in NG technology for advanced cardiovascular sensing and monitoring. Based on the application targets, the discuss covers implantable NGs on hearts, implantable NGs for blood vessel grafts and patches, and wearable NGs with various sensing functions. The applications of NGs as a power source and as an electromechanical sensing element are both discussed. At the end, current challenges in this direction and future research perspectives are elaborated. This emerging and impactful application direction reviewed in this article is expected to inspire many new research and commercialization opportunities in the field of NG technology.

Feasibility of a remotely monitored blood alcohol concentration device to facilitate treatment motivation

Background

Consistent monitoring of blood alcohol concentration through breathalyzers is critical for identifying reoccurrence. Little research has effectively utilized convenient wireless enabled breathalyzers that can measure blood alcohol concentration while enhancing treatment motivation for outpatient care. The current study attempted to understand the impact of wireless breathalyzers on treatment motivation and self-efficacy in remaining sober for individuals diagnosed with alcohol use disorder in an outpatient treatment facility.

Methods

Participants were assigned to one of two conditions: the experimental breathalyzer and the treatment as usual group. The groups were assessed by the University of Rhode Island Change Assessment (URICA), and on self-efficacy, measured by the Alcohol Abstinence Self-Efficacy Scale (AASE). The evaluation period took place over three months with a six-week follow-up evaluation. During the entirety of the evaluation period and post-study follow up, interviews occurred.

Results

As a secondary analysis, the URICA's motivational scores were higher for participants receiving the experimental intervention at a two-month evaluation and at the six-week follow-up. The AASE's temptation to reoccurrence scores significantly reduced over time for both groups. The confidence to resist temptation was not significant. Three major themes emerged from the interviews, including the benefit of the breathalyzer facilitating their treatment, ease of device use, and technical issues.

Conclusions

The insights gained from this study will be important to develop cost-effective ancillary interventions for comprehensive alcohol dependence treatment. On-going monitoring enabled by new technology allows treatment providers to take an individualized disease-management approach as well as facilitating timely interventions by the treatment provider.

The Emergence of AI-Based Wearable Sensors for Digital Health Technology: A Review

Disease diagnosis and monitoring using conventional healthcare services is typically expensive and has limited accuracy. Wearable health technology based on flexible electronics has gained tremendous attention in recent years for monitoring patient health owing to attractive features, such as lower medical costs, quick access to patient health data, ability to operate and transmit data in harsh environments, storage at room temperature, non-invasive implementation, mass scaling, etc. This technology provides an opportunity for disease pre-diagnosis and immediate therapy. Wearable sensors have opened a new area of personalized health monitoring by accurately measuring physical states and biochemical signals. Despite the progress to date in the development of wearable sensors, there are still several limitations in the accuracy of the data collected, precise disease diagnosis, and early treatment. This necessitates advances in applied materials and structures and using artificial intelligence (AI)-enabled wearable sensors to extract target signals for accurate clinical decision-making and efficient medical care. In this paper, we review two significant aspects of smart wearable sensors. First, we offer an overview of the most recent progress in improving wearable sensor performance for physical, chemical, and biosensors, focusing on materials, structural configurations, and transduction mechanisms. Next, we review the use of AI technology in combination with wearable technology for big data processing, self-learning, power-efficiency, real-time data acquisition and processing, and personalized health for an intelligent sensing platform. Finally, we present the challenges and future opportunities associated with smart wearable sensors.

The Role of Wearable Sensors in the Future Primary Healthcare - Preferences of the Adult Swiss Population: A Mixed Methods Approach

Wearable sensors have the potential to increase continuity of care and reduce healthcare expenditure. The user concerns and preferences regarding wearable sensors are the least addressed topic in related literature. Therefore, this study aimed first, to examine the preferences of the adult Swiss population regarding the use of wearable sensors in primary healthcare. Second, the study aimed to explain and learn more about these preferences and why such wearable sensors would or would not be used. An explanatory sequential design was used to reach the two aims. In the initial quantitative phase preferences of a nationwide survey were analyzed descriptively and a multivariable ordered logistic regression was used to identify key characteristics, that influence the preferences. In the second phase, eight semi-structured interviews were conducted. The cleaned study sample of the survey included 687 participants, 46% of whom gave a positive rating regarding the use of wearable sensors. In contrast, 44% gave a negative rating and 10% were neutral. The interviews showed that sensors should be small, not flashy and be compatible with everyday activities. Individuals without a current health risk or existing chronic disease showed lower preferences for using wearable sensors, particularly because they fear losing control over their own body. In contrast, individuals with increased risk or with an existing chronic disease were more likely to use wearable sensors as they can increase the personal safety and provide real-time health information to physicians. Therefore, an important deciding factor for and against the use of wearable sensors seems to be the perceived personal susceptibility for potential health problems.

Smart Technologies used as Smart Tools in the Management of Cardiovascular Disease and their Future Perspective

Cardiovascular disease (CVD) is a leading cause of morbidity and mortality worldwide. The advent of smart technologies has significantly impacted the management of CVD, offering innovative tools and solutions to improve patient outcomes. Smart technologies have revolutionized and transformed the management of CVD, providing innovative tools to improve patient care, enhance diagnostics, and enable more personalized treatment approaches. These smart tools encompass a wide range of technologies, including wearable devices, mobile applications,3D printing technologies, artificial intelligence (AI), remote monitoring systems, and electronic health records (EHR). They offer numerous advantages, such as real-time monitoring, early detection of abnormalities, remote patient management, and data-driven decision-making. However, they also come with certain limitations and challenges, including data privacy concerns, technical issues, and the need for regulatory frameworks. In this review, despite these challenges, the future of smart technologies in CVD management looks promising, with advancements in AI algorithms, telemedicine platforms, and bio fabrication techniques opening new possibilities for personalized and efficient care. In this article, we also explore the role of smart technologies in CVD management, their advantages and disadvantages, limitations, current applications, and their smart future.

Remote monitoring for long-term physical health conditions: an evidence and gap map

Background

Remote monitoring involves the measurement of an aspect of a patient's health without that person being seen face to face. It could benefit the individual and aid the efficient provision of health services. However, remote monitoring can be used to monitor different aspects of health in different ways. This evidence map allows users to find evidence on different forms of remote monitoring for different conditions easily to support the commissioning and implementation of interventions.

Objectives

The aim of this map was to provide an overview of the volume, diversity and nature of recent systematic reviews on the effectiveness, acceptability and implementation of remote monitoring for adults with long-term physical health conditions.

Data sources

We searched MEDLINE, nine further databases and Epistemonikos for systematic reviews published between 2018 and March 2022, PROSPERO for continuing reviews, and completed citation chasing on included studies.

Review methods

(Study selection and Study appraisal): Included systematic reviews focused on adult populations with a long-term physical health condition and reported on the effectiveness, acceptability or implementation of remote monitoring. All forms of remote monitoring where data were passed to a healthcare professional as part of the intervention were included. Data were extracted on the characteristics of the remote monitoring intervention and outcomes assessed in the review. AMSTAR 2 was used to assess quality. Results were presented in an interactive evidence and gap map and summarised narratively. Stakeholder and public and patient involvement groups provided feedback throughout the project.

Results

We included 72 systematic reviews. Of these, 61 focus on the effectiveness of remote monitoring and 24 on its acceptability and/or implementation, with some reviews reporting on both. The majority contained studies from North America and Europe (38 included studies from the United Kingdom). Patients with cardiovascular disease, diabetes and respiratory conditions were the most studied populations. Data were collected predominantly using common devices such as blood pressure monitors and transmitted via applications, websites, e-mail or patient portals, feedback provided via telephone call and by nurses. In terms of outcomes, most reviews focused on physical health, mental health and well-being, health service use, acceptability or implementation. Few reviews reported on less common conditions or on the views of carers or healthcare professionals. Most reviews were of low or critically low quality.

Limitations

Many terms are used to describe remote monitoring; we searched as widely as possible but may have missed some relevant reviews. Poor reporting of remote monitoring interventions may mean some included reviews contain interventions that do not meet our definition, while relevant reviews might have been excluded. This also made the interpretation of results difficult.

Conclusions and future work

The map provides an interactive, visual representation of evidence on the effectiveness of remote monitoring and its acceptability and successful implementation. This evidence could support the commissioning and delivery of remote monitoring interventions, while the limitations and gaps could inform further research and technological development. Future reviews should follow the guidelines for conducting and reporting systematic reviews and investigate the application of remote monitoring in less common conditions.

Review registration

A protocol was registered on the OSF registry (https://doi.org/10.17605/OSF.IO/6Q7P4).

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Health Services and Delivery Research programme (NIHR award ref: NIHR135450) as part of a series of evidence syntheses under award NIHR130538. For more information, visit https://fundingawards.nihr.ac.uk/award/NIHR135450 and https://fundingawards.nihr.ac.uk/award/NIHR130538. The report is published in full in Health and Social Care Delivery Research; Vol. 11, No. 22. See the NIHR Funding and Awards website for further project information.

Rehabilitation Technologies for Chronic Conditions: Will We Sink or Swim?

INTRODUCTION

Chronic conditions such as stroke, Parkinson's disease, spinal cord injury, multiple sclerosis, vestibular disorders, chronic pain, arthritis, diabetes, chronic obstructive pulmonary disease (COPD), and heart disease are leading causes of disability among middle-aged and older adults. While evidence-based treatment can optimize clinical outcomes, few people with chronic conditions engage in the recommended levels of exercise for clinical improvement and successful management of their condition. Rehabilitation technologies that can augment therapeutic care-i.e., exoskeletons, virtual/augmented reality, and remote monitoring-offer the opportunity to bring evidence-based rehabilitation into homes. Successful integration of rehabilitation techniques at home could help recovery and access and foster long term self-management. However, widespread uptake of technology in rehabilitation is still limited, leaving many technologies developed but not adopted.

METHODS

In this narrative review, clinical need, efficacy, and obstacles and suggestions for implementation are discussed. The use of three technologies is reviewed in the management of the most prevalent chronic diseases that utilize rehabilitation services, including common neurological, musculoskeletal, metabolic, pulmonary, and cardiac conditions. The technologies are (i) exoskeletons, (ii) virtual and augmented reality, and (iii) remote monitoring.

RESULTS

Effectiveness evidence backing the use of technology in rehabilitation is growing but remains limited by high heterogeneity, lack of long-term outcomes, and lack of adoption outcomes.

CONCLUSION

While rehabilitation technologies bring opportunities to bridge the gap between clinics and homes, there are many challenges with adoption. Hybrid effectiveness and implementation trials are a possible path to successful technology development and adoption.

Strain-Driven Negative Resistance Switching of Conductive Fibers with Adjustable Sensitivity for Wearable Healthcare Monitoring Systems with Near-Zero Standby Power

Recently, one of the primary concerns in e-textile-based healthcare monitoring systems for chronic illness patients has been reducing wasted power consumption, as the system should be always-on to capture diverse biochemical and physiological characteristics. However, the general conductive fibers, a major component of the existing wearable monitoring systems, have a positive gauge-factor (GF) that increases electrical resistance when stretched, so that the systems have no choice but to consume power continuously. Herein, a twisted conductive-fiber-based negatively responsive switch-type (NRS) strain-sensor with an extremely high negative GF (resistance change ratio ≈ 3.9 × 108 ) that can significantly increase its conductivity from insulating to conducting properties is developed. To this end, a precision cracking technology is devised, which could induce a difference in the Young's modulus of the encapsulated layer on the fiber through selective ultraviolet-irradiation treatment. Owing to this technology, the NRS strain-sensors can allow for effective regulation of the mutual contact resistance under tensile strain while maintaining superior durability for over 5000 stretching cycles. For further practical demonstrations, three healthcare monitoring systems (E-fitness pants, smart-masks, and posture correction T-shirts) with near-zero standby power are also developed, which opens up advancements in electronic textiles by expanding the utilization range of fiber strain-sensors.

Clinical Nurses' Identification of a Wearable Universal Serial Bus Used for Pediatric Oncology Clinical Trial Participant Safety Management

The expanded access to clinical trials has provided more patients the opportunity to participate in novel therapeutics research. There is an increased likelihood of a patient, as a pediatric oncology clinical trial participant, to present for clinical care outside the research site, such as at an emergency room or urgent care center. A novel wearable universal serial bus device is a proposed technology to bridge potential communication gaps, pertaining to critical information such as side effects and permitted therapies, between research teams and clinical teams where investigational agents may be contraindicated to standard treatments. Fifty-five emergency and urgent care nurses across the United States were presented, via online survey without priming to the context of clinical trials or the device, a picture of a pediatric patient wearing the novel wearable device prompted to identify significant, environmental cues important for patient care. Of the 40 nurses observing the patient photo, three identified the wearable device within Situational Awareness Global Assessment Tool formatted narrative response fields. Analysis of the narrative nurse-participant responses of significant clinical findings upon initial assessment of the pediatric patient photo is described, as well as the implications for subsequent prototyping of the novel universal serial bus prototype.

The use of digital health in heart rhythm care

INTRODUCTION

Digital health is a broad term that includes telecommunication technologies to collect, share and manipulate health information to improve patient health and health care services. With the growing use of wearables, artificial intelligence, machine learning, and other novel technologies, digital health is particularly relevant to the field of cardiac arrhythmias, with roles pertinent to education, prevention, diagnosis, management, prognosis, and surveillance.

AREAS COVERED

This review summarizes information on the clinical use of digital health technology in arrhythmia care and discusses its opportunities and challenges.

EXPERT OPINION

Digital health has begun to play an essential role in arrhythmia care regarding diagnostics, long-term monitoring, patient education and shared decision making, management, medication adherence, and research. Despite remarkable advances, integrating digital health technologies into healthcare faces challenges, including patient usability, privacy, system interoperability, physician liability, analysis and incorporation of the huge amount of real-time information from wearables, and reimbursement. Successful implementation of digital health technologies requires clear objectives and deep changes to existing workflows and responsibilities.

Classification of Breathing Signals According to Human Motions by Combining 1D Convolutional Neural Network and Embroidered Textile Sensor

Research on healthcare and body monitoring has increased in recent years, with respiratory data being one of the most important factors. Respiratory measurements can help prevent diseases and recognize movements. Therefore, in this study, we measured respiratory data using a capacitance-based sensor garment with conductive electrodes. To determine the most stable measurement frequency, we conducted experiments using a porous Eco-flex and selected 45 kHz as the most stable frequency. Next, we trained a 1D convolutional neural network (CNN) model, which is a type of deep learning model, to classify the respiratory data according to four movements (standing, walking, fast walking, and running) using one input. The final test accuracy for classification was >95%. Therefore, the sensor garment developed in this study can measure respiratory data for four movements and classify them using deep learning, making it a versatile wearable in the form of a textile. We expect that this method will advance in various healthcare fields.

A Review of the Role of Artificial Intelligence in Healthcare

Artificial intelligence (AI) applications have transformed healthcare. This study is based on a general literature review uncovering the role of AI in healthcare and focuses on the following key aspects: (i) medical imaging and diagnostics, (ii) virtual patient care, (iii) medical research and drug discovery, (iv) patient engagement and compliance, (v) rehabilitation, and (vi) other administrative applications. The impact of AI is observed in detecting clinical conditions in medical imaging and diagnostic services, controlling the outbreak of coronavirus disease 2019 (COVID-19) with early diagnosis, providing virtual patient care using AI-powered tools, managing electronic health records, augmenting patient engagement and compliance with the treatment plan, reducing the administrative workload of healthcare professionals (HCPs), discovering new drugs and vaccines, spotting medical prescription errors, extensive data storage and analysis, and technology-assisted rehabilitation. Nevertheless, this science pitch meets several technical, ethical, and social challenges, including privacy, safety, the right to decide and try, costs, information and consent, access, and efficacy, while integrating AI into healthcare. The governance of AI applications is crucial for patient safety and accountability and for raising HCPs' belief in enhancing acceptance and boosting significant health consequences. Effective governance is a prerequisite to precisely address regulatory, ethical, and trust issues while advancing the acceptance and implementation of AI. Since COVID-19 hit the global health system, the concept of AI has created a revolution in healthcare, and such an uprising could be another step forward to meet future healthcare needs.

Wearable Devices in Cardiovascular Medicine

Wearable devices, such as smartwatches and activity trackers, are commonly used by patients in their everyday lives to manage their health and well-being. These devices collect and analyze long-term continuous data on measures of behavioral or physiologic function, which may provide clinicians with a more comprehensive view of a patients' health compared with the traditional sporadic measures captured by office visits and hospitalizations. Wearable devices have a wide range of potential clinical applications ranging from arrhythmia screening of high-risk individuals to remote management of chronic conditions such as heart failure or peripheral artery disease. As the use of wearable devices continues to grow, we must adopt a multifaceted approach with collaboration among all key stakeholders to effectively and safely integrate these technologies into routine clinical practice. In this Review, we summarize the features of wearable devices and associated machine learning techniques. We describe key research studies that illustrate the role of wearable devices in the screening and management of cardiovascular conditions and identify directions for future research. Last, we highlight the challenges that are currently hindering the widespread use of wearable devices in cardiovascular medicine and provide short- and long-term solutions to promote increased use of wearable devices in clinical care.

Design Principle and Proofing of a New Smart Textile Material That Acts as a Sensor for Immobility in Severe Bed-Confined Patients

The immobility of patients confined to continuous bed rest continues to raise a couple of very serious challenges for modern medicine. In particular, the overlooking of sudden onset immobility (as in acute stroke) and the delay in addressing the underlying conditions are of utmost importance for the patient and, in the long term, for the medical and social systems. This paper describes the design principles and concrete implementation of a new smart textile material that can form the substrate of intensive care bedding, that acts as a mobility/immobility sensor in itself. The textile sheet acts as a multi-point pressure-sensitive surface that sends continuous capacitance readings through a connector box to a computer running a dedicated software. The design of the capacitance circuit ensures enough individual points to provide an accurate description of the overlying shape and weight. We describe the textile composition and circuit design as well as the preliminary data collected during testing to demonstrate the validity of the complete solution. These results suggest that the smart textile sheet is a very sensitive pressure sensor and can provide continuous discriminatory information to allow for the very sensitive, real-time detection of immobility.

Personalized LSTM Models for ECG Lead Transformations Led to Fewer Diagnostic Errors Than Generalized Models: Deriving 12-Lead ECG from Lead II, V2, and V6

BACKGROUND AND OBJECTIVE

The prevalence of chronic cardiovascular diseases (CVDs) has risen globally, nearly doubling from 1990 to 2019. ECG is a simple, non-invasive measurement that can help identify CVDs at an early and treatable stage. A multi-lead ECG, up to 15 leads in a wearable form factor, is desirable. We seek to derive multiple ECG leads from a select subset of leads so that the number of electrodes can be reduced in line with a patient-friendly wearable device. We further compare personalized derivations to generalized derivations.

METHODS

Long-Short Term Memory (LSTM) networks using Lead II, V2, and V6 as input are trained to obtain generalized models using Bayesian Optimization for hyperparameter tuning for all patients and personalized models for each patient by applying transfer learning to the generalized models. We compare quantitatively using error metrics Root Mean Square Error (RMSE), R², and Pearson correlation (ρ). We compare qualitatively by matching ECG interpretations of board-certified cardiologists.

RESULTS

ECG interpretations from personalized models, when corrected for an intra-observer variance, were identical to the original ECGs, whereas generalized models led to errors. Mean performance values for generalized and personalized models were (RMSE-74.31 µV, R²-72.05, ρ-0.88) and (RMSE-26.27 µV, R²-96.38, ρ-0.98), respectively.

CONCLUSIONS

Diagnostic accuracy based on derived ECG is the most critical validation of ECG derivation methods. Personalized transformation should be sought to derive ECGs. Performing a personalized calibration step to wearable ECG systems and LSTM networks could yield ambulatory 15-lead ECGs with accuracy comparable to clinical ECGs.

Student perspectives on the integration of artificial intelligence into healthcare services

Background

Healthcare workers are often overworked, underfunded, and face many challenges. Integration of artificial intelligence into healthcare service provision can tackle these challenges by relieving burdens on healthcare workers. Since healthcare students are our future healthcare workers, we assessed the knowledge, attitudes, and perspectives of current healthcare students at Qatar University on the implementation of artificial intelligence into healthcare services.

Methods

This was a cross-sectional study of QU-Health Cluster students via an online survey over a three-week period in November 2021. Chi-squared tests and gamma coefficients were used to compare differences between categorical variables.

Results

One hundred and ninety-three QU-Health students responded. Most participants had a positive attitude towards artificial intelligence, finding it useful and reliable. The most popular perceived advantage of artificial intelligence was its ability to speed up work processes. Around 40% expressed concern about a threat to job security from artificial intelligence, and a majority believed that artificial intelligence cannot provide sympathetic care (57.9%). Participants who felt that artificial intelligence can better make diagnoses than humans also agreed that artificial intelligence could replace their job (p  =  0.005). Male students had more knowledge (p  =  0.005) and received more training (p  =  0.005) about healthcare artificial intelligence. Participants cited a lack of expert mentorship as a barrier to obtaining knowledge about artificial intelligence, followed by lack of dedicated courses and funding.

Conclusions

More resources are required for students to develop a good understanding about artificial intelligence. Education needs to be supported by expert mentorship. Further work is needed on how best to integrate artificial intelligence teaching into university curricula.

Attitudes and perspectives of nurses and physicians in South Korea towards the clinical use of person-generated health data

This study aimed to explore the adoption of person-generated health data in clinical settings and discern the factors influencing clinicians' willingness to use it. A web-based survey containing 48 questions was developed based on prior research and the Unified Theory of Acceptance and Use of Technology 2 model. The survey was administered to a convenience sample of 486 nurses and physicians in South Korea recruited through an online community and snowball sampling. Of these, 70.7% were physicians. While 65% had used mobile health apps and devices, only 12.8% were familiar with person-generated health data. Still, a promising 73.3% expressed interest in incorporating person-generated health data into patient care, particularly data on blood glucose and vital signs. The findings of the study also indicated that clinicians specializing in internal medicine (OR: 1.9, CI: 1.16-3.19), familiar with person-generated health data (OR: 2.6, CI: 1.58-4.29), with a positive view of information and communication technology adoption (OR: 2.6, CI: 1.65-4.13), and who see the value in person-generated health data (OR: 3.9, CI: 2.55-6.09) showed higher inclination to utilize it. However, those in outpatient settings (OR: 0.4, CI: 0.19-0.73) showed less enthusiasm. The findings of this study suggest that despite the willingness of clinicians to use person-generated health data, various barriers must be addressed first, including a lack of knowledge regarding its use, concerns about data reliability and quality, and a lack of provider incentives. Overcoming these challenges demands concerted organizational or policy support. This research underscores person-generated health data's untapped potential in healthcare and the pressing need for strategies that facilitate its clinical integration.

Exploring the Potential Use of Wearable Devices as a Prognostic Tool among Patients in Hospice Care

Background: Smartphones and wearable devices have become a part and parcel of the healthcare industry. The use of wearable technology has already proved its potentials in improving healthcare research, clinical work, and patient care. The real time data allows the care providers to monitor the patients' symptoms remotely, prioritize the patients' visits, assist in decision-making, and carry out advanced care planning. Objectives: The primary objective of our study was to investigate the potential use of wearable devices as a prognosis tool among patients in hospice care and palliative care, and the secondary objective was to examine the association between wearable devices and clinical data in the context of patient outcomes, such as discharge and deceased at various time intervals. Methods: We employed a prospective observational research approach to continuously monitor the hand movements of the selected 68 patients between December 2019 and June 2022 via an actigraphy device at hospice or palliative care ward of Taipei Medical University Hospital (TMUH) in Taiwan. Results: The results revealed that the patients with higher scores in the Karnofsky Performance Status (KPS), and Palliative Performance Scale (PPS) tended to live at discharge, while Palliative Prognostic Score (PaP) and Palliative prognostic Index (PPI) also shared the similar trend. In addition, the results also confirmed that all these evaluating tools only suggested rough rather than accurate and definite prediction. The outcomes (May be Discharge (MBD) or expired) were positively correlated with accumulated angle and spin values, i.e., the patients who survived had higher angle and spin values as compared to those who died/expired. Conclusion: The outcomes had higher correlation with angle value compared to spin and ACT. The correlation value increased within the first 48 h and then began to decline. We recommend rigorous prospective observational studies/randomized control trials with many participants for the investigations in the future.

Ubiquitous and smart healthcare monitoring frameworks based on machine learning: A comprehensive review

During the COVID-19 pandemic, the patient care delivery paradigm rapidly shifted to remote technological solutions. Rising rates of life expectancy of older people, and deaths due to chronic diseases (CDs) such as cancer, diabetes and respiratory disease pose many challenges to healthcare. While the feasibility of Remote Patient Monitoring (RPM) with a Smart Healthcare Monitoring (SHM) framework was somewhat questionable before the COVID-19 pandemic, it is now a proven commodity and is on its way to becoming ubiquitous. More health organizations are adopting RPM to enable CD management in the absence of individual monitoring. The current studies on SHM have reviewed the applications of IoT and/or Machine Learning (ML) in the domain, their architecture, security, privacy and other network related issues. However, no study has analyzed the AI and ubiquitous computing advances in SHM frameworks. The objective of this research is to identify and map key technical concepts in the SHM framework. In this context an interesting and meaningful classification of the research articles surveyed for this work is presented. The comprehensive and systematic review is based on the "Preferred Reporting Items for Systematic Review and Meta-Analysis" (PRISMA) approach. A total of 2540 papers were screened from leading research archives from 2016 to March 2021, and finally, 50 articles were selected for review. The major advantages, developments, distinctive architectural structure, components, technical challenges and possibilities in SHM are briefly discussed. A review of various recent cloud and fog computing based architectures, major ML implementation challenges, prospects and future trends is also presented. The survey primarily encourages the data driven predictive analytics aspects of healthcare and the development of ML models for health empowerment.

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.

Monitoring gait at home with radio waves in Parkinson's disease: A marker of severity, progression, and medication response

Parkinson's disease (PD) is the fastest-growing neurological disease in the world. A key challenge in PD is tracking disease severity, progression, and medication response. Existing methods are semisubjective and require visiting the clinic. In this work, we demonstrate an effective approach for assessing PD severity, progression, and medication response at home, in an objective manner. We used a radio device located in the background of the home. The device detected and analyzed the radio waves that bounce off people's bodies and inferred their movements and gait speed. We continuously monitored 50 participants, with and without PD, in their homes for up to 1 year. We collected over 200,000 gait speed measurements. Cross-sectional analysis of the data shows that at-home gait speed strongly correlates with gold-standard PD assessments, as evaluated by the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part III subscore and total score. At-home gait speed also provides a more sensitive marker for tracking disease progression over time than the widely used MDS-UPDRS. Further, the monitored gait speed was able to capture symptom fluctuations in response to medications and their impact on patients' daily functioning. Our study shows the feasibility of continuous, objective, sensitive, and passive assessment of PD at home and hence has the potential of improving clinical care and drug clinical trials.

A systematic review of chiropractic care for fall prevention: rationale, state of the evidence, and recommendations for future research

BACKGROUND

Falls in older adults are a significant and growing public health concern. There are multiple risk factors associated with falls that may be addressed within the scope of chiropractic training and licensure. Few attempts have been made to summarize existing evidence on multimodal chiropractic care and fall risk mitigation. Therefore, the broad purpose of this review was to summarize this research to date. BODY: Systematic review was conducted following PRISMA guidelines. Databases searched included PubMed, Embase, Cochrane Library, PEDro, and Index of Chiropractic Literature. Eligible study designs included randomized controlled trials (RCT), prospective non-randomized controlled, observational, and cross-over studies in which multimodal chiropractic care was the primary intervention and changes in gait, balance and/or falls were outcomes. Risk of bias was also assessed using the 8-item Cochrane Collaboration Tool. The original search yielded 889 articles; 21 met final eligibility including 10 RCTs. One study directly measured the frequency of falls (underpowered secondary outcome) while most studies assessed short-term measurements of gait and balance. The overall methodological quality of identified studies and findings were mixed, limiting interpretation regarding the potential impact of chiropractic care on fall risk to qualitative synthesis.

CONCLUSION

Little high-quality research has been published to inform how multimodal chiropractic care can best address and positively influence fall prevention. We propose strategies for building an evidence base to inform the role of multimodal chiropractic care in fall prevention and outline recommendations for future research to fill current evidence gaps.

Hurdles to developing and scaling remote patients' health management tools and systems: a scoping review

BACKGROUND

Despite all the excitement and hype generated regarding the expected transformative impact of digital technology on the healthcare industry, traditional healthcare systems around the world have largely remained unchanged and resultant improvements in developed countries are slower than anticipated. One area which was expected to significantly improve the quality of and access to primary healthcare services in particular is remote patient monitoring and management. Based on a combination of rapid advances in body sensors and information and communication technologies (ICT), it was hoped that remote patient management tools and systems (RPMTSs) would significantly reduce the care burden on traditional healthcare systems as well as health-related costs. However, the uptake or adoption of above systems has been extremely slow and their roll out has not yet properly taken off especially in developing countries where they ought to have made the greatest positive impact.

AIM

The aim of the study was to assess whether or not recent, relevant literature would support the development of in-community, design, deployment and implementation framework based on three factors thought to be important drivers and levers of RPMTS's adoption and scalability.

METHODS

A rapid, scoping review conducted on relevant articles obtained from PubMed, MEDLINE, PMC and Cochrane databases and grey literature on Google and published between 2012 and May 2020, by combining a number of relevant search terms and phrases.

RESULTS

Most RPMTSs are targeted at and focused on a single disease, do not extensively involve patients and clinicians in their early planning and design phases, are not designed to best serve a specific catchment area and are mainly directed at post-hospital, disease management settings. This may be leading to a situation where patients, potential patients and clinicians simply do not make use of these tools, leading to low adoption and scalability thereof.

CONCLUSION

The development of a user-centred, context-dependent, customizable design and deployment framework could potentially increase the adoption and scalability of RPMTSs, if such framework addressed a combination of diseases, prevalent in a given specific catchment area, especially in developing countries with limited financial resources.

eHealth Literacy of Australian Undergraduate Health Profession Students: A Descriptive Study

Rapid growth in digital health technologies has increased demand for eHealth literacy of all stakeholders within health and social care environments. The digital future of health care services requires the next generation of health professionals to be well-prepared to confidently provide high-quality and safe health care. The aim of this study was to explore the eHealth literacy of undergraduate health profession students to inform undergraduate curriculum development to promote work-readiness. A cross-sectional survey was undertaken at an Australian university using the seven-domain eHealth Literacy Questionnaire (eHLQ), with 610 students participating. A one-way Multivariate Analysis of Variance (MANOVA) with follow-up univariate analysis (ANOVA) was used to determine if there were differences in eHLQ scores across 11 sociodemographic variables. Students generally had good knowledge of health (Scale 2); however, they had concerns over the security of online health data (Scale 4). There were also significant differences in age and ownership of digital devices. Students who were younger reported higher scores across all seven eHLQ scales than older students. This research provided an understanding of eHealth literacy of health profession students and revealed sub-groups that have lower eHealth literacy, suggesting that digital health skills should be integrated into university curriculums, especially related to practice-based digital applications with special focus to address privacy and security concerns. Preparation of health profession students so they can efficiently address their own needs, and the needs of others, is recommended to minimise the digital divide within health and social care environments.

MaD GUI: An Open-Source Python Package for Annotation and Analysis of Time-Series Data

Developing machine learning algorithms for time-series data often requires manual annotation of the data. To do so, graphical user interfaces (GUIs) are an important component. Existing Python packages for annotation and analysis of time-series data have been developed without addressing adaptability, usability, and user experience. Therefore, we developed a generic open-source Python package focusing on adaptability, usability, and user experience. The developed package, Machine Learning and Data Analytics (MaD) GUI, enables developers to rapidly create a GUI for their specific use case. Furthermore, MaD GUI enables domain experts without programming knowledge to annotate time-series data and apply algorithms to it. We conducted a small-scale study with participants from three international universities to test the adaptability of MaD GUI by developers and to test the user interface by clinicians as representatives of domain experts. MaD GUI saves up to 75% of time in contrast to using a state-of-the-art package. In line with this, subjective ratings regarding usability and user experience show that MaD GUI is preferred over a state-of-the-art package by developers and clinicians. MaD GUI reduces the effort of developers in creating GUIs for time-series analysis and offers similar usability and user experience for clinicians as a state-of-the-art package.

Prescribing Time in Nature for Human Health and Well-Being: Study Protocol for Tailored Park Prescriptions

Background

eHealth technologies offer an efficient method to integrate park prescriptions into clinical practice by primary health care (PHC) providers to help patients improve their health via tailored, nature-based health behavior interventions. This paper describes the protocol of the GoalRx Prescription Intervention (GPI) which was designed to leverage community resources to provide tailored park prescriptions for PHC patients.

Methods

The GPI study was designed as a 3-arm, multi-site observational study. We enrolled low-income, rural adults either at-risk of or living with hypertension or diabetes (n = 75) from Federally Qualified Health Centers (FQHC) in two counties in North Carolina, USA into the 3-month intervention. Eligible participants self-selected to receive (1) a tailored park prescription intervention; (2) a tailored home/indoor PA prescription intervention; or (3) a healthy eating prescription (with no PA prescription beyond standard PA counseling advice that is already routinely provided in PHC) as the comparison group. The GPI app paired patient health data from the electronic health record with stated patient preferences and triggered app-integrated SMS motivation and compliance messaging directly to the patient. Patients were assessed at baseline and at a 3-month follow-up upon the completion of the intervention. The primary outcome (mean difference in weekly physical activity from baseline (T0) to post-intervention (T1) as measured by the Fitbit Flex 2) was assessed at 3 months. Secondary outcomes included assessment of the relationship between the intervention and biological markers of health, including body mass index (BMI), systolic and diastolic blood pressure, HbA1c or available glucose test (if applicable), and a depression screen score using the Patient Health Questionnaire 9. Secondary outcomes also included the total number of SMS messages sent, number of SMS messages responded to, number of SMS messages ignored, and opt-out rate.

Discussion

The goal was to create a protocol utilizing eHealth technologies that addressed the specific needs of rural low-income communities and fit into the natural rhythms and processes of the selected FQHC clinics in North Carolina. This protocol offered a higher standard of health care by connecting patients to their PHC teams and increasing patient motivation to make longer-lasting health behavior changes.

Applicability of Cloud Native-based Healthcare Monitoring Platform (CN-HMP) in Older Adult Facilities

Over the past few decades, the world has faced the huge demographic change in the aging population, which makes significant challenges in healthcare systems. The increasing older adult population along with the current health workforce shortage creates a struggling situation for current facilities and personnel to meet the demand. To tackle this situation, cloud computing is a fast-growing area in digital healthcare and it allows to settle up a modern distributed system environment, capable of scaling to tens of thousands of self healing multitenant nodes for healthcare applications. In addition, cloud native architecture is recently getting focused as an ideal structure for multi-node based healthcare monitoring system due to its high scalability, low latency, and rapid and stable maintainability. In this study, we proposed a cloud native-based rapid, robust, and productive digital healthcare platform which allows to manage and care for a large number of patient groups. To validate our platform, we simulated our Cloud Nativebased Healthcare Monitoring Platform (CN-HMP) with real-time setup and evaluated the performance in terms of request response time, data packets delivery, and end-to-end latency. We found it showing less than 0.1 ms response time in at least 92.5% of total requests up to 3K requests, and no data packet loss along with more than 28% of total data packets with no latency and only ≈ 0.6% of those with maximum latency (3 ms) in 24-hour observation. Clinical Relevance- This study and relevant experiment demonstrate the suitability of the CN-HMP to support providers and nurses for elderly patients healthcare with regular monitoring in older adult facilities.

Emerging Artificial Intelligence–Empowered mHealth: Scoping Review

Background

Artificial intelligence (AI) has revolutionized health care delivery in recent years. There is an increase in research for advanced AI techniques, such as deep learning, to build predictive models for the early detection of diseases. Such predictive models leverage mobile health (mHealth) data from wearable sensors and smartphones to discover novel ways for detecting and managing chronic diseases and mental health conditions.

Objective

Currently, little is known about the use of AI-powered mHealth (AIM) settings. Therefore, this scoping review aims to map current research on the emerging use of AIM for managing diseases and promoting health. Our objective is to synthesize research in AIM models that have increasingly been used for health care delivery in the last 2 years.

Methods

Using Arksey and O’Malley’s 5-point framework for conducting scoping reviews, we reviewed AIM literature from the past 2 years in the fields of biomedical technology, AI, and information systems. We searched 3 databases, PubsOnline at INFORMS, e-journal archive at MIS Quarterly, and Association for Computing Machinery (ACM) Digital Library using keywords such as “mobile healthcare,” “wearable medical sensors,” “smartphones”, and “AI.” We included AIM articles and excluded technical articles focused only on AI models. We also used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) technique for identifying articles that represent a comprehensive view of current research in the AIM domain.

Results

We screened 108 articles focusing on developing AIM models for ensuring better health care delivery, detecting diseases early, and diagnosing chronic health conditions, and 37 articles were eligible for inclusion, with 31 of the 37 articles being published last year (76%). Of the included articles, 9 studied AI models to detect serious mental health issues, such as depression and suicidal tendencies, and chronic health conditions, such as sleep apnea and diabetes. Several articles discussed the application of AIM models for remote patient monitoring and disease management. The considered primary health concerns belonged to 3 categories: mental health, physical health, and health promotion and wellness. Moreover, 14 of the 37 articles used AIM applications to research physical health, representing 38% of the total studies. Finally, 28 out of the 37 (76%) studies used proprietary data sets rather than public data sets. We found a lack of research in addressing chronic mental health issues and a lack of publicly available data sets for AIM research.

Conclusions

The application of AIM models for disease detection and management is a growing research domain. These models provide accurate predictions for enabling preventive care on a broader scale in the health care domain. Given the ever-increasing need for remote disease management during the pandemic, recent AI techniques, such as federated learning and explainable AI, can act as a catalyst for increasing the adoption of AIM and enabling secure data sharing across the health care industry.

Multimode optical fiber specklegram smart bed sensor array

SIGNIFICANCE

Monitoring the movement and vital signs of patients in hospitals and other healthcare environments is a significant burden on healthcare staff. Early warning systems using smart bed sensors hold promise to relieve this burden and improve patient outcomes. We propose a scalable and cost-effective optical fiber sensor array that can be embedded into a mattress to detect movement, both sensitively and spatially.

AIM

Proof-of-concept demonstration that a multimode optical fiber (MMF) specklegram sensor array can be used to detect and image movement on a bed.

APPROACH

Seven MMFs are attached to the upper surface of a mattress such that they cross in a 3  ×  4 array. The specklegram output is monitored using a single laser and single camera and movement on the fibers is monitored by calculating a rolling zero-normalized cross-correlation. A 3  ×  4 image is formed by comparing the signal at each crossing point between two fibers.

RESULTS

The MMF sensor array can detect and image movement on a bed, including getting on and off the bed, rolling on the bed, and breathing.

CONCLUSIONS

The sensor array shows a high sensitivity to movement, which can be used for monitoring physiological parameters and patient movement for potential applications in healthcare settings.

A Remote Patient-Monitoring System for Intensive Care Medicine: Mixed Methods Human-Centered Design and Usability Evaluation

Background

Continuous monitoring of vital signs is critical for ensuring patient safety in intensive care units (ICUs) and is becoming increasingly relevant in general wards. The effectiveness of health information technologies such as patient-monitoring systems is highly determined by usability, the lack of which can ultimately compromise patient safety. Usability problems can be identified and prevented by involving users (ie, clinicians).

Objective

In this study, we aim to apply a human-centered design approach to evaluate the usability of a remote patient-monitoring system user interface (UI) in the ICU context and conceptualize and evaluate design changes.

Methods

Following institutional review board approval (EA1/031/18), a formative evaluation of the monitoring UI was performed. Simulated use tests with think-aloud protocols were conducted with ICU staff (n=5), and the resulting qualitative data were analyzed using a deductive analytic approach. On the basis of the identified usability problems, we conceptualized informed design changes and applied them to develop an improved prototype of the monitoring UI. Comparing the UIs, we evaluated perceived usability using the System Usability Scale, performance efficiency with the normative path deviation, and effectiveness by measuring the task completion rate (n=5). Measures were tested for statistical significance using a 2-sample t test, Poisson regression with a generalized linear mixed-effects model, and the N-1 chi-square test. P<.05 were considered significant.

Results

We found 37 individual usability problems specific to monitoring UI, which could be assigned to six subcodes: usefulness of the system, response time, responsiveness, meaning of labels, function of UI elements, and navigation. Among user ideas and requirements for the UI were high usability, customizability, and the provision of audible alarm notifications. Changes in graphics and design were proposed to allow for better navigation, information retrieval, and spatial orientation. The UI was revised by creating a prototype with a more responsive design and changes regarding labeling and UI elements. Statistical analysis showed that perceived usability improved significantly (System Usability Scale design A: mean 68.5, SD 11.26, n=5; design B: mean 89, SD 4.87, n=5; P=.003), as did performance efficiency (normative path deviation design A: mean 8.8, SD 5.26, n=5; design B: mean 3.2, SD 3.03, n=5; P=.001), and effectiveness (design A: 18 trials, failed 7, 39% times, passed 11, 61% times; design B: 20 trials, failed 0 times, passed 20 times; P=.002).

Conclusions

Usability testing with think-aloud protocols led to a patient-monitoring UI with significantly improved usability, performance, and effectiveness. In the ICU work environment, difficult-to-use technology may result in detrimental outcomes for staff and patients. Technical devices should be designed to support efficient and effective work processes. Our results suggest that this can be achieved by applying basic human-centered design methods and principles.

Trial Registration

ClinicalTrials.gov NCT03514173; https://clinicaltrials.gov/ct2/show/NCT03514173

Remote Health Monitoring in Clinical Trial using Machine Learning Techniques: A Conceptual Framework

Monitoring any process is crucial and very necessary, this is to ensure that standard protocols and procedures are strictly adhered to, monitoring clinical trials is not an exception. It is one of the most crucial processes that should be monitored because human subjects are involved. In trying to monitor clinical trial, information and communication technology techniques can be deployed to facilitate the process and hence improve accuracy. This research formulates a new conceptual framework for monitoring clinical trial using Support Vector Machine and Artificial Neural Network classifiers with physiological datasets from a wearable device. The proposed framework prototype consists of data collection module, data transmission module, and data analysis and prediction module. The data analytic and prediction module is the core section of the proposed framework tailored with data analysis. These datasets are preprocessed and transformed and then used to train and test the system, through different experimental analysis including bagging Support Vector Machine (SVM) and Artificial Neural Network (ANN). The outcome of the analysis presents classification into three different categories, such as fit, unfit, and undecided participants. These various classifications are used to determine if a participant should be allowed to continue in the trial or not. This research provides a framework that is useful in monitoring clinical trial remotely, thereby informing the decision-making process of the research team.

Wearable gait analysis systems: ready to be used by medical practitioners in geriatric wards?

Aim

To investigate the feasibility of wearable gait analysis in geriatric wards by testing the effectiveness and acceptance of the system.

Findings

Wearable gait analysis can be implemented into geriatric wards, showing its readiness for a transformation from a pure research tool to a practically usable gait analysis system.

Message

Despite good transferability into clinical practice, future research should aim to increase functionality and applicability of wearable gait analysis systems in clinical contexts.

Purpose

We assess feasibility of wearable gait analysis in geriatric wards by testing the effectiveness and acceptance of the system.

Methods

Gait parameters of 83 patients (83.34 ± 5.88 years, 58/25 female/male) were recorded at admission and/or discharge to/from two geriatric inpatient wards. Gait parameters were tested for statistically significant differences between admission and discharge. Walking distance measured by a wearable gait analysis system was correlated with distance assessed by physiotherapists. Examiners rated usability using the system usability scale. Patients reported acceptability on a five-point Likert-scale.

Results

The total distance measures highly correlate (r = 0.89). System Usability Scale is above the median threshold of 68, indicating good usability. Majority of patients does not have objections regarding the use of the system. Among other gait parameters, mean heel strike angle changes significantly between admission and discharge.

Conclusion

Wearable gait analysis system is objectively and subjectively usable in a clinical setting and accepted by patients. It offers a reasonably valid assessment of gait parameters and is a feasible way for instrumented gait analysis.

Remote Healthcare for Elderly People Using Wearables: A Review

The growth of health care spending on older adults with chronic diseases faces major concerns that require effective measures to be adopted worldwide. Among the main concerns is whether recent technological advances now offer the possibility of providing remote health care for the aging population. The benefits of suitable prevention and adequate monitoring of chronic diseases by using emerging technological paradigms such as wearable devices and the Internet of Things (IoT) can increase the detection rates of health risks to raise the quality of life for the elderly. Specifically, on the subject of remote health monitoring in older adults, a first approach is required to review devices, sensors, and wearables that serve as tools for obtaining and measuring physiological parameters in order to identify progress, limitations, and areas of opportunity in the development of health monitoring schemes. For these reasons, a review of articles on wearable devices was presented in the first instance to identify whether the selected articles addressed the needs of aged adults. Subsequently, the direct review of commercial and prototype wearable devices with the capability to read physiological parameters was presented to identify whether they are optimal or usable for health monitoring in older adults.

Factors Influencing Perceived Helpfulness and Participation in Innovative Research: A Pilot Study of Individuals with and without Mood Symptoms

Little is known about how individuals with and without mood disorders perceive the inherent risks and helpfulness of participating in innovative psychiatric research, or about the factors that influence their willingness to participate. We conducted an online survey with 80 individuals (self-reported mood disorder [n = 25], self-reported good health [n = 55]) recruited via MTurk. We assessed respondents' perceptions of risk and helpfulness in study vignettes associated with two innovative research projects (intravenous ketamine therapy and wearable devices), as well as their willingness to participate in these projects. Respondents with and without mood disorders perceived risk similarly across projects. Respondents with no mood disorders viewed both projects as more helpful to society than to research volunteers, while respondents with mood disorders viewed the projects as equally helpful to volunteers and society. Individuals with mood disorders perceived ketamine research, and the two projects on average, as more helpful to research volunteers than did individuals without mood disorders. Our findings add to a limited empirical literature on the perspectives of volunteers in innovative psychiatric research.

Development and Initial Validation of an Acute Readiness Monitoring Scale in Military Personnel

Personnel in many professions must remain “ready” to perform diverse activities. Managing individual and collective capability is a common concern for leadership and decision makers. Typical existing approaches for monitoring readiness involve keeping detailed records of training, health and equipment maintenance, or – less commonly – data from wearable devices that can be difficult to interpret as well as raising privacy concerns. A widely applicable, simple psychometric measure of perceived readiness would be invaluable in generating rapid evaluations of current capability directly from personnel. To develop this measure, we conducted exploratory factor analysis and confirmatory factor analysis with a sample of 770 Australian military personnel. The 32-item Acute Readiness Monitoring Scale (ARMS) demonstrated good model fit, and comprised nine factors: overall readiness; physical readiness; physical fatigue; cognitive readiness; cognitive fatigue; threat-challenge (i.e., emotional/coping) readiness; skills-and-training readiness; group-team readiness, and equipment readiness. Readiness factors were negatively correlated with recent stress, current negative affect and distress, and positively correlated with resilience, wellbeing, current positive affect and a supervisor’s rating of solider readiness. The development of the ARMS facilitates a range of new research opportunities: enabling quick, simple and easily interpreted assessment of individual and group readiness.

A Novel Elderly Tracking System Using Machine Learning to Classify Signals from Mobile and Wearable Sensors

A health or activity monitoring system is the most promising approach to assisting the elderly in their daily lives. The increase in the elderly population has increased the demand for health services so that the existing monitoring system is no longer able to meet the needs of sufficient care for the elderly. This paper proposes the development of an elderly tracking system using the integration of multiple technologies combined with machine learning to obtain a new elderly tracking system that covers aspects of activity tracking, geolocation, and personal information in an indoor and an outdoor environment. It also includes information and results from the collaboration of local agencies during the planning and development of the system. The results from testing devices and systems in a case study show that the k-nearest neighbor (k-NN) model with k = 5 was the most effective in classifying the nine activities of the elderly, with 96.40% accuracy. The developed system can monitor the elderly in real-time and can provide alerts. Furthermore, the system can display information of the elderly in a spatial format, and the elderly can use a messaging device to request help in an emergency. Our system supports elderly care with data collection, tracking and monitoring, and notification, as well as by providing supporting information to agencies relevant in elderly care.

Assessing the Current Integration of Multiple Personalised Wearable Sensors for Environment and Health Monitoring

The ever-growing development of sensor technology brings new opportunities to investigate impacts of the outdoor environment on human health at the individual level. However, there is limited literature on the use of multiple personalized sensors in urban environments. This review paper focuses on examining how multiple personalized sensors have been integrated to enhance the monitoring of co-exposures and health effects in the city. Following PRISMA guidelines, two reviewers screened 4898 studies from Scopus, Web of Science, ProQuest, Embase, and PubMed databases published from January 2010 to April 2021. In this case, 39 articles met the eligibility criteria. The review begins by examining the characteristics of the reviewed papers to assess the current situation of integrating multiple sensors for health and environment monitoring. Two main challenges were identified from the quality assessment: choosing sensors and integrating data. Lastly, we propose a checklist with feasible measures to improve the integration of multiple sensors for future studies.

Chronic Heart Failure Patients’ Attitudes towards Digital Device Data for Self-Documentation and Research in Germany: A Cross-Sectional Survey Study (Preprint)

Background

In recent years, the use of digital mobile measurement devices (DMMDs) for self-documentation in cardiovascular care in Western industrialized health care systems has increased. For patients with chronic heart failure (cHF), digital self-documentation plays an increasingly important role in self-management. Data from DMMDs can also be integrated into telemonitoring programs or data-intensive medical research to collect and evaluate patient-reported outcome measures through data sharing. However, the implementation of data-intensive devices and data sharing poses several challenges for doctors and patients as well as for the ethical governance of data-driven medical research.

Objective

This study aims to explore the potential and challenges of digital device data in cardiology research from patients’ perspectives. Leading research questions of the study concerned the attitudes of patients with cHF toward health-related data collected in the use of digital devices for self-documentation as well as sharing these data and consenting to data sharing for research purposes.

Methods

A cross-sectional survey of patients of a research in cardiology was conducted at a German university medical center (N=159) in 2020 (March to July). Eligible participants were German-speaking adult patients with cHF at that center. A pen-and-pencil questionnaire was sent by mail.

Results

Most participants (77/105, 73.3%) approved digital documentation, as they expected the device data to help them observe their body and its functions more objectively. Digital device data were believed to provide cognitive support, both for patients’ self-assessment and doctors’ evaluation of their patients’ current health condition. Interestingly, positive attitudes toward DMMD data providing cognitive support were, in particular, voiced by older patients aged >65 years. However, approximately half of the participants (56/105, 53.3%) also reported difficulty in dealing with self-documented data that lay outside the optimal medical target range. Furthermore, our findings revealed preferences for the self-management of DMMD data disclosed for data-intensive medical research among German patients with cHF, which are best implemented with a dynamic consent model.

Conclusions

Our findings provide potentially valuable insights for introducing DMMD in cardiovascular research in the German context. They have several practical implications, such as a high divergence in attitudes among patients with cHF toward different data-receiving organizations as well as a large variance in preferences for the modes of receiving information included in the consenting procedure for data sharing for research. We suggest addressing patients’ multiple views on consenting and data sharing in institutional normative governance frameworks for data-intensive medical research.

Biopotential Signal Monitoring Systems in Rehabilitation: A Review

Monitoring physical activity in medical and clinical rehabilitation, in sports environments or as a wellness indicator is helpful to measure, analyze and evaluate physiological parameters involving the correct subject’s movements. Thanks to integrated circuit (IC) technologies, wearable sensors and portable devices have expanded rapidly in monitoring physical activities in sports and tele-rehabilitation. Therefore, sensors and signal acquisition devices became essential in the tele-rehabilitation path to obtain accurate and reliable information by analyzing the acquired physiological signals. In this context, this paper provides a state-of-the-art review of the recent advances in electroencephalogram (EEG), electrocardiogram (ECG) and electromyogram (EMG) signal monitoring systems and sensors that are relevant to the field of tele-rehabilitation and health monitoring. Mostly, we focused our contribution in EMG signals to highlight its importance in rehabilitation context applications. This review focuses on analyzing the implementation of sensors and biomedical applications both in literature than in commerce. Moreover, a final review discussion about the analyzed solutions is also reported at the end of this paper to highlight the advantages of physiological monitoring systems in rehabilitation and individuate future advancements in this direction. The main contributions of this paper are (i) the presentation of interesting works in the biomedical area, mainly focusing on sensors and systems for physical rehabilitation and health monitoring between 2016 and up-to-date, and (ii) the indication of the main types of commercial sensors currently being used for biomedical applications.