Wearables and the medical revolution

Digital health in pharmacy education: Elective practical course integrating wearable devices and their generated health data

The widespread adoption of wearable devices (wearables) for monitoring vital signs, including blood pressure and glucose levels, has experienced a considerable surge in recent times. This surge has led to the generation of a substantial amount of health data, accessible to pharmacists during patient consultations as the healthcare sector advances in digitalization. To enhance the digital competencies of future pharmacists required by the rapidly changing digital health landscape, the Institute of Clinical Pharmacy and Pharmacotherapy, Heinrich Heine University (HHU) Duesseldorf has developed an innovative elective practical course aimed to bolster pharmacy students' competencies in handling wearables and the health data generated. The three-week practical elective course employed wearables FreeStyle Libre® 3 (Continuous Glucose Monitoring, CGM) and Aktiia (Cuffless Blood Pressure Monitoring). The hands-on activities allowed participants to obtain and interpret wearable-generated health-related data and acquainted them with simulated patient cases. Final-year pharmacy students' subjective assessments before and after the course depicted the increased knowledge and competence regarding analysing wearables data.

Perceptions of HIV-Related Comorbidities and Usability of a Virtual Environment for Cardiovascular Disease Prevention Education in Sexual Minority Men With HIV: Formative Phases of a Pilot Randomized Controlled Trial

BACKGROUND

Sexual minority men with HIV are at an increased risk of cardiovascular disease (CVD) and have been underrepresented in behavioral research and clinical trials.

OBJECTIVE

This study aims to explore perceptions of HIV-related comorbidities and assess the interest in and usability of a virtual environment for CVD prevention education in Black and Latinx sexual minority men with HIV.

METHODS

This is a 3-phase pilot behavioral randomized controlled trial. We report on formative phases 1 and 2 that informed virtual environment content and features using qualitative interviews, usability testing, and beta testing with a total of 25 individuals. In phase 1, a total of 15 participants completed interviews exploring HIV-related illnesses of concern that would be used to tailor the virtual environment. In phase 2, usability testing and beta testing were conducted with 10 participants to assess interest, features, and content.

RESULTS

In phase 1, we found that CVD risk factors included high blood pressure, myocardial infarction, stroke, and diabetes. Cancer (prostate, colon, and others) was a common concern, as were mental health conditions. In phase 2, all participants completed the 12-item usability checklist with favorable feedback within 30 to 60 minutes. Beta-testing interviews suggested (1) mixed perceptions of health and HIV, (2) high risk for comorbid conditions, (3) virtual environment features were promising, and (4) the need for diverse avatar representations.

CONCLUSIONS

We identified several comorbid conditions of concern, and findings carry significant implications for mitigating barriers to preventive health screenings, given the shared risk factors between HIV and related comorbidities. Highly rated aspects of the virtual environment were anonymity; meeting others with HIV who identify as gay or bisexual; validating lesbian, gay, bisexual, transgender, queer, and others (LGBTQ+) images and content; and accessibility to CVD prevention education. Critical end-user feedback from beta testing suggested more options for avatar customization in skin, hair, and body representation. Our next phase will test the virtual environment as a new approach to advancing cardiovascular health equity in ethnic and racial sexual minority men with HIV.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04061915; https://clinicaltrials.gov/study/NCT05242952.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/38348.

The rise of bioelectronic medicine

Bioelectronic Medicine (BEM), which uses implantable electronic medical devices to interface with electrically active tissues, aspires to revolutionize the way we understand and fight disease. By leveraging knowledge from microelectronics, materials science, information technology, neuroscience and medicine, BEM promises to offer novel solutions that address unmet clinical needs and change the concept of therapeutics. This perspective communicates our vision for the future of BEM and presents the necessary steps that need to be taken and the challenges that need to be faced before this new technology can flourish.

Traumatic brain injury and sleep in military and veteran populations: A literature review

BACKGROUND

Traumatic brain injury (TBI) is a hallmark of wartime injury and is related to numerous sleep wake disorders (SWD), which persist long term in veterans. Current knowledge gaps in pathophysiology have hindered advances in diagnosis and treatment.

OBJECTIVE

We reviewed TBI SWD pathophysiology, comorbidities, diagnosis and treatment that have emerged over the past two decades.

METHODS

We conducted a literature review of English language publications evaluating sleep disorders (obstructive sleep apnea, insomnia, hypersomnia, parasomnias, restless legs syndrome and periodic limb movement disorder) and TBI published since 2000. We excluded studies that were not specifically evaluating TBI populations.

RESULTS

Highlighted areas of interest and knowledge gaps were identified in TBI pathophysiology and mechanisms of sleep disruption, a comparison of TBI SWD and post-traumatic stress disorder SWD. The role of TBI and glymphatic biomarkers and management strategies for TBI SWD will also be discussed.

CONCLUSION

Our understanding of the pathophysiologic underpinnings of TBI and sleep health, particularly at the basic science level, is limited. Developing an understanding of biomarkers, neuroimaging, and mixed-methods research in comorbid TBI SWD holds the greatest promise to advance our ability to diagnose and monitor response to therapy in this vulnerable population.

Patterns of Ownership and Usage of Wearable Devices in the United States, 2020-2022: Survey Study

BACKGROUND

Although wearable technology has become increasingly common, comprehensive studies examining its ownership across different sociodemographic groups are limited.

OBJECTIVE

The aims of this study were to (1) measure wearable device ownership by sociodemographic characteristics in a cohort of US consumers and (2) investigate how these devices are acquired and used for health-related purposes.

METHODS

Data from the Rock Health Digital Health Consumer Adoption Survey collected from 2020 to 2022 with 23,974 US participants were analyzed. The sample was US Census-matched for demographics, including age, race/ethnicity, gender, and income. The relationship between sociodemographic factors and wearable ownership was explored using descriptive analysis and multivariate logistic regression.

RESULTS

Of the 23,974 respondents, 10,679 (44.5%) owned wearables. Ownership was higher among younger individuals, those with higher incomes and education levels, and respondents living in urban areas. Compared to those aged 18-24 years, respondents 65 years and older had significantly lower odds of wearable ownership (odds ratio [OR] 0.18, 95% CI 0.16-0.21). Higher annual income (≥US $200,000; OR 2.27, 95% CI 2.01-2.57) and advanced degrees (OR 2.23, 95% CI 2.01-2.48) were strong predictors of ownership. Living in rural areas reduced ownership odds (OR 0.65, 95% CI 0.60-0.72). There was a notable difference in ownership based on gender and health insurance status. Women had slightly higher ownership odds than men (OR 1.10, 95% CI 1.04-1.17). Private insurance increased ownership odds (OR 1.28, 95% CI 1.17-1.40), whereas being uninsured (OR 0.41, 95% CI 0.36-0.47) or on Medicaid (OR 0.75, 95% CI 0.68-0.82) decreased the odds of ownership. Interestingly, minority groups such as non-Hispanic Black (OR 1.14, 95% CI 1.03-1.25) and Hispanic/Latine (OR 1.20, 95% CI 1.10-1.31) respondents showed slightly higher ownership odds than other racial/ethnic groups.

CONCLUSIONS

Our findings suggest that despite overall growth in wearable ownership, sociodemographic divides persist. The data indicate a need for equitable access strategies as wearables become integral to clinical and public health domains.

3D-Printed Flexible Microfluidic Health Monitor for In Situ Sweat Analysis and Biomarker Detection

Wearable sweat biosensors have shown great progress in noninvasive, in situ, and continuous health monitoring to demonstrate individuals' physiological states. Advances in novel nanomaterials and fabrication methods promise to usher in a new era of wearable biosensors. Here, we introduce a three-dimensional (3D)-printed flexible wearable health monitor fabricated through a unique one-step continuous manufacturing process with self-supporting microfluidic channels and novel single-atom catalyst-based bioassays for measuring the sweat rate and concentration of three biomarkers. Direct ink writing is adapted to print the microfluidic device with self-supporting structures to harvest human sweat, which eliminates the need for removing sacrificial supporting materials and addresses the contamination and sweat evaporation issues associated with traditional sampling methods. Additionally, the pick-and-place strategy is employed during the printing process to accurately integrate the bioassays, improving manufacturing efficiency. A single-atom catalyst is developed and utilized in colorimetric bioassays to improve sensitivity and accuracy. A feasibility study on human skin successfully demonstrates the functionality and reliability of our health monitor, generating reliable and quantitative in situ results of sweat rate, glucose, lactate, and uric acid concentrations during physical exercise.

Design, Fabrication, and Evaluation of 3D Biopotential Electrodes and Intelligent Garment System for Sports Monitoring

This study presents the development and evaluation of an innovative intelligent garment system, incorporating 3D knitted silver biopotential electrodes, designed for long-term sports monitoring. By integrating advanced textile engineering with wearable monitoring technologies, we introduce a novel approach to real-time physiological signal acquisition, focusing on enhancing athletic performance analysis and fatigue detection. Utilizing low-resistance silver fibers, our electrodes demonstrate significantly reduced skin-to-electrode impedance, facilitating improved signal quality and reliability, especially during physical activities. The garment system, embedded with these electrodes, offers a non-invasive, comfortable solution for continuous ECG and EMG monitoring, addressing the limitations of traditional Ag/AgCl electrodes, such as skin irritation and signal degradation over time. Through various experimentation, including impedance measurements and biosignal acquisition during cycling activities, we validate the system's effectiveness in capturing high-quality physiological data. Our findings illustrate the electrodes' superior performance in both dry and wet conditions. This study not only advances the field of intelligent garments and biopotential monitoring, but also provides valuable insights for the application of intelligent sports wearables in the future.

Artificial Intelligence in Otology and Neurotology

Clinical applications of artificial intelligence (AI) have grown exponentially with increasing computational power and Big Data. Data rich fields such as Otology and Neurotology are still in the infancy of harnessing the power of AI but are increasingly involved in training and developing ways to incorporate AI into patient care. Current studies involving AI are focused on accessible datasets; health care wearables, tabular data from electronic medical records, electrophysiologic measurements, imaging, and "omics" provide huge amounts of data to utilize. Health care wearables, such as hearing aids and cochlear implants, are a ripe environment for AI implementation.

Unveiling wearables: exploring the global landscape of biometric applications and vital signs and behavioral impact

The development of neuroscientific techniques enabling the recording of brain and peripheral nervous system activity has fueled research in cognitive science. Recent technological advancements offer new possibilities for inducing behavioral change, particularly through cost-effective Internet-based interventions. However, limitations in laboratory equipment volume have hindered the generalization of results to real-life contexts. The advent of Internet of Things (IoT) devices, such as wearables, equipped with sensors and microchips, has ushered in a new era in behavior change techniques. Wearables, including smartwatches, electronic tattoos, and more, are poised for massive adoption, with an expected annual growth rate of 55% over the next five years. These devices enable personalized instructions, leading to increased productivity and efficiency, particularly in industrial production. Additionally, the healthcare sector has seen a significant demand for wearables, with over 80% of global consumers willing to use them for health monitoring. This research explores the primary biometric applications of wearables and their impact on users' well-being, focusing on the integration of behavior change techniques facilitated by IoT devices. Wearables have revolutionized health monitoring by providing real-time feedback, personalized interventions, and gamification. They encourage positive behavior changes by delivering immediate feedback, tailored recommendations, and gamified experiences, leading to sustained improvements in health. Furthermore, wearables seamlessly integrate with digital platforms, enhancing their impact through social support and connectivity. However, privacy and data security concerns must be addressed to maintain users' trust. As technology continues to advance, the refinement of IoT devices' design and functionality is crucial for promoting behavior change and improving health outcomes. This study aims to investigate the effects of behavior change techniques facilitated by wearables on individuals' health outcomes and the role of wearables in promoting a healthier lifestyle.

Role of artificial intelligence in colorectal cancer

The sphere of artificial intelligence (AI) is ever expanding. Applications for clinical practice have been emerging over recent years. Although its uptake has been most prominent in endoscopy, this represents only one aspect of holistic patient care. There are a multitude of other potential avenues in which gastrointestinal care may be involved. We aim to review the role of AI in colorectal cancer as a whole. We performed broad scoping and focused searches of the applications of AI in the field of colorectal cancer. All trials including qualitative research were included from the year 2000 onwards. Studies were grouped into pre-operative, intra-operative and post-operative aspects. Pre-operatively, the major use is with endoscopic recognition. Colonoscopy has embraced the use for human derived classifications such as Narrow-band Imaging International Colorectal Endoscopic, Japan Narrow-band Imaging Expert Team, Paris and Kudo. However, novel detection and diagnostic methods have arisen from advances in AI classification. Intra-operatively, adjuncts such as image enhanced identification of structures and assessment of perfusion have led to improvements in clinical outcomes. Post-operatively, monitoring and surveillance have taken strides with potential socioeconomic and environmental savings. The uses of AI within the umbrella of colorectal surgery are multiple. We have identified existing technologies which are already augmenting cancer care. The future applications are exciting and could at least match, if not surpass human standards.

Ethological computational psychiatry: Challenges and opportunities

Studying the intricacies of individual subjects' moods and cognitive processing over extended periods of time presents a formidable challenge in medicine. While much of systems neuroscience appropriately focuses on the link between neural circuit functions and well-constrained behaviors over short timescales (e.g., trials, hours), many mental health conditions involve complex interactions of mood and cognition that are non-stationary across behavioral contexts and evolve over extended timescales. Here, we discuss opportunities, challenges, and possible future directions in computational psychiatry to quantify non-stationary continuously monitored behaviors. We suggest that this exploratory effort may contribute to a more precision-based approach to treating mental disorders and facilitate a more robust reverse translation across animal species. We conclude with ethical considerations for any field that aims to bridge artificial intelligence and patient monitoring.

Imaging of the vascular distribution of the outer ear using optical coherence tomography angiography for highly accurate positioning of a hearable sensor

Novel hearable technology is securely and comfortably positioned within the ear canal minimizing inaccuracies caused by accessory movements during activities. Despite extensive research on hearable technologies within the outer ear, there is a lack of research in the field of vascular imaging and quantitative analysis in the outer ear in vivo, which is one of the crucial factors to select the appropriate sensor position. Therefore, in this paper, we introduced optical coherence tomography angiography (OCTA)-based qualitative and quantitative analyses to visualize the inner vasculature of the outer ear to acquire vascular maps for microvascular assessments in vivo. By generating maximum amplitude projection images from three-dimensional blood vascular volume, we identified variations of blood vessel signal caused by the different biological characteristics and curvature of the ear among individuals. The performance of micro-vascular mapping using the proposed method was validated through the comparison and analysis of individual vascular parameters using extracted 20 vascular-related variables. In addition, we extracted pulsatile blood flow signals, demonstrating its potential to provide photoplethysmographic signals and ear blood maps simultaneously. Therefore, our proposed OCTA-based method for ear vascular mapping successfully provides quantitative information about ear vasculature, which is potentially used for determining the position of system-on-chip sensors for health monitoring in hearable devices.

Use of Patient-Generated Health Data From Consumer-Grade Devices by Health Care Professionals in the Clinic: Systematic Review

BACKGROUND

Mobile health (mHealth) uses mobile technologies to promote wellness and help disease management. Although mHealth solutions used in the clinical setting have typically been medical-grade devices, passive and active sensing capabilities of consumer-grade devices like smartphones and activity trackers have the potential to bridge information gaps regarding patients' behaviors, environment, lifestyle, and other ubiquitous data. Individuals are increasingly adopting mHealth solutions, which facilitate the collection of patient-generated health data (PGHD). Health care professionals (HCPs) could potentially use these data to support care of chronic conditions. However, there is limited research on real-life experiences of HPCs using PGHD from consumer-grade mHealth solutions in the clinical context.

OBJECTIVE

This systematic review aims to analyze existing literature to identify how HCPs have used PGHD from consumer-grade mobile devices in the clinical setting. The objectives are to determine the types of PGHD used by HCPs, in which health conditions they use them, and to understand the motivations behind their willingness to use them.

METHODS

A systematic literature review was the main research method to synthesize prior research. Eligible studies were identified through comprehensive searches in health, biomedicine, and computer science databases, and a complementary hand search was performed. The search strategy was constructed iteratively based on key topics related to PGHD, HCPs, and mobile technologies. The screening process involved 2 stages. Data extraction was performed using a predefined form. The extracted data were summarized using a combination of descriptive and narrative syntheses.

RESULTS

The review included 16 studies. The studies spanned from 2015 to 2021, with a majority published in 2019 or later. Studies showed that HCPs have been reviewing PGHD through various channels, including solutions portals and patients' devices. PGHD about patients' behavior seem particularly useful for HCPs. Our findings suggest that PGHD are more commonly used by HCPs to treat conditions related to lifestyle, such as diabetes and obesity. Physicians were the most frequently reported users of PGHD, participating in more than 80% of the studies.

CONCLUSIONS

PGHD collection through mHealth solutions has proven beneficial for patients and can also support HCPs. PGHD have been particularly useful to treat conditions related to lifestyle, such as diabetes, cardiovascular diseases, and obesity, or in domains with high levels of uncertainty, such as infertility. Integrating PGHD into clinical care poses challenges related to privacy and accessibility. Some HCPs have identified that though PGHD from consumer devices might not be perfect or completely accurate, their perceived clinical value outweighs the alternative of having no data. Despite their perceived value, our findings reveal their use in clinical practice is still scarce.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/39389.

Wearable Sensors in Other Medical Domains with Application Potential for Orthopedic Trauma Surgery-A Narrative Review

The use of wearable technology is steadily increasing. In orthopedic trauma surgery, where the musculoskeletal system is directly affected, focus has been directed towards assessing aspects of physical functioning, activity behavior, and mobility/disability. This includes sensors and algorithms to monitor real-world walking speed, daily step counts, ground reaction forces, or range of motion. Several specific reviews have focused on this domain. In other medical fields, wearable sensors and algorithms to monitor digital biometrics have been used with a focus on domain-specific health aspects such as heart rate, sleep, blood oxygen saturation, or fall risk. This review explores the most common clinical and research use cases of wearable sensors in other medical domains and, from it, derives suggestions for the meaningful transfer and application in an orthopedic trauma context.

Telemonitoring of pediatric asthma in outpatient settings: A systematic review

Telemonitoring technologies are rapidly evolving, offering a promising solution for remote monitoring and timely management of asthma acute episodes. We aimed to describe current pediatric asthma telemonitoring technologies. A systematic review was conducted until September 2023 on Medline, Scopus, and Web of Science. We included studies of children (0-18 years) with asthma or recurrent wheezing whose respiratory condition was telemonitored outside the healthcare setting. A narrative synthesis was performed. We identified 40 telemonitoring technologies described in 40 studies. The more frequently used technologies for telemonitoring were mobile applications (n = 21) and web-based systems (n = 14). Telemonitoring duration varied between 2 weeks and 32 months. Data collection included asthma symptoms (n = 30), patient-reported outcome measures (PROMs) (n = 11), spirometry/peak flow readings (n = 20), medication adherence (n = 17), inhaler technique (n = 3), air quality (n = 2), and respiratory sounds (n = 2). Both parents and children were the technology target users in most studies (n = 23). Technology training was reported in 23 studies of which 3 provided ongoing support. Automatic feedback was found in 30 studies, mostly related with asthma control. HCP were involved in data management in 27 studies. Technologies were tested in samples from 4 to 327 children, with most studies including school-aged children and/or adolescents (n = 38) and eight including preschool children. This review provides an overview of existing technologies for the outpatient telemonitoring of pediatric asthma. Specific technologies for preschool children represent a gap in the literature that needs to be specifically addressed in future research.

Patient and Provider Perspectives About the Use of Patient-Generated Health Data During Pregnancy: Qualitative Exploratory Study

BACKGROUND

There is increasing interest in using patient-generated health data (PGHD) to improve patient-centered care during pregnancy. However, little research has examined the perspectives of patients and providers as they report, collect, and use PGHD to inform obstetric care.

OBJECTIVE

This study aims to explore the perspectives of patients and providers about the use of PGHD during pregnancy, including the benefits and challenges of reporting, collecting, and using these data, as well as considerations for expanding the use of PGHD to improve obstetric care.

METHODS

We conducted one-on-one interviews with 30 pregnant or postpartum patients and 14 health care providers from 2 obstetrics clinics associated with an academic medical center. Semistructured interview guides included questions for patients about their experience and preferences for sharing PGHD and questions for providers about current processes for collecting PGHD, opportunities to improve or expand the collection of PGHD, and challenges faced when collecting and using this information. Interviews were conducted by phone or videoconference and were audio recorded, transcribed verbatim, and deidentified. Interview transcripts were analyzed deductively and inductively to characterize and explore themes in the data.

RESULTS

Patients and providers described how PGHD, including physiologic measurements and experience of symptoms, were currently collected during and between in-person clinic visits for obstetric care. Both patients and providers reported positive perceptions about the collection and use of PGHD during pregnancy. Reported benefits of collecting PGHD included the potential to use data to directly inform patient care (eg, identify issues and adjust medication) and to encourage ongoing patient involvement in their care (eg, increase patient attention to their health). Patients and providers had suggestions for expanding the collection and use of PGHD during pregnancy, and providers also shared considerations about strategies that could be used to expand PGHD collection and use. These strategies included considering the roles of both patients and providers in reporting and interpreting PGHD. Providers also noted the need to consider the unintended consequences of using PGHD that should be anticipated and addressed.

CONCLUSIONS

Acknowledging the challenges, suggestions, and considerations voiced by patients and providers can inform the development and implementation of strategies to effectively collect and use PGHD to support patient-centered care during pregnancy.

Towards E-textiles in augmentative and alternative communication - user scenarios developed by speech and language therapists

PURPOSE

E-textiles have been the focus of interest in health technology, but little research has been done so far on how they could support persons with complex communication needs. A global estimate is that 97 million people may benefit from Augmentative and Alternative Communication (AAC). Unfortunately, despite the growing body of research, many persons with complex communication needs are left without functional means to communicate. This study aimed to address the lack of research in textile-based AAC and to build a picture of the issues that affect novel textile-based technology development.

MATERIALS AND METHODS

We arranged a focus group study for altogether 12 speech and language therapists to elicit user scenarios to understand needs, activities, and contexts when implementing a novel, textile-based technology in a user-centred approach.

RESULTS AND CONCLUSION

As a result, we present six user scenarios that were created for children to enhance their social interaction in everyday life when using textile-based technology that recognizes touch or detects motion. The persistent availability and the individual design to meet a person's capability along with ease of use and personalization were perceived important requirements. Through these scenarios, we identified technological constraints regarding the development of e-textile technology and its use in the AAC field, such as issues regarding sensors and providing power supply. Resolving the design constraints will lead to a feasible and portable e-textile AAC system.

Precision Assessment of Real-World Associations Between Stress and Sleep Duration Using Actigraphy Data Collected Continuously for an Academic Year: Individual-Level Modeling Study

BACKGROUND

Heightened stress and insufficient sleep are common in the transition to college, often co-occur, and have both been linked to negative health outcomes. A challenge concerns disentangling whether perceived stress precedes or succeeds changes in sleep. These day-to-day associations may vary across individuals, but short study periods and group-level analyses in prior research may have obscured person-specific phenotypes.

OBJECTIVE

This study aims to obtain stable estimates of lead-lag associations between perceived stress and objective sleep duration in the individual, unbiased by the group, by developing an individual-level linear model that can leverage intensive longitudinal data while remaining parsimonious.

METHODS

In total, 55 college students (n=6, 11% second-year students and n=49, 89% first-year students) volunteered to provide daily self-reports of perceived stress via a smartphone app and wore an actigraphy wristband for the estimation of daily sleep duration continuously throughout the academic year (median usable daily observations per participant: 178, IQR 65.5). The individual-level linear model, developed in a Bayesian framework, included the predictor and outcome of interest and a covariate for the day of the week to account for weekly patterns. We validated the model on the cohort of second-year students (n=6, used as a pilot sample) by applying it to variables expected to correlate positively within individuals: objective sleep duration and self-reported sleep quality. The model was then applied to the fully independent target sample of first-year students (n=49) for the examination of bidirectional associations between daily stress levels and sleep duration.

RESULTS

Proof-of-concept analyses captured expected associations between objective sleep duration and subjective sleep quality in every pilot participant. Target analyses revealed negative associations between sleep duration and perceived stress in most of the participants (45/49, 92%), but their temporal association varied. Of the 49 participants, 19 (39%) showed a significant association (probability of direction>0.975): 8 (16%) showed elevated stress in the day associated with shorter sleep later that night, 5 (10%) showed shorter sleep associated with elevated stress the next day, and 6 (12%) showed both directions of association. Of note, when analyzed using a group-based multilevel model, individual estimates were systematically attenuated, and some even reversed sign.

CONCLUSIONS

The dynamic interplay of stress and sleep in daily life is likely person specific. Paired with intensive longitudinal data, our individual-level linear model provides a precision framework for the estimation of stable real-world behavioral and psychological dynamics and may support the personalized prioritization of intervention targets for health and well-being.

Heart Failure Management through Telehealth: Expanding Care and Connecting Hearts

Heart failure (HF) is a leading cause of morbidity worldwide, imposing a significant burden on deaths, hospitalizations, and health costs. Anticipating patients' deterioration is a cornerstone of HF treatment: preventing congestion and end organ damage while titrating HF therapies is the aim of the majority of clinical trials. Anyway, real-life medicine struggles with resource optimization, often reducing the chances of providing a patient-tailored follow-up. Telehealth holds the potential to drive substantial qualitative improvement in clinical practice through the development of patient-centered care, facilitating resource optimization, leading to decreased outpatient visits, hospitalizations, and lengths of hospital stays. Different technologies are rising to offer the best possible care to many subsets of patients, facing any stage of HF, and challenging extreme scenarios such as heart transplantation and ventricular assist devices. This article aims to thoroughly examine the potential advantages and obstacles presented by both existing and emerging telehealth technologies, including artificial intelligence.

Brave new world: expanding home care in stem cell transplantation and advanced therapies with new technologies

Hematopoietic stem cell transplantation and cell therapies like CAR-T are costly, complex therapeutic procedures. Outpatient models, including at-home transplantation, have been developed, resulting in similar survival results, reduced costs, and increased patient satisfaction. The complexity and safety of the process can be addressed with various emerging technologies (artificial intelligence, wearable sensors, point-of-care analytical devices, drones, virtual assistants) that allow continuous patient monitoring and improved decision-making processes. Patients, caregivers, and staff can also benefit from improved training with simulation or virtual reality. However, many technical, operational, and above all, ethical concerns need to be addressed. Finally, outpatient or at-home hematopoietic transplantation or CAR-T therapy creates a different, integrated operative system that must be planned, designed, and carefully adapted to the patient's characteristics and distance from the hospital. Patients, clinicians, and their clinical environments can benefit from technically improved at-home transplantation.

Self-supervised learning for human activity recognition using 700,000 person-days of wearable data

Accurate physical activity monitoring is essential to understand the impact of physical activity on one's physical health and overall well-being. However, advances in human activity recognition algorithms have been constrained by the limited availability of large labelled datasets. This study aims to leverage recent advances in self-supervised learning to exploit the large-scale UK Biobank accelerometer dataset-a 700,000 person-days unlabelled dataset-in order to build models with vastly improved generalisability and accuracy. Our resulting models consistently outperform strong baselines across eight benchmark datasets, with an F1 relative improvement of 2.5-130.9% (median 24.4%). More importantly, in contrast to previous reports, our results generalise across external datasets, cohorts, living environments, and sensor devices. Our open-sourced pre-trained models will be valuable in domains with limited labelled data or where good sampling coverage (across devices, populations, and activities) is hard to achieve.

Bioelectronic modulation of carotid sinus nerve to treat type 2 diabetes: current knowledge and future perspectives

Bioelectronic medicine are an emerging class of treatments aiming to modulate body nervous activity to correct pathological conditions and restore health. Recently, it was shown that the high frequency electrical neuromodulation of the carotid sinus nerve (CSN), a small branch of the glossopharyngeal nerve that connects the carotid body (CB) to the brain, restores metabolic function in type 2 diabetes (T2D) animal models highlighting its potential as a new therapeutic modality to treat metabolic diseases in humans. In this manuscript, we review the current knowledge supporting the use of neuromodulation of the CSN to treat T2D and discuss the future perspectives for its clinical application. Firstly, we review in a concise manner the role of CB chemoreceptors and of CSN in the pathogenesis of metabolic diseases. Secondly, we describe the findings supporting the potential therapeutic use of the neuromodulation of CSN to treat T2D, as well as the feasibility and reversibility of this approach. A third section is devoted to point up the advances in the neural decoding of CSN activity, in particular in metabolic disease states, that will allow the development of closed-loop approaches to deliver personalized and adjustable treatments with minimal side effects. And finally, we discuss the findings supporting the assessment of CB activity in metabolic disease patients to screen the individuals that will benefit therapeutically from this bioelectronic approach in the future.

Use of artificial intelligence to develop predictive algorithms of cough and PCR-confirmed COVID-19 infections based on inputs from clinical-grade wearable sensors

There have been over 769 million cases of COVID-19, and up to 50% of infected individuals are asymptomatic. The purpose of this study aimed to assess the use of a clinical-grade physiological wearable monitoring system, ANNE One, to develop an artificial intelligence algorithm for (1) cough detection and (2) early detection of COVID-19, through the retrospective analysis of prospectively collected physiological data from longitudinal wear of ANNE sensors in a multicenter single arm study of subjects at high risk for COVID-19 due to occupational or home exposures. The study employed a two-fold approach: cough detection algorithm development and COVID-19 detection algorithm development. For cough detection, healthy individuals wore an ANNE One chest sensor during scripted activity. The final performance of the algorithm achieved an F-1 score of 83.3% in twenty-seven healthy subjects during biomarker validation. In the COVID-19 detection algorithm, individuals at high-risk for developing COVID-19 because of recent exposures received ANNE One sensors and completed daily symptom surveys. An algorithm analyzing vital parameters (heart rate, respiratory rate, cough count, etc.) for early COVID-19 detection was developed. The COVID-19 detection algorithm exhibited a sensitivity of 0.47 and specificity of 0.72 for detecting COVID-19 in 325 individuals with recent exposures. Participants demonstrated high adherence (≥ 4 days of wear per week). ANNE One shows promise for detection of COVID-19. Inclusion of respiratory biomarkers (e.g., cough count) enhanced the algorithm's predictive ability. These findings highlight the potential value of wearable devices in early disease detection and monitoring.

Real-world walking behaviors are associated with early-stage heart failure: a Project Baseline Health Study

BACKGROUND

Data collected via wearables may complement in-clinic assessments to monitor subclinical heart failure (HF).

OBJECTIVES

Evaluate the association of sensor-based digital walking measures with HF stage and characterize their correlation with in-clinic measures of physical performance, cardiac function and participant reported outcomes (PROs) in individuals with early HF.

METHODS

The analyzable cohort included participants from the Project Baseline Health Study (PBHS) with HF stage 0, A, or B, or adaptive remodeling phenotype (without risk factors but with mild echocardiographic change, termed RF-/ECHO+) (based on available first-visit in-clinic test and echocardiogram results) and with sufficient sensor data. We computed daily values per participant for 18 digital walking measures, comparing HF subgroups vs stage 0 using multinomial logistic regression and characterizing associations with in-clinic measures and PROs with Spearman's correlation coefficients, adjusting all analyses for confounders.

RESULTS

In the analyzable cohort (N=1265; 50.6% of the PBHS cohort), one standard deviation decreases in 17/18 walking measures were associated with greater likelihood for stage-B HF (multivariable-adjusted odds ratios [ORs] vs stage 0 ranging from 1.18-2.10), or A (ORs vs stage 0, 1.07-1.45), and lower likelihood for RF-/ECHO+ (ORs vs stage 0, 0.80-0.93). Peak 30-minute pace demonstrated the strongest associations with stage B (OR vs stage 0=2.10; 95% CI:1.74-2.53) and A (OR vs stage 0=1.43; 95% CI:1.23-1.66). Decreases in 13/18 measures were associated with greater likelihood for stage-B HF vs stage A. Strength of correlation with physical performance tests, echocardiographic cardiac-remodeling and dysfunction indices and PROs was greatest in stage B, then A, and lowest for 0.

CONCLUSIONS

Digital measures of walking captured by wearable sensors could complement clinic-based testing to identify and monitor pre-symptomatic HF.

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.

E-Tattoos: Toward Functional but Imperceptible Interfacing with Human Skin

The human body continuously emits physiological and psychological information from head to toe. Wearable electronics capable of noninvasively and accurately digitizing this information without compromising user comfort or mobility have the potential to revolutionize telemedicine, mobile health, and both human-machine or human-metaverse interactions. However, state-of-the-art wearable electronics face limitations regarding wearability and functionality due to the mechanical incompatibility between conventional rigid, planar electronics and soft, curvy human skin surfaces. E-Tattoos, a unique type of wearable electronics, are defined by their ultrathin and skin-soft characteristics, which enable noninvasive and comfortable lamination on human skin surfaces without causing obstruction or even mechanical perception. This review article offers an exhaustive exploration of e-tattoos, accounting for their materials, structures, manufacturing processes, properties, functionalities, applications, and remaining challenges. We begin by summarizing the properties of human skin and their effects on signal transmission across the e-tattoo-skin interface. Following this is a discussion of the materials, structural designs, manufacturing, and skin attachment processes of e-tattoos. We classify e-tattoo functionalities into electrical, mechanical, optical, thermal, and chemical sensing, as well as wound healing and other treatments. After discussing energy harvesting and storage capabilities, we outline strategies for the system integration of wireless e-tattoos. In the end, we offer personal perspectives on the remaining challenges and future opportunities in the field.

Technological Advances of Wearable Device for Continuous Monitoring of In Vivo Glucose

Managing diabetes is a chronic challenge today, requiring monitoring and timely insulin injections to maintain stable blood glucose levels. Traditional clinical testing relies on fingertip or venous blood collection, which has facilitated the emergence of continuous glucose monitoring (CGM) technology to address data limitations. Continuous glucose monitoring technology is recognized for tracking long-term blood glucose fluctuations, and its development, particularly in wearable devices, has given rise to compact and portable continuous glucose monitoring devices, which facilitates the measurement of blood glucose and adjustment of medication. This review introduces the development of wearable CGM-based technologies, including noninvasive methods using body fluids and invasive methods using implantable electrodes. The advantages and disadvantages of these approaches are discussed as well as the use of microneedle arrays in minimally invasive CGM. Microneedle arrays allow for painless transdermal puncture and are expected to facilitate the development of wearable CGM devices. Finally, we discuss the challenges and opportunities and look forward to the biomedical applications and future directions of wearable CGM-based technologies in biological research.

A Wavelet-Based Approach for Motion Artifact Reduction in Ambulatory Seismocardiography

Wearable sensing has become a vital approach to cardiac health monitoring, and seismocardiography (SCG) is emerging as a promising technology in this field. However, the applicability of SCG is hindered by motion artifacts, including those encountered in practice of which the strongest source is walking. This holds back the translation of SCG to clinical settings. We therefore investigated techniques to enhance the quality of SCG signals in the presence of motion artifacts. To simulate ambulant recordings, we corrupted a clean SCG dataset with real-walking-vibrational noise. We decomposed the signal using several empirical-mode-decomposition methods and the maximum overlap discrete wavelet transform (MODWT). By combining MODWT, time-frequency masking, and nonnegative matrix factorization, we developed a novel algorithm which leveraged the vertical axis accelerometer to reduce walking vibrations in dorsoventral SCG. The accuracy and applicability of our method was verified using heart rate estimation. We used an interactive selection approach to improve estimation accuracy. The best decomposition method for reduction of motion artifact noise was the MODWT. Our algorithm improved heart rate estimation from 0.1 to 0.8 r-squared at -15 dB signal-to-noise ratio (SNR). Our method reduces motion artifacts in SCG signals up to a SNR of -19 dB without requiring any external assistance from electrocardiography (ECG). Such a standalone solution is directly applicable to the usage of SCG in daily life, as a content-rich replacement for other wearables in clinical settings, and other continuous monitoring scenarios. In applications with higher noise levels, ECG may be incorporated to further enhance SCG and extend its usable range. This work addresses the challenges posed by motion artifacts, enabling SCG to offer reliable cardiovascular insights in more difficult scenarios, and thereby facilitating wearable monitoring in daily life and the clinic.

The Fundamentals and Applications of Wearable Sensor Devices in Sports Medicine: A Scoping Review

PURPOSE

To (1) characterize the various forms of wearable sensor devices (WSDs) and (2) review the peer-reviewed literature of applied wearable technology within sports medicine.

METHODS

A systematic search of PubMed and EMBASE databases, from inception through 2023, was conducted to identify eligible studies using WSDs within sports medicine. Data extraction was performed of study demographics and sensor specifications. Included studies were categorized by application: athletic training, rehabilitation, and research.

RESULTS

In total, 43 studies met criteria for inclusion in this review. Forms of WSDs include pedometers, accelerometers, encoders (consisting of magnetometers and gyroscopes), force sensors, global positioning system trackers, and inertial measurement units. Outcome metrics include step counts; gait, limb motion, and angular positioning; foot and skin pressure; change of direction and inclination, including analysis of both body parts and athletes on a field; displacement and velocity of body segments and joints; heart rate; plethysmography; sport-specific kinematics; range of motion, symmetry, and alignment; head impact; sleep; throwing biomechanics; and kinetic and spatiotemporal running metrics. WSDs are used in athletic training to assess sport-specific biomechanics and workload with a goal of injury prevention and training optimization, as well as for rehabilitation monitoring and research such as for risk predicting and aiding diagnosis.

CONCLUSIONS

WSDs enable real-time monitoring of human performance across a variety of implementations and settings, allowing collection of metrics otherwise not achievable. WSDs are powerful tools with multiple applications within athletic training, patient rehabilitation, and orthopaedic and sports medicine research.

CLINICAL RELEVANCE

Wearable technology may represent the missing link to quantitatively addressing return to play and previous performance. WSDs are commercially available and portable adjuncts that allow clinicians, trainers, and individual athletes to monitor biomechanical parameters, workload, and recovery status to better contextualize personalized training, injury risk, and rehabilitation.

Biomimetic Wearable Sensors: Emerging Combination of Intelligence and Electronics

Owing to the advancement of interdisciplinary concepts, for example, wearable electronics, bioelectronics, and intelligent sensing, during the microelectronics industrial revolution, nowadays, extensively mature wearable sensing devices have become new favorites in the noninvasive human healthcare industry. The combination of wearable sensing devices with bionics is driving frontier developments in various fields, such as personalized medical monitoring and flexible electronics, due to the superior biocompatibilities and diverse sensing mechanisms. It is noticed that the integration of desired functions into wearable device materials can be realized by grafting biomimetic intelligence. Therefore, herein, the mechanism by which biomimetic materials satisfy and further enhance system functionality is reviewed. Next, wearable artificial sensory systems that integrate biomimetic sensing into portable sensing devices are introduced, which have received significant attention from the industry owing to their novel sensing approaches and portabilities. To address the limitations encountered by important signal and data units in biomimetic wearable sensing systems, two paths forward are identified and current challenges and opportunities are presented in this field. In summary, this review provides a further comprehensive understanding of the development of biomimetic wearable sensing devices from both breadth and depth perspectives, offering valuable guidance for future research and application expansion of these devices.

Wearable Devices: Implications for Precision Medicine and the Future of Health Care

Wearable devices are integrated analytical units equipped with sensitive physical, chemical, and biological sensors capable of noninvasive and continuous monitoring of vital physiological parameters. Recent advances in disciplines including electronics, computation, and material science have resulted in affordable and highly sensitive wearable devices that are routinely used for tracking and managing health and well-being. Combined with longitudinal monitoring of physiological parameters, wearables are poised to transform the early detection, diagnosis, and treatment/management of a range of clinical conditions. Smartwatches are the most commonly used wearable devices and have already demonstrated valuable biomedical potential in detecting clinical conditions such as arrhythmias, Lyme disease, inflammation, and, more recently, COVID-19 infection. Despite significant clinical promise shown in research settings, there remain major hurdles in translating the medical uses of wearables to the clinic. There is a clear need for more effective collaboration among stakeholders, including users, data scientists, clinicians, payers, and governments, to improve device security, user privacy, data standardization, regulatory approval, and clinical validity. This review examines the potential of wearables to offer affordable and reliable measures of physiological status that are on par with FDA-approved specialized medical devices. We briefly examine studies where wearables proved critical for the early detection of acute and chronic clinical conditions with a particular focus on cardiovascular disease, viral infections, and mental health. Finally, we discuss current obstacles to the clinical implementation of wearables and provide perspectives on their potential to deliver increasingly personalized proactive health care across a wide variety of conditions.

Skin temperature influence on transcutaneous carbon dioxide (CO2) conductivity and skin blood flow in healthy human subjects at the arm and wrist

Objective: present transcutaneous carbon dioxide (CO2)-tcpCO2-monitors suffer from limitations which hamper their widespread use, and call for a new tcpCO2 measurement technique. However, the progress in this area is hindered by the lack of knowledge in transcutaneous CO2 diffusion. To address this knowledge gap, this study focuses on investigating the influence of skin temperature on two key skin properties: CO2 permeability and skin blood flow. Methods: a monocentric prospective exploratory study including 40 healthy adults was undertaken. Each subject experienced a 90 min visit split into five 18 min sessions at different skin temperatures-Non-Heated (NH), 35, 38, 41, and 44°C. At each temperature, custom sensors measured transcutaneous CO2 conductivity and exhalation rate at the arm and wrist, while Laser Doppler Flowmetry (LDF) assessed skin blood flow at the arm. Results: the three studied metrics sharply increased with rising skin temperature. Mean values increased from the NH situation up to 44°C from 4.03 up to 8.88 and from 2.94 up to 8.11 m·s-1 for skin conductivity, and from 80.4 up to 177.5 and from 58.7 up to 162.3 cm3·m-2·h-1 for exhalation rate at the arm and wrist, respectively. Likewise, skin blood flow increased elevenfold for the same temperature increase. Of note, all metrics already augmented significantly in the 35-38°C skin temperature range, which may be reached without active heating-i.e. only using a warm clothing. Conclusion: these results are extremely encouraging for the development of next-generation tcpCO2 sensors. Indeed, the moderate increase (× 2) in skin conductivity from NH to 44°C tends to indicate that heating the skin is not critical from a response time point of view, i.e. little to no skin heating would only result in a doubled sensor response time in the worst case, compared to a maximal heating at 44°C. Crucially, a skin temperature within the 35-38°C range already sharply increases the skin blood flow, suggesting that tcpCO2 correlates well with the arterial paCO2 even at such low skin temperatures. These two conclusions further strengthen the viability of non-heated tcpCO2 sensors, thereby paving the way for the development of wearable transcutaneous capnometers.

Acceptance of Digital Health Technologies in Palliative Care Patients

Background

Digital health technologies have potential to transform palliative care (PC) services. The global aging population poses unique challenges for PC, which digital health technologies may help overcome. Evaluation of attitudes and perceptions combined with quantification of prior use habits favor an understanding of psychological barriers to PC patient acceptance of digital health technologies including artificial intelligence (AI).

Objectives

We aimed to evaluate the attitudes and perceptions of PC patients regarding a broad range of digital health technologies used in their routine monitoring and treatment and identify barriers to use.

Methods

We used a 39-item questionnaire to evaluate acceptance and use of smartphone-based electronic patient report outcome measures, wearables, AI, data privacy, and virtual reality (VR) in 29 female and male PC inpatients.

Results

A majority of patients indicated an interest in (69.0%) and positive attitude toward (75.9%) digital health technologies. Nearly all (93.1%) patients believe that digital health technologies will become more important in medicine in the future. Most patients would consider using their smartphone (79.3%) or wearable (69.0%) more often for their health. The most feasible technologies were smartphones, wearables, and VR. Barriers to acceptance included unfamiliarity, data security, errors in data interpretation, and loss of personal interaction through AI.

Conclusion

In this patient survey, acceptance of new technologies in a PC patient population was high, encouraging its use also at the end-of-life.

Digital patient twins for personalized therapeutics and pharmaceutical manufacturing

Digital twins are virtual models of physical artefacts that may or may not be synchronously connected, and that can be used to simulate their behavior. They are widely used in several domains such as manufacturing and automotive to enable achieving specific quality goals. In the health domain, so-called digital patient twins have been understood as virtual models of patients generated from population data and/or patient data, including, for example, real-time feedback from wearables. Along with the growing impact of data science technologies like artificial intelligence, novel health data ecosystems centered around digital patient twins could be developed. This paves the way for improved health monitoring and facilitation of personalized therapeutics based on management, analysis, and interpretation of medical data via digital patient twins. The utility and feasibility of digital patient twins in routine medical processes are still limited, despite practical endeavors to create digital twins of physiological functions, single organs, or holistic models. Moreover, reliable simulations for the prediction of individual drug responses are still missing. However, these simulations would be one important milestone for truly personalized therapeutics. Another prerequisite for this would be individualized pharmaceutical manufacturing with subsequent obstacles, such as low automation, scalability, and therefore high costs. Additionally, regulatory challenges must be met thus calling for more digitalization in this area. Therefore, this narrative mini-review provides a discussion on the potentials and limitations of digital patient twins, focusing on their potential bridging function for personalized therapeutics and an individualized pharmaceutical manufacturing while also looking at the regulatory impacts.

When "virtual" works and when it doesn't: A survey of physician and patient experiences with virtual care during the COVID-19 pandemic

Objective

To assess the experience of virtual care among both patients and physicians across a range of clinical scenarios during the COVID-19 pandemic.

Methods

A web-based survey was disseminated to patients and physicians through a variety of media and healthcare communications from May 2020 to July 2021. Demographic details and attitudes across a range of virtual care domains were collected. Quantitative responses were analyzed descriptively. Open-text responses were gathered to contrast when a virtual visit was superior or inferior to an in-person one, and a thematic content analysis was used.

Results

There were 197 patients and 93 physician respondents, representing a range of demographic and practice characteristics. Patients noted several benefits of virtual care and felt it should continue to be available. Physicians felt they could do a lot of their care virtually. Common themes related to the superiority of virtual care were for "quick" visits, reviewing test results, chronic disease monitoring, and medication needs. Virtual care was less ideal when a physical exam was needed, and was not perceived as a good fit for an individual's cultural, language, or emotional needs. Certain conditions were identified as both ideal and non-ideal for the virtual format (e.g. mental healthcare).

Discussion

Certain situations are more amenable to virtual care with personal preferences among both patients and physicians. Future priorities should ensure that virtual care is effective across the range of clinical situations in which it may be used and that both virtual and in-person options are equally available to those who want them.

Haptic Nudging Using a Wearable Device to Promote Upper Limb Activity during Stroke Rehabilitation: Exploring Diurnal Variation, Repetition, and Duration of Effect

Haptic nudging via wearable devices promotes physical activity and may increase upper limb movement in stroke rehabilitation. This study investigated the optimal approach to haptic nudging by examining diurnal variation, duration of effect, and repeated nudging. The study analysed data from a multiple-period randomised crossover study. A 12 h inpatient rehabilitation day was divided into 72 intervals in which participants with stroke (n = 20) randomly received either a 'nudge' or 'no nudge'. Upper limb movement was observed, classified, and analysed using longitudinal mixed models. The odds of affected upper limb movement following a nudge compared with no nudge were significantly higher during active periods such as breakfast, lunch, and morning and afternoon activities (odds ratios (ORs) 2.01-4.63, 95% CIs [1.27-2.67, 3.17-8.01]), but not dinner (OR 1.36, 95% CI [0.86, 2.16]). The effect of nudging was no longer statistically significant at 50-60 s post-nudge. Consecutive delays in nudging significantly decreased the odds of moving when a nudge was eventually delivered. Contrary to expectations, people with stroke appear more responsive to haptic nudging during active periods rather than periods of inactivity. By understanding the optimal timing and frequency of haptic nudging, the design of wearable devices can be optimised to maximise their therapeutic benefits.

Wolf Creek XVII part 3: Automated cardiac arrest diagnosis

Introduction

Automated cardiac arrest diagnosis offers the possibility to significantly shorten the interval between onset of out-of-hospital cardiac arrest (OHCA) and notification of EMS, providing the opportunity for earlier resuscitation and possibly increased survival.

Methods

Automated cardiac arrest diagnosis was one of six focus topics for the Wolf Creek XVII Conference held on June 14-17 2023 in Ann Arbor, Michigan, USA. Conference invitees included international thought leaders and scientists in the field of cardiac arrest resuscitation from academia and industry. Participants submitted via online survey knowledge gaps, barriers to translation and research priorities for each focus topic. Expert panels used the survey results and their own perspectives and insights to create and present a preliminary unranked list for each category that was debated, revised and ranked by all attendees to identify the top 5 for each category.

Results

Top knowledge gaps include the accuracy of automated OHCA detection technologies and the feasibility and reliability of automated EMS activation. The main barriers to translation are the risk of false positives potentially overburdening EMS, development and application costs of technology and the challenge of integrating new technology in EMS IT systems. The top research priorities are large-scale evaluation studies to measure real world performance and user research regarding the willingness to adopt these technologies.

Conclusion

Automated cardiac arrest diagnosis has the potential to significantly impact time to resuscitation and survival of OHCA because it could convert unwitnessed events into witnessed events. Validation and feasibility studies are needed. The specificity of the technology must be high not to overburden limited EMS resources. If adequate event classification is achieved, future research could shift toward event prediction, focusing on identifying potential digital biomarkers and signatures of imminent cardiac arrest. Implementation could be challenging due to high costs of development, regulatory considerations and instantiation logistics.

Are Nursing Faculty Future-Ready? The Effects of Emerging Technologies on Nursing Education

BACKGROUND

Preparing the next generation of nurses requires consideration for the integration of emerging technologies. This refers specifically to technologies that will shape and affect how care is delivered-not in today's health care facilities but in facilities 20 years from now and beyond.

METHOD

A cross-sectional survey examining how seven emerging technologies were integrated into nursing curricula was developed and distributed to nursing faculty. A multiple linear regression model was used to analyze the data for the level of integration. The model was adjusted for all demographic measures, and the independent variables of interest were developed from unified theory of acceptance and use of technology (UTAUT) constructs.

RESULTS

Performance, attitude, social influence, and effort were insignificant. Education and training, costs, and complexity were noted as top barriers to adoption.

CONCLUSION

A literature gap regarding integrating emerging technologies in nursing curricula exists. Further research is warranted to understand factors influencing integration. [J Nurs Educ. 2023;62(12):689-700.].

Diagnostic Accuracy of Single-Lead Electrocardiograms Using the Kardia Mobile App and the Apple Watch 4: Validation Study

BACKGROUND

To date, the 12-lead electrocardiogram (ECG) is the gold standard for cardiological diagnosis in clinical settings. With the advancements in technology, a growing number of smartphone apps and gadgets for recording, visualizing, and evaluating physical performance as well as health data is available. Although this new smart technology is innovative and time- and cost-efficient, less is known about its diagnostic accuracy and reliability.

OBJECTIVE

This study aimed to examine the agreement between the mobile single-lead ECG measurements of the Kardia Mobile App and the Apple Watch 4 compared to the 12-lead gold standard ECG in healthy adults under laboratory conditions. Furthermore, it assessed whether the measurement error of the devices increases with an increasing heart rate.

METHODS

This study was designed as a prospective quasi-experimental 1-sample measurement, in which no randomization of the sampling was carried out. In total, ECGs at rest from 81 participants (average age 24.89, SD 8.58 years; n=58, 72% male) were recorded and statistically analyzed. Bland-Altman plots were created to graphically illustrate measurement differences. To analyze the agreement between the single-lead ECGs and the 12-lead ECG, Pearson correlation coefficient (r) and Lin concordance correlation coefficient (CCCLin) were calculated.

RESULTS

The results showed a higher agreement for the Apple Watch (mean deviation QT: 6.85%; QT interval corrected for heart rate using Fridericia formula [QTcF]: 7.43%) than Kardia Mobile (mean deviation QT: 9.53%; QTcF: 9.78%) even if both tend to underestimate QT and QTcF intervals. For Kardia Mobile, the QT and QTcF intervals correlated significantly with the gold standard (rQT=0.857 and rQTcF=0.727; P<.001). CCCLin corresponded to an almost complete heuristic agreement for the QT interval (0.835), whereas the QTcF interval was in the range of strong agreement (0.682). Further, for the Apple Watch, Pearson correlations were highly significant and in the range of a large effect (rQT=0.793 and rQTcF=0.649; P<.001). CCCLin corresponded to a strong heuristic agreement for both the QT (0.779) and QTcF (0.615) intervals. A small negative correlation between the measurement error and increasing heart rate could be found of each the devices and the reference.

CONCLUSIONS

Smart technology seems to be a promising and reliable approach for nonclinical health monitoring. Further research is needed to broaden the evidence regarding its validity and usability in different target groups.

Fractal complexity of daily physical activity and cognitive function in a midlife cohort

High stability of fluctuation in physiological patterns across fixed time periods suggest healthy fractal complexity, while greater randomness in fluctuation patterns may indicate underlying disease processes. The importance of fractal stability in mid-life remains unexplored. We quantified fractal regulation patterns in 24-h accelerometer data and examined associations with cognitive function in midlife. Data from 5097 individuals (aged 46) from the 1970 British Cohort Study were analyzed. Participants wore thigh-mounted accelerometers for seven days and completed cognitive tests (verbal fluency, memory, processing speed; derived composite z-score). Detrended fluctuation analysis (DFA) was used to examine temporal correlations of acceleration magnitude across 25 time scales (range: 1 min-10 h). Linear regression examined associations between DFA scaling exponents (DFAe) and each standardised cognitive outcome. DFAe was normally distributed (mean ± SD: 0.90 ± 0.06; range: 0.72-1.25). In males, a 0.10 increase in DFAe was associated with a 0.30 (95% Confidence Interval: 0.14, 0.47) increase in composite cognitive z-score in unadjusted models; associations were strongest for verbal fluency (0.10 [0.04, 0.16]). Associations remained in fully-adjusted models for verbal fluency only (0.06 [0.00, 0.12]). There was no association between DFA and cognition in females. Greater fractal stability in men was associated with better cognitive function. This could indicate mechanisms through which fractal complexity may scale up to and contribute to cognitive clinical endpoints.

Wearable Devices to Diagnose and Monitor the Progression of COVID-19 Through Heart Rate Variability Measurement: Systematic Review and Meta-Analysis

BACKGROUND

Recent studies have linked low heart rate variability (HRV) with COVID-19, indicating that this parameter can be a marker of the onset of the disease and its severity and a predictor of mortality in infected people. Given the large number of wearable devices that capture physiological signals of the human body easily and noninvasively, several studies have used this equipment to measure the HRV of individuals and related these measures to COVID-19.

OBJECTIVE

The objective of this study was to assess the utility of HRV measurements obtained from wearable devices as predictive indicators of COVID-19, as well as the onset and worsening of symptoms in affected individuals.

METHODS

A systematic review was conducted searching the following databases up to the end of January 2023: Embase, PubMed, Web of Science, Scopus, and IEEE Xplore. Studies had to include (1) measures of HRV in patients with COVID-19 and (2) measurements involving the use of wearable devices. We also conducted a meta-analysis of these measures to reduce possible biases and increase the statistical power of the primary research.

RESULTS

The main finding was the association between low HRV and the onset and worsening of COVID-19 symptoms. In some cases, it was possible to predict the onset of COVID-19 before a positive clinical test. The meta-analysis of studies reported that a reduction in HRV parameters is associated with COVID-19. Individuals with COVID-19 presented a reduction in the SD of the normal-to-normal interbeat intervals and root mean square of the successive differences compared with healthy individuals. The decrease in the SD of the normal-to-normal interbeat intervals was 3.25 ms (95% CI -5.34 to -1.16 ms), and the decrease in the root mean square of the successive differences was 1.24 ms (95% CI -3.71 to 1.23 ms).

CONCLUSIONS

Wearable devices that measure changes in HRV, such as smartwatches, rings, and bracelets, provide information that allows for the identification of COVID-19 during the presymptomatic period as well as its worsening through an indirect and noninvasive self-diagnosis.

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.

Wearable technologies for health research: Opportunities, limitations, and practical and conceptual considerations

One of the most notable limitations of laboratory-based health research is its inability to continuously monitor health-relevant physiological processes as individuals go about their daily lives. As a result, we have generated large amounts of data with unknown generalizability to real-world situations and also created a schism between where data are collected (i.e., in the lab) and where we need to intervene to prevent disease (i.e., in the field). Devices using noninvasive wearable technology are changing all of this, however, with their ability to provide high-frequency assessments of peoples' ever-changing physiological states in daily life in a manner that is relatively noninvasive, affordable, and scalable. Here, we discuss critical points that every researcher should keep in mind when using these wearables in research, spanning device and metric decisions, hardware and software selection, and data quality and sampling rate issues, using research on stress and health as an example throughout. We also address usability and participant acceptability issues, and how wearable "digital biomarker" and behavioral data can be integrated to enhance basic science and intervention studies. Finally, we summarize 10 key questions that should be addressed to make every wearable study as strong as possible. Collectively, keeping these points in mind can improve our ability to study the psychobiology of human health, and to intervene, precisely where it matters most: in peoples' daily lives.

The contribution of the exposome to the burden of cardiovascular disease

Large epidemiological and health impact assessment studies at the global scale, such as the Global Burden of Disease project, indicate that chronic non-communicable diseases, such as atherosclerosis and diabetes mellitus, caused almost two-thirds of the annual global deaths in 2020. By 2030, 77% of all deaths are expected to be caused by non-communicable diseases. Although this increase is mainly due to the ageing of the general population in Western societies, other reasons include the increasing effects of soil, water, air and noise pollution on health, together with the effects of other environmental risk factors such as climate change, unhealthy city designs (including lack of green spaces), unhealthy lifestyle habits and psychosocial stress. The exposome concept was established in 2005 as a new strategy to study the effect of the environment on health. The exposome describes the harmful biochemical and metabolic changes that occur in our body owing to the totality of different environmental exposures throughout the life course, which ultimately lead to adverse health effects and premature deaths. In this Review, we describe the exposome concept with a focus on environmental physical and chemical exposures and their effects on the burden of cardiovascular disease. We discuss selected exposome studies and highlight the relevance of the exposome concept for future health research as well as preventive medicine. We also discuss the challenges and limitations of exposome studies.

Artificial Intelligence's Impact on Drug Discovery and Development From Bench to Bedside

Artificial intelligence (AI) techniques have the potential to revolutionize drug release modeling, optimize therapy for personalized medicine, and minimize side effects. By applying AI algorithms, researchers can predict drug release profiles, incorporate patient-specific factors, and optimize dosage regimens to achieve tailored and effective therapies. This AI-based approach has the potential to improve treatment outcomes, enhance patient satisfaction, and advance the field of pharmaceutical sciences. International collaborations and professional organizations play vital roles in establishing guidelines and best practices for data collection and sharing. Open data initiatives can enhance transparency and scientific progress, facilitating algorithm validation.

A novel smart somatosensory wearable assistive device for older adults' home rehabilitation during the COVID-19 pandemic

Background

Due to the Coronavirus disease 19 (COVID-19) related social distancing measures and health service suspension, physical activity has declined, leading to increased falling risk and disability, and consequently, compromising the older adult health. How to improve the quality of older adult life has become a crucial social issue.

Objective

In traditional rehabilitation, manual and repetitive muscle training cannot identify the patient's rehabilitation effect, and increasing the willingness to use it is not easy. Therefore, based on the usability perspective, this study aims to develop a novel smart somatosensory wearable assistive device (called SSWAD) combined with wireless surface electromyography (sEMG) and exergame software and hardware technology. The older adult can do knee extension, ankle dorsiflexion, and ankle plantar flexion rehabilitation exercises at home. Meanwhile, sEMG values can be digitally recorded to assist physicians (or professionals) in judgment, treatment, or diagnosis.

Methods

To explore whether the novel SSWAD could improve the older adult willingness to use and motivation for home rehabilitation, 25 frail older adult (12 males and 13 females with an average age of 69.3) perform the rehabilitation program with the SSWAD, followed by completing the system usability scale (SUS) questionnaire and the semi-structured interview for the quantitative and qualitative analyses. In addition, we further investigate whether the factor of gender or prior rehabilitation experience would affect the home rehabilitation willingness or not.

Results

According to the overall SUS score, the novel SSWAD has good overall usability performance (77.70), meaning that the SSWAD makes older adult feel interested and improves their willingness for continuous rehabilitation at home. In addition, the individual item scores of SUS are shown that female older adult with prior rehabilitation experience perform better in "Learnability" (t = 2.35, p = 0.03) and "Confidence" (t = -3.24, p = 0.01). On the contrary, male older adult without rehabilitation experience are more willing to adopt new technologies (t = -2.73, p = 0.02), and perform better in "Learnability" (t = 2.18, p = 0.04) and "Confidence" (t = -3.75, p < 0.001) with the SSWAD. In addition, the result of the semi-structured interview shows that the operation of the SSWAD is highly flexible, thus reducing older adult burden during the rehabilitation exercise and using them long-term.

Conclusion

This novel SSWAD receives consistently positive feedback regardless of the gender or prior rehabilitation experience of elders. The SSWAD could be used as a novel way of home rehabilitation for elders, especially during the COVID-19 pandemic. Older adult can do rehabilitation exercises at home, and physicians could make proper judgments or adjust suitable treatments online according to the sEMG data, which older adult can know their rehabilitation progress at the same time. Most importantly, older adult do not have to go to the hospital every time for rehabilitation, which significantly reduces time and the risk of infection.

Monitoring Resistance Training in Real Time with Wearable Technology: Current Applications and Future Directions

Resistance training is an exercise modality that involves using weights or resistance to strengthen and tone muscles. It has become popular in recent years, with numerous people including it in their fitness routines to ameliorate their strength, muscle mass, and overall health. Still, resistance training can be complex, requiring careful planning and execution to avoid injury and achieve satisfactory results. Wearable technology has emerged as a promising tool for resistance training, as it allows monitoring and adjusting training programs in real time. Several wearable devices are currently available, such as smart watches, fitness trackers, and other sensors that can yield detailed physiological and biomechanical information. In resistance training research, this information can be used to assess the effectiveness of training programs and identify areas for improvement. Wearable technology has the potential to revolutionize resistance training research, providing new insights and opportunities for developing optimized training programs. This review examines the types of wearables commonly used in resistance training research, their applications in monitoring and optimizing training programs, and the potential limitations and challenges associated with their use. Finally, it discusses future research directions, including the development of advanced wearable technologies and the integration of artificial intelligence in resistance training research.

Digital Platform for Continuous Monitoring of Patients Using a Smartwatch: Longitudinal Prospective Cohort Study

BACKGROUND

Since the COVID-19 pandemic, there has been a boost in the digital transformation of the human society, where wearable devices such as a smartwatch can already measure vital signs in a continuous and naturalistic way; however, the security and privacy of personal data is a challenge to expanding the use of these data by health professionals in clinical follow-up for decision-making. Similar to the European General Data Protection Regulation, in Brazil, the Lei Geral de Proteção de Dados established rules and guidelines for the processing of personal data, including those used for patient care, such as those captured by smartwatches. Thus, in any telemonitoring scenario, there is a need to comply with rules and regulations, making this issue a challenge to overcome.

OBJECTIVE

This study aimed to build a digital solution model for capturing data from wearable devices and making them available in a safe and agile manner for clinical and research use, following current laws.

METHODS

A functional model was built following the Brazilian Lei Geral de Proteção de Dados (2018), where data captured by smartwatches can be transmitted anonymously over the Internet of Things and be identified later within the hospital. A total of 80 volunteers were selected for a 24-week follow-up clinical trial divided into 2 groups, one group with a previous diagnosis of COVID-19 and a control group without a previous diagnosis of COVID-19, to measure the synchronization rate of the platform with the devices and the accuracy and precision of the smartwatch in out-of-hospital conditions to simulate remote monitoring at home.

RESULTS

In a 35-week clinical trial, >11.2 million records were collected with no system downtime; 66% of continuous beats per minute were synchronized within 24 hours (79% within 2 days and 91% within a week). In the limit of agreement analysis, the mean differences in oxygen saturation, diastolic blood pressure, systolic blood pressure, and heart rate were -1.280% (SD 5.679%), -1.399 (SD 19.112) mm Hg, -1.536 (SD 24.244) mm Hg, and 0.566 (SD 3.114) beats per minute, respectively. Furthermore, there was no difference in the 2 study groups in terms of data analysis (neither using the smartwatch nor the gold-standard devices), but it is worth mentioning that all volunteers in the COVID-19 group were already cured of the infection and were highly functional in their daily work life.

CONCLUSIONS

On the basis of the results obtained, considering the validation conditions of accuracy and precision and simulating an extrahospital use environment, the functional model built in this study is capable of capturing data from the smartwatch and anonymously providing it to health care services, where they can be treated according to the legislation and be used to support clinical decisions during remote monitoring.

A system for bioelectronic delivery of treatment directed toward wound healing

The development of wearable bioelectronic systems is a promising approach for optimal delivery of therapeutic treatments. These systems can provide continuous delivery of ions, charged biomolecules, and an electric field for various medical applications. However, rapid prototyping of wearable bioelectronic systems for controlled delivery of specific treatments with a scalable fabrication process is challenging. We present a wearable bioelectronic system comprised of a polydimethylsiloxane (PDMS) device cast in customizable 3D printed molds and a printed circuit board (PCB), which employs commercially available engineering components and tools throughout design and fabrication. The system, featuring solution-filled reservoirs, embedded electrodes, and hydrogel-filled capillary tubing, is assembled modularly. The PDMS and PCB both contain matching through-holes designed to hold metallic contact posts coated with silver epoxy, allowing for mechanical and electrical integration. This assembly scheme allows us to interchange subsystem components, such as various PCB designs and reservoir solutions. We present three PCB designs: a wired version and two battery-powered versions with and without onboard memory. The wired design uses an external voltage controller for device actuation. The battery-powered PCB design uses a microcontroller unit to enable pre-programmed applied voltages and deep sleep mode to prolong battery run time. Finally, the battery-powered PCB with onboard memory is developed to record delivered currents, which enables us to verify treatment dose delivered. To demonstrate the functionality of the platform, the devices are used to deliver H[Formula: see text] in vivo using mouse models and fluoxetine ex vivo using a simulated wound environment. Immunohistochemistry staining shows an improvement of 35.86% in the M1/M2 ratio of H[Formula: see text]-treated wounds compared with control wounds, indicating the potential of the platform to improve wound healing.

The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives

Artificial intelligence (AI) and machine learning (ML) represent significant advancements in computing, building on technologies that humanity has developed over millions of years-from the abacus to quantum computers. These tools have reached a pivotal moment in their development. In 2021 alone, the U.S. Food and Drug Administration (FDA) received over 100 product registration submissions that heavily relied on AI/ML for applications such as monitoring and improving human performance in compiling dossiers. To ensure the safe and effective use of AI/ML in drug discovery and manufacturing, the FDA and numerous other U.S. federal agencies have issued continuously updated, stringent guidelines. Intriguingly, these guidelines are often generated or updated with the aid of AI/ML tools themselves. The overarching goal is to expedite drug discovery, enhance the safety profiles of existing drugs, introduce novel treatment modalities, and improve manufacturing compliance and robustness. Recent FDA publications offer an encouraging outlook on the potential of these tools, emphasizing the need for their careful deployment. This has expanded market opportunities for retraining personnel handling these technologies and enabled innovative applications in emerging therapies such as gene editing, CRISPR-Cas9, CAR-T cells, mRNA-based treatments, and personalized medicine. In summary, the maturation of AI/ML technologies is a testament to human ingenuity. Far from being autonomous entities, these are tools created by and for humans designed to solve complex problems now and in the future. This paper aims to present the status of these technologies, along with examples of their present and future applications.