Search

Background: The field of smart devices and physical activity is evolving rapidly, with a wide range of devices measuring a wide range of parameters. Scientific articles look at very different populations in terms of the impact of smart devices but do not take into account which characteristics of the devices are important for the group and which may influence the effectiveness of the device. In our study, we aimed to analyse articles about the impact of smart devices on physical activity and identify the characteristics of different target groups. Methods: Queries were run on two major databases (PubMed and Web of Science) between 2017 and 2024. Duplicates were filtered out, and according to a few main criteria, inappropriate studies were excluded so that 37 relevant articles were included in a more detailed analysis. Results: Four main target groups were identified: healthy individuals, people with chronic diseases, elderly people, and competitive athletes. We identified the essential attributes of smart devices by target groups. For the elderly, an easy-to-use application is needed. In the case of women, children, and elderly people, gamification can be used well, but for athletes, specific measurement tools and accuracy may have paramount importance. For most groups, regular text messages or notifications are important. Conclusions: The use of smart devices can have a positive impact on physical activity, but the context and target group must be taken into account to achieve effectiveness. [ABSTRACT FROM AUTHOR]

In elite sports, athletic excellence demands meticulous performance preparation and a sound health status. This paper overviews the current propositions and applications of pervasive computing and data analytics and our vision on how they should be used in future frameworks to contribute to the optimal balance of athletes' performance and health requirements. Two main areas will be discussed. The first area is Sports Performance Optimization, in which we consider interesting recent advancements in data analytics for performance improvement, equipment design, and team member recruitment and selection. We will also briefly discuss how the betting industry has been relaying and developing sports analytics. The second area is Athlete's Wellness and Wellbeing, which will discuss how wearables and data analytics have been used to assess physical activity and sedentary behavior profiles, sleep and circadian rhythm, nutrition and eating behavior, menstrual cycles, and training/performance readiness. In the final part of this paper, we argue that a critical issue for managers to enhance their decision making is the standardization of acquired information and decision-making processes, while introducing an adaptable, personalized approach. Thus, we present and discuss new theoretical and practical approaches that could potentially address this problem and identify precision medicine as a recommended methodology. This conceptualization involves the integration of pervasive computing and data analytics by employing predictive models that are constantly updated with the outcomes from monitoring tools and athletes' feedback interventions. This framework has the potential to revolutionize how athletes' performance and well-being are monitored, assessed, and optimized, contributing to a new era of precision in sports science and medicine. [ABSTRACT FROM AUTHOR]

Introduction: Wearables are potentially valuable tools for understanding mobility behavior in individuals with neurological disorders and how it changes depending on health status, such as after rehabilitation. However, the accurate detection of gait events, which are crucial for the evaluation of gait performance and quality, is challenging due to highly individual-specific patterns that also vary greatly in movement and speed, especially after stroke. Therefore, the purpose of this study was to assess the accuracy, concurrent validity, and test--retest reliability of a commercially available insole system in the detection of gait events and the calculation of stance duration in individuals with chronic stroke. Methods: Pressure insole data were collected from 17 individuals with chronic stroke during two measurement blocks, each comprising three 10-min walking tests conducted in a clinical setting. The gait assessments were recorded with a video camera that served as a ground truth, and pressure insoles as an experimental system. We compared the number of gait events and stance durations between systems. Results and discussion: Over all 3,820 gait events, 90.86% were correctly identified by the insole system. Recall values ranged from 0.994 to 1, with a precision of 1 for all measurements. The F1 score ranged from 0.997 to 1. Excellent absolute agreement (Intraclass correlation coefficient, ICC = 0.874) was observed for the calculation of the stance duration, with a slightly longer stance duration recorded by the insole system (difference of -0.01 s). Bland-- Altmann analysis indicated limits of agreement of 0.33 s that were robust to changes in walking speed. This consistency makes the system well-suited for individuals post-stroke. The test--retest reliability between measurement timepoints T1 and T2 was excellent (ICC = 0.928). The mean difference in stance duration between T1 and T2 was 0.03 s. We conclude that the insole system is valid for use in a clinical setting to quantitatively assess continuous walking in individuals with stroke. [ABSTRACT FROM AUTHOR]

Metallic nanowires have a wide range of potential applications as interconnects in next-generation electronic devices, such as flexible electronics, due to their excellent electrical and mechanical properties. However, for their successful application, it is necessary to assess their electrical reliability, namely, their behavior under electromigration, a common failure mode for electronic interconnects. Here, for the first time, we have experimentally characterized the electromigration reliability of individual penta-twinned gold nanowires, conducting time-to-failure tests for 30 samples as a function of temperature. At each temperature, the distribution of results was modeled with a log-normal distribution to find the median time to failure (MTTF), an important metric for interconnects. The electromigration activation energy and other relevant constants were then determined by fitting the MTTF results to the classic Black's electromigration model. The activation energy found (0.47–0.58 eV) suggests that surface diffusion is a significant contributor to electromigration, which indicates that further improvements in lifetime and reliability could be obtained by surface passivation of the nanowires' surface. [ABSTRACT FROM AUTHOR]

Cough is a common symptom of multiple respiratory diseases, such as asthma and chronic obstructive pulmonary disorder. Various research works targeted cough detection as a means for continuous monitoring of these respiratory health conditions. This has been mainly achieved using sophisticated machine learning or deep learning algorithms fed with audio recordings. In this work, we explore the use of an alternative detection method, since audio can generate privacy and security concerns related to the use of always-on microphones. This study proposes the use of a non-contact tri-axial accelerometer for motion detection to differentiate between cough and noncough events/movements. A total of 43 time-domain features were extracted from the acquired tri-axial accelerometry signals. These features were evaluated and ranked for their importance using six methods with adjustable conditions, resulting in a total of 11 feature rankings. The ranking methods included model-based feature importance algorithms, first principal component, leave-one-out, permutation, and recursive features elimination (RFE). The ranking results were further used in the feature selection of the top 10, 20, and 30 for use in cough detection. A total of 68 classification models using a simple logistic regression classifier are reported, using two approaches for data splitting: subject-record-split and leave-one-subject-out (LOSO). The best-performing model out of the 34 using subject-record-split obtained an accuracy of 92.20%, sensitivity of 90.87%, specificity of 93.52%, and F1 score of 92.09% using only 20 features selected by the RFE method. The bestperforming model out of the 34 using LOSO obtained an accuracy of 89.57%, sensitivity of 85.71%, specificity of 93.43%, and F1 score of 88.72% using only 10 features selected by the RFE method. These results demonstrate the ability for future implementation of a motion-based wearable cough detector. [ABSTRACT FROM AUTHOR]

Background: The escalating global prevalence of type 2 diabetes and prediabetes presents a major public health challenge. Physical activity plays a critical role in managing (pre)diabetes; however, adherence to physical activity recommendations remains low. The ENERGISED trial was designed to address these challenges by integrating mHealth tools into the routine practice of general practitioners, aiming for a significant, scalable impact in (pre)diabetes patient care through increased physical activity and reduced sedentary behaviour. Methods: The mHealth intervention for the ENERGISED trial was developed according to the mHealth development and evaluation framework, which includes the active participation of (pre)diabetes patients. This iterative process encompasses four sequential phases: (a) conceptualisation to identify key aspects of the intervention; (b) formative research including two focus groups with (pre)diabetes patients (n = 14) to tailor the intervention to the needs and preferences of the target population; (c) pre-testing using think-aloud patient interviews (n = 7) to optimise the intervention components; and (d) piloting (n = 10) to refine the intervention to its final form. Results: The final intervention comprises six types of text messages, each embodying different behaviour change techniques. Some of the messages, such as those providing interim reviews of the patients' weekly step goal or feedback on their weekly performance, are delivered at fixed times of the week. Others are triggered just in time by specific physical behaviour events as detected by the Fitbit activity tracker: for example, prompts to increase walking pace are triggered after 5 min of continuous walking; and prompts to interrupt sitting following 30 min of uninterrupted sitting. For patients without a smartphone or reliable internet connection, the intervention is adapted to ensure inclusivity. Patients receive on average three to six messages per week for 12 months. During the first six months, the text messaging is supplemented with monthly phone counselling to enable personalisation of the intervention, assistance with technical issues, and enhancement of adherence. Conclusions: The participatory development of the ENERGISED mHealth intervention, incorporating just-in-time prompts, has the potential to significantly enhance the capacity of general practitioners for personalised behavioural counselling on physical activity in (pre)diabetes patients, with implications for broader applications in primary care. [ABSTRACT FROM AUTHOR]

Stress has emerged as a prominent and multifaceted health concern in contemporary society, manifesting detrimental effects on individuals' physical and mental health and well-being. The ability to accurately predict stress levels in real time holds significant promise for facilitating timely interventions and personalized stress management strategies. The increasing incidence of stress-related physical and mental health issues highlights the importance of thoroughly understanding stress prediction mechanisms. Given that stress is a contributing factor to a wide array of mental and physical health problems, objectively assessing stress is crucial for behavioral and physiological studies. While numerous studies have assessed stress levels in controlled environments, the objective evaluation of stress in everyday settings still needs to be explored, primarily due to contextual factors and limitations in self-report adherence. This short review explored the emerging field of real-time stress prediction, focusing on utilizing physiological data collected by wearable devices. Stress was examined from a comprehensive standpoint, acknowledging its effects on both physical and mental well-being. The review synthesized existing research on the development and application of stress prediction models, underscoring advancements, challenges, and future directions in this rapidly evolving domain. Emphasis was placed on examining and critically evaluating the existing research and literature on stress prediction, physiological data analysis, and wearable devices for stress monitoring. The synthesis of findings aimed to contribute to a better understanding of the potential of wearable technology in objectively assessing and predicting stress levels in real time, thereby informing the design of effective interventions and personalized stress management approaches. [ABSTRACT FROM AUTHOR]

Introduction: Current cognitive assessments suffer from floor/ceiling and practice effects, poor psychometric performance in mild cases, and repeated assessment effects. This study explores the use of digital speech analysis as an alternative tool for determining cognitive impairment. The study specifically focuses on identifying the digital speech biomarkers associated with cognitive impairment and its severity. Methods: We recruited older adults with varying cognitive health. Their speech data, recorded via a wearable microphone during the reading aloud of a standard passage, were processed to derive digital biomarkers such as timing, pitch, and loudness. Cohen's d effect size highlighted group differences, and correlations were drawn to the Montreal Cognitive Assessment (MoCA). A stepwise approach using a Random Forest model was implemented to distinguish cognitive states using speech data and predict MoCA scores based on highly correlated features. Results: The study comprised 59 participants, with 36 demonstrating cognitive impairment and 23 serving as cognitively intact controls. Among all assessed parameters, similarity, as determined by Dynamic Time Warping (DTW), exhibited the most substantial positive correlation (rho = 0.529, p < 0.001), while timing parameters, specifically the ratio of extra words, revealed the strongest negative correlation (rho = −0.441, p < 0.001) with MoCA scores. Optimal discriminative performance was achieved with a combination of four speech parameters: total pause time, speech-to-pause ratio, similarity via DTW, and intelligibility via DTW. Precision and balanced accuracy scores were found to be 88.1 ± 1.2% and 76.3 ± 1.3%, respectively. Discussion: Our research proposes that reading-derived speech data facilitates the differentiation between cognitively impaired individuals and cognitively intact, age-matched older adults. Specifically, parameters based on timing and similarity within speech data provide an effective gauge of cognitive impairment severity. These results suggest speech analysis as a viable digital biomarker for early detection and monitoring of cognitive impairment, offering novel approaches in dementia care. [ABSTRACT FROM AUTHOR]

Continuous monitoring of multiple physiological parameters, such as glucose levels, temperature, and heart rate variability (HRV) is crucial for effective diabetes management and mitigating the risks associated with hypoglycemic events. These events often occur without apparent symptoms, posing a challenge for diabetic patients in managing their condition. Therefore, a non‐invasive wearable device capable of continuously measuring multiple body signals to predict hypoglycemic events would be highly beneficial. In this study, a wearable patch that continuously measures glucose, temperature, and HRV is presented. The device uses a novel power harvesting system to convert radiofrequency (RF) signals with the frequency of 2.45 GHz to direct current (DC) signals to extend the battery life for further continuous monitoring. The patch is small and has a conformal structure that can easily fit onto different body parts. The screen‐printed glucose sensor demonstrates a sensitivity of 10.3 nA cm−2 µM−1, a limit of detection (LOD) of 8.9 µM, and a limit of quantification (LOQ) of 27 µM. The device employs a photoplethysmography (PPG) module with a peak‐finding algorithm to calculate the HRV values. In vivo experiments demonstrate the validation of the device's proper operation in glucose, HRV, and temperature measurement. [ABSTRACT FROM AUTHOR]

Although the twenty-first century has seen major advances in evidence-based medicine to improve health, athletic performance, and injury prevention, our inability to implement these best practices across underserved American communities has limited the impact of these breakthroughs in sports medicine. Rochester, NY is stereotypical of American communities in which an economically challenged racially diverse urban center with grossly underperforming public schools is surrounded by adequately resourced predominantly Caucasian state-of-the-art education systems. As these great disparities perpetuate and further degrade our society in the absence of interventions, the need for community engagement initiatives is self-evident. [ABSTRACT FROM AUTHOR]

Thermal physiology and psychophysics are complex and nuanced, with significant variability between individuals. Wearable devices have the potential to offer customizable microclimate control. However, individual experiences with different supplemental heating strategies are likely to vary considerably in unconstrained environments. The physiological responses, psychophysical effects, and qualitative experiences of participants using five readily available heating strategies were collected in a quasi-field study environment (n =17). Although all devices maintained or increased fingertip temperature, effects observed from controlled studies of thermal physiology are not clearly seen. Physiological, perceptual, and experiential data are presented, exploring heating technologies and thermal comfort in typical indoor environments. [ABSTRACT FROM AUTHOR]

Wearable digital technologies capable of measuring everyday behaviors could improve the early detection of dementia‐causing diseases. We conducted two systematic reviews following Preferred Reporting Items for Systematic reviews and Meta‐Analyses (PRISMA) guidelines to establish the evidence base for measuring navigation and gait, two everyday behaviors affected early in AD and non‐AD disorders and not adequately measured in current practice. PubMed and Web of Science databases were searched for studies on asymptomatic and early‐stage symptomatic individuals at risk of dementia, with the Newcastle–Ottawa Scale used to assess bias and evaluate methodological quality. Of 316 navigation and 2086 gait records identified, 27 and 83, respectively, were included in the final sample. We highlight several measures that may identify at‐risk individuals, whose quantifiability with different devices mitigates the risk of future technological obsolescence. Beyond navigation and gait, this review also provides the framework for evaluating the evidence base for future digital measures of behaviors considered for early disease detection. [ABSTRACT FROM AUTHOR]

The history of accessible digital and mobile technologies, from mobile phones and tablets to smartwatches and wearables, offers a case study in how such devices have made a significant difference in the lives of people with disabilities and older users. A review of more than 20 years of mobile phone development also demonstrates how the three-pronged approach of policy initiatives, industry efforts, and end-user advocacy resulted in a wide range of accessibility features. These features not only facilitate the use of the device and access to digital content, but more and more, include assistive functions that improve access to the environment as well. In the absence of a comprehensive chronicle of mobile accessibility, we refer to the GARI database, founded in 2008 and since then reporting on accessibility features of mobile phones to facilitate consumer choice. GARI serves as a proxy to analyse how far we have come in making consumer electronics accessible. With this information we can look back and take account of where we are in 2023 and explore how accessible and assistive technologies are becoming closely integrated within the mobile device. The lessons learned, and this lengthy experience provides a framework for the future development of innovative and disruptive technologies to ensure that the progress made is taken forward into a new generation of technologies. [ABSTRACT FROM AUTHOR]

The productivity of work meetings is mostly assessed through post-hoc questionnaires. These questionnaires are impractical as they require additional time after the meeting has ended. Thus, measuring meeting productivity in a non-intrusive manner is of practical and theoretical importance. Extending research on physiological arousal and the healthy physiological variability thesis to the context of work meetings, we take a novel approach and investigate whether physiological arousal and the variability in implicit body signals of meeting participants (heart rate, arm movements, and speech intensity) can be accurate predictors of perceived meeting productivity. In a preliminary field study, we used smartwatches and tracked the body signals of 16 team members in 26 team meetings. The perceived meeting productivity was assessed at the end of the meetings. Partly supporting our assumptions, multilevel analysis showed that the variance in arm acceleration was a significant predictor of perceived meeting productivity. Further, using a random forest classifier, we accurately predicted perceived meeting productivity in roughly 60% of the cases with body signals. This study adds to previous work on meeting effectiveness by tapping into the potential of wearables to provide valid information about perceived meeting productivity. Cultivating our findings, we discuss lessons learned for future research. [ABSTRACT FROM AUTHOR]

Despite advanced health facilities in many developed countries, diabetic patients face multifold health challenges. Type 2 diabetes mellitus (T2DM) go along with conspicuous symptoms due to frequent peaks, hypoglycemia <=70 mg/dL (while fasting), or hyperglycemia >=180 mg/dL two hours postprandial, according to the American Diabetes Association (ADA)). The worse effects of Type 2 diabetes mellitus are precisely associated with the poor lifestyle adopted by patients. In particular, a healthy diet and nutritious food are the key to success for such patients. This study was done to help T2DM patients improve their health by developing a favorable lifestyle under an AI-assisted Continuous glucose monitoring (CGM) digital system. This study aims to reduce the blood glucose level fluctuations of such patients by rectifying their daily diet and maintaining their exertion vs. food consumption records. In this study, a well-precise prediction is obtained by training the ML model on a dataset recorded from CGM sensor devices attached to T2DM patients under observation. As the data obtained from the CGM sensor is time series, to predict blood glucose levels, the time series analysis and forecasting are done with XGBoost, SARIMA, and Prophet. The results of different Models are then compared based on performance metrics. This helped in monitoring various trends, specifically irregular patterns of the patient's glucose data, collected by the CGM sensor. Later, keeping track of these trends and seasonality, the diet is adjusted accordingly by adding or removing particular food and keeping track of its nutrients with the intervention of a commercially available all-in-one AI solution for food recognition. This created an interactive assistive system, where the predicted results are compared to food contents to bring the blood glucose levels within the normal range for maintaining a healthy lifestyle and to alert about blood glucose fluctuations before the time that are going to occur sooner. This study will help T2DM patients get in managing diabetes and ultimately bring HbA1c within the normal range (<= 5.7%) for diabetic and pre-diabetic patients, three months after the intervention. [ABSTRACT FROM AUTHOR]

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.

Abstract Wearable strain sensors have attracted research interest owing to their potential within digital healthcare, offering smarter tracking, efficient diagnostics, and lower costs. Unlike rigid sensors, fiber‐based ones compete with their flexibility, durability, adaptability to body structures as well as eco‐friendliness to environment. Here, the sustainable fiber‐based wearable strain sensors for digital health are reviewed, and material, fabrication, and practical healthcare aspects are explored. Typical strain sensors predicated on various sensing modalities, be it resistive, capacitive, piezoelectric, or triboelectric, are explained and analyzed according to their strengths and weaknesses toward fabrication and applications. The applications in digital healthcare spanning from body area sensing networks, intelligent health management, and medical rehabilitation to multifunctional healthcare systems are also evaluated. Moreover, to create a more complete digital health network, wired and wireless methods of data collection and examples of machine learning are elaborated in detail. Finally, the prevailing challenges and prospective insights into the advancement of novel fibers, enhancement of sensing precision and wearability, and the establishment of seamlessly integrated systems are critically summarized and offered. This endeavor not only encapsulates the present landscape but also lays the foundation for future breakthroughs in fiber‐based wearable strain sensor technology within the domain of digital health.

Cardiovascular diseases (CVDs) are a global burden that requires attention. For the detection and diagnosis of CVDs, the 12-lead ECG is a key tool. With technological advancements, ECG devices are becoming smaller and available for home use. Most of these devices contain a limited number of leads and are aimed to detect atrial fibrillation (AF). To investigate whether a four-electrode arrangement could provide enough information to diagnose other CVDs, further research is necessary. At the University Medical Center Utrecht in a multidisciplinary team, we developed the miniECG, a four-electrode ECG handheld system for scientific research in clinical environments (TRL6). This paper describes the process followed during the development of the miniECG. From assembling a multidisciplinary team, which includes engineers, cardiologists, and clinical physicians to the contribution of team members in the design input, design, and testing for safety and functionality of the device. Finally, we detail how the development process was composed by iterative design steps based on user input and intended use evolution. The miniECG is a device compliant for scientific research with patients within Dutch Medical Centers. We believe that hospital-based development led to a streamlined process, which could be applied for the design and development of other technologies used for scientific research in clinical environments.

Abstract Anastomotic leakage (AL) is the leaking of non‐sterile gastrointestinal contents into a patient's abdominal cavity. AL is one of the most dreaded complications following gastrointestinal surgery, with mortality rates reaching up to 27%. The current diagnostic methods for anastomotic leaks are limited in sensitivity and specificity. Since the timing of detection directly impacts patient outcomes, developing new, fast, and simple methods for early leak detection is crucial. Here, a naked eye‐readable, electronic‐free macromolecular network drain fluid sensor is introduced for continuous monitoring and early detection of AL at the patient's bedside. The sensor array comprises three different macromolecular network sensing elements, each tailored for selectivity toward the three major digestive enzymes found in the drainage fluid during a developing AL. Upon digestion of the macromolecular network structure by the respective digestive enzymes, the sensor produces an optical shift discernible to the naked eye. The diagnostic efficacy and clinical applicability of these sensors are demonstrated using clinical samples from 32 patients, yielding a Receiver Operating Characteristic Area Under the Curve (ROC AUC) of 1.0. This work has the potential to significantly contribute to improved patient outcomes through continuous monitoring and early, low‐cost, and reliable AL detection.

Background Wearables are increasingly used by patients with atrial fibrillation (AF) for symptom monitoring and health management, but their impact on patient health care use and psychological well‐being is not well understood. Methods and Results In this retrospective, propensity‐matched study of patients with AF, survey and electronic health record data were merged to compare AF‐specific health care use (outpatient/inpatient visits, rhythm‐related testing, and procedures) and informal health care use (telephone calls and patient portal messages) over a 9‐month period between wearable users and nonusers. We also examined the effects of wearable cardiac monitoring features (eg, heart rate alerts, irregular rhythm notification, and ECG) on patient behavior and well‐being. Of 172 patients with AF in this analysis (age, 72.6±9.0 years; 42% women), 83 used a wearable. Compared with nonusers, wearable users reported higher rates of symptom monitoring and preoccupation (P=0.03) and more AF treatment concerns (P=0.02). Moreover, 20% of wearable users experienced anxiety and always contacted their doctors in response to irregular rhythm notifications. After matching, AF‐specific health care use was significantly greater among wearable users compared with nonusers (P=0.04), including significantly higher rates of ECGs, echocardiograms/transesophageal echocardiogram, and ablation. Wearable users were also significantly more likely to use informal health care resources compared with nonusers (P=0.05). Conclusions Wearables were associated with higher rates of symptom monitoring and preoccupation, AF treatment concerns, AF‐specific health care use, and use of informal health care resources. Prospective, randomized studies are needed to understand the net effects of wearables and their alerts on patients, providers, and the health care system.

Given the popularity of wrist-worn devices, particularly smartwatches, the identification of manual movement patterns has become of utmost interest within the research field of Human Activity Recognition (HAR) systems. In this context, by leveraging the numerous sensors natively embedded in smartwatches, the HAR functionalities that can be implemented in a watch via software and in a very cost-efficient way cover a wide variety of applications, ranging from fitness trackers to gesture detectors aimed at disabled individuals (e.g., for sending alarms), promoting behavioral activation or healthy lifestyle habits. In this regard, for the development of artificial intelligence algorithms capable of effectively discriminating these activities, it is of great importance to have repositories of movements that allow the scientific community to train, evaluate, and benchmark new proposals of movement detectors. The UMAHand dataset offers a collection of files containing the signals captured by a Shimmer 3 sensor node, which includes an accelerometer, a gyroscope, a magnetometer and a barometer, during the execution of different typical hand movements. For that purpose, the measurements from these four sensors, gathered at a sampling rate of 100 Hz, were taken from a group of 25 volunteers (16 females and 9 males), aged between 18 and 56, during the performance of 29 daily life activities involving hand mobility. Participants wore the sensor node on their dominant hand throughout the experiments.

People are increasingly open to sharing personal data collected by wearables, while concerns have emerged on how companies, governments and organisations process this data. This paper applies Nissenbaum's theory of contextual integrity to explore the perceived appropriateness of information flows linked to wearables. A vignette study was conducted (N = 500) to examine the influence of the type of data shared, its purpose, and the sender, on the appropriateness of different wearables' information flow scenarios. Results revealed a significant impact of information type, sharing purpose, and sender on the perceived appropriateness of data sharing. Notably, data collected for research purposes or to develop new functionalities was deemed most appropriate, while data used for advertising was viewed unfavourably. Further, the user-controlled sharing received higher appropriateness ratings. This research underscores the need for meaningful consent in data sharing and suggests that manufacturers of wearable devices should utilise user agency to supplement information flow automation based on societal and contextual privacy norms.

Timed Up and Go (TUG) test is one of the most popular clinical tools aimed at the assessment of functional mobility and fall risk in older adults. The automation of the analysis of TUG movements is of great medical interest not only to speed up the test but also to maximize the information inferred from the subjects under study. In this context, this article describes a dataset collected from a cohort of 69 experimental subjects (including 30 adults over 60 years), during the execution of several repetitions of the TUG test. In particular, the dataset includes the measurements gathered with four wearables devices embedding four sensors (accelerometer, gyroscope magnetometer and barometer) located on four body locations (waist, wrist, ankle and chest). As a particularity, the dataset also includes the same measurements recorded when the young subjects repeat the test while wearing a commercial geriatric simulator, consisting of a set of weighted vests and other elements intended to replicate the limitations caused by aging. Thus, the generated dataset also enables the investigation into the potential of such tools to emulate the actual dynamics of older individuals.

Solid-contact (SC) ion-selective electrodes (ISEs) are often employed in wearables for electrolytes detection owing to their simplicity and ease of miniaturization. However, to mitigate their inherently unstable open circuit potential signal, ISEs require long hours of conditioning and frequent calibration prior to and during operation, limiting their practicality in wearable applications. Inspired by strategies to address water crossover and flooding in polyelectrolyte fuel cells, we demonstrated a SCISE with minimal conditioning time and long-term stability by modulating the rate-limiting step between mass transfer of water and hydrated ions and redox kinetics in the conducting polymer (CP). Our strategy comprised a wearable ISE with a superhydrophobic CP, PEDOT:TFPB, which reduced water and ion fluxes within the ISE, resulting in a stable and less-swollen CP and diminished water layer formation while maintaining CP's high capacitance. Our PEDOT:TFPB based ISEs functioned after a short conditioning time of 30 min and exhibited extended stability with a reduced signal deviation of only 0.16 % per hour (0.02 mV h−1) during 48 h of continuous measurement. Through systematic studies, we showed that ISE performance could be further tuned by tailoring the thickness of the ion-selective membrane as well as the hydrophobicity and polymerization charges of the CP. Without the need for recurrent calibration, our ISEs sustain high accuracy and prolonged stability upon integration into a wearable format for on-body perspiration analysis. Our strategy allows wearable ion-selective sensors with minimal maintenance at the user-end for long-term continuous monitoring, unveiling their potential in sports, healthcare, and diagnosis fields.

Resumen: El uso de dispositivos inteligentes como los teléfonos móviles (smpartphones) o relojes inteligentes (smartwatch) para promover la actividad física y el bienestar se ha incrementado en los últimos años entre los pacientes y los profesionales en atención primaria. Este cambio se ve impulsado por el acceso de los pacientes y de los profesionales a un gran catálogo de aplicaciones en salud, las cuales pueden complementar la prestación de servicios y promover el empoderamiento de los pacientes en su propia salud y los estilos de vida. Estas aplicaciones se están empezando a integrar con áreas como la inteligencia artificial (IA), el internet de las cosas médicas (IoMT) y el almacenamiento de datos en la nube, entre otros sistemas tecnológicos emergentes, ofreciendo un nuevo enfoque complementario a la práctica clínica conocida hasta el momento. A pesar del gran potencial, existen numerosas limitaciones y grandes desafíos para su implementación plena en la práctica clínica. Abstract: The use of smart devices such as mobile phones (smartphones) or smart watches (smartwatch) to promote physical activity and well-being has increased in recent years among patients and professionals in primary care. This change is driven by the access of patients and professionals to a large catalog of health applications, which can complement the provision of services and promote the empowerment of patients in their own health and lifestyles. These applications are beginning to be integrated with areas such as Artificial Intelligence (AI), the Internet of Medical Things (IoMT) and data storage in the cloud, among other emerging technological systems, offering a new complementary approach to clinical practice known so far. Despite the great potential, there are numerous limitations and major challenges for its full implementation in clinical practice.

Seizures have a profound impact on quality of life and mortality, in part because they can be challenging both to detect and forecast. Seizure detection relies upon accurately differentiating transient neurological symptoms caused by abnormal epileptiform activity from similar symptoms with different causes. Seizure forecasting aims to identify when a person has a high or low likelihood of seizure, which is related to seizure prediction. Machine learning and artificial intelligence are data-driven techniques integrated with neurodiagnostic monitoring technologies that attempt to accomplish both of those tasks. In this narrative review, we describe both the existing software and hardware approaches for seizure detection and forecasting, as well as the concepts for how to evaluate the performance of new technologies for future application in clinical practice. These technologies include long-term monitoring both with and without electroencephalography (EEG) that report very high sensitivity as well as reduced false positive detections. In addition, we describe the implications of seizure detection and forecasting upon the evaluation of novel treatments for seizures within clinical trials. Based on these existing data, long-term seizure detection and forecasting with machine learning and artificial intelligence could fundamentally change the clinical care of people with seizures, but there are multiple validation steps necessary to rigorously demonstrate their benefits and costs, relative to the current standard.

The development of graphene-based electrodes for application in energy storage and energy harvesting devices represents an important strategy for producing wearable devices with requisites of flexibility and good electrochemical performance. Herein, the use of laser-induced graphene (LIG) has been explored as a simple and efficient method for the production of interdigitated microsupercapacitors (μSCs) and back electrodes for triboelectric nanogenerators (TENGs) active layers by direct production of graphene from Kapton polyimide and by the transference of the pattern to polydimethylsiloxane (a typical tribonegative layer for TENG). An open circuit voltage of 189.7 V, short circuit current of 39.8 μA, and power of 302.5 μW (power density of 20.2 μW/cm2) was observed for the conventional TENG while an areal capacitance of 2.5 mF/cm2 with good retention in the energy generation and cyclability in energy storage was observed for the microsupercapacitor. The most relevant aspect to be considered is a single-step method for transference of back-electrode to the Poly(dimethylsiloxane) requiring minimal processing steps for morphology control in the friction layer and self-powered behavior for integration of TENG/microsupercapacitor in a power unit cell.

Summary: Background: Limited knowledge exists regarding behavioral and biomarker shifts during the period from respiratory infection exposure to testing decisions (the diagnostic decision period), a key phase affecting transmission dynamics and public health strategy development. This study aims to examine the changes in behavior and biomarkers during the diagnostic decision period for COVID-19, influenza, and group A streptococcus (GAS). Methods: We analyzed data from a two-year prospective cohort study involving 4795 participants in Israel, incorporating smartwatch data, self-reported symptoms, and medical records. Our analysis focused on three critical phases: the digital incubation period (from exposure to physiological anomalies detected by smartwatches), the symptomatic incubation period (from exposure to onset of symptoms), and the diagnostic decision period for influenza, COVID-19, and GAS. Findings: The delay between initial symptom reporting and testing was 39 [95% confidence interval (CI): 34–45] hours for influenza, 53 [95% CI: 49–58] hours for COVID-19, and 38 [95% CI: 32–46] hours for GAS, with 73 [95% CI: 67–78] hours from anomalies in heart measures to symptom onset for influenza, 23 [95% CI: 18–27] hours for COVID-19, and 62 [95% CI: 54–68] hours for GAS. Analyzing the entire course of infection of each individual, the greatest changes in heart rates were detected 67.6 [95% CI: 62.8–72.5] hours prior to testing for influenza, 64.1 [95% CI: 61.4–66.7] hours prior for COVID-19, and 58.2 [95% CI: 52.1–64.2] hours prior for GAS. In contrast, the greatest reduction in physical activities and social contacts occurred after testing. Interpretation: These findings highlight the delayed response of patients in seeking medical attention and reducing social contacts and demonstrate the transformative potential of smartwatches for identifying infection and enabling timely public health interventions. Funding: This work was supported by the European Research Council, project #949850, the Israel Science Foundation (ISF), grant No. 3409/19, within the Israel Precision Medicine Partnership program, and a Koret Foundation gift for Smart Cities and Digital Living.

Noise and distractions are commonly associated with stress. Our objective was to identify the impact of noise and distracting environments on the stress levels of library patrons, as measured by wearable devices. In this study, we explored the relationship between heart rate variability (HRV) and skin temperature measures using consumer wearable devices. Additionally, we analyzed our data through the perspective of established personas for library patrons to determine if purpose in visiting the library had any effect on observed stress. For those who were expected to be more stressed, there was no significant difference between loud and quiet conditions. Our results showed that patrons in both quiet and loud conditions were significantly more stressed than their baseline measurements outside of the library, but equally so. This was consistent even accounting for differing purpose in library attendance. Our findings suggest that noisy environments in the library may not be as problematic for library patrons as is often perceived. [ABSTRACT FROM AUTHOR]

Abstract Background The escalating global prevalence of type 2 diabetes and prediabetes presents a major public health challenge. Physical activity plays a critical role in managing (pre)diabetes; however, adherence to physical activity recommendations remains low. The ENERGISED trial was designed to address these challenges by integrating mHealth tools into the routine practice of general practitioners, aiming for a significant, scalable impact in (pre)diabetes patient care through increased physical activity and reduced sedentary behaviour. Methods The mHealth intervention for the ENERGISED trial was developed according to the mHealth development and evaluation framework, which includes the active participation of (pre)diabetes patients. This iterative process encompasses four sequential phases: (a) conceptualisation to identify key aspects of the intervention; (b) formative research including two focus groups with (pre)diabetes patients (n = 14) to tailor the intervention to the needs and preferences of the target population; (c) pre-testing using think-aloud patient interviews (n = 7) to optimise the intervention components; and (d) piloting (n = 10) to refine the intervention to its final form. Results The final intervention comprises six types of text messages, each embodying different behaviour change techniques. Some of the messages, such as those providing interim reviews of the patients’ weekly step goal or feedback on their weekly performance, are delivered at fixed times of the week. Others are triggered just in time by specific physical behaviour events as detected by the Fitbit activity tracker: for example, prompts to increase walking pace are triggered after 5 min of continuous walking; and prompts to interrupt sitting following 30 min of uninterrupted sitting. For patients without a smartphone or reliable internet connection, the intervention is adapted to ensure inclusivity. Patients receive on average three to six messages per week for 12 months. During the first six months, the text messaging is supplemented with monthly phone counselling to enable personalisation of the intervention, assistance with technical issues, and enhancement of adherence. Conclusions The participatory development of the ENERGISED mHealth intervention, incorporating just-in-time prompts, has the potential to significantly enhance the capacity of general practitioners for personalised behavioural counselling on physical activity in (pre)diabetes patients, with implications for broader applications in primary care.

Abstract Continuous glucose monitoring (CGM) device adoption in non- and pre-diabetics for preventive healthcare has uncovered a paucity of benchmarking data on glycemic control and insulin resistance for the high-risk Indian/South Asian demographic. Furthermore, the correlational efficacy between digital applications-derived health scores and glycemic indices lacks clear supportive evidence. In this study, we acquired glycemic variability (GV) using the Ultrahuman (UH) M1 CGM, and activity metrics via the Fitbit wearable for Indians/South Asians with normal glucose control (non-diabetics) and those with pre-diabetes (N = 53 non-diabetics, 52 pre-diabetics) for 14 days. We examined whether CGM metrics could differentiate between the two groups, assessed the relationship of the UH metabolic score (MetSc) with clinical biomarkers of dysglycemia (OGTT, HbA1c) and insulin resistance (HOMA-IR); and tested which GV metrics maximally correlated with inflammation (Hs-CRP), stress (cortisol), sleep, step count and heart rate. We found significant inter-group differences for mean glucose levels, restricted time in range (70–110 mg/dL), and GV-by-SD, all of which improved across days. Inflammation was strongly linked with specific GV metrics in pre-diabetics, while sleep and activity correlated modestly in non-diabetics. Finally, MetSc displayed strong inverse relationships with insulin resistance and dysglycemia markers. These findings present initial guidance GV data of non- and pre-diabetic Indians and indicate that digitally-derived metabolic scores can positively influence glucose management.

Background: Fatigue is a prominent symptom in many diseases and is strongly associated with impaired daily function. The measurement of daily function is currently almost always done with questionnaires, which are subjective and imprecise. With the recent advances of digital wearable technologies, novel approaches to evaluate daily function quantitatively and objectively in real-life conditions are increasingly possible. This also creates new possibilities to measure fatigue-related changes of daily function using such technologies. Summary: This review examines which digitally assessable parameters in immune-mediated inflammatory and neurodegenerative diseases may have the greatest potential to reflect fatigue-related changes of daily function. Key Messages: Results of a standardized analysis of the literature reporting about perception-, capacity-, and performance-evaluating assessment tools indicate that changes of the following parameters: physical activity, independence of daily living, social participation, working life, mental status, cognitive and aerobic capacity, and supervised and unsupervised mobility performance have the highest potential to reflect fatigue-related changes of daily function. These parameters thus hold the greatest potential for quantitatively measuring fatigue in representative diseases in real-life conditions, e.g., with digital wearable technologies. Furthermore, to the best of our knowledge, this is a new approach to analysing evidence for the design of performance-based digital assessment protocols in human research, which may stimulate further systematic research in this area.

Abstract Background Traditionally, existing studies assessing the health associations of accelerometer-measured movement behaviors have been performed with few averaged values, mainly representing the duration of physical activities and sedentary behaviors. Such averaged values cannot naturally capture the complex interplay between the duration, timing, and patterns of accumulation of movement behaviors, that altogether may be codependently related to health outcomes in adults. In this study, we introduce a novel approach to visually represent recorded movement behaviors as images using original accelerometer outputs. Subsequently, we utilize these images for cluster analysis employing deep convolutional autoencoders. Methods Our method involves converting minute-by-minute accelerometer outputs (activity counts) into a 2D image format, capturing the entire spectrum of movement behaviors performed by each participant. By utilizing convolutional autoencoders, we enable the learning of these image-based representations. Subsequently, we apply the K-means algorithm to cluster these learned representations. We used data from 1812 adult (20–65 years) participants in the National Health and Nutrition Examination Survey (NHANES, 2003–2006 cycles) study who worn a hip-worn accelerometer for 7 seven consecutive days and provided valid accelerometer data. Results Deep convolutional autoencoders were able to learn the image representation, encompassing the entire spectrum of movement behaviors. The images were encoded into 32 latent variables, and cluster analysis based on these learned representations for the movement behavior images resulted in the identification of four distinct movement behavior profiles characterized by varying levels, timing, and patterns of accumulation of movement behaviors. After adjusting for potential covariates, the movement behavior profile characterized as “Early-morning movers” and the profile characterized as “Highest activity” both had lower levels of insulin (P

Patient-generated health data (PGHD) have great potential to improve clinical outcomes. As providers consider whether and how to incorporate PGHD into their clinical workflows, platforms by Apple and Amazon stand to fundamentally alter the landscape. With the aim to examine the conditions under which providers would adopt PGHD and possibly sign on with a platform, we analyzed the incentives and optimal strategies of two healthcare providers, a monopoly platform, and consumers using stylized game-theoretic models and solve for potential equilibria. We found that consumer surplus always increased with PGHD adoption, but social welfare may drop. The larger provider had more incentive to adopt PGHD than the smaller provider, but these incentives were reversed in the case of platform adoption. Accordingly, the platform enrolled the smaller provider first and possibly both providers. The emergence of the platform raised provider surplus, potentially at the expense of the consumers, despite offering its service to them for free. These results illustrate the importance of economic incentives regarding whether and how PGHD could be incorporated into our current healthcare system.

Abstract Background When job demand exceeds job resources, burnout occurs. Burnout in healthcare workers extends beyond negatively affecting their functioning and physical and mental health; it also has been associated with poor medical outcomes for patients. Data-driven technology holds promise for the prediction of occupational burnout before it occurs. Early warning signs of burnout would facilitate preemptive institutional responses for preventing individual, organizational, and public health consequences of occupational burnout. This protocol describes the design and methodology for the decentralized Burnout PRedictiOn Using Wearable aNd ArtIficial IntelligEnce (BROWNIE) Study. This study aims to develop predictive models of occupational burnout and estimate burnout-associated costs using consumer-grade wearable smartwatches and systems-level data. Methods A total of 360 registered nurses (RNs) will be recruited in 3 cohorts. These cohorts will serve as training, testing, and validation datasets for developing predictive models. Subjects will consent to one year of participation, including the daily use of a commodity smartwatch that collects heart rate, step count, and sleep data. Subjects will also complete online baseline and quarterly surveys assessing psychological, workplace, and sociodemographic factors. Routine administrative systems-level data on nursing care outcomes will be abstracted weekly. Discussion The BROWNIE study was designed to be decentralized and asynchronous to minimize any additional burden on RNs and to ensure that night shift RNs would have equal accessibility to study resources and procedures. The protocol employs novel engagement strategies with participants to maintain compliance and reduce attrition to address the historical challenges of research using wearable devices. Trial Registration NCT05481138.

While resistance training promotes muscle hypertrophy and strength, accessibility of equipment is a barrier. This study evaluated a wearable VAriable Resistance Suit (VARS) as a novel and alternative method to achieve muscle strength improvement. It was hypothesized that by providing adjustable, bi-directional and speed dependent resistance, VARS can target specific muscles to improve muscle strength via an accessible and portable device. Twelve untrained healthy male adults (22.08 ± 4.1 years old) participated in an 8-week long resistance training using VARS to strengthen four muscles (biceps brachii, triceps brachii, biceps femoris, rectus femoris) of their non-dominant arm and leg using VARS. The results showed significant improvements in the muscle strength measured by isokinetic dynamometer – 49.9±9.6% increase in isokinetic force and 30.6±7.6% increase in isometric force. Muscle size and body composition were also assessed using ultrasound imaging and bioelectrical impedance analysis, which did not show significant changes. The study demonstrates the efficacy and feasibility of VARS as a resistance training tool to achieve muscle strength improvement and its potential extension to clinical populations.

Tele-rehabilitation has garnered significant interest among clinicians and researchers with its potential to transform cardiac rehabilitation, affecting millions of patients annually. A critical requirement of tele-cardiac rehabilitation is a fail-safe, highly interactive system with timely feedback to both the patient and the therapists or physicians. Current systems often assume ideal network conditions, neglecting the nuances of real-world deployment. We have designed, developed, and tested an end-to-end tele-cardiac rehabilitation system that seamlessly combines Internet of Medical Things (IoMT) devices and AI-powered abnormality and activity detection, providing a fail-safe and real-time actionable closed-feedback loop system for the patient and the doctor. A pilot study evaluates system performance across diverse mobile networks in varying conditions (stable or unstable). The RESNET-18 model for cardiac abnormality detection (0.71 F1-score) and the VGG-16 model for human activity classification (0.89 F1-score) demonstrate significant performance. Furthermore, we optimize these models for edge devices, demonstrating significant speed improvements compared to cloud servers (up to 33 times faster).

One of the most relevant physiological parameters in dogs is respiratory rate (RR). The aim of this paper is to present a novel wearable system that allows to accurately estimate RR in dogs, and to compare it to a gold standard in static conditions. Data from 12 dogs were acquired while the animals were anesthetized and attached to a vital signs monitor. The experimental setup consisted of three Inertial Measurement Units (IMUs) applied on the dog, and a video camera filming the RR value shown on the monitor. The range of RR values analyzed in the study is 0 to 29 breaths per minute, read by the vital signs monitor. The mean RMSE for the data acquisitions is 1.68 breaths per minute. The values of the filtering parameters that allow to obtain the best performance depend on the specific acquisition. This result demonstrates that adaptive filtering is a viable method for the application. Future developments include tests on a larger dataset, and trials on dogs in unconstrained environments and during movement.

Postural instability is associated with disease status and fall risk in Persons with Multiple Sclerosis (PwMS). However, assessments of postural instability, known as postural sway, leverage force platforms or wearable accelerometers, and are most often conducted in laboratory environments and are thus not broadly accessible. Remote measures of postural sway captured during daily life may provide a more accessible alterative, but their ability to capture disease status and fall risk has not yet been established. We explored the utility of remote measures of postural sway in a sample of 33 PwMS. Remote measures of sway differed significantly from lab-based measures, but still demonstrated moderately strong associations with patient-reported measures of balance and mobility impairment. Machine learning models for predicting fall risk trained on lab data provided an Area Under Curve (AUC) of 0.79, while remote data only achieved an AUC of 0.51. Remote model performance improved to an AUC of 0.74 after a new, subject-specific k-means clustering approach was applied for identifying the remote data most appropriate for modelling. This cluster-based approach for analyzing remote data also strengthened associations with patient-reported measures, increasing their strength above those observed in the lab. This work introduces a new framework for analyzing data from remote patient monitoring technologies and demonstrates the promise of remote postural sway assessment for assessing fall risk and characterizing balance impairment in PwMS.

Decentralized clinical trials (DCTs) are gaining momentum in clinical research as these studies can be executed remotely through telemedicine and mobile/local health-care providers. The COVID-19 pandemic has further accelerated advances and adoption in this area. In the past few years, there has been significant development and growth in the use of remote data that are electronically transmitted from a clinical trial (CT) participant, from outside the clinical setting, to a data repository. Such data may include laboratory data, safety data, or outcome measures reported by the participant, the clinician or the observer. Similarly, wearable health monitoring devices are being increasingly used in health-care and CT settings. Digital biomarkers, which can support continuous measurement of physiologic parameters outside the physical confines of the clinical environment, are also creating new and improved opportunities for patient care and biomedical research, enabling remote monitoring and DCTs. There are several benefits to using remote data capture, wearables, and digital biomarkers in clinical health-care research; however, several questions and challenges still need to be addressed. In an effort to understand the adoption of these technologies in DCTs, and the challenges therein, the authors of this workstream conducted an online survey of clinical research stakeholders across India and reviewed 80 responses. The review article summarizes the key findings from this online survey.

Objective: Panic attacks are an impairing mental health problem that affects 11% of adults every year. Current criteria describe them as occurring without warning, despite evidence suggesting individuals can often identify attack triggers. We aimed to prospectively explore qualitative and quantitative factors associated with the onset of panic attacks. Results: Of 87 participants, 95% retrospectively identified a trigger for their panic attacks. Worse individually reported mood and state-level mood, as indicated by Twitter ratings, were related to greater likelihood of next-day panic attack. In a subsample of participants who uploaded their wearable sensor data (n = 32), louder ambient noise and higher resting heart rate were related to greater likelihood of next-day panic attack. Conclusions: These promising results suggest that individuals who experience panic attacks may be able to anticipate their next attack which could be used to inform future prevention and intervention efforts.

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.