A review of wearable and unobtrusive sensing technologies for chronic disease management

Revolutionizing biosensing with wearable microneedle patches: innovations and applications

Wearable microneedle (MN) patches have emerged as a transformative platform for biosensing, offering a minimally invasive and user-friendly approach to real-time health monitoring and disease diagnosis. Primarily designed to access interstitial fluid (ISF) through shallow skin penetration, MNs enable precise and continuous sampling of biomarkers such as glucose, lactate, and electrolytes. Additionally, recent innovations have integrated MN arrays with microfluidic and porous structures to support sweat-based analysis, where MNs act as structural or functional components in hybrid wearable systems. This review explores the design, fabrication, and functional integration of MNs into wearable devices, highlighting advances in multi-analyte detection, wireless data transmission, and self-powered sensing. Challenges related to material biocompatibility, sensor stability, scalability, and user variability are addressed, alongside emerging opportunities in microfluidics, artificial intelligence, and soft materials. Overall, MN-based biosensing platforms are poised to redefine personalized healthcare by enabling dynamic, decentralized, and accessible health monitoring.

Seasonal, weekly, and individual variations in long-term use of wearable medical devices for diabetes management

Wearable medical-grade devices are transforming the standard of care for prevalent chronic conditions like diabetes. Yet, adoption and long-term use remain a challenge for many people. In this study, we investigate patterns of consistent versus disrupted use of continuous glucose monitors (CGMs) through analysis of more than 118,000 days of data, with over 22 million blood glucose samples, from 108 young adults with type 1 diabetes (average: 3 years of CGM data per person). In this population, we found more consistent CGM use at the start and end of the year (e.g., January, December), and more disrupted CGM use in the middle of the year/warmer months (i.e., May to July). We also found more consistent CGM use on weekdays (Monday to Thursday) and during waking hours (6AM - 6PM), but more disrupted CGM use on weekends (Friday to Sunday) and during evening/night hours (7PM - 5AM). Only 52.7% of participants (57 out of 108) had consistent and sustained CGM use over the years (i.e., over 70% daily wear time for more than 70% of their data duration). From semi-structured interviews, we unpack factors contributing to sustained CGM use (e.g., easier and better blood glucose management) and factors contributing to disrupted CGM use (e.g., changes in insurance coverage, issues with sensor adhesiveness/lifespan, and college/life transitions). We leverage insights from this study to elicit implications for next-generation technology and interventions that can circumvent seasonal and other factors that disrupt sustained use of wearable medical devices for the goal of improving health outcomes.

Digital biomarkers for real-life, home-based monitoring of frailty: a systematic review and meta-analysis

BACKGROUND

Frailty, characterised by decreased physiological function and increased vulnerability to stressors, was associated with an increase in numerous adverse outcomes. Although the number of digital biomarkers for detecting frailty in older adults is increasing, there remains a lack of evidence regarding their effectiveness for early detection and follow-up in real-world, home-based settings.

METHODS

Five databases were searched from inception until 1 August 2024. Standardised forms were utilised for data extraction. The Quality Assessment of Diagnostic Accuracy Studies was used to assess the risk of bias and applicability of included studies. A meta-analysis was conducted to assess the overall sensitivity and specificity for frailty detection.

RESULTS

The systematic review included 16 studies, identifying digital biomarkers relevant for frailty detection, including gait, activity, sleep, heart rate, hand movements and room transition. Meta-analysis further revealed pooled sensitivity of 0.78 [95% confidence interval (CI): 0.70-0.86] and specificity of 0.79 (95% CI: 0.72-0.86) to classify robust and pre-frailty/frailty participants. The overall risk of bias indicated that all the included studies were characterised as having a high or unclear risk of bias.

CONCLUSION

This study offers a thorough characterisation of digital biomarkers for detecting frailty, underscoring their potential for early prediction in home settings. These findings are instrumental in bridging the gap between evidence and practice, enabling more proactive and personalised healthcare monitoring. Further longitudinal studies involving larger sample sizes are necessary to validate the effectiveness of these digital biomarkers as diagnostic tools or prognostic indicators.

Overcoming Challenges in Video-Based Health Monitoring: Real-World Implementation, Ethics, and Data Considerations

In the context of evolving healthcare technologies, this study investigates the application of AI and machine learning in video-based health monitoring systems, focusing on the challenges and potential of implementing such systems in real-world scenarios, specifically for knowledge workers. The research underscores the criticality of addressing technological, ethical, and practical hurdles in deploying these systems outside controlled laboratory environments. Methodologically, the study spanned three months and employed advanced facial recognition technology embedded in participants' computing devices to collect physiological metrics such as heart rate, blinking frequency, and emotional states, thereby contributing to a stress detection dataset. This approach ensured data privacy and aligns with ethical standards. The results reveal significant challenges in data collection and processing, including biases in video datasets, the need for high-resolution videos, and the complexities of maintaining data quality and consistency, with 42% (after adjustments) of data lost. In conclusion, this research emphasizes the necessity for rigorous, ethical, and technologically adapted methodologies to fully realize the benefits of these systems in diverse healthcare contexts.

Advances in the application of human-machine collaboration in healthcare: insights from China

In the context of the technological revolution and the digital intelligence era, the contradiction between the rising incidence of diseases and the uneven distribution of quality medical resources is highlighted, and the diagnosis and prevention of diseases, and the prognosis and management of diseases are particularly important in the current society of aging population. "Human-machine collaboration" is based on an intelligent algorithmic system that utilizes the complementary strengths of humans and machines for data exchange, task allocation, decision making and collaborative work to provide more decision support. The traditional healthcare model is highly dependent on the unified management of hospitals, which further increases the burden on the healthcare system and often makes it difficult to formulate and implement personalized and precise rehabilitation programs for patients, which seriously affects their prognosis and quality of life, and increases the risk of re-admission to hospitals. In view of this, human-computer collaboration, an innovation-driven technology, is a groundbreaking solution to the outstanding healthcare issues of today. We use the subject words "Human-machine collaboration" OR "Human-Computer Interaction" OR "HCI" AND "chronic disease" OR "Health management" OR "Precision medicine "were searched for CNKI, Wanfang Data, VIP, CBM, PubMed, Web of science, Embase, Cochrane Library and other Chinese and English databases to identify all relevant studies and compare their results, and finally include 68 relevant literature articles, we identified the broad application of HCI in five main areas: disease screening and treatment, health management, medical education, traditional medicine, and the integration and processing of medical data. The aim is to review the concept of human-computer collaboration, its application in global healthcare environments, and the challenges it faces, with a view to continually driving innovation in healthcare models, optimizing the allocation of healthcare resources, and providing new paradigms for the development and application of innovative technologies in healthcare.

Invisible Monitoring for Athlete Health and Performance: A Call for a Better Conceptualization and Practical Recommendations

BACKGROUND

Practices to routinely monitor athletes are rapidly changing. With the concurrent exponential rise in wearable technologies and advanced data analysis, tracking training exposures and responses is widespread and more frequent in the athlete-coach decision-making process. Within this scenario, the concept of invisible monitoring emerged, which was initially vaguely defined as testing athletes without testing them. Despite sound practical applications and benefits (eg, reduced burden on player staff and more frequent measurement), a clear lack of constitutive definition has led to multiple cleavages in both research and practice, including ethical concerns.

PURPOSE

The purpose of this study is to (1) extend the current conceptualization of invisible monitoring by considering subdimensions of the concept and (2) its data-related and ethical challenges and (3) provide practical considerations to implement invisible monitoring. Monitoring burden (degree of obtrusion and frequency of measurement) and the number of constructs a single measurement tool can assess have been proposed as subdimensions of the concept of invisible monitoring. Challenges include the governance and analysis of data required to make estimates, validity and reliability of an invisible monitoring measure, and communication to athletes.

CONCLUSIONS

This commentary presents a first attempt to conceptualize invisible monitoring in the context of elite sport and provide subdimensions of the concept that can be used to classify choices of measurement tools. A consensus is required from both researchers and practitioners regarding its definition and operationalization to optimize current monitoring services to elite athletes.

Nerve-Inspired Optical Waveguide Stretchable Sensor Fusing Wireless Transmission and AI Enabling Smart Tele-Healthcare

Flexible strain monitoring of hand and joint muscle movement is recognized as an effective method for the diagnosis and rehabilitation of neurological diseases such as stroke and Parkinson's disease. However, balancing high sensitivity and large strain, improving wearing comfort, and solving the separation of diagnosis and treatment are important challenges for further building tele-healthcare systems. Herein, a hydrogel-based optical waveguide stretchable (HOWS) sensor is proposed in this paper. A double network structure is adopted to allow the HOWS sensor to exhibit high stretchability of the tensile strain up to 600% and sensitivity of 0.685 mV %-1. Additionally, the flexible smart bionic fabric embedding the HOWS sensor, produced through the warp and weft knitting, significantly enhances wearing comfort. A small circuit board is prepared to enable wireless signal transmission of the designed sensor, thereby improving the daily portability. A speech recognition and human-machine interaction system, based on sensor signal acquisition, is constructed, and the convolutional neural network algorithm is integrated for disease assessment. By integrating sensing, wireless transmission, and artificial intelligence (AI), a smart tele-healthcare system based on HOWS sensors is demonstrated to hold great potential for early warning and rehabilitation monitoring of neurological diseases.

Experimental economics for machine learning-a methodological contribution on lie detection

In this paper, we investigate how technology has contributed to experimental economics in the past and illustrate how experimental economics can contribute to technological progress in the future. We argue that with machine learning (ML), a new technology is at hand, where for the first time experimental economics can contribute to enabling substantial improvement of technology. At the same time, ML opens up new questions for experimental research because it can generate previously impossible observations. To demonstrate this, we focus on algorithms trained to detect lies. Such algorithms are of high relevance for research in economics as they deal with the ability to retrieve otherwise private information. We deduce that most of the commonly applied data sets for the training of lie detection algorithms could be improved by applying the toolbox of experimental economics. To illustrate this, we replicate the "lies in disguise-experiment" by Fischbacher and Föllmi-Heusi with a modification regarding monitoring. The modified setup guarantees a certain level of privacy from the experimenter yet allows to record the subjects as they lie to the camera. Despite monitoring, our results indicate the same lying behavior as in the original experiment. Yet, our experiment allows an individual-level analysis of experimental data and the generation of a lie detection algorithm with an accuracy rate of 67%, which we present in this article.

Wearable Devices for Supporting Chronic Disease Self-Management: Scoping Review

BACKGROUND

People with chronic diseases can benefit from wearable devices in managing their health and encouraging healthy lifestyle habits. Wearables such as activity trackers or blood glucose monitoring devices can lead to positive health impacts, including improved physical activity adherence or better management of type 2 diabetes. Few literature reviews have focused on the intersection of various chronic diseases, the wearable devices used, and the outcomes evaluated in intervention studies, particularly in the context of primary health care.

OBJECTIVE

This study aims to identify and describe (1) the chronic diseases represented in intervention studies, (2) the types or combinations of wearables used, and (3) the health or health care outcomes assessed and measured.

METHODS

We conducted a scoping review following the Joanna Briggs Institute guidelines, searching the MEDLINE and Web of Science databases for studies published between 2012 and 2022. Pairs of reviewers independently screened titles and abstracts, applied the selection criteria, and performed full-text screening. We included interventions using wearables that automatically collected and transmitted data to adult populations with at least one chronic disease. We excluded studies with participants with only a predisposition to develop a chronic disease, hospitalized patients, patients with acute diseases, patients with active cancer, and cancer survivors. We included randomized controlled trials and cohort, pretest-posttest, observational, mixed methods, and qualitative studies.

RESULTS

After the removal of 1987 duplicates, we screened 4540 titles and abstracts. Of the remaining 304 articles after exclusions, we excluded 215 (70.7%) full texts and included 89 (29.3%). Of these 89 texts, 10 (11%) were related to the same interventions as those in the included studies, resulting in 79 studies being included. We structured the results according to chronic disease clusters: (1) diabetes, (2) heart failure, (3) other cardiovascular conditions, (4) hypertension, (5) multimorbidity and other combinations of chronic conditions, (6) chronic obstructive pulmonary disease, (7) chronic pain, (8) musculoskeletal conditions, and (9) asthma. Diabetes was the most frequent health condition (18/79, 23% of the studies), and wearable activity trackers were the most used (42/79, 53% of the studies). In the 79 included studies, 74 clinical, 73 behavioral, 36 patient technology experience, 28 health care system, and 25 holistic or biopsychosocial outcomes were reported.

CONCLUSIONS

This scoping review provides an overview of the wearable devices used in chronic disease self-management intervention studies, revealing disparities in both the range of chronic diseases studied and the variety of wearable devices used. These findings offer researchers valuable insights to further explore health care outcomes, validate the impact of concomitant device use, and expand their use to other chronic diseases.

TRIAL REGISTRATION

Open Science Framework Registries (OSF) s4wfm; https://osf.io/s4wfm.

Motion artifacts in capacitive ECG monitoring systems: a review of existing models and reduction techniques

Current research focuses on improving electrocardiogram (ECG) monitoring systems to enable real-time and long-term usage, with a specific focus on facilitating remote monitoring of ECG data. This advancement is crucial for improving cardiovascular health by facilitating early detection and management of cardiovascular disease (CVD). To efficiently meet these demands, user-friendly and comfortable ECG sensors that surpass wet electrodes are essential. This has led to increased interest in ECG capacitive electrodes, which facilitate signal detection without requiring gel preparation or direct conductive contact with the body. This feature makes them suitable for wearables or integrated measurement devices. However, ongoing research is essential as the signals they measure often lack sufficient clinical accuracy due to susceptibility to interferences, particularly Motion Artifacts (MAs). While our primary focus is on studying MAs, we also address other limitations crucial for designing a high Signal-to-Noise Ratio (SNR) circuit and effectively mitigating MAs. The literature on the origins and models of MAs in capacitive electrodes is insufficient, which we aim to address alongside discussing mitigation methods. We bring attention to digital signal processing approaches, especially those using reference signals like Electrode-Tissue Impedance (ETI), as highly promising. Finally, we discuss its challenges, proposed solutions, and offer insights into future research directions.

Rehabilitation clinicians' use of mainstream wireless technologies in practice: a scoping review

PURPOSE

This scoping review was conducted to understand the barriers, facilitators, and education and training needs of rehabilitation clinicians in their use of mainstream wireless technologies (MWT) to support people with disabilities and older adults. It was also conducted to understand the functional skills of clients that were targeted with MWT use.

MATERIALS AND METHODS

This scoping review was reported using PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) and the Population (or Participants)/Concept/Context) framework. We searched PubMed; ProQuest to access APA PsycINFO; Web of Science Core Collection; and EBSCOhost to access Cumulated Index to Nursing and Allied Health Literature (CINAHL), Ovid MEDLINE ALL, and Education Resources Information Center (ERIC). Articles published between 2015-2022 were retrieved.

RESULTS

A total of 90 articles were included. Most interventions were apps, smartphones, and tablets; were geared toward adults; and targeted motor, cognitive and speech skills. An infographic on barriers and facilitators was generated as a decision support tool for clinicians when implementing MWT. The topic, format, timing, and source of information clinicians need are also delineated.

CONCLUSION

MWT such as apps, smartphones and tablets are being used by rehabilitation clinicians to address motor, cognitive, and speech skills, most commonly in adults. Clinicians voice a need for more education and training. Barriers and facilitators exist at the clinician-, technology-, client-, institution-, and policy levels.

Electrochemical sensing at the fingertips: Wearable glove-based sensors for detection of 4-nitrophenol, picric acid and diazepam

A wearable glove-based sensor is a portable and practical approach for onsite detection/monitoring of a variety of chemical threats. Herein, we report a flexible and sensitive wearable sensor fabricated on the nitrile glove fingertips by stencil-printing technique. The working electrodes were modified with multiwalled carbon nanotubes (MWCNTs)/poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) for sensitive and real-time analyses of hazardous or chemical treats, as picric acid (PA) explosive, diazepam (DZ) as drug-facilitated crimes and the emerging pollutant 4-nitrophenol (4-NP). The multi-sensing platform towards PA, 4-NP, and DZ offers the ability of in-situ qualitative and quantitative analyses of powder and liquid samples. A simple sampling by touching or swiping the fingertip sensor on the sample or surface under investigation using an ionic hydrogel combined with fast voltammetry measurement provides timely point-of-need analyses. The wearable glove-based sensor uses the square wave voltammetry (SWV) technique and exhibited excellent performance to detect PA, 4-NP, and DZ, resulting in limits of detection (LOD) of 0.24 μM, 0.35 μM, 0.06 μM, respectively, in a wide concentration range (from 0.5 μM to 100 μM). Also, we obtained excellent manufacturing reproducibility with relative standard deviations (RSD) in the range of 3.65%-4.61% using 7 different wearable devices (n = 7) and stability in the range of 4.86%-6.61% using different electrodes stored for 10 days at room temperature (n = 10), demonstrating the excellent sensor-to-sensor reproducibility and stability for reliable in-field measurements. The stretchable sensor presented great mechanical robustness, supporting up to 80 bending or stretching deformation cycles without significant voltammetric changes. Collectively, our wearable glove-based sensor may be employed for analyses of chemical contaminants of concern, such as explosives (PA), drugs (DZ), and emerging pollutants (4-NP), helping in environmental and public safety control.

Direct Memory Access-Based Data Storage for Long-Term Acquisition Using Wearables in an Energy-Efficient Manner

This study introduces a lightweight storage system for wearable devices, aiming to optimize energy efficiency in long-term and continuous monitoring applications. Utilizing Direct Memory Access and the Serial Peripheral Interface protocol, the system ensures efficient data transfer, significantly reduces energy consumption, and enhances the device autonomy. Data organization into Time Block Data (TBD) units, rather than files, significantly diminishes control overhead, facilitating the streamlined management of periodic data recordings in wearable devices. A comparative analysis revealed marked improvements in energy efficiency and write speed over existing file systems, validating the proposed system as an effective solution for boosting wearable device performance in health monitoring and various long-term data acquisition scenarios.

Noncontact Sensors for Vital Signs Measurement: A Narrative Review

Vital signs are crucial for monitoring changes in patient health status. This review compared the performance of noncontact sensors with traditional methods for measuring vital signs and investigated the clinical feasibility of noncontact sensors for medical use. We searched the Medical Literature Analysis and Retrieval System Online (MEDLINE) database for articles published through September 30, 2023, and used the key search terms "vital sign," "monitoring," and "sensor" to identify relevant articles. We included studies that measured vital signs using traditional methods and noncontact sensors and excluded articles not written in English, case reports, reviews, and conference presentations. In total, 129 studies were identified, and eligible articles were selected based on their titles, abstracts, and full texts. Three articles were finally included in the review, and the types of noncontact sensors used in each selected study were an impulse radio ultrawideband radar, a microbend fiber-optic sensor, and a mat-type air pressure sensor. Participants included neonates in the neonatal intensive care unit, patients with sleep apnea, and patients with coronavirus disease. Their heart rate, respiratory rate, blood pressure, body temperature, and arterial oxygen saturation were measured. Studies have demonstrated that the performance of noncontact sensors is comparable to that of traditional methods of vital signs measurement. Noncontact sensors have the potential to alleviate concerns related to skin disorders associated with traditional skin-contact vital signs measurement methods, reduce the workload for healthcare providers, and enhance patient comfort. This article reviews the medical use of noncontact sensors for measuring vital signs and aimed to determine their potential clinical applicability.

[Development of flexible multi-phase barium titanate piezoelectric sensor for physiological health and action behavior monitoring]

Self-powered wearable piezoelectric sensing devices demand flexibility and high voltage electrical properties to meet personalized health and safety management needs. Aiming at the characteristics of piezoceramics with high piezoelectricity and low flexibility, this study designs a high-performance piezoelectric sensor based on multi-phase barium titanate (BTO) flexible piezoceramic film, namely multi-phase BTO sensor. The substrate-less self-supported multi-phase BTO films had excellent flexibility and could be bent 180° at a thickness of 33 μm, and exhibited good bending fatigue resistance in 1 × 10 4 bending cycles at a thickness of 5 μm. The prepared multi-phase BTO sensor could maintain good piezoelectric stability after 1.2 × 10 4 piezoelectric cycle tests. Based on the flexibility, high piezoelectricity, wearability, portability and battery-free self-powered characteristics of this sensor, the developed smart mask could monitor the respiratory signals of different frequencies and amplitudes in real time. In addition, by mounting the sensor on the hand or shoulder, different gestures and arm movements could also be detected. In summary, the multi-phase BTO sensor developed in this paper is expected to develop convenient and efficient wearable sensing devices for physiological health and behavioral activity monitoring applications.

Enhancing the sensitivity and stability of electrochemical aptamer-based sensors by AuNPs@MXene nanocomposite for continuous monitoring of biomarkers

Electrochemical aptamer-based (E-AB) sensors offer exciting potential for real-time tracking of various biomarkers, such as proteins and small molecules, due to their exceptional selectivity and adaptability. However, most E-AB sensors rely on planar gold structures, which inherently limit their sensitivity and operational stability for continuous monitoring of biomarkers. Although gold nanostructures have recently enhanced E-AB sensor performance, no studies have explored the combination of gold nanostructure with other types of nanomaterials for continuous molecular monitoring. To fill this gap, we employed gold nanoparticles and MXene Ti3C2 (AuNPs@MXene), a versatile nanocomposite, in designing an E-AB sensor targeted at vascular endothelial growth factor (VEGF), a crucial human signaling protein. Remarkably, the AuNPs@MXene nanocomposite achieved over thirty-fold and half-fold increases in active surface area compared to bare and AuNPs-modified gold electrodes, respectively, significantly elevating the analytical capabilities of E-AB sensors during continuous operation. After a systematic optimization and characterization process, the newly developed E-AB sensor, powered by AuNPs@MXene nanocomposite, demonstrated both enhanced stability and heightened sensitivity. Overall, our findings open new avenues for the incorporation of nanocomposites in E-AB sensor design, enabling the creation of more sensitive and durable real-time monitoring systems.

Accurate wavelet thresholding method for ECG signals

Current wavelet thresholding methods for cardiogram signals captured by flexible wearable sensors face a challenge in achieving both accurate thresholding and real-time signal denoising. This paper proposes a real-time accurate thresholding method based on signal estimation, specifically the normalized ACF, as an alternative to traditional noise estimation without the need for parameter fine-tuning and extensive data training. This method is experimentally validated using a variety of electrocardiogram (ECG) signals from different databases, each containing specific types of noise such as additive white Gaussian (AWG) noise, baseline wander noise, electrode motion noise, and muscle artifact noise. Although this method only slightly outperforms other methods in removing AWG noise in ECG signals, it far outperforms conventional methods in removing other real noise. This is attributed to the method's ability to accurately distinguish not only AWG noise that is significantly different spectrum of the ECG signal, but also real noise with similar spectra. In contrast, the conventional methods are effective only for AWG noise. In additional, this method improves the denoising visualization of the measured ECG signals and can be used to optimize other parameters of other wavelet methods to enhancing the denoised periodic signals, thereby improving diagnostic accuracy.

Recent Advances of Nanogenerator Technology for Cardiovascular Sensing and Monitoring

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

Continuous Patient-Independent Estimation of Respiratory Rate and Blood Pressure Using Robust Spectro-Temporal Features Derived From Photoplethysmogram Only

Objective: A patient-independent approach for continuous estimation of vital signs using robust spectro-temporal features derived from only photoplethysmogram (PPG) signal. Methods: In the pre-processing stage, we remove baseline shifts and artifacts of the PPG signal using Incremental Merge Segmentation with adaptive thresholding. From the cleaned PPG, we extract multiple parameters independent of individual patient PPG morphology for both Respiration Rate (RR) and Blood Pressure (BP). In addition, we derived a set of novel spectral and statistical features strongly correlated to BP. We proposed robust correlation-based feature selection methods for accurate RR estimates. For fewer computations and accurate measurements of BP, the most significant features are selected using correlation and mutual information measures in the feature engineering part. Finally, RR and BP are estimated using breath counting and a neural network regression model, respectively. Results: The proposed approach outperforms the current state-of-the-art in both RR and BP. The RR algorithm results in mean absolute errors (median, 25th-75th percentiles) of 0.4 (0.1-0.7) for CapnoBase dataset and 0.5(0.3-2.8) for BIDMC dataset without discarding any data window. Similarly, BP approach has been validated on a large dataset derived from MIMIC-II ([Formula: see text]1700 records) which has errors (mean absolute, standard deviation) of 5.0(6.3) and 3.0(4.0) for systolic and diastolic BP, respectively. The results meet the American Association for the Advancement of Medical Instrumentation (AAMI) and British Hypertension Society (BHS) Class A criteria. Conclusion: By using robust features and feature selection methods, we alleviated patient dependency to have reliable estimates of vitals.

The use of commercial wrist-worn technology to track physiological outcomes in behavioral interventions

PURPOSE OF REVIEW

The aim of this review is to provide an overview of the use of commercial wrist-worn mobile health devices to track and monitor physiological outcomes in behavioral interventions as well as discuss considerations for selecting the optimal device.

RECENT FINDINGS

Wearable technology can enhance intervention design and implementation. The use of wrist-worn wearables provides the opportunity for tracking physiological outcomes, thus providing a unique approach for assessment and delivery of remote interventions. Recent findings support the utility, acceptability, and benefits of commercial wrist-worn wearables in interventions, and they can be used to continuously monitor outcomes, remotely administer assessments, track adherence, and personalize interventions. Wrist-worn devices show acceptable accuracy when measuring heart rate, blood pressure, step counts, and physical activity; however, accuracy is dependent on activity type, intensity, and device brand. These factors should be considered when designing behavioral interventions that utilize wearable technology.

SUMMARY

With the continuous advancement in technology and frequent product upgrades, the capabilities of commercial wrist-worn devices will continue to expand, thus increasing their potential use in intervention research. Continued research is needed to examine and validate the most recent devices on the market to better inform intervention design and implementation.

Identification of heart rate change during the teaching process

Internet of Things (IoT) technology can be used in many areas of everyday life. The objective of this paper is to obtain physiological functions in a non-invasive manner using commonly available IoT devices. The aim of the research is to point out the possibility of using physiological functions as an identifier of changes in students' level of arousal during the teaching process. The motivation of the work is to find a correlation between the change in heart rate, the student's level of arousal and the student's partial and final learning results. The research was focused on the collection of physiological data, namely heart rate and the evaluation of these data in the context of identification of arousal during individual teaching activities of the teaching process. The experiment was carried out during the COVID-19 pandemic via distance learning. During the teaching process, individual activities were recorded in time and HR was assigned to them. The benefit of the research is the proposed methodology of the system, which can identify changes in students' arousal in order to increase the efficiency of the teaching process. Based on the results of the designed system, they could also alert teachers who should be able to modify their teaching style in specific situations so that it is suitable for students and provides a basis for better teaching and understanding of educational materials. The presented methodology will be able to guarantee an increase in the success of the teaching process itself in terms of students' understanding of the teaching materials.

Personalized Technological Support for Informal Caregivers of Older People with Dementia: A Co-Design Approach Involving Potential End Users and Healthcare Professionals in Three Focus Groups in Italy

Informal/family caregivers (ICs) of older people with dementia (PwD) can suffer from depression and burnout. However, digital solutions can potentially provide innovative ways to facilitate care provision. The aim of this study was to analyze the opinions of end users (EUs), i.e., PwD aged 65 years and over and their ICs and healthcare professionals (HPs), on the use of digital technology to support care activities. Qualitative data were collected during the co-design phase of the European project "DemiCare-Personalized support for informal caregivers of people with dementia". This study focused on the Italian context and included two PwD, three ICs, and seven HPs. Three focus groups were held in April-June 2022. Qualitative data were analyzed using MaxQDA 2020 software. Seven thematic areas were identified: daily activities, care tasks, information needs, support received, relationship with and expectations from technology, functionality of the DemiCare integrated system, and ethical issues. Smart devices seemed to be received positively by ICs and HPs, although older PwD seemed to have difficulty accepting the technology. Overall, despite the low number of participants, it seems important and potentially effective to consider the needs and preferences of PwD during exploratory co-design to allow social interactions with them. This study was not registered.

Fabrication of three-dimensional orthodontic force detecting brackets and preliminary clinical test for tooth movement simulation

This study aimed to develop an ultraminiature pressure sensor array to measure the force exerted on teeth. Orthodontic force plays an important role in effective, rapid, and safe tooth movement. However, owing to the lack of an adequate tool to measure the orthodontic force in vivo, it remains challenging to determine the best orthodontic loading in clinical and basic research. In this study, a three-dimensional (3D) orthodontic force detection system based on piezoresistive absolute pressure sensors was designed. The 3D force sensing array was constructed using five pressure sensors on a single chip. The size of the sensor array was only 4.1 × 2.6 mm, which can be placed within the bracket base area. Based on the barometric calibration, conversion formulas for the output voltage and pressure of the five channels were constructed. Subsequently, a 3D linear mechanical simulation model of the voltage and stress distribution was established using 312 tests of the applied force in 13 operating modes. Finally, the output voltage was first converted to pressure and then to the resultant force. The 3D force-detection chip was then tested to verify the accuracy of force measurement on the teeth. Based on the test results, the average output force error was only 0.0025 N (0.7169%) (p = 0.958), and the average spatial positioning error was only 0.058 mm (p = 0.872) on the X-axis and 0.050 mm (p = 0.837) on the Y-axis. The simulation results were highly consistent with the actual force applied (intraclass correlation efficient (ICC): 0.997-1.000; p < 0.001). Furthermore, through in vivo measurements and a finite element analysis, the movement trends generated when the measured orthodontic forces that acted on the teeth were simulated. The results revealed that the device can accurately measure the orthodontic force, representing the first clinical test of an orthodontic-force monitoring system. Our study provides a hardware basis for clinical research on efficient, safe, and optimal orthodontic forces, and has considerable potential for application in monitoring the biomechanics of tooth movement.

Remote Patient Monitoring and Teleconsultation to Improve Health Outcomes and Reduce Health Care Utilization of Pediatric Asthma (ALPACA Study): Protocol for a Randomized Controlled Effectiveness Trial

BACKGROUND

Childhood asthma is imposing a great financial burden on the pediatric health care system. Asthma costs are directly related to the level of asthma control. A substantial part of these costs may be preventable by the timely and adequate assessment of asthma deterioration in daily life and proper asthma management. The use of eHealth technology may assist such timely and targeted medical anticipation.

OBJECTIVE

This paper describes the Ambulatory Pediatric Asthma Care (ALPACA) study protocol to investigate the effectiveness of an eHealth intervention consisting of remote patient monitoring and teleconsultation integrated into the daily clinical care of pediatric patients with asthma. This intervention aims to reduce health care utilization and costs and improve health outcomes compared to a control group that receives standard care. In addition, this study aims to improve future eHealth pediatric asthma care by gaining insights from home-monitoring data.

METHODS

This study is a prospective randomized controlled effectiveness trial. A total of 40 participants will be randomized to either 3 months of eHealth care (intervention group) or standard care (control group). The eHealth intervention consists of remote patient monitoring (spirometry, pulse oximetry, electronic medication adherence tracking, and asthma control questionnaire) and web-based teleconsultation (video sharing, messages). All participants will have a 3-month follow-up with standard care to evaluate whether the possible effects of eHealth care are longer lasting. During the entire study and follow-up period, all participants will use blinded observational home monitoring (sleep, cough/wheeze sounds, air quality in bedroom) as well.

RESULTS

This study was approved by the Medical Research Ethics Committees United. Enrollment began in February 2023, and the results of this study are expected to be submitted for publication in July 2024.

CONCLUSIONS

This study will contribute to the existing knowledge on the effectiveness of eHealth interventions that combine remote patient monitoring and teleconsultation for health care utilization, costs, and health outcomes. Furthermore, the observational home-monitoring data can contribute to improved identification of early signs of asthma deterioration in pediatric patients. Researchers and technology developers could use this study to guide and improve eHealth development, while health care professionals, health care institutions, and policy makers may employ our results to make informed decisions to steer toward high-quality, efficient pediatric asthma care.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05517096; https://clinicaltrials.gov/ct2/show/NCT05517096.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/45585.

A Multi-Parameter Fusion Method for Cuffless Continuous Blood Pressure Estimation Based on Electrocardiogram and Photoplethysmogram

Blood pressure (BP) is an essential physiological indicator to identify and determine health status. Compared with the isolated BP measurement conducted by traditional cuff approaches, cuffless BP monitoring can reflect the dynamic changes in BP values and is more helpful to evaluate the effectiveness of BP control. In this paper, we designed a wearable device for continuous physiological signal acquisition. Based on the collected electrocardiogram (ECG) and photoplethysmogram (PPG), we proposed a multi-parameter fusion method for noninvasive BP estimation. An amount of 25 features were extracted from processed waveforms and Gaussian copula mutual information (MI) was introduced to reduce feature redundancy. After feature selection, random forest (RF) was trained to realize systolic BP (SBP) and diastolic BP (DBP) estimation. Moreover, we used the records in public MIMIC-III as the training set and private data as the testing set to avoid data leakage. The mean absolute error (MAE) and standard deviation (STD) for SBP and DBP were reduced from 9.12 ± 9.83 mmHg and 8.31 ± 9.23 mmHg to 7.93 ± 9.12 mmHg and 7.63 ± 8.61 mmHg by feature selection. After calibration, the MAE was further reduced to 5.21 mmHg and 4.15 mmHg. The result showed that MI has great potential in feature selection during BP prediction and the proposed multi-parameter fusion method can be used for long-term BP monitoring.

Biocompatible and Long-Term Monitoring Strategies of Wearable, Ingestible and Implantable Biosensors: Reform the Next Generation Healthcare

Sensors enable the detection of physiological indicators and pathological markers to assist in the diagnosis, treatment, and long-term monitoring of diseases, in addition to playing an essential role in the observation and evaluation of physiological activities. The development of modern medical activities cannot be separated from the precise detection, reliable acquisition, and intelligent analysis of human body information. Therefore, sensors have become the core of new-generation health technologies along with the Internet of Things (IoTs) and artificial intelligence (AI). Previous research on the sensing of human information has conferred many superior properties on sensors, of which biocompatibility is one of the most important. Recently, biocompatible biosensors have developed rapidly to provide the possibility for the long-term and in-situ monitoring of physiological information. In this review, we summarize the ideal features and engineering realization strategies of three different types of biocompatible biosensors, including wearable, ingestible, and implantable sensors from the level of sensor designing and application. Additionally, the detection targets of the biosensors are further divided into vital life parameters (e.g., body temperature, heart rate, blood pressure, and respiratory rate), biochemical indicators, as well as physical and physiological parameters based on the clinical needs. In this review, starting from the emerging concept of next-generation diagnostics and healthcare technologies, we discuss how biocompatible sensors revolutionize the state-of-art healthcare system unprecedentedly, as well as the challenges and opportunities faced in the future development of biocompatible health sensors.

Behavioral Interpretation of Willingness to Use Wearable Health Devices in Community Residents: A Cross-Sectional Study

Wearable health devices (WHDs) have become increasingly advantageous in long-term health monitoring and patient management. However, most people have not yet benefited from such innovative technologies, and the willingness to accept WHDs and their influencing factors are still unclear. Based on two behavioral theories: the theory of planned behavior (TPB) and the diffusion of innovation (DOI), this study aims to explore the influencing factors of willingness to use WHDs in community residents from the perspective of both internal and external factors. A convenience sample of 407 community residents were recruited from three randomly selected Community Health Service Centers (CHSCs) in Nanjing, China, and were investigated with a self-developed questionnaires. The mean score of willingness to use WHDs was 17.00 (range 5-25). In the dimensions of TPB, perceived behavioral control (β = 1.979, p < 0.001) was the strongest influencing factor. Subjective norms (β = 1.457, p < 0.001) and attitudes (β = 0.651, p = 0.016) were also positively associated with willingness. In innovation characteristics of DOI, compatibility (β = 0.889, p < 0.001) and observability (β = 0.576, p = 0.003) had positive association with the willingness to wear a WHD. This study supports the applicability of the two behavioral theories to interpret the willingness to use WHDs in Chinese community residents. Compared with the innovative features of WHDs, individual cognitive factors were more critical predictors of willingness to use.

A protocol for a systematic review of electronic early warning/track-and-trigger systems (EW/TTS) to predict clinical deterioration: Focus on automated features, technologies, and algorithms

BACKGROUND

This is a systematic review protocol to identify automated features, applied technologies, and algorithms in the electronic early warning/track and triage system (EW/TTS) developed to predict clinical deterioration (CD).

METHODOLOGY

This study will be conducted using PubMed, Scopus, and Web of Science databases to evaluate the features of EW/TTS in terms of their automated features, technologies, and algorithms. To this end, we will include any English articles reporting an EW/TTS without time limitation. Retrieved records will be independently screened by two authors and relevant data will be extracted from studies and abstracted for further analysis. The included articles will be evaluated independently using the JBI critical appraisal checklist by two researchers.

DISCUSSION

This study is an effort to address the available automated features in the electronic version of the EW/TTS to shed light on the applied technologies, automated level of systems, and utilized algorithms in order to smooth the road toward the fully automated EW/TTS as one of the potential solutions of prevention CD and its adverse consequences.

TRIAL REGISTRATION

Systematic review registration: PROSPERO CRD42022334988.

A non-interventive monitoring system prototype for chronic patients with hypertension and asthma

BACKGROUND

Monitoring the vital signs of chronic patients with hypertension, asthma, and chronic obstructive pulmonary disease (COPD) aids in disease prevention.

OBJECTIVE

This study enhances the patient quality of life while adding to the corpus of information about electronic medical devices.

METHOD

The requirements for both the functional and non-functional system architecture were determined and designs were made. Diagrams were used to describe the system's parts, behaviour, and connections before implementation.

RESULTS

Although the project's development produced a remote monitoring system prototype with outcomes comparable to those of patented and regarded as reliable devices, CCFHAC is not yet prepared to be considered a fully finished good that can be used to define a person's health status with absolute certainty.

CONCLUSION

This endeavour marks a step in investigating how the Internet of Things might improve the quality of life for Jordanian patients.

Wearables for Measuring Health Effects of Climate Change-Induced Weather Extremes: Scoping Review

BACKGROUND

Although climate change is one of the biggest global health threats, individual-level and short-term data on direct exposure and health impacts are still scarce. Wearable electronic devices (wearables) present a potential solution to this research gap. Wearables have become widely accepted in various areas of health research for ecological momentary assessment, and some studies have used wearables in the field of climate change and health. However, these studies vary in study design, demographics, and outcome variables, and existing research has not been mapped.

OBJECTIVE

In this review, we aimed to map existing research on wearables used to detect direct health impacts and individual exposure during climate change-induced weather extremes, such as heat waves or wildfires.

METHODS

We conducted a scoping review according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) framework and systematically searched 6 databases (PubMed [MEDLINE], IEEE Xplore, CINAHL [EBSCOhost], WoS, Scopus, Ovid [MEDLINE], and Google Scholar). The search yielded 1871 results. Abstracts and full texts were screened by 2 reviewers (MK and IM) independently using the inclusion and exclusion criteria. The inclusion criteria comprised studies published since 2010 that used off-the-shelf wearables that were neither invasive nor obtrusive to the user in the setting of climate change-related weather extremes. Data were charted using a structured form, and the study outcomes were narratively synthesized.

RESULTS

The review included 55,284 study participants using wearables in 53 studies. Most studies were conducted in upper-middle-income and high-income countries (50/53, 94%) in urban environments (25/53, 47%) or in a climatic chamber (19/53, 36%) and assessed the health effects of heat exposure (52/53, 98%). The majority reported adverse health effects of heat exposure on sleep, physical activity, and heart rate. The remaining studies assessed occupational heat stress or compared individual- and area-level heat exposure. In total, 26% (14/53) of studies determined that all examined wearables were valid and reliable for measuring health parameters during heat exposure when compared with standard methods.

CONCLUSIONS

Wearables have been used successfully in large-scale research to measure the health implications of climate change-related weather extremes. More research is needed in low-income countries and vulnerable populations with pre-existing conditions. In addition, further research could focus on the health impacts of other climate change-related conditions and the effectiveness of adaptation measures at the individual level to such weather extremes.

Evaluating and Visualizing the Contribution of ECG Characteristic Waveforms for PPG-Based Blood Pressure Estimation

Non-invasive continuous blood pressure monitoring is of great significance for the preventing, diagnosing, and treating of cardiovascular diseases (CVDs). Studies have demonstrated that photoplethysmogram (PPG) and electrocardiogram (ECG) signals can effectively and continuously predict blood pressure (BP). However, most of the BP estimation models focus on the waveform features of the PPG signal, while the peak value of R-wave in ECG is only used as a time reference, and few references investigated the ECG waveforms. This paper aims to evaluate the influence of three characteristic waveforms in ECG on the improvement of BP estimation. PPG is the primary signal, and five input combinations are formed by adding ECG, P wave, QRS complex, T wave, and none. We employ five common convolutional neural networks (CNN) to validate the consistency of the contribution. Meanwhile, with the visualization of Gradient-weighted class activation mapping (Grad-CAM), we generate the heat maps and further visualize the distribution of CNN's attention to each waveform of PPG and ECG. The heat maps show that networks pay more attention to the QRS complex and T wave. In the comparison results, the QRS complex and T wave have more contribution to minimizing errors than P wave. By separately adding P wave, QRS complex, and T wave, the average MAE of these networks reaches 7.87 mmHg, 6.57 mmHg, and 6.21 mmHg for systolic blood pressure (SBP), and 4.27 mmHg, 3.65 mmHg, and 3.73 mmHg, respectively, for diastolic blood pressure (DBP). The results of the experiment show that QRS complex and T wave deserves more attention and feature extraction like PPG waveform features in the continuous BP estimation.

Accurate detection of heart rate using in-ear photoplethysmography in a clinical setting

Background

Recent research has shown that photoplethysmography (PPG) based wearable sensors offer a promising potential for chronic disease monitoring. The aim of the present study was to assess the performance of an in-ear wearable PPG sensor in acquiring valid and reliable heart rate measurements in a clinical setting, with epileptic patients.

Methods

Patients undergoing video-electroencephalography (EEG) monitoring with concomitant one-lead electrocardiographic (ECG) recordings were equipped with an in-ear sensor developed by cosinuss°.

Results

In total, 2,048 h of recording from 97 patients with simultaneous ECG and in-ear heart rate data were included in the analysis. The comparison of the quality-filtered in-ear heart rate data with the reference ECG resulted in a bias of 0.78 bpm with a standard deviation of ±2.54 bpm; Pearson's Correlation Coefficient PCC = 0.83; Intraclass Correlation Coefficient ICC = 0.81 and mean absolute percentage error MAPE = 2.57.

Conclusion

These data confirm that the in-ear wearable PPG sensor provides accurate heart rate measurements in comparison with ECG under realistic clinical conditions, especially with a signal quality indicator. Further research is required to investigate whether this technology is helpful in identifying seizure-related cardiovascular changes.

Wearables for Engagement Detection in Learning Environments: A Review

Appropriate teaching-learning strategies lead to student engagement during learning activities. Scientific progress and modern technology have made it possible to measure engagement in educational settings by reading and analyzing student physiological signals through sensors attached to wearables. This work is a review of current student engagement detection initiatives in the educational domain. The review highlights existing commercial and non-commercial wearables for student engagement monitoring and identifies key physiological signals involved in engagement detection. Our findings reveal that common physiological signals used to measure student engagement include heart rate, skin temperature, respiratory rate, oxygen saturation, blood pressure, and electrocardiogram (ECG) data. Similarly, stress and surprise are key features of student engagement.

A novel early diagnostic framework for chronic diseases with class imbalance

Chronic diseases are one of the most severe health issues in the world, due to their terrible clinical presentations such as long onset cycle, insidious symptoms, and various complications. Recently, machine learning has become a promising technique to assist the early diagnosis of chronic diseases. However, existing works ignore the problems of feature hiding and imbalanced class distribution in chronic disease datasets. In this paper, we present a universal and efficient diagnostic framework to alleviate the above two problems for diagnosing chronic diseases timely and accurately. Specifically, we first propose a network-limited polynomial neural network (NLPNN) algorithm to efficiently capture high-level features hidden in chronic disease datasets, which is data augmentation in terms of its feature space and can also avoid over-fitting. Then, to alleviate the class imbalance problem, we further propose an attention-empowered NLPNN algorithm to improve the diagnostic accuracy for sick cases, which is also data augmentation in terms of its sample space. We evaluate the proposed framework on nine public and two real chronic disease datasets (partly with class imbalance). Extensive experiment results demonstrate that the proposed diagnostic algorithms outperform state-of-the-art machine learning algorithms, and can achieve superior performances in terms of accuracy, recall, F1, and G_mean. The proposed framework can help to diagnose chronic diseases timely and accurately at an early stage.

Wearable Devices for Physical Monitoring of Heart: A Review

Cardiovascular diseases (CVDs) are the leading cause of death globally. An effective strategy to mitigate the burden of CVDs has been to monitor patients' biomedical variables during daily activities with wearable technology. Nowadays, technological advance has contributed to wearables technology by reducing the size of the devices, improving the accuracy of sensing biomedical variables to be devices with relatively low energy consumption that can manage security and privacy of the patient's medical information, have adaptability to any data storage system, and have reasonable costs with regard to the traditional scheme where the patient must go to a hospital for an electrocardiogram, thus contributing a serious option in diagnosis and treatment of CVDs. In this work, we review commercial and noncommercial wearable devices used to monitor CVD biomedical variables. Our main findings revealed that commercial wearables usually include smart wristbands, patches, and smartwatches, and they generally monitor variables such as heart rate, blood oxygen saturation, and electrocardiogram data. Noncommercial wearables focus on monitoring electrocardiogram and photoplethysmography data, and they mostly include accelerometers and smartwatches for detecting atrial fibrillation and heart failure. However, using wearable devices without healthy personal habits will cause disappointing results in the patient's health.

Remote Healthcare for Elderly People Using Wearables: A Review

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

Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare

Chronic diseases are a growing concern worldwide, with nearly 25% of adults suffering from one or more chronic health conditions, thus placing a heavy burden on individuals, families, and healthcare systems. With the advent of the "Smart Healthcare" era, a series of cutting-edge technologies has brought new experiences to the management of chronic diseases. Among them, smart wearable technology not only helps people pursue a healthier lifestyle but also provides a continuous flow of healthcare data for disease diagnosis and treatment by actively recording physiological parameters and tracking the metabolic state. However, how to organize and analyze the data to achieve the ultimate goal of improving chronic disease management, in terms of quality of life, patient outcomes, and privacy protection, is an urgent issue that needs to be addressed. Artificial intelligence (AI) can provide intelligent suggestions by analyzing a patient's physiological data from wearable devices for the diagnosis and treatment of diseases. In addition, blockchain can improve healthcare services by authorizing decentralized data sharing, protecting the privacy of users, providing data empowerment, and ensuring the reliability of data management. Integrating AI, blockchain, and wearable technology could optimize the existing chronic disease management models, with a shift from a hospital-centered model to a patient-centered one. In this paper, we conceptually demonstrate a patient-centric technical framework based on AI, blockchain, and wearable technology and further explore the application of these integrated technologies in chronic disease management. Finally, the shortcomings of this new paradigm and future research directions are also discussed.