Wearable Devices Research Articles

Effectiveness of the Support From Community Health Workers and Health Care Professionals on the Sustained Use of Wearable Monitoring Devices Among Community-Dwelling Older Adults: Feasibility Randomized Controlled Trial

Background The wearable monitoring device (WMD) is emerging as a promising tool for community-dwelling older adults to monitor personal health, enhance awareness of their activities, and promote healthy behaviors. However, the sustained use of WMDs among this population remains a significant challenge. Objective This study aims to implement an interventional program that promotes and motivates the continued use of WMDs among older adults through a peer and professional support approach. This program will facilitate the integration of WMDs into their daily lives. Methods This feasibility trial examined the following: (1) the usability of the WMD from the users’ perspectives; (2) the feasibility of the Live With Wearable Monitoring Device program; and (3) the effectiveness of the Live With Wearable Monitoring Device program among community-dwelling older adults. The intervention, based on Self-Determination Theory, involved using the Live With Wearable Monitoring Device program over a 3-month period, with ongoing professional and peer support provided by community health workers, aided by a nurse and social workers. This support included 1 home visit and biweekly communication via WhatsApp. Data were collected at baseline and at 1, 3, and 6 months. Results A total of 39 participants were enrolled in the intervention group, while 37 participants were in the control group. The recruitment rate was high (76/89, 85%), and the attrition rate was low (8/76, 11%), indicating that the program is feasible for older adults. Participants in the intervention group exhibited higher self-efficacy, lower anxiety levels, and used the smartwatch more frequently, in terms of both days and hours, compared with the control group. A between-group difference was observed in self-efficacy between the intervention and control groups (β=3.31, 95% CI 0.36-6.25, P=.03), with statistically significant higher mean values recorded at all 4 time points. Conclusions It is clear that merely providing a WMD to older adults does not guarantee its usage, particularly for those unfamiliar with how to utilize its health-related functions in their daily routines. This study implemented a theory-based program aimed at enhancing the ongoing use of WMDs among older adults, suggesting that continuous professional and peer support may significantly influence WMD usage. Trial Registration ClinicalTrials.gov NCT05269303; https://clinicaltrials.gov/ct2/show/NCT05269303

Rapid Fabrication of Graphene Fibers and Fiber-Based Thermistors for Wearable Devices

Rapid Fabrication of Graphene Fibers and Fiber-Based Thermistors for Wearable Devices

Integrating genome-wide information and wearable device data to explore the link of anxiety and antidepressants with pulse rate variability

Integrating genome-wide information and wearable device data to explore the link of anxiety and antidepressants with pulse rate variability

Commercial Wearables for the Management of People with Autism Spectrum Disorder: A Review

Autism Spectrum Disorder (ASD) necessitates comprehensive management, addressing complex challenges in social communication, behavioral regulation, and sensory processing, for which wearable technologies offer valuable tools to monitor and support interventions. Therefore, this review explores recent advancements in wearable technology, categorizing devices based on executive function, psychomotor skills, and the behavioral/emotional/sensory domain, highlighting their potential to improve ongoing management and intervention. To ensure rigor and comprehensiveness, the review employs a PRISMA-based methodology. Specifically, literature searches were conducted across diverse databases, focusing on studies published between 2014 and 2024, to identify the most commonly used wearables in ASD research. Notably, 55.45% of the 110 devices analyzed had an undefined FDA status, 23.6% received 510(k) clearance, and only a small percentage were classified as FDA Breakthrough Devices or in the submission process. Additionally, approximately 50% of the devices utilized sensors like ECG, EEG, PPG, and EMG, highlighting their widespread use in real-time physiological monitoring. Our work comprehensively analyzes a wide array of wearable technologies, including emerging and advanced. While these technologies have the potential to transform ASD management through real-time data collection and personalized interventions, improved clinical validation and user-centered design are essential for maximizing their effectiveness and user acceptance.

Toward a better understanding of barriers to wayfinding technology use for people with disabilities

ABSTRACT Wayfinding is the process of navigating from one’s present location to their desired location. While wayfinding technologies are increasingly used by people with disabilities, there is little understanding of the barriers specific to wayfinding technology. The objective of this study was to understand the wayfinding technology barriers experienced by Canadians with disabilities. A total of 213 participants with varying disabilities (i.e. mobility, visual, hearing, memory and learning disabilities) completed a survey of open-ended questions about their personal experiences with different types of technologies. Technologies were categorized into public (i.e. digital and tactile interfaces) and personalized (i.e. mobile/website applications, wearable devices, smart assistive devices), and qualitative content analysis was used. Main themes were identified and either common across both groups (i.e. compatibility, demands on personal resources, information provision, interactability) or specific to one technology type (i.e. stigma, specific to personalized technology). Detailed subthemes provided greater specificity on the types of barriers encountered. For example, infection risk was noted as a barrier to public technology and high costs was a barrier for personalized technology. Results support the inclusion of wayfinding technology within accessibility standards and provide insights to clinicians on how to best support people with disabilities and their use of technology.

Low-Temperature Fabrication of Stable Black-Phase CsPbI3 Perovskite Flexible Photodetectors Toward Wearable Health Monitoring.

Flexible wearable optoelectronic devices fabricated from organic-inorganic hybrid perovskites significantly accelerate the development of portable energy, biomedicine, and sensing fields, but their poor thermal stability hinders further applications. Conversely, all-inorganic perovskites possess excellent thermal stability, but black-phase all-inorganic perovskite film usually requires high-temperature annealing steps, which increases energy consumption and is not conducive to the fabrication of flexible wearable devices. In this work, an unprecedented low-temperature fabrication of stable black-phase CsPbI3 perovskite films is demonstrated by the in situ hydrolysis reaction of diphenylphosphinic chloride additive. The released diphenyl phosphate and chloride ions during the hydrolysis reaction significantly lower the phase transition temperature and effectively passivate the defects in the perovskite films, yielding high-performance photodetectors with a responsivity of 42.1A W-1 and a detectivity of 1.3 × 1014Jones. Furthermore, high-fidelity image and photoplethysmography sensors are demonstrated based on the fabricated flexible wearable photodetectors. This work provides a new perspective for the low-temperature fabrication of large-area all-inorganic perovskite flexible optoelectronic devices.

Preoperative determinants of normative postoperative recovery rate following minimally invasive repair of pectus excavatum.

Recovery after minimally invasive repair of pectus excavatum (MIRPE) is prolonged. The purpose of this prospective study was to enhance our understanding of post-MIRPE recovery by following patients' recovery through postoperative day (POD) 60 using wearable devices and determine if recovery rate is impacted by PE severity and preoperative physical activity (PA) level. Children ≤ 18years who underwent MIRPE with cryoablation between 8/2023 and 1/2024 wore a Fitbit™ for ≥ 3days preoperatively to determine preoperative PA and through POD 60. The recovery trajectory, defined by postoperative daily step count divided by mean preoperative daily step count, was fit by power function through POD 60 among patients with uncomplicated recovery. Subgroup analyses were performed to compare recovery by PE severity and preoperative PA level. Sixteen patients met criteria (68.8% male, mean [SD] age 15.4 [1.6] years). Recovery trajectory analysis demonstrated recovery on POD 60 was 84.8% (95CI 79.0-90.6%). On subgroup analysis, patients with Correction Index > 40% and preoperative mean steps/day ≥ 10,000 had faster recovery. Patients undergoing MIRPE with cryotherapy who are more active preoperatively or have higher Correction Indices were found to have accelerated recovery trajectories. These results may provide insight for preoperative counselling and interventions to optimize post-MIRPE recovery.

An Artificial Intelligence-Assisted Flexible and Wearable Mechanoluminescent Strain Sensor System.

The complex wiring, bulky data collection devices, and difficulty in fast and on-site data interpretation significantly limit the practical application of flexible strain sensors as wearable devices. To tackle these challenges, this work develops an artificial intelligence-assisted, wireless, flexible, and wearable mechanoluminescent strain sensor system (AIFWMLS) by integration of deep learning neural network-based color data processing system (CDPS) with a sandwich-structured flexible mechanoluminescent sensor (SFLC) film. The SFLC film shows remarkable and robust mechanoluminescent performance with a simple structure for easy fabrication. The CDPS system can rapidly and accurately extract and interpret the color of the SFLC film to strain values with auto-correction of errors caused by the varying color temperature, which significantly improves the accuracy of the predicted strain. A smart glove mechanoluminescent sensor system demonstrates the great potential of the AIFWMLS system in human gesture recognition. Moreover, the versatile SFLC film can also serve as a encryption device. The integration of deep learning neural network-based artificial intelligence and SFLC film provides a promising strategy to break the "color to strain value" bottleneck that hinders the practical application of flexible colorimetricstrain sensors, which could promote the development of wearable and flexible strain sensors from laboratory research to consumer markets.

Development of a Real-Time Wearable Humming Detector Device

This study focuses on the development of a wearable real-time Humming Detector Device (HDD) aimed at enhancing the control of assistive devices through humming. As the need for portable user-friendly tools in assistive technology grows, the HDD offers a non-invasive solution to detect vocal cord vibrations. Vibrations, detected thanks to an accelerometer worn on the neck, are processed in real time using a Fast Fourier Transform (FFT) to identify specific humming frequencies, which are then translated into commands for controlling assistive devices via Bluetooth Low Energy (BLE) transmission. The device was tested with 13 healthy subjects to validate its potential and determine the optimal number of distinct commands that users can achieve. The HDD’s portability and precision make it a promising alternative to traditional voice recognition systems, particularly for individuals with speech impairments.

Hybrid Machine Learning Approach for Early Stroke Prediction in Elderly People

Stroke represents a significant global health challenge, often leading to severe disability and mortality. The timely prediction and intervention of stroke are paramount in enhancing patient outcomes and reducing healthcare burdens. This project proposes a machine learning-based approach to predict stroke risk using multi-modal biosignals, specifically electrocardiogram (ECG) data. By leveraging advanced algorithms, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, the system aims to classify patient health data into critical risk categories such as Normal, Abnormal, Ischemic, and Hemorrhagic. The study utilizes a comprehensive dataset consisting of ECG signals and incorporates techniques for data preprocessing, class balancing, and feature extraction. The predictive model is trained and validated using robust evaluation metrics, including accuracy, precision, recall, and F1-score. The findings underscore the efficacy of the proposed system in providing real-time stroke risk assessments, offering a cost-effective alternative to traditional diagnostic methods. Furthermore, this research explores the integration of wearable technology with machine learning, highlighting its potential for continuous patient monitoring and early detection of stroke symptoms. By creating a user-friendly interface for healthcare professionals and patients, the system aims to facilitate prompt decision-making and intervention, ultimately improving the overall quality of care for individuals at risk of stroke

The role and impact of new technologies on healthcare systems

AbstractThe integration of new technologies into healthcare systems is revolutionizing the industry by significantly enhancing the capabilities of healthcare providers, improving patient outcomes, and optimizing administrative operations. This comprehensive examination critically assesses the impact of these technologies on various stakeholders within the healthcare ecosystem, highlighting both the benefits and challenges. Emerging technologies such as artificial intelligence (AI), which refers to the simulation of human intelligence by machines, the Internet of Things (IoT), which involves interconnected devices that collect and exchange health-related data in real time, Blockchain, which is a decentralized and tamper-proof ledger technology used to securely manage patient data, and wearable devices are explored for their potential applications in diagnostics, treatment, and patient monitoring. Despite their transformative potential, these technologies also bring forth a range of limitations and challenges, including ethical concerns regarding patient privacy, legal implications of data security, and technical hurdles related to interoperability and implementation. By addressing these issues, the healthcare industry can better navigate the complexities of technological advancement, ensuring that innovation contributes positively to patient care and operational efficiency.

A Bayesian Approach to Modeling Variance of Intensive Longitudinal Biomarker Data as a Predictor of Health Outcomes.

Intensive longitudinal biomarker data are increasingly common in scientific studies that seek temporally granular understanding of the role of behavioral and physiological factors in relation to outcomes of interest. Intensive longitudinal biomarker data, such as those obtained from wearable devices, are often obtained at a high frequency typically resulting in several hundred to thousand observations per individual measured over minutes, hours, or days. Often in longitudinal studies, the primary focus is on relating the means of biomarker trajectories to an outcome, and the variances are treated as nuisance parameters, although they may also be informative for the outcomes. In this paper, we propose a Bayesian hierarchical model to jointly model a cross-sectional outcome and the intensive longitudinal biomarkers. To model the variability of biomarkers and deal with the high intensity of data, we develop subject-level cubic B-splines and allow the sharing of information across individuals for both the residual variability and the random effects variability. Then different levels of variability are extracted and incorporated into an outcome submodel for inferential and predictive purposes. We demonstrate the utility of the proposed model via an application involving bio-monitoring of hertz-level heart rate information from a study on social stress.

Wearable Technology and Chronic Illness: Balancing Justice and Care Ethics

Wearable Technology and Chronic Illness: Balancing Justice and Care Ethics

Discriminating Parkinson's disease patients from healthy controls using nasal respiratory airflow.

Breathing patterns may inform on health. We note that the sites of earliest brain damage in Parkinson's disease (PD) house the neural pace-makers of respiration. We therefore hypothesized that ongoing long-term temporal dynamics of respiration may be altered in PD. We applied a wearable device that precisely logs nasal airflow over time in 28 PD patients (mostly H&Y stage-II) and 33 matched healthy controls. Each participant wore the device for 24 h of otherwise routine daily living. We observe significantly altered temporal patterns of nasal airflow in PD, where inhalations are longer and less variable than in matched controls (mean PD = -1.22 ± 1.9 (combined respiratory features score), Control = 1.04 ± 2.16, Wilcoxon rank-sum test, z = -4.1, effect size Cliff's δ = -0.61, 95% confidence interval = -0.79 - (-0.34), P = 4.3 × 10-5). The extent of alteration is such that using only 30 min of recording we detect PD at 87% accuracy (AUC = 0.85, 79% sensitivity (22 of 28), 94% specificity (31 of 33), z = 5.7, p = 3.5 × 10-9), and also predict disease severity (correlation with UPDRS-Total score: r = 0.49; P = 0.008). We conclude that breathing patterns are altered by H&Y stage-II in the disease cascade, and our methods may be further refined in the future to provide an indication with diagnostic and prognostic value.

Telehealth to Address Preventable Maternal Deaths: A Call to Action.

Over 80% of maternal deaths are preventable. Telehealth approaches can help address disparities by increasing access to quality maternal health care. In this position statement, we advocate for the utility of telehealth to address maternal mortality disparities, focusing specifically on the postpartum period, where most maternal deaths occur. Specifically, we describe how telehealth visits, mobile health applications, and wearable devices for remote patient monitoring can be used to promote the uptake of postpartum care and adherence to evidence-based treatment for the most common causes of maternal death (i.e., cardiovascular conditions and mental health-related conditions). We discuss challenges that must be overcome to ensure the broad and equitable reach of telehealth and identify specific action steps to address this pressing public health issue.

Sex-specific changes in sleep quality with aging: Insights from wearable device analysis.

Prior studies evaluating sleep quality have found that women often have better polysomnography-defined sleep quality than men, but women subjectively report a greater frequency of sleep disturbances. Although these studies can be partially attributed to study design, it is currently unclear what may be causing these discrepancies. In this study, we aim to identify potential differences in objectively assessed sleep quality between men and women with further emphasis on subgroup analysis based on age. We hypothesize that women's sleep worsens after menopause. Sleep quality was assessed by comparing the Sleep Quality Index, Arousal Index, sleep efficiency and apnea-hypopnea index, as provided by the SleepImage Ring@ 2.3.0, between men and women, with a sub-group analysis performed by age (18-40 years, 41-50 years, 51-60 years, 61-70 years, and > 70 years), run separately for women and men. In total, 1444 subjects (704 women and 740 men) with a mean age of 53.6 ± 14.71 years were enrolled in this analysis. In women, a significant drop in Sleep Quality Index was noted after age 51 years. Regression analysis demonstrated that age, Arousal Index, sleep efficiency and apnea-hypopnea index correlated significantly with Sleep Quality Index - with age, Arousal Index and apnea-hypopnea index negatively correlated, and sleep efficiency positively correlated. The highest correlation coefficient was obtained for Arousal Index in both women and men. In women, age older than 50 years was associated with a more rapid decrease of sleep quality than men, as defined by an increase in Arousal Index and apnea-hypopnea index with a concurrent decrease in Sleep Quality Index.

Predicting wearable technology readiness in a South African government department: Exploring the influence of wearable technology acceptance and positive attitudes

Wearable technologies are anticipated to flourish in the fourth industrial revolution, enabling employers to integrate data into organizational systems. This study examined whether readiness for wearables can predict employees’ acceptance of these devices in the workplace. It explored the factors of technology readiness influencing acceptance and attitudes towards wearable use. A quantitative research approach was adopted, involving 8,081 employees from the South African Department of Employment and Labour (DEL). Census sampling was employed, with questionnaires distributed electronically, resulting in 351 responses. The Technology Readiness and Acceptance Model (TRAM) was the measuring instrument. Four hypotheses were formulated to investigate the relationship between readiness and acceptance of wearables. Results indicated that eagerness, optimism, and discomfort in the technology readiness scales significantly predicted technology acceptance. Specifically, optimism and eagerness were consistent positive predictors, while discomfort negatively impacted two of the three acceptance dimensions. Insecurity was not found to be a predictor of acceptance. The study recommends revising DEL's digital transformation policy to embrace wearables, emphasizing their potential for seamless integration. Additionally, it identifies a positive attitude as a new dimension of the TRAM, contributing to establishing a Wearable Readiness and Acceptance Model (WRAM), which has not been previously introduced in the literature.

Identifying and Assessing Physiological Biomarkers for Food and Energy Intake: A Systematic Review with Meta-Analysis Protocol

Background Traditional dietary assessments are often inaccurate and prone to self-reporting biases. Tracking the physiological responses associated with eating and digestion events via wearable technologies may provide an effective approach for continuously monitoring food intake and estimating energy consumption. Eating and digestion are accompanied by a series of changes in the heart rate, skin temperature, blood oxygen saturation, and blood pressure. These changes can be tracked by wearable devices, such as smartwatches, which have been widely accepted in the market. This systematic review is the first to evaluate the effectiveness of tracking such physiological biomarkers in differentiating between high- and low-calorie meals, potentially paving the way for more accurate dietary monitoring. Methods Following the PRISMA-P guidelines, we will conduct a systematic literature search through MEDLINE, EMBASE, and PubMed for clinical trials that investigated physiological responses following meal intake in healthy subjects. Two independent reviewers will screen and select articles based on pre-defined eligibility criteria, with a third review to resolve any discrepancies. This will be followed by data extraction and quality assessment of the included studies. Statistical analyses, including meta-analyses, will be performed using R Studio software. Our primary outcome will be the comparison of physiological biomarkers before and after meal intake, while secondary outcomes will include comparisons of physiological biomarkers between high- and low-calorie meal consumption and the correlation between the caloric content of consumed meals and postprandial physiological changes. Discussion This systematic review and meta-analysis will identify physiological indicators for eating events and inform the design of wearable sensors that estimate food intake in healthy subjects. Systematic Review Registration PROSPERO Registration ID: CRD42024544353

Novel Multicomponent Digital Care Assistant and Support Program for People After Stroke or Transient Ischaemic Attack: A Pilot Feasibility Study

Evidence is increasing for digital health programs targeting the secondary prevention of stroke. We aimed to determine the feasibility of the novel Care Assistant and support Program for people after Stroke (CAPS) or transient ischaemic attack (TIA) by combining person-centred goal setting and risk-factor monitoring through a web-based clinician portal, SMS messages, a mobile application (app), and a wearable device. We conducted a 12-week mixed-methods, open-label feasibility study. Participants (6 months–3 years after stroke or TIA, access to the internet via a smartphone/tablet) were recruited via the Australian Stroke Clinical Registry. Participants set one or two secondary prevention goals with a researcher and provided access and training in technology use. Feasibility outcomes included recruitment, retention, usability, acceptability, and satisfaction. Secondary outcomes included goal attainment, health outcomes, and program costs. Following 600 invitations, 58 responded, 34/36 (94%) eligible participants commenced the program (one withdrawal; 97% retention), and 10 were interviewed. Participants (27% female, 33% TIA) generally rated the usability of the mobile application as ‘Good’ to ‘Excellent’ (System Usability Scale). Most (94%) agreed the program helped with engagement in health self-monitoring. Overall, 52 goals were set, predominantly regarding exercise (21/52), which were the most frequently achieved (9/21). At 12 weeks, participants reported significant improvements (p < 0.05) in self-efficacy (Cohen’s d = 0.40), cardiovascular health (d = 0.71), and the mental health domain of the PROMIS GH (d = 0.63). CAPS was acceptable, with good retention and engagement of participants. Evaluation of this program in a randomised controlled trial is warranted.

RF sensing enabled tracking of human facial expressions using machine learning algorithms.

Automatic analysis of facial expressions has emerged as a prominent research area in the past decade. Facial expressions serve as crucial indicators for understanding human behavior, enabling the identification and assessment of positive and negative emotions. Moreover, facial expressions provide insights into various aspects of mental activities, social connections, and physiological information. Currently, most facial expression detection systems rely on cameras and wearable devices. However, these methods have drawbacks, including privacy concerns, issues with poor lighting and line of sight blockage, difficulties in training with longer video sequences, computational complexities, and disruptions to daily routines. To address these challenges, this study proposes a novel and privacy-preserving human behavior recognition system that utilizes Frequency Modulated Continuous Wave (FMCW) radar combined with Machine Learning (ML) techniques for classifying facial expressions. Specifically, the study focuses on five common facial expressions: Happy, Sad, Fear, Surprise, and Neutral. The recorded data is obtained in the form of a Micro-Doppler signal, and state-of-the-art ML models such as Super Learner, Linear Discriminant Analysis, Random Forest, K-Nearest Neighbor, Long Short-Term Memory, and Logistic Regression are employed to extract relevant features. These extracted features from the radar data are then fed into ML models for classification. The results show a highly promising classification accuracy of 91%. The future applications of the proposed work will lead to advancements in technology, healthcare, security, and communication, thereby improving overall human well-being and societal functioning.