Fully Integrated Biosensing System for Dynamic Monitoring of Sweat Glucose and Real-Time pH Adjustment Based on 3D Graphene MXene Aerogel.

The development of noninvasive glucose sensors capable of continuous monitoring without restricting user mobility is crucial, particularly for managing diabetes, which demands consistent and long-term observation. Traditional sensors often face challenges with accuracy and stability that curtail their practical applications. To address these issues, we have innovatively applied a three-dimensional porous aerogel composed of Ti3C2Tx MXene and reduced graphene oxide (MX-rGO) in electrochemical sensing. It significantly reduces the electron-transfer distance between the enzyme's redox center and the electrode surface while firmly anchoring the enzyme layer to effectively prevent any leakage. Another pivotal advancement in our study is the integration of the sensor with a real-time adaptive calibration mechanism tailored specifically for analyzing sweat glucose. This sensor not only measures glucose levels but also dynamically monitors and adjusts to pH fluctuations in sweat. Such capabilities ensure the precise delivery of physiological data during physical activities, providing strong support for personalized health management.

Effectiveness of wearable technology-based physical activity interventions for adults with type 2 diabetes mellitus: A systematic review and meta-regression.

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder with the increasing prevalence of a modern sedentary lifestyle. Wearable technology-based physical activity interventions (WT-BPAI) might provide a channel to improve diabetic self-management. The study aimed to (1) evaluate the effectiveness of WT-BPAI on PA levels, glycemic levels, and other outcomes (blood pressure [BP], body mass index [BMI], and serum lipid profile) in adults with T2DM, and (2) investigate the potential covariates affecting aforementioned outcomes. Eight databases were searched thoroughly using three steps from inception until January 16, 2024. The quality of the studies and overall evidence were evaluated. The package meta of the R software program version 4.3.1. was utilized for meta-analyses, subgroup analyses, and meta-regression analyses. A total of 19 randomized controlled trials (RCTs) were found. Meta-analyses revealed that WT-BPAI significantly increased 1583 steps per day and decreased systolic BP (SBP) by 2.46 mmHg. Subgroup and meta-regression analyses found that function, duration of intervention, and age were significant covariates. According to the risk of bias version 2, more than half of the trials raised some concerns about the randomization process, deviations from the intended intervention, and missing outcome data. The certainty of the evidence was very low for all outcomes based on the Grading of Recommendations Assessment, Development and Evaluation (GRADE) criteria. WT-BPAI can be considered a supplementary intervention to increase the steps per day and decrease SBP, especially when used for short periods in young adults with T2DM. However, we need more well-designed research with long-term outcomes.

Wearable technology mediated biofeedback to modulate spine motor control: a scoping review.

BACKGROUND: Lower back pain (LBP) is a disability that affects a large proportion of the population and treatment for this condition has been shifting towards a more individualized, patient-centered approach. There has been a recent uptake in the utilization and implementation of wearable sensors that can administer biofeedback in various industrial, clinical, and performance-based settings. Despite this, there is a strong need to investigate how wearable sensors can be used in a sensorimotor (re)training approach, including how sensory biofeedback from wearable sensors can be used to improve measures of spinal motor control and proprioception. RESEARCH QUESTION: The purpose of this scoping review was to examine the wide range of wearable sensor-mediated biofeedback frameworks currently being utilized to enhance spine posture and motor function. METHODS: A comprehensive scoping review was conducted in adherence with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Guidelines extension for Scoping Reviews (PRISMA-ScR) across the following databases: Embase, PubMed, Scopus, Cochrane, and IEEEXplore. Articles related to wearable biofeedback and spine movement were reviewed dated from 1980 - 2020. Extracted data was collected as per a predetermined checklist including the type, timing, trigger, location, and magnitude of sensory feedback being applied to the body. RESULTS: A total of 23 articles were reviewed and analysed. The most used wearable sensor to inform biofeedback were inertial measurement units (IMUs). Haptic (vibrotactile) feedback was the most common sensory stimulus. Most studies used an instantaneous online trigger to initiate sensory feedback derived from information pertaining to gross lumbar angles or the absolute orientations of the thorax or pelvis. CONCLUSIONS: This is the first study to review wearable sensor-derived sensory biofeedback to modulate spine motor control. Although the type of wearable sensor and feedback were common, this study highlights the lack of consensus regarding the timing and structure of sensory feedback, suggesting the need to optimize any sensory feedback to a specific use case. The findings from this study help to improve the understanding surrounding the ecological utility of wearable sensor-mediated biofeedback in industrial, clinical, and performance settings to enhance the sensorimotor control of the lumbar spine.

Towards the Instrumentation of Facemasks Used as Personal Protective Equipment for Unobtrusive Breathing Monitoring of Workers.

This study focuses on the integration and validation of a filtering face piece 3 (FFP3) facemask module for monitoring breathing activity in industrial environments. The key objective is to ensure accurate, real-time respiratory rate (RR) monitoring while maintaining workers' comfort. RR monitoring is conducted through temperature variations detected using temperature sensors tested in two configurations: sensor t1, integrated inside the exhalation valve and necessitating structural mask modifications, and sensor t2, mounted externally in a 3D-printed structure, thus preserving its certification as a piece of personal protective equipment (PPE). Ten healthy volunteers participated in static and dynamic tests, simulating typical daily life and industrial occupational activities while wearing the breathing activity monitoring module and a chest strap as a reference instrument. These tests were carried out in both indoor and outdoor settings. The results demonstrate comparable mean absolute error (MAE) for t1 and t2 in both indoor (i.e., 0.31 bpm and 0.34 bpm) and outdoor conditions (i.e., 0.43 bpm and 0.83 bpm). During simulated working activities, both sensors showed consistency with MAE values in static tests and were not influenced by motion artifacts, with more than 97% of RR estimated errors within ±2 bpm. These findings demonstrate the effectiveness of integrating a smart module into protective masks, enhancing occupational health monitoring by providing continuous and precise RR data without requiring additional wearable devices.

The acceptability of wearable technology for long-term respiratory disease: A cross-sectional survey.

Few studies have investigated the acceptability of wearable technology in patients with long-term respiratory disease. We conducted a 24-item cross-sectional survey (September 2022-February 2023), developed using four common themes universal to previously described models of technology acceptance and social behavioural therapy, to explore the acceptability of wearable technology spanning the breadth of chronic respiratory disease. A total of 74 valid survey responses were analysed with 50 % aged 51-70years; 72 % female; 63 % white British ethnicity; 79 % having an income less than £50,000, and 93 % having at least obstructive airways disease. A third of participants current used wearables with 85 % using smart watches. Most of these participants used wearables to monitor their symptoms (69 %) and as a general health measurement device (85 %). Likert scale questions (ranked 1-7) showed that participants valued accuracy and approval of wearables by regulatory bodies (median (IQR) rank score 7 (Huberty et al., 2015; Preusse et al., 2016) 6-76-7 and felt that wearables would increase their confidence in managing their long-term health condition (median (IQR) rank score 6 (Huberty et al., 2015; Preusse et al., 2016) 6-76-7. Favourable product characteristics for wearables were accuracy (73 %), easy to learn (63 %) and easy to use (50 %). They were less concerned about aesthetics (23 %) and battery life (27 %). This survey will guide future developers to produce a wearable for a population with chronic respiratory disease which will improve acceptability, usability and longevity.

A Comprehensive Review on Harnessing Wearable Technology for Enhanced Depression Treatment.

Depression is a prevalent and debilitating mental health disorder that significantly impacts individuals, families, and societies worldwide. Despite advancements in treatment, challenges remain in effectively managing and monitoring depressive symptoms. Wearable technology, which encompasses devices that can monitor physiological and behavioral parameters in real time, offers promising new avenues for enhancing depression treatment. This comprehensive review explores the potential of wearable technology in managing and treating depression. It examines how wearables can monitor depressive symptoms, improve patient engagement and adherence to treatment plans, and provide valuable data for personalized treatment strategies. The review covers the integration of wearable technology in clinical settings, the role of wearables in remote monitoring and telemedicine, and the ethical and privacy considerations associated with their use. Additionally, it highlights case studies and pilot programs demonstrating the practical applications and outcomes of wearable technology interventions. Future directions and innovations are discussed, identifying potential advancements and challenges in this emerging field. This review aims to inform healthcare professionals, researchers, and policymakers about the opportunities and challenges of integrating wearable technology into depression treatment, ultimately contributing to improved mental healthcare outcomes.

ToPick: Time-of-Pickup Measurement for the Elderly using Wearables.

The ability to pick up objects off the floor can degrade over time with elderly individuals, leading to a reduced quality of life and an increase in the risk of falling. Healthcare professionals have expressed an interest in monitoring the decline in pickup ability of a subject over extended periods of time and intervening when it becomes hazardous to the subject's health. The current means of evaluating pickup ability involving in-clinic patient visits is both time and financially expensive. There is a clear need for a cost-effective, remote means of pickup evaluation to ease the burden on both patients and physicians. To address these challenges, we introduce a Time-of-Pickup (ToP) solution, called ToPick, designed for the automatic assessment of pickup ability over time. The practical performance of ToPick is evident, demonstrated by a minimal median error of approximately 100 milliseconds in evaluating 20 pickup events among 10 elderly individuals. Furthermore, ToPick exhibits a high level of reliability, achieving perfect accuracy, precision, and recall scores for pickup event detection. We actualize our research findings by designing an application intended for adoption by both healthcare practitioners and elderly individuals. The app aims to reduce both time and financial costs while enabling mobile treatment for users.

Relationship between activity and sleep, as measured through a wearable accelerometer, and appropriate cardioverter-defibrillator interventions: a prospective SafeHeart substudy.

INTRODUCTION: Physical activity has shown association with ventricular arrhythmia, however, the role of specific behavioral patterns over a 24-hour cycle remains unknown. Therefore, we aimed to explore associations between physical behavior and appropriate implantable cardioverter-defibrillator (ICD) therapy. METHODS: We included patients with an ICD at two European sites, who wore wrist-based accelerometers capturing 24-hour movement and sleep behaviors for 28 days. Behavioral measures included activity volume, duration and intensity, sleep duration and efficiency. Patients were followed for 12 months for the outcome of appropriate ICD therapy. Cox proportional hazard models with restricted cubic splines were used for the analysis. Lastly, the predictive capacity was tested. RESULTS: : A total of 253 ICD patients were included (mean age 63.8 (±10.2), 50 (19.8%) female). During follow-up, 40 patients (15.8%) received appropriate ICD therapy; 32 ATP only (12.6%), 5 shock only (2.0%) and 3 combined ATP and shock (1.2%). In the adjusted model, high inactive duration (HR 1.40 (95% 1.10-1.78), peak walking cadence (HR 1.07 (95% 1.03-1.12) and total sleep duration (HR 1.50 (1.02-2.22) were associated with the outcome. The dose-response relationship was U-shaped for inactive duration with a cutoff at 16 hours, and linear for peak cadence and sleep. The prediction model reached an AUROC of 0.70 ±0.03, with highest accuracy in the first months. CONCLUSION: Wearable-derived 24-hour movement and sleep behaviors collected over 28 days were associated with later appropriate ICD therapy risk. Testing of the predictive value of digital biomarkers for enhanced risk stratification of ventricular arrhythmia warrants larger prospective studies. TRIAL REGISTRATION: National Trial Registration (NL9218, http://onderzoekmetmensen.nl/).

Optimising flow without congestion using the venous-arterial Doppler enhanced resuscitation framework.

Introduction: Ultrasonography as a guide for intravenous (IV) fluid therapy is increasingly accepted within the spheres of acute care. Initial investigations and protocols often focused on measures of arterial flow as an objective approach for personalising organ 'perfusion.' More recently, and with literature associating excessive IV fluid with adverse outcomes, venous ultrasound as a measure of organ 'congestion' is taking hold. Yet, arterial (i.e., 'perfusion') and venous (i.e., 'congestion') Doppler ultrasound measures are often performed separately and can be time-consuming, especially for novices. Methods: We report a case, wherein venous and arterial Doppler were simultaneously measured using a wireless, wearable ultrasound as a means to optimise flow without congestion. Results: Before IV volume expansion, the patient had Doppler measures consistent with low central venous pressure (CVP) and stroke volume (SV). Following IV volume expansion, venous Doppler remained the same; however, carotid corrected flow time (ccFT) increased significantly. Conclusion: A framework for venous-arterial Doppler enhanced resuscitation (VADER) can be used to guide IV volume in patients at risk for venous congestion.

Wearable Technology and Its Influence on Motor Development and Biomechanical Analysis.

The convergence among biomechanics, motor development, and wearable technology redefines our understanding of human movement. These technologies allow for the continuous monitoring of motor development and the state of motor abilities from infancy to old age, enabling early and personalized interventions to promote healthy motor skills. For athletes, they offer valuable insights to optimize technique and prevent injuries, while in old age, they help maintain mobility and prevent falls. Integration with artificial intelligence further extends these capabilities, enabling sophisticated data analysis. Wearable technology is transforming the way we approach motor development and maintenance of motor skills, offering unprecedented possibilities for improving health, performance, and quality of life at every stage of life. The promising future of these technologies paves the way for an era of more personalized and effective healthcare, driven by innovation and interdisciplinary collaboration.

Advancements in Textile-Based sEMG Sensors for Muscle Fatigue Detection: A Journey from Material Evolution to Technological Integration.

Textile-based surface electromyography (sEMG) electrodes have emerged as a prominent tool in muscle fatigue assessment, marking a significant shift toward innovative, noninvasive methods. This review examines the transition from metallic fibers to novel conductive polymers, elastomers, and advanced material-based electrodes, reflecting on the rapid evolution of materials in sEMG sensor technology. It highlights the pivotal role of materials science in enhancing sensor adaptability, signal accuracy, and longevity, crucial for practical applications in health monitoring, while examining the balance of clinical precision with user comfort. Additionally, it maps the global sEMG research landscape of diverse regional contributors and their impact on technological progress, focusing on the integration of Eastern manufacturing prowess with Western technological innovations and exploring both the opportunities and challenges in this global synergy. The integration of such textile-based sEMG innovations with artificial intelligence, nanotechnology, energy harvesting, and IoT connectivity is also anticipated as future prospects. Such advancements are poised to revolutionize personalized preventive healthcare. As the exploration of textile-based sEMG electrodes continues, the transformative potential not only promises to revolutionize integrated wellness and preventive healthcare but also signifies a seamless transition from laboratory innovations to real-world applications in sports medicine, envisioning the future of truly wearable material technologies.

A Digital Intervention for Capturing the Real-Time Health Data Needed for Epilepsy Seizure Forecasting: Protocol for a Formative Co-Design and Usability Study (The ATMOSPHERE Study).

BACKGROUND: Epilepsy is a chronic neurological disorder affecting individuals globally, marked by recurrent and apparently unpredictable seizures that pose significant challenges, including increased mortality, injuries, and diminished quality of life. Despite advancements in treatments, a significant proportion of people with epilepsy continue to experience uncontrolled seizures. The apparent unpredictability of these events has been identified as a major concern for people with epilepsy, highlighting the need for innovative seizure forecasting technologies. OBJECTIVE: The ATMOSPHERE study aimed to develop and evaluate a digital intervention, using wearable technology and data science, that provides real-time, individualized seizure forecasting for individuals living with epilepsy. This paper reports the protocol for one of the workstreams focusing on the design and testing of a prototype to capture real-time input data needed for predictive modeling. The first aim was to collaboratively design the prototype (work completed). The second aim is to conduct an "in-the-wild" study to assess usability and refine the prototype (planned research). METHODS: This study uses a person-based approach to design and test the usability of a prototype for real-time seizure precipitant data capture. Phase 1 (work completed) involved co-design with individuals living with epilepsy and health care professionals. Sessions explored users' requirements for the prototype, followed by iterative design of low-fidelity, static prototypes. Phase 2 (planned research) will be an "in-the-wild" usability study involving the deployment of a mid-fidelity, functional prototype for 4 weeks, with the collection of mixed methods usability data to assess the prototype's real-world application, feasibility, acceptability, and engagement. This phase involves primary participants (adults diagnosed with epilepsy) and, optionally, their nominated significant other. The usability study will run in 3 rounds of deployment and data collection, aiming to recruit 5 participants per round, with prototype refinement between rounds. RESULTS: The phase-1 co-design study engaged 22 individuals, resulting in the development of a mid-fidelity, functional prototype based on identified requirements, including the tracking of evidence-based and personalized seizure precipitants. The upcoming phase-2 usability study is expected to provide insights into the prototype's real-world usability, identify areas for improvement, and refine the technology for future development. The estimated completion date of phase 2 is the last quarter of 2024. CONCLUSIONS: The ATMOSPHERE study aims to make a significant step forward in epilepsy management, focusing on the development of a user-centered, noninvasive wearable device for seizure forecasting. Through a collaborative design process and comprehensive usability testing, this research aims to address the critical need for predictive seizure forecasting technologies, offering a promising approach to improving the lives of individuals with epilepsy. By leveraging predictive analytics and personalized machine learning models, this technology seeks to offer a novel approach to managing epilepsy, potentially improving clinical outcomes, including quality of life, through increased predictability and seizure management. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/60129.

Signal quality evaluation of an in-ear EEG device in comparison to a conventional cap system.

Introduction: Wearable in-ear electroencephalographic (EEG) devices hold significant promise for integrating brain monitoring technologies into real-life applications. However, despite the introduction of various in-ear EEG systems, there remains a necessity for validating these technologies against gold-standard, clinical-grade devices. This study aims to evaluate the signal quality of a newly developed mobile in-ear EEG device compared to a standard scalp EEG system among healthy volunteers during wakefulness and sleep. Methods: The study evaluated an in-ear EEG device equipped with dry electrodes in a laboratory setting, recording a single bipolar EEG channel using a cross-ear electrode configuration. Thirty healthy participants were recorded simultaneously using the in-ear EEG device and a conventional EEG cap system with 64 wet electrodes. Based on two recording protocols, one during a resting state condition involving alternating eye opening and closure with a low degree of artifact contamination and another consisting of a daytime nap, several quality measures were used for a quantitative comparison including root mean square (RMS) analysis, artifact quantification, similarities of relative spectral power (RSP), signal-to-noise ratio (SNR) based on alpha peak criteria, and cross-signal correlations of alpha activity during eyes-closed conditions and sleep activities. The statistical significance of our results was assessed through nonparametric permutation tests with False Discovery Rate (FDR) control. Results: During the resting state, in-ear and scalp EEG signals exhibited similar fluctuations, characterized by comparable RMS values. However, intermittent signal alterations were noticed in the in-ear recordings during nap sessions, attributed to movements of the head and facial muscles. Spectral analysis indicated similar patterns between in-ear and scalp EEG, showing prominent peaks in the alpha range (8-12 Hz) during rest and in the low-frequency range during naps (particularly in the theta range of 4-7 Hz). Analysis of alpha wave characteristics during eye closures revealed smaller alpha wave amplitudes and slightly lower signal-to-noise ratio (SNR) values in the in-ear EEG compared to scalp EEG. In around 80% of cases, cross-correlation analysis between in-ear and scalp signals, using a contralateral bipolar montage of 64 scalp electrodes, revealed significant correlations with scalp EEG (p < 0.01), particularly evident in the FT11-FT12 and T7-T8 electrode derivations. Conclusion: Our findings support the feasibility of using in-ear EEG devices with dry-contact electrodes for brain activity monitoring, compared to a standard scalp EEG, notably for wakefulness and sleep uses. Although marginal signal degradation is associated with head and facial muscle contractions, the in-ear device offers promising applications for long-term EEG recordings, particularly in scenarios requiring enhanced comfort and user-friendliness.

Effects of a Wearable-Based Intervention on the Hemiparetic Upper Limb in Persons With Stroke: A Randomized Controlled Trial.

INTRODUCTION: Wearables have emerged as a transformative rehabilitation tool to provide self-directed training in the home. Objective. In this study, we examined the efficacy of a novel wearable device, "Smart Reminder" (SR), to provide home-based telerehabilitation for hemiparetic upper limb (UL) training in persons with stroke. METHODS: Forty stroke survivors from community support groups were randomized (stratified by the period after stroke onset and impairment severity) to either the SR group or the sham device group. Participants received either 20 hours of telerehabilitation using the SR device or training with pictorial handouts and a sham device over 4 weeks. In addition, all participants wore a standard accelerometer for 3 hours each day, 5 times a week, outside the prescribed training. Participants were assessed by a masked assessor at baseline, post-intervention (week 4), and follow-up (week 8). The outcome measures included Fugl-Meyer Assessment for Upper Extremity (FMA-UE), Action Research Arm Test, Motor Activity Log, muscle strength, active range of motion and amount of movement of the UL, and compliance rate of training. RESULTS: The SR group improved substantially in their FMA-UE scores after treatment (mean difference = 2.05, P = .036) compared to the sham group. Also, adherence to the training using the SR device was significantly higher, 97%, than the sham group, 82.3% (P = .038). CONCLUSION: The 4-week telerehabilitation program using a "SR" device demonstrated potential efficacy in improving FMA-UE scores of the hemiparetic upper limb. However, it did not significantly enhance the performance of the affected limb in daily activities. The trial was registered on ClinicalTrial.gov (URL: http://www.clinicaltrials.gov) with the identifier NCT05877183.

Impact of the Promoting Physical Activity in Regional and Remote Cancer Survivors intervention on health-related quality of life in breast and colorectal cancer survivors.

Background: The PPARCS trial examined the efficacy of a distance-based wearable and health coaching intervention to increase physical activity (PA) in breast and colorectal cancer (CRC) survivors living in non-metropolitan areas. This paper examines the effects of the intervention on health-related quality of life (HRQoL) at 12 weeks (T2; end of intervention) and 24 weeks (T3; follow-up). Methods: Participants that were insufficiently physically active and had successfully completed cancer treatment were randomised to an intervention or control group. PA was assessed using an ActiGraph (GT9X) at baseline, T2, and T3. Intervention effects on HRQoL were analysed using quantile regression comparing treatment groups across time. Results: A total of 87 were randomised to intervention and control groups. There were generally no statistically significant differences between the groups on any HRQoL item except for pain. There was an arm (F(1, 219) = 5.0. p = 0.027) and time (F(2,221) = 4.8, p = 0.009) effect, reflecting the higher pain scores in the control group when collapsed across time points (median difference 16.7, CI 1.9 to 31.4, p = 0.027). For global HRQoL, the intervention group increased by 8.3 points between T1 and T2. The overall group median when collapsed across time was 16.7 points CI 8.2 to 25.2, p <0.001) greater in the intervention group than controls. Conclusions: While the PPARCS intervention resulted in significant increases in PA, participants indicated a high HRQoL at baseline, leaving little room for improvement. Findings suggest that PA may improve global HRQoL and pain in breast and CRC survivors.

Wearable Multi-Sensor Positioning Prototype for Rowing Technique Evaluation.

The goal of this study is to determine the feasibility of a wearable multi-sensor positioning prototype to be used as a training tool to evaluate rowing technique and to determine the positioning accuracy using multiple mathematical models and estimation methods. The wearable device consists of an inertial measurement unit (IMU), an ultra-wideband (UWB) transceiver, and a global navigation satellite system (GNSS) receiver. An experiment on a rowing shell was conducted to evaluate the performance of the system on a rower's wrist, against a centimeter-level GNSS reference trajectory. This experiment analyzed the rowing motion in multiple navigation frames and with various positioning methods. The results show that the wearable device prototype is a viable option for rowing technique analysis; the system was able to provide the position, velocity, and attitude of a rower's wrist, with a positioning accuracy ranging between ±0.185 m and ±1.656 m depending on the estimation method.

Neuro Receptor Signal Detecting and Monitoring Smart Devices for Biological Changes in Cognitive Health Conditions.

Background: Currently, wearable sensors significantly impact health care through continuous monitoring and event prediction. The types and clinical applications of wearable technology for the prevention of mental illnesses, as well as associated health authority rules, are covered in the current review. Summary: The technologies behind wearable ECG monitors, biosensors, electronic skin patches, neural interfaces, retinal prosthesis, and smart contact lenses were discussed. We described how sensors will examine neuronal impulses using verified machine-learning algorithms running in real-time. These sensors will closely monitor body signals and demonstrate continuous sensing with wireless functionality. The wearable applications in the following medical fields were covered in our review: sleep, neurology, mental health, anxiety, depression, Parkinson's disease, epilepsy, seizures, and schizophrenia. These mental health conditions can cause serious issues, even death. Inflammation brought on by mental health problems can worsen hypothalamic-pituitary-adrenal axis dysfunction and interfere with certain neuroregulatory systems such as the neural peptide Y, serotonergic, and cholinergic systems. Severe depressive disorder symptoms are correlated with elevated Interleukin (IL-6) levels. On the basis of previous and present data collected utilizing a variety of sensory modalities, researchers are currently investigating ways to identify or detect the current mental state. Key message: This review explores the potential of various mental health monitoring technologies. The types and clinical uses of wearable technology, such as ECG monitors, biosensors, electronic skin patches, brain interfaces, retinal prostheses, and smart contact lenses, were covered in the current review will be beneficial for patients with mental health problems like Alzheimer, epilepsy, dementia. The sensors will closely monitor bodily signals with wireless functionality while using machine learning algorithms to analyse neural impulses in real time.

Using Video Technology and AI within Parkinson's Disease Free-Living Fall Risk Assessment.

Falls are a major concern for people with Parkinson's disease (PwPD), but accurately assessing real-world fall risk beyond the clinic is challenging. Contemporary technologies could enable the capture of objective and high-resolution data to better inform fall risk through measurement of everyday factors (e.g., obstacles) that contribute to falls. Wearable inertial measurement units (IMUs) capture objective high-resolution walking/gait data in all environments but are limited by not providing absolute clarity on contextual information (i.e., obstacles) that could greatly influence how gait is interpreted. Video-based data could compliment IMU-based data for a comprehensive free-living fall risk assessment. The objective of this study was twofold. First, pilot work was conducted to propose a novel artificial intelligence (AI) algorithm for use with wearable video-based eye-tracking glasses to compliment IMU gait data in order to better inform free-living fall risk in PwPD. The suggested approach (based on a fine-tuned You Only Look Once version 8 (YOLOv8) object detection algorithm) can accurately detect and contextualize objects (mAP50 = 0.81) in the environment while also providing insights into where the PwPD is looking, which could better inform fall risk. Second, we investigated the perceptions of PwPD via a focus group discussion regarding the adoption of video technologies and AI during their everyday lives to better inform their own fall risk. This second aspect of the study is important as, traditionally, there may be clinical and patient apprehension due to ethical and privacy concerns on the use of wearable cameras to capture real-world video. Thematic content analysis was used to analyse transcripts and develop core themes and categories. Here, PwPD agreed on ergonomically designed wearable video-based glasses as an optimal mode of video data capture, ensuring discreteness and negating any public stigma on the use of research-style equipment. PwPD also emphasized the need for control in AI-assisted data processing to uphold privacy, which could overcome concerns with the adoption of video to better inform IMU-based gait and free-living fall risk. Contemporary technologies (wearable video glasses and AI) can provide a holistic approach to fall risk that PwPD recognise as helpful and safe to use.

Development of a Concourse for Two Q-Method Studies Exploring Perspectives of Patients with Multiple Sclerosis and Healthcare Professionals on the Use of Wearable Technology-Lessons Learnt.

This paper presents the generation of a Q-set for two Q studies investigating the perspectives of (1) patients with multiple sclerosis and (2) their healthcare professionals on the use of wearable technology. It describes the adopted Q methodology and how it was applied in different phases. The concourse is derived from the relevant literature, based on the unified theory of acceptance and use of technology (UTAUT2) model, with privacy as a moderator, and Hofstede's "Cultural Dimensions" framework, incorporates statements drawn from the concourse following review by experts This is followed by a pilot study involving 4 stakeholders to improve the relevance and quality of the research. A 43-statement Q-sample was developed for the first Q study, and a 32-statement Q-sample was developed for the second Q study. This preliminary study reported the development of a legitimate and reliable concourse in a transparent and comprehensive manner. The lessons learnt from developing the concourse in this study could be beneficial for future research when conducted in a similar digital healthcare context and in the context of MS where individuals often experience symptoms related to vision, sensation, coordination, and movement.

User Engagement with the User Manuals of Smart Medical Wearable Technologies and Relevant Questions of User Behavior.

In this paper, engagement with smart medical wearables and with their user manuals, as well as related user behavior are studied. A research questionnaire containing 15 single-choice questions was completed by 1381 test participants to address relevant topics of the investigated area, including trust in measured medical data, device calibration, technical terminologies and function discovery in the documentation, information sources beyond the documentation, and wearing such devices to bed. The questionnaire particularly focused on device functionalities and characteristics that initially led to the purchase of the smart medical wearable.