Applying artificial intelligence on EDA sensor data to predict stress on minimally invasive robotic-assisted surgery.

PURPOSE: This study aims predicting the stress level based on the ergonomic (kinematic) and physiological (electrodermal activity-EDA, blood pressure and body temperature) parameters of the surgeon from their records collected in the previously immediate situation of a minimally invasive robotic surgery activity. METHODS: For this purpose, data related to the surgeon's ergonomic and physiological parameters were collected during twenty-six robotic-assisted surgical sessions completed by eleven surgeons with different experience levels. Once the dataset was generated, two preprocessing techniques were applied (scaled and normalized), these two datasets were divided into two subsets: with 80% of data for training and cross-validation, and 20% of data for test. Three predictive techniques (multiple linear regression-MLR, support vector machine-SVM and multilayer perceptron-MLP) were applied on training dataset to generate predictive models. Finally, these models were validated on cross-validation and test datasets. After each session, surgeons were asked to complete a survey of their feeling of stress. These data were compared with those obtained using predictive models. RESULTS: The results showed that MLR combined with the scaled preprocessing achieved the highest R2 coefficient and the lowest error for each parameter analyzed. Additionally, the results for the surgeons' surveys were highly correlated to the results obtained by the predictive models (R2 = 0.8253). CONCLUSIONS: The linear models proposed in this study were successfully validated on cross-validation and test datasets. This fact demonstrates the possibility of predicting factors that help us to improve the surgeon's health during robotic surgery.

Putting the usability of wearable technology in forensic psychiatry to the test: a randomized crossover trial.

Introduction: Forensic psychiatric patients receive treatment to address their violent and aggressive behavior with the aim of facilitating their safe reintegration into society. On average, these treatments are effective, but the magnitude of effect sizes tends to be small, even when considering more recent advancements in digital mental health innovations. Recent research indicates that wearable technology has positive effects on the physical and mental health of the general population, and may thus also be of use in forensic psychiatry, both for patients and staff members. Several applications and use cases of wearable technology hold promise, particularly for patients with mild intellectual disability or borderline intellectual functioning, as these devices are thought to be user-friendly and provide continuous daily feedback. Method: In the current randomized crossover trial, we addressed several limitations from previous research and compared the (continuous) usability and acceptance of four selected wearable devices. Each device was worn for one week by staff members and patients, amounting to a total of four weeks. Two of the devices were general purpose fitness trackers, while the other two devices used custom made applications designed for bio-cueing and for providing insights into physiological reactivity to daily stressors and events. Results: Our findings indicated significant differences in usability, acceptance and continuous use between devices. The highest usability scores were obtained for the two fitness trackers (Fitbit and Garmin) compared to the two devices employing custom made applications (Sense-IT and E4 dashboard). The results showed similar outcomes for patients and staff members. Discussion: None of the devices obtained usability scores that would justify recommendation for future use considering international standards; a finding that raises concerns about the adaptation and uptake of wearable technology in the context of forensic psychiatry. We suggest that improvements in gamification and motivational aspects of wearable technology might be helpful to tackle several challenges related to wearable technology.

Identifying Key Training Load and Intensity Indicators in Ice Hockey Using Unsupervised Machine Learning.

To identify key training load (TL) and intensity indicators in ice hockey, practice, and game data were collected using a wearable 200-Hz accelerometer and heart rate (HR) recording throughout a four-week (29 days) competitive period (23 practice sessions and 8 competitive games in 17 elite Danish players (n = 427 observations). Within-individual correlations among accelerometer- (total accelerations [Acctot], accelerations >2 m·s-2 [Acc2], total accelerations [Dectot], decelerations <- 2 m·s-2 [Dec2]), among HR-derived (time >85% maximum HR [t85%HRmax], Edwards' TL and modified training impulse) TL indicators, and between acceleration- and HR-derived TL parameters were large to almost perfect (r = 0.69-0.99). No significant correlations were observed between accelerometer- and HR-derived intensity indicators. Three between- and two within-components were found. The K-means++ cluster analysis revealed five and four clusters for between- and within-loadings, respectively. The least Euclidean distance from their centroid for each cluster was reported by session-duration, Acctot, Dec2, TRIMPMOD, %t85HRmax for between-loadings, whereas session-duration, Acc2, t85HRmax and Dec2/min for within-loadings. Specific TL or intensity variables might be relevant to identify similar between-subject groups (e.g. individual player, playing positions), or temporal patterns (e.g. changes in TL or intensity over time). Our study provides insights about the redundancy associated with the use of multiple TL and intensity variables in ice hockey.

Promoting child and adolescent health through wearable technology: A systematic review.

Background: Wearable technology is used in healthcare to monitor the health of individuals. This study presents an updated systematic literature review of the use of wearable technology in promoting child and adolescent health, accompanied by recommendations for future research. Methods: This review focuses on studies involving children and adolescents aged between 2 and 18 years, regardless of their health condition or disabilities. Studies that were published from 2016 to 2024, and which met the inclusion criteria, were extracted from four academic databases (i.e. PubMed, Cochrane, Embase, and Web of Science) using the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) protocol. Data on intervention purposes, interventions deployed, intervention duration, measurements, and the main outcomes of the studies were collected. Results: A total of 53 studies involving 14,852 participants were reviewed. They focused on various aspects, including the ownership and use of wearable devices (n = 3), the feasibility (n = 22), effectiveness (n = 4), and adherence (n = 2) of intervention strategies, or a combination of multiple aspects (n = 22). Among the interventions deployed, Fitbit was the most frequently used, featuring in 26 studies, followed by ActiGraph (n = 11). In intervention studies, the majority of studies focused on pre-morbidity prevention (n = 26) and the treatment of illnesses (n = 20), with limited attention given to postoperative monitoring (n = 4). Conclusions: The use of wearable technology by children and adolescents has proven to be both feasible and effective for health promotion. This systematic review summarizes existing research by exploring the use of wearable technology in promoting health across diverse youth populations, including healthy and unhealthy individuals. It examines health promotion at various stages of the disease continuum, including pre-disease prevention, in-disease treatment, and postoperative monitoring. Additionally, the review provides directions for future research.

A Novel IMU-Based System for Work-Related Musculoskeletal Disorders Risk Assessment.

This study introduces a novel wearable Inertial Measurement Unit (IMU)-based system for an objective and comprehensive assessment of Work-Related Musculoskeletal Disorders (WMSDs), thus enhancing workplace safety. The system integrates wearable technology with a user-friendly interface, providing magnetometer-free orientation estimation, joint angle measurements, and WMSDs risk evaluation. Tested in a cable manufacturing facility, the system was evaluated with ten female employees. The evaluation involved work cycle identification, inter-subject comparisons, and benchmarking against standard WMSD risk assessments like RULA, REBA, Strain Index, and Rodgers Muscle Fatigue Analysis. The evaluation demonstrated uniform joint patterns across participants (ICC=0.72±0.23) and revealed a higher occurrence of postures warranting further investigation, which is not easily detected by traditional methods such as RULA. The experimental results showed that the proposed system's risk assessments closely aligned with the established methods and enabled detailed and targeted risk assessments, pinpointing specific bodily areas for immediate ergonomic interventions. This approach not only enhances the detection of ergonomic risks but also supports the development of personalized intervention strategies, addressing common workplace issues such as tendinitis, low back pain, and carpal tunnel syndrome. The outcomes highlight the system's sensitivity and specificity in identifying ergonomic hazards. Future efforts should focus on broader validation and exploring the relative influence of various WMSDs risk factors to refine risk assessment and intervention strategies for improved applicability in occupational health.

Assessing Trail Running Biomechanics: A Comparative Analysis of the Reliability of StrydTM and GARMINRP Wearable Devices.

This study investigated biomechanical assessments in trail running, comparing two wearable devices-Stryd Power Meter and GARMINRP. With the growing popularity of trail running and the complexities of varied terrains, there is a heightened interest in understanding metabolic pathways, biomechanics, and performance factors. The research aimed to assess the inter- and intra-device agreement for biomechanics under ecological conditions, focusing on power, speed, cadence, vertical oscillation, and contact time. The participants engaged in trail running sessions while wearing two Stryd and two Garmin devices. The intra-device reliability demonstrated high consistency for both GARMINRP and StrydTM, with strong correlations and minimal variability. However, distinctions emerged in inter-device agreement, particularly in power and contact time uphill, and vertical oscillation downhill, suggesting potential variations between GARMINRP and StrydTM measurements for specific running metrics. The study underscores that caution should be taken in interpreting device data, highlighting the importance of measuring with the same device, considering contextual and individual factors, and acknowledging the limited research under real-world trail conditions. While the small sample size and participant variations were limitations, the strength of this study lies in conducting this investigation under ecological conditions, significantly contributing to the field of biomechanical measurements in trail running.

Using wearable technology for Posture Regulation to Improve Surgical Ergonomics in the paediatric operating room: the UPRISE trial: a pilot study.

BACKGROUND: The rising prevalence of work-related musculoskeletal disorders has numerous physical, financial, and mental repercussions for surgeons. This study aims to establish whether the use of a wearable posture device can improve the operating time spent in suboptimal, high-risk postures. METHODS: Surgeons were recruited in Phase 1 of this prospective randomised study and baseline postural data was obtained. In Phase 2, participants were randomised to receive either a traditional educational workshop or intraoperative vibrations from the device to correct postural lapses. During minor elective day cases, intraoperative postural data was collected and stratified by forward flexion angle, into five risk categories (negligible to very high). Participants' experience with the sensor was also assessed. RESULTS: A total of 100 surgical procedures (Phase 1: n = 50; Phase 2: n = 50) were performed by eight surgeons of varying seniority. Exposure to the educational intervention increased time spent in suboptimal posture (Phase 1 vs. Phase 2); 47.5% vs. 67.8%, p = 0.05. However, the vibrational intervention significantly reduced this time; 50.0% vs. 20.7%, p = 0.005. Procedure type didn't influence posture although, laparoscopic interventions spent most time in negligible-risk postures; 47.7% vs. 49.3%, compared to open procedures. Surgical consultants spent less time in suboptimal posture compared to fellow/registrars; 30.3% vs. 72.6% (Phase 1) and 33.8% vs. 65.3% (Phase 2). CONCLUSION: Vibrational intervention from the device significantly decreased the time spent in suboptimal, high-risk postures. As procedure type wasn't correlated with postural changes, surgeon-specific factors in regulating posture are paramount. Finally, surgeon experience was positively correlated with improved surgical ergonomics.

Innovative solution or cause for concern? The use of continuous glucose monitors in people not living with diabetes: A narrative review.

AIMS: Continuous glucose monitors (CGMs) have expanded their scope beyond indicated uses for diabetes management and are gaining traction among people not living with diabetes (PNLD). CGMs track in time glucose levels and are proposed as tools for the early detection of abnormal glucose and a potential solution for its normalisation through behavioural change, particularly, diet personalisation and motivation of physical activity. This becomes relevance given the growing incidence of metabolic conditions, such as type 2 diabetes mellitus (T2DM). Clinical guidelines, however, do not recommend CGMs in contexts outside type 1 diabetes (T1DM) or insulin-treated T2DM. Therefore, there is a visible disconnect between the indicated and real-world usage of these medical devices. While the commercial market for CGMs in PNLD is expanding rapidly, a comprehensive and evidence-based evaluation of the devices' utility in this population has not been done. Therefore, this review aims to formulate a working model for CGM utility in PNLD as proposed by the 'health and wellness' market that advertises and distributes it to these individuals. METHODS: We aim to critically analyse the available research addressing components of the working model, that is (1) detection of abnormal glucose; (2) behavioural change, and (3) metabolic health improvement. RESULTS: We find a lack of consistent and high-quality evidence to support the utility of CGMs for these purposes. We identify significantly under-reserved areas including clinical benchmarks and scoring procedures for CGM measures, device acceptability, and potential adverse effects of CGMs on eating habits in PNLD. We also raise concerns about the robustness of available CGM research. CONCLUSION: In the face of these research gaps, we urge for the commercial claims suggesting the utility of the device in PNLD to be labelled as misleading. We argue that there is a regulatory inadequacy that fuels 'off-label' CGM distribution and calls for the strengthening of post-market clinical follow-up oversight for CGMs. We hope this will help to avert the continued misinformation risk to PNLD and 'off-label' exacerbation of health disparities.

Challenges and Solutions for Better Management of Side Effects in Geriatric Oncology.

This editorial discusses the difficulties encountered in the management of cancer among the geriatric population. Although cancer research has made substantial advancements, treatments frequently fail to consider the long-lasting consequences and adverse effects on elderly people. We advocate for enhanced geriatric oncology care, embodying enhanced evaluation techniques, the incorporation of complementary therapies, and the utilisation of wearable technologies for remote surveillance. Additionally, we suggest modifying future clinical trials to take into account the cognitive well-being of senior individuals. Implementing these modifications would greatly enhance cancer treatment for geriatric cancer patients.

The use of wearable devices on physical activity levels among individuals living with diabetes: 2017 BRFSS.

AIM: This study aims to examine the association between wearing wearable devices and physical activity levels among people living with diabetes. METHODS: 1298 wearable device users and nonusers living with diabetes from eight states of the 2017 Behavioral Risk Factors Surveillance System were included in the analysis. Unadjusted and adjusted linear regression was performed to determine the association between self-reported physical activity per week (min) and wearable device usage (users and nonusers) among people living with diabetes using survey analysis. RESULTS: 84.97 % (95 % CI [80.39, 88.89]) of participants were nonusers of wearable devices, while 15.03 % (95 % CI [11.11, 19.61]) were users. Across the sample, the average weekly physical activity was 427.39 mins (95 % Cl [356.43, 498.35]). Nonusers had a higher physical activity per week with 433.83 mins (95 % CI [353.59, 514.07]), while users only had 392.59 mins (95 % CI [253.48, 531.69]) of physical activity per week. However, the differences between the two groups were non-statistically significant (p=.61). In both adjusted and unadjusted linear regressions between physical activity per week and wearable device usage, statistically significant associations were not found (unadjusted: ß=-41.24, p=.62; adjusted: ß=-56.41, p=.59). CONCLUSION: Further research is needed to determine the effectiveness of wearable devices in promoting physical activity among people with diabetes. Additionally, there is a need to determine how people with diabetes use wearable devices that could promote physical activity levels.

A Comparison of Personalized and Generalized Approaches to Emotion Recognition Using Consumer Wearable Devices: Machine Learning Study.

BACKGROUND: There are a wide range of potential adverse health effects, ranging from headaches to cardiovascular disease, associated with long-term negative emotions and chronic stress. Because many indicators of stress are imperceptible to observers, the early detection of stress remains a pressing medical need, as it can enable early intervention. Physiological signals offer a noninvasive method for monitoring affective states and are recorded by a growing number of commercially available wearables. OBJECTIVE: We aim to study the differences between personalized and generalized machine learning models for 3-class emotion classification (neutral, stress, and amusement) using wearable biosignal data. METHODS: We developed a neural network for the 3-class emotion classification problem using data from the Wearable Stress and Affect Detection (WESAD) data set, a multimodal data set with physiological signals from 15 participants. We compared the results between a participant-exclusive generalized, a participant-inclusive generalized, and a personalized deep learning model. RESULTS: For the 3-class classification problem, our personalized model achieved an average accuracy of 95.06% and an F1-score of 91.71%; our participant-inclusive generalized model achieved an average accuracy of 66.95% and an F1-score of 42.50%; and our participant-exclusive generalized model achieved an average accuracy of 67.65% and an F1-score of 43.05%. CONCLUSIONS: Our results emphasize the need for increased research in personalized emotion recognition models given that they outperform generalized models in certain contexts. We also demonstrate that personalized machine learning models for emotion classification are viable and can achieve high performance.

A Worksite Intervention Program for Obese Sedentary Women Using Wearable Technology.

BACKGROUND: The sedentary aspects of work have been associated with increased health risks. The purpose of this study was to compare the effects of high intensity interval training (HIIT) and increased steps on anthropometric, body mass, and body composition changes over a 12-week period. METHODS: 12 sedentary, obese, body mass index (BMI) = 32.98 ± 3.21 kg/m2, adult (46.10 ± 9.56 years), females volunteered for the study and were randomly assigned into one of the two groups, the HIIT group and the STEP group. During the 12-week study, all participants' movements were monitored during their workday, via an accelerometer, a Movband™, 5 days/week. FINDINGS: The HIIT group (n = 5) engaged in structured exercise (~15.0 ± 3.5 minutes), defined as total body moves which consisted of eight different routines: upper and lower extremity, two cardio segments, two total body, yoga, and abdominal exercises. The STEP group (n = 7) averaged ~7,000 steps/day throughout 12 weeks. Pre- and post-program measurements included: five anthropometric measurements (biceps, waist, abdomen, hips, and thigh), along with body mass and body composition measures: relative (%) body fat via dual x-ray absorptiometry (DEXA) scan, fat mass, fat-free mass, and lean mass. CONCLUSIONS: Statistical significance was determined among participants for biceps, hips, and thigh measurements along with body mass and body composition changes for improved health. APPLICATION TO PRACTICE: This work is suggestive that a physical activity intervention integrated into the workplace via work processes and/or structured exercise is supportive in reducing anthropometric and body composition measurements, while changing body mass, to increase health and reduce obesity-related chronic disease risks in sedentary women.

Examining health apps and wearable use in improving physical and mental well-being across U.S., China, and Singapore.

Health apps and wearables are touted to improve physical health and mental well-being. However, it is unclear from existing research the extent to which these health technologies are efficacious in improving physical and mental well-being at a population level, particularly for the underserved groups from the perspective of health equity and social determinants. Also, it is unclear if the relationship between health apps and wearables use and physical and mental well-being differs across individualistic, collectivistic, and a mix of individual-collectivistic cultures. A large-scale online survey was conducted in the U.S. (individualist culture), China (collectivist culture), and Singapore (mix of individual-collectivist culture) using quota sampling after obtaining ethical approval from the Institutional Review Board (IRB-2021-262) of Nanyang Technological University (NTU), Singapore. There was a total of 1004 respondents from the U.S., 1072 from China, and 1017 from Singapore. Data were analyzed using multiple regression and negative binomial regression. The study found that income consistently had the strongest relationship with physical and mental well-being measures in all three countries, while the use of health apps and wearables only had a moderate association with psychological well-being only in the US. Health apps and wearables were associated with the number of times people spent exercising and some mental health outcomes in China and Singapore, but they were only positively associated with psychological well-being in the US. The study emphasizes the importance of considering the social determinants, social-cultural context of the population, and the facilitating conditions for the effective use of digital health technologies. The study suggests that the combined use of both health apps and wearables is most strongly associated with better physical and mental health, though this association is less pronounced when individuals use only apps or wearables.

Are Gait Patterns during In-Lab Running Representative of Gait Patterns during Real-World Training? An Experimental Study.

Biomechanical assessments of running typically take place inside motion capture laboratories. However, it is unclear whether data from these in-lab gait assessments are representative of gait during real-world running. This study sought to test how well real-world gait patterns are represented by in-lab gait data in two cohorts of runners equipped with consumer-grade wearable sensors measuring speed, step length, vertical oscillation, stance time, and leg stiffness. Cohort 1 (N = 49) completed an in-lab treadmill run plus five real-world runs of self-selected distances on self-selected courses. Cohort 2 (N = 19) completed a 2.4 km outdoor run on a known course plus five real-world runs of self-selected distances on self-selected courses. The degree to which in-lab gait reflected real-world gait was quantified using univariate overlap and multivariate depth overlap statistics, both for all real-world running and for real-world running on flat, straight segments only. When comparing in-lab and real-world data from the same subject, univariate overlap ranged from 65.7% (leg stiffness) to 95.2% (speed). When considering all gait metrics together, only 32.5% of real-world data were well-represented by in-lab data from the same subject. Pooling in-lab gait data across multiple subjects led to greater distributional overlap between in-lab and real-world data (depth overlap 89.3-90.3%) due to the broader variability in gait seen across (as opposed to within) subjects. Stratifying real-world running to only include flat, straight segments did not meaningfully increase the overlap between in-lab and real-world running (changes of <1%). Individual gait patterns during real-world running, as characterized by consumer-grade wearable sensors, are not well-represented by the same runner's in-lab data. Researchers and clinicians should consider "borrowing" information from a pool of many runners to predict individual gait behavior when using biomechanical data to make clinical or sports performance decisions.

Highlighting Unseen Activity Through 48-Hour Continuous Measurement in Subacute Stroke Rehabilitation: Preliminary Cohort Study.

BACKGROUND: Motor impairments not only lead to a significant reduction in patient activity levels but also trigger a further deterioration in motor function due to deconditioning, which is an issue that is particularly pronounced during hospitalization. This deconditioning can be countered by sustaining appropriate activity levels. Activities that occur outside of scheduled programs, often overlooked, are critical in this context. Wearable technology, such as smart clothing, provides a means to monitor these activities. OBJECTIVE: This study aimed to observe activity levels in patients who had strokes during the subacute phase, focusing on both scheduled training sessions and other nontraining times in an inpatient rehabilitation environment. A smart clothing system is used to simultaneously measure heart rate and acceleration, offering insights into both the amount and intensity of the physical activity. METHODS: In this preliminary cohort study, 11 individuals undergoing subacute stroke rehabilitation were enrolled. The 48-hour continuous measurement system, deployed at admission and reassessed 4 weeks later, monitored accelerometry data for physical activity (quantified with a moving SD of acceleration [MSDA]) and heart rate for intensity (quantified with percent heart rate reserve). The measurements were performed using a wearable activity monitoring system, the hitoe (NTT Corporation and Toray Industries, Inc) system comprising a measuring garment (wear or strap) with integrated electrodes, a data transmitter, and a smartphone. The Functional Independence Measure was used to assess the patients' daily activity levels. This study explored factors such as differences in activity during training and nontraining periods, correlations with activities of daily living (ADLs) and age, and changes observed after 4 weeks. RESULTS: A significant increase was found in the daily total MSDA after the 4-week program, with the average percent heart rate reserve remaining consistent. Physical activity during training positively correlated with ADL levels both at admission (ρ=0.86, P<.001) and 4 weeks post admission (ρ=0.96, P<.001), whereas the correlation between age and MSDA was not significant during training periods at admission (ρ=-0.41, P=.21) or 4 weeks post admission (ρ=-0.25, P=.45). Conversely, nontraining activity showed a negative correlation with age, with significant negative correlations with age at admission (ρ=-0.82, P=.002) and 4 weeks post admission (ρ=-0.73, P=.01). CONCLUSIONS: Inpatient rehabilitation activity levels were positively correlated with ADL levels. Further analysis revealed a strong positive correlation between scheduled training activities and ADL levels, whereas nontraining activities showed no such correlation. Instead, a negative correlation between nontraining activities and age was observed. These observations suggest the importance of providing activity opportunities for older patients, while it may also suggest the need for adjusting the activity amount to accommodate the potentially limited fitness levels of this demographic. Future studies with larger patient groups are warranted to validate and further elucidate these findings.

Innova4Health: an integrated approach for prevention of recurrence and personalized treatment of Major Depressive Disorder.

Background: Major Depressive Disorder (MDD) is a prevalent mental health condition characterized by persistent low mood, cognitive and physical symptoms, anhedonia (loss of interest in activities), and suicidal ideation. The World Health Organization (WHO) predicts depression will become the leading cause of disability by 2030. While biological markers remain essential for understanding MDD's pathophysiology, recent advancements in social signal processing and environmental monitoring hold promise. Wearable technologies, including smartwatches and air purifiers with environmental sensors, can generate valuable digital biomarkers for depression assessment in real-world settings. Integrating these with existing physical, psychopathological, and other indices (autoimmune, inflammatory, neuroradiological) has the potential to improve MDD recurrence prevention strategies. Methods: This prospective, randomized, interventional, and non-pharmacological integrated study aims to evaluate digital and environmental biomarkers in adolescents and young adults diagnosed with MDD who are currently taking medication. The study implements a sensor-integrated platform built around an open-source "Pothos" air purifier system. This platform is designed for scalability and integration with third-party devices. It accomplishes this through software interfaces, a dedicated app, sensor signal pre-processing, and an embedded deep learning AI system. The study will enroll two experimental groups (10 adolescents and 30 young adults each). Within each group, participants will be randomly allocated to Group A or Group B. Only Group B will receive the technological equipment (Pothos system and smartwatch) for collecting digital biomarkers. Blood and saliva samples will be collected at baseline (T0) and endpoint (T1) to assess inflammatory markers and cortisol levels. Results: Following initial age-based stratification, the sample will undergo detailed classification at the 6-month follow-up based on remission status. Digital and environmental biomarker data will be analyzed to explore intricate relationships between these markers, depression symptoms, disease progression, and early signs of illness. Conclusion: This study seeks to validate an AI tool for enhancing early MDD clinical management, implement an AI solution for continuous data processing, and establish an AI infrastructure for managing healthcare Big Data. Integrating innovative psychophysical assessment tools into clinical practice holds significant promise for improving diagnostic accuracy and developing more specific digital devices for comprehensive mental health evaluation.

Applications of wearable activity monitors for prostate cancer survivors: A systematic scoping review.

BACKGROUND: Wearable technology is used to monitor and motivate physical activity (PA) and provides continuous, objective PA and sleep data outside the clinical setting. We reviewed the literature to understand how wearables are integrated into prostate cancer (PC) investigations in order to identify current practices, gaps, and research opportunities. METHODS: We conducted a literature search for articles using wearables, among PC survivors published between 2012 and 2022. We extracted study details, interventions and outcomes, participant baseline characteristics, and device characteristics and grouped them by study type: randomized control trials (RCTs) and non-randomized studies. RESULTS: Of 354 articles screened, 44 met eligibility criteria (23 RCTs, and 21 non-randomized). 89% used wearables to monitor PA metrics, 11%, sleep metrics, and 6.8%, both. Most studies involved exercise (70% RCTs, 9% non-randomized studies) or lifestyle interventions (30% RCTs, 9% non-randomized studies). Intervention delivery methods included personalized computer-based (48%), in-person (e.g., trainer) (20%), and education web or print-based (20%). Interventions occurred at the participant's home (48%) or at a gym (20%). 57% of the studies evaluated the feasibility and acceptability of the wearable as an activity-measuring device or as part of a remotely delivered computer-based intervention. Studies used wearables to monitor adherence to PA interventions, motivate behavior change, to assess patient outcomes (e.g., patient function, quality of life, mood), or as data collection tools. CONCLUSIONS: Wearables are primarily being used to assess daily activity and monitor adherence to exercise interventions in clinical studies involving PC survivors. Findings suggest that they are feasible for use in this population. More research is needed to understand how to integrate wearables into routine clinical care, expand their use to predict clinical outcomes, or to deliver tailored interventions for PC survivors.

Bio-based epoxy resin/carbon nanotube coatings applied on cotton fabrics for smart wearable systems.

Electroactive coatings for smart wearable textiles based on a furan bio-epoxy monomer (BOMF) crosslinked with isophorone diamine (IPD) and additivated with carbon nanotubes (CNTs) are reported herein. The effect of BOMF/IPD molar ratio on the curing reaction, as well as on the properties of the crosslinked resins was first assessed, and it was found that 1.5:1 BOMF/IPD molar ratio provided higher heat of reaction, glass transition temperature, and mechanical performance. The resin was then modified with CNT to prepare electrically conductive nanocomposite films, which exhibited conductivity values increased by eight orders of magnitude upon addition of 5 phr of CNTs. The epoxy/CNT nanocomposites were finally applied as coatings onto a cotton fabric to develop electrically conductive, hydrophobic and breathable textiles. Notably, the integration of CNTs imparted efficient and reversible electrothermal behavior to the cotton fabric, showcasing its potential application in smart and comfortable wearable electronic devices.

Recent Advances in Ferroelectret Fabrication, Performance Optimization, and Applications.

The growing demand for wearable devices has sparked a significant interest in ferroelectret films. They possess flexibility and exceptional piezoelectric properties due to strong macroscopic dipoles formed by charges trapped at the interface of their internal cavities. This review of ferroelectrets focuses on the latest progress in fabrication techniques for high temperature resistant ferroelectrets with regular and engineered cavities, strategies for optimizing their piezoelectric performance, and novel applications. The charging mechanisms of bipolar and unipolar ferroelectrets with closed and open-cavity structures are explained first. Next, the preparation and piezoelectric behavior of ferroelectret films with closed, open, and regular cavity structures using various materials are discussed. Three widely used models for predicting the piezoelectric coefficients (d33) are outlined. Methods for enhancing the piezoelectric performance such as optimized cavity design, utilization of fabric electrodes, injection of additional ions, application of DC bias voltage, and synergy of foam structure and ferroelectric effect are illustrated. A variety of applications of ferroelectret films in acoustic devices, wearable monitors, pressure sensors, and energy harvesters are presented. Finally, the future development trends of ferroelectrets toward fabrication and performance optimization are summarized along with its potential for integration with intelligent systems and large-scale preparation.

Examining the Relationship Between E-Cigarette Status and Wearable Device Use on Physical Activity Levels in U.S. Adults.

PURPOSE: The aim of this analysis is to investigate physical activity levels amongst e-cigarette users based on their wearable device use. DESIGN: Cross-sectional secondary data analysis using 2017 Behavioral Risk Factor Surveillance Survey (BRFSS). SETTING: Data from the 2017 BRFSS were used. SAMPLE: 5,562 U.S. adults (age 18+). MEASURES: Self-reported physical activity related variables from U.S. adults (age 18+). ANALYSIS: Separate unadjusted and adjusted linear regression models were performed for each of the dependent variables using survey analysis. RESULTS: Non-users of wearable devices and e-cigarettes account for 96.6% (95%CI [95.7, 97.6]) of the sample, whereas users of wearable devices and e-cigarettes account for 3.3% (95%CI [1.2, 5.4]) of the sample. Those who use e-cigarettes participate in almost 50% less vigorous physical activity minutes per week than nonusers, 46 (95%CI [0.43, 91.57]) and 93 (95%CI [80.59, 106.34]) minutes respectively. Individuals who use e-cigarettes and use wearable devices were found to spend significantly more time in total physical activity per week in both the unadjusted and adjusted linear regressions, P = =0.01 and P = =0.04 respectively. CONCLUSION: The use of e-cigarettes, wearable devices, or both technologies may influence the physical activity levels of its users. Additional research is needed to better understand the association between physical activity levels and the usage of these technologies.