Highly elastic, fatigue-resistant, antibacterial, conductive, and nanocellulose-enhanced hydrogels with selenium nanoparticles loading as strain sensors

The fabrication of highly elastic, fatigue-resistant and conductive hydrogels with antibacterial properties is highly desirable in the field of wearable devices. However, it remains challenging to simultaneously realize the above properties within one hydrogel without compromising excellent sensing ability. Herein, we fabricated a highly elastic, fatigue-resistant, conductive, antibacterial and cellulose nanocrystal (CNC) enhanced hydrogel as a sensitive strain sensor by the synergistic effect of biosynthesized selenium nanoparticles (BioSeNPs), MXene and nanocellulose. The structure and potential mechanism to generate biologically synthesized SeNPs (BioSeNPs) were systematically investigated, and the role of protease A (PrA) in enhancing the adsorption between proteins and SeNPs was demonstrated. Additionally, owing to the incorporation of BioSeNPs, CNC and MXene, the synthesized hydrogels showed high elasticity, excellent fatigue resistance and antibacterial properties. More importantly, the sensitivity of hydrogels determined by the gauge factor was as high as 6.24 when a high strain was applied (400-700 %). This study provides a new horizon to synthesize high-performance antibacterial and conductive hydrogels for soft electronics applications.

Biosignals, facial expressions, and speech as measures of workplace stress: Workstress3d dataset

WorkStress3D is a comprehensive collection of multimodal data for the research of stress in the workplace. This dataset contains biosignals, facial expressions, and speech signals, making it an invaluable resource for stress analysis and related studies. The ecological validity of the dataset was ensured by the fact that the data were collected in actual workplace environments. The biosignal data contains measurements of electrodermal activity, blood volume pressure, and cutaneous temperature, among others. High-resolution video recordings were used to capture facial expressions, allowing for a comprehensive analysis of facial cues associated with tension. In order to capture vocal characteristics indicative of tension, speech signals were recorded. The dataset contains samples from both stress-free and stressful work situations, providing a proportionate representation of various stress levels. The dataset is accompanied by extensive metadata and annotations, which facilitate in-depth analysis and interpretation. WorkStress3D is a valuable resource for developing and evaluating stress detection models, examining the impact of work environments on stress levels, and exploring the potential of multimodal data fusion for stress analysis.

Miniaturized therapeutic systems for ultrasound-modulated drug delivery to the central and peripheral nervous system

Ultrasound is a promising technology to address challenges in drug delivery, including limited drug penetration across physiological barriers and ineffective targeting. Here we provide an overview of the significant advances made in recent years in overcoming technical and pharmacological barriers using ultrasound-assisted drug delivery to the central and peripheral nervous system. We commence by exploring the fundamental principles of ultrasound physics and its interaction with tissue. The mechanisms of ultrasonic-enhanced drug delivery are examined, as well as the relevant tissue barriers. We highlight drug transport through such tissue barriers utilizing insonation alone, in combination with ultrasound contrast agents (e.g., microbubbles), and through innovative particulate drug delivery systems. Furthermore, we review advances in systems and devices for providing therapeutic ultrasound, as their practicality and accessibility are crucial for clinical application.

Diagnostic accuracy of apple watch ECG outputs in identifying dysrhythmias: A comparison with 12-Lead ECG in emergency department

BACKGROUND

Wearable devices, particularly smartwatches like the Apple Watch (AW), can record important cardiac information, such as single‑lead electrocardiograms (ECGs). Although they are increasingly used to detect conditions such as atrial fibrillation (AF), research on their effectiveness in detecting a wider range of dysrhythmias and abnormal ECG findings remains limited. The primary objective of this study is to evaluate the accuracy of the AW in detecting various cardiac rhythms by comparing it with standard ECG's lead-I.

METHODS

This single-center prospective observational study was conducted in a tertiary care emergency department (ED) between 1.10.2023 and 31.10.2023. The study population consisted of all patients assessed in the critical care areas of the ED, all of whom underwent standard 12‑lead ECGs for various clinical reasons. Participants in the study were included consecutively. An AW was attached to patients' wrists and an ECG lead-I printout was obtained. Heart rate, rhythm and abnormal findings were evaluated and compared with the lead-I of standard ECG. Two emergency medicine specialists performed the ECG evaluations. Rhythms were categorized as normal sinus rhythm and abnormal rhythms, while ECG findings were categorized as the presence or absence of abnormal findings. AW and 12‑lead ECG outputs were compared using the McNemar test. Predictive performance analyses were also performed for subgroups. Bland-Altman analysis using absolute mean differences and concordance correlation coefficients was used to assess the level of heart rate agreement between devices.

RESULTS

The study was carried out on 721 patients. When analyzing ECG rhythms and abnormal findings in lead-I, the effectiveness of AW in distinguishing between normal and abnormal rhythms was similar to standard ECGs (p = 0.52). However, there was a significant difference between AW and standard ECGs in identifying abnormal findings in lead-I (p < 0.05). Using Bland-Altman analysis for heart rate assessment, the absolute mean difference for heart rate was 0.81 ± 6.12 bpm (r = 0.94). There was strong agreement in 658 out of 700 (94%) heart rate measurements.

CONCLUSION

Our study indicates that the AW has the potential to detect cardiac rhythms beyond AF. ECG tracings obtained from the AW may help evaluate cardiac rhythms prior to the patient's arrival in the ED. However, further research with a larger patient cohort is essential, especially for specific diagnoses.

A randomized controlled mHealth trial that evaluates social comparison-oriented gamification to improve physical activity, sleep quantity, and quality of life in young adults

INTRODUCTION

The integration of gamification in mHealth interventions presents a novel approach to enhance user engagement and health outcomes. This study aims to evaluate whether comparison-oriented gamification can effectively improve various aspects of health and well-being, including physical activity, sedentary behavior, sleep, and overall quality of life among young adults.

METHODS

Potential 107 young adults (from 19 to 28 years old) participated in an 8-week trial. Participants were assigned to either a gamified mHealth intervention (LevantApp) with daily leaderboards and progress bars (n = 53, 26 % dropped-out), or a control condition without gamification (n = 52, 29 % dropped-out). Physical activity (number of steps, moderate and moderate-to-vigorous physical activity -MVPA-) and sleep quantity were measured objectively via accelerometry and subjectively using the International Physical Activity Questionnaire(IPAQ), Pittsburgh Sleep Quality Index(PSQI), Sedentary Behavior Questionnaire(SBQ), and Short Form Health Survey(SF-36).

RESULTS

This mHealth intervention with social comparison-oriented gamification significantly improved moderate physical activity to a greater extent than the control group. Additionally, the intervention group showed improvements in the number of steps, moderate physical activity, sedentary time, emotional wellbeing, and social functioning. However, no significant group by time interaction was observed. No significant differences were observed in sleep quality or quantity.

CONCLUSION

s: The LevantApp gamified mHealth intervention was effective in improving moderate physical activity, physical functioning, and role-emotional in young adults. No significant effects were found on step counts, MVPA or sleep, suggesting that while gamification can enhance specific aspects of physical activity and quality of life, its impact may vary across different outcomes.

Use of a wearable device to compare subjective and objective fatigue in lung cancer patients and cancer-free controls

PURPOSE

The study evaluates the use of heart rate variability (HRV), a measure of autonomic nervous system (ANS) modulation via wearable smart bands, to objectively assess cancer-related fatigue (CRF) levels. It aims to enhance understanding of fatigue by distinguishing between LF/HF ratios and LF/HF disorder ratios through HRV and photoplethysmography (PPG), identifying them as potential biomarkers.

METHODS

Seventy-one lung cancer patients and 75 non-cancer controls wore smart bands for one week. Fatigue was assessed using Brief Fatigue Inventory, alongside sleep quality and daily interference. HRV parameters were analyzed to compare groups.

RESULTS

Cancer patients showed higher fatigue and interference levels than controls (64.8% vs. 54.7%). Those with mild fatigue had elevated LF/HF disorder ratios during sleep (40% vs. 20%, P = 0.01), similar to those with moderate to severe fatigue (50% vs. 20%, P = 0.01), indicating more significant autonomic dysregulation. Notably, mild fatigue patients had higher mean LF/HF ratios than controls (1.9 ± 1.34 vs. 1.2 ± 0.6, P = 0.01), underscoring the potential of disorder ratios in signaling fatigue severity.

CONCLUSIONS

Utilizing wearable smart bands for HRV-based analysis is feasible for objectively assess CRF levels in cancer patients, especially during sleep. By distinguishing between LF/HF ratios and LF/HF disorder ratios, our findings suggest that wearable technology and detailed HRV analysis offer promising avenues for real-time fatigue monitoring. This approach has the potential to significantly improve cancer care by providing new methods for managing and intervening in CRF, particularly with a focus on autonomic dysregulation as a crucial factor.

Identifying changes in dynamic plantar pressure associated with radiological knee osteoarthritis based on machine learning and wearable devices

BACKGROUND

Knee osteoarthritis (KOA) is an irreversible degenerative disease that characterized by pain and abnormal gait. Radiography is typically used to detect KOA but has limitations. This study aimed to identify changes in plantar pressure that are associated with radiological knee osteoarthritis (ROA) and to validate them using machine learning algorithms.

METHODS

This study included 92 participants with variable degrees of KOA. A modified Kellgren-Lawrence scale was used to classify participants into non-ROA and ROA groups. The total feature set included 210 dynamic plantar pressure features captured by a wearable in-shoe system as well as age, gender, height, weight, and body mass index. Filter and wrapper methods identified the optimal features, which were used to train five types of machine learning classification models for further validation: k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), AdaBoost, and eXtreme gradient boosting (XGBoost).

RESULTS

Age, the standard deviation (SD) of the peak plantar pressure under the left lateral heel (f_L8PPP_std), the SD of the right second peak pressure (f_Rpeak2_std), and the SD of the variation in the anteroposterior displacement of center of pressure (COP) in the right foot (f_RYcopstd_std) were most associated with ROA. The RF model with an accuracy of 82.61% and F1 score of 0.8000 had the best generalization ability.

CONCLUSION

Changes in dynamic plantar pressure are promising mechanical biomarkers that distinguish between non-ROA and ROA. Combining a wearable in-shoe system with machine learning enables dynamic monitoring of KOA, which could help guide treatment plans.

Support Through Remote Observation and Nutrition Guidance (STRONG), a digital health intervention to reduce malnutrition among pancreatic cancer patients: A study protocol for a pilot randomized controlled trial

Background

Malnutrition is a common and distressing condition among pancreatic cancer patients. Fewer than a quarter of pancreatic cancer patients receive medical nutrition therapy (MNT), important for improving nutritional status, weight maintenance, quality of life and survival. System, provider, and patient level barriers limit access to MNT. We propose to examine the feasibility of a 12-week multi-level, digital health intervention designed to expand MNT access among pancreatic cancer patients.

Methods

Individuals with advanced pancreatic cancer starting chemotherapy (N = 80) will be 1:1 randomized to the intervention or usual care. The Support Through Remote Observation and Nutrition Guidance (STRONG) intervention includes system-level (e.g., routine malnutrition and screening), provider-level (e.g., dietitian training and web-based dashboard), and patient-level strategies (e.g., individualized nutrition plan, self-monitoring of dietary intake via Fitbit, ongoing goal monitoring and feedback). Individuals receiving usual care will be referred to dietitians based on their oncologists' discretion. Study assessments will be completed at baseline, 4-, 8-, 12-, and 16-weeks.

Results

Primary outcomes will be feasibility (e.g., recruitment, retention, assessment completion) and acceptability. We will collect additional implementation outcomes, such as intervention adherence, perceived usability, and feedback on intervention quality via an exit interview. We will collect preliminary data on outcomes that may be associated with the intervention including malnutrition, quality of life, treatment outcomes, and survival.

Conclusion

This study will advance our knowledge on the feasibility of a digital health intervention to reduce malnutrition among individuals with advanced pancreatic cancer. Trial registration: NCT05675059, registered on December 9, 2022.

The role of wearable home blood pressure monitoring in detecting out-of-office control status

Ambulatory blood pressure (ABP) and home blood pressure (HBP) monitoring is currently recommended for management of hypertension. Nonetheless, traditional HBP protocols could overlook diurnal fluctuations, which could also be linked with adverse cardiovascular outcomes. In this observational study, we studied among a group of treated hypertensive patients (N = 62, age: 52.4 ± 10.4 years) by using out-of-office ABP and wearable HBP. They received one session of 24-h ABP measurement with an oscillometric upper-arm monitor, and totally three sessions of 7-day/6-time-daily wearable HBP measurement separated in each month with HeartGuide. Controlled hypertension is defined as an average BP <130/80 mmHg for both daytime ABP and HBP. There was substantial reliability (intraclass correlation coefficient, ICC 0.883-0.911) and good reproducibility (Cohen's kappa = 0.600) for wearable HBP measurement, especially before breakfast and after dinner. Among all patients, 27.4% had both uncontrolled HBP and ABP, 30.6% had uncontrolled HBP only, while 6.5% had uncontrolled ABP only. Female gender and increased numbers of anti-hypertensive agents are correlated with controlled hypertension. Patients with uncontrolled hypertension had a significantly higher maximal daytime blood pressure, which was previously signified as an imperial marker for cardiovascular risk. In conclusion, wearable HBP monitoring in accordance with a dedicated daily-living schedule results in good reliability and reproducibility. Patients with an uncontrolled wearable HBP should benefit from repeated HBP or ABP measurement for risk stratification.

Effectiveness of a Lifestyle Improvement Support App in Combination with a Wearable Device in Japanese People with Type 2 Diabetes Mellitus: STEP-DM Study

INTRODUCTION

Although the use of application (app)s and wearable devices supporting diabetes treatment has spread rapidly in recent years, evidence of their impact, especially in combination of them, is limited. TOMOCO™ is a lifestyle improvement support app that features interactive virtual conversations according to the programmed algorithm guiding users toward their goals of lifestyle improvement. We hypothesized that TOMOCO™ in combination with Fitbit, which accurately tracks users' activity level, would encourage people with type 2 diabetes mellitus (T2DM) to change their lifestyles and improve their glycated hemoglobin (HbA1c) levels without changes in conventional therapy. Thus, we performed the present study to explore the effectiveness of this combination in Japanese participants with T2DM who had not achieved their glycemic targets.

METHODS

In this single-arm exploratory study, participants with T2DM used the TOMOCO™ and Fitbit in addition to the conventional diet/exercise therapy and anti-diabetic drug for 12 weeks. They were provided with feedback/advice by health care providers based on the TOMOCO™ and Fitbit records. The primary endpoint was the change in HbA1c from baseline to the end of the observation period. Data were expressed as mean ± standard deviation.

RESULTS

Fifty-nine (96.7%) of the 61 participants (male, 42 [71.2%]; age, 60.1 ± 8.7 years; HbA1c level, 7.48 ± 0.37% at screening) completed the study. At the end of the observation period, the HbA1c was significantly reduced (- 0.41 ± 0.41%, p < 0.001). This trend was consistent across the preselected patient characteristics, including sex, age, and body mass index. However, it was more pronounced in the participants with earlier stages of behavioral changes defined by the transtheoretical model at baseline.

CONCLUSIONS

The unique features of TOMOCO™ in combination with Fitbit, together with conventional therapy, may promote a healthy lifestyle and thus contribute to improving HbA1c in people with T2DM.

CLINICAL TRIAL REGISTRATION

jRCT1070220007.

Implementation difficulties and solutions for a smart-clothes assisted home nursing care program for older adults with dementia or recovering from hip fracture

BACKGROUND

Wearable devices have the advantage of always being with individuals, enabling easy detection of their movements. Smart clothing can provide feedback to family caregivers of older adults with disabilities who require in-home care.

METHODS

This study describes the process of setting up a smart technology-assisted (STA) home-nursing care program, the difficulties encountered, and strategies applied to improve the program. The STA program utilized a smart-vest, designed specifically for older persons with dementia or recovering from hip-fracture surgery. The smart-vest facilitated nurses' and family caregivers' detection of a care receiver's movements via a remote-monitoring system. Movements included getting up at night, time spent in the bathroom, duration of daytime immobility, leaving the house, and daily activity. Twelve caregivers of older adults and their care receiver participated; care receivers included persons recovering from hip fracture (n = 5) and persons living with dementia (n = 7). Data about installation of the individual STA in-home systems, monitoring, and technical difficulties encountered were obtained from researchers' reports. Qualitative data about the caregivers' and care receivers' use of the system were obtained from homecare nurses' reports, which were explored with thematic analysis.

RESULTS

Compiled reports from the research team identified three areas of difficulty with the system: incompatibility with the home environment, which caused extra hours of manpower and added to the cost of set-up and maintenance; interruptions in data transmissions, due to system malfunctions; and inaccuracies in data transmissions, due to sensors on the smart-vest. These difficulties contributed to frustration experienced by caregivers and care receivers.

CONCLUSIONS

The difficulties encountered impeded implementation of the STA home nursing care. Each of these difficulties had their own unique problems and strategies to resolve them. Our findings can provide a reference for future implementation of similar smart-home systems, which could facilitate ease-of-use for family caregivers.

Cross-linkable fullerene enables elastic and conductive grain boundaries for efficient and wearable tin-based perovskite solar cells

Tin-based perovskite solar cells (TPSCs) have received increasing attention due to their low toxicity, high theoretical efficiency, and potential applications as wearable devices. However, the inherent fast and uncontrollable crystallization process of tin-based perovskites results in high defect density in the film. Meanwhile, when fabricated into flexible devices, the prepared perovskite film exhibits inevitable brittleness and high Young's modulus, seriously weakening the mechanical stability. In this work, we design and synthesize a cross-linkable fullerene, thioctic acid functionalized C60 fulleropyrrolidinium iodide (FTAI), which has multiple interactions with perovskite components and can finely regulate the crystallization quality of perovskite film. The obtained perovskite film shows an increased grain size and a more matched energy level with the electron transport material, effectively improving the carrier extraction efficiency. The FTAI-based rigid device achieves a champion efficiency of 14.91% with enhanced stability. More importantly, the FTAI located at the perovskite grain boundaries could spontaneously cross-link during the perovskite annealing process, which effectively improves the conductivity and elasticity of grain boundaries, thereby giving the film excellent bending resistance. Finally, the FTAI-based wearable device yields a record efficiency of 12.35% and displays robust bending durability, retaining about 90% of the initial efficiency after 10,000 bending times.

Validity and reliability of the Oura Ring Generation 3 (Gen3) with Oura sleep staging algorithm 2.0 (OSSA 2.0) when compared to multi-night ambulatory polysomnography: A validation study of 96 participants and 421,045 epochs

PURPOSE

To evaluate the validity and the reliability of the Oura Ring Generation 3 (Gen3) with Oura Sleep Staging Algorithm 2.0 (OSSA 2.0) through multi-night polysomnography (PSG).

PARTICIPANTS AND METHODS

Participants were 96 generally healthy Japanese men and women aged between 20 and 70 years contributing with 421,045 30-s epochs. Sleep scoring was performed according to American Academy of Sleep Medicine criteria. Each participant could contribute with a maximum of three polysomnography (PSG) nights. Within-participant means were created for each sleep measure and paired t-tests were used to compare equivalent measures obtained from the PSG and Oura Rings (non-dominant and dominant hand). Agreement between sleep measures were assessed using Bland-Altman plots. Interrater reliability for epoch accuracy was determined by prevalence-adjusted and bias-adjusted kappa (PABAK).

RESULTS

The Oura Ring did not significantly differ from PSG for the measures time in bed, total sleep time, sleep onset latency, sleep period time, wake after sleep onset, time spent in light sleep, and time spent in deep sleep. Oura Rings worn on the non-dominant- and dominant-hand underestimated sleep efficiency by 1.1 %-1.5 % and time spent in REM sleep by 4.1-5.6 min. The Oura Ring had a sensitivity of 94.4 %-94.5 %, specificity of 73.0 %-74.6 %, a predictive value for sleep of 95.9 %-96.1 %, a predictive value for wake of 66.6 %-67.0 %, and accuracy of 91.7 %-91.8 %. PABAK was 0.83-0.84 and reliability was 94.8 %. Sleep staging accuracy ranged between 75.5 % (light sleep) and 90.6 % (REM sleep).

CONCLUSIONS

The Oura Ring Gen3 with OSSA 2.0 shows good agreement with PSG for global sleep measures and time spent in light and deep sleep.

A low-cost, wireless, 4-channel EEG measurement system used in virtual reality environments

The combination of Virtual Reality (VR) technology and Electroencephalography (EEG) measurements has shown tremendous potential in the fields of psychology and neuroscience research. However, the majority of EEG measurement devices currently available are expensive, bulky, uncomfortable to wear, and difficult to integrate with VR headsets. These limitations have hindered the development of related research fields. This study describes a low-cost (60.07 USD), small-sized, wireless, high-precision, low-power consumption 4-channel EEG measurement system (NeuroVista) for frontal area EEG measurements, which can be used with a VR headset, enabling EEG measurements in VR environments. The system has an input-referred noise of less than 0.9480 μ V r m s , a common mode rejection ratio of over 96 dB, a measurement resolution of less than 0.1 μ V , a bandwidth of 0.5 ∼ 45 Hz, and works at a sampling rate of 250 Hz. It also supports metal dry electrodes and includes a built-in analog bandpass filter, right-leg drive circuit, and built-in digital lowpass filter and notch filter, which can reduce noise during measurement. Researchers can reconstruct the electrode system to measure regions of interest according to their needs.

Associations between performance-based and patient-reported physical functioning and real-world mobile sensor metrics in older cancer survivors: A pilot study

INTRODUCTION

Older cancer survivors are at increased risk for impaired physical functioning, but current assessments of function are difficult to implement in busy oncology clinics. Mobile devices measuring continuous activity and mobility in daily life may be useful for estimating physical functioning. The goal of this pilot study was to examine the associations between consumer wearable device (a wrist-worn activity tracker) and smartphone sensor data and commonly used clinical measures of physical function in cancer survivors aged 65 and older.

MATERIALS AND METHODS

Older adults within five years of completing primary treatment for any cancer completed standardized questionnaires and performance-based tests to measure physical functioning. Continuous passive data from smartphones and consumer wearable devices were collected for four weeks and linked to patient-reported and performance-based physical functioning as well as patient-reported falls or near falls at the end of the four-week monitoring period. To examine associations between sensor variables and physical functioning, we conducted bivariate Pearson correlations as well as multivariable linear regression analyses. To examine associations between sensor variables and falls, we conducted exploratory receiver operating characteristic curve and multivariable logistic regression analyses.

RESULTS

We enrolled 40 participants (mean age 73 years old, range 65-83; 98% White; 50% female). In bivariate analyses, consumer wearable device features reflecting greater amount and speed and lower fragmentation of walking in daily life were significantly related to better patient-reported function (r= 0.43-0.65) and performance-based physical function (r = 0.56-0.72), while smartphone features reflecting more geographic mobility were related to better performance-based physical function (r = 0.40-0.42) but not patient-reported function. After adjusting for age and comorbidities, only consumer wearable device features remained associated with performance-based physical functioning. In exploratory analyses, peak gait cadence was associated with fall risk even after covariate adjustment.

DISCUSSION

This study provides preliminary evidence that real-world data from consumer devices may be useful for estimating functional performance among older cancer survivors and potentially for remotely and longitudinally monitoring functioning in older patients during and after cancer treatment.

Predictors of physical activity recovery after total hip arthroplasty: a prospective observational study

PURPOSE

Little is known about the changes and the factors in physical activity as following total hip arthroplasty (THA). There are potential discrepancies between subjective and objective measurements of physical activity. Thus, our porpose is to compare objective and subjective measurements of activity levels in patients undergoing THA preoperatively, three months and one year following surgery, and investigated the factors that predicts the objective activity level after THA.

METHODS

This prospective observational study included 42 patients with unilateral symptomatic hip osteoarthritis who underwent THA. The objective activity level (step counts, sit-to-stands, and upright time) by using a tri-axial accelerometer, PRO (Oxford hip score; OHS and University of California, Los Angeles activity score; UCLA score), and muscle volume around the hip joint by using preoperative computed tomography were measured.

RESULTS

The number of steps, OHS, and UCLA score before, at three months, and one year after THA averaged 5092, 6532, and 6545 steps, 30.3, 43.0, and 44.2 points, and 3.4, 4.8, and 4.6 points, respectively, with significant postoperative increases (P < 0.05). No significant difference was found between three months and one year postoperatively. In multivariate analysis, younger age and higher preoperative contralateral gluteal medius volume were the predictors of higher postoperative step counts (P < 0.05).

CONCLUSIONS

Physical activity, including step counts, OHS, and UCLA score increased significantly until three months after unilateral THA. Early surgical intervention before contralateral muscle declines and preoperative rehabilitation including contralateral side may additionally improve postoperative activity levels.

Continuous timely monitoring of core temperature with two wearable devices in pediatric patients undergoing chemotherapy for cancer - a comparison study

PURPOSE

Pediatric patients with cancer often develop chemotherapy-induced fever in neutropenia (FN), requiring emergency broad-spectrum antibiotics. Continuous temperature monitoring can lead to earlier FN detection and therapy with improved outcomes. We aimed to compare the feasibility of continuous core temperature monitoring with timely data availability between two wearable devices (WDs) in pediatric oncology patients undergoing chemotherapy.

METHODS

In this prospective observational two-center study, 20 patients (median age: 8 years) undergoing chemotherapy simultaneously wore two WDs (CORE®, Everion®) for 14 days. The predefined goal was core temperature recorded in sufficient quality and available within ≤ 30 min during ≥ 18/24 h for ≥ 7/14 days in more than 15 patients.

RESULTS

More patients reached the goal with CORE® (n = 13) versus Everion® (n = 3) (difference, 50% p < 0.001). After correcting for the transmission bottleneck caused by two WDs transmitting via one gateway, these numbers increased (n = 15 versus n = 14; difference, 5%; p = 0.69). CORE® measurements corresponded better to ear temperatures (n = 528; mean bias, - 0.07 °C; mean absolute difference, 0.35 °C) than Everion® measurements (n = 532; - 1.06 °C; 1.10 °C). Acceptance rates for the WDs were 95% for CORE® and 89% for Everion®.

CONCLUSION

The CORE® fulfilled the predefined feasibility criterion (15 of 20 patients) after correction for transmission bottleneck, and the Everion® nearly fulfilled it. Continuous core temperature recording of good quality and with timely data availability was feasible from preschool to adolescent patients undergoing chemotherapy for cancer. These results encourage the design of randomized controlled trials on continuously monitored core temperature in pediatric patients.

TRIAL REGISTRATION

CLINICALTRIALS

gov (NCT04914702) on June 7, 2021.

Early changes in skin surface temperature predict body temperature increases in patients with fever: A pilot study

OBJECTIVE

To investigate the correlation between body temperature and skin surface temperature in intensive care unit patients and to identify specific indicators of skin surface temperature for early fever detection.

RESEARCH METHODOLOGY/DESIGN

This pilot study was a prospective, observational investigation conducted at National Cheng Kung University Hospital in Tainan, Taiwan. A total of 54 patients admitted to the Surgical Intensive Care Unit of a tertiary hospital between April and August 2020 were included. Patients utilized the wearable device HEARThremoTM to continuously monitor skin surface temperature and heart rate. Analysis of Variance was applied to identify the association of skin surface temperature with different body temperature groups. The comparison between skin surface temperature and fever over eight time intervals was studied using a generalized estimating equation.

RESULTS

In 34 patients (63 %) with a fever (≥38 °C), skin surface temperature increased (P < 0.001) when body temperature increased. The maximum skin surface temperature was significantly associated with fever 180-210 min before the fever events occurred (OR: 2.22, 95 % CI: 1.30-3.80). The mean skin surface temperature was associated with fever 120-150 min before the fever events (OR: 8.70, 95 % CI: 2.08-36.36).

CONCLUSIONS

Skin surface temperature can be an important early predictive sign before the onset of fever. Continuous temperature monitoring can detect fever early and initiate treatment in advance. This study serves as a preliminary exploration in this area, laying the groundwork for future comprehensive research.

IMPLICATIONS FOR CLINICAL PRACTICE

Continuous monitoring of skin surface temperature empowers nurses to swiftly detect fever, transcending conventional methods. This proactive approach allows for the early identification of physiological abnormalities, facilitating the prompt initiation of further physical assessments and relevant examinations for early treatment commencement.

Deep Learning Models for Predicting Left Heart Abnormalities From Single-Lead Electrocardiogram for the Development of Wearable Devices

BACKGROUND

Left heart abnormalities are risk factors for heart failure. However, echocardiography is not always available. Electrocardiograms (ECGs), which are now available from wearable devices, have the potential to detect these abnormalities. Nevertheless, whether a model can detect left heart abnormalities from single Lead I ECG data remains unclear.Methods and Results: We developed Lead I ECG models to detect low ejection fraction (EF), wall motion abnormality, left ventricular hypertrophy (LVH), left ventricular dilatation, and left atrial dilatation. We used a dataset comprising 229,439 paired sets of ECG and echocardiography data from 8 facilities, and validated the model using external verification with data from 2 facilities. The area under the receiver operating characteristic curves of our model was 0.913 for low EF, 0.832 for wall motion abnormality, 0.797 for LVH, 0.838 for left ventricular dilatation, and 0.802 for left atrial dilatation. In interpretation tests with 12 cardiologists, the accuracy of the model was 78.3% for low EF and 68.3% for LVH. Compared with cardiologists who read the 12-lead ECGs, the model's performance was superior for LVH and similar for low EF.

CONCLUSIONS

From a multicenter study dataset, we developed models to predict left heart abnormalities using Lead I on the ECG. The Lead I ECG models show superior or equivalent performance to cardiologists using 12-lead ECGs.

Multi-night measurement for diagnosis and simplified monitoring of obstructive sleep apnoea

Substantial night-to-night variability in obstructive sleep apnoea (OSA) severity has raised misdiagnosis and misdirected treatment concerns with the current prevailing single-night diagnostic approach. In-home, multi-night sleep monitoring technology may provide a feasible complimentary diagnostic pathway to improve both the speed and accuracy of OSA diagnosis and monitor treatment efficacy. This review describes the latest evidence on night-to-night variability in OSA severity, and its impact on OSA diagnostic misclassification. Emerging evidence for the potential impact of night-to-night variability in OSA severity to influence important health risk outcomes associated with OSA is considered. This review also characterises emerging diagnostic applications of wearable and non-wearable technologies that may provide an alternative, or complimentary, approach to traditional OSA diagnostic pathways. The required evidence to translate these devices into clinical care is also discussed. Appropriately sized randomised controlled trials are needed to determine the most appropriate and effective technologies for OSA diagnosis, as well as the optimal number of nights needed for accurate diagnosis and management. Potential risks versus benefits, patient perspectives, and cost-effectiveness of these novel approaches should be carefully considered in future trials.

Consumer Wearables Define a Normative Recovery Trajectory Following the Modified Nuss Procedure

INTRODUCTION

The modified Nuss procedure is an elective procedure associated with a lengthy recovery, uncontrolled pain, and risk of infrequent, yet life-threatening complications. The absence of objective measures of normative postoperative recovery creates uncertainty about the postdischarge period, which remains highly dependent on the patients' and their caregivers' expectations and management of recovery. We aimed to describe an objective-normative, physical activity recovery trajectory after the modified Nuss procedure, using step counts from the Fitbit.

METHODS

This observational study enrolled children ≤18 y with pectus excavatum who underwent the modified Nuss procedure from 2021 to 2022. The Fitbit was worn for 21 postoperative days. Postdischarge outcomes and health-care utilization were evaluated. For patients without postoperative complications, piecewise linear regression analysis was conducted to generate a normative recovery trajectory model of daily step counts.

RESULTS

Of 80 patients enrolled, 66 (86%) met eligibility criteria (mean age, 15.1 ± 1.3 y; 89.4% male, 62.1% non-Hispanic White). The mean number of telephone and electronic message encounters regarding concerns with the patient's recovery within 30 d postoperatively was 2.1 (standard deviation = 2.7). Ten patients (15.2%) returned to the emergency department (ED) within the 30-d postoperative period. Seven patients (10.6%) presented to the ED one time, and three patients (4.5%) presented to the ED twice. Thirty-day readmission rate was four patients (6.0%). Piecewise regression model showed that patients without complications steadily increased their daily step count on each postoperative day and plateaued on day 18.

CONCLUSIONS

We have developed a normative recovery trajectory following the modified Nuss procedure using step count data collected by a consumer wearable device. This offers the potential to inform preoperative patient expectations and reduce avoidable health-care utilization through informed preoperative counseling, thus laying the ground work for the use of consumer wearable devices as a postdischarge remote monitoring tool.

Blood pressure estimation and classification using a reference signal-less photoplethysmography signal: a deep learning framework

The markers that help to predict th function of a cardiovascular system are hemodynamic parameters like blood pressure (BP), stroke volume, heart rate, and cardiac output. Continuous analysis of hemodynamic parameters such as BP can detect abnormalities earlier, preventing cardiovascular diseases (CVDs). However, sometimes due to motion artifacts, it becomes difficult to monitor the BP accurately and classify it. This work presents an optimized deep learning model having the capability to estimate the systolic blood pressure (SBP) and diastolic blood pressure (DBP) and classify the BP stages simultaneously from the same network using only a single channel photoplethysmography (PPG) signal. The proposed model is designed by exploiting the deep learning framework of a convolutional neural network (CNN), exhibiting the inherent ability to extract features automatically. Moreover, the proposed framework utilizes the superlet transform method to transform a 1-D PPG signal into a 2-D super-resolution time-frequency (TF) spectrogram. A superlet transform separates the peaks related to true PPG signal components and motion artifacts components. Thus, the superlet provides a robust realtime approach to accurately estimating and classifying BP using a single PPG sensor signal and does not require additional ECG and PPG sensor signals for reference. Using a super-resolution spectrogram and CNN model makes the method profitable in motion artifact removal, feature selection, and extraction. Hence the proposed framework becomes less complex for deployment on wearable devices having limited battery resources. The performance of the proposed framework is demonstrated on the publicly available larger dataset MIMIC-III. This work obtained a mean absolute error (MAE) of 2.71 mmHg and 2.42 mmHg for SBP and DBP, respectively. The classification accuracy for the SBP prediction is about 96.79%, whereas it is 98.94% for DBP. From a motion artifact-affected PPG signal, SBP and DBP are estimated. Then the estimated BP is classified into three categories: normotension, prehypertension, and hypertension, and is compared with the state of art methods to show the effectiveness of the proposed optimized framework.

[Correlation Between 6 Characteristics of Perioperative Hypothermia and Allogeneic Red Blood Cell Transfusion in Abdominal Surgery Patients]

Objective

To explore the correlation between six characteristics of perioperative hypothermia and allogeneic red blood cell (RBC) transfusions in patients who underwent abdominal surgeries.

Methods

Patients who underwent abdominal surgeries at West China Hospital, Sichuan University between October 2019 and July 2021 were retrospectively enrolled. A wearable wireless temperature sensor was used to continuously monitor the core body temperature of patients throughout the perioperative period. The perioperative temperature nadir, maximum temperature loss, percentage of time with hypothermia, time-weighted average temperature, area under the curve (AUC) at 36 ℃, and AUC at 37 ℃ were calculated for the period from entering the operation room to 24 hours after the end of anesthesia. The restricted cubic spline (RCS) and multiple logistic regression models were used to explore the correlation between these temperature characteristics and perioperative allogeneic RBC transfusions.

Results

A total of 3119 patients were included in the study, with an allogeneic RBC transfusion rate of 2.8%. The RCS model showed that allogeneic RBC transfusion was associated with the perioperative temperature nadir (Poverall=0.048) and AUC at 36 ℃ (Poverall=0.026) and no statistical significance was found in the nonlinear test. The association between allogeneic RBC transfusions and other temperature characteristics was not statistically significant. According to the RCS model results, cut-off points were taken to form groups based on the body temperature characteristics. Multivariate logistic regression showed that the perioperative temperature nadir<35.5 ℃ (odds ratio [OR]=2.47, 95% confidence interval [CI]: 1.21-5.03) and AUC at 36 ℃≥100 ℃·min (OR=2.24, 95% CI:1.09-4.58) were associated with increased demand for allogeneic RBC transfusion.

Conclusion

Hypothermia is associated with an increased need for perioperative allogeneic RBC transfusions and has a cumulative effect over time. For patients at high risk of bleeding, attention should be paid to the prevention of perioperative hypothermia and reduction in the cumulative exposure to hypothermia, thereby reducing the need for blood transfusion.

Effects of overground gait training assisted by a wearable exoskeleton in patients with Parkinson's disease

BACKGROUND

In the recent past, wearable devices have been used for gait rehabilitation in patients with Parkinson's disease. The objective of this paper is to analyze the outcome of a wearable hip orthosis whose assistance adapts in real time to the patient's gait kinematics via adaptive oscillators. In particular, this study focuses on a metric characterizing natural gait variability, i.e., the level of long-range autocorrelations (LRA) in series of stride durations.

METHODS

Eight patients with Parkinson's disease (Hoehn and Yahr stages 1[Formula: see text]2.5) performed overground gait training three times per week for four consecutive weeks, assisted by a wearable hip orthosis. Gait was assessed based on performance metrics such as the hip range of motion, speed, stride length and duration, and the level of LRA in inter-stride time series assessed using the Adaptive Fractal Analysis. These metrics were measured before, directly after, and 1 month after training.

RESULTS

After training, patients increased their hip range of motion, their gait speed and stride length, and decreased their stride duration. These improvements were maintained 1 month after training. Regarding long-range autocorrelations, the population's behavior was standardized towards a metric closer to the one of healthy individuals after training, but with no retention after 1 month.

CONCLUSION

This study showed that an overground gait training with adaptive robotic assistance has the potential to improve key gait metrics that are typically affected by Parkinson's disease and that lead to higher prevalence of fall.

TRIAL REGISTRATION

ClinicalTrials.gov Identifer NCT04314973. Registered on 11 April 2020.

Field effect transistor based wearable biosensors for healthcare monitoring

The rapid advancement of wearable biosensors has revolutionized healthcare monitoring by screening in a non-invasive and continuous manner. Among various sensing techniques, field-effect transistor (FET)-based wearable biosensors attract increasing attention due to their advantages such as label-free detection, fast response, easy operation, and capability of integration. This review explores the innovative developments and applications of FET-based wearable biosensors for healthcare monitoring. Beginning with an introduction to the significance of wearable biosensors, the paper gives an overview of structural and operational principles of FETs, providing insights into their diverse classifications. Next, the paper discusses the fabrication methods, semiconductor surface modification techniques and gate surface functionalization strategies. This background lays the foundation for exploring specific FET-based biosensor designs, including enzyme, antibody and nanobody, aptamer, as well as ion-sensitive membrane sensors. Subsequently, the paper investigates the incorporation of FET-based biosensors in monitoring biomarkers present in physiological fluids such as sweat, tears, saliva, and skin interstitial fluid (ISF). Finally, we address challenges, technical issues, and opportunities related to FET-based biosensor applications. This comprehensive review underscores the transformative potential of FET-based wearable biosensors in healthcare monitoring. By offering a multidimensional perspective on device design, fabrication, functionalization and applications, this paper aims to serve as a valuable resource for researchers in the field of biosensing technology and personalized healthcare.

Effects of error rates and target sizes on neck and shoulder biomechanical loads during augmented reality interactions

Augmented reality (AR) interactions have been associated with increased biomechanical loads on the neck and shoulders. To provide a better understanding of the factors that may impact such biomechanical loads, this repeated-measures laboratory study evaluated the effects of error rates and target sizes on neck and shoulder biomechanical loads during two standardized AR tasks (omni-directional pointing and cube placing). Twenty participants performed the two AR tasks with different error rates and target sizes. During the tasks, angles, moments, and muscle activity in the neck and shoulders were measured. The results showed that the target sizes and error rates significantly affected angles, moments, and muscle activity in the neck and shoulder regions. Specifically, the presence of errors increased neck extension, shoulder flexion angles and associated moments. Muscle activity in the neck (splenius capitis) and shoulder (anterior and medial deltoids) also increased when the errors were introduced. Moreover, interacting with larger targets resulted in greater neck extension moments and shoulder abduction angles along with higher muscle activity in the splenius capitis and upper trapezius muscles. These findings indicate the importance of reducing errors and incorporating appropriate target sizes in the AR interfaces to minimize risks of musculoskeletal discomfort and injuries in the neck and shoulders.

A flexible and wearable three-electrode electrochemical sensing system consisting of a two-in-one enzyme-mimic working electrode

In this work, a wearable and flexible three-electrode electrochemical sensing system (TESS) by using a two-in-one enzyme-mimic working electrode (TIOWE) is reported. The integrated three-electrode, including working electrodes, reference electrodes, and counter electrodes are formed by transfer printing of Ni2P-based composite electrode ink (Ni2P/G ink), Ag/AgCl ink, and carbon ink onto PDMS substrate, respectively. The Ni2P/G ink-based working electrodes have both good conductivity and enzyme-mimic catalytic activity towards glucose. Under optimized conditions, the TIOWE-TESS has a low detection limit of 0.37 μM and wide linear ranges of 0.001 mM-0.1 mM and 0.1 mM-1.4 mM. Furthermore, the TIOWE-TESS has good applicability in serum samples and reveals remarkable electrochemical performance at fluctuant working temperatures. The proposed TIOWE-TESS can be integrated on a waterproof bandage to fabricate a skin-friendly patch device for sweet glucose monitoring, which highlights its potential applications in flexible and wearable commercial devices for health-monitoring.

Effectiveness of social media with or without wearable devices to improve physical activity and reduce sedentary behavior: A randomized controlled trial of Chinese postgraduates

The present study was aimed to verify whether an integrating of wearable activity tracker device and a social media intervention strategy would be better than a standalone social media intervention for improving physical activity (PA) and reducing sedentary time for Chinese postgraduate population. A total of 42 full-time postgraduate students participated in this study, which were randomized to receive a 4-week social media intervention through WeChat either with (Wearable Device group) or without (control group) a wearable activity tracker device. Energy expenditure, step counts, moderate to vigorous physical activity time (MVPA) and sedentary time were assessed before and after the intervention. Besides, anthropometric parameters of body weight, body mass index, body fat rate, waist-to-hip ratio, as well as self-reported quality of life were also evaluated. It was found that both energy expenditure and step counts were significantly increased, while sedentary time was significantly reduced during the post-intervention test compared to the baseline test for Wearable Device group. No significant difference of PA was found for the control group. The results demonstrated that the integrating of wearable activity tracker device and a social media intervention was effective in promoting PA, while a standalone social media intervention may have no effect on the influence of PA for Chinese postgraduates.

Association of tibial acceleration during walking to pain and impact loading in adults with knee osteoarthritis

BACKGROUND

Higher impact loading during walking is implicated in the pathogenesis of knee osteoarthritis. Accelerometry enables the measurement of peak tibial acceleration outside the laboratory. We characterized the relations of peak tibial acceleration to knee pain and impact loading during walking in adults with knee osteoarthritis.

METHODS

Adults with knee osteoarthritis reported knee pain then walked at a self-selected speed on an instrumented treadmill for 3 min with an ankle-worn inertial measurement unit. Ground reaction forces and tibial acceleration data were sampled for 1 min. Vertical impact peaks, and average and peak instantaneous load rates were determined and averaged across 10 steps. Peak tibial acceleration was extracted for all steps and averaged. Pearson's correlations and multiple linear regression analyses assessed the relation of peak tibial acceleration to pain and impact loading metrics, independently and after controlling for gait speed and pain.

FINDINGS

Higher peak tibial acceleration was associated with worse knee pain (r = 0.39; p = 0.01), and higher vertical average (r = 0.40; p = 0.01) and instantaneous (r = 0.46; p = 0.004) load rates. After adjusting for gait speed and pain, peak tibial acceleration was a significant predictor of vertical average (R2 = 0.33; p = 0.003) and instantaneous (R2 = 0.28; p = 0.02) load rates, but not strongly associated with vertical impact peak.

INTERPRETATIONS

Peak tibial acceleration during walking is associated with knee pain and vertical load rates in those with knee osteoarthritis. Clinicians can easily access measures of peak tibial acceleration with wearable sensors equipped with accelerometers. Future work should determine the feasibility of improving patient outcomes by using peak tibial acceleration to inform clinical management.

We-VoltamoStat: A wearable potentiostat for voltammetry analysis with a smartphone interface

Wearable technology, such as electronic components integrated into clothing or worn as accessories, is becoming increasingly prevalent in fields like healthcare and biomedical monitoring. These devices allow for continuous monitoring of important biomarkers for medical diagnosis, monitoring of physiological health, and evaluation. However, an open-source wearable potentiostat is a relatively new technology that still faces several design limitations such as short battery lifetime, bulky size, heavy weight, and the requirement for a wire for data transmission, which affects comfortability during long periods of measurement. In this work, an open-source wearable potentiostat device named We-VoltamoStat is developed to allow interested parties to use and modify the device for creating new products, research, and teaching purposes. The proposed device includes improved and added features, such as wireless real-time signal monitoring and data collection. It also has an ultra-low power consumption battery estimated to deliver 15 mA during operating mode for 33 h and 20 min and 5 mA during standby mode for 100 h without recharging. Its convenience for wearable applications, tough design, and compact size of 67x54x38 mm make it suitable for wearable applications. Cost-effectiveness is another advantage, with a price less than 120 USD. Validation performance tests indicate that the device has good accuracy, with an R2 value of 0.99 for linear regression of test accuracy on milli-, micro-, and nano-Ampere detection. In the future, it is recommended to improve the design and add more features to the device, including new applications for wearable potentiostats.

A consumer wearable device for tracking sleep respiratory events

PURPOSE

The diagnosis of obstructive sleep apnea (OSA) is instrument, operator, and time-dependent and therefore requires long waiting times. In recent decades, technological development has produced useful devices to monitor the health status of the population, including sleep. Therefore, the aim of this study was to evaluate a wearable device (WD) in a group of individuals at high risk of OSA.

METHODS

The study was conducted on consecutive subjects with high risk of OSA assessed by sleep questionnaires and clinical evaluation. All subjects performed cardio-respiratory monitoring (CRM) and WD simultaneously on a single night, after which the parameters of the two sleep investigations were compared.

RESULTS

Of 20 individuals enrolled, 60% were men and mean age was 57.3 ± 10.7 years. The apnea-hypopnea index (AHI) for the CRM was 23.1 ± 19.6 events·h-1 while it was 10.3 ± 8.3 events·h-1 for the WD. Correlation analysis between the results of the two investigations showed r = 0.19 (p = 0.40) for AHI and r = 0.4076 (p = 0.07) for sO2%. The accuracy for different stages of OSA severity was 70% in OSA cases and 60% in moderate to severe cases with sensitivity and specificity varying a great deal.

CONCLUSION

Small and low-cost devices may prove to be a valuable resource to reduce costs and waiting times for a sleep investigation in suspected OSA. However, diagnosis of sleep apnea requires valid and reliable instruments, so validation tests are necessary before a device can be commercialized.

Wearables for personalized monitoring of masticatory muscle activity - opportunities, challenges, and the future

Wearable devices are worn on or remain in close proximity of the human body. The use of wearable devices specific to the orofacial region is steadily increasing. Orofacial applications of wearable devices include supplementing diagnosis, tracking treatment progress, monitoring patient compliance, and understanding oral parafunctional behaviours. In this short communication, the role of wearable devices in advancing personalized dental medicine are highlighted with a specific focus on masticatory muscle activity monitoring in naturalistic settings. Additionally, challenges, opportunities, as well as future research areas for successful use of wearable devices for precise, personalized care of muscle disorders are discussed.

Identifying biomarkers of drug use recurrence using wearable device technologies and phone applications

BACKGROUND

Identifying predictors of drug use recurrence (DUR) is critical to combat the addiction epidemic. Wearable devices and phone-based applications for obtaining self-reported assessments in the patient's natural environment (e.g., ecological momentary assessment; EMA) have been used in various healthcare settings. However, the utility of combining these technologies to predict DUR in substance use disorder (SUD) has not yet been explored. This study investigates the combined use of wearable technologies and EMA as a potential mechanism for identifying physiological/behavioral biomarkers of DUR.

METHODS

Participants, recruited from an SUD treatment program, were provided with a commercially available wearable device that continuously monitors biometric signals (e.g., heart rate/variability [HR/HRV], sleep characteristics). They were also prompted daily to complete an EMA via phone-based application (EMA-APP) that included questionnaires regarding mood, pain, and craving.

RESULTS

Seventy-seven participants are included in this pilot study (34 participants experienced a DUR during enrollment). Wearable technologies revealed that physiological markers were significantly elevated in the week prior to DUR relative to periods of sustained abstinence (p<0.001). Results from the EMA-APP revealed that those who experienced a DUR reported greater difficulty concentrating, exposure to triggers associated with substance use, and increased isolation the day prior to DUR (p<0.001). Compliance with study procedures during the DUR week was lower than any other period of measurement (p<0.001).

CONCLUSIONS

These results suggest that data acquired via wearable technologies and the EMA-APP may serve as a method of predicting near-term DUR, thereby potentially prompting intervention before drug use occurs.

Investigating the effects of stress on achievement: BIOSTRESS dataset

The effects of chronic stress on academic and professional achievement can have a substantial impact. This relationship is highlighted through a dataset that includes questionnaires and physiological data from a group of individuals. The questionnaire data of 48 individuals, the physiological data of 20 individuals during sessions with a psychologist, and the exam data of 8 individuals were analyzed. The questionnaire data collected includes demographic information and scores on the TOAD stress scale. Physiological data was captured using the Empatica e4, a wearable device, which measured various signals such as blood volume pulse, electrodermal activity, body temperature, interbeat intervals, heart rate, and 3-axis accelerometer data. These measurements were taken under different stress conditions, both high and low, during therapy sessions and an exam respectively. Overall, this study significantly contributes to our understanding of how stress affects achievement. By providing a large dataset consisting of questionnaires and physiological data, this research helps researchers gain a better understanding of the complex relationship between stress and achievement. It also enables them to develop innovative strategies for managing stress and enhancing academic and professional success.

Functional Materials and Innovative Strategies for Wearable Thermal Management Applications

Highlights

This article systematically reviews the thermal management wearables with a specific emphasis on materials and strategies to regulate the human body temperature. Thermal management wearables are subdivided into the active and passive thermal managing methods. The strength and weakness of each thermal regulatory wearables are discussed in details from the view point of practical usage in real-life.

Abstract

Thermal management is essential in our body as it affects various bodily functions, ranging from thermal discomfort to serious organ failures, as an example of the worst-case scenario. There have been extensive studies about wearable materials and devices that augment thermoregulatory functionalities in our body, employing diverse materials and systematic approaches to attaining thermal homeostasis. This paper reviews the recent progress of functional materials and devices that contribute to thermoregulatory wearables, particularly emphasizing the strategic methodology to regulate body temperature. There exist several methods to promote personal thermal management in a wearable form. For instance, we can impede heat transfer using a thermally insulating material with extremely low thermal conductivity or directly cool and heat the skin surface. Thus, we classify many studies into two branches, passive and active thermal management modes, which are further subdivided into specific strategies. Apart from discussing the strategies and their mechanisms, we also identify the weaknesses of each strategy and scrutinize its potential direction that studies should follow to make substantial contributions to future thermal regulatory wearable industries.

The preventive effect of PNF stretching exercise on sports injuries in physical education based on IoT data monitoring

In physical education, in order to prevent sports injuries of students, it is necessary to study and build a set of physical education Internet of Things data monitoring training system to prevent sports injuries of students. This system is mainly composed of sensors, smart phones and cloud servers. Wearable devices equipped with sensors are used to complete data acquisition and transmission by means of the Internet of Things system, and relevant parameters are sorted and monitored by combining data analysis technology. The system makes a more in-depth, comprehensive and accurate analysis and processing of the collected data, so as to better evaluate the status and quality of students' sports, find out the existing problems in time, and put forward the corresponding solutions. By analyzing students' sports data and health data, the system generates personalized training programs, including training intensity, training time, training frequency and other parameters, so as to meet the needs and actual conditions of different students and avoid sports injuries caused by overtraining. This system can better analyze and process the collected data, provide teachers with more comprehensive and in-depth assessment and monitoring of students' sports status, and provide students with more personalized and scientific training programs, so as to effectively prevent the occurrence of students' sports injuries.

Assessing the association between overcrowding and human physiological stress response in different urban contexts: a case study in Salzburg, Austria

Overcrowding in densely populated urban areas is increasingly becoming an issue for mental health disorders. Yet, only few studies have examined the association between overcrowding in cities and physiological stress responses. Thus, this study employed wearable sensors (a wearable camera, an Empatica E4 wristband and a smartphone-based GPS) to assess the association between overcrowding and human physiological stress response in four types of urban contexts (green space, transit space, commercial space, and blue space). A case study with 26 participants was conducted in Salzburg, Austria. We used Mask R-CNN to detect elements related to overcrowding such as human crowds, sitting facilities, vehicles and bikes from first-person video data collected by wearable cameras, and calculated a change score (CS) to assess human physiological stress response based on galvanic skin response (GSR) and skin temperature from the physiological data collected by the wristband, then this study used statistical and spatial analysis to assess the association between the change score and the above elements. The results demonstrate the feasibility of using sensor-based measurement and quantitative analysis to investigate the relationship between human stress and overcrowding in relation to different urban elements. The findings of this study indicate the importance of considering human crowds, sitting facilities, vehicles and bikes to assess the impact of overcrowding on human stress at street level.

Deep cross-modal feature learning applied to predict acutely decompensated heart failure using in-home collected electrocardiography and transthoracic bioimpedance

BACKGROUND

Deep learning has been successfully applied to ECG data to aid in the accurate and more rapid diagnosis of acutely decompensated heart failure (ADHF). Previous applications focused primarily on classifying known ECG patterns in well-controlled clinical settings. However, this approach does not fully capitalize on the potential of deep learning, which directly learns important features without relying on a priori knowledge. In addition, deep learning applications to ECG data obtained from wearable devices have not been well studied, especially in the field of ADHF prediction.

METHODS

We used ECG and transthoracic bioimpedance data from the SENTINEL-HF study, which enrolled patients (≥21 years) who were hospitalized with a primary diagnosis of heart failure or with ADHF symptoms. To build an ECG-based prediction model of ADHF, we developed a deep cross-modal feature learning pipeline, termed ECGX-Net, that utilizes raw ECG time series and transthoracic bioimpedance data from wearable devices. To extract rich features from ECG time series data, we first adopted a transfer learning approach in which ECG time series were transformed into 2D images, followed by feature extraction using ImageNet-pretrained DenseNet121/VGG19 models. After data filtering, we applied cross-modal feature learning in which a regressor was trained with ECG and transthoracic bioimpedance. Then, we concatenated the DenseNet121/VGG19 features with the regression features and used them to train a support vector machine (SVM) without bioimpedance information.

RESULTS

The high-precision classifier using ECGX-Net predicted ADHF with a precision of 94 %, a recall of 79 %, and an F1-score of 0.85. The high-recall classifier with only DenseNet121 had a precision of 80 %, a recall of 98 %, and an F1-score of 0.88. We found that ECGX-Net was effective for high-precision classification, while DenseNet121 was effective for high-recall classification.

CONCLUSION

We show the potential for predicting ADHF from single-channel ECG recordings obtained from outpatients, enabling timely warning signs of heart failure. Our cross-modal feature learning pipeline is expected to improve ECG-based heart failure prediction by handling the unique requirements of medical scenarios and resource limitations.

A comparison of perceived image quality between computer display monitors and augmented reality smart glasses

INTRODUCTION

Augmented-reality (AR) smart glasses provide an alternative to standard computer display monitors (CDM). AR smart glasses may provide an opportunity to improve visualisation during fluoroscopy and interventional radiology (IR) procedures when there can be difficulty in viewing intra-procedural images on a CDM. The aim of this study was to evaluate radiographer perception of image quality (IQ) when comparing CDM and AR smart glasses.

METHODS

38 radiographers attending an international congress evaluated ten fluoroscopic-guided surgery and IR images on both a CDM (1920 × 1200 pixels) and a set of Epson Moverio BT-40 AR smart glasses (1920 × 1080 pixels). Participants provided oral responses to pre-defined IQ questions generated by study researchers. Summative IQ scores for each participant/image were compared between CDM and AR smart glasses.

RESULTS

Of the 38 participants, the mean age was 39 ± 1 years. 23 (60.5%) participants required corrective glasses. In terms of generalisability, participants were from 12 different countries, the majority (n = 9, 23.7%) from the United Kingdom. For eight out of ten images, the AR smart glasses demonstrated a statistically significant increase in perceived IQ (median [IQR] 2.0 [-1.0 to 7.0] points) when compared to the CDM.

CONCLUSION

AR smart glasses appear to show improvements in perceived IQ when compared to a CDM. AR smart glasses could provide an option for improving the experiences of radiographers involved in image-guided procedures and should be subject to further clinical evaluations.

IMPLICATIONS FOR PRACTICE

Opportunities exist to improve perceived IQ for radiographers when reviewing fluoroscopy and IR images. AR smart glasses should be further evaluated as a potential opportunity to improve practice when visual attention is split between positioning equipment and image review.

All fabric and flexible wearable sensors for simultaneous sweat metabolite detection and high-efficiency collection

Wearable sweat electrochemical sensors have attracted wide attention due to their advantages of non-invasive, portable, real-time monitoring, etc. However, existing sensors still have some problems with efficient sweat collection. Microfluidic channel technology and electrospinning technology are commonly used to collect sweat efficiently, but there are some limitations such as complex channel design and multiple spinning parameters. Besides, existing sensors are mostly based on flexible polymers, such as, PET, PDMS, PI and PI, which have limited wearability and permeability. Based on the above, all fabric and dual-function flexible wearable sweat electrochemical sensor is proposed in this paper. This sensor uses fabric as the raw material to implement the directional transport of sweat and the multi-component integrated detection dual functions. Meanwhile, the high-efficiency collection of sweat is obtained by a Janus fabric, wherein one side of the selected silk is superhydrophobic graft treated and the other side is hydrophilic plasma treated. Therefore, the resulting Janus fabric can effectively transfer sweat from the skin side to the electrode, and the minimum sweat droplet can reach 0.2 μL to achieve micro-volume collection. Besides, the patterned sensor, made of silk-based carbon cloth, is fabricated using a simple laser engraving, which could detect Na⁺, pH, and glucose instantaneously. As a result, these proposed sensors can achieve good sensing performance and high-efficiency sweat collection dual functionality; moreover, it has good flexibility and comfortable wearability.

Modified proximal thigh kinematics captured with a novel smartphone app in individuals with a history of recurrent ankle sprains and altered dorsiflexion with walking

BACKGROUND

We examined sagittal-plane thigh angular kinematics in individuals with and without recurrent ankle sprains using a clinical smartphone app called AccWalker. Sagittal-plane ankle kinematics were also compared to ascertain that altered ankle dorsiflexion, which is typically displayed with chronic ankle instability, is also present in individuals with recurrent ankle sprains.

METHODS

Participants with (n = 22) and without (n = 22) recurrent ankle sprains were evaluated on average sagittal-plane ankle kinematics during walking and average sagittal-plane thigh angular kinematics during stepping-in-place with AccWalker.

FINDINGS

Significant group-by-limb interactions were found for sagittal-plane ankle kinematics (F_(1,42) = 63.786, P < .010) during walking and sagittal-plane average thigh angular range-of-motion (F_(1,42) = 6.166, P = .017) with AccWalker. Individuals with recurrent ankle sprains displayed more ankle dorsiflexion in affected (P < .001) and unaffected (P = .001) limbs during walking than healthy controls and exhibited more ankle dorsiflexion in their affected-limb compared to their unaffected-limb (P < .001). The average sagittal-plane thigh angular range-of-motion was lower in the unaffected-limb for recurrent ankle sprains compared to their affected-limb (P = .038) and the assigned unaffected-limb of healthy controls (P = .035).

INTERPRETATION

Increased dorsiflexion was present in both limbs of the recurrent ankle sprain group with walking. AccWalker does not assess ankle movement, but uniquely identified thigh motion impairments associated with recurrent ankle sprains in their unaffected-limb, potentially identifying central deficits associated with recurrent ankle sprains. This app has clinical implications for assessing potential pathological movement that can be corrected through rehabilitation.

Use of new technologies for the promotion of physical activity in patients with mental illness: A systematic review

BACKGROUND

Physical exercise is an underutilized tool for the management of mental disorders. New technologies have made a breakthrough in health care, and one of its possible applications (apps) could be that of customizing exercise programs for special populations, such as patients with mental disorders. However, the app of the so-called e-health to mental health care is still limited.

AIM

To know the efficacy of apps to promote physical activity in patients with mental disorders.

METHODS

We conducted a systematic review of the PubMed and Embase databases with the aim of exploring the use of new technologies for the enhancement of physical exercise in patients with a psychiatric illness. Following the selection process, 10 articles were included in the review.

RESULTS

The most commonly used devices in this type of intervention are wearable devices and web platforms. Good results in terms of effectiveness and acceptability were obtained in most of the studies.

CONCLUSION

Our findings suggest that the use of new technologies in mental health represents a feasible strategy with great potential in clinical practice.

Cloth-based microfluidic devices integrated onto the patch as wearable colorimetric sensors for simultaneous sweat analysis

Introduction

In this work, a flexible, and wearable point-of-care (POC) device integrated on a pain relief patch as wearable colorimetric sensors have been developed for sweat analysis, such as lactic acid, sodium ions, and pH simultaneously. Herein, the patch has still functioned as pain relief, while it allows for sweat monitoring during exercise, and in daily activities.

Methods

It was constructed on cotton cloth using wax printing technology (batik stamp) as cloth-based microfluidic devices (CMDs). Here, it uses micro volumes of samples to perform the reaction in the sensing zones, where the sensitive reagents are immobilized so that it can collect and analyze the sweat (lactic acid, sodium ions, and pH) as the model for sweat analytes. The colorimetric analysis was conducted via a smartphone camera by using a free app (Color Grab) for a color image analysis that uses for quantitative analysis or naked eye for semi-qualitative analysis.

Results

The ∆RGB value of the CMDS shows the excellent linear correlation vs analytes concentration, where the coefficient of correlations was found for lactic acid (R2 = 0.994), sodium ion (R2 = 0.998), and pH (R2 = 0.994). The ∆RGB value shows the appropriate color value for the linear correlation of the analyte target concentrations in the sweat samples. Here, the limit of detection (LOD) was found at 45.73 µg/mL for lactic acid and 56.46 µg/mL for sodium ions. The reproducibility was found at 0.79% and 0.89%, for lactic acid and sodium ions respectively.

Conclusion

It was applied for sweat analysis during exercise, and the results show in agreement with the standard methods used in a clinical laboratory.

Data of worldwide observational studies of adults with accelerometry-measured physical activity and sedentary behavior

A compendium of observational studies of adults that collected accelerometry to assess physical activity and sedentary behavior (i.e., physical behaviors) could facilitate cross-study comparisons, meta-analyses, and future research collaborations. Therefore, we performed a systematic search to identify observational studies, including surveillance systems, that collected accelerometry-measured physical activity and sedentary behavior among adults. We performed a search using PubMed, Web of Science, and SPORTDiscus for studies published on or before June 1, 2021. After screening 5686 abstracts and 1027 full text articles, we included 155 unique studies that collected accelerometry on at least 500 adults 18 years or older. Most studies used one accelerometer (n=146), although eight studies used two accelerometers and one study used four accelerometers. The country of data collection, age range, and accelerometer characteristics were abstracted and checked by a second reviewer. These datasets summarizing relevant observational studies of adults can be a resource to researchers seeking to identify data sources for accelerometer-measured physical activity and sedentary behavior from around the world.

The long-term and short-term effects of ambient air pollutants on sleep characteristics in the Chinese population: big data analysis from real world by sleep records of consumer wearable devices

Several studies on long-term air pollution exposure and sleep have reported inconsistent results. Large-scale studies on short-term air pollution exposures and sleep have not been conducted. We investigated the associations of long- and short-term exposure to ambient air pollutants with sleep in a Chinese population based on over 1 million nights of sleep data from consumer wearable devices. Air pollution data including particulate matter (PM_2.5, PM₁₀), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), carbon monoxide (CO), and ozone (O₃) were collected from the Ministry of Ecology and Environment. Short-term exposure was defined as a moving average of the exposure level for different lag days from Lag0 to Lag0-6. A 365-day moving average of air pollution was regarded as long-term exposure. Sleep data were recorded using wearable devices from 2017 to 2019. The mixed-effects model was used to evaluate the associations. We observed that sleep parameters were associated with long-term exposure to all air pollutants. Higher levels of air pollutant concentrations were associated with longer total sleep and light sleep duration, shorter deep sleep duration, and decreases in wake after sleep onset (WASO), with stronger associations of exposures to NO₂ and CO [a 1-interquartile range (IQR) increased NO₂ (10.3 μg/m³) was associated with 8.7 min (95% CI: 8.08 to 9.32) longer sleep duration, a 1-IQR increased CO (0.3 mg/m³) was associated with 5.0 min (95% CI: - 5.13 to - 4.89) shorter deep sleep duration, 7.7 min (95% CI: 7.46 to 7.85) longer light sleep duration, and 0.5% (95% CI: - 0.5 to - 0.4%) lower proportion of WASO duration to total sleep]. The cumulative effect of short-term exposure on Lag0-6 is similar to long-term exposure but relatively less. Subgroup analyses indicated generally greater effects on individuals who were female, younger (< 45 years), slept longer (≥ 7 h), and during cold seasons, but the pattern of effects was mixed. We supplemented two additional types of stratified analyses to reduce repeated measures of outcomes and exposures while accounting for individual variation. The results were consistent with the overall results, proving the robustness of the overall results. In summary, both short- and long-term exposure to air pollution affect sleep, and the effects are comparable. Although people tend to have prolonged total sleep duration with increasing air pollutant concentrations, their sleep quality might remain poor because of the reduction in deep sleep.

Roles of MXenes in biomedical applications: recent developments and prospects

....With the development of nanomedical technology, the application of various novel nanomaterials in the biomedical field has been greatly developed in recent years. MXenes, which are new inorganic nanomaterials with ultrathin atomic thickness, consist of layered transition metal carbides and nitrides or carbonitrides and have the general structural formula M_n+1X_nT_x (n = 1-3). Based on the unique structural features of MXenes, such as ultrathin atomic thickness and high specific surface area, and their excellent physicochemical properties, such as high photothermal conversion efficiency and antibacterial properties, MXenes have been widely applied in the biomedical field. This review systematically summarizes the application of MXene-based materials in biomedicine. The first section is a brief summary of their synthesis methods and surface modification strategies, which is followed by a focused overview and analysis of MXenes applications in biosensors, diagnosis, therapy, antibacterial agents, and implants, among other areas. We also review two popular research areas: wearable devices and immunotherapy. Finally, the difficulties and research progress in the clinical translation of MXene-based materials in biomedical applications are briefly discussed.

Wearable Device-Delivered Intensive Sleep Retraining as an Adjunctive Treatment to Kickstart Cognitive-Behavioral Therapy for Insomnia

Intensive Sleep Retraining is a behavioral treatment for sleep onset insomnia that produces substantial benefits in symptoms after a single treatment session. This technique involves falling asleep and waking up shortly afterward repeatedly: a process that is thought to retrain people to fall asleep quickly when attempting sleep. Although originally confined to the sleep laboratory, recent technological developments mean that this technique is feasible to self-administer at home. With multiple randomised controlled trials required to confirm its efficacy, Intensive Sleep Retraining may serve as an adjunctive treatment to cognitive-behavioral therapy for insomnia, improving short-term efficacy by kick-starting treatment gains.

Four Sessions of Combining Wearable Devices and Heart Rate Variability (HRV) Biofeedback are Needed to Increase HRV Indices and Decrease Breathing Rates

Heart rate variability biofeedback (HRVB) is a behavioral intervention that uses resonance frequency breathing to synchronize the heart rate and breathing patterns. This study aimed to explore how many sessions of wearable HRVB devices are needed to increase the HRV index and decrease breathing rates and to compare the HRVB protocol with other psychological intervention programs in HRV indices and breathing rates. Sixty-four participants were randomly assigned to either the HRVB or relaxation training (RT) group. Both groups received interbeat intervals (IBIs) and breathing rates measurement at the pre-training baseline, during training, and post-training baseline from weeks 1 to 4. IBIs were transformed into HRV indices as the index of the autonomic nervous system. The Group × Week interaction effects significantly in HRV indices and breathing rates. The between-group comparison found a significant increase in HRV indices and decreased breathing rates in the HRVB group than in the RT group at week 4. The within-session comparison in the HRVB group revealed significantly increased HRV indices and decreased breathing rates at weeks 3 and 4 than at weeks 1 and 2. There was a significant increase in HRV indices and a decrease in breathing rates at mid- and post-training than pre-training in the HRVB group. Therefore, 4 weeks of HRVB combined with a wearable device are needed in increasing HRV indices and decrease breathing rates compared to the relaxation training. Three weeks of HRVB training are the minimum requirement for increasing HRV indices and reducing breathing rates compared to the first week of HRVB.

Effect of wearable exoskeleton on post-stroke gait: A systematic review and meta-analysis

BACKGROUND

Wearable exoskeletons are a recently developed technology.

OBJECTIVES

The present systematic review aimed to investigate the effect of a wearable exoskeleton on post-stroke walking by considering its use in a gait training system and simply as an orthosis assisting walking.

METHODS

We systematically searched for randomised and quasi-randomised controlled trials in PubMed, Scopus, CINAHL and Embase databases from their earliest publication record to July 2021. We chose reports of trials investigating the effects of exoskeleton-assisted training or the effects of wearing an exoskeleton to assist walking. A meta-analysis was conducted to explore the benefits of the wearable exoskeleton on mobility capacity, walking speed, motor function, balance, endurance and activities of daily living.

RESULTS

We included 13 studies (492 participants) comparing exoskeleton-assisted training with dose-matched conventional gait training. Studies addressing the effect of wearing a wearable exoskeleton were unavailable. As compared with conventional gait training at the end of the intervention, exoskeleton-assisted training was superior for walking speed (mean difference [MD] 0.13 m/s, 95% CI 0.05; 0.21) and balance (standardized MD [SMD] 0.3, 95% CI 0.07; 0.54). The subgroup with chronic stroke (i.e., > 6 months) presented the outcome favouring exoskeleton-assisted training regarding overall mobility capacity (SMD 0.37, 95% CI 0.04; 0.69). At the end of follow-up, exoskeleton-assisted training was superior to conventional gait training in overall mobility (SMD 0.45, 95% CI 0.07; 0.84) and endurance (MD 46.23 m, 95% CI 9.90; 82.56).

CONCLUSIONS

Exoskeleton-assisted training was superior to dose-matched conventional gait training in several gait-related outcomes at the end of the intervention and follow-up in this systematic review and meta-analysis, which may support the use of exoskeleton-assisted training in the rehabilitation setting. Whether wearing versus not wearing a wearable exoskeleton is beneficial during walking remains unknown.