Automated detection of tonic seizures using wearable movement sensor and artificial neural network.

Although several validated wearable devices are available for detection of generalized tonic-clonic seizures, automated detection of tonic seizures is still a challenge. In this phase 1 study, we report development and validation of an artificial neural network (ANN) model for automated detection of tonic seizures with visible clinical manifestation using a wearable wristband movement sensor (accelerometer and gyroscope). The dataset prospectively recorded for this study included 70 tonic seizures from 15 patients (seven males, age 3-46 years, median = 19 years). We trained an ANN model to detect tonic seizures. The independent test dataset comprised nocturnal recordings, including 10 tonic seizures from three patients and additional (distractor) data from three subjects without seizures. The ANN model detected nocturnal tonic seizures with visible clinical manifestation with a sensitivity of 100% (95% confidence interval = 69%-100%) and with an average false alarm rate of .16/night. The mean detection latency was 14.1 s (median = 10 s), with a maximum of 47 s. These data suggest that nocturnal tonic seizures can be reliably detected with movement sensors using ANN. Large-scale, multicenter prospective (phase 3) trials are needed to provide compelling evidence for the clinical utility of this device and detection algorithm.

Flame Retardant Exposure in Vehicles Is Influenced by Use in Seat Foam and Temperature.

Flame retardants (FRs) are added to vehicles to meet flammability standards, such as US Federal Motor Vehicle Safety Standard FMVSS 302. However, an understanding of which FRs are being used, sources in the vehicle, and implications for human exposure is lacking. US participants (n = 101) owning a vehicle of model year 2015 or newer hung a silicone passive sampler on their rearview mirror for 7 days. Fifty-one of 101 participants collected a foam sample from a vehicle seat. Organophosphate esters (OPEs) were the most frequently detected FR class in the passive samplers. Among these, tris(1-chloro-isopropyl) phosphate (TCIPP) had a 99% detection frequency and was measured at levels ranging from 0.2 to 11,600 ng/g of sampler. TCIPP was also the dominant FR detected in the vehicle seat foam. Sampler FR concentrations were significantly correlated with average ambient temperature and were 2-5 times higher in the summer compared to winter. The presence of TCIPP in foam resulted in ∼4 times higher median air sampler concentrations in winter and ∼9 times higher in summer. These results suggest that FRs used in vehicle interiors, such as in seat foam, are a source of OPE exposure, which is increased in warmer temperatures.

Silicone wristbands reveal ubiquitous human exposure to ortho-phthalates and non-ortho-phthalate plasticizers in Southern California.

In the United States and abroad, ortho-phthalates and non-ortho-phthalate plasticizers continue to be used within a diverse array of consumer products. Prior California-specific biomonitoring programs for ortho-phthalates have focused on rural, agricultural communities and, to our knowledge, these programs have not measured the potential for exposure to non-ortho-phthalate plasticizers. Therefore, the potential for human exposure to ortho-phthalates and non-ortho-phthalate plasticizers have not been adequately addressed in regions of California that have higher population density. Since there are numerous sources of ortho-phthalates and non-ortho-phthalate plasticizers in population-dense, urban regions, the objective of this study was to leverage silicone wristbands to quantify aggregate ortho-phthalate and non-ortho-phthalate plasticizer exposure over a 5-day period across two different cohorts (2019 and 2020) of undergraduate students at the University of California, Riverside (UCR) that commute from all over Southern California. Based on 5 d of aggregate exposure across two different cohorts, total ortho-phthalate plus non-ortho-phthalate plasticizer concentrations ranged, on average, from ∼100,000-1,000,000 ng/g. Based on the distribution of individual ortho-phthalate and non-ortho-phthalate plasticizer concentrations, the concentrations of di-isononyl phthalate (DiNP, a high molecular weight ortho-phthalate), di (2-ethylhexyl) phthalate (DEHP, a high molecular weight ortho-phthalate), and di-2-ethylhexyl terephthalate (DEHT, a non-ortho-phthalate plasticizer) detected within wristbands were higher than the remaining seven ortho-phthalates and non-ortho-phthalate plasticizers measured, accounting for approximately 94-97% of the total mass depending on the cohort. Overall, our findings raise concerns about chronic DiNP, DEHP, and DEHT exposure in urban, population-dense regions throughout California.

Associations of environmental chemical exposures measured in personal silicone wristbands with sociodemographic factors, COVID-19 restrictions, and child respiratory health.

BACKGROUND: Although human biomonitoring of environmental chemicals has been considered a gold standard, these methods can be costly, burdensome, and prone to unwanted sources of variability that may cause confounding. Silicone wristbands have recently emerged as innovative passive samplers for measuring personal exposures. METHODS: In a pilot study from 2019 to 2021 involving 55 children aged 5-9 years in Seattle and Yakima, Washington, we utilized silicone wristbands to explore associations of sociodemographic variables and COVID-19-related restrictions, including school closures, with exposures to numerous chemicals including brominated and organophosphate ester (OPE) flame retardants, polychlorinated biphenyls, polycyclic aromatic hydrocarbons (PAHs), phthalates, and pesticides. We additionally conducted the first analysis testing silicone wristband chemicals as predictors of child wheeze, individually and in mixtures via logistic weighted quantile sum regression (WQS). RESULTS: Among 109 semi-volatile organic compounds measured, we detected 40 in >60% of wristbands worn by children continuously for an average of 5 days. Chemicals were generally positively correlated, especially within the same class. Male sex and increasing age were linked with higher exposures across several chemical classes; Hispanic/Latino ethnicity was linked with higher exposures to some phthalates and OPEs. COVID-19 restrictions were associated with lower wristband concentrations of brominated and triaryl OPE flame retardants. Each one-decile higher WQS exposure index was suggestively associated with 2.11-fold [95% CI: 0.93-4.80] higher odds of child wheeze. Risk of child wheeze was higher per 10-fold increase in the PAH chrysene (RR = 1.93[1.07-3.49]), the pesticide cis-permethrin (3.31[1.23-8.91]), and di-isononyl phthalate (DINP) (5.40[1.22-24.0]) CONCLUSIONS: Our identification of demographic factors including sex, age, and ethnicity associated with chemical exposures may aid efforts to mitigate exposure disparities. Lower exposures to flame retardants during pandemic restrictions corroborates prior evidence of higher levels of these chemicals in school versus home environments. Future research in larger cohorts is needed to validate these findings.

Imagery rescripting and cognitive restructuring for inpatients with moderate and severe depression - a controlled pilot study.

BACKGROUND: This controlled pilot study investigates the effect of the combined use of cognitive restructuring (CR) and imagery rescripting (IR) compared to treatment as usual among inpatients with moderate and severe depression. Alongside expert ratings and self-report tools, fitness wristbands were used as an assessment tool. METHODS: In addition to the standard inpatient care (SIC) program, 33 inpatients with moderate and severe depression were randomly assigned to an intervention group (two sessions of IR and CR) or an active treatment-as-usual (TAU) control group (two sessions of problem-solving and build-up of positive activity). Depression severity was assessed by the Hamilton Depression Rating Scale-21 (HDRS-21), the Beck Depression Inventory-II (BDI-II), and as a diagnostic adjunct daily step count via the Fitbit Charge 3™. We applied for analyses of HDRS-21 and BDI-II, 2 × 2 repeated-measures analysis of variance (ANOVA), and an asymptotic Wilcoxon test for step count. RESULTS: The main effect of time on both treatments was η2 = .402. Based on the data from the HDRS-21, patients in the intervention group achieved significantly greater improvements over time than the TAU group (η2 = .34). The BDI-II data did not demonstrate a significant interaction effect by group (η2 = .067). The daily hourly step count for participants of the intervention group was significantly higher (r = .67) than the step count for the control group. CONCLUSIONS: The findings support the utilization of imagery-based interventions for treating depression. They also provide insights into using fitness trackers as psychopathological assessment tools for depressed patients. TRIAL REGISTRATION: The trial is registered at the German Clinical Trials Register (Deutsches Register Klinischer Studien) under the registration number: DRKS00030809.

Association between wrist-worn free-living accelerometry and hand grip strength in middle-aged and older adults.

INTRODUCTION: Wrist-worn activity monitors have seen widespread adoption in recent times, particularly in young and sport-oriented cohorts, while their usage among older adults has remained relatively low. The main limitations are in regards to the lack of medical insights that current mainstream activity trackers can provide to older subjects. One of the most important research areas under investigation currently is the possibility of extrapolating clinical information from these wearable devices. METHODS: The research question of this study is understanding whether accelerometry data collected for 7-days in free-living environments using a consumer-based wristband device, in conjunction with data-driven machine learning algorithms, is able to predict hand grip strength and possible conditions categorized by hand grip strength in a general population consisting of middle-aged and older adults. RESULTS: The results of the regression analysis reveal that the performance of the developed models is notably superior to a simple mean-predicting dummy regressor. While the improvement in absolute terms may appear modest, the mean absolute error (6.32 kg for males and 4.53 kg for females) falls within the range considered sufficiently accurate for grip strength estimation. The classification models, instead, excel in categorizing individuals as frail/pre-frail, or healthy, depending on the T-score levels applied for frailty/pre-frailty definition. While cut-off values for frailty vary, the results suggest that the models can moderately detect characteristics associated with frailty (AUC-ROC: 0.70 for males, and 0.76 for females) and viably detect characteristics associated with frailty/pre-frailty (AUC-ROC: 0.86 for males, and 0.87 for females). CONCLUSIONS: The results of this study can enable the adoption of wearable devices as an efficient tool for clinical assessment in older adults with multimorbidities, improving and advancing integrated care, diagnosis and early screening of a number of widespread diseases.

Silicone wristband as a sampling tool for insecticide exposure assessment of vegetable farmers.

The use of passive sampling devices (PSDs) as an appropriate alternative to conventional methods of assessing human exposure to environmental toxicants was studied. One-time purposive sampling by a silicone wristband was used to measure insecticide residues in 35 volunteer pepper farmers in the Vea irrigation scheme in the Guinea savannah and the Weija irrigation scheme in the coastal savannah ecological zones of Ghana. A GC-MS/MS method was developed and validated for quantifying 18 insecticides used by farmers in Ghana. Limits of detection (LODs) and quantitation (LOQs) ranged from 0.64 to 67 and 2.2-222 ng per wristband, respectively. The selected insecticides showed a range of concentrations in the various silicone wristbands from not detected to 27 µg/wristband. The concentrations of 13 insecticides were above their LOQs. Chlorpyrifos had the highest detection frequencies and concentrations, followed by cyhalothrin and then allethrin. This study shows that silicone wristbands can be used to detect individual insecticide exposures, providing a valuable tool for future exposure studies. Ghanaian vegetable farmers are substantially exposed to insecticides. Hence, the use of appropriate personal protective equipment is recommended.

Do cats mirror their owner? Paired exposure assessment using silicone bands to measure residential PAH exposure.

It has been suggested that domestic animals can serve as sentinels for human exposures. In this study our objectives were to demonstrate that i) silicone collars can be used to measure environmental exposures of (domestic) animals, and that ii) domestic animals can be used as sentinels for human residential exposure. For this, we simultaneously measured polycyclic aromatic hydrocarbons (PAHs) using silicone bands worn by 30 pet cats (collar) and their owner (wristband). Collars and wristbands were worn for 7 days and analyzed via targeted Gas Chromatography-Mass Spectrometry (GC-MS). Demographics and daily routines were collected for humans and cats. Out of 16 PAHs, 9 were frequently detected (>50% of samples) in both wristbands and collars, of which Phenanthrene and Fluorene were detected in all samples. Concentrations of wristbands and collars were moderately correlated for these 9 PAHs (Median Spearman's r = 0.51 (range 0.16-0.68)). Determinants of PAH concentrations of cats and humans showed considerable overlap, with vacuum cleaning resulting in higher exposures and frequent changing of bed sheets in lower exposures. This study adds proof-of-principle data for the use of silicone collars to measure (domestic) animal exposure and shows that cats can be used as sentinels for human residential exposure.

Accuracy of a Standalone Atrial Fibrillation Detection Algorithm Added to a Popular Wristband and Smartwatch: Prospective Diagnostic Accuracy Study.

BACKGROUND: Silent paroxysmal atrial fibrillation (AF) may be difficult to diagnose, and AF burden is hard to establish. In contrast to conventional diagnostic devices, photoplethysmography (PPG)-driven smartwatches or wristbands allow for long-term continuous heart rhythm assessment. However, most smartwatches lack an integrated PPG-AF algorithm. Adding a standalone PPG-AF algorithm to these wrist devices might open new possibilities for AF screening and burden assessment. OBJECTIVE: The aim of this study was to assess the accuracy of a well-known standalone PPG-AF detection algorithm added to a popular wristband and smartwatch, with regard to discriminating AF and sinus rhythm, in a group of patients with AF before and after cardioversion (CV). METHODS: Consecutive consenting patients with AF admitted for CV in a large academic hospital in Amsterdam, the Netherlands, were asked to wear a Biostrap wristband or Fitbit Ionic smartwatch with Fibricheck algorithm add-on surrounding the procedure. A set of 1-min PPG measurements and 12-lead reference electrocardiograms was obtained before and after CV. Rhythm assessment by the PPG device-software combination was compared with the 12-lead electrocardiogram. RESULTS: A total of 78 patients were included in the Biostrap-Fibricheck cohort (156 measurement sets) and 73 patients in the Fitbit-Fibricheck cohort (143 measurement sets). Of the measurement sets, 19/156 (12%) and 7/143 (5%), respectively, were not classifiable by the PPG algorithm due to bad quality. The diagnostic performance in terms of sensitivity, specificity, positive predictive value, negative predictive value, and accuracy was 98%, 96%, 96%, 99%, 97%, and 97%, 100%, 100%, 97%, and 99%, respectively, at an AF prevalence of ~50%. CONCLUSIONS: This study demonstrates that the addition of a well-known standalone PPG-AF detection algorithm to a popular PPG smartwatch and wristband without integrated algorithm yields a high accuracy for the detection of AF, with an acceptable unclassifiable rate, in a semicontrolled environment.

AI-Enabled Smart Wristband Providing Real-Time Vital Signs and Stress Monitoring.

This work introduces the design, architecture, implementation, and testing of a low-cost and machine-learning-enabled device to be worn on the wrist. The suggested wearable device has been developed for use during emergency incidents of large passenger ship evacuations, and enables the real-time monitoring of the passengers' physiological state, and stress detection. Based on a properly preprocessed PPG signal, the device provides essential biometric data (pulse rate and oxygen saturation level) and an efficient unimodal machine learning pipeline. The stress detecting machine learning pipeline is based on ultra-short-term pulse rate variability, and has been successfully integrated into the microcontroller of the developed embedded device. As a result, the presented smart wristband is able to provide real-time stress detection. The stress detection system has been trained with the use of the publicly available WESAD dataset, and its performance has been tested through a two-stage process. Initially, evaluation of the lightweight machine learning pipeline on a previously unseen subset of the WESAD dataset was performed, reaching an accuracy score equal to 91%. Subsequently, external validation was conducted, through a dedicated laboratory study of 15 volunteers subjected to well-acknowledged cognitive stressors while wearing the smart wristband, which yielded an accuracy score equal to 76%.

Comparative Assessment of Pesticide Exposures in Domestic Dogs and Their Owners Using Silicone Passive Samplers and Biomonitoring.

Pesticides are used extensively in residential settings for lawn maintenance and in homes to control household pests including application directly on pets to deter fleas and ticks. Pesticides are commonly detected in the home environment where people and pets can be subject to chronic exposure. Due to increased interest in using companion animals as sentinels for human environmental health studies, we conducted a comparative pesticide exposure assessment in 30 people and their pet dogs to determine how well silicone wristbands and silicone dog tags can predict urinary pesticide biomarkers of exposure. Using targeted gas chromatography-mass spectrometry analyses, we quantified eight pesticides in silicone samplers and used a suspect screening approach for additional pesticides. Urine samples were analyzed for 15 pesticide metabolite biomarkers. Several pesticides were detected in >70% of silicone samplers including permethrin, N,N-diethyl-meta-toluamide (DEET), and chlorpyrifos. Significant and positive correlations were observed between silicone sampler levels of permethrin and DEET with their corresponding urinary metabolites (rs = 0.50-0.96, p < 0.05) in both species. Significantly higher levels of fipronil were observed in silicone samplers from participants who reported using flea and tick products containing fipronil on their dog. This study suggests that people and their dogs have similar pesticide exposures in a home environment.

Partial dust removal in vehicles does not mitigate human exposure to organophosphate esters.

Organophosphate esters (OPEs) have been detected within car interior dust, suggesting that the indoor microenvironment of vehicles may represent a potential route of human exposure to OPEs. We recently showed that people with longer commutes are exposed to higher concentrations of tris(1,3-dichloro-2-isopropyl)phosphate (TDCIPP) - a widely used OPE - and other studies have suggested that dust removal may lead to lower exposure to chemicals. Therefore, the overall objective of this study was to determine if a decrease in interior car dust results in mitigation of personal OPE exposure. Participants (N = 49) were asked to wear silicone wristbands, and a subset of them wiped interior parts at the front of their vehicles prior to one study week (N = 25) or both study weeks (N = 11). There were no significant differences in total OPE concentrations (77.79-13,660 ng/g) nor individual OPE concentrations (0.04-4852.81 ng/g) across the different wiping groups nor in relation to participant residence ZIP codes and AC/Heater usage. These findings suggest that higher exposure to TDCIPP for participants with longer commutes may be independent of dust located on interior parts at the front of the vehicle. Therefore, our study demonstrates that there is a need for research on the potential contribution of other sources of TDCIPP exposure within car interiors.

Are You Wearing a Mask? Detecting If a Person Wears a Mask Using a Wristband.

Coronavirus 2019 (COVID-19) has posed a serious threat to the lives and health of the majority of people worldwide. Since the early days of the outbreak, South Korea's government and citizens have made persistent efforts to provide effective prevention against further spread of the disease. In particular, the participation of individual citizens in complying with the necessary code of conduct to prevent spread of the infection, through measures such as social distancing and mask wearing, is as instrumental as the geographical tracking of the trajectory of the infected. In this paper, we propose an activity recognition method based on a wristband equipped with an IR array and inertial measurement unit (IMU) to detect individual compliance with codes of personal hygiene management, such as mask wearing, which are recommended to prevent the spread of infectious diseases. The results of activity recognition were comparatively analyzed by applying conventional machine learning algorithms and convolutional neural networks (CNNs) to the IMU time series and IR array thermal images collected from 25 subjects. When CNN and 24 × 32 thermal images were used, 97.8% accuracy was achieved (best performance), and when 6 × 8 low-resolution thermal images were used, similar performance with 97.1% accuracy was obtained. In the case of using IMU, the performance of activity recognition was lower than that obtained with the IR array, but an accuracy of 93% was achieved even in the case of applying machine learning algorithms, indicating that it is more suitable for wearable devices with low computational capability.

A Novel Personal Passive Sampler for Collecting Gaseous Phthalates.

Dermal absorption of gaseous chemicals is an important contributor to increased health risk and has yet to be adequately addressed due to the lack of available sampling techniques. In the present study, a novel personal passive sampler consisting of a housing (embracing a polydimethylsiloxane (PDMS) disk as the sorbent phase, a membrane filter, and a stainless-steel mesh) and a watchband (traditional wristband) was constructed and used to characterize gaseous phthalates (PAEs) near the air-skin interface. In a real-life setting, the utility of the passive sampler was validated by comparing the composition profiles of PAEs in the PDMS disks and in active samples and watchbands. The compositions of PAEs were consistent in disks and gaseous constituents from ambient air, with low-molecular-weight (<306 g mol-1) PAEs accounting for 87-100% and approximately 100%, respectively. Appreciable amounts of diisononyl phthalate, diisodecyl phthalate, dinonyl phthalate, and skin lipid (e.g., squalene) were detected in watchbands but not in disks. Apparently, the passive sampler can prevent particles and skin-related chemicals from adhering to the disk and collect gaseous PAEs only. The vast majority of PAEs in watchbands was associated with nongaseous constituents. The present study demonstrated that the sampling strategy is a key factor in exposure assessment.

Wearable Transcutaneous CO2 Monitor Based on Miniaturized Nondispersive Infrared Sensor.

Transcutaneous oxygen and carbon dioxide provide the status of pulmonary gas exchange and are of importance in diagnosis and management of respiratory diseases. Though significant progress has been made in oximetry, not much has been explored in developing wearable technologies for continuous monitoring of transcutaneous carbon dioxide. This research reports the development of a truly wearable sensor for continuous monitoring of transcutaneous carbon dioxide using miniaturized nondispersive infrared sensor augmented by hydrophobic membrane to address the humidity interference. The wearable transcutaneous CO2 monitor shows well-behaved response curve to humid CO2 with linear response to CO2 concentration. The profile of transcutaneous CO2 monitored by the wearable device correlates well with the end-tidal CO2 trend in human test. The feasibility of the wearable device for passive and unobstructed tracking of transcutaneous CO2 in free-living conditions has also been demonstrated in field test. The wearable transcutaneous CO2 monitoring technology developed in this research can be widely used in remote assessment of pulmonary gas exchange efficiency for patients with respiratory diseases, such as COVID-19, sleep apnea, and chronic obstructive pulmonary disease (COPD).

Application of a Machine Learning Algorithms in a Wrist-Wearable Sensor for Patient Health Monitoring during Autonomous Hospital Bed Transport.

Smart sensors, coupled with artificial intelligence (AI)-enabled remote automated monitoring (RAMs), can free a nurse from the task of in-person patient monitoring during the transportation process of patients between different wards in hospital settings. Automation of hospital beds using advanced robotics and sensors has been a growing trend exacerbated by the COVID crisis. In this exploratory study, a polynomial regression (PR) machine learning (ML) RAM algorithm based on a Dreyfusian descriptor for immediate wellbeing monitoring was proposed for the autonomous hospital bed transport (AHBT) application. This method was preferred over several other AI algorithm for its simplicity and quick computation. The algorithm quantified historical data using supervised photoplethysmography (PPG) data for 5 min just before the start of the autonomous journey, referred as pre-journey (PJ) dataset. During the transport process, the algorithm continued to quantify immediate measurements using non-overlapping sets of 30 PPG waveforms, referred as in-journey (IJ) dataset. In combination, this algorithm provided a binary decision condition that determined if AHBT should continue its journey to destination by checking the degree of polynomial (DoP) between PJ and IJ. Wrist PPG was used as algorithm's monitoring parameter. PPG data was collected simultaneously from both wrists of 35 subjects, aged 21 and above in postures mimicking that in AHBT and were given full freedom of upper limb and wrist movement. It was observed that the top goodness-of-fit which indicated potentials for high data accountability had 0.2 to 0.6 cross validation score mean (CVSM) occurring at 8th to 10th DoP for PJ datasets and 0.967 to 0.994 CVSM at 9th to 10th DoP for IJ datasets. CVSM was a reliable metric to pick out the best PJ and IJ DoPs. Central tendency analysis showed that coinciding DoP distributions between PJ and IJ datasets, peaking at 8th DoP, was the precursor to high algorithm stability. Mean algorithm efficacy was 0.20 as our proposed algorithm was able to pick out all signals from a conscious subject having full freedom of movement. This efficacy was acceptable as a first ML proof of concept for AHBT. There was no observable difference between subjects' left and right wrists.

Hand Gesture Recognition on a Resource-Limited Interactive Wristband.

Most of the reported hand gesture recognition algorithms require high computational resources, i.e., fast MCU frequency and significant memory, which are highly inapplicable to the cost-effectiveness of consumer electronics products. This paper proposes a hand gesture recognition algorithm running on an interactive wristband, with computational resource requirements as low as Flash < 5 KB, RAM < 1 KB. Firstly, we calculated the three-axis linear acceleration by fusing accelerometer and gyroscope data with a complementary filter. Then, by recording the order of acceleration vectors crossing axes in the world coordinate frame, we defined a new feature code named axis-crossing code. Finally, we set templates for eight hand gestures to recognize new samples. We compared this algorithm's performance with the widely used dynamic time warping (DTW) algorithm and recurrent neural network (BiLSTM and GRU). The results show that the accuracies of the proposed algorithm and RNNs are higher than DTW and that the time cost of the proposed algorithm is much less than those of DTW and RNNs. The average recognition accuracy is 99.8% on the collected dataset and 97.1% in the actual user-independent case. In general, the proposed algorithm is suitable and competitive in consumer electronics. This work has been volume-produced and patent-granted.

Analysis of Older Adults in Spanish Care Facilities, Risk of Falling and Daily Activity Using Xiaomi Mi Band 2.

BACKGROUND: Presently the use of technological devices such as wearable devices has emerged. Physical activity monitoring with wearable sensors is an easy and non-intrusive approach to encourage preventive care for older adults. It may be useful to follow a continuous assessment of the risk of falling. The objective is to explore the relationship between the daily activity measured by Xiaomi Mi Band 2 and the risk of falling of older adults residing in or attending care facilities. METHODS: A cross-sectional study was conducted on three different institutions located in Galicia (autonomous community) (Spain). RESULTS: A total of 31 older adults were included in the study, with a mean age of 84 ± 8.71 years old. The main findings obtained were that a greater number of steps and distance could be related to a lower probability of falling, of dependency in basic activities of daily living, or of mobility problems. CONCLUSIONS: The importance of focusing on daily steps, intrinsically related to the objective assessment of daily physical activity, is that it is a modifiable factor that impacts different aspects of health and quality of life.

Status epilepticus admissions during the COVID-19 pandemic in Salzburg-A population-based study.

Several emergencies were admitted less frequently to the hospital during the coronavirus disease 2019 (COVID-19) pandemic. To investigate whether this also occurred with status epilepticus (SE) we compared admissions due to first SE from March to April 2020 ("Time of COVID," TOC) with January to February 2020 ("pre-COVID," preCOV). We also compared admission numbers in TOC and preCOV with the respective 2-month periods in 2018 and 2019 in a retrospective cohort analysis. Two investigators independently searched the hospital patient database for various forms of SE. There was no significant change in the 2-month incidences of first SE in the city of Salzburg from preCOV of 6.1 (95% confidence interval [CI] 2.9-12.3) to TOC of 6.9/100 000 adults (95% CI 3.4-13.3). Admission numbers did not differ significantly from previous years. Estimated adjusted incidence was in line with a recent 5-year epidemiological study in Salzburg. However, a trend toward less-frequent nonconvulsive SE (NCSE) and loss of female predominance were indirect hints of underdiagnosing SE. In contrast to other medical conditions, SE most often presents clinically with impaired consciousness, which may promote admission to emergency departments even in times of lock-down. Further research of medical support of women and patients with NCSE during pandemic-related restrictions is warranted.

Feasibility of smart wristbands for continuous monitoring during pregnancy and one month after birth.

BACKGROUND: Smart wristbands enable the continuous monitoring of health parameters, for example, in maternity care. Understanding the feasibility and acceptability of these devices in an authentic context is essential. The aim of this study was to evaluate the feasibility of using a smart wristband to collect continuous activity, sleep and heart rate data from the beginning of the second trimester until one month postpartum. METHODS: The feasibility of a smart wristband was tested prospectively through pregnancy in nulliparous women (n = 20). The outcomes measured were the wear time of the device and the participants' experiences with the smart wristband. The data were collected from the wristbands, phone interviews, questionnaires, and electronic patient records. The quantitative data were analyzed with hierarchical linear mixed models for repeated measures, and qualitative data were analyzed using content analysis. RESULTS: Participants (n = 20) were recruited at a median of 12.9 weeks of gestation. They used the smart wristbands for an average of 182 days during the seven-month study period. The daily use of the devices was similar during the second (17.9 h, 95% CI 15.2 to 20.7) and third trimesters (16.7 h, 95% CI 13.8 to 19.5) but decreased during the postpartum period (14.4 h, 95% CI 11.4 to 17.4, p = 0.0079). Participants who could not wear smart wristbands at work used the device 300 min less per day than did those with no use limitations. Eight of the participants did not wear the devices or wore them only occasionally after giving birth. Nineteen participants reported that the smart wristband did not have any permanent effects on their behavior. Problems with charging and synchronizing the devices, perceiving the devices as uncomfortable, or viewing the data as unreliable, and the fear of scratching their babies with the devices were the main reasons for not using the smart wristbands. CONCLUSIONS: A smart wristband is a feasible tool for continuous monitoring during pregnancy. However, the daily use decreased after birth. The results of this study may support the planning of future studies and help with overcoming barriers related to the use of smart wristbands on pregnant women.