Frailty assessment using a novel approach based on combined motor and cardiac functions: a pilot study.

BACKGROUND: Previous research showed association between frailty and an impaired autonomic nervous system; however, the direct effect of frailty on heart rate (HR) behavior during physical activity is unclear. The purpose of the current study was to determine the association between HR increase and decrease with frailty during a localized upper-extremity function (UEF) task to establish a multimodal frailty test. METHODS: Older adults aged 65 or older were recruited and performed the UEF task of rapid elbow flexion for 20 s with the right arm. Wearable gyroscopes were used to measure forearm and upper-arm motion, and electrocardiography were recorded using leads on the left chest. Using this setup, HR dynamics were measured, including time to peak HR, recovery time, percentage increase in HR during UEF, and percentage decrease in HR during recovery after UEF. RESULTS: Fifty-six eligible participants were recruited, including 12 non-frail (age = 76.92 ± 7.32 years), and 40 pre-frail (age = 80.53 ± 8.12 years), and four frail individuals (age = 88.25 ± 4.43 years). Analysis of variance models showed that the percentage increase in HR during UEF and percentage decrease in HR during recovery were both 47% smaller in pre-frail/frail older adults compared to non-frails (p < 0.01, effect size = 0.70 and 0.62 for increase and decrease percentages). Using logistic models with both UEF kinematics and HR parameters as independent variables, frailty was predicted with a sensitivity of 0.82 and specificity of 0.83. CONCLUSION: Current findings showed evidence of strong association between HR dynamics and frailty. It is suggested that combining kinematics and HR data in a multimodal model may provide a promising objective tool for frailty assessment.

Pervasive Lying Posture Tracking.

Automated lying-posture tracking is important in preventing bed-related disorders, such as pressure injuries, sleep apnea, and lower-back pain. Prior research studied in-bed lying posture tracking using sensors of different modalities (e.g., accelerometer and pressure sensors). However, there remain significant gaps in research regarding how to design efficient in-bed lying posture tracking systems. These gaps can be articulated through several research questions, as follows. First, can we design a single-sensor, pervasive, and inexpensive system that can accurately detect lying postures? Second, what computational models are most effective in the accurate detection of lying postures? Finally, what physical configuration of the sensor system is most effective for lying posture tracking? To answer these important research questions, in this article we propose a comprehensive approach for designing a sensor system that uses a single accelerometer along with machine learning algorithms for in-bed lying posture classification. We design two categories of machine learning algorithms based on deep learning and traditional classification with handcrafted features to detect lying postures. We also investigate what wearing sites are the most effective in the accurate detection of lying postures. We extensively evaluate the performance of the proposed algorithms on nine different body locations and four human lying postures using two datasets. Our results show that a system with a single accelerometer can be used with either deep learning or traditional classifiers to accurately detect lying postures. The best models in our approach achieve an F1 score that ranges from 95.2% to 97.8% with a coefficient of variation from 0.03 to 0.05. The results also identify the thighs and chest as the most salient body sites for lying posture tracking. Our findings in this article suggest that, because accelerometers are ubiquitous and inexpensive sensors, they can be a viable source of information for pervasive monitoring of in-bed postures.

Cardiac arrhythmia detection using deep learning: A review.

Due to its simplicity and low cost, analyzing an electrocardiogram (ECG) is the most common technique for detecting cardiac arrhythmia. The massive amount of ECG data collected every day, in home and hospital, may preclude data review by human operators/technicians. Therefore, several methods are proposed for either fully automatic arrhythmia detection or event selection for further verification by human experts. Traditional machine learning approaches have made significant progress in the past years. However, those methods rely on hand-crafted feature extraction, which requires in-depth domain knowledge and preprocessing of the signal (e.g., beat detection). This, plus the high variability in wave morphology among patients and the presence of noise, make it challenging for computerized interpretation to achieve high accuracy. Recent advances in deep learning make it possible to perform automatic high-level feature extraction and classification. Therefore, deep learning approaches have gained interest in arrhythmia detection. In this work, we reviewed the recent advancement of deep learning methods for automatic arrhythmia detection. We summarized existing literature from five aspects: utilized dataset, application, type of input data, model architecture, and performance evaluation. We also reported limitations of reviewed papers and potential future opportunities.

Densely connected convolutional networks for detection of atrial fibrillation from short single-lead ECG recordings.

The development of new technology such as wearables that record high-quality single channel ECG, provides an opportunity for ECG screening in a larger population, especially for atrial fibrillation screening. The main goal of this study is to develop an automatic classification algorithm for normal sinus rhythm (NSR), atrial fibrillation (AF), other rhythms (O), and noise from a single channel short ECG segment (9-60 s). For this purpose, we combined a signal quality index (SQI) algorithm, to assess noisy instances, and trained densely connected convolutional neural networks to classify ECG recordings. Two convolutional neural network (CNN) models (a main model that accepts 15 s ECG segments and a secondary model that processes shorter 9 s segments) were trained using the training data set. If the recording is determined to be of low quality by SQI, it is immediately classified as noisy. Otherwise, it is transformed to a time-frequency representation and classified with the CNN as NSR, AF, O, or noise. The results achieved on the 2017 PhysioNet/Computing in Cardiology challenge test dataset were an overall F1 score of 0.82 (F1 for NSR, AF, and O were 0.91, 0.83, and 0.72, respectively). Compared with 80 challenge entries, this was the third best overall score achieved on the evaluation dataset.

Postural Transitions during Activities of Daily Living Could Identify Frailty Status: Application of Wearable Technology to Identify Frailty during Unsupervised Condition.

BACKGROUND: Impairment of physical function is a major indicator of frailty. Functional performance tests have been shown to be useful for identification of frailty in older adults. However, these tests are often not translatable into unsupervised and remote monitoring of frailty status at home and/or community settings. OBJECTIVE: In this study, we explored daily postural transition quantified using a chest-worn wearable technology to identify frailty in community-dwelling older adults. METHODS: Spontaneous daily physical activity was monitored over 24 h in 120 community-dwelling elderly (age: 78 ± 8 years) using an unobtrusive wearable sensor (PAMSys™, BioSensics LLC, Watertown, MA, USA). Participants were classified as non-frail and pre-frail/frail using Fried's criteria. A validated software package was used to identify body postures and postural transition between each independent postural activity such as sit-to-stand, stand-to-sit, stand-to-walk, and walk-to-stand. The transition from walking to sitting was further classified as quick sitting and cautious sitting based on presence/absence of a standing posture pause between sitting and walking. A general linear model univariate test was used for between-group comparison. Pearson's correlation was used to determine the association between sensor-derived parameters and age. Logistic regression model was used to identify independent predictors of frailty. RESULTS: According to Fried's criteria, 63% of participants were pre-frail/frail. The total number of postural transitions, stand-to-walk, and walk-to-stand were, respectively, 25.2, 30.2, and 30.6% lower in the pre-frail/frail group when compared to the non-frail group (p < 0.05, Cohen's d = 0.73-0.79). Furthermore, the ratio of cautious sitting was significantly higher by 6.2% in pre-frail/frail compared to non-frail (p = 0.025, Cohen's d = 0.22). Total number of postural transitions and the ratio of cautious sitting also showed significant negative and positive correlations with age, respectively (r = -0.51 and 0.29, p < 0.05). After applying a logistic regression model, among tested parameters, walk-to-stand (odds ratio [OR] = 0.997 p = 0.013), quick sitting (OR = 1.036, p = 0.05), and age (OR = 1.073, p = 0.016) were recognized as independent variables to identify frailty status. CONCLUSIONS: This study demonstrated that daily number of specific postural transitions such as walk-to-stand and quick sitting could be used for monitoring frailty status by unsupervised monitoring of daily physical activity. Further study is warranted to explore whether tracking the daily number of specific postural transitions is also sensitive to track change in the status of frailty over time.

Regulation of Cardiac Autonomic Nervous System Control across Frailty Statuses: A Systematic Review.

BACKGROUND: Frailty is a geriatric syndrome that leads to impairment in interrelated physiological systems and progressive homeostatic dysregulation in physiological systems. OBJECTIVE: The focus of the present systematic review was to study the association between the activity of the cardiac autonomic nervous system (ANS) and frailty. METHODS: A systematic literature search was conducted in multiple databases: PubMed/MEDLINE, Embase, Cochrane Library, Web of Science, CINAHL, and ClinicalTrials.gov; the last search was performed in March 2015. Inclusion criteria were: (1) that the studied population was classified for frailty according to a standard definition, such as Fried's criteria; (2) that the study had a nonfrail control group, and (3) that heart rate (HR) and/or heart rate variability (HRV) were parameters of interest in the study. RESULTS: Of the 1,544 articles screened, 54 were selected for full-text review and 6 studies met the inclusion criteria. Assessment of HRV using different standard time domain, frequency domain, and nonlinear domain approaches confirmed the presence of an impaired cardiac ANS function in frail compared to nonfrail participants. Furthermore, HR changes while performing a clinical test (e.g., the seated step test or the lying-to-standing orthostatic test) were decreased in the frail group compared to the nonfrail group. CONCLUSIONS: The current systematic review provides evidence that the cardiac ANS is impaired in frail compared to nonfrail older adults, as indicated by a reduction in the complexity of HR dynamics, reduced HRV, and reduced HR changes in response to daily activities. Four out of 6 included articles recruited only female participants, and in the other 2 articles the effect of gender on impairment of cardiac ANS was insufficiently investigated. Therefore, further studies are required to study the association between cardiac ANS impairments and frailty in males. Furthermore, HRV was studied only during static postures such as sitting, or without considering the level of activity as a potential confounder. Accordingly, simultaneous measurement of both physiological (i.e., HRV) and kinematic (e.g., using wearable sensor technology) information may provide a better understanding of cardiac ANS impairments with frailty while controlling for activity.

Sensor-Based Interactive Balance Training with Visual Joint Movement Feedback for Improving Postural Stability in Diabetics with Peripheral Neuropathy: A Randomized Controlled Trial.

BACKGROUND: Individuals with diabetic peripheral neuropathy (DPN) have deficits in sensory and motor skills leading to inadequate proprioceptive feedback, impaired postural balance and higher fall risk. OBJECTIVE: This study investigated the effect of sensor-based interactive balance training on postural stability and daily physical activity in older adults with diabetes. METHODS: Thirty-nine older adults with DPN were enrolled (age 63.7 ± 8.2 years, BMI 30.6 ± 6, 54% females) and randomized to either an intervention (IG) or a control (CG) group. The IG received sensor-based interactive exercise training tailored for people with diabetes (twice a week for 4 weeks). The exercises focused on shifting weight and crossing virtual obstacles. Body-worn sensors were implemented to acquire kinematic data and provide real-time joint visual feedback during the training. Outcome measurements included changes in center of mass (CoM) sway, ankle and hip joint sway measured during a balance test while the eyes were open and closed at baseline and after the intervention. Daily physical activities were also measured during a 48-hour period at baseline and at follow-up. Analysis of covariance was performed for the post-training outcome comparison. RESULTS: Compared with the CG, the patients in the IG showed a significantly reduced CoM sway (58.31%; p = 0.009), ankle sway (62.7%; p = 0.008) and hip joint sway (72.4%; p = 0.017) during the balance test with open eyes. The ankle sway was also significantly reduced in the IG group (58.8%; p = 0.037) during measurements while the eyes were closed. The number of steps walked showed a substantial but nonsignificant increase (+27.68%; p = 0.064) in the IG following training. CONCLUSION: The results of this randomized controlled trial demonstrate that people with DPN can significantly improve their postural balance with diabetes-specific, tailored, sensor-based exercise training. The results promote the use of wearable technology in exercise training; however, future studies comparing this technology with commercially available systems are required to evaluate the benefit of interactive visual joint movement feedback.

Predicting the spontaneous termination of atrial fibrillation based on poincare section in the electrocardiogram phase space.

Atrial fibrillation (AF) is a commonly encountered cardiac arrhythmia. Predicting the conditions under which AF terminates spontaneously is an important task that would bring great benefit to both patients and clinicians. In this study, a new method was proposed to predict spontaneous AF termination by employing the points of section (POS) coordinates along a Poincare section in the electrocardiogram (ECG) phase space. The AF Termination Database provided by PhysioNet for the Computers in Cardiology Challenge 2004 was applied in the present study. It includes one training dataset and two testing datasets, A and B. The present investigation was initiated by producing a two-dimensional reconstructed phase space (RPS) of the ECG. Then, a Poincare line was drawn in a direction that included the maximum point distribution in the RPS and also passed through the origin of the RPS coordinate system. Afterward, the coordinates of the RPS trajectory intersections with this Poincare line were extracted to capture the local behavior related to the arrhythmia under investigation. The POS corresponding to atrial activity were selected with regard to the fact that similar ECG morphologies such as P waves, which are corresponding to atrial activity, distribute in a specific region of the RPS. Thirteen features were extracted from the selected intersection points to quantify their distributions. To select the best feature subset, a genetic algorithm (GA), in combination with a support vector machine (SVM), was applied to the training dataset. Based on the selected features and trained SVM, the performance of the proposed method was evaluated using the testing datasets. The results showed that 86.67% of dataset A and 80% of dataset B were correctly classified. This classification accuracy is in the same range as or higher than that of recent studies in this area. These results show that the proposed method, in which no complicated QRST cancelation algorithm was used, has the potential to predict AF termination.

Boosting Lying Posture Classification with Transfer Learning.

Automatic lying posture tracking is an important factor in human health monitoring. The increasing popularity of the wrist-based trackers provides the means for unobtrusive, affordable, and long-term monitoring with minimized privacy concerns for the end-users and promising results in detecting the type of physical activity, step counting, and sleep quality assessment. However, there is limited research on development of accurate and efficient lying posture tracking models using wrist-based sensor. Our experiments demonstrate a major drop in the accuracy of the lying posture tracking using wrist-based accelerometer sensor due to the unpredictable noise from arbitrary wrist movements and rotations while sleeping. In this paper, we develop a deep transfer learning method that improves performance of lying posture tracking using noisy data from wrist sensor by transferring the knowledge from an initial setting which contains both clean and noisy data. The proposed solution develops an optimal mapping model from the noisy data to the clean data in the initial setting using LSTM sequence regression, and reconstruct clean synthesized data in another setting where no noisy sensor data is available. This increases the lying posture tracking F1-Score by 24.9% for 'left-wrist' and by 18.1% for 'right-wrist' sensors comparing to the case without mapping.

Frailty Identification Using Heart Rate Dynamics: A Deep Learning Approach.

Previous research showed that frailty can influence autonomic nervous system and consequently heart rate response to physical activities, which can ultimately influence the homeostatic state among older adults. While most studies have focused on resting state heart rate characteristics or heart rate monitoring without controlling for physical activities, the objective of the current study was to classify pre-frail/frail vs non-frail older adults using heart rate response to physical activity (heart rate dynamics). Eighty-eight older adults (≥65 years) were recruited and stratified into frailty groups based on the five-component Fried frailty phenotype. Groups consisted of 27 non-frail (age = 78.80±7.23) and 61 pre-frail/frail (age = 80.63±8.07) individuals. Participants performed a normal speed walking as the physical task, while heart rate was measured using a wearable electrocardiogram recorder. After creating heart rate time series, a long short-term memory model was used to classify participants into frailty groups. In 5-fold cross validation evaluation, the long short-term memory model could classify the two above-mentioned frailty classes with a sensitivity, specificity, F1-score, and accuracy of 83.0%, 80.0%, 87.0%, and 82.0%, respectively. These findings showed that heart rate dynamics classification using long short-term memory without any feature engineering may provide an accurate and objective marker for frailty screening.

The association between heart rate behavior and gait performance: The moderating effect of frailty.

INTRODUCTION: Research suggests that frailty not only influence individual systems, but also it affects the interconnection between them. However, no study exists to show how the interplay between cardiovascular and motor performance is compromised with frailty. AIM: To investigate the effect of frailty on the association between heart rate (HR) dynamics and gait performance. METHODS: Eighty-five older adults (≥65 years and able to walk 9.14 meters) were recruited (October 2016-March 2018) and categorized into 26 non-frail (age = 78.65±7.46 years) and 59 pre-frail/frail individuals (age = 81.01±8.17) based on the Fried frailty phenotype. Participants performed gait tasks while equipped with a wearable electrocardiogram (ECG) sensor attached to the chest, as well as wearable gyroscopes for gait assessment. HR dynamic parameters were extracted, including time to peak HR and percentage increase in HR in response to walking. Using the gyroscope sensors gait parameters were recorded including stride length, stride velocity, mean swing velocity, and double support. RESULTS: Among the pre-frail/frail group, time to peak HR was significantly correlated with all gait parameters (p<0.0001, r = 0.51-0.59); however, for the non-frail group, none of the correlations between HR dynamics and gait performance parameters were significant (p>0.45, r = 0.03-0.15). The moderation analysis of time to peak HR, demonstrated a significant interaction effect of HR dynamics and frailty status on walking velocity (p<0.01), and the interaction effect was marginally non-significant for other gait parameters (p>0.10). CONCLUSIONS: Current findings, for the first time, suggest that a compromised motor and cardiac autonomic interaction exist among pre-frail/frail older adults; an impaired HR performance (i.e., slower increase of HR in response to stressors) may lead to a slower walking performance. Assessing physical performance and its corresponding HR behavior should be studied as a tool for frailty screening and providing insights about the underlying cardiovascular-related mechanism leading to physical frailty.

Frailty and heart response to physical activity.

BACKGROUND: Although previous studies showed that frail older adults are more susceptible to develop cardiovascular diseases, the underlying effect of frailty on heart rate dynamics is still unclear. The goal of the current study was to measure heart rate changes due to normal speed and rapid walking among non-frail and pre-frail/frail older adults, and to implement heart rate dynamic measures to identify frailty status. METHODS: Eighty-eight older adults (≥65 years) were recruited and stratified into frailty groups based on the five-component Fried frailty phenotype. While performing gait tests, heart rate was recorded using a wearable ECG and accelerometer sensors. Groups consisted of 27 non-frail (age = 78.70 ± 7.32) and 61 pre-frail/frail individuals (age = 81.00 ± 8.14). The parameters of interest included baseline heart rate measures (mean heart rate and heart rate variability), and heart rate dynamics due to walking (percentage change in heart rate and required time to reach the maximum heart rate). RESULTS: Respectively for normal and rapid walking conditions, pre-frail/frail participants had 46% and 44% less increase in heart rate, and 49% and 27% slower occurrence of heart rate peak, when compared to non-frail older adults (p < 0.04, effect size = 0.71 ± 0.12). Measures of heart rate dynamics showed stronger associations with frailty status compared to baseline resting-state measures (sensitivity = 0.75 and specificity = 0.65 using heart rate dynamics measures, compared to sensitivity = 0.64 and specificity = 0.62 using baseline parameters). CONCLUSIONS: These findings suggest that measures of heart rate dynamics in response to daily activities may provide meaningful markers for frailty screening.

Can motor function uncertainty and local instability within upper-extremity dual-tasking predict amnestic mild cognitive impairment and early-stage Alzheimer's disease?

In this study, we examined the uncertainty and local instability of motor function for cognitive impairment screening using a previously validated upper-extremity function (UEF). This approach was established based upon the fact that elders with an impaired executive function have trouble in the simultaneous execution of a motor and a cognitive task (dual-tasking). Older adults aged 65 years and older were recruited and stratified into 1) cognitive normal (CN), 2) amnestic MCI of the Alzheimer's type (aMCI), and 3) early-stage Alzheimer's Disease (AD). Participants performed normal-paced repetitive elbow flexion without counting and while counting backward by ones and threes. The influence of cognitive task on motor function was measured using uncertainty (measured by Shannon entropy), and local instability (measured by the largest Lyapunov exponent) of elbow flexion and compared between cognitive groups using ANOVAs, while adjusting for age, sex, and BMI. We developed logistic ordinal regression models for predicting cognitive groups based on these nonlinear measures. A total of 81 participants were recruited, including 35 CN (age = 83.8 ± 6.9), 30 aMCI (age = 83.9 ± 6.9), and 16 early AD (age = 83.2 ± 6.6). Uncertainty of motor function demonstrated the strongest associations with cognitive impairment, with an effect size of 0.52, 0.88, and 0.51 for CN vs. aMCI, CN vs. AD, and aMCI vs. AD comparisons, respectively. Ordinal logistic models predicted cognitive impairment (aMCI and AD combined) with a sensitivity and specificity of 0.82. The findings accentuate the potential of employing nonlinear dynamical features of motor functions during dual-tasking, especially uncertainty, in detecting cognitive impairment.

Path to precision: prevention of post-operative atrial fibrillation.

Development of post-operative atrial fibrillation (POAF) following open-heart surgery is a significant clinical and economic burden. Despite advancements in medical therapies, the incidence of POAF remains elevated at 25-40%. Early work focused on detecting arrhythmias from electrocardiograms as well as identifying pre-operative risk factors from medical records. However, further progress has been stagnant, and a deeper understanding of pathogenesis and significant influences is warranted. With the advent of more complex machine learning (ML) algorithms and high-throughput sequencing, we have an unprecedented ability to capture and predict POAF in real-time. Integration of multimodal heterogeneous data and application of ML can generate a paradigm shift for diagnosis and treatment. This will require a concerted effort to consolidate and streamline real-time data. Herein, we will review the current literature and emerging opportunities aimed at predictive targets and new insights into the mechanisms underlying long-term sequelae of POAF.

Monitoring of Patient Blanket Coverage using 3D Camera Data.

Nurses in a hospital are responsible for the monitoring and care of a large number of patients. Regularly checking if patients are sufficiently covered by their blankets is part of a nurse's normal workload. In this paper, an algorithm based on 3D images is proposed that can automatically and unobtrusively detect if a patient is covered with a blanket or not. The depth images for this study was recorded by a Microsoft Kinect™ sensor in a simulated hospital environment. The training dataset consisted of 20 images with blanket and 10 images without blanket. Additionally, held-out test data (20 with and 10 without blanket) was created in both good lighting conditions and under a greater variation of lighting conditions to test performance of trained classifiers. We first extract traditional features (textural features and a statistical measures) and new features based on a cross-section profile of the data from a training data set. The skewness of pixel values over the region of interest and the minimum of the differentiate of smoothed cross-section profile were selected as the most important feature using mutual information. Several classifiers, including support vector machines (SVM), k-nearest neighbors, and logistic regression were built with the selected features. The best performing was linear SVM classifier with overall accuracy of 93% on entire held-out dataset, with sensitivity of 90% and specificity of 85%.

Analyzing single-lead short ECG recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation.

OBJECTIVE: The prevalence of atrial fibrillation (AF) in the general population is 0.5%-1%. As AF is the most common sustained cardiac arrhythmia that is associated with an increased morbidity and mortality, its timely diagnosis is clinically desirable. The main aim of this study as our contribution to the PhysioNet/CinC Challenge 2017 was to develop an automatic algorithm for classification of normal sinus rhythm (NSR), AF, other rhythm (O), and noise using a short single-channel ECG. Furthermore, the impact of changing labels/annotations on performance of the proposed algorithm was studied in this article. APPROACH: The challenge training dataset (8528 ECG recordings) and a complementary dataset (6312 ECG recordings) from other sources were used for algorithm development. Version 3 (v3), which is an updated version of the annotations at the official phase of the challenge (v2), was used in this study. In the proposed algorithm, densely connected convolutional networks were combined with feature-based post-processing after initial signal quality analysis for the classification of ECG recordings. MAIN RESULTS: The F1 scores for classification of NSR, AF, and O were 0.91, 0.83, and 0.72, respectively, which led to a F1 of 0.82. There was a small or no performance difference between the top entries in the official phase of the challenge and our proposed method. An increase of 2.5% in F1 score was observed when the same annotations for training and test was used (using v3 annotations) compared to using different annotations (v2 annotations for training and v3 annotations for the test). SIGNIFICANCE: Our promising results suggest that the availability of more data with improved labeling along with improvement in signal quality analysis make our algorithm suitable for practical clinical applications.

Stress among surgical attending physicians and trainees: A quantitative assessment during trauma activation and emergency surgeries.

BACKGROUND: The adverse effects of stress on the wellness of trauma team members are well established; however, the level of stress has never been quantitatively assessed. The aim of our study was to assess the level of stress using subjective data and objective heart rate variability (HRV) among attending surgeons (ASs), junior residents (JRs) (PGY2/PGY3), and senior residents (SRs) (PGY5/PGY6) during trauma activation and emergency surgery. METHODS: We preformed a prospective study enrolling participants over eight 24-hour calls in our Level I trauma center. Stress was assessed based on decrease in HRV, which was recorded using body worn sensors. Stress was defined as HRV of less than 85% of baseline HRV. We collected subjective data on stress for each participant during calls. Three groups (ASs, JRs, SRs) were compared for duration of different stress levels through trauma activation and emergency surgery. RESULTS: A total of 22 participants (ASs: n = 8, JRs: n = 7, SRs: n = 7) were evaluated over 192 hours, which included 33 trauma activations and 50 emergency surgeries. Stress level increased during trauma activations and operations regardless of level of training. The ASs had significantly lower stress when compared with SRs and JRs during trauma activation (21.9 ± 10.7 vs. 51.9 ± 17.2 vs. 64.5 ± 11.6; p < 0.001) and emergency surgery (30.8 ± 7.0 vs. 53.33 ± 6.9 vs. 56.1 ± 3.8; p < 0.001). The level of stress was similar between JRs and SRs during trauma activation (p = 0.37) and emergency surgery (p = 0.19). There was no correlation between objectively measured stress level and subjectively measured stress using State-Trait Anxiety Inventory (R = 0.16; p = 0.01) among surgeons or residents. CONCLUSIONS: Surgeon wellness is a significant concern, and this study provides empirical evidence that trauma and acute care surgeons encounter mental strain and fail to recognize it. Stress management and burnout are very important in this high-intensity field, and this research may provide some insight in finding those practitioners who are at risk. LEVEL OF EVIDENCE: Epidemiologic study, level II.

Motor Performance Assessment in Parkinson's Disease: Association between Objective In-Clinic, Objective In-Home, and Subjective/Semi-Objective Measures.

Advances in wearable technology allow for the objective assessment of motor performance in both in-home and in-clinic environments and were used to explore motor impairments in Parkinson's disease (PD). The aims of this study were to: 1) assess differences between in-clinic and in-home gait speed, and sit-to-stand and stand-to-sit duration in PD patients (in comparison with healthy controls); and 2) determine the objective physical activity measures, including gait, postural balance, instrumented Timed-up-and-go (iTUG), and in-home spontaneous physical activity (SPA), with the highest correlation with subjective/semi-objective measures, including health survey, fall history (fallers vs. non-fallers), fear of falling, pain, Unified Parkinson's Disease Rating Scale, and PD stage (Hoehn and Yahr). Objective assessments of motor performance were made by measuring physical activities in the same sample of PD patients (n = 15, Age: 71.2±6.3 years) and age-matched healthy controls (n = 35, Age: 71.9±3.8 years). The association between in-clinic and in-home parameters, and between objective parameters and subjective/semi-objective evaluations in the PD group was assessed using linear regression-analysis of variance models and reported as Pearson correlations (R). Both in-home SPA and in-clinic assessments demonstrated strong discriminatory power in detecting impaired motor function in PD. However, mean effect size (0.94±0.37) for in-home measures was smaller compared to in-clinic assessments (1.30±0.34) for parameters that were significantly different between PD and healthy groups. No significant correlation was observed between identical in-clinic and in-home parameters in the PD group (R = 0.10-0.25; p>0.40), while the healthy showed stronger correlation in gait speed, sit-to-stand duration, and stand-to-sit duration (R = 0.36-0.56; p<0.03). This suggests a better correlation between supervised and unsupervised motor function assessments in healthy controls compared to PD group. In the PD group, parameters related to velocity and range-of-motion of lower extremity within gait assessment (R = 0.58-0.84), and turning duration and velocity within iTUG test (R = 0.62-0.77) demonstrated strong correlations with PD stage (p<0.01).