Harnessing Consumer Wearable Digital Biomarkers for Individualized Recognition of Postpartum Depression Using the All of Us Research Program Data Set: Cross-Sectional Study.

BACKGROUND: Postpartum depression (PPD) poses a significant maternal health challenge. The current approach to detecting PPD relies on in-person postpartum visits, which contributes to underdiagnosis. Furthermore, recognizing PPD symptoms can be challenging. Therefore, we explored the potential of using digital biomarkers from consumer wearables for PPD recognition. OBJECTIVE: The main goal of this study was to showcase the viability of using machine learning (ML) and digital biomarkers related to heart rate, physical activity, and energy expenditure derived from consumer-grade wearables for the recognition of PPD. METHODS: Using the All of Us Research Program Registered Tier v6 data set, we performed computational phenotyping of women with and without PPD following childbirth. Intraindividual ML models were developed using digital biomarkers from Fitbit to discern between prepregnancy, pregnancy, postpartum without depression, and postpartum with depression (ie, PPD diagnosis) periods. Models were built using generalized linear models, random forest, support vector machine, and k-nearest neighbor algorithms and evaluated using the κ statistic and multiclass area under the receiver operating characteristic curve (mAUC) to determine the algorithm with the best performance. The specificity of our individualized ML approach was confirmed in a cohort of women who gave birth and did not experience PPD. Moreover, we assessed the impact of a previous history of depression on model performance. We determined the variable importance for predicting the PPD period using Shapley additive explanations and confirmed the results using a permutation approach. Finally, we compared our individualized ML methodology against a traditional cohort-based ML model for PPD recognition and compared model performance using sensitivity, specificity, precision, recall, and F1-score. RESULTS: Patient cohorts of women with valid Fitbit data who gave birth included <20 with PPD and 39 without PPD. Our results demonstrated that intraindividual models using digital biomarkers discerned among prepregnancy, pregnancy, postpartum without depression, and postpartum with depression (ie, PPD diagnosis) periods, with random forest (mAUC=0.85; κ=0.80) models outperforming generalized linear models (mAUC=0.82; κ=0.74), support vector machine (mAUC=0.75; κ=0.72), and k-nearest neighbor (mAUC=0.74; κ=0.62). Model performance decreased in women without PPD, illustrating the method's specificity. Previous depression history did not impact the efficacy of the model for PPD recognition. Moreover, we found that the most predictive biomarker of PPD was calories burned during the basal metabolic rate. Finally, individualized models surpassed the performance of a conventional cohort-based model for PPD detection. CONCLUSIONS: This research establishes consumer wearables as a promising tool for PPD identification and highlights personalized ML approaches, which could transform early disease detection strategies.

Monitoring Adolescent and Young Adult Patients With Cancer via a Smart T-Shirt: Prospective, Single-Cohort, Mixed Methods Feasibility Study (OncoSmartShirt Study).

Background: Wearables that measure vital parameters can be potential tools for monitoring patients at home during cancer treatment. One type of wearable is a smart T-shirt with embedded sensors. Initially, smart T-shirts were designed to aid athletes in their performance analyses. Recently however, researchers have been investigating the use of smart T-shirts as supportive tools in health care. In general, the knowledge on the use of wearables for symptom monitoring during cancer treatment is limited, and consensus and awareness about compliance or adherence are lacking. objectives: The aim of this study was to evaluate adherence to and experiences with using a smart T-shirt for the home monitoring of biometric sensor data among adolescent and young adult patients undergoing cancer treatment during a 2-week period. Methods: This study was a prospective, single-cohort, mixed methods feasibility study. The inclusion criteria were patients aged 18 to 39 years and those who were receiving treatment at Copenhagen University Hospital - Rigshospitalet, Denmark. Consenting patients were asked to wear the Chronolife smart T-shirt for a period of 2 weeks. The smart T-shirt had multiple sensors and electrodes, which engendered the following six measurements: electrocardiogram (ECG) measurements, thoracic respiration, abdominal respiration, thoracic impedance, physical activity (steps), and skin temperature. The primary end point was adherence, which was defined as a wear time of >8 hours per day. The patient experience was investigated via individual, semistructured telephone interviews and a paper questionnaire. Results: A total of 10 patients were included. The number of days with wear times of >8 hours during the study period (14 d) varied from 0 to 6 (mean 2 d). Further, 3 patients had a mean wear time of >8 hours during each of their days with data registration. The number of days with any data registration ranged from 0 to 10 (mean 6.4 d). The thematic analysis of interviews pointed to the following three main themes: (1) the smart T-shirt is cool but does not fit patients with cancer, (2) the technology limits the use of the smart T-shirt, and (3) the monitoring of data increases the feeling of safety. Results from the questionnaire showed that the patients generally had confidence in the device. Conclusions: Although the primary end point was not reached, the patients' experiences with using the smart T-shirt resulted in the knowledge that patients acknowledged the need for new technologies that improve supportive cancer care. The patients were positive when asked to wear the smart T-shirt. However, technical and practical challenges in using the device resulted in low adherence. Although wearables might have potential for home monitoring, the present technology is immature for clinical use.

A non-invasive wearable stress patch for real-time cortisol monitoring using a pseudoknot-assisted aptamer.

Stress is part of everyone's life and is exacerbated by traumatic events such as pandemics, disasters, violence, lifestyle changes, and health disorders. Chronic stress has many detrimental health effects and can even be life-threatening. Long-term stress monitoring outside of a hospital is often accomplished by measuring heart rate variability. While easy to measure, this digital biomarker has low specificity, greatly limiting its utility. To address this shortcoming, we report a non-invasive, wearable biomolecular sensor to monitor cortisol levels in sweat. Cortisol is a neuroendocrine hormone that regulates homeostasis as part of the stress pathway. Cortisol is detected using an electrochemical sensor functionalized with a pseudoknot-assisted aptamer and a flexible microfluidic sweat sampling system. The skin-worn microfluidic sampler provides rapid sweat collection while separating old and new sweat. The conformation-switching aptamer provides high specificity towards cortisol while being regenerable, allowing it to monitor temporal changes continuously. The aptamer was engineered to add a pseudoknot, restricting it to only two states, thus minimizing the background signal and enabling high sensitivity. An electrochemical pH sensor allows pH-corrected amperometric measurements. Device operation was demonstrated invitro with a broad linear dynamic range (1 pM - 1 µM) covering the physiological range and a sub-picomolar (0.2 pM) limit of detection in sweat. Real-time, on-body measurements were collected from human subjects using an induced stress protocol, demonstrating in-situ signal regeneration and the ability to detect dynamic cortisol fluctuations continuously for up to 90 min. The reported device has the potential to improve prognosis and enable personalized treatments.

A wearable cardiac ultrasound imager.

Continuous imaging of cardiac functions is highly desirable for the assessment of long-term cardiovascular health, detection of acute cardiac dysfunction and clinical management of critically ill or surgical patients1-4. However, conventional non-invasive approaches to image the cardiac function cannot provide continuous measurements owing to device bulkiness5-11, and existing wearable cardiac devices can only capture signals on the skin12-16. Here we report a wearable ultrasonic device for continuous, real-time and direct cardiac function assessment. We introduce innovations in device design and material fabrication that improve the mechanical coupling between the device and human skin, allowing the left ventricle to be examined from different views during motion. We also develop a deep learning model that automatically extracts the left ventricular volume from the continuous image recording, yielding waveforms of key cardiac performance indices such as stroke volume, cardiac output and ejection fraction. This technology enables dynamic wearable monitoring of cardiac performance with substantially improved accuracy in various environments.

Effects of Healthcare Technologies on the Promotion of Physical Activities in Older Persons: A Systematic Review.

This study aimed to explore the effects of health technologies on the promotion of health through physical activities of older persons. Following PRISMA guidelines, a systematic review of relevant articles published prior to 2020 was conducted from selected indices such as COCHRANE, PubMed, Science Direct, Proquest, including the use of hand search procedure. Twenty-seven articles were analyzed with significant findings influential to older people nursing: types of health technologies used for promoting physical activity; effects of technology use in promoting physical activity of older person care; and aspects that need to be considered in technology use among older persons. Characteristics of technologies were accuracy, usefulness, reliability, comfort, safety, and relevancy. Most technologies promoting physical activities for older people were wearable technologies that use artificial intelligence. Altogether, these technologies influenced overall healthcare behaviors of older persons. With healthcare technology efficiencies, proficiencies, and dependencies, technology-based healthcare have served older people well. Most technologies for older people care, such as wearables, reliably produce characteristics enhancing dependency and accuracy of bio-behavioral information influencing physical activities of older persons. Health technologies foster the values of physical activities among older persons thereby promoting healthy living.

Validation of the Perception Neuron system for full-body motion capture.

Recent advancements in Inertial Measurement Units (IMUs) offers the possibility of its use as a cost effective and portable alternative to traditional optoelectronic motion capture systems in analyzing biomechanical performance. One such commercially available IMU is the Perception Neuron motion capture system (PNS). The accuracy of the PNS had been tested and was reported to be a valid method for assessing the upper body range of motion to within 5° RMSE. However, testing of the PNS was limited to upper body motion involving functional movement within a single plane. Therefore, the purpose of this study is to further validate the Perception Neuron system with reference to a conventional optoelectronic motion capture system (VICON) through the use of dynamic movements (e.g., walking, jogging and a multi-articular sports movement with object manipulation) and to determine its feasibility through full-body kinematic analysis. Validation was evaluated using Pearson's R correlation, RMSE and Bland-Altman estimates. Present findings suggest that the PNS performed well against the VICON motion analysis system with most joint angles reporting a RMSE of < 4° and strong average Pearson's R correlation of 0.85, with the exception of the shoulder abduction/adduction where RMSE was larger and Pearson's R correlation at a moderate level. Bland-Altman analysis revealed that most joint angles across the different movements had a mean bias of less than 10°, except for the shoulder abduction/adduction and elbow flexion/extension measurements. It was concluded that the PNS may not be the best substitute for traditional motion analysis technology if there is a need to replicate raw joint angles. However, there was adequate sensitivity to measure changes in joint angles and would be suitable when normalized joint angles are compared and the focus of analysis is to identify changes in movement patterns.

Highly efficient patterning technique for silver nanowire electrodes by electrospray deposition and its application to self-powered triboelectric tactile sensor.

A patterned transparent electrode is a crucial component of state-of-the-art wearable devices and optoelectronic devices. However, most of the patterning methods using silver nanowires (AgNWs), which is one of the outstanding candidate materials for the transparent electrode, wasted a large amount of unused AgNWs during the patterning process. Here, we report a highly efficient patterning of AgNWs using electrospray deposition with grounded electrolyte solution (EDGE). During electrospray deposition, a patterned electrolyte solution collector attracted AgNWs by strong electrostatic attraction and selectively deposited them only on the patterned collector, minimizing AgNW deposited elsewhere. The enhanced patterning efficiency was verified through a comparison between the EDGE and conventional process by numerical simulation and experimental validation. As a result, despite the same electrospray deposition conditions for both cases except for the existence of the electrolyte solution collector, the coverage ratio of AgNWs fabricated by the EDGE process was at least six times higher than that of AgNWs produced by the conventional process. Furthermore, the EDGE process provided high design flexibility in terms of not only the material of the substrate, including a polymer and a ceramic but also the shape of the substrate, including a 2D flat and 3D curved surface. As an application of the EDGE process, a self-powered touch sensor exploiting the triboelectric effect was demonstrated. Thus, the EDGE process would be utilized in further application in wearable or implantable devices in the field of biomedicine, intelligent robots, and human-machine interface.

A multi-point heart rate monitoring using a soft wearable system based on fiber optic technology.

Early diagnosis can be crucial to limit both the mortality and economic burden of cardiovascular diseases. Recent developments have focused on the continuous monitoring of cardiac activity for a prompt diagnosis. Nowadays, wearable devices are gaining broad interest for a continuous monitoring of the heart rate (HR). One of the most promising methods to estimate HR is the seismocardiography (SCG) which allows to record the thoracic vibrations with high non-invasiveness in out-of-laboratory settings. Despite significant progress on SCG, the current state-of-the-art lacks both information on standardized sensor positioning and optimization of wearables design. Here, we introduce a soft wearable system (SWS), whose novel design, based on a soft polymer matrix embedding an array of fiber Bragg gratings, provides a good adhesion to the body and enables the simultaneous recording of SCG signals from multiple measuring sites. The feasibility assessment on healthy volunteers revealed that the SWS is a suitable wearable solution for HR monitoring and its performance in HR estimation is strongly influenced by sensor positioning and improved by a multi-sensor configuration. These promising characteristics open the possibility of using the SWS in monitoring patients with cardiac pathologies in clinical (e.g., during cardiac magnetic resonance procedures) and everyday life settings.

Real-time quantitation of thyroidal radioiodine uptake in thyroid disease with monitoring by a collar detection device.

Radioactive iodine (RAI) is safe and effective in most patients with hyperthyroidism but not all individuals are cured by the first dose, and most develop post-RAI hypothyroidism. Postoperative RAI therapy for remnant ablation is successful in 80-90% of thyroid cancer patients and sometimes induces remission of nonresectable cervical and/or distant metastatic disease but the effective tumor dose is usually not precisely known and must be moderated to avoid short- and long-term adverse effects on other tissues. The Collar Therapy Indicator (COTI) is a radiation detection device embedded in a cloth collar secured around the patient's neck and connected to a recording and data transmission box. In previously published experience, the data can be collected at multiple time points, reflecting local cervical RAI exposure and correlating well with conventional methods. We evaluated the real-time uptake of RAI in patients with hyperthyroid Graves' disease and thyroid cancer. We performed a pilot feasibility prospective study. Data were analyzed using R© (version 4.0.3, The R Foundation for Statistical Computing, 2020), and Python (version 3.6, Matplotlib version 3.0.3). The COTI was able to provide a quantitative temporal pattern of uptake within the thyroid in persons with Graves' disease and lateralized the remnant tissue in persons with thyroid cancer. The study has demonstrated that the portable collar radiation detection device outside of a healthcare facility is accurate and feasible for use after administration of RAI for diagnostic studies and therapy to provide a complete collection of fractional target radioactivity data compared to that traditionally acquired with clinic-based measurements at one or two time-points.Clinical Trials Registration NCT03517579, DOR 5/7/2018.

Evaluation of the Accuracy of Cuffless Blood Pressure Measurement Devices: Challenges and Proposals.

Several novel cuffless wearable devices and smartphone applications claiming that they can measure blood pressure (BP) are appearing on the market. These technologies are very attractive and promising, with increasing interest among health care professionals for their potential use. Moreover, they are becoming popular among patients with hypertension and healthy people. However, at the present time, there are serious issues about BP measurement accuracy of cuffless devices and the 2021 European Society of Hypertension Guidelines on BP measurement do not recommend them for clinical use. Cuffless devices have special validation issues, which have been recently recognized. It is important to note that the 2018 Universal Standard for the validation of automated BP measurement devices developed by the American Association for the Advancement of Medical Instrumentation, the European Society of Hypertension, and the International Organization for Standardization is inappropriate for the validation of cuffless devices. Unfortunately, there is an increasing number of publications presenting data on the accuracy of novel cuffless BP measurement devices, with inadequate methodology and potentially misleading conclusions. The objective of this review is to facilitate understanding of the capabilities and limitations of emerging cuffless BP measurement devices. First, the potential and the types of these devices are described. Then, the unique challenges in evaluating the BP measurement accuracy of cuffless devices are explained. Studies from the literature and computer simulations are employed to illustrate these challenges. Finally, proposals are given on how to evaluate cuffless devices including presenting and interpreting relevant study results.

A catalog of validity indices for step counting wearable technologies during treadmill walking: the CADENCE-Kids study.

BACKGROUND: Wearable technologies play an important role in measuring physical activity (PA) and promoting health. Standardized validation indices (i.e., accuracy, bias, and precision) compare performance of step counting wearable technologies in young people. PURPOSE: To produce a catalog of validity indices for step counting wearable technologies assessed during different treadmill speeds (slow [0.8-3.2 km/h], normal [4.0-6.4 km/h], fast [7.2-8.0 km/h]), wear locations (waist, wrist/arm, thigh, and ankle), and age groups (children, 6-12 years; adolescents, 13-17 years; young adults, 18-20 years). METHODS: One hundred seventeen individuals (13.1 ± 4.2 years, 50.4% female) participated in this cross-sectional study and completed 5-min treadmill bouts (0.8 km/h to 8.0 km/h) while wearing eight devices (Waist: Actical, ActiGraph GT3X+, NL-1000, SW-200; Wrist: ActiGraph GT3X+; Arm: SenseWear; Thigh: activPAL; Ankle: StepWatch). Directly observed steps served as the criterion measure. Accuracy (mean absolute percentage error, MAPE), bias (mean percentage error, MPE), and precision (correlation coefficient, r; standard deviation, SD; coefficient of variation, CoV) were computed. RESULTS: Five of the eight tested wearable technologies (i.e., Actical, waist-worn ActiGraph GT3X+, activPAL, StepWatch, and SW-200) performed at < 5% MAPE over the range of normal speeds. More generally, waist (MAPE = 4%), thigh (4%) and ankle (5%) locations displayed higher accuracy than the wrist location (23%) at normal speeds. On average, all wearable technologies displayed the lowest accuracy across slow speeds (MAPE = 50.1 ± 35.5%), and the highest accuracy across normal speeds (MAPE = 15.9 ± 21.7%). Speed and wear location had a significant effect on accuracy and bias (P < 0.001), but not on precision (P > 0.05). Age did not have any effect (P > 0.05). CONCLUSIONS: Standardized validation indices focused on accuracy, bias, and precision were cataloged by speed, wear location, and age group to serve as important reference points when selecting and/or evaluating device performance in young people moving forward. Reduced performance can be expected at very slow walking speeds (0.8 to 3.2 km/h) for all devices. Ankle-worn and thigh-worn devices demonstrated the highest accuracy. Speed and wear location had a significant effect on accuracy and bias, but not precision. TRIAL REGISTRATION: Clinicaltrials.gov NCT01989104 . Registered November 14, 2013.

Interventions Using Wearable Physical Activity Trackers Among Adults With Cardiometabolic Conditions: A Systematic Review and Meta-analysis.

Importance: Wearable physical activity (PA) trackers, such as accelerometers, fitness trackers, and pedometers, are accessible technologies that may encourage increased PA levels in line with current recommendations. However, whether their use is associated with improvements in PA levels in participants who experience 1 or more cardiometabolic conditions, such as diabetes, prediabetes, obesity, and cardiovascular disease, is unknown. Objective: To assess the association of interventions using wearable PA trackers (accelerometers, fitness trackers, and pedometers) with PA levels and other health outcomes in adults with cardiometabolic conditions. Data Sources: For this systematic review and meta-analysis, searches of MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and PsycINFO were performed from January 1, 2000, until December 31, 2020, with no language restriction. A combination of Medical Subject Heading terms and text words of diabetes, obesity, cardiovascular disease, pedometers, accelerometers, and Fitbits were used. Study Selection: Randomized clinical trials or cluster randomized clinical trials that evaluated the use of wearable PA trackers, such as pedometers, accelerometers, or fitness trackers, were included. Trials were excluded if they assessed the trackers only as measuring tools of PA before and after another intervention, they required participants to be hospitalized, assessors were not blinded to the trackers, or they used a tracker to measure the effect of a pharmacological treatment on PA among individuals. Data Extraction and Synthesis: The study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline. A random-effects model was used for the meta-analysis. Main Outcomes and Measures: The primary outcome was mean difference in PA levels. When the scale was different across studies, standardized mean differences were calculated. Heterogeneity was quantified using the I2 statistic and explored using mixed-effects metaregression. Results: A total of 38 randomized clinical trials with 4203 participants were eligible in the systematic review; 29 trials evaluated pedometers, and 9 evaluated accelerometers or fitness trackers. Four studies did not provide amenable outcome data, leaving 34 trials (3793 participants) for the meta-analysis. Intervention vs comparator analysis showed a significant association of wearable tracker use with increased PA levels overall (standardized mean difference, 0.72; 95% CI, 0.46-0.97; I2 = 88%; 95% CI, 84.3%-90.8%; P < .001) in studies with short to medium follow-up for median of 15 (range, 12-52) weeks. Multivariable metaregression showed an association between increased PA levels and interventions that involved face-to-face consultations with facilitators (23 studies; ß = -0.04; 95% CI, -0.11 to -0.01), included men (23 studies; ß = 0.48; 95% CI, 0.01-0.96), and assessed pedometer-based interventions (26 studies; ß = 0.20; 95% CI, 0.02-0.32). Conclusions and Relevance: In this systematic review and meta-analysis, interventions that combined wearable activity trackers with health professional consultations were associated with significant improvements in PA levels among people with cardiometabolic conditions.

Automatic methods of hoof-on and -off detection in horses using wearable inertial sensors during walk and trot on asphalt, sand and grass.

Detection of hoof-on and -off events are essential to gait classification in horses. Wearable sensors have been endorsed as a convenient alternative to the traditional force plate-based method. The aim of this study was to propose and validate inertial sensor-based methods of gait event detection, reviewing different sensor locations and their performance on different gaits and exercise surfaces. Eleven horses of various breeds and ages were recruited to wear inertial sensors attached to the hooves, pasterns and cannons. Gait events detected by pastern and cannon methods were compared to the reference, hoof-detected events. Walk and trot strides were recorded on asphalt, grass and sand. Pastern-based methods were found to be the most accurate and precise for detecting gait events, incurring mean errors of between 1 and 6ms, depending on the limb and gait, on asphalt. These methods incurred consistent errors when used to measure stance durations on all surfaces, with mean errors of 0.1 to 1.16% of a stride cycle. In conclusion, the methods developed and validated here will enable future studies to reliably detect equine gait events using inertial sensors, under a wide variety of field conditions.

Accuracy and precision of wrist-worn actigraphy for measuring steps taken during over-ground and treadmill walking in adults with Parkinson's disease.

INTRODUCTION: Step counts represent a straight-forward method of measuring physical activity in adults with Parkinson's disease (PD). The present study examined the absolute and relative accuracy and precision of a wrist-worn research-grade accelerometer (i.e., ActiGraph GT3X+) for measuring step counts during over-ground and treadmill walking in adults with PD and controls without PD. METHODS: Participants (PD: n = 29; controls: n = 31) wore two ActiGraph GT3X + accelerometers, one on each wrist, and completed an over-ground walking bout followed by a treadmill walking bout at the same speed. Step counts were measured manually using a hand-held tally counter. Accuracy and precision were based on absolute and relative metrics. RESULTS: The ActiGraph GT3X + underestimated step counts in both participants with PD (4.7-11% error) and controls without PD (8.8-17% error), with a greater discrepancy in controls. The ActiGraph GT3X + provided more accurate and precise estimates of step counts when placed on the more affected wrist and non-dominant wrist for participants with PD and controls, respectively, and was more accurate and precise during over-ground walking compared with treadmill walking for both groups. CONCLUSIONS: Our results suggest that placement of the device (i.e., dominant vs. non-dominant), type of activity (i.e., over-ground vs. treadmill walking), and presence of clinical conditions may impact the accuracy and precision of data when using the research-grade ActiGraph GT3X + accelerometer for measuring step counts.

Bio-Inspired Multi-Mode Pain-Perceptual System (MMPPS) with Noxious Stimuli Warning, Damage Localization, and Enhanced Damage Protection.

The multi-mode pain-perceptual system (MMPPS) is essential for the human body to perceive noxious stimuli in all circumstances and make an appropriate reaction. Based on the central sensitization mechanism, the MMPPS can switch between different working modes and thus offers a smarter protection mechanism to human body. Accordingly, before injury MMPPS can offer warning of excessive pressure with normal pressure threshold. After injury, extra care on the periphery of damage will be activated by decreasing the pressure threshold. Furthermore, the MMPPS will gradually recover back to a normal state as damage heals. Although current devices can realize basic functions like damage localization and nociceptor signal imitating, the development of a human-like MMPPS is still a great challenge. Here, a bio-inspired MMPPS is developed for prosthetics protection, in which all working modes is realized and controlled by mimicking the central sensitization mechanism. Accordingly, the system warns one of a potential injury, identifies the damaged area, and subsequently offers extra care. The proposed system can open new avenues for designing next-generation prosthetics, especially make other smart sensing systems operate under complete protection against injuries.

Development and validation of smartwatch-based activity recognition models for rigging crew workers on cable logging operations.

Analysis of high-resolution inertial sensor and global navigation satellite system (GNSS) data collected by mobile and wearable devices is a relatively new methodology in forestry and safety research that provides opportunities for modeling work activities in greater detail than traditional time study analysis. The objective of this study was to evaluate whether smartwatch-based activity recognition models could quantify the activities of rigging crew workers setting and disconnecting log chokers on cable logging operations. Four productive cycle elements (travel to log, set choker, travel away, clear) were timed for choker setters and four productive cycle elements (travel to log, unhook, travel away, clear) were timed for chasers working at five logging sites in North Idaho. Each worker wore a smartwatch that recorded accelerometer data at 25 Hz. Random forest machine learning was used to develop predictive models that classified the different cycle elements based on features extracted from the smartwatch acceleration data using 15 sliding window sizes (1 to 15 s) and five window overlap levels (0%, 25%, 50%, 75%, and 90%). Models were compared using multiclass area under the Receiver Operating Characteristic (ROC) curve, or AUC. The best choker setter model was created using a 3-s window with 90% overlap and had sensitivity values ranging from 76.95% to 83.59% and precision values ranging from 41.42% to 97.08%. The best chaser model was created using a 1-s window with 90% overlap and had sensitivity values ranging from 71.95% to 82.75% and precision values ranging from 14.74% to 99.16%. These results have demonstrated the feasibility of quantifying forestry work activities using smartwatch-based activity recognition models, a basic step needed to develop real-time safety notifications associated with high-risk job functions and to advance subsequent, comparative analysis of health and safety metrics across stand, site, and work conditions.

Comparing subjective and objective response to medications in Parkinson's disease patients using the Personal KinetiGraph™.

BACKGROUND: Management of motor symptoms in Parkinson's Disease(PD) relies on subjective information provided by patients, the quality of which can be affected by many factors. RATIONALE: Objective data collected during daily life could complement this information and improve management of motor symptoms. OBJECTIVES: To assess the usefulness of the Personal KinetiGraph (PKG) in characterizing the intensity and timing of motor symptoms in PD patients. METHODS: Retrospective study of all PD patients followed at a tertiary academic movement disorders center assessed by PKG between December 1, 2016 and October 30, 2018. PKG was worn for 7 days prior to the clinical visit. We compared the information obtained from the interview and the clinical visit, and assessed the impact of the PKG on treatment decision making. RESULTS: 170 PKG results were reviewed. PKG complemented patient input in 82.9%(141/170) and led to medication changes in 71%(100/141) of the complemented inputs. PKG contributed the least to correcting or complementing patients' input when patients self-reported as undertreated (22%) and the most when patient were unable to answer all questions regarding motor response to individual doses (100%) (Fisher, p < 0.0001). The majority of patient undergoing 3 or 4 PKG encounters did not reach a controlled state as defined by PKG until the 3rd or 4th encounter, suggesting that repeated use of the PKG might be needed to help optimize motor control as therapy changes done after one encounter might not be enough. CONCLUSIONS: PKG might be useful in supplementing patient-provided information for accurate assessment and treatment plan.

Automated seizure detection using wearable devices: A clinical practice guideline of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology.

The objective of this clinical practice guideline (CPG) is to provide recommendations for healthcare personnel working with patients with epilepsy, on the use of wearable devices for automated seizure detection in patients with epilepsy, in outpatient, ambulatory settings. The Working Group of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology developed the CPG according to the methodology proposed by the ILAE Epilepsy Guidelines Working Group. We reviewed the published evidence using The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement and evaluated the evidence and formulated the recommendations following the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. We found high level of evidence for the accuracy of automated detection of generalized tonic-clonic seizures (GTCS) and focal-to-bilateral tonic-clonic seizures (FBTCS) and recommend use of wearable automated seizure detection devices for selected patients when accurate detection of GTCS and FBTCS is recommended as a clinical adjunct. We also found moderate level of evidence for seizure types without GTCs or FBTCs. However, it was uncertain whether the detected alarms resulted in meaningful clinical outcomes for the patients. We recommend using clinically validated devices for automated detection of GTCS and FBTCS, especially in unsupervised patients, where alarms can result in rapid intervention (weak/conditional recommendation). At present, we do not recommend clinical use of the currently available devices for other seizure types (weak/conditional recommendation). Further research and development are needed to improve the performance of automated seizure detection and to document their accuracy and clinical utility.

Automated seizure detection using wearable devices: A clinical practice guideline of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology.

The objective of this clinical practice guideline (CPG) is to provide recommendations for healthcare personnel working with patients with epilepsy on the use of wearable devices for automated seizure detection in patients with epilepsy, in outpatient, ambulatory settings. The Working Group of the International League Against Epilepsy (ILAE) and the International Federation of Clinical Neurophysiology (IFCN) developed the CPG according to the methodology proposed by the ILAE Epilepsy Guidelines Working Group. We reviewed the published evidence using The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement and evaluated the evidence and formulated the recommendations following the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. We found high level of evidence for the accuracy of automated detection of generalized tonic-clonic seizures (GTCS) and focal-to-bilateral tonic-clonic seizures (FBTCS) and recommend the use of wearable automated seizure detection devices for selected patients when accurate detection of GTCS and FBTCS is recommended as a clinical adjunct. We also found a moderate level of evidence for seizure types without GTCS or FBTCS. However, it was uncertain whether the detected alarms resulted in meaningful clinical outcomes for the patients. We recommend using clinically validated devices for automated detection of GTCS and FBTCS, especially in unsupervised patients, where alarms can result in rapid intervention (weak/conditional recommendation). At present, we do not recommend clinical use of the currently available devices for other seizure types (weak/conditional recommendation). Further research and development are needed to improve the performance of automated seizure detection and to document their accuracy and clinical utility.

Reliability and Validity of the Polar V800 Sports Watch for Estimating Vertical Jump Height.

This study aimed to assess the reliability and validity of the Polar V800 to measure vertical jump height. Twenty-two physically active healthy men (age: 22.89 ± 4.23 years; body mass: 70.74 ± 8.04 kg; height: 1.74 ± 0.76 m) were recruited for the study. The reliability was evaluated by comparing measurements acquired by the Polar V800 in two identical testing sessions one week apart. Validity was assessed by comparing measurements simultaneously obtained using a force platform (gold standard), high-speed camera and the Polar V800 during squat jump (SJ) and countermovement jump (CMJ) tests. In the test-retest reliability, high intraclass correlation coefficients (ICCs) were observed (mean: 0.90, SJ and CMJ) in the Polar V800. There was no significant systematic bias ± random errors (p > 0.05) between test-retest. Low coefficients of variation (<5%) were detected in both jumps in the Polar V800. In the validity assessment, similar jump height was detected among devices (p > 0.05). There was almost perfect agreement between the Polar V800 compared to a force platform for the SJ and CMJ tests (Mean ICCs = 0.95; no systematic bias ± random errors in SJ mean: -0.38 ± 2.10 cm, p > 0.05). Mean ICC between the Polar V800 versus high-speed camera was 0.91 for the SJ and CMJ tests, however, a significant systematic bias ± random error (0.97 ± 2.60 cm; p = 0.01) was detected in CMJ test. The Polar V800 offers valid, compared to force platform, and reliable information about vertical jump height performance in physically active healthy young men.