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Patch validation: an observational study protocol for the evaluation of a multisignal wearable sensor in patients during anaesthesia and in the postanaesthesia care unit.

ABSTRACT: INTRODUCTION:Except for operating rooms, postanaesthesia care units and intensive care units, where the monitoring of vital signs is continuous, intermittent care is standard practice. However, at a time when only the patients with the most serious conditions are hospitalised and only a fraction of these patients are in intensive care units, this type of monitoring is no longer sufficient. Wireless monitoring has been proposed, but it requires rigorous validation. The aim of this observational study is to compare vital signs obtained from a precordial patch sensor to those obtained with conventional monitoring. METHODS AND ANALYSIS:This patch validation trial will be an observational, prospective, single-centre open study of 115 anaesthetised adult patients monitored with both a wireless sensor (myAngel VitalSigns, Devinnova, Montpellier, France) and a standard bedside monitor (Carescape Monitor B850, GE Healthcare, Chicago, Illinois). Both sensors will be used to record peripheral oxygen saturation, respiratory rate, heart rate, body temperature and blood pressure (systolic and diastolic). The main objective will be to assess the degree of agreement between the two systems during the patients' stay in the postanaesthesia care unit, both at the raw signal level and at the clinical parameter level. The secondary objectives will be to assess the same performance under anaesthesia, the frequency of missing data or artefacts, the diagnostic performance of the systems, the influence of patients' characteristics on agreement between the two systems, the adverse events and the acceptability of the patch to patients. Bland-Altman plots will be used in the main analysis to detect discrepancies and estimate the limits of agreement. ETHICS AND DISSEMINATION:Ethics approval was obtained from the Ethical Committee (Toulouse, France) on 10 April 2020. We are not yet recruiting subjects for this study. The results will be submitted for publication in peer-reviewed journals. TRIAL REGISTRATION NUMBER:NCT04344093.

SUBMITTER: Le Guen M

PROVIDER: S-EPMC7520837 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Patch validation: an observational study protocol for the evaluation of a multisignal wearable sensor in patients during anaesthesia and in the postanaesthesia care unit.

Le Guen Morgan M Squara Pierre P Ma Sabrina S Adjavon Shérifa S Trillat Bernard B Merzoug Messaouda M Aegerter Philippe P Fischler Marc M

BMJ open 20200925 9

<h4>Introduction</h4>Except for operating rooms, postanaesthesia care units and intensive care units, where the monitoring of vital signs is continuous, intermittent care is standard practice. However, at a time when only the patients with the most serious conditions are hospitalised and only a fraction of these patients are in intensive care units, this type of monitoring is no longer sufficient. Wireless monitoring has been proposed, but it requires rigorous validation. The aim of this observa ...[more]

PMID: 32978206

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Project description:BACKGROUND AND OBJECTIVES:Intermittent vital signs measurements are the current standard on hospital wards, typically recorded once every 8?hours. Early signs of deterioration may therefore be missed. Recent innovations have resulted in 'wearable' sensors, which may capture patient deterioration at an earlier stage. The objective of this study was to determine whether a wireless 'patch' sensor is able to reliably measure respiratory and heart rate continuously in high-risk surgical patients. The secondary objective was to explore the potential of the wireless sensor to serve as a safety monitor. DESIGN:In an observational methods comparisons study, patients were measured with both the wireless sensor and bedside routine standard for at least 24?hours. SETTING:University teaching hospital, single centre. PARTICIPANTS:Twenty-five postoperative surgical patients admitted to a step-down unit. OUTCOME MEASURES:Primary outcome measures were limits of agreement and bias of heart rate and respiratory rate. Secondary outcome measures were sensor reliability, defined as time until first occurrence of data loss. RESULTS:1568?hours of vital signs data were analysed. Bias and 95% limits of agreement for heart rate were -1.1 (-8.8 to 6.5) beats per minute. For respiration rate, bias was -2.3 breaths per minute with wide limits of agreement (-15.8 to 11.2 breaths per minute). Median filtering over a 15 min period improved limits of agreement of both respiration and heart rate. 63% of the measurements were performed without data loss greater than 2?min. Overall data loss was limited (6% of time). CONCLUSIONS:The wireless sensor is capable of accurately measuring heart rate, but accuracy for respiratory rate was outside acceptable limits. Remote monitoring has the potential to contribute to early recognition of physiological decline in high-risk patients. Future studies should focus on the ability to detect patient deterioration on low care environments and at home after discharge.

Project description:IntroductionPostoperative delirium can be a serious consequence of major surgery, associated with longer hospital stays, readmission, cognitive and functional deterioration and mortality. Delirium is an acute, reversible disorder characterised by fluctuating course, inattention, disorganised thinking and altered level of consciousness. Delirium occurring in the hours immediately following anaesthesia and delirium occurring in the postoperative period of 1-5 days have been described as distinct clinical entities. This protocol describes an observational study with the aim of determining if delirium in the first hour following tracheal tube removal is a predictor of delirium in the 5 subsequent postoperative days. Improved understanding regarding the development of postoperative delirium would improve patient care and allow more effective implementation of delirium prevention measures.Methods and analysisPatients enrolled to the Electroencephalography Guidance of Anesthesia to Alleviate Geriatric Syndromes (ENGAGES) randomised controlled trial will be eligible for this substudy. A validated delirium assessment method, the 3-min Diagnostic Confusion Assessment Method and the Richmond Agitation and Sedation Scale will be used to assess 100 patients for delirium at 30 min and 60 min following tracheal tube removal. Patients will also be assessed for delirium over postoperative days 1-5 using three validated methods, the Confusion Assessment Method (CAM), CAM for the Intensive Care Unit and structured chart review. Logistic regression analysis will then be performed to test whether immediately postoperative delirium independently predicts subsequent postoperative delirium.Ethics and disseminationThis observational substudy of ENGAGES has been approved by the ethics board of Washington University School of Medicine. Enrolment began in June 2016 and will continue until June 2017. Dissemination plans include presentations at scientific conferences and scientific publications.Trial registration numberNCT02241655.

Project description:Formulift is a newly developed mobile health (mHealth) app that connects to a single inertial measurement unit (IMU) worn on the left thigh. The IMU captures users' movements as they exercise, and the app analyzes the data to count repetitions in real time and classify users' exercise technique. The app also offers feedback and guidance to users on exercising safely and effectively.The aim of this study was to assess the Formulift system with three different and realistic types of potential users (beginner gym-goers, experienced gym-goers, and qualified strength and conditioning [S&C] coaches) under a number of categories: (1) usability, (2) functionality, (3) the perceived impact of the system, and (4) the subjective quality of the system. It was also desired to discover suggestions for future improvements to the system.A total of 15 healthy volunteers participated (12 males; 3 females; age: 23.8 years [SD 1.80]; height: 1.79 m [SD 0.07], body mass: 78.4 kg [SD 9.6]). Five participants were beginner gym-goers, 5 were experienced gym-goers, and 5 were qualified and practicing S&C coaches. IMU data were first collected from each participant to create individualized exercise classifiers for them. They then completed a number of nonexercise-related tasks with the app. Following this, a workout was completed using the system, involving squats, deadlifts, lunges, and single-leg squats. Participants were then interviewed about their user experience and completed the System Usability Scale (SUS) and the user version of the Mobile Application Rating Scale (uMARS). Thematic analysis was completed on all interview transcripts, and survey results were analyzed.Qualitative and quantitative analysis found the system has "good" to "excellent" usability. The system achieved a mean (SD) SUS usability score of 79.2 (8.8). Functionality was also deemed to be good, with many users reporting positively on the systems repetition counting, technique classification, and feedback. A number of bugs were found, and other suggested changes to the system were also made. The overall subjective quality of the app was good, with a median star rating of 4 out of 5 (interquartile range, IQR: 3-5). Participants also reported that the system would aid their technique, provide motivation, reassure them, and help them avoid injury.This study demonstrated an overall positive evaluation of Formulift in the categories of usability, functionality, perceived impact, and subjective quality. Users also suggested a number of changes for future iterations of the system. These findings are the first of their kind and show great promise for wearable sensor-based exercise biofeedback systems.

Project description:Evaluation of surgical skills during minimally invasive surgeries is needed when recruiting new surgeons. Although surgeons' differentiation by skill level is highly complex, performance in specific clinical tasks such as pegboard transfer and knot tying could be determined using wearable EMG and accelerometer sensors. A wireless wearable platform has made it feasible to collect movement and muscle activation signals for quick skill evaluation during surgical tasks. However, it is challenging since the placement of multiple wireless wearable sensors may interfere with their performance in the assessment. This study utilizes machine learning techniques to identify optimal muscles and features critical for accurate skill evaluation. This study enrolled a total of twenty-six surgeons of different skill levels: novice (n = 11), intermediaries (n = 12), and experts (n = 3). Twelve wireless wearable sensors consisting of surface EMGs and accelerometers were placed bilaterally on bicep brachii, tricep brachii, anterior deltoid, flexor carpi ulnaris (FCU), extensor carpi ulnaris (ECU), and thenar eminence (TE) muscles to assess muscle activations and movement variability profiles. We found features related to movement complexity such as approximate entropy, sample entropy, and multiscale entropy played a critical role in skill level identification. We found that skill level was classified with highest accuracy by i) ECU for Random Forest Classifier (RFC), ii) deltoid for Support Vector Machines (SVM) and iii) biceps for Naïve Bayes Classifier with classification accuracies 61%, 57% and 47%. We found RFC classifier performed best with highest classification accuracy when muscles are combined i) ECU and deltoid (58%), ii) ECU and biceps (53%), and iii) ECU, biceps and deltoid (52%). Our findings suggest that quick surgical skill evaluation is possible using wearables sensors, and features from ECU, deltoid, and biceps muscles contribute an important role in surgical skill evaluation.

Project description:BACKGROUND:Long-term management of Parkinson's disease does not reach its full potential because we lack knowledge about individual variations in clinical presentation and disease progression. Continuous and longitudinal assessments in real-life (ie, within the patients' own home environment) might fill this knowledge gap. OBJECTIVE:The primary aim of the Parkinson@Home study is to evaluate the feasibility and compliance of using multiple wearable sensors to collect clinically relevant data. Our second aim is to address the usability of these data for answering clinical research questions. Finally, we aim to build a database for future validation of novel algorithms applied to sensor-derived data from Parkinson's patients during daily functioning. METHODS:The Parkinson@Home study is a two-phase observational study involving 1000 Parkinson's patients and 250 physiotherapists. Disease status is assessed using a short version of the Parkinson's Progression Markers Initiative protocol, performed by certified physiotherapists. Additionally, participants will wear a set of sensors (smartwatch, smartphone, and fall detector), and use these together with a customized smartphone app (Fox Insight), 24/7 for 3 months. The sensors embedded within the smartwatch and fall detector may be used to estimate physical activity, tremor, sleep quality, and falls. Medication intake and fall incidents will be measured via patients' self-reports in the smartphone app. Phase one will address the feasibility of the study protocol. In phase two, mathematicians will distill relevant summary statistics from the raw sensor signals, which will be compared against the clinical outcomes. RESULTS:Recruitment of 300 participants for phase one was concluded in March, 2016, and the follow-up period will end in June, 2016. Phase two will include the remaining participants, and will commence in September, 2016. CONCLUSIONS:The Parkinson@Home study is expected to generate new insights into the feasibility of integrating self-collected information from wearable sensors into both daily routines and clinical practices for Parkinson's patients. This study represents an important step towards building a reliable system that translates and integrates real-life information into clinical decisions, with the long-term aim of delivering personalized disease management support. CLINICALTRIAL:ClinicalTrials.gov NCT02474329; https://clinicaltrials.gov/ct2/show/NCT02474329 (Archived at http://www.webcitation.org/6joEc5P1v).

Project description:Purpose Surface electromyography (sEMG) is often used for biofeedback during swallowing rehabilitation. However, commercially available sEMG electrodes are not optimized for the head and neck area, have rigid form, and are mostly available in large medical centers. We developed an ultrathin, soft, and flexible sEMG patch, specifically designed to conform to the submental anatomy and which will be ultimately incorporated into a telehealth system. To validate this first-generation sEMG patch, we compared its safety, efficiency, and signal quality in monitoring submental muscle activity with that of widely used conventional sEMG electrodes. Method A randomized crossover design was used to compare the experimental sEMG patch with conventional (snap-on) sEMG electrodes. Participants completed the same experimental protocol with both electrodes in counterbalanced order. Swallow trials included five trials of 5- and 10-ml water. Comparisons were made on (a) signal-related factors: signal-to-noise ratio (SNR), baseline amplitude, normalized mean amplitude, and sEMG burst duration and (b) safety/preclinical factors: safety/adverse effects, efficiency of electrode placement, and satisfaction/comfort. Noninferiority and equivalence tests were used to examine signal-related factors. Paired t tests and descriptive statistics were used to examine safety/preclinical factors. Results Forty healthy adults participated (24 women, M age = 67.5 years). Signal-related factors: SNR of the experimental patch was not inferior to the SNR of the conventional electrodes (p < .0056). Similarly, baseline amplitude obtained with the experimental patch was not inferior to that obtained with conventional electrodes (p < .0001). Finally, normalized amplitude values were equivalent across swallows (5 ml: p < .025; 10 ml: p < .0012), and sEMG burst duration was also equivalent (5 ml: p < .0001; 10 ml: p < .0001). Safety/preclinical factors: The experimental patch resulted in fewer mild adverse effects. Participant satisfaction was higher with the experimental patch (p = .0476, d = 0.226). Conclusions Our new wearable sEMG patch is equivalent with widely used conventional sEMG electrodes in terms of technical performance. In addition, our patch is safe, and healthy older adults are satisfied with it. With lessons learned from the current COVID-19 pandemic, efforts to develop optimal swallowing telerehabilitation devices are more urgent than ever. Upon further validation, this new technology has the potential to improve rehabilitation and telerehabilitation efforts for patients with dysphagia. Supplemental Material https://doi.org/10.23641/asha.12915509.

Dataset Information

Patch validation: an observational study protocol for the evaluation of a multisignal wearable sensor in patients during anaesthesia and in the postanaesthesia care unit.

Publications

Patch validation: an observational study protocol for the evaluation of a multisignal wearable sensor in patients during anaesthesia and in the postanaesthesia care unit.

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