Continuous Remote Monitoring Research Articles

A Self-Supervised Metric Learning Framework for the Arising-From-Chair Assessment of Parkinsonians With Graph Convolutional Networks

The onset and progression of Parkinson’s disease (PD) gradually affect the patient’s motor functions and quality of life. The PD motor symptoms are usually assessed using the Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). Automated MDS-UPDRS assessment has been recently required as an invaluable tool for PD diagnosis and telemedicine, especially with the recent novel coronavirus pandemic outbreak. This paper proposes a novel vision-based method for automated assessment of the arising-from-chair task, which is one of the key MDS-UPDRS components. The proposed method is based on a self-supervised metric learning scheme with a graph convolutional network (SSM-GCN). Specifically, for human skeleton sequences extracted from videos, a self-supervised intra-video quadruplet learning strategy is proposed to construct a metric learning formulation with prior knowledge, for improving the spatial-temporal representations. Afterwards, a vertex-specific convolution operation is designed to achieve effective aggregation of all skeletal joint features, where each joint or feature is weighted differently based on its relative factor of importance. Finally, a graph representation supervised mechanism is developed to maximize the potential consistency between the joint and bone information streams. Experimental results on a clinical dataset demonstrate the superiority of the proposed method over the existing sensor-based methods, with an accuracy of 70.60% and an acceptable accuracy of 98.65%. The analysis of discriminative spatial connections makes our predictions more clinically interpretable. This method can achieve reliable automated PD assessment using only easily-obtainable videos, thus providing an effective tool for real-time PD diagnosis or remote continuous monitoring.

Wearable-Based Stair Climb Power Estimation and Activity Classification.

Stair climb power (SCP) is a clinical measure of leg muscular function assessed in-clinic via the Stair Climb Power Test (SCPT). This method is subject to human error and cannot provide continuous remote monitoring. Continuous monitoring using wearable sensors may provide a more comprehensive assessment of lower-limb muscular function. In this work, we propose an algorithm to classify stair climbing periods and estimate SCP from a lower-back worn accelerometer, which strongly agrees with the clinical standard (r = 0.92, p < 0.001; ICC = 0.90, [0.82, 0.94]). Data were collected in-lab from healthy adults (n = 65) performing the four-step SCPT and a walking assessment while instrumented (accelerometer + gyroscope), which allowed us to investigate tradeoffs between sensor modalities. Using two classifiers, we were able to identify periods of stair ascent with >89% accuracy [sensitivity = >0.89, specificity = >0.90] using two ensemble machine learning algorithms, trained on accelerometer signal features. Minimal changes in model performances were observed using the gyroscope alone (±0–6% accuracy) versus the accelerometer model. While we observed a slight increase in accuracy when combining gyroscope and accelerometer (about +3–6% accuracy), this is tolerable to preserve battery life in the at-home environment. This work is impactful as it shows potential for an accelerometer-based at-home assessment of SCP.

Continuous Remote Patient Monitoring in Patients With Heart Failure (Cascade Study): Protocol for a Mixed Methods Feasibility Study.

BackgroundHeart failure (HF) is a prevalent chronic disease and is associated with increases in mortality and morbidity. HF is a leading cause of hospitalizations and readmissions in the United States. A potentially promising area for preventing HF readmissions is continuous remote patient monitoring (CRPM).ObjectiveThe primary aim of this study is to determine the feasibility and preliminary efficacy of a CRPM solution in patients with HF at NorthShore University HealthSystem.MethodsThis study is a feasibility study and uses a wearable biosensor to continuously remotely monitor patients with HF for 30 days after discharge. Eligible patients admitted with an HF exacerbation at NorthShore University HealthSystem are being recruited, and the wearable biosensor is placed before discharge. The biosensor collects physiological ambulatory data, which are analyzed for signs of patient deterioration. Participants are also completing a daily survey through a dedicated study smartphone. If prespecified criteria from the physiological data and survey results are met, a notification is triggered, and a predetermined electronic health record–based pathway of telephonic management is completed. In phase 1, which has already been completed, 5 patients were enrolled and monitored for 30 days after discharge. The results of phase 1 were analyzed, and modifications to the program were made to optimize it. After analysis of the phase 1 results, 15 patients are being enrolled for phase 2, which is a calibration and testing period to enable further adjustments to be made. After phase 2, we will enroll 45 patients for phase 3. The combined results of phases 1, 2, and 3 will be analyzed to determine the feasibility of a CRPM program in patients with HF. Semistructured interviews are being conducted with key stakeholders, including patients, and these results will be analyzed using the affective adaptation of the technology acceptance model.ResultsDuring phase 1, of the 5 patients, 2 (40%) were readmitted during the study period. The study completion rate for phase 1 was 80% (4/5), and the study attrition rate was 20% (1/5). There were 57 protocol deviations out of 150 patient days in phase 1 of the study. The results of phase 1 were analyzed, and the study protocol was adjusted to optimize it for phases 2 and 3. Phase 2 and phase 3 results will be available by the end of 2022.ConclusionsA CRPM program may offer a low-risk solution to improve care of patients with HF after hospital discharge and may help to decrease readmission of patients with HF to the hospital. This protocol may also lay the groundwork for the use of CRPM solutions in other groups of patients considered to be at high risk.International Registered Report Identifier (IRRID)DERR1-10.2196/36741

Digital Monitoring System for Gas Emissions of Industrial Waste Landfills Using Unmanned Aerial Vehicles

Architecture, hardware, software and information support and digital modes of operation were developed for monitoring emissions from industrial waste landfills using unmanned aerial vehicles (UAVs). It was shown that systems for monitoring gas emissions from landfills using UAVs allow real-time remote continuous operational monitoring due to installed temperature and gas sensors, as well as methane emissions of the large areas of landfills and providing operators with gas distribution maps and and data on location of potential leaks.

GENDER BIAS IN ICM RELATED OUTCOMES AND POST-ICM INSERTION PROCEDURES

Over the past 5 years insertable cardiac monitors (ICMs) have revolutionized cardiac care through continuous remote monitoring to help identify AF (Atrial Fibrillation) conditions and help clinicians (and specialists) take appropriate timely actions for at-risk patients who need ICDs, pacemakers or ablations. ICMs are implanted devices offering continuous ambulatory electrocardiogram monitoring. ICMs have been proven to identify AF conditions in unexplained syncope and palpitations, as well as in AF driven medication management.

Synthesis of decision making in a distributed intelligent personnel health management system on offshore oil platform

This paper proposes a methodological approach for the decision synthesis in a geographically distributed intelligent health management system for oil workers working in offshore industry. The decision-making methodology is based on the concept of a person-centered approach to managing the health and safety of personnel, which implies the inclusion of employees as the main component in the control loop. This paper develops a functional model of the health management system for workers employed on offshore oil platforms and implements it through three phased operations that is monitoring and assessing the health indicators and environmental parameters of each employee, and making decisions. These interacting operations combine the levels of a distributed intelligent health management system. The paper offers the general principles of functioning of a distributed intelligent system for managing the health of workers in the context of structural components and computing platforms. It presents appropriate approaches to the implementation of decision support processes and describes one of the possible methods for evaluating the generated data and making decisions using fuzzy pattern recognition. The models of a fuzzy ideal image and fuzzy real images of the health status of an employee are developed and an algorithm is described for assessing the deviation of generated medical parameters from the norm. The paper also compiles the rules to form the knowledge bases of a distributed intelligent system for remote continuous monitoring. It is assumed that embedding this base into the intelligent system architecture will objectively assess the trends in the health status of workers and make informed decisions to eliminate certain problems

Towards Remote Continuous Monitoring of Cytokine Release Syndrome.

Cytokine release syndrome (CRS) is a noninfec-tious systemic inflammatory response syndrome condition and a principle severe adverse event common in oncology patients treated with immunotherapies. Accurate monitoring and timely prediction of CRS severity remain a challenge. This study presents an XGBoost-based machine learning algorithm for forecasting CRS severity (no CRS, mild- and severe-CRS classes) in the 24 hours following the time of prediction utilizing the common vital signs and Glasgow coma scale (GCS) questionnaire inputs. The CRS algorithm was developed and evaluated on a cohort of patients (n=1,139) surgically treated for neoplasm with no ICD9 codes for infection or sepsis during a collective 9,892 patient-days of monitoring in ICU settings. Different models were trained with unique feature sets to mimic practical monitoring environments where different types of data availability will exist. The CRS models that incorporated all time series features up to the prediction time showcased a micro-average area under curve (AUC) statistic for the receiver operating characteristic curve (ROC) of 0.94 for the 3 classes of CRS grades. Models developed on a second cohort requiring data within the 24 hours preceding prediction time showcased a relatively lower 0.88 micro-average AUROC as these models did not benefit from implicit information in the data availability. Systematic removal of blood pressure and/or GCS inputs revealed significant decreases (p<0.05) in model performances that confirm the importance of such features for CRS prediction. Accurate CRS prediction and timely intervention can reverse CRS adverse events and maximize the benefit of immunotherapies in oncology patients.

A Remote Digital Monitoring Platform to Assess Cognitive and Motor Symptoms in Huntington Disease: Cross-sectional Validation Study.

BackgroundRemote monitoring of Huntington disease (HD) signs and symptoms using digital technologies may enhance early clinical diagnosis and tracking of disease progression, guide treatment decisions, and monitor response to disease-modifying agents. Several recent studies in neurodegenerative diseases have demonstrated the feasibility of digital symptom monitoring.ObjectiveThe aim of this study was to evaluate a novel smartwatch- and smartphone-based digital monitoring platform to remotely monitor signs and symptoms of HD.MethodsThis analysis aimed to determine the feasibility and reliability of the Roche HD Digital Monitoring Platform over a 4-week period and cross-sectional validity over a 2-week interval. Key criteria assessed were feasibility, evaluated by adherence and quality control failure rates; test-retest reliability; known-groups validity; and convergent validity of sensor-based measures with existing clinical measures. Data from 3 studies were used: the predrug screening phase of an open-label extension study evaluating tominersen (NCT03342053) and 2 untreated cohorts—the HD Natural History Study (NCT03664804) and the Digital-HD study. Across these studies, controls (n=20) and individuals with premanifest (n=20) or manifest (n=179) HD completed 6 motor and 2 cognitive tests at home and in the clinic.ResultsParticipants in the open-label extension study, the HD Natural History Study, and the Digital-HD study completed 89.95% (1164/1294), 72.01% (2025/2812), and 68.98% (1454/2108) of the active tests, respectively. All sensor-based features showed good to excellent test-retest reliability (intraclass correlation coefficient 0.89-0.98) and generally low quality control failure rates. Good overall convergent validity of sensor-derived features to Unified HD Rating Scale outcomes and good overall known-groups validity among controls, premanifest, and manifest participants were observed. Among participants with manifest HD, the digital cognitive tests demonstrated the strongest correlations with analogous in-clinic tests (Pearson correlation coefficient 0.79-0.90).ConclusionsThese results show the potential of the HD Digital Monitoring Platform to provide reliable, valid, continuous remote monitoring of HD symptoms, facilitating the evaluation of novel treatments and enhanced clinical monitoring and care for individuals with HD.

CONTINUOUS REMOTE PATIENT MONITORING SHOWS EARLY CARDIOVASCULAR CHANGES IN COVID-19 PATIENTS

Objective: COVID-19 exerts deleterious cardiopulmonary effects, leading to a worse prognosis in the most affected. This study aimed to analyze the trajectories of key vitals amongst hospitalized COVID-19 patients using a chest-patch wearable medical-grade monitor providing continuous remote patient monitoring of numerous vital signs. Design and method: This retrospective multicenter observational cohort study was conducted in five COVID-19 isolation units. 492 COVID-19 patients were included in the final analysis. Physiological parameters were measured every 15 minutes. Results: More than 3 million measurements were collected including heart rate, systolic and diastolic blood pressure, cardiac output, cardiac index, systemic vascular resistance, respiratory rate, blood oxygen saturation, and body temperature. Cardiovascular deterioration appeared early after admission and in parallel with changes in the respiratory parameters, showing a significant difference in trajectories within sub-populations at high risk. Conclusions: Early detection of cardiovascular deterioration of COVID-19 patients is achievable when using frequent remote patient monitoring.

974-P: Implementation of Remote Continuous Data Monitoring within a Clinical Setting for the Management of Type 2 Diabetes Mellitus

Introduction: The challenge of managing type 2 diabetes mellitus is well known and was especially highlighted during the pandemic. Advances in the use of technology have resulted in improved glycemic control. The GluCare program uses Remote Continuous Data Monitoring (RCDM) and leverages connectivity and shifting diabetes management away from episodic care to continuous care. Methods and Analysis: Forty type 2 DM patients were managed, with half opting "in" to monitoring using the GluCare RCDM program and half opting "out" to not be monitored. Data was collected from electronic medical reports and statistical analysis was conducted to compare clinical parameters between groups. Outcomes: The primary outcome was the difference in mean HbA1c at 3 months follow-up between Glucare opt-in and opt-out patients with both groups managed by the same health care professional and facility. Secondary outcomes included analysis of body mass index (BMI) , LDL cholesterol, HDL cholesterol, triglycerides (TG) , uric acid, high-sensitivity C-reactive protein and cardiovascular disease (CVD) risk reduction. Results: According to our findings, HbA1c levels from the opt-in RCDM group decreased significantly by a mean of 2.38 points and were statistically superior than the opt-out group (p &amp;lt; 0.001) . The improvements in BMI, TG, and CVD risk reduction also were higher in opt-in patients (p =0.046, p=0.018, p=0.001, respectively) . Conclusion: The GluCare RCDM opt-in group had significantly superior results in HbA1c and other metabolic parameters at 3 months follow up suggesting RCDM post first visit may be an efffective way to improve diabetes care models. Disclosure M.Caccelli: Speaker's Bureau; Novo Nordisk, Servier Laboratories. Y.Said: None. J.D.Mojado: None. C.Palsky: None. R.Colodetti: None. I.Almarzooqi: None. A.Hashemi: None.

Early diagnosis of sepsis using an E-health application for a clinical early warning system outside of the intensive care unit: a case report

BackgroundElderly and frail patients who are unable to call for help in case of vital distress can develop complications during their hospitalization. As a supplement to clinical monitoring by the nursing staff, these patients can also be monitored in real time, with the Sensium E-health technology. An application notifies clinical staff of any change in their vital signs (heart rate, respiratory rate, temperature) outside of normal ranges, suggestive of physiological decline. Nurses and physicians are notified of these abnormal changes by email and also via mobile application (iPhone or iPad), allowing early intervention to prevent further deterioration.Case presentationAn 86-year-old Caucasian female, with chronic kidney disease, was hospitalized in our medical unit for pyelonephritis associated with a moderate deterioration of serum creatinine. Remote continuous monitoring allowed us to diagnose clinical deterioration early and adjust her treatment. The treatment improved her clinical condition and amended the secondary sepsis with circulation failure in 2 days.ConclusionsThe prognosis for patients with acute complicated pyelonephritis is much worse than for those with uncomplicated pyelonephritis. Remote continuous monitoring might be helpful to early diagnose urosepsis. This technology leads to improved prognosis of patients without initial vital distress, allowing early treatment and admission to intensive care unit.

Technological Requirements and Challenges in Wireless Body Area Networks for Health Monitoring: A Comprehensive Survey.

With the rapid growth in healthcare demand, an emergent, novel technology called wireless body area networks (WBANs) have become promising and have been widely used in the field of human health monitoring. A WBAN can collect human physical parameters through the medical sensors in or around the patient’s body to realize real-time continuous remote monitoring. Compared to other wireless transmission technologies, a WBAN has more stringent technical requirements and challenges in terms of power efficiency, security and privacy, quality of service and other specifications. In this paper, we review the recent WBAN medical applications, existing requirements and challenges and their solutions. We conducted a comprehensive investigation of WBANs, from the sensor technology for the collection to the wireless transmission technology for the transmission process, such as frequency bands, channel models, medium access control (MAC) and networking protocols. Then we reviewed its unique safety and energy consumption issues. In particular, an application-specific integrated circuit (ASIC)-based WBAN scheme is presented to improve its security and privacy and achieve ultra-low energy consumption.

P382. Replication of Personalized Relapse Prediction in Patients With Major Depressive Disorder Using Digital Biomarkers

Relapses and recurrences contribute to the significant morbidity and mortality in Major Depressive Disorder (MDD). Recent advances in mobile technologies and wearable sensors have enabled continuous remote monitoring of patients with MDD. Here we discuss the replication results of personalized (N of 1) relapse prediction framework using actigraphy data and self-reported symptoms to predict relapse and recurrence in MDD.

Creation of an Evidence-Based Implementation Framework for Digital Health Technology in the Intensive Care Unit: Qualitative Study.

BackgroundDigital health technologies such as continuous remote monitoring and artificial intelligence–driven clinical decision support systems could improve clinical outcomes in intensive care medicine. However, comprehensive evidence and guidelines for the successful implementation of digital health technologies into specific clinical settings such as the intensive care unit (ICU) are scarce. We evaluated the implementation of a remote patient monitoring platform and derived a framework proposal for the implementation of digital health technology in an ICU.ObjectiveThis study aims to investigate barriers and facilitators to the implementation of a remote patient monitoring technology and to develop a proposal for an implementation framework for digital health technology in the ICU.MethodsThis study was conducted from May 2018 to March 2020 during the implementation of a tablet computer–based remote patient monitoring system. The system was installed in the ICU of a large German university hospital as a supplementary monitoring device. Following a hybrid qualitative approach with inductive and deductive elements, we used the Consolidated Framework for Implementation Research and the Expert Recommendations for Implementing Change to analyze the transcripts of 7 semistructured interviews with clinical ICU stakeholders and descriptive questionnaire data. The results of the qualitative analysis, together with the findings from informal meetings, field observations, and previous explorations, provided the basis for the derivation of the proposed framework.ResultsThis study revealed an insufficient implementation process due to lack of staff engagement and few perceived benefits from the novel solution. Further implementation barriers were the high staff presence and monitoring coverage in the ICU. The implementation framework includes strategies to be applied before and during implementation, targeting the implementation setting by involving all ICU stakeholders, assessing the intervention’s adaptability, facilitating the implementation process, and maintaining a vital feedback culture. Setting up a unit responsible for implementation, considering the guidance of an implementation advisor, and building on existing institutional capacities could improve the institutional context of implementation projects in the ICU.ConclusionsImplementation of digital health in the ICU should involve a thorough preimplementation assessment of the ICU’s need for innovation and its readiness to change, as well as an ongoing evaluation of the implementation conditions. Involvement of all stakeholders, transparent communication, and continuous feedback in an equal atmosphere are essential, but leadership roles must be clearly defined and competently filled. Our proposed framework may guide health care providers with concrete, evidence-based, and step-by-step recommendations for implementation practice, facilitating the introduction of digital health in intensive care.Trial RegistrationClinicalTrials.gov NCT03514173; https://clinicaltrials.gov/ct2/show/NCT03514173

Nursing staff perspectives of continuous remote vital signs monitoring on surgical wards: Theory elicitation for a realist evaluation.

Rationale, Aims and ObjectivesContinuous remote monitoring (CRM) provides a novel solution to the challenges of monitoring patients' vital signs in hospital, but the results of quantitative studies have been mixed. Acceptance by staff is a crucial determinant of the success of healthcare technologies and may explain these discrepancies. Drawing on the approach of realist evaluation, this paper aims to identify theories about how, why and in what conditions nursing staff perceptions vary regarding the CRM of patients' vital signs.MethodsMultiple methods were used to elicit theories about factors likely to facilitate or impede the successful implementation of continuous remote vital signs monitoring. This included a literature review, consultation with patients and observational work conducted during a randomized controlled trial (RCT) of CRM. In addition, a priori theories developed through informal interactions with patients and ward staff during the day‐to‐day set‐up of the trial were included.ResultsThe findings suggest that the perceptions of nursing staff regarding remote monitoring can be influenced by the type of patients under their care and their previous experience of telemetry. Factors which may undermine the engagement of staff are perceived staff burden, which can be dependent on contextual factors such as staffing levels, time of day and senior staff attitudes. Staff attitudes are also likely to be influenced by patient perspectives and the utility of the devices associated with remote monitoring. The successful implementation of CRM may be dependent on staff training, research staff input and hospital culture.ConclusionsTheories regarding nursing staff engagement with remote monitoring are numerous, varied and contradictory. The theories elicited in this initial phase will be refined during interviews with the nursing staff involved with the RCT.

In-Vitro Demonstration of Ultra-Reliable, Wireless and Batteryless Implanted Intracranial Sensors Operated on Loci of Exceptional Points.

Vital signal monitoring, such as pulse, respiration rate, intra-organ and intra-vascular pressure, can provide important information for determination of clinic diagnosis, treatments, and surgical protocols. Nowadays, micromachined bioimplants, equipped with antennas for converting bio-signals to modulated radio transmissions, may allow remote continuous monitoring of patients' vital signs. Yet, current passive biotelemetry techniques usually suffer from poor signal reproducibility and robustness in light of inevitable misalignment between transmitting and receiving antennas. Here, we seek to address this long-existing challenge and to robustly acquire information from a passive wireless intracranial pressure (or brain pressure) sensor by introducing a novel, high-performance biotelemetry system. In spite of variable inductive links, this biotelemetry system may have absolute accuracy by leveraging the uniqueness of loci of exceptional points (EPs) in non-Hermitian radio-frequency (RF) electronic systems with parity-time (PT) symmetry. Our in-vitro experimental demonstration shows that the proposed intracranial (ICP) monitoring system can provide a sub-mmHg resolution in the ICP range of 0-20 mmHg and ultra-robust wireless data acquisition against the misalignment-induced weakening of inductive link. Our results could provide a practical pathway toward reliable, real-time wireless monitoring of ICP, and other vital signals generated by bio-implants and wearables.

Multi-ROI Spectral Approach for the Continuous Remote Cardio-Respiratory Monitoring from Mobile Device Built-In Cameras.

Heart rate (HR) and respiratory rate (fR) can be estimated by processing videos framing the upper body and face regions without any physical contact with the subject. This paper proposed a technique for continuously monitoring HR and fR via a multi-ROI approach based on the spectral analysis of RGB video frames recorded with a mobile device (i.e., a smartphone’s camera). The respiratory signal was estimated by the motion of the chest, whereas the cardiac signal was retrieved from the pulsatile activity at the level of right and left cheeks and forehead. Videos were recorded from 18 healthy volunteers in four sessions with different user-camera distances (i.e., 0.5 m and 1.0 m) and illumination conditions (i.e., natural and artificial light). For HR estimation, three approaches were investigated based on single or multi-ROI approaches. A commercially available multiparametric device was used to record reference respiratory signals and electrocardiogram (ECG). The results demonstrated that the multi-ROI approach outperforms the single-ROI approach providing temporal trends of both the vital parameters comparable to those provided by the reference, with a mean absolute error (MAE) consistently below 1 breaths·min−1 for fR in all the scenarios, and a MAE between 0.7 bpm and 6 bpm for HR estimation, whose values increase at higher distances.

Live capture and handling of Taiwanese leopard cats Prionailurus bengalensis: an evaluation of trap designs and capture protocol

Behavioural and ecological studies often require capture and handling of animals for marking or collaring. Cage traps, in various designs, are commonly used for live‐capture of small mammals. Apart from costs and practicality, trap designs need to balance trap success and safety for the trapped animal. Inevitably, trapping and handling causes stress for the animals involved and can even result in injury or mortality. To optimise trap methods, it is important to share experiences. In this study, we describe our trapping and handling protocol for leopard cat, an elusive small felid which can be challenging to trap. We determine which factors influence trap success and compare three custom made cage trap designs: aviary‐, box‐ and side‐traps. We found that trap chance was affected by trap design, location and seasonality but, despite being causes of leopard cat mortality, not by human or domestic dog presence. The side‐trap provided the best tradeoff between trap success and safety, and was economic and easy to handle. Sacrificing live‐bait was unnecessary to successfully trap leopard cats. More male than female leopard cats visited the traps, resulting in a sex‐bias in trapped individuals. Due to a long response time, especially when continuous trap monitoring was absent, the time between leopard cat capture and release was &gt; 10 h (mean ± SE = 16:18 ± 01:57 h). We therefore recommend to always use remote continuous monitoring devices when trapping animals. Although not related to drug dosage, recovery time was negatively related to the distance leopard cats moved after release, whereas total time between capture and release was not. To minimize exposure to human and domestic dog related threats, we released leopard cats after nightfall within their natural 18:00–06:00 h activity time, and only released individuals once fully responsive, which took &gt; 4 h.

Remote continuous monitoring with wireless wearable sensors in clinical practice, nurses perspectives on factors affecting implementation: a qualitative study

BackgroundContinuous monitoring using wireless wearable sensors is a promising solution for use in clinical practice and in the home setting. It is important to involve nurses to ensure successful implementation. This paper aims to provide an overview of 1) factors affecting implementation of continuous monitoring using wireless wearable sensors by evaluating nurses’ experiences with its use on the nursing ward, and 2) nurses’ expectations for use in the home setting.MethodsSemi-structured interviews were conducted with 16 nurses from three teaching hospitals in the Netherlands, covering constructs from the Consolidated Framework for Implementation Research (CFIR). A deductive approach of directed content analysis was applied. One additional factor was added using the Unified Theory for Acceptance of Technology (UTAUT). The quotes and domains were rated on valence (positive, neutral, negative) and strength (strong: − 2, + 2, neutral 0, and weak: − 1, + 1).ResultsData was collected on 27 CFIR constructs and 1 UTAUT construct. In the experience of at least 8 nurses, five constructs had a strong positive influence on implementation on the nursing ward, including relative advantage (e.g., early detection of deterioration), patient needs and resources (e.g. feeling safe), networks and communications (e.g. execute tasks together), personal attributes (e.g. experience with intervention), and implementation leaders (e.g., project leader). Five constructs had a strong negative influence: evidence strength and quality (e.g. lack of evidence from practical experience), complexity (e.g. number of process steps), design quality and packaging (e.g., bad sensor quality), compatibility (e.g, change in work) and facilitating conditions (e.g, Wi-Fi connection). Nurses expected continuous monitoring in the home setting to be hindered by compatibility with work processes and to be facilitated by staff’s access to information. Technical facilitating conditions (e.g. interoperability) were suggested to be beneficial for further development.ConclusionsThis paper provides an overview, of factors influencing implementation of continuous monitoring including relative importance, based on nurses’ experiences with use on nursing wards, and their perspectives for use in the home setting. Implementation of continuous monitoring is affected by a wide range of factors. This overview may be used as a guideline for future implementations.

A prospective observational study evaluating the use of remote patient monitoring in ED discharged COVID-19 patients in NYC

ObjectivesWe investigated whether continuous remote patient monitoring (RPM) could significantly reduce return Emergency Department (ED) revisits among coronavirus disease 2019 (COVID-19) patients discharged from the emergency Department. Materials and methodsA prospective observational study was conducted from a total of 2833 COVID-19 diagnosed patients who presented to the Montefiore Medical Center ED between September 2020–March 2021. Study patients were remotely monitored through a digital platform that was supervised 24/7 by licensed healthcare professionals. Age and time-period matched controls were randomly sampled through retrospective review. The primary outcome was ED revisit rates among the two groups. ResultsIn our study, 150 patients enrolled in the RPM program and 150 controls were sampled for a total of 300 patients. Overall, 59.1% of the patients identified as Hispanic/Latino. The RPM group had higher body mass index (BMI) (29 (25–35) vs. 27 (25–31) p-value 0.020) and rates of hypertension (50.7% (76) vs. 35.8% (54) p-value 0.009). There were no statistically significant differences in rates of ED revisit between the RPM group (8% (12)) and control group (9.3% (14)) (OR: 0.863; 95% CI:0.413–1. 803; p- 0.695). Discussion and conclusionOur study explored the impact of continuous monitoring versus intermittent monitoring for reducing ED revisits in a largely underrepresented population of the Bronx. Our study demonstrated that continuous remote patient monitoring showed no significant difference in preventing ED revisits compared to non-standardized intermittent monitoring. However, potential other acute care settings where RPM may be useful for identifying high-risk patients for early interventions warrant further study.