Remote Monitoring of Vital and Activity Parameters in Chronic Transfusion-Dependent Patients: A Feasibility Pilot Using Wearable Biosensors.

Introduction: Little is known if, and to what extent, outpatient red blood cell (RBC) transfusions benefit chronic transfusion-dependent patients. Costs, labour, and potential side effects of RBC transfusions cause a restrictive transfusion strategy to be the standard of care. However, effects on the actual performance and quality of life of patients who require RBCs on a regular basis are hardly studied. The aim of this study was to assess if new technologies and techniques like wearable biosensor devices and web-based testing can be used to measure physiological changes, functional activity, and hence eventually better assess quality of life in a cohort of transfusion-dependent patients. Methods: We monitored 5 patients who regularly receive transfusions during one transfusion cycle with the accelerateIQ biosensor platform, the Withings Steel HR, and web-based cognitive and quality of life testing. Results: Data collection by the deployed devices was shown to be feasible; the AccelerateIQ platform rendered data of which 97.8% was of high quality and usable; of the data the Withings Steel HR rendered, 98.9% was of high quality and usable. Furthermore, heart rate decreased and cognition improved significantly following RBC transfusions. Activity and quality of life measures did not show transfusion-induced changes. Conclusion: In a 5-patient cohort of transfusion-dependent patients, we found that the accelerateIQ, Withings Steel HR, and CANTAB platforms enable acquisition of high-quality data. The collected data suggest that RBC transfusions significantly and reversibly decrease heart rate and increase sustained attention in this cohort. This feasibility study justifies larger validation trials to confirm that these wearables can indeed help to determine personalized RBC transfusion strategies and thus optimization of each patient's quality of life.

Detection and Monitoring of Viral Infections via Wearable Devices and Biometric Data.

Mounting clinical evidence suggests that viral infections can lead to detectable changes in an individual's normal physiologic and behavioral metrics, including heart and respiration rates, heart rate variability, temperature, activity, and sleep prior to symptom onset, potentially even in asymptomatic individuals. While the ability of wearable devices to detect viral infections in a real-world setting has yet to be proven, multiple recent studies have established that individual, continuous data from a range of biometric monitoring technologies can be easily acquired and that through the use of machine learning techniques, physiological signals and warning signs can be identified. In this review, we highlight the existing knowledge base supporting the potential for widespread implementation of biometric data to address existing gaps in the diagnosis and treatment of viral illnesses, with a particular focus on the many important lessons learned from the coronavirus disease 2019 pandemic.

Motion Artifact Reduction In Photoplethysmography For Reliable Signal Selection.

Photoplethysmography (PPG) is a non-invasive and economical technique to extract vital signs of the human body. Although it has been widely used in consumer and research grade wrist devices to track a user's physiology, the PPG signal is very sensitive to motion which can corrupt the signal's quality. Existing Motion Artifact (MA) reduction techniques have been developed and evaluated using either synthetic noisy signals or signals collected during high-intensity activities - both of which are difficult to generalize for real-life scenarios. Therefore, it is valuable to collect realistic PPG signals while performing Activities of Daily Living (ADL) to develop practical signal denoising and analysis methods. In this work, we propose an automatic pseudo clean PPG generation process for reliable PPG signal selection. For each noisy PPG segment, the corresponding pseudo clean PPG reduces the MAs and contains rich temporal details depicting cardiac features. Our experimental results show that 71% of the pseudo clean PPG collected from ADL can be considered as high quality segment where the derived MAE of heart rate and respiration rate are 1.46 BPM and 3.93 BrPM, respectively. Therefore, our proposed method can determine the reliability of the raw noisy PPG by considering quality of the corresponding pseudo clean PPG signal.

Wearable sensor derived decompensation index for continuous remote monitoring of COVID-19 diagnosed patients.

The COVID-19 pandemic has accelerated the adoption of innovative healthcare methods, including remote patient monitoring. In the setting of limited healthcare resources, outpatient management of individuals newly diagnosed with COVID-19 was commonly implemented, some taking advantage of various personal health technologies, but only rarely using a multi-parameter chest-patch for continuous monitoring. Here we describe the development and validation of a COVID-19 decompensation index (CDI) model based on chest patch-derived continuous sensor data to predict COVID-19 hospitalizations in outpatient-managed COVID-19 positive individuals, achieving an overall AUC of the ROC Curve of 0.84 on 308 event negative participants, and 22 event positive participants, out of an overall study cohort of 400 participants. We retrospectively compare the performance of CDI to standard of care modalities, finding that the machine learning model outperforms the standard of care modalities in terms of both numbers of events identified and with a lower false alarm rate. While only a pilot phase study, the CDI represents a promising application of machine learning within a continuous remote patient monitoring system.