Enabling Wearable Pulse Transit Time-Based Blood Pressure Estimation for Medically Underserved Areas and Health Equity: Comprehensive Evaluation Study

Measuring gait parameters from a single chest-worn accelerometer in healthy individuals: a validation study

Digital health technologies (DHTs) are increasingly being adopted in clinical trials, as they enable objective evaluations of health parameters in free-living environments. Although lumbar accelerometers notably provide reliable gait parameters, embedding accelerometers in chest devices, already used for vital signs monitoring, could capture a more comprehensive picture of participants' wellbeing, while reducing the burden of multiple devices. Here we assess the validity of gait parameters measured from a chest accelerometer. Twenty healthy adults (13 females, mean ± sd age: 33.9 ± 9.1 years) instrumented with lumbar and chest accelerometers underwent in-lab and outside-lab walking tasks, while monitored with reference devices (an instrumented mat, and a 6-accelerometers set). Gait parameters were extracted from chest and lumbar accelerometers using our open-source Scikit Digital Health gait (SKDH-gait) algorithm, and compared against reference values via Bland-Altman plots, Pearson's correlation, and intraclass correlation coefficient. Mixed effects regression models were performed to investigate the effect of device, task, and their interaction. Gait parameters derived from chest and lumbar accelerometers showed no significant difference and excellent agreement across all tasks, as well as good-to-excellent agreement and strong correlation against reference values, thus supporting the deployment of a single multimodal chest device in clinical trials, to simultaneously measure gait and vital signs.Trial Registration: The study was reviewed and approved by the Advarra IRB (protocol number: Pro00043100).

AI-enabled epidermal electronic system to automatically monitor a prognostic parameter for hypertension with a smartphone

We present a wearable, flexible, wireless and smartphone-enabled epidermal electronic system (EES) for the continuous monitoring of a prognostic parameter for hypertension. The thin and lightweight EES can be tightly attached to the chest of a patient and synchronously monitor first lead electrocardiograms (ECG) and seismocardiograms (SCG). To demonstrate the concept, we developed the EES using state-of-the-art cleanroom technologies. Two types of sensors were integrated: A pair of metal electrodes to contact the skin and to record ECG and a vibration sensor based on a thin piezoelectric polymer to record SCG from the same location of the chest, simultaneously. The complete EES was powered by the near field communication functionality of the smartphone. We developed a machine-learning algorithm and trained it on public ECG data and recorded SCG signals to extract characteristic features of the recordings. Binary classifiers were used to automatically annotate peaks. After training, the algorithm was transferred to the smartphone to continuously analyze the timing between particular ECG and SCG peaks and to extract the Weissler's index as a prognostic parameter for hypertension. Tests with data of healthy control persons and clinical experiments with patients diagnosed with cardio-pulmonary hypertension showed a promising prognostic performance. The presented EES technology could be utilized for pre-screening of cardio-pulmonary hypertension, which is a strong burden in our today's healthcare system.

Development of a PPG-based hardware and software system deployable on elbow and thumb for real-time estimation of pulse transit time

Blood pressure (BP) is a vital parameter used by clinicians to diagnose issues in the human cardiovascular system. Cuff-based BP devices are currently the standard method for on-the-spot and ambulatory BP measurements. However, cuff-based devices are not comfortable and are not suitable for long-term BP monitoring. Many studies have reported a significant correlation between pulse transit time (PTT) with blood pressure. However, this relation is impacted by many internal and external factors which might lower the accuracy of the PTT method. In this paper, we present a novel hardware system consisting of two custom photoplethysmography (PPG) sensors designed particularly for the estimation of PTT. In addition, a software interface and algorithms have been implemented to perform a real-time assessment of the PTT and other features of interest from signals gathered between the brachial artery and the thumb. A preclinical study has been conducted to validate the system. Five healthy volunteer subjects were tested and the results were then compared with those gathered using a reference device. The analysis reports a mean difference among subjects equal to -3.75±7.28 ms. Moreover, the standard deviation values obtained for each individual showed comparable results with the reference device, proving to be a valuable tool to investigate the factors impacting the BP-PTT relationship.Clinical Relevance- The proposed system proved to be a feasible solution to detect blood volume changes providing good quality signals to be used in the study of BP-PTT relationship.

Security Risks and User Perception towards Adopting Wearable Internet of Medical Things

The Wearable Internet of Medical Things (WIoMT) is a collective term for all wearable medical devices connected to the internet to facilitate the collection and sharing of health data such as blood pressure, heart rate, oxygen level, and more. Standard wearable devices include smartwatches and fitness bands. This evolving phenomenon due to the IoT has become prevalent in managing health and poses severe security and privacy risks to personal information. For better implementation, performance, adoption, and secured wearable medical devices, observing users' perception is crucial. This study examined users' perspectives of trust in the WIoMT while also exploring the associated security risks. Data analysed from 189 participants indicated a significant variance (R² = 0.553) on intention to use WIoMT devices, which was determined by the significant predictors (95% Confidence Interval; p < 0.05) perceived usefulness, perceived ease of use, and perceived security and privacy. These were found to have important consequences, with WIoMT users intending to use the devices based on the trust factors of usefulness, easy to use, and security and privacy features. Further outcomes of the study identified how users' security matters while adopting the WIoMT and provided implications for the healthcare industry to ensure regulated devices that secure confidential data.

Use of Self-Measured Blood Pressure Monitoring to Improve Hypertension Equity

PURPOSE OF REVIEW

To evaluate how self-measured blood pressure (SMBP) monitoring interventions impact hypertension equity.

RECENT FINDINGS

While a growing number of studies have recruited participants from safety-net settings, racial/ethnic minority groups, rural areas, or lower socio-economic backgrounds, few have reported on clinical outcomes with many choosing to evaluate only patient-reported outcomes (e.g., satisfaction, engagement). The studies with clinical outcomes demonstrate that SMBP monitoring (a) can be successfully adopted by historically excluded patient populations and safety-net settings and (b) improves outcomes when paired with clinical support. There are few studies that explicitly evaluate how SMBP monitoring impacts hypertension disparities and among rural, low-income, and some racial/ethnic minority populations. Researchers need to design SMBP monitoring studies that include disparity reduction outcomes and recruit from broader populations that experience worse hypertension outcomes. In addition to assessing effectiveness, studies must also evaluate how to mitigate multi-level barriers to real-world implementation of SMBP monitoring programs.

A Data-Driven Model with Feedback Calibration Embedded Blood Pressure Estimator Using Reflective Photoplethysmography

Ambulatory blood pressure (BP) monitoring (ABPM) is vital for screening cardiovascular activity. The American College of Cardiology/American Heart Association guideline for the prevention, detection, evaluation, and management of BP in adults recommends measuring BP outside the office setting using daytime ABPM. The recommendation to use night–day BP measurements to confirm hypertension is consistent with the recommendation of several other guidelines. In recent studies, ABPM was used to measure BP at regular intervals, and it reduces the effect of the environment on BP. Out-of-office measurements are highly recommended by almost all hypertension organizations. However, traditional ABPM devices based on the oscillometric technique usually interrupt sleep. For all-day ABPM purposes, a photoplethysmography (PPG)-based wrist-type device has been developed as a convenient tool. This optical, noninvasive device estimates BP using morphological characteristics from PPG waveforms. As measurement can be affected by multiple variables, calibration is necessary to ensure that the calculated BP values are accurate. However, few studies focused on adaptive calibration. A novel adaptive calibration model, which is data-driven and embedded in a wearable device, was proposed. The features from a 15 s PPG waveform and personal information were input for estimation of BP values and our data-driven calibration model. The model had a feedback calibration process using the exponential Gaussian process regression method to calibrate BP values and avoid inter- and intra-subject variability, ensuring accuracy in long-term ABPM. The estimation error of BP (ΔBP = actual BP—estimated BP) of systolic BP was −0.1776 ± 4.7361 mmHg; ≤15 mmHg, 99.225%, and of diastolic BP was −0.3846 ± 6.3688 mmHg; ≤15 mmHg, 98.191%. The success rate was improved, and the results corresponded to the Association for the Advancement of Medical Instrumentation standard and British Hypertension Society Grading criteria for medical regulation. Using machine learning with a feedback calibration model could be used to assess ABPM for clinical purposes.

Enabling Continuous Wearable Reflectance Pulse Oximetry at the Sternum

In light of the recent Coronavirus disease (COVID-19) pandemic, peripheral oxygen saturation (SpO₂) has shown to be amongst the vital signs most indicative of deterioration in persons with COVID-19. To allow for the continuous monitoring of SpO₂, we attempted to demonstrate accurate SpO₂ estimation using our custom chest-based wearable patch biosensor, capable of measuring electrocardiogram (ECG) and photoplethysmogram (PPG) signals with high fidelity. Through a breath-hold protocol, we collected physiological data with a wide dynamic range of SpO₂ from 20 subjects. The ratio of ratios (R) used in pulse oximetry to estimate SpO₂ was robustly extracted from the red and infrared PPG signals during the breath-hold segments using novel feature extraction and PPG_green-based outlier rejection algorithms. Through subject independent training, we achieved a low root-mean-square error (RMSE) of 2.64 ± 1.14% and a Pearson correlation coefficient (PCC) of 0.89. With subject-specific calibration, we further reduced the RMSE to 2.27 ± 0.76% and increased the PCC to 0.91. In addition, we showed that calibration is more efficiently accomplished by standardizing and focusing on the duration of breath-hold rather than the resulting range in SpO₂. The accurate SpO₂ estimation provided by our custom biosensor and the algorithms provide research opportunities for a wide range of disease and wellness monitoring applications.