Current Status and Prospects of Health-Related Sensing Technology in Wearable Devices

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).

Were Frailty Identification Criteria Created Equal? A Comparative Case Study on Continuous Non-Invasively Collected Neurocardiovascular Signals during an Active Standing Test in the Irish Longitudinal Study on Ageing (TILDA)

BACKGROUND

In this observational study, we compared continuous physiological signals during an active standing test in adults aged 50 years and over, characterised as frail by three different criteria, using data from The Irish Longitudinal Study on Ageing (TILDA).

METHODS

This study utilised data from TILDA, an ongoing landmark prospective cohort study of community-dwelling adults aged 50 years or older in Ireland. The initial sampling strategy in TILDA was based on random geodirectory sampling. Four independent groups were identified: those characterised as frail only by one of the frailty tools used (the physical Frailty Phenotype (FP), the 32-item Frailty Index (FI), or the Clinical Frailty Scale (CFS) classification tree), and a fourth group where participants were not characterised as frail by any of these tools. Continuous non-invasive physiological signals were collected during an active standing test, including systolic (sBP) and diastolic (dBP) blood pressure, as well as heart rate (HR), using digital artery photoplethysmography. Additionally, the frontal lobe cerebral oxygenation (Oxy), deoxygenation (Deoxy), and tissue saturation index (TSI) were also non-invasively measured using near-infrared spectroscopy (NIRS). The signals were visualised across frailty groups and statistically compared using one-dimensional statistical parametric mapping (SPM).

RESULTS

A total of 1124 participants (mean age of 63.5 years; 50.2% women) were included: 23 were characterised as frail only by the FP, 97 by the FI, 38 by the CFS, and 966 by none of these criteria. The SPM analyses revealed that only the group characterised as frail by the FI had significantly different signals (p < 0.001) compared to the non-frail group. Specifically, they exhibited an attenuated gain in HR between 10 and 15 s post-stand and larger deficits in sBP and dBP between 15 and 20 s post-stand.

CONCLUSIONS

The FI proved to be more adept at capturing distinct physiological responses to standing, likely due to its direct inclusion of cardiovascular morbidities in its definition. Significant differences were observed in the dynamics of cardiovascular signals among the frail populations identified by different frailty criteria, suggesting that caution should be taken when employing frailty identification tools on physiological signals, particularly the neurocardiovascular signals in an active standing test.

An Algorithm to Classify Real-World Ambulatory Status From a Wearable Device Using Multimodal and Demographically Diverse Data: Validation Study

BACKGROUND

Measuring the amount of physical activity and its patterns using wearable sensor technology in real-world settings can provide critical insights into health status.

OBJECTIVE

This study's aim was to develop and evaluate the analytical validity and transdemographic generalizability of an algorithm that classifies binary ambulatory status (yes or no) on the accelerometer signal from wrist-worn biometric monitoring technology.

METHODS

Biometric monitoring technology algorithm validation traditionally relies on large numbers of self-reported labels or on periods of high-resolution monitoring with reference devices. We used both methods on data collected from 2 distinct studies for algorithm training and testing, one with precise ground-truth labels from a reference device (n=75) and the second with participant-reported ground-truth labels from a more diverse, larger sample (n=1691); in total, we collected data from 16.7 million 10-second epochs. We trained a neural network on a combined data set and measured performance in multiple held-out testing data sets, overall and in demographically stratified subgroups.

RESULTS

The algorithm was accurate at classifying ambulatory status in 10-second epochs (area under the curve 0.938; 95% CI 0.921-0.958) and on daily aggregate metrics (daily mean absolute percentage error 18%; 95% CI 15%-20%) without significant performance differences across subgroups.

CONCLUSIONS

Our algorithm can accurately classify ambulatory status with a wrist-worn device in real-world settings with generalizability across demographic subgroups. The validated algorithm can effectively quantify users' walking activity and help researchers gain insights on users' health status.

Medical Health Data-Driven Physical Education Scheme: Public Environment-Oriented Exercise Health Management

Smart wearable devices can encourage users to take an active part in exercise to a certain extent. The most important reason is that their entertainment function can bring users a better exercise experience, which is also one of the reasons why most people wear smart wearable devices during exercise. Another reason is that the medical and health data feedback of smart wearable devices can play a stimulating role, which is also the motivation of this study. Through the feedback of health data, students can perceive their own exercise situation, and teachers can plan more targeted exercise courses based on medical and health data, so as to improve the quality of physical education. This paper studies the origin and development of health data-driven learning; clarifies the logical mechanism between health data-driven learning and physical education; analyzes the internal needs of physical education design; discusses the characteristics of thinking, process, tools, and other elements of health data-driven learning in the new era of physical education reform; and carries out physical education teaching practice. Experiments have proved that health data-driven physical education is beneficial for improving students’ emotional attitude and values, optimizing learning process and methods, and strengthening knowledge literacy and exercise skills. However, in today’s poor public environment, the medical and health data-driven physical education scheme proposed in this paper is also applicable to the public-oriented exercise health management. Medical Health; Public Environment; Health Management; Data Analysis.

Public health implications of adapting HIV pre-exposure prophylaxis programs for virtual service delivery in the context of the COVID-19 pandemic: a systematic review

Background

The novel coronavirus disease COVID-19 caused by SARS-CoV-2 threatens to disrupt global progress toward HIV epidemic control. Opportunities exist to leverage ongoing public health responses to mitigate the impacts of COVID-19 on HIV services, and novel approaches to care provision might help address both epidemics.

Objective

As the COVID-19 pandemic continues, novel approaches to maintain comprehensive HIV prevention service delivery are needed. The aim of this study was to summarize the related literature to highlight adaptations that could address potential COVID-19–related service interruptions.

Methods

We performed a systematic review and searched six databases, OVID/Medline, Scopus, Cochrane Library, CINAHL, PsycINFO, and Embase, for studies published between January 1, 2010, and October 26, 2021, related to recent technology-based interventions for virtual service delivery. Search terms included “telemedicine,” “telehealth,” “mobile health,” “eHealth,” “mHealth,” “telecommunication,” “social media,” “mobile device,” and “internet,” among others. Of the 6685 abstracts identified, 1259 focused on HIV virtual service delivery, 120 of which were relevant for HIV prevention efforts; 48 pertained to pre-exposure prophylaxis (PrEP) and 19 of these focused on evaluations of interventions for the virtual service delivery of PrEP. Of the 16 systematic reviews identified, three were specific to PrEP. All 35 papers were reviewed for outcomes of efficacy, feasibility, and/or acceptability. Limitations included heterogeneity of the studies’ methodological approaches and outcomes; thus, a meta-analysis was not performed. We considered the evidence-based interventions found in our review and developed a virtual service delivery model for HIV prevention interventions. We also considered how this platform could be leveraged for COVID-19 prevention and care.

Results

We summarize 19 studies of virtual service delivery of PrEP and 16 relevant reviews. Examples of technology-based interventions that were effective, feasible, and/or acceptable for PrEP service delivery include: use of SMS, internet, and smartphone apps such as iText (50% [95% CI 16%-71%] reduction in discontinuation of PrEP) and PrEPmate (OR 2.62, 95% CI 1.24-5.5.4); telehealth and eHealth platforms for virtual visits such as PrEPTECH and IowaTelePrEP; and platforms for training of health care workers such as Extension for Community Healthcare Outcomes (ECHO). We suggest a virtual service delivery model for PrEP that can be leveraged for COVID-19 using the internet and social media for demand creation, community-based self-testing, telehealth platforms for risk assessment and follow-up, applications for support groups and adherence/appointment reminders, and applications for monitoring.

Conclusions

Innovations in the virtual service provision of PrEP occurred before COVID-19 but have new relevance during the COVID-19 pandemic. The innovations we describe might strengthen HIV prevention service delivery during the COVID-19 pandemic and in the long run by engaging traditionally hard-to-reach populations, reducing stigma, and creating a more accessible health care platform. These virtual service delivery platforms can mitigate the impacts of the COVID-19 pandemic on HIV services, which can be leveraged to facilitate COVID-19 pandemic control now and for future responses.

Home blood pressure monitoring: methodology, clinical relevance and practical application: a 2021 position paper by the Working Group on Blood Pressure Monitoring and Cardiovascular Variability of the European Society of Hypertension

The present paper provides an update of previous recommendations on Home Blood Pressure Monitoring from the European Society of Hypertension (ESH) Working Group on Blood Pressure Monitoring and Cardiovascular Variability sequentially published in years 2000, 2008 and 2010. This update has taken into account new evidence in this field, including a recent statement by the American Heart association, as well as technological developments, which have occurred over the past 20 years. The present document has been developed by the same ESH Working Group with inputs from an international team of experts, and has been endorsed by the ESH.

Personalized Blood Pressure Estimation Using Photoplethysmography: A Transfer Learning Approach

In this paper, we present a personalized deep learning approach to estimate blood pressure (BP) using the photoplethysmogram (PPG) signal. We propose a hybrid neural network architecture consisting of convolutional, recurrent, and fully connected layers that operates directly on the raw PPG time series and provides BP estimation every 5 seconds. To address the problem of limited personal PPG and BP data for individuals, we propose a transfer learning technique that personalizes specific layers of a network pre-trained with abundant data from other patients. We use the MIMIC III database which contains PPG and continuous BP data measured invasively via an arterial catheter to develop and analyze our approach. Our transfer learning technique, namely BP-CRNN-Transfer, achieves a mean absolute error (MAE) of 3.52 and 2.20 mmHg for SBP and DBP estimation, respectively, outperforming existing methods. Our approach satisfies both the BHS and AAMI blood pressure measurement standards for SBP and DBP. Moreover, our results demonstrate that as little as 50 data samples per person are required to train accurate personalized models. We carry out Bland-Altman and correlation analysis to compare our method to the invasive arterial catheter, which is the gold-standard BP measurement method.

Instrumented Analysis of the Sit-to-Stand Movement for Geriatric Screening: A Systematic Review

The Sit-to-Stand (STS) is a widely used test of physical function to screen older people at risk of falls and frailty and is also one of the most important components of standard screening for sarcopenia. There have been many recent studies in which instrumented versions of the STS (iSTS) have been developed to provide additional parameters that could improve the accuracy of the STS test. This systematic review aimed to identify whether an iSTS is a viable alternative to a standard STS to identify older people at risk of falling, frailty, and sarcopenia. A total of 856 articles were found using the search strategy developed, with 12 articles retained in the review after screening based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Six studies evaluated the iSTS in fallers, five studies in frailty and only one study in both fallers and frailty. The results showed that power and velocity parameters extracted from an iSTS have the potential to improve the accuracy of screening when compared to a standard STS. Future work should focus on standardizing the segmentation of the STS into phases to enable comparison between studies and to develop devices integrated into the chair used for the test to improve usability.

A Comparison of Four Approaches to Evaluate the Sit-to-Stand Movement

The sit-to-stand test (STS) is a simple test of function in older people that can identify people at risk of falls. The aim of this study was to develop two novel methods of evaluating performance in the STS using a low-cost RGB camera and another an instrumented chair containing load cells in the seat of the chair to detect center of pressure movements and ground reaction forces. The two systems were compared to a Kinect and a force plate. Twenty-one younger subjects were tested when performing two 5STS movements at self-selected slow and normal speeds while 16 older fallers were tested when performing one 5STS at a self-selected pace. All methods had acceptable limits of agreement with an expert for total STS time for younger subjects and older fallers, with smaller errors observed for the chair (-0.18 ± 0.17 s) and force plate (-0.19 ± 0.79 s) than for the RGB camera (-0.30 ± 0.51 s) and the Kinect (-0.38 ± 0.50 s) for older fallers. The chair had the smallest limits of agreement compared to the expert for both younger and older participants. The new device was also able to estimate movement velocity, which could be used to estimate muscle power during the STS movement. Subsequent studies will test the device against opto-electronic systems, incorporate additional sensors, and then develop predictive equations for measures of physical function.