Continuous physiological monitoring using wearable technology to inform individual management of infectious diseases, public health and outbreak responses

Optimizing COVID-19 testing resources use with wearable sensors

The timely identification of infectious pre-symptomatic and asymptomatic cases is key towards preventing the spread of a viral illness like COVID-19. Early identification has been done through routine testing programs, which are indeed costly and potentially burdensome for individuals who should be tested with high frequency. A supplemental tool is represented by wearable technology, that can passively monitor and identify individuals at high risk, alerting them to take a test. We designed a Markov chain model and simulated a routine testing and a wearable testing strategy to estimate the number of tests required and the average number of days in which an individual is infectious and undetected. According to our model, with 2 test per month available, we have that the number of infectious and undetected days is 4.1 in the case of routine testing, while it decreases by 46% and 27% with a wearable testing strategy in the presence or absence of self-reported symptoms. The proposed parametric model can be used for different viral illnesses by tuning its parameters. It shows that wearable technology informing a testing strategy can significantly reduce the number of infectious days in which an individuals can spread the virus. With the same number of infectious days, by using wearables we can potentially reduce the number of required tests and the cost of the testing strategy.

Patterns of Ownership and Usage of Wearable Devices in the United States, 2020-2022: Survey Study

BACKGROUND

Although wearable technology has become increasingly common, comprehensive studies examining its ownership across different sociodemographic groups are limited.

OBJECTIVE

The aims of this study were to (1) measure wearable device ownership by sociodemographic characteristics in a cohort of US consumers and (2) investigate how these devices are acquired and used for health-related purposes.

METHODS

Data from the Rock Health Digital Health Consumer Adoption Survey collected from 2020 to 2022 with 23,974 US participants were analyzed. The sample was US Census-matched for demographics, including age, race/ethnicity, gender, and income. The relationship between sociodemographic factors and wearable ownership was explored using descriptive analysis and multivariate logistic regression.

RESULTS

Of the 23,974 respondents, 10,679 (44.5%) owned wearables. Ownership was higher among younger individuals, those with higher incomes and education levels, and respondents living in urban areas. Compared to those aged 18-24 years, respondents 65 years and older had significantly lower odds of wearable ownership (odds ratio [OR] 0.18, 95% CI 0.16-0.21). Higher annual income (≥US $200,000; OR 2.27, 95% CI 2.01-2.57) and advanced degrees (OR 2.23, 95% CI 2.01-2.48) were strong predictors of ownership. Living in rural areas reduced ownership odds (OR 0.65, 95% CI 0.60-0.72). There was a notable difference in ownership based on gender and health insurance status. Women had slightly higher ownership odds than men (OR 1.10, 95% CI 1.04-1.17). Private insurance increased ownership odds (OR 1.28, 95% CI 1.17-1.40), whereas being uninsured (OR 0.41, 95% CI 0.36-0.47) or on Medicaid (OR 0.75, 95% CI 0.68-0.82) decreased the odds of ownership. Interestingly, minority groups such as non-Hispanic Black (OR 1.14, 95% CI 1.03-1.25) and Hispanic/Latine (OR 1.20, 95% CI 1.10-1.31) respondents showed slightly higher ownership odds than other racial/ethnic groups.

CONCLUSIONS

Our findings suggest that despite overall growth in wearable ownership, sociodemographic divides persist. The data indicate a need for equitable access strategies as wearables become integral to clinical and public health domains.

Assessment of Heat Exposure and Health Outcomes in Rural Populations of Western Kenya by Using Wearable Devices: Observational Case Study

BACKGROUND

Climate change increasingly impacts health, particularly of rural populations in sub-Saharan Africa due to their limited resources for adaptation. Understanding these impacts remains a challenge, as continuous monitoring of vital signs in such populations is limited. Wearable devices (wearables) present a viable approach to studying these impacts on human health in real time.

OBJECTIVE

The aim of this study was to assess the feasibility and effectiveness of consumer-grade wearables in measuring the health impacts of weather exposure on physiological responses (including activity, heart rate, body shell temperature, and sleep) of rural populations in western Kenya and to identify the health impacts associated with the weather exposures.

METHODS

We conducted an observational case study in western Kenya by utilizing wearables over a 3-week period to continuously monitor various health metrics such as step count, sleep patterns, heart rate, and body shell temperature. Additionally, a local weather station provided detailed data on environmental conditions such as rainfall and heat, with measurements taken every 15 minutes.

RESULTS

Our cohort comprised 83 participants (42 women and 41 men), with an average age of 33 years. We observed a positive correlation between step count and maximum wet bulb globe temperature (estimate 0.06, SE 0.02; P=.008). Although there was a negative correlation between minimum nighttime temperatures and heat index with sleep duration, these were not statistically significant. No significant correlations were found in other applied models. A cautionary heat index level was recorded on 194 (95.1%) of 204 days. Heavy rainfall (>20 mm/day) occurred on 16 (7.8%) out of 204 days. Despite 10 (21%) out of 47 devices failing, data completeness was high for sleep and step count (mean 82.6%, SD 21.3% and mean 86.1%, SD 18.9%, respectively), but low for heart rate (mean 7%, SD 14%), with adult women showing significantly higher data completeness for heart rate than men (2-sided t test: P=.003; Mann-Whitney U test: P=.001). Body shell temperature data achieved 36.2% (SD 24.5%) completeness.

CONCLUSIONS

Our study provides a nuanced understanding of the health impacts of weather exposures in rural Kenya. Our study's application of wearables reveals a significant correlation between physical activity levels and high temperature stress, contrasting with other studies suggesting decreased activity in hotter conditions. This discrepancy invites further investigation into the unique socioenvironmental dynamics at play, particularly in sub-Saharan African contexts. Moreover, the nonsignificant trends observed in sleep disruption due to heat expose the need for localized climate change mitigation strategies, considering the vital role of sleep in health. These findings emphasize the need for context-specific research to inform policy and practice in regions susceptible to the adverse health effects of climate change.

Towards a machine-learning assisted non-invasive classification of dengue severity using wearable PPG data: a prospective clinical study

BACKGROUND

Dengue epidemics impose considerable strain on healthcare resources. Real-time continuous and non-invasive monitoring of patients admitted to the hospital could lead to improved care and outcomes. We evaluated the performance of a commercially available wearable (SmartCare) utilising photoplethysmography (PPG) to stratify clinical risk for a cohort of hospitalised patients with dengue in Vietnam.

METHODS

We performed a prospective observational study for adult and paediatric patients with a clinical diagnosis of dengue at the Hospital for Tropical Disease, Ho Chi Minh City, Vietnam. Patients underwent PPG monitoring early during admission alongside standard clinical care. PPG waveforms were analysed using machine learning models. Adult patients were classified between 3 severity classes: i) uncomplicated (ward-based), ii) moderate-severe (emergency department-based), and iii) severe (ICU-based). Data from paediatric patients were split into 2 classes: i) severe (during ICU stay) and ii) follow-up (14-21 days after the illness onset). Model performances were evaluated using standard classification metrics and 5-fold stratified cross-validation.

FINDINGS

We included PPG and clinical data from 132 adults and 15 paediatric patients with a median age of 28 (IQR, 21-35) and 12 (IQR, 9-13) years respectively. 1781 h of PPG data were available for analysis. The best performing convolutional neural network models (CNN) achieved a precision of 0.785 and recall of 0.771 in classifying adult patients according to severity class and a precision of 0.891 and recall of 0.891 in classifying between disease and post-disease state in paediatric patients.

INTERPRETATION

We demonstrate that the use of a low-cost wearable provided clinically actionable data to differentiate between patients with dengue of varying severity. Continuous monitoring and connectivity to early warning systems could significantly benefit clinical care in dengue, particularly within an endemic setting. Work is currently underway to implement these models for dynamic risk predictions and assist in individualised patient care.

FUNDING

EPSRC Centre for Doctoral Training in High-Performance Embedded and Distributed Systems (HiPEDS) (Grant: EP/L016796/1) and the Wellcome Trust (Grants: 215010/Z/18/Z and 215688/Z/19/Z).

A qualitative exploration of the experiences of transdermal alcohol sensor devices amongst people in receipt of treatment for alcohol use disorder

Introduction

Transdermal alcohol sensors (TAS) have the potential to be used as a clinical tool in alcohol treatment, but there is limited research with individuals with alcohol dependence using TAS. Our study is a qualitative evaluation of the views of people attending alcohol treatment and their experiences of wearing the BACtrack Skyn, within alcohol services in South London.

Methods

Participants with alcohol dependence wore a BACtrack Skyn TAS for one week and met with the researcher every two days, for a total of four meetings (for example: Monday, Wednesday, Friday, and Monday). In the final meeting, a post-wear survey (on their physical, social and comfort experience of the TAS) and semi-structured interview were completed. The Technology Acceptance Model (TAM) informed the topic guide and data analysis.

Results

Adults (N = 16) receiving alcohol treatment were recruited. Three core topics guided analysis: perceived usefulness, perceived ease of use and attitudes towards use. Participants found the TAS easy to wear and felt positive about its appearance and comfort. The only challenges reported were side effects, mostly skin irritation. The main two perceived uses were 1) TAS working as a drinking deterrent and 2) reducing daily breathalyser visits during detox.

Conclusion

Findings support the use of TAS amongst alcohol service users. Wearing the TAS for one week was acceptable and feasible for objective alcohol concentration measurement. Participants reported high perceived ease of use and usefulness of the Skyn in the context of alcohol treatment. These results are encouraging for the use of TAS in clinical settings.

Short- and Long-Term Predicted and Witnessed Consequences of Digital Surveillance During the COVID-19 Pandemic: Scoping Review

BACKGROUND

The COVID-19 pandemic has prompted the deployment of digital technologies for public health surveillance globally. The rapid development and use of these technologies have curtailed opportunities to fully consider their potential impacts (eg, for human rights, civil liberties, privacy, and marginalization of vulnerable groups).

OBJECTIVE

We conducted a scoping review of peer-reviewed and gray literature to identify the types and applications of digital technologies used for surveillance during the COVID-19 pandemic and the predicted and witnessed consequences of digital surveillance.

METHODS

Our methodology was informed by the 5-stage methodological framework to guide scoping reviews: identifying the research question; identifying relevant studies; study selection; charting the data; and collating, summarizing, and reporting the findings. We conducted a search of peer-reviewed and gray literature published between December 1, 2019, and December 31, 2020. We focused on the first year of the pandemic to provide a snapshot of the questions, concerns, findings, and discussions emerging from peer-reviewed and gray literature during this pivotal first year of the pandemic. Our review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) reporting guidelines.

RESULTS

We reviewed a total of 147 peer-reviewed and 79 gray literature publications. Based on our analysis of these publications, we identified a total of 90 countries and regions where digital technologies were used for public health surveillance during the COVID-19 pandemic. Some of the most frequently used technologies included mobile phone apps, location-tracking technologies, drones, temperature-scanning technologies, and wearable devices. We also found that the literature raised concerns regarding the implications of digital surveillance in relation to data security and privacy, function creep and mission creep, private sector involvement in surveillance, human rights, civil liberties, and impacts on marginalized groups. Finally, we identified recommendations for ethical digital technology design and use, including proportionality, transparency, purpose limitation, protecting privacy and security, and accountability.

CONCLUSIONS

A wide range of digital technologies was used worldwide to support public health surveillance during the COVID-19 pandemic. The findings of our analysis highlight the importance of considering short- and long-term consequences of digital surveillance not only during the COVID-19 pandemic but also for future public health crises. These findings also demonstrate the ways in which digital surveillance has rendered visible the shifting and blurred boundaries between public health surveillance and other forms of surveillance, particularly given the ubiquitous nature of digital surveillance.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-https://doi.org/10.1136/bmjopen-2021-053962.

Advancements in Airborne Viral Nucleic Acid Detection with Wearable Devices

Wearable health sensors for an expanding range of physiological parameters have experienced rapid development in recent years and are poised to disrupt the way healthcare is tracked and administered. The monitoring of environmental contaminants with wearable technologies is an additional layer of personal and public healthcare and is also receiving increased focus. Wearable sensors that detect exposure to airborne viruses could alert wearers of viral exposure and prompt proactive testing and minimization of viral spread, benefitting their own health and decreasing community risk. With the high levels of asymptomatic spread of COVID-19 observed during the pandemic, such devices could dramatically enhance our pandemic response capabilities in the future. To facilitate advancements in this area, this review summarizes recent research on airborne viral detection using wearable sensing devices as well as technologies suitable for wearables. Since the low concentration of viral particles in the air poses significant challenges to detection, methods for airborne viral particle collection and viral sensing are discussed in detail. A special focus is placed on nucleic acid-based viral sensing mechanisms due to their enhanced ability to discriminate between viral subtypes. Important considerations for integrating airborne viral collection and sensing on a single wearable device are also discussed.

Feasibility of wearable monitors to detect heart rate variability in children with hand, foot and mouth disease

Hand foot and mouth disease (HFMD) is caused by a variety of enteroviruses, and occurs in large outbreaks in which a small proportion of children deteriorate rapidly with cardiopulmonary failure. Determining which children are likely to deteriorate is difficult and health systems may become overloaded during outbreaks as many children require hospitalization for monitoring. Heart rate variability (HRV) may help distinguish those with more severe diseases but requires simple scalable methods to collect ECG data.We carried out a prospective observational study to examine the feasibility of using wearable devices to measure HRV in 142 children admitted with HFMD at a children's hospital in Vietnam. ECG data were collected in all children. HRV indices calculated were lower in those with enterovirus A71 associated HFMD compared to those with other viral pathogens.HRV analysis collected from wearable devices is feasible in a low and middle income country (LMIC) and may help classify disease severity in HFMD.

Data-driven approach to quantify trust in medical devices using Bayesian networks

Bayesian networks are increasingly used to quantify the uncertainty of subjective and stochastic concepts such as trust. In this article, we propose a data-driven approach to estimate Bayesian parameters in the domain of wearable medical devices. Our approach extracts the probability of a trust factor being in a specific state directly from the devices (e.g. sensor quality). The strength of the relationship between related factors is defined by expert knowledge and incorporated into the model. We use propagation rules from requirements engineering to estimate how much each trust factor contributes to the related intermediate nodes in the network and ultimately compute the trust score. The trust score is a relative measure of trustworthiness when different devices are evaluated in the same test conditions and using the same Bayesian structure. To evaluate our approach, we developed Bayesian networks for the trust quantification of similar wearable devices from two manufacturers under identical test conditions and noise levels. The results demonstrated the learnability and generalizability of our approach.

Utilization of digital tools to enhance COVID-19 and tuberculosis testing and linkage to care: A cross-sectional evaluation study among Bodaboda motorbike riders in the Nairobi Metropolis, Kenya

Kenya has registered over 300,000 cases of COVID-19 and is a high-burden tuberculosis country. Tuberculosis diagnosis was significantly disrupted by the pandemic. Access to timely diagnosis, which is key to effective management of tuberculosis and COVID-19, can be expanded and made more efficient through integrated screening. Decentralized testing at community level further increases access, especially for underserved populations, and requires robust systems for data and process management. This study delivered integrated COVID-19 and tuberculosis testing to commercial motorbike (Bodaboda) riders, a population at increased risk of both diseases with limited access to services, in four counties: Nairobi, Kiambu, Machakos and Kajiado. Testing sheds were established where riders congregate, with demand creation carried out by the Bodaboda association. Integrated symptom screening for tuberculosis and COVID-19 was conducted through a digital questionnaire which automatically flagged participants who should be tested for either, or both, diseases. Rapid antigen-detecting tests (Ag-RDTs) for COVID-19 were conducted onsite, while sputum samples were collected and transported to laboratories for tuberculosis diagnosis. End-to-end patient data were captured using digital tools. 5663 participants enrolled in the study, 4946 of whom were tested for COVID-19. Ag-RDT positivity rate was 1% but fluctuated widely across counties in line with broader regional trends. Among a subset tested by PCR, positivity was greater in individuals flagged as high risk by the digital tool (8% compared with 4% overall). Of 355 participants tested for tuberculosis, 7 were positive, with the resulting prevalence rate higher than the national average. Over 40% of riders had elevated blood pressure or abnormal sugar levels. The digital tool successfully captured complete end-to-end data for 95% of all participants. This study revealed high rates of undetected disease among Bodaboda riders and demonstrated that integrated diagnosis can be delivered effectively in communities, with the support of digital tools, to maximize access.

Assessing Adherence to Multi-Modal Oura Ring Wearables From COVID-19 Detection Among Healthcare Workers

Background Identifying early signs of a SARS-CoV-2 infection in healthcare workers could be a critical tool in reducing disease transmission. To provide this information, both daily symptom surveys and wearable device monitoring could have utility, assuming there is a sufficiently high level of participant adherence. Purpose The aim of this study is to evaluate adherence to a daily symptom survey and a wearable device (Oura Ring) among healthcare professionals (attending physicians and other clinical staff) and trainees (residents and medical students) in a hospital setting during the early stages of the COVID-19 pandemic. Methods In this mixed-methods observational study, the data were a subset (N=91) of those collected as part of the larger TemPredict Study. Demographic data analyses were conducted with descriptive statistics. Participant adherence to the wearable device protocol was reported as the percentage of days that sleep was recorded, and adherence to the daily survey was reported as the percentage of days with submitted surveys. Comparisons for the primary (wearable and survey adherence of groups) and secondary (adherence patterns among subgroups) outcomes were conducted using descriptive statistics, two-tailed independent t-tests, and Welch's ANOVA with post hoc analysis using Games-Howell. Results Wearable device adherence was significantly higher than the daily symptom survey adherence for most participants. Overall, participants were highly adherent to the wearable device, wearing the device an average of 87.8 ± 11.6% of study nights compared to survey submission, showing an average of 63.8 ± 27.4% of study days. In subgroup analysis, we found that healthcare professionals (HCPs) and medical students had the highest adherence to wearing the wearable device, while medical residents had lower adherence in both wearable adherence and daily symptom survey adherence. Conclusions These results indicated high participant adherence to wearable devices to monitor for impending infection in the course of a research study conducted as part of clinical practice. Subgroup analysis indicated HCPs and medical students maintained high adherence, but residents' adherence was lower, which is likely multifactorial, with differences in work demands and stress contributing to the findings. These results can guide the development of adherence strategies for a wearable device to increase the quality of data collection and assist in disease detection in this and future pandemics.

Responsible design and implementation of technologies for the prevention of infectious diseases: towards a values-based assessment framework for the Dutch government

OBJECTIVES

The Dutch government implemented the apps 'CoronaMelder' and 'CoronaCheck' to prevent the transmission of SARS-CoV-2. They faced many questions on how to responsibly implement such technologies. Here, we aim to develop an assessment framework to support the Dutch national government with the responsible design and implementation of technologies for the prevention of future infectious diseases.

STUDY DESIGN

Three-stage web-based Delphi process.

METHODS

The assessment framework was developed through two research phases. During the Initial Design phase, a conceptual version of the assessment framework was developed through a scoping review and semistructured interviews with a scientific board. The Consensus phase involved a three-stage web-based Delphi process with an expert community.

RESULTS

The final assessment framework consists of five development phases, 10 values, and a total of 152 questions.

CONCLUSIONS

Technology assessment frameworks help policymakers to make informed decisions and contribute to the responsible implementation of technologies in society. The framework is now available for the Dutch government and other stakeholders to use in future pandemics. We discuss the possibilities of using the framework transnationally.

A Wearable Multimodal Wireless Sensing System for Respiratory Monitoring and Analysis

Wireless sensing systems are required for continuous health monitoring and data collection. It allows for patient data collection in real time rather than through time-consuming and expensive hospital or lab visits. This technology employs wearable sensors, signal processing, and wireless data transfer to remotely monitor patients' health. The research offers a novel approach to providing primary diagnostics remotely with a digital health system for monitoring pulmonary health status using a multimodal wireless sensor device. The technology uses a compact wearable with new integration of acoustics and biopotentials sensors to monitor cardiovascular and respiratory activity to provide comprehensive and fast health status monitoring. Furthermore, the small wearable sensor size may stick to human skin and record heart and lung activities to monitor respiratory health. This paper proposes a sensor data fusion method of lung sounds and cardiograms for potential real-time respiration pattern diagnostics, including respiratory episodes like low tidal volume and coughing. With a p-value of 0.003 for sound signals and 0.004 for electrocardiogram (ECG), preliminary tests demonstrated that it was possible to detect shallow breathing and coughing at a meaningful level.

Mental Stress Detection Using a Wearable In-Ear Plethysmography

This study presents an ear-mounted photoplethysmography (PPG) system that is designed to detect mental stress. Mental stress is a prevalent condition that can negatively impact an individual's health and well-being. Early detection and treatment of mental stress are crucial for preventing related illnesses and maintaining overall wellness. The study used data from 14 participants that were collected in a controlled environment. The participants were subjected to stress-inducing tasks such as the Stroop color-word test and mathematical calculations. The raw PPG signal was then preprocessed and transformed into scalograms using continuous wavelet transform (CWT). A convolutional neural network classifier was then used to classify the transformed signals as stressed or non-stressed. The results of the study show that the PPG system achieved high levels of accuracy (92.04%) and F1-score (90.8%). Furthermore, by adding white Gaussian noise to the raw PPG signals, the results were improved even more, with an accuracy of 96.02% and an F1-score of 95.24%. The proposed ear-mounted device shows great promise as a reliable tool for the early detection and treatment of mental stress, potentially revolutionizing the field of mental health and well-being.

Mapping patient pathways and understanding clinical decision-making in dengue management to inform the development of digital health tools

BACKGROUND

Dengue is a common viral illness and severe disease results in life-threatening complications. Healthcare services in low- and middle-income countries treat the majority of dengue cases worldwide. However, the clinical decision-making processes which result in effective treatment are poorly characterised within this setting. In order to improve clinical care through interventions relating to digital clinical decision-support systems (CDSS), we set out to establish a framework for clinical decision-making in dengue management to inform implementation.

METHODS

We utilised process mapping and task analysis methods to characterise existing dengue management at the Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam. This is a tertiary referral hospital which manages approximately 30,000 patients with dengue each year, accepting referrals from Ho Chi Minh city and the surrounding catchment area. Initial findings were expanded through semi-structured interviews with clinicians in order to understand clinical reasoning and cognitive factors in detail. A grounded theory was used for coding and emergent themes were developed through iterative discussions with clinician-researchers.

RESULTS

Key clinical decision-making points were identified: (i) at the initial patient evaluation for dengue diagnosis to decide on hospital admission and the provision of fluid/blood product therapy, (ii) in those patients who develop severe disease or other complications, (iii) at the point of recurrent shock in balancing the need for fluid therapy with complications of volume overload. From interviews the following themes were identified: prioritising clinical diagnosis and evaluation over existing diagnostics, the role of dengue guidelines published by the Ministry of Health, the impact of seasonality and caseload on decision-making strategies, and the potential role of digital decision-support and disease scoring tools.

CONCLUSIONS

The study highlights the contemporary priorities in delivering clinical care to patients with dengue in an endemic setting. Key decision-making processes and the sources of information that were of the greatest utility were identified. These findings serve as a foundation for future clinical interventions and improvements in healthcare. Understanding the decision-making process in greater detail also allows for development and implementation of CDSS which are suited to the local context.

Use of digital technologies for public health surveillance during the COVID-19 pandemic: A scoping review

Throughout the COVID-19 pandemic, a variety of digital technologies have been leveraged for public health surveillance worldwide. However, concerns remain around the rapid development and deployment of digital technologies, how these technologies have been used, and their efficacy in supporting public health goals. Following the five-stage scoping review framework, we conducted a scoping review of the peer-reviewed and grey literature to identify the types and nature of digital technologies used for surveillance during the COVID-19 pandemic and the success of these measures. We conducted a search of the peer-reviewed and grey literature published between 1 December 2019 and 31 December 2020 to provide a snapshot of questions, concerns, discussions, and findings emerging at this pivotal time. A total of 147 peer-reviewed and 79 grey literature publications reporting on digital technology use for surveillance across 90 countries and regions were retained for analysis. The most frequently used technologies included mobile phone devices and applications, location tracking technologies, drones, temperature scanning technologies, and wearable devices. The utility of digital technologies for public health surveillance was impacted by factors including uptake of digital technologies across targeted populations, technological capacity and errors, scope, validity and accuracy of data, guiding legal frameworks, and infrastructure to support technology use. Our findings raise important questions around the value of digital surveillance for public health and how to ensure successful use of technologies while mitigating potential harms not only in the context of the COVID-19 pandemic, but also during other infectious disease outbreaks, epidemics, and pandemics.

Past, Present and Future of Research on Wearable Technologies for Healthcare: A Bibliometric Analysis Using Scopus

Currently, wearable technology is present in different fields that aim to satisfy our needs in daily life, including the improvement of our health in general, the monitoring of patient health, ensuring the safety of people in the workplace or supporting athlete training. The objective of this bibliometric analysis is to examine and map the scientific advances in wearable technologies in healthcare, as well as to identify future challenges within this field and put forward some proposals to address them. In order to achieve this objective, a search of the most recent related literature was carried out in the Scopus database. Our results show that the research can be divided into two periods: before 2013, it focused on design and development of sensors and wearable systems from an engineering perspective and, since 2013, it has focused on the application of this technology to monitoring health and well-being in general, and in alignment with the Sustainable Development Goals wherever feasible. Our results reveal that the United States has been the country with the highest publication rates, with 208 articles (34.7%). The University of California, Los Angeles, is the institution with the most studies on this topic, 19 (3.1%). Sensors journal (Switzerland) is the platform with the most studies on the subject, 51 (8.5%), and has one of the highest citation rates, 1461. We put forward an analysis of keywords and, more specifically, a pennant chart to illustrate the trends in this field of research, prioritizing the area of data collection through wearable sensors, smart clothing and other forms of discrete collection of physiological data.

Sepsis Mortality Prediction Using Wearable Monitoring in Low-Middle Income Countries

Sepsis is associated with high mortality-particularly in low-middle income countries (LMICs). Critical care management of sepsis is challenging in LMICs due to the lack of care providers and the high cost of bedside monitors. Recent advances in wearable sensor technology and machine learning (ML) models in healthcare promise to deliver new ways of digital monitoring integrated with automated decision systems to reduce the mortality risk in sepsis. In this study, firstly, we aim to assess the feasibility of using wearable sensors instead of traditional bedside monitors in the sepsis care management of hospital admitted patients, and secondly, to introduce automated prediction models for the mortality prediction of sepsis patients. To this end, we continuously monitored 50 sepsis patients for nearly 24 h after their admission to the Hospital for Tropical Diseases in Vietnam. We then compared the performance and interpretability of state-of-the-art ML models for the task of mortality prediction of sepsis using the heart rate variability (HRV) signal from wearable sensors and vital signs from bedside monitors. Our results show that all ML models trained on wearable data outperformed ML models trained on data gathered from the bedside monitors for the task of mortality prediction with the highest performance (area under the precision recall curve = 0.83) achieved using time-varying features of HRV and recurrent neural networks. Our results demonstrate that the integration of automated ML prediction models with wearable technology is well suited for helping clinicians who manage sepsis patients in LMICs to reduce the mortality risk of sepsis.

Consumer-grade wearables identify changes in multiple physiological systems during COVID-19 disease progression

Consumer-grade wearables are needed to track disease, especially in the ongoing pandemic, as they can monitor patients in real time. We show that decomposing heart rate from low-cost wearable technologies into signals from different systems can give a multidimensional description of physiological changes due to COVID-19 infection. We find that the separate physiological features of basal heart rate, heart rate response to physical activity, circadian variation in heart rate, and autocorrelation of heart rate are significantly altered and can classify symptomatic versus healthy periods. Increased heart rate and autocorrelation begin at symptom onset, while the heart rate response to activity increases soon after symptom onset and increases more in individuals exhibiting cough. Symptom onset is associated with a blunting of circadian variation in heart rate, as measured by the uncertainty in the phase estimate. This work establishes an innovative data analytic approach to monitor disease progression remotely using consumer-grade wearables.

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We separate wearable heart rate into cardiopulmonary, circadian, and other signals

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Parameters from different physiological systems enable disease tracking

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Individual signals change in distinct ways around COVID-19 symptom onset

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Together, the parameter changes can distinguish healthy from infection periods

The compensatory reserve index predicts recurrent shock in patients with severe dengue

BACKGROUND

Dengue shock syndrome (DSS) is one of the major clinical phenotypes of severe dengue. It is defined by significant plasma leak, leading to intravascular volume depletion and eventually cardiovascular collapse. The compensatory reserve Index (CRI) is a new physiological parameter, derived from feature analysis of the pulse arterial waveform that tracks real-time changes in central volume. We investigated the utility of CRI to predict recurrent shock in severe dengue patients admitted to the ICU.

METHODS

We performed a prospective observational study in the pediatric and adult intensive care units at the Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam. Patients were monitored with hourly clinical parameters and vital signs, in addition to continuous recording of the arterial waveform using pulse oximetry. The waveform data was wirelessly transmitted to a laptop where it was synchronized with the patient's clinical data.

RESULTS

One hundred three patients with suspected severe dengue were recruited to this study. Sixty-three patients had the minimum required dataset for analysis. Median age was 11 years (IQR 8-14 years). CRI had a negative correlation with heart rate and moderate negative association with blood pressure. CRI was found to predict recurrent shock within 12 h of being measured (OR 2.24, 95% CI 1.54-3.26), P < 0.001). The median duration from CRI measurement to the first recurrent shock was 5.4 h (IQR 2.9-6.8). A CRI cutoff of 0.4 provided the best combination of sensitivity and specificity for predicting recurrent shock (0.66 [95% CI 0.47-0.85] and 0.86 [95% CI 0.80-0.92] respectively).

CONCLUSION

CRI is a useful non-invasive method for monitoring intravascular volume status in patients with severe dengue.

Novel Clinical Monitoring Approaches for Reemergence of Diphtheria Myocarditis, Vietnam

Diphtheria is a life-threatening, vaccine-preventable disease caused by toxigenic Corynebacterium bacterial species that continues to cause substantial disease and death worldwide, particularly in vulnerable populations. Further outbreaks of vaccine-preventable diseases are forecast because of health service disruptions caused by the coronavirus disease pandemic. Diphtheria causes a spectrum of clinical disease, ranging from cutaneous forms to severe respiratory infections with systemic complications, including cardiac and neurologic. In this synopsis, we describe a case of oropharyngeal diphtheria in a 7-year-old boy in Vietnam who experienced severe myocarditis complications. We also review the cardiac complications of diphtheria and discuss how noninvasive bedside imaging technologies to monitor myocardial function and hemodynamic parameters can help improve the management of this neglected infectious disease.

Applied machine learning for the risk-stratification and clinical decision support of hospitalised patients with dengue in Vietnam

BACKGROUND

Identifying patients at risk of dengue shock syndrome (DSS) is vital for effective healthcare delivery. This can be challenging in endemic settings because of high caseloads and limited resources. Machine learning models trained using clinical data could support decision-making in this context.

METHODS

We developed supervised machine learning prediction models using pooled data from adult and paediatric patients hospitalised with dengue. Individuals from 5 prospective clinical studies in Ho Chi Minh City, Vietnam conducted between 12th April 2001 and 30th January 2018 were included. The outcome was onset of dengue shock syndrome during hospitalisation. Data underwent random stratified splitting at 80:20 ratio with the former used only for model development. Ten-fold cross-validation was used for hyperparameter optimisation and confidence intervals derived from percentile bootstrapping. Optimised models were evaluated against the hold-out set.

FINDINGS

The final dataset included 4,131 patients (477 adults and 3,654 children). DSS was experienced by 222 (5.4%) of individuals. Predictors were age, sex, weight, day of illness at hospitalisation, indices of haematocrit and platelets over first 48 hours of admission and before the onset of DSS. An artificial neural network model (ANN) model had best performance with an area under receiver operator curve (AUROC) of 0.83 (95% confidence interval [CI], 0.76-0.85) in predicting DSS. When evaluated against the independent hold-out set this calibrated model exhibited an AUROC of 0.82, specificity of 0.84, sensitivity of 0.66, positive predictive value of 0.18 and negative predictive value of 0.98.

INTERPRETATION

The study demonstrates additional insights can be obtained from basic healthcare data, when applied through a machine learning framework. The high negative predictive value could support interventions such as early discharge or ambulatory patient management in this population. Work is underway to incorporate these findings into an electronic clinical decision support system to guide individual patient management.

Wearable Sensors for Remote Health Monitoring: Potential Applications for Early Diagnosis of Covid-19

Wearable sensors are emerging as a new technology to detect physiological and biochemical markers for remote health monitoring. By measuring vital signs such as respiratory rate, body temperature, and blood oxygen level, wearable sensors offer tremendous potential for the noninvasive and early diagnosis of numerous diseases such as Covid-19. Over the past decade, significant progress has been made to develop wearable sensors with high sensitivity, accuracy, flexibility, and stretchability, bringing to reality a new paradigm of remote health monitoring. In this review paper, the latest advances in wearable sensor systems that can measure vital signs at an accuracy level matching those of point-of-care tests are presented. In particular, the focus of this review is placed on wearable sensors for measuring respiratory behavior, body temperature, and blood oxygen level, which are identified as the critical signals for diagnosing and monitoring Covid-19. Various designs based on different materials and working mechanisms are summarized. This review is concluded by identifying the remaining challenges and future opportunities for this emerging field.

Exploring Early Pre-Symptomatic Detection of Influenza Using Continuous Monitoring of Advanced Physiological Parameters during a Randomized Controlled Trial

Early detection of influenza may improve responses against outbreaks. This study was part of a clinical study assessing the efficacy of a novel influenza vaccine, aiming to discover distinct, highly predictive patterns of pre-symptomatic illness based on changes in advanced physiological parameters using a novel wearable sensor. Participants were frequently monitored 24 h before and for nine days after the influenza challenge. Viral load was measured daily, and self-reported symptoms were collected twice a day. The Random Forest classifier model was used to classify the participants based on changes in the measured parameters. A total of 116 participants with ~3,400,000 data points were included. Changes in parameters were detected at an early stage of the disease, before the development of symptomatic illness. Heart rate, blood pressure, cardiac output, and systemic vascular resistance showed the greatest changes in the third post-exposure day, correlating with viral load. Applying the classifier model identified participants as flu-positive or negative with an accuracy of 0.81 ± 0.05 two days before major symptoms appeared. Cardiac index and diastolic blood pressure were the leading predicting factors when using data from the first and second day. This study suggests that frequent remote monitoring of advanced physiological parameters may provide early pre-symptomatic detection of flu.

Patient monitoring in the nonoperating room anesthesia (NORA) setting: current advances in technology

PURPOSE OF REVIEW

Nonoperating room anesthesia (NORA) procedures continue to increase in type and complexity as procedural medicine makes technical advances. Patients presenting for NORA procedures are also older and sicker than ever. Commensurate with the requirements of procedural medicine, anesthetic monitoring must meet the American Society of Anesthesiologists standards for basic monitoring.

RECENT FINDINGS

There have been improvements in the required monitors that are used for intraoperative patient care. Some of these changes have been with new technologies and others have occurred with software refinements. In addition, specialized monitoring devises have also been introduced into NORA locations (depth of hypnosis, respiratory monitoring, point-of care ultrasound). These additions to the monitoring tools available to the anesthesiologist working in the NORA-environment push the boundaries of procedures which may be accomplished in this setting.

SUMMARY

NORA procedures constitute a growing percentage of total administered anesthetics. There is no difference in the monitoring standard between that of an anesthetic administered in an operating room and a NORA location. Anesthesiologists in the NORA setting must have the same compendium of monitors available as do their colleagues working in the operating suite.

Classifying signals from a wearable accelerometer device to measure respiratory rate

Background

Automatic measurement of respiratory rate in general hospital patients is difficult. Patient movement degrades the signal and variation of the breathing cycle means that accurate observation for ≥60 s is needed for adequate precision.

Methods

We studied acutely ill patients recently admitted to a teaching hospital. Breath duration was measured from a triaxial accelerometer attached to the chest wall and compared with a signal from a nasal cannula. We randomly divided the patient records into a training (n=54) and a test set (n=7). We used machine learning to train a neural network to select reliable signals, automatically identifying signal features associated with accurate measurement of respiratory rate. We used the test records to assess the accuracy of the device, indicated by the median absolute difference between respiratory rates, provided by the accelerometer and by the nasal cannula.

Results

In the test set of patients, machine classification of the respiratory signal reduced the median absolute difference (interquartile range) from 1.25 (0.56–2.18) to 0.48 (0.30–0.78) breaths per min. 50% of the recording periods were rejected as unreliable and in one patient, only 10% of the signal time was classified as reliable. However, even only 10% of observation time would allow accurate measurement for 6 min in an hour of recording, giving greater reliability than nurse charting, which is based on much less observation time.

Conclusion

Signals from a body-mounted accelerometer yield accurate measures of respiratory rate, which could improve automatic illness scoring in adult hospital patients.

A machine learning method was developed to classify sections of breathing records from acutely ill patients wearing a small wireless motion sensor. This would allow accurate and automatic measurement, recording, and charting of respiratory rate.https://bit.ly/301P8XW

Real-Time Continuous Glucose Monitoring During the Coronavirus Disease 2019 Pandemic and Its Impact on Time in Range

Background: The coronavirus disease 2019 (COVID-19) pandemic disrupted the lives of people with diabetes. Use of real-time continuous glucose monitoring (rtCGM) helped manage diabetes effectively. Some of these disruptions may be reflected in population-scale changes to metrics of glycemic control, such as time-in-range (TIR). Methods: We examined data from 65,067 U.S.-based users of the G6 rtCGM System (Dexcom, Inc., San Diego, CA) who had uploaded data before and during the COVID-19 pandemic. Users associated with three counties that included the cities of Los Angeles, Chicago, and New York or with five regions designated by the Centers for Disease Control and Prevention (CDC) were compared. Public data were used to associate regions with prepandemic and intrapandemic glycemic parameters, COVID-19 mortality, and median household income. Results: Compared with an 8-week prepandemic interval before stay-at-home orders (January 6, 2020, to March 1, 2020), overall mean (standard deviation) TIR improved from 59.0 (20.1)% to 61.0 (20.4)% during the early pandemic period (April 20, 2020 to June 14, 2020, P < 0.001). TIR improvements were noted in all three counties and in all five CDC-designated regions. Higher COVID-19 mortality was associated with higher proportions of individuals experiencing TIR improvements of ≥5 percentage points. Users in economically wealthier zip codes had higher pre- and intrapandemic TIR values and greater relative improvements in TIR. TIR and pandemic-related improvements in TIR varied across CDC-designated regions. Conclusions: Population-level rtCGM data may be used to monitor changes in glycemic control with temporal and geographic specificity. The COVID-19 pandemic is associated with improvements in TIR, which were not evenly distributed across the United States.

Academic general practice/family medicine in times of COVID-19 - Perspective of WONCA Europe

COVID-19 outbreak has significantly changed all aspects of general practice in Europe. This article focuses on the academic challenges for the discipline, mainly in the field of education, research, and quality assurance. The efforts of the European Region of the World Organisation of National Colleges, Academies, and Academic Associations of General Practitioners/Family Physicians (WONCA Europe) to support academic sustainability of the discipline in the time of pandemic are presented. Medical education was affected by the pandemic, threatening both its productivity and quality. Emerging new educational methods might be promising, but the results of their rapid implementation remain uncertain. A relatively small number of publications related to COVID-19 and general practice is available in the medical literature. There is a shortage of original data from general practice settings. This contrasts with the crucial role of GPs in fighting a pandemic. COVID-19 outbreak has opened widely new research areas, which should be explored by GPs. Maintaining the quality of care and safety of all patients during the COVID-19 pandemic is the utmost priority. Many of them suffer from poor access or inadequate management of their problems. Rapid implementation of telemedicine brought both threats and opportunities. The COVID-19 pandemic also challenged doctors' safety and well-being. These aspects will require discussion and remedy to prevent deterioration of the quality of primary care. WONCA Europe is making a multi-faceted effort to support GPs in difficult times of the pandemic. It is ready to support future efforts to uphold the integrity of family medicine as an academic discipline.

Citizen-Centered Mobile Health Apps Collecting Individual-Level Spatial Data for Infectious Disease Management: Scoping Review

BACKGROUND

The novel coronavirus SARS-CoV-2 rapidly spread around the world, causing the disease COVID-19. To contain the virus, much hope is placed on participatory surveillance using mobile apps, such as automated digital contact tracing, but broad adoption is an important prerequisite for associated interventions to be effective. Data protection aspects are a critical factor for adoption, and privacy risks of solutions developed often need to be balanced against their functionalities. This is reflected by an intensive discussion in the public and the scientific community about privacy-preserving approaches.

OBJECTIVE

Our aim is to inform the current discussions and to support the development of solutions providing an optimal balance between privacy protection and pandemic control. To this end, we present a systematic analysis of existing literature on citizen-centered surveillance solutions collecting individual-level spatial data. Our main hypothesis is that there are dependencies between the following dimensions: the use cases supported, the technology used to collect spatial data, the specific diseases focused on, and data protection measures implemented.

METHODS

We searched PubMed and IEEE Xplore with a search string combining terms from the area of infectious disease management with terms describing spatial surveillance technologies to identify studies published between 2010 and 2020. After a two-step eligibility assessment process, 27 articles were selected for the final analysis. We collected data on the four dimensions described as well as metadata, which we then analyzed by calculating univariate and bivariate frequency distributions.

RESULTS

We identified four different use cases, which focused on individual surveillance and public health (most common: digital contact tracing). We found that the solutions described were highly specialized, with 89% (24/27) of the articles covering one use case only. Moreover, we identified eight different technologies used for collecting spatial data (most common: GPS receivers) and five different diseases covered (most common: COVID-19). Finally, we also identified six different data protection measures (most common: pseudonymization). As hypothesized, we identified relationships between the dimensions. We found that for highly infectious diseases such as COVID-19 the most common use case was contact tracing, typically based on Bluetooth technology. For managing vector-borne diseases, use cases require absolute positions, which are typically measured using GPS. Absolute spatial locations are also important for further use cases relevant to the management of other infectious diseases.

CONCLUSIONS

We see a large potential for future solutions supporting multiple use cases by combining different technologies (eg, Bluetooth and GPS). For this to be successful, however, adequate privacy-protection measures must be implemented. Technologies currently used in this context can probably not offer enough protection. We, therefore, recommend that future solutions should consider the use of modern privacy-enhancing techniques (eg, from the area of secure multiparty computing and differential privacy).