Real-time, personalized medicine through wearable sensors and dynamic predictive modeling: a new paradigm for clinical medicine

The Impact of Stress and Social Determinants on Diet in Cardiovascular Prevention in Young Women

The prevention of cardiovascular diseases is a fundamental pillar for reducing morbidity and mortality caused by non-communicable diseases. Social determinants, such as socioeconomic status, education, neighborhood, physical environment, employment, social support networks, and access to health care, play a crucial role in influencing health outcomes and health inequities within populations. Social determinants and stress in women are interconnected factors that can significantly impact women's health and well-being. Pregnancy is a good time to engage young women and introduce them to beneficial behaviors, such as adopting essential life skills, especially diet, and learning stress management techniques. Stress influences diet, and women are more likely to engage in unhealthy eating behaviors such as emotional eating or coping with stress with food. Strong action is needed to improve women's lifestyle starting at a young age considering that this lays the foundation for a lower cardiovascular risk in adults and the elderly. The objective of this review is to examine cardiovascular primary prevention in young healthy women, focusing particularly on unresolved issues and the influence of social determinants, as well as the correlation with stressors and their influence on diet.

Dyadic and Individual Variation in 24-Hour Heart Rates of Cancer Patients and Their Caregivers

BACKGROUND

Twenty-four-hour heart rate (HR) integrates multiple physiological and psychological systems related to health and well-being, and can be continuously monitored in high temporal resolution over several days with wearable HR monitors. Using HR data from two independent datasets of cancer patients and their caregivers, we aimed to identify dyadic and individual patterns of 24 h HR variation and assess their relationship to demographic, environmental, psychological, and clinical variables of interest.

METHODS

a novel regularized approach to high-dimensional canonical correlation analysis (CCA) was used to identify factors reflecting dyadic and individual variation in the 24 h (circadian) HR trajectories of 430 people in 215 dyads, then regression analysis was used to relate these patterns to explanatory variables.

RESULTS

Four distinct factors of dyadic covariation in circadian HR were found, contributing approximately 7% to overall circadian HR variation. These factors, along with non-dyadic factors reflecting individual variation exhibited diverse and statistically robust patterns of association with explanatory variables of interest.

CONCLUSIONS

Both dyadic and individual anomalies are present in the 24 h HR patterns of cancer patients and their caregivers. These patterns are largely synchronous, and their presence robustly associates with multiple explanatory variables. One notable finding is that higher mood scores in cancer patients correspond to an earlier HR nadir in the morning and higher HR during the afternoon.

From numbers to medical knowledge: harnessing combinatorial data patterns to predict COVID-19 resource needs and distinguish patient subsets

Background

The COVID-19 pandemic intensified the use of scarce resources, including extracorporeal membrane oxygenation (ECMO) and mechanical ventilation (MV). The combinatorial features of the immune system may be considered to estimate such needs and facilitate continuous open-ended knowledge discovery.

Materials and methods

Computer-generated distinct data patterns derived from 283 white blood cell counts collected within five days after hospitalization from 97 COVID-19 patients were used to predict patient's use of hospital resources.

Results

Alone, data on separate cell types-such as neutrophils-did not identify patients that required MV/ECMO. However, when structured as multicellular indicators, distinct data patterns displayed by such markers separated patients later needing or not needing MV/ECMO. Patients that eventually required MV/ECMO also revealed increased percentages of neutrophils and decreased percentages of lymphocytes on admission.

Discussion/conclusion

Future use of limited hospital resources may be predicted when combinations of available blood leukocyte-related data are analyzed. New methods could also identify, upon admission, a subset of COVID-19 patients that reveal inflammation. Presented by individuals not previously exposed to MV/ECMO, this inflammation differs from the well-described inflammation induced after exposure to such resources. If shown to be reproducible in other clinical syndromes and populations, it is suggested that the analysis of immunological combinations may inform more and/or uncover novel information even in the absence of pre-established questions.

Machine learning prediction will be part of future treatment of depression

Machine learning (ML) is changing the way that medicine is practiced. While already clinically utilised in diagnostic radiology and outcome prediction in intensive care unit, ML approaches in psychiatry remain nascent. Implementing ML algorithms in psychiatry, particularly in the treatment of depression, is significantly more challenging than other areas of medicine in part because of the less demarcated disease nosology and greater variability in practice. Given the current exiguous capacity of clinicians to predict patient and treatment outcomes in depression, there is a significantly greater need for better predictive capability. Early studies have shown promising results. ML predictions were significantly better than chance within the sequenced treatment alternatives to relieve depression (STAR*D) trial (accuracy 64.6%, p < 0.0001) and combining medications to enhance depression outcomes (COMED) randomised Controlled Trial (RCT) (accuracy 59.6%, p = 0.043), with similar results found in larger scale, retrospective studies. The greater flexibility and dimensionality of ML approaches has been demonstrated in studies incorporating diverse input variables including electroencephalography scans, achieving 88% accuracy for treatment response, and cognitive test scores, achieving up to 72% accuracy for treatment response. The predicting response to depression treatment (PReDicT) trial tested ML informed prescribing of antidepressants against standard therapy and found there was both better outcomes for anxiety and functional endpoints despite the algorithm only having a balanced accuracy of 57.5%. Impeding the progress of ML algorithms in psychiatry are pragmatic hurdles, including accuracy, expense, acceptability and comprehensibility, and ethical hurdles, including medicolegal liability, clinical autonomy and data privacy. Notwithstanding impediments, it is clear that ML prediction algorithms will be part of depression treatment in the future and clinicians should be prepared for their arrival.

Wearables for personalized monitoring of masticatory muscle activity - opportunities, challenges, and the future

Wearable devices are worn on or remain in close proximity of the human body. The use of wearable devices specific to the orofacial region is steadily increasing. Orofacial applications of wearable devices include supplementing diagnosis, tracking treatment progress, monitoring patient compliance, and understanding oral parafunctional behaviours. In this short communication, the role of wearable devices in advancing personalized dental medicine are highlighted with a specific focus on masticatory muscle activity monitoring in naturalistic settings. Additionally, challenges, opportunities, as well as future research areas for successful use of wearable devices for precise, personalized care of muscle disorders are discussed.

A Review of Digital Health and Biotelemetry: Modern Approaches towards Personalized Medicine and Remote Health Assessment

With the prevalence of digitalization in all aspects of modern society, health assessment is becoming digital too. Taking advantage of the most recent technological advances and approaching medicine from an interdisciplinary perspective has allowed for important progress in healthcare services. Digital health technologies and biotelemetry devices have been more extensively employed for preventing, detecting, diagnosing, monitoring, and predicting the evolution of various diseases, without requiring wires, invasive procedures, or face-to-face interaction with medical personnel. This paper aims to review the concepts correlated to digital health, classify and describe biotelemetry devices, and present the potential of digitalization for remote health assessment, the transition to personalized medicine, and the streamlining of clinical trials.

Research on Digital Technology Use in Cardiology: Bibliometric Analysis

Background

Digital technology uses in cardiology have become a popular research focus in recent years. However, there has been no published bibliometric report that analyzed the corresponding academic literature in order to derive key publishing trends and characteristics of this scientific area.

Objective

We used a bibliometric approach to identify and analyze the academic literature on digital technology uses in cardiology, and to unveil popular research topics, key authors, institutions, countries, and journals. We further captured the cardiovascular conditions and diagnostic tools most commonly investigated within this field.

Methods

The Web of Science electronic database was queried to identify relevant papers on digital technology uses in cardiology. Publication and citation data were acquired directly from the database. Complete bibliographic data were exported to VOSviewer, a dedicated bibliometric software package, and related to the semantic content of titles, abstracts, and keywords. A term map was constructed for findings visualization.

Results

The analysis was based on data from 12,529 papers. Of the top 5 most productive institutions, 4 were based in the United States. The United States was the most productive country (4224/12,529, 33.7%), followed by United Kingdom (1136/12,529, 9.1%), Germany (1067/12,529, 8.5%), China (682/12,529, 5.4%), and Italy (622/12,529, 5.0%). Cardiovascular diseases that had been frequently investigated included hypertension (152/12,529, 1.2%), atrial fibrillation (122/12,529, 1.0%), atherosclerosis (116/12,529, 0.9%), heart failure (106/12,529, 0.8%), and arterial stiffness (80/12,529, 0.6%). Recurring modalities were electrocardiography (170/12,529, 1.4%), angiography (127/12,529, 1.0%), echocardiography (127/12,529, 1.0%), digital subtraction angiography (111/12,529, 0.9%), and photoplethysmography (80/12,529, 0.6%). For a literature subset on smartphone apps and wearable devices, the Journal of Medical Internet Research (20/632, 3.2%) and other JMIR portfolio journals (51/632, 8.0%) were the major publishing venues.

Conclusions

Digital technology uses in cardiology target physicians, patients, and the general public. Their functions range from assisting diagnosis, recording cardiovascular parameters, and patient education, to teaching laypersons about cardiopulmonary resuscitation. This field already has had a great impact in health care, and we anticipate continued growth.

Coupled Multiphysics Modelling of Sensors for Chemical, Biomedical, and Environmental Applications with Focus on Smart Materials and Low-Dimensional Nanostructures

Low-dimensional nanostructures have many advantages when used in sensors compared to the traditional bulk materials, in particular in their sensitivity and specificity. In such nanostructures, the motion of carriers can be confined from one, two, or all three spatial dimensions, leading to their unique properties. New advancements in nanosensors, based on low-dimensional nanostructures, permit their functioning at scales comparable with biological processes and natural systems, allowing their efficient functionalization with chemical and biological molecules. In this article, we provide details of such sensors, focusing on their several important classes, as well as the issues of their designs based on mathematical and computational models covering a range of scales. Such multiscale models require state-of-the-art techniques for their solutions, and we provide an overview of the associated numerical methodologies and approaches in this context. We emphasize the importance of accounting for coupling between different physical fields such as thermal, electromechanical, and magnetic, as well as of additional nonlinear and nonlocal effects which can be salient features of new applications and sensor designs. Our special attention is given to nanowires and nanotubes which are well suited for nanosensor designs and applications, being able to carry a double functionality, as transducers and the media to transmit the signal. One of the key properties of these nanostructures is an enhancement in sensitivity resulting from their high surface-to-volume ratio, which leads to their geometry-dependant properties. This dependency requires careful consideration at the modelling stage, and we provide further details on this issue. Another important class of sensors analyzed here is pertinent to sensor and actuator technologies based on smart materials. The modelling of such materials in their dynamics-enabled applications represents a significant challenge as we have to deal with strongly nonlinear coupled problems, accounting for dynamic interactions between different physical fields and microstructure evolution. Among other classes, important in novel sensor applications, we have given our special attention to heterostructures and nucleic acid based nanostructures. In terms of the application areas, we have focused on chemical and biomedical fields, as well as on green energy and environmentally-friendly technologies where the efficient designs and opportune deployments of sensors are both urgent and compelling.

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

Field-Based Assessments of Behavioral Patterns During Shiftwork in Police Academy Trainees Using Wearable Technology

Circadian misalignment, as occurs in shiftwork, is associated with numerous negative health outcomes. Here, we sought to improve data labeling accuracy from wearable technology using a novel data pre-processing algorithm in 27 police trainees during shiftwork. Secondarily, we explored changes in four metabolic salivary biomarkers of circadian rhythm during shiftwork. Using a two-group observational study design, participants completed in-class training during dayshift for 6 weeks followed by either dayshift or nightshift field-training for 6 weeks. Using our novel algorithm, we imputed labels of circadian misaligned sleep episodes that occurred during daytime, which were previously were mislabeled as non-sleep by Garmin, supported by algorithm performance analysis. We next assessed changes to resting heart rate and sleep regularity index during dayshift versus nightshift field-training. We also examined changes in field-based assessments of salivary cortisol, uric acid, testosterone, and melatonin during dayshift versus nightshift. Compared to dayshift, nightshift workers experienced larger changes to resting heart rate, sleep regularity index (indicating reduced sleep regularity), and alterations in sleep/wake activity patterns accompanied by blunted salivary cortisol. Salivary uric acid and testosterone did not change. These findings show wearable technology combined with specialized data pre-processing can be used to monitor changes in behavioral patterns during shiftwork.

How to Assess the Measurement Performance of Mobile/Wearable Point-of-Care Testing Devices? A Systematic Review Addressing Sweat Analysis

Recent advances in technologies for biosensor integration in mobile or wearable devices have highlighted the need for the definition of proper validation procedures and technical standards that enable testing, verification and validation of the overall performance of these solutions. Thus, reliable assessment—in terms of limits of detection/quantitation, linearity, range, analytical and diagnostic sensitivity/specificity, accuracy, repeatability, reproducibility, cross-reactivity, diagnostic efficiency, and positive/negative prediction—still represents the most critical and challenging aspect required to progress beyond the status of feasibility studies. Considering this picture, this work aims to review and discuss the literature referring to the available methods and criteria reported in the assessment of the performance of point-of-care testing (PoCT) devices within their specific applications. In particular, without losing generality, we focused on mobile or wearable systems able to analyze human sweat. In performing this review, the focus was on the main challenges and trends underlined in the literature, in order to provide specific hints that can be used to set shared procedures and improve the overall reliability of the identified solutions, addressing the importance of sample management, the sensing components, and the electronics. This review can contribute to supporting an effective validation of mobile or wearable PoCT devices and thus to spreading the use of reliable approaches outside hospitals and clinical laboratories.

Data Integration Challenges for Machine Learning in Precision Medicine

A main goal of Precision Medicine is that of incorporating and integrating the vast corpora on different databases about the molecular and environmental origins of disease, into analytic frameworks, allowing the development of individualized, context-dependent diagnostics, and therapeutic approaches. In this regard, artificial intelligence and machine learning approaches can be used to build analytical models of complex disease aimed at prediction of personalized health conditions and outcomes. Such models must handle the wide heterogeneity of individuals in both their genetic predisposition and their social and environmental determinants. Computational approaches to medicine need to be able to efficiently manage, visualize and integrate, large datasets combining structure, and unstructured formats. This needs to be done while constrained by different levels of confidentiality, ideally doing so within a unified analytical architecture. Efficient data integration and management is key to the successful application of computational intelligence approaches to medicine. A number of challenges arise in the design of successful designs to medical data analytics under currently demanding conditions of performance in personalized medicine, while also subject to time, computational power, and bioethical constraints. Here, we will review some of these constraints and discuss possible avenues to overcome current challenges.

Evaluating mobile Health technology use among cancer caregivers in the digital era

Introduction

Digital health technology-based interventions have the potential to support cancer caregivers in caregiving responsibilities and in managing their own health and well-being. The objective of this study was to examine the association between caregiving characteristics and different types of digital health technologies used in a national sample of caregivers of patients undergoing hematopoietic cell transplantation (HCT).

Methods

We conducted an online, cross-sectional survey of 948 HCT caregivers.

Results

Spousal caregivers comprised nearly one-third of respondents (27.1%) with a median age of 59 years (range: 18-80 years), compared with parents (32.9%: 38 years), adult children (28.9%: 38 years), and other (11.1%; e.g. friend, other family member: 36 years). Almost two-thirds (65.4%) of all respondents reported using an app for fitness or step counting and 41.3% reported using a smartwatch. However, spousal caregivers were the least likely group to use mobile apps (0.72; P < 0.005) or smartwatches (OR = 0.46; P < 0.005) compared with parent caregivers in models adjusted for demographics and coping style. Caregiving for six months or greater was associated with the use of fewer apps compared with caregiving for less than six months in adjusted models (OR = 0.80, P < 0.005). Caregivers of patients receiving an allogeneic transplant (i.e. non-self-donor) used more apps on average than caregivers of patients receiving an autologous transplant (i.e. self-donor) in adjusted models (OR = 1.36, P < 0.005).

Conclusion

Digital health technologies reflect promising avenues for supporting cancer caregivers. While digital technologies are becoming increasingly pervasive, older caregivers remain an underserved population. Future research should integrate older adult caregivers in the co-design and development activities of technology-driven caregiver support products.

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.

[Digital Palliative Care]

Digital Palliative Care Abstract. Palliative care is becoming more and more digital. This article illuminates how digital approaches can help identify patients who qualify for palliative care offers and who wish to make use of them. Digital approaches can be used to monitor patients through apps and wearables, but digital methods are also becoming more important in psychosocial and spiritual support. One case demonstrates the therapeutic use of virtual reality. Work organization is digital, and teaching has also become digital during the corona crisis. In spite of all the advantages, however, the potential risks of digitization must also be considered.

Capacitive Sensing for Monitoring Stent Patency in the Central Airway

Central airway obstruction (CAO) is a respiratory disorder characterized by the blockage of the trachea and/or the main bronchi that can be life-threatening. Airway stenting is a palliative procedure for CAO commonly used given its efficacy. However, mucus impaction, secretion retention, and granulation tissue growth are known complications that can counteract the stent's benefits. To prevent these situations, patients are routinely brought into the hospital to check stent patency, incurring a burden for the patient and the health care system, unnecessarily when no problems are found. In this paper, we introduce a capacitive sensor embedded in a stent that can detect solid and colloidal obstructions in the stent, as such obstructions alter the capacitor's dielectric relative permittivity. In the case of colloidal obstructions (e.g., mucus), volumes as low as 0.1 ml can be detected. Given the small form factor of the sensor, it could be adapted to a variety of stent types without changing the standard bronchoscopy insertion method. The proposed system is a step forward in the development of smart airway stents that overcome the limitations of current stenting technology.Clinical Relevance- This establishes the foundation for smart stent technology to monitor stent patency as an alternative to rutinary bronchoscopies.

Using an Individual-Centered Approach to Gain Insights From Wearable Data in the Quantified Flu Platform: Netnography Study

BACKGROUND

Wearables have been used widely for monitoring health in general, and recent research results show that they can be used to predict infections based on physiological symptoms. To date, evidence has been generated in large, population-based settings. In contrast, the Quantified Self and Personal Science communities are composed of people who are interested in learning about themselves individually by using their own data, which are often gathered via wearable devices.

OBJECTIVE

This study aims to explore how a cocreation process involving a heterogeneous community of personal science practitioners can develop a collective self-tracking system for monitoring symptoms of infection alongside wearable sensor data.

METHODS

We engaged in a cocreation and design process with an existing community of personal science practitioners to jointly develop a working prototype of a web-based tool for symptom tracking. In addition to the iterative creation of the prototype (started on March 16, 2020), we performed a netnographic analysis to investigate the process of how this prototype was created in a decentralized and iterative fashion.

RESULTS

The Quantified Flu prototype allowed users to perform daily symptom reporting and was capable of presenting symptom reports on a timeline together with resting heart rates, body temperature data, and respiratory rates measured by wearable devices. We observed a high level of engagement; over half of the users (52/92, 56%) who engaged in symptom tracking became regular users and reported over 3 months of data each. Furthermore, our netnographic analysis highlighted how the current Quantified Flu prototype was a result of an iterative and continuous cocreation process in which new prototype releases sparked further discussions of features and vice versa.

CONCLUSIONS

As shown by the high level of user engagement and iterative development process, an open cocreation process can be successfully used to develop a tool that is tailored to individual needs, thereby decreasing dropout rates.

Gas Sensor Array Using a Hybrid Structure Based on Zeolite and Oxide Semiconductors for Multiple Bio-Gas Detection

Semiconductor-type gas sensors, composed of metal-oxide semiconductors and porous zeolite materials, are attractive devices for bio-gas detection, particularly when used as bio-gas sensors such as electronic nose application. Previous studies have shown such detection can be obtained with a separate gas concentrator and a sensor device using zeolites and oxide semiconductors of WO₃ nanoparticles. By applying the gas concentrator, porous molecular structures alter both the gas sensitivity and the selectivity, and even can be used to define the sensor characteristics. Based on such a gas sensor design, we investigated the properties of an array of three sensors made of a layer of WO₃ nanoparticles coated with zeolites with different interactions between gas molecule adsorption and desorption. The array was tested with four volatile organic compounds, each measured at different concentrations. The results confirm that the features of individual zeolites combined with the hybrid gas sensor behavior, along with the differences among the sensors, are sufficient for enabling the discrimination of volatile compounds when disregarding their concentration.

Monitoring beliefs and physiological measures in students at risk for COVID-19 using wearable sensors and smartphone technology: Protocol for a mobile health study

BACKGROUND

The COVID-19 pandemic has impacted lives significantly and greatly affected an already vulnerable population, college students, in relation to mental health and public safety. Social distancing and isolation have brought about challenges to student's mental health. Mobile health apps and wearable sensors may help to monitor students at risk for COVID-19 and support their mental well-being.

OBJECTIVE

Through the use of a wearable sensor and smartphone-based survey completion, this study aimed to monitor students at risk for COVID-19.

METHODS

We conducted a prospective study of students, undergraduate and graduate, at a public university in the Midwest. Students were instructed to download the Fitbit, Social Rhythms, and Roadmap 2.0 apps onto their personal mobile devices (Android or iOS). Subjects consented to provide up to 10 saliva samples during the study period. Surveys were administered through the Roadmap 2.0 app at five timepoints - at baseline, 1-month later, 2-months later, 3-months later, and at study completion. The surveys gathered information regarding demographics, COVID-19 diagnoses and symptoms, and mental health resilience, with the aim of documenting the impact of COVID-19 on the college student population.

RESULTS

This study enrolled 2,158 college students between September 2020 and January 2021. Subjects are currently being followed on-study for one academic year. Data collection and analysis are ongoing.

CONCLUSIONS

This study examined student health and well-being during the COVID-19 pandemic. It also assessed the feasibility of wearable sensor use and survey completion in a college student population, which may inform the role of our mobile health tools on student health and well-being. Finally, using wearable sensor data, biospecimen collection, and self-reported COVID-19 diagnosis, our results may provide key data towards the development of a model for the early prediction and detection of COVID-19.

CLINICALTRIAL

ClinicalTrials.gov NCT04766788.

Evaluating health technology engagement among family caregivers of patients undergoing hematopoietic cell transplantation

Purpose:

Digital health technology-based interventions have the potential to support caregivers in their caregiving responsibilities and in managing their own health and well-being. Designing digital health technologies to support caregivers of patients undergoing hematopoietic cell transplantation requires evaluating their engagement with these technologies. The objective of this study was to examine the association between caregiving characteristics and different types of digital health technologies used.

Methods:

We conducted an online cross-sectional, national survey of 948 unpaid family caregivers of patients undergoing hematopoietic cell transplantation.

Results:

Almost two-thirds (65.4%) of respondents reported using an app for fitness or step counting, while 41.3% reported using a smartwatch. The average number of apps used was 3.3 (range 0-9). In adjusted models, adult children who were caregivers (OR=5.82, p<0.005) and caregivers of another relative (OR=2.51, p<0.005) were significantly more likely to use a fitness tracker than caregivers of a child. Caregiving for six months or greater was associated with use of fewer apps compared with caregiving for less than six months in adjusted models (OR=0.80, p<0.005). Caregivers of patients receiving an allogeneic transplant used more apps on average than caregivers of patients receiving an autologous transplant, in adjusted (OR=1.36, p<0.005) models.

Conclusion:

Digital health technologies may reflect promising avenues for supporting caregivers of patients undergoing HCT. The rapid insurgence of telehealth, propelled by the current COVID-19 pandemic, emphasizes the need for a better understanding of digital health technology for future study design.

Monitoring Health Care Workers at Risk for COVID-19 Using Wearable Sensors and Smartphone Technology: Protocol for an Observational mHealth Study

BACKGROUND

Health care workers (HCWs) have been working on the front lines of the COVID-19 pandemic with high risks of viral exposure, infection, and transmission. Standard COVID-19 testing is insufficient to protect HCWs from these risks and prevent the spread of disease. Continuous monitoring of physiological data with wearable sensors, self-monitoring of symptoms, and asymptomatic COVID-19 testing may aid in the early detection of COVID-19 in HCWs and may help reduce further transmission among HCWs, patients, and families.

OBJECTIVE

By using wearable sensors, smartphone-based symptom logging, and biospecimens, this project aims to assist HCWs in self-monitoring COVID-19.

METHODS

We conducted a prospective, longitudinal study of HCWs at a single institution. The study duration was 1 year, wherein participants were instructed on the continuous use of two wearable sensors (Fitbit Charge 3 smartwatch and TempTraq temperature patches) for up to 30 days. Participants consented to provide biospecimens (ie, nasal swabs, saliva swabs, and blood) for up to 1 year from study entry. Using a smartphone app called Roadmap 2.0, participants entered a daily mood score, submitted daily COVID-19 symptoms, and completed demographic and health-related quality of life surveys at study entry and 30 days later. Semistructured qualitative interviews were also conducted at the end of the 30-day period, following completion of daily mood and symptoms reporting as well as continuous wearable sensor use.

RESULTS

A total of 226 HCWs were enrolled between April 28 and December 7, 2020. The last participant completed the 30-day study procedures on January 16, 2021. Data collection will continue through January 2023, and data analyses are ongoing.

CONCLUSIONS

Using wearable sensors, smartphone-based symptom logging and survey completion, and biospecimen collections, this study will potentially provide data on the prevalence of COVID-19 infection among HCWs at a single institution. The study will also assess the feasibility of leveraging wearable sensors and self-monitoring of symptoms in an HCW population.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04756869; https://clinicaltrials.gov/ct2/show/NCT04756869.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/29562.

Multiplex Molecular Point-of-Care Test for Syndromic Infectious Diseases

Point-of-care (POC) molecular diagnostics for clinical microbiology and virology has primarily focused on the detection of a single pathogen. More recently, it has transitioned into a comprehensive syndromic approach that employs multiplex capabilities, including the simultaneous detection of two or more pathogens. Multiplex POC tests provide higher accuracy to for actionable decisionmaking in critical care, which leads to pathogen-specific treatment and standardized usages of antibiotics that help prevent unnecessary processes. In addition, these tests can be simple enough to operate at the primary care level and in remote settings where there is no laboratory infrastructure. This review focuses on state-of-the-art multiplexed molecular point-of-care tests (POCT) for infectious diseases and efforts to overcome their limitations, especially related to inadequate throughput for the identification of syndromic diseases. We also discuss promising and imperative clinical POC approaches, as well as the possible hurdles of their practical applications as front-line diagnostic tests.

Biological variation: Understanding why it is so important?

This Review will describe the increasing importance of the concepts of biological variation to clinical chemists. The idea of comparison to 'reference' is fundamental in measurement. For the biological measurands, that reference is the relevant patient population, a clinical decision point based on a trial or an individual patient's previous results. The idea of using biological variation to set quality goals was then realised for setting Quality Control (QC) and External Quality Assurance (EQA) limits. The current phase of BV integration into practice is using Patient-Based Real-Time Quality Control (PBRTQC) and Patient Based Quality Assurance (PBQA) to detect a change in assay performance. The challenge of personalised medicine is to determine an individual reference interval. The Athletes Biological Passport may provide the solution.

Detecting and distinguishing tipping points using spectral early warning signals

Theory and observation tell us that many complex systems exhibit tipping points—thresholds involving an abrupt and irreversible transition to a contrasting dynamical regime. Such events are commonly referred to as critical transitions. Current research seeks to develop early warning signals (EWS) of critical transitions that could help prevent undesirable events such as ecosystem collapse. However, conventional EWS do not indicate the type of transition, since they are based on the generic phenomena of critical slowing down. For instance, they may fail to distinguish the onset of oscillations (e.g. Hopf bifurcation) from a transition to a distant attractor (e.g. Fold bifurcation). Moreover, conventional EWS are less reliable in systems with density-dependent noise. Other EWS based on the power spectrum (spectral EWS) have been proposed, but they rely upon spectral reddening, which does not occur prior to critical transitions with an oscillatory component. Here, we use Ornstein–Uhlenbeck theory to derive analytic approximations for EWS prior to each type of local bifurcation, thereby creating new spectral EWS that provide greater sensitivity to transition proximity; higher robustness to density-dependent noise and bifurcation type; and clues to the type of approaching transition. We demonstrate the advantage of applying these spectral EWS in concert with conventional EWS using a population model, and show that they provide a characteristic signal prior to two different Hopf bifurcations in data from a predator–prey chemostat experiment. The ability to better infer and differentiate the nature of upcoming transitions in complex systems will help humanity manage critical transitions in the Anthropocene Era.