Integrating Personalized Technology in Toxicology: Sensors, Smart Glass, and Social Media Applications in Toxicology Research

Discriminating cocaine use from other sympathomimetics using wearable electrocardiographic (ECG) sensors

BACKGROUND

Our group has established the feasibility of using on-body electrocardiographic (ECG) sensors to detect cocaine use in the human laboratory. The purpose of the current study was to test whether ECG sensors and features are capable of discriminating cocaine use from other non-cocaine sympathomimetics.

METHODS

Eleven subjects with cocaine use disorder wore the Zephyr BioHarness™ 3 chest band under six experimental (drug and non-drug) conditions, including 1) laboratory, intravenous cocaine self-administration, 2) after a single oral dose of methylphenidate, 3) during aerobic exercise, 4) during tobacco use (N=7 who smoked tobacco), and 5) during routine activities of daily inpatient living (unit activity). Three ECG-derived feature sets served as primary outcome measures, including 1) the RR interval (i.e., heart rate), 2) a group of ECG interval proxies (i.e., PR, QS, QT and QTc intervals), and 3) the full ECG waveform. Discriminatory power between cocaine and non-cocaine conditions for each of the three outcomes measures was expressed as the area under the receiver operating characteristics (AUROC) curve.

RESULTS

All three outcomes successfully discriminated cocaine use from unit activity, exercise, tobacco, and methylphenidate conditions with a mean AUROC values ranging from 0.66 to 0.99 and with least squares means values all statistically different/higher than 0.5 among all subjects [F(3, 99) = 3.38, p =0.02] and among those with tobacco use [F(4, 84) = 5.39, p = 0.0007].

CONCLUSIONS

These preliminary results support discriminatory power of wearable ECG sensors for detecting cocaine use.

Digital Transformation in Toxicology: Improving Communication and Efficiency in Risk Assessment

Toxicology is undergoing a digital revolution, with mobile apps, sensors, artificial intelligence (AI), and machine learning enabling better record-keeping, data analysis, and risk assessment. Additionally, computational toxicology and digital risk assessment have led to more accurate predictions of chemical hazards, reducing the burden of laboratory studies. Blockchain technology is emerging as a promising approach to increase transparency, particularly in the management and processing of genomic data related with food safety. Robotics, smart agriculture, and smart food and feedstock offer new opportunities for collecting, analyzing, and evaluating data, while wearable devices can predict toxicity and monitor health-related issues. The review article focuses on the potential of digital technologies to improve risk assessment and public health in the field of toxicology. By examining key topics such as blockchain technology, smoking toxicology, wearable sensors, and food security, this article provides an overview of how digitalization is influencing toxicology. As well as highlighting future directions for research, this article demonstrates how emerging technologies can enhance risk assessment communication and efficiency. The integration of digital technologies has revolutionized toxicology and has great potential for improving risk assessment and promoting public health.

Mapping precision public health definitions, terminology and applications: a scoping review protocol

INTRODUCTION

Precision public health is an emerging and evolving field. Academic communities are divided regarding terminology and definitions, and what the scope, parameters and goals of precision public health should include. This protocol summarises the procedure for a scoping review which aims to identify and describe definitions, terminology, uses of the term and concepts in current literature.

METHODS AND ANALYSIS

A scoping review will be undertaken to gather existing literature on precision public health. We will search CINAHL, PubMed, Scopus, Web of Science and Google Scholar, and include all documents published in English that mention precision public health. A critical discourse analysis of the resulting papers will generate an account of precision public health terminology, definitions and uses of the term and the use and meaning of language. The analysis will occur in stages: first, descriptive information will be extracted and descriptive statistics will be calculated in order to characterise the literature. Second, occurrences of the phrase 'precision public health' and alternative terms in documents will be enumerated and mapped, and definitions collected. The third stage of discourse analysis will involve analysis and interpretation of the meaning of precision public health, including the composition, organisation and function of discourses. Finally, discourse analysis of alternative phrases to precision public health will be undertaken. This will include analysis and interpretation of what alternative phrases to precision public health are used to mean, how the phrases relate to each other and how they are compared or contrasted to precision public health. Results will be grouped under headings according to how they answer the research questions.

ETHICS AND DISSEMINATION

No ethical approval will be required for the scoping review. Results of the scoping review will be used as part of a doctoral thesis, and may be published in journals, conference proceedings or elsewhere.

Integration of Extended Reality and a High-Fidelity Simulator in Team-Based Simulations for Emergency Scenarios

Wearable devices such as smart glasses are considered promising assistive tools for information exchange in healthcare settings. We aimed to evaluate the usability and feasibility of smart glasses for team-based simulations constructed using a high-fidelity simulator. Two scenarios of patients with arrhythmia were developed to establish a procedure for interprofessional interactions via smart glasses using 15-h simulation training. Three to four participants formed a team and played the roles of remote supporter or bed-side trainee with smart glasses. Usability, attitudes towards the interprofessional health care team and learning satisfaction were assessed. Using a 5-point Likert scale, from 1 (strongly disagree) to 5 (strongly agree), 31 participants reported that the smart glasses were easy to use (3.61 ± 0.95), that they felt confident during use (3.90 ± 0.87), and that that responded positively to long-term use (3.26 ± 0.89) and low levels of physical discomfort (1.96 ± 1.06). The learning satisfaction was high (4.65 ± 0.55), and most (84%) participants found the experience favorable. Key challenges included an unstable internet connection, poor resolution and display, and physical discomfort while using the smart glasses with accessories. We determined the feasibility and acceptability of smart glasses for interprofessional interactions within a team-based simulation. Participants responded favorably toward a smart glass-based simulation learning environment that would be applicable in clinical settings.

Deployment of a wearable biosensor system in the emergency department: a technical feasibility study

Wearable devices to detect changes in health status are increasingly adopted by consumers, yet hospitals remain slow to assimilate these devices into clinical practice. Despite the clear benefits of capturing clinical information in acutely ill patients, such technology remains difficult to implement in emergency medicine. To improve adoption, barriers must first be removed. In our technical feasibility and acceptability trial, we studied the deployment of a wearable wireless biosensor that collects physiological data. We enrolled 44 adult patients receiving care in an emergency department observation unit. After we consented patients for participation, we applied biosensors to their chest and collected basic demographic and clinical information. We then collected biosensor data on an isolated system and measured patient experience via an exit survey. Throughout this process we documented and studied technical challenges. Overall, the technology was feasible to deploy in the emergency department observation unit and was acceptable to participants. Such technologies have tremendous future operational and clinical implications in settings ranging from emergency to home-care.

Mobile Health Applications for Prenatal Assessment and Monitoring

BACKGROUND

A mobile health application is an exciting, fast-paced domain that is likely to improve prenatal care.

METHODS

In this narrative review, we summarise the use of mobile health applications in this setting with a special emphasis on both the benefits of remote monitoring devices and the potential pitfalls of their use, highlighting the need for robust regulations and guidelines before their widespread introduction into prenatal care.

RESULTS

Remote monitoring devices for four areas of prenatal care are reported: (1) cardio-tocography; (2) blood glucose levels; (3) blood pressure; and (4) prenatal ultrasound. The majority of publications are pilot projects on remote consultation, education, coaching, screening, monitoring and selective booking, mostly reporting potential medical and/or economic benefits by mobile health applications over conventional care for very specific situations, indications and locations, but not always generalizable.

CONCLUSIONS

Despite the potential advantages of these devices, some caution must be taken when implementing this technology into routine daily practice. To date, the majority of published research on mobile health in the prenatal setting consists of observational studies and there is a need for high-quality randomized controlled trials to confirm the reported clinical and economic benefits as well as the safety of this technology. There is also a need for guidance and governance on the development and validation of new apps and devices and for the implementation of mobile health technology into healthcare systems in both high and low-income settings. Finally, digital communication technologies offer perspectives towards exploration and development of the very new domain of tele-pharmacology.

Combining ecological momentary assessment with objective, ambulatory measures of behavior and physiology in substance-use research

Whereas substance-use researchers have long combined self-report with objective measures of behavior and physiology inside the laboratory, developments in mobile/wearable electronic technology are increasingly allowing for the collection of both subjective and objective information in participants' daily lives. For self-report, ecological momentary assessment (EMA), as implemented on contemporary smartphones or personal digital assistants, can provide researchers with near-real-time information on participants' behavior and mood in their natural environments. Data from portable/wearable electronic sensors measuring participants' internal and external environments can be combined with EMA (e.g., by timestamps recorded on questionnaires) to provide objective information useful in determining the momentary context of behavior and mood and/or validating participants' self-reports. Here, we review three objective ambulatory monitoring techniques that have been combined with EMA, with a focus on detecting drug use and/or measuring the behavioral or physiological correlates of mental events (i.e., emotions, cognitions): (1) collection and processing of biological samples in the field to measure drug use or participants' physiological activity (e.g., hypothalamic-pituitary-adrenal axis activity); (2) global positioning system (GPS) location information to link environmental characteristics (disorder/disadvantage, retail drug outlets) to drug use and affect; (3) ambulatory electronic physiological monitoring (e.g., electrocardiography) to detect drug use and mental events, as advances in machine learning algorithms make it possible to distinguish target changes from confounds (e.g., physical activity). Finally, we consider several other mobile/wearable technologies that hold promise to be combined with EMA, as well as potential challenges faced by researchers working with multiple mobile/wearable technologies simultaneously in the field.

The Role of Technology-Based Interventions for Substance Use Disorders in Primary Care: A Review of the Literature

The burden of alcohol and drug use disorders (substance use disorders [SUDs]) has intensified efforts to expand access to cost-effective psychosocial interventions and pharmacotherapies. This article provides an overview of technology-based interventions (eg, computer-based and Web-based interventions, text messaging, interactive voice recognition, smartphone apps, and emerging technologies) that are extending the reach of effective addiction treatments both in substance use treatment and primary care settings. It discusses the efficacy of existing technology-based interventions for SUDs, prospects for emerging technologies, and special considerations when integrating technologies in primary care (eg, privacy and regulatory protocols) to enhance the management of SUDs.

Big Data's Role in Precision Public Health

Precision public health is an emerging practice to more granularly predict and understand public health risks and customize treatments for more specific and homogeneous subpopulations, often using new data, technologies, and methods. Big data is one element that has consistently helped to achieve these goals, through its ability to deliver to practitioners a volume and variety of structured or unstructured data not previously possible. Big data has enabled more widespread and specific research and trials of stratifying and segmenting populations at risk for a variety of health problems. Examples of success using big data are surveyed in surveillance and signal detection, predicting future risk, targeted interventions, and understanding disease. Using novel big data or big data approaches has risks that remain to be resolved. The continued growth in volume and variety of available data, decreased costs of data capture, and emerging computational methods mean big data success will likely be a required pillar of precision public health into the future. This review article aims to identify the precision public health use cases where big data has added value, identify classes of value that big data may bring, and outline the risks inherent in using big data in precision public health efforts.