Analysis of Fluoride-Free Content on Twitter: Topic Modeling Study.

BACKGROUND: Although social media has the potential to spread misinformation, it can also be a valuable tool for elucidating the social factors that contribute to the onset of negative beliefs. As a result, data mining has become a widely used technique in infodemiology and infoveillance research to combat misinformation effects. On the other hand, there is a lack of studies that specifically aim to investigate misinformation about fluoride on Twitter. Web-based individual concerns on the side effects of fluoridated oral care products and tap water stimulate the emergence and propagation of convictions that boost antifluoridation activism. In this sense, a previous content analysis-driven study demonstrated that the term fluoride-free was frequently associated with antifluoridation interests. OBJECTIVE: This study aimed to analyze "fluoride-free" tweets regarding their topics and frequency of publication over time. METHODS: A total of 21,169 tweets published in English between May 2016 and May 2022 that included the keyword "fluoride-free" were retrieved by the Twitter application programming interface. Latent Dirichlet allocation (LDA) topic modeling was applied to identify the salient terms and topics. The similarity between topics was calculated through an intertopic distance map. Moreover, an investigator manually assessed a sample of tweets depicting each of the most representative word groups that determined specific issues. Lastly, additional data visualization was performed regarding the total count of each topic of fluoride-free record and its relevance over time, using Elastic Stack software. RESULTS: We identified 3 issues by applying the LDA topic modeling: "healthy lifestyle" (topic 1), "consumption of natural/organic oral care products" (topic 2), and "recommendations for using fluoride-free products/measures" (topic 3). Topic 1 was related to users' concerns about leading a healthier lifestyle and the potential impacts of fluoride consumption, including its hypothetical toxicity. Complementarily, topic 2 was associated with users' personal interests and perceptions of consuming natural and organic fluoride-free oral care products, whereas topic 3 was linked to users' recommendations for using fluoride-free products (eg, switching from fluoridated toothpaste to fluoride-free alternatives) and measures (eg, consuming unfluoridated bottled water instead of fluoridated tap water), comprising the propaganda of dental products. Additionally, the count of tweets on fluoride-free content decreased between 2016 and 2019 but increased again from 2020 onward. CONCLUSIONS: Public concerns toward a healthy lifestyle, including the adoption of natural and organic cosmetics, seem to be the main motivation of the recent increase of "fluoride-free" tweets, which can be boosted by the propagation of fluoride falsehoods on the web. Therefore, public health authorities, health professionals, and legislators should be aware of the spread of fluoride-free content on social media to create and implement strategies against their potential health damage for the population.

Estimating the Effects of Non-Pharmaceutical Interventions and Population Mobility on Daily COVID-19 Cases: Evidence from Ontario.

This study uses coronavirus disease 2019 (COVID-19) case counts and Google mobility data for 12 of Ontario's largest Public Health Units from Spring 2020 until the end of January 2021 to evaluate the effects of non-pharmaceutical interventions (NPIs; policy restrictions on business operations and social gatherings) and population mobility on daily cases. Instrumental variables (IV) estimation is used to account for potential simultaneity bias, because both daily COVID-19 cases and NPIs are dependent on lagged case numbers. IV estimates based on differences in lag lengths to infer causal estimates imply that the implementation of stricter NPIs and indoor mask mandates are associated with reductions in COVID-19 cases. Moreover, estimates based on Google mobility data suggest that increases in workplace attendance are correlated with higher case counts. Finally, from October 2020 to January 2021, daily Ontario forecasts from Box-Jenkins time-series models are more accurate than official forecasts and forecasts from a susceptible-infected-removed epidemiology model.

Cette étude cherche à évaluer les effets des interventions non pharmaceutiques (INPs; restrictions sur les activités commerciales et rassemblements sociaux) et de la mobilité de la population sur le nombre de cas d'infection par jour, en utilisant les nombres de cas d'infection par la maladie à coronavirus 2019 (COVID-19) et les données de mobilité de Google pour 12 des plus grands Bureaux de Santé publique de l'Ontario entre le printemps 2020 et la fin janvier 2021. La méthode des variables instrumentales (VI) permet de rendre compte d'un biais potentiel de simultanéité puisque les taux quotidiens de COVID-19 et les INPs dépendent, tous les deux, du nombre de cas décalés. Les estimations par les VI basées sur les différences de durée des décalages d'ajustement pour inférer des estimations causales impliquent que de plus strictes INPs et le port obligatoire du masque dans les endroits fermés sont associés à une réduction de cas d'infection. Par ailleurs, Les estimations basées sur les données de mobilité de Google montrent que la présence accrue sur le lieu du travail est corrélée avec un plus grand nombre de cas d'infection. Finalement, d'octobre 2020 à Janvier 2021, les prévisions faites à partir de modèles de Box-Jenkins en série chronologique s'avèrent plus précises que les prévisions officielles et que celles utilisant le modèle épidémiologique susceptible ­ infecté ­ retiré.

Biobehavioral Aspects of the COVID-19 Pandemic: A Review.

OBJECTIVE: This review highlights the scope and significance of the coronavirus disease 2019 (COVID-19) pandemic with a focus on biobehavioral aspects and critical avenues for research. METHODS: A narrative review of the published research literature was undertaken, highlighting major empirical findings emerging during the first and second waves of the COVID-19 pandemic. RESULTS: Interactions among biological, behavioral, and societal processes were prominent across all regions of the globe during the first year of the COVID-19 emergency. Affective, cognitive, behavioral, socioeconomic, and technological factors all played a significant role in the spread of infection, response precautions, and outcomes of mitigation efforts. Affective symptoms, suicidality, and cognitive dysfunction have been widely described consequences of the infection, the economic fallout, and the necessary public health mitigation measures themselves. The impact of COVID-19 may be especially serious for those living with severe mental illness and/or chronic medical diseases, given the confluence of several adverse factors in a manner that appears to have syndemic potential. CONCLUSIONS: The COVID-19 pandemic has made clear that biological and behavioral factors interact with societal processes in the infectious disease context. Empirical research examining mechanistic pathways from infection and recovery to immunological, behavioral, and emotional outcomes is critical. Examination of how emotional and behavioral factors relate to the pandemic-both as causes and as effects-can provide valuable insights that can improve management of the current pandemic and future pandemics to come.

The Service of Research Analytics to Optimize Digital Health Evidence Generation: Multilevel Case Study.

BACKGROUND: The widespread adoption of digital health interventions for chronic disease self-management has catalyzed a paradigm shift in the selection of methodologies used to evidence them. Recently, the application of digital health research analytics has emerged as an efficient approach to evaluate these data-rich interventions. However, there is a growing mismatch between the promising evidence base emerging from analytics mediated trials and the complexity of introducing these novel research methods into evaluative practice. OBJECTIVE: This study aimed to generate transferable insights into the process of implementing research analytics to evaluate digital health interventions. We sought to answer the following two research questions: (1) how should the service of research analytics be designed to optimize digital health evidence generation? and (2) what are the challenges and opportunities to scale, spread, and sustain this service in evaluative practice? METHODS: We conducted a qualitative multilevel embedded single case study of implementing research analytics in evaluative practice that comprised a review of the policy and regulatory climate in Ontario (macro level), a field study of introducing a digital health analytics platform into evaluative practice (meso level), and interviews with digital health innovators on their perceptions of analytics and evaluation (microlevel). RESULTS: The practice of research analytics is an efficient and effective means of supporting digital health evidence generation. The introduction of a research analytics platform to evaluate effective engagement with digital health interventions into a busy research lab was ultimately accepted by research staff, became routinized in their evaluative practice, and optimized their existing mechanisms of log data analysis and interpretation. The capacity for research analytics to optimize digital health evaluations is highest when there is (1) a collaborative working relationship between research client and analytics service provider, (2) a data-driven research agenda, (3) a robust data infrastructure with clear documentation of analytic tags, (4) in-house software development expertise, and (5) a collective tolerance for methodological change. CONCLUSIONS: Scientific methods and practices that can facilitate the agile trials needed to iterate and improve digital health interventions warrant continued implementation. The service of research analytics may help to accelerate the pace of digital health evidence generation and build a data-rich research infrastructure that enables continuous learning and evaluation.

The usability of ventilators: a comparative evaluation of use safety and user experience.

BACKGROUND: The design complexity of critical care ventilators (CCVs) can lead to use errors and patient harm. In this study, we present the results of a comparison of four CCVs from market leaders, using a rigorous methodology for the evaluation of use safety and user experience of medical devices. METHODS: We carried out a comparative usability study of four CCVs: Hamilton G5, Puritan Bennett 980, Maquet SERVO-U, and Dräger Evita V500. Forty-eight critical care respiratory therapists participated in this fully counterbalanced, repeated measures study. Participants completed seven clinical scenarios composed of 16 tasks on each ventilator. Use safety was measured by percentage of tasks with use errors or close calls (UE/CCs). User experience was measured by system usability and workload metrics, using the Post-Study System Usability Questionnaire (PSSUQ) and the National Aeronautics and Space Administration Task Load Index (NASA-TLX). RESULTS: Nine of 18 post hoc contrasts between pairs of ventilators were significant after Bonferroni correction, with effect sizes between 0.4 and 1.09 (Cohen's d). There were significantly fewer UE/CCs with SERVO-U when compared to G5 (p = 0.044) and V500 (p = 0.020). Participants reported higher system usability for G5 when compared to PB980 (p = 0.035) and higher system usability for SERVO-U when compared to G5 (p < 0.001), PB980 (p < 0.001), and V500 (p < 0.001). Participants reported lower workload for G5 when compared to PB980 (p < 0.001) and lower workload for SERVO-U when compared to PB980 (p < 0.001) and V500 (p < 0.001). G5 scored better on two of nine possible comparisons; SERVO-U scored better on seven of nine possible comparisons. Aspects influencing participants' performance and perception include the low sensitivity of G5's touchscreen and the positive effect from the quality of SERVO-U's user interface design. CONCLUSIONS: This study provides empirical evidence of how four ventilators from market leaders compare and highlights the importance of medical technology design. Within the boundaries of this study, we can infer that SERVO-U demonstrated the highest levels of use safety and user experience, followed by G5. Based on qualitative data, differences in outcomes could be explained by interaction design, quality of hardware components used in manufacturing, and influence of consumer product technology on users' expectations.

Technological aspects of hospital communication challenges: an observational study.

OBJECTIVE: To gain insights into how technological communication tools impact effective communication among clinicians, which is critical for patient safety. DESIGN: This multi-site observational study analyzes inter-clinician communication and interaction with information technology, with a focus on the critical process of patient transfer from the Emergency Department to General Internal Medicine. SETTING: Mount Sinai Hospital, Sunnybrook Health Sciences Centre and Toronto General Hospital. PARTICIPANTS: At least five ED and general internal medicine nurses and physicians directly involved in patient transfers were observed on separate occasions at each institution. INTERVENTIONS: N/A. MAIN OUTCOME MEASURES: N/A. RESULTS: The study provides insight into clinician workflow, evaluates current hospital communication systems and identifies key issues affecting communication: interruptions, issues with numeric pagers, lack of integrated communication tools, lack of awareness of consultation status, inefficiencies related to the paper chart, unintuitive user interfaces, mixed use of electronic and paper systems and lack of up-to-date contact information. It also identifies design trade-offs to be negotiated: synchronous communication vs. reducing interruptions, notification of patient status vs. reducing interruptions and speed vs. quality of handovers. CONCLUSIONS: The issues listed should be considered in the design of new technology for hospital communications.

What is the impact of artificial intelligence-based chatbots on infodemic management?

Artificial intelligence (AI) chatbots have the potential to revolutionize online health information-seeking behavior by delivering up-to-date information on a wide range of health topics. They generate personalized responses to user queries through their ability to process extensive amounts of text, analyze trends, and generate natural language responses. Chatbots can manage infodemic by debunking online health misinformation on a large scale. Nevertheless, system accuracy remains technically challenging. Chatbots require training on diverse and representative datasets, security to protect against malicious actors, and updates to keep up-to-date on scientific progress. Therefore, although AI chatbots hold significant potential in assisting infodemic management, it is essential to approach their outputs with caution due to their current limitations.

Revealing the Mysteries of Population Mobility Amid the COVID-19 Pandemic in Canada: Comparative Analysis With Internet of Things-Based Thermostat Data and Google Mobility Insights.

BACKGROUND: The COVID-19 pandemic necessitated public health policies to limit human mobility and curb infection spread. Human mobility, which is often underestimated, plays a pivotal role in health outcomes, impacting both infectious and chronic diseases. Collecting precise mobility data is vital for understanding human behavior and informing public health strategies. Google's GPS-based location tracking, which is compiled in Google Mobility Reports, became the gold standard for monitoring outdoor mobility during the pandemic. However, indoor mobility remains underexplored. OBJECTIVE: This study investigates in-home mobility data from ecobee's smart thermostats in Canada (February 2020 to February 2021) and compares it directly with Google's residential mobility data. By assessing the suitability of smart thermostat data, we aim to shed light on indoor mobility patterns, contributing valuable insights to public health research and strategies. METHODS: Motion sensor data were acquired from the ecobee "Donate Your Data" initiative via Google's BigQuery cloud platform. Concurrently, residential mobility data were sourced from the Google Mobility Report. This study centered on 4 Canadian provinces-Ontario, Quebec, Alberta, and British Columbia-during the period from February 15, 2020, to February 14, 2021. Data processing, analysis, and visualization were conducted on the Microsoft Azure platform using Python (Python Software Foundation) and R programming languages (R Foundation for Statistical Computing). Our investigation involved assessing changes in mobility relative to the baseline in both data sets, with the strength of this relationship assessed using Pearson and Spearman correlation coefficients. We scrutinized daily, weekly, and monthly variations in mobility patterns across the data sets and performed anomaly detection for further insights. RESULTS: The results revealed noteworthy week-to-week and month-to-month shifts in population mobility within the chosen provinces, aligning with pandemic-driven policy adjustments. Notably, the ecobee data exhibited a robust correlation with Google's data set. Examination of Google's daily patterns detected more pronounced mobility fluctuations during weekdays, a trend not mirrored in the ecobee data. Anomaly detection successfully identified substantial mobility deviations coinciding with policy modifications and cultural events. CONCLUSIONS: This study's findings illustrate the substantial influence of the Canadian stay-at-home and work-from-home policies on population mobility. This impact was discernible through both Google's out-of-house residential mobility data and ecobee's in-house smart thermostat data. As such, we deduce that smart thermostats represent a valid tool for facilitating intelligent monitoring of population mobility in response to policy-driven shifts.

Application of artificial intelligence in active assisted living for aging population in real-world setting with commercial devices - A scoping review.

BACKGROUND: The aging population is steadily increasing, posing new challenges and opportunities for healthcare systems worldwide. Technological advancements, particularly in commercially available Active Assisted Living devices, offer a promising alternative. These readily accessible products, ranging from smartwatches to home automation systems, are often equipped with Artificial Intelligence capabilities that can monitor health metrics, predict adverse events, and facilitate a safer living environment. However, there is no review exploring how Artificial Intelligence has been integrated into commercially available Active Assisted Living technologies, and how these devices monitor health metrics and provide healthcare solutions in a real-world environment for healthy aging. This review is essential because it fills a knowledge gap in understanding AI's integration in Active Assisted Living technologies in promoting healthy aging in real-world settings, identifying key issues that require to be addressed in future studies. OBJECTIVE: The aim of this overview is to outline current understanding, identify potential research opportunities, and highlight research gaps from published studies regarding the use of Artificial Intelligence in commercially available Active Assisted Living technologies that assists older individuals aging at home. METHODS: A comprehensive search was conducted in six databases-PubMed, CINAHL, IEEE Xplore, Scopus, ACM Digital Library, and Web of Science-to identify relevant studies published over the past decade from 2013 to 2024. Our methodology adhered to the PRISMA extension for scoping reviews to ensure rigor and transparency throughout the review process. After applying predefined inclusion and exclusion criteria on 825 retrieved articles, a total of 64 papers were included for analysis and synthesis. RESULTS: Several trends emerged from our analysis of the 64 selected papers. A majority of the work (39/64, 61%) was published after the year 2020. Geographically, most of the studies originated from East Asia and North America (36/64, 56%). The primary application goal of Artificial Intelligence in the reviewed literature was focused on activity recognition (34/64, 53%), followed by daily monitoring (10/64, 16%). Methodologically, tree-based and neural network-based approaches were the most prevalent Artificial Intelligence algorithms used in studies (32/64, 50% and 31/64, 48% respectively). A notable proportion of the studies (32/64, 50%) carried out their research using specially designed smart home testbeds that simulate the conditions in real-world. Moreover, ambient technology was a common thread (49/64, 77%), with occupancy-related data (such as motion and electrical appliance usage logs) and environmental sensors (indicators like temperature and humidity) being the most frequently used. CONCLUSION: Our results suggest that Artificial Intelligence has been increasingly deployed in the real-world Active Assisted Living context over the past decade, offering a variety of applications aimed at healthy aging and facilitating independent living for the older adults. A wide range of smart home indicators were leveraged for comprehensive data analysis, exploring and enhancing the potentials and effectiveness of solutions. However, our review has identified multiple research gaps that need further investigation. First, most research has been conducted in controlled testbed environments, leaving a lack of real-world applications that could validate the technologies' efficacy and scalability. Second, there is a noticeable absence of research leveraging cloud technology, an essential tool for large-scale deployment and standardized data collection and management. Future work should prioritize these areas to maximize the potential benefits of Artificial Intelligence in Active Assisted Living settings.

Health Monitoring Using Smart Home Technologies: Scoping Review.

BACKGROUND: The Internet of Things (IoT) has become integrated into everyday life, with devices becoming permanent fixtures in many homes. As countries face increasing pressure on their health care systems, smart home technologies have the potential to support population health through continuous behavioral monitoring. OBJECTIVE: This scoping review aims to provide insight into this evolving field of research by surveying the current technologies and applications for in-home health monitoring. METHODS: Peer-reviewed papers from 2008 to 2021 related to smart home technologies for health care were extracted from 4 databases (PubMed, Scopus, ScienceDirect, and CINAHL); 49 papers met the inclusion criteria and were analyzed. RESULTS: Most of the studies were from Europe and North America. The largest proportion of the studies were proof of concept or pilot studies. Approximately 78% (38/49) of the studies used real human participants, most of whom were older females. Demographic data were often missing. Nearly 60% (29/49) of the studies reported on the health status of the participants. Results were primarily reported in engineering and technology journals. Almost 62% (30/49) of the studies used passive infrared sensors to report on motion detection where data were primarily binary. There were numerous data analysis, management, and machine learning techniques employed. The primary challenges reported by authors were differentiating between multiple participants in a single space, technology interoperability, and data security and privacy. CONCLUSIONS: This scoping review synthesizes the current state of research on smart home technologies for health care. We were able to identify multiple trends and knowledge gaps-in particular, the lack of collaboration across disciplines. Technological development dominates over the human-centric part of the equation. During the preparation of this scoping review, we noted that the health care research papers lacked a concrete definition of a smart home, and based on the available evidence and the identified gaps, we propose a new definition for a smart home for health care. Smart home technology is growing rapidly, and interdisciplinary approaches will be needed to ensure integration into the health sector.

Cloud Native Remote Monitoring Data Ecosystem for Aging Population based on Commercial AAL Sensors.

In the recent years, Active Assisted Living (AAL) technologies used for autonomous tracking and activity recognition have started to play major roles in geriatric care. From fall detection to remotely monitoring behavioral patterns, vital functions and collection of air quality data, AAL has become pervasive in the modern era of independent living for the elderly section of the population. However, even with the current rate of progress, data access and data reliability has become a major hurdle especially when such data is intended to be used in new age modelling approaches such as those using machine learning. This paper presents a comprehensive data ecosystem comprising remote monitoring AAL sensors along with extensive focus on cloud native system architecture, secured and confidential access to data with easy data sharing. Results from a validation study illustrate the feasibility of using this system for remote healthcare surveillance. The proposed system shows great promise in multiple fields from various AAL studies to development of data driven policies by local governments in promoting healthy lifestyles for the elderly alongside a common data repository that can be beneficial to other research communities worldwide.Clinical Relevance- This study creates a cloud-based smart home data ecosystem, which can achieve the remote healthcare monitoring for aging population, enabling them to live more independently and decreasing hospital admission rates.

Exploring the Role of Active Assisted Living in the Continuum of Care for Older Adults: Thematic Analysis.

BACKGROUND: Active assisted living (AAL) refers to systems designed to improve the quality of life, aid in independence, and create healthier lifestyles for those who need assistance at any stage of their lives. As the population of older adults in Canada grows, there is a pressing need for nonintrusive, continuous, adaptable, and reliable health monitoring tools to support aging in place and reduce health care costs. AAL has great potential to support these efforts with the wide variety of solutions currently available; however, additional work is required to address the concerns of care recipients and their care providers with regard to the integration of AAL into care. OBJECTIVE: This study aims to work closely with stakeholders to ensure that the recommendations for system-service integrations for AAL aligned with the needs and capacity of health care and allied health systems. To this end, an exploratory study was conducted to understand the perceptions of, and concerns with, AAL technology use. METHODS: A total of 18 semistructured group interviews were conducted with stakeholders, with each group comprising several participants from the same organization. These participant groups were categorized into care organizations, technology development organizations, technology integration organizations, and potential care recipient or patient advocacy groups. The results of the interviews were coded using a thematic analysis to identify future steps and opportunities regarding AAL. RESULTS: The participants discussed how the use of AAL systems may lead to improved support for care recipients through more comprehensive monitoring and alerting, greater confidence in aging in place, and increased care recipient empowerment and access to care. However, they also raised concerns regarding the management and monetization of data emerging from AAL systems as well as general accountability and liability. Finally, the participants discussed potential barriers to the use and implementation of AAL systems, especially addressing the question of whether AAL systems are even worth it considering the investment required and encroachment on privacy. Other barriers raised included issues with the institutional decision-making process and equity. CONCLUSIONS: Better definition of roles is needed in terms of who can access the data and who is responsible for acting on the gathered data. It is important for stakeholders to understand the trade-off between using AAL technologies in care settings and the costs of AAL technologies, including the loss of patient privacy and control. Finally, further work is needed to address the gaps, explore the equity in AAL access, and develop a data governance framework for AAL in the continuum of care.

Ethical principles for infodemiology and infoveillance studies concerning infodemic management on social media.

Big data originating from user interactions on social media play an essential role in infodemiology and infoveillance outcomes, supporting the planning and implementation of public health actions. Notably, the extrapolation of these data requires an awareness of different ethical elements. Previous studies have investigated and discussed the adoption of conventional ethical approaches in the contemporary public health digital surveillance space. However, there is a lack of specific ethical guidelines to orient infodemiology and infoveillance studies concerning infodemic on social media, making it challenging to design digital strategies to combat this phenomenon. Hence, it is necessary to explore if traditional ethical pillars can support digital purposes or whether new ones must be proposed since we are confronted with a complex online misinformation scenario. Therefore, this perspective provides an overview of the current scenario of ethics-related issues of infodemiology and infoveillance on social media for infodemic studies.

Development of an eHealth Tool for Capturing and Analyzing the Immune-related Adverse Events (irAEs) in Cancer Treatment.

Introduction: Immunotherapy has revolutionized the treatment of many different types of cancer, but it is associated with a myriad of immune-related adverse events (irAEs). Patient-reported outcome (PRO) measures have been identified as valuable tools for continuously collecting patient-centered data and are frequently used in oncology trials. However, few studies still research an ePRO follow-up approach on patients treated with Immunotherapy, potentially reflecting a lack of support services for this population. Methods: The team co-developed a digital platform (V-Care) using ePROs to create a new follow-up pathway for cancer patients receiving immunotherapy. To operationalize the first 3 phases of the CeHRes roadmap, we employed multiple methods that were integrated throughout the development process, rather than being performed in a linear fashion. The teams employed an agile approach in a dynamic and iterative manner, engaging key stakeholders throughout the process. Results: The development of the application was categorized into 2 phases: "user interface" (UI) and "user experience" (UX) designs. In the first phase, the pages of the application were segmented into general categories, and feedback from all stakeholders was received and used to modify the application. In phase 2, mock-up pages were developed and sent to the Figma website. Moreover, the Android Package Kit (APK) of the application was installed and tested multiple times on a mobile phone to proactively detect and fix any errors. After resolving some technical issues and adjusting errors on the Android version to improve the user experience, the iOS version of the application was developed. Discussion: By incorporating the latest technological developments, V-Care has enabled cancer patients to have access to more comprehensive and personalized care, allowing them to better manage their condition and be better informed about their health decisions. These advances have also enabled healthcare professionals to be better equipped with the knowledge and tools to provide more effective and efficient care. In addition, the advances in V-Care technology have allowed patients to connect with their healthcare providers more easily, providing a platform to facilitate communication and collaboration. Although usability testing is necessary to evaluate the efficacy and user experience of the app, it can be a significant investment of time and resources. Conclusion: The V-Care platform can be used to investigate the reported symptoms experienced by cancer patients receiving Immune checkpoint inhibitors (ICIs) and to compare them with the results from clinical trials. Furthermore, the project will utilize ePRO tools to collect symptoms from patients and provide insight into whether the reported symptoms are linked to the treatment. Clinical Relevance: V-Care provides a secure, easy-to-use interface for patient-clinician communication and data exchange. Its clinical system stores and manages patient data in a secure environment, while its clinical decision support system helps clinicians make decisions that are more informed, efficient, and cost-effective. This system has the potential to improve patient safety and quality of care, while also helping to reduce healthcare costs.

Exploring the dynamic transitions of polysubstance use patterns among Canadian youth using Latent Markov Models on COMPASS data.

Background: Understanding what factors lead to youth polysubstance use (PSU) patterns and how the transitions between use patterns can inform the design and implementation of PSU prevention programs. We explore the dynamics of PSU patterns from a large cohort of Canadian secondary school students using machine learning techniques. Methods: We employed a multivariate latent Markov model (LMM) on COMPASS data, with a linked sample (N = 8824) of three-annual waves, Wave I (WI, 2016-17, as baseline), Wave II (WII, 2017-18), and Wave III (WIII, 2018-19). Substance use indicators, i.e., cigarette, e-cigarette, alcohol and marijuana, were self-reported and were categorized into never/occasional/current use. Outcomes: Four distinct use patterns were identified: no-use (S1), single-use of alcohol (S2), dual-use of e-cigarettes and alcohol (S3), and multi-use (S4). S1 had the highest prevalence (60.5%) at WI, however, S3 became the prominent use pattern (32.5%) by WIII. Most students remained in the same subgroup over time, particularly S4 had the highest transition probability (0.87) across the three-wave. With time, those who transitioned typically moved towards a higher use pattern, with the most and least likely transition occurring S2→S3 (0.45) and S3→S2 (<0.01), respectively. Among all covariates being examined, truancy, being measured by the # of classes skipped, significantly affected transition probabilities from any low→high (e.g., ORS2→S4 = 2.41, 95% CI [2.11, 2.72], p < 0.00001) and high→low (e.g., ORS3→S1 = 0.38, 95% CI [0.33, 0.44], p < 0.00001) use directions over time. Older students, blacks (vs. whites), and breakfast eaters were less likely to transition from low→high use direction. Students with more weekly allowance, with more friends that smoked, longer sedentary time, and attended attended school unsupportive to resist or quit drug/alcohol were more likely to transition from low→high use direction. Except for truancy, all other covariates had inconsistent effects on the transition probabilities from the high→low use direction. Interpretation: This is the first study to ascertain the dynamics of use patterns and factors in youth PSU utilizing LMM with population-based longitudinal health surveys, providing evidence in developing programs to prevent youth PSU. Funding: The Applied Health Sciences scholarship; the Microsoft AI for Good grant; the Canadian Institutes of Health Research, Health Canada, the Canadian Centre on Substance Abuse, the SickKids Foundation, the Ministère de la Santé et des Services sociaux of the province of Québec.

Occurrence detection and selection procedures in healthcare facilities: a comparison across Canada and Brazil.

Healthcare institutions face high levels of risk on a daily basis. Efforts have been made to address these risks and turn this complex environment into a safer environment for patients, staff, and visitors. However, healthcare institutions need more advanced risk management tools to achieve the safety levels currently seen in other industries. One of these potential tools is occurrence investigation systems. In order to be investigated, occurrences must be detected and selected for investigation, since not all institutions have enough resources to investigate all occurrences. A survey was conducted in healthcare institutions in Canada and Brazil to evaluate currently used risk management tools, the difficulties faced, and the possibilities for improvement. The findings include detectability difficulties, lack of resources, lack of support, and insufficient staff involvement.

Situation awareness and risk management understanding the notification issues.

Healthcare institutions are known to be risky environments that still lag behind other industries in the development and application of risk management tools. Awareness of risk is an important aspect of a risk management program. People depend on high awareness to take precautions to manage risk. The Situation Awareness framework describes how a person perceives elements of the environment, comprehends and projects its actions into the future, and analyzes the cognitive process used. Consequently, it allows the integration of the cognitive model and the risk assessment model into one single framework, provides a means of examining if the risk awareness is calibrated to the true risk levels of the institutions, and a better understanding of the issues with adverse events notification systems. In this paper we discuss how the situation awareness model can be used in the assessment of risk awareness, for understanding risk awareness and safety culture, and finally, for designing more effective risk management systems. For the purpose of this paper, we focus on the adverse event notification system.

Patient-Generated Data Analytics of Health Behaviors of People Living With Type 2 Diabetes: Scoping Review.

BACKGROUND: Complications due to type 2 diabetes (T2D) can be mitigated through proper self-management that can positively change health behaviors. Technological tools are available to help people living with, or at risk of developing, T2D to manage their condition, and such tools provide a large repository of patient-generated health data (PGHD). Analytics can provide insights into the health behaviors of people living with T2D. OBJECTIVE: The aim of this review is to investigate what can be learned about the health behaviors of those living with, or at risk of developing, T2D through analytics from PGHD. METHODS: A scoping review using the Arksey and O'Malley framework was conducted in which a comprehensive search of the literature was conducted by 2 reviewers. In all, 3 electronic databases (PubMed, IEEE Xplore, and ACM Digital Library) were searched using keywords associated with diabetes, behaviors, and analytics. Several rounds of screening using predetermined inclusion and exclusion criteria were conducted, after which studies were selected. Critical examination took place through a descriptive-analytical narrative method, and data extracted from the studies were classified into thematic categories. These categories reflect the findings of this study as per our objective. RESULTS: We identified 43 studies that met the inclusion criteria for this review. Although 70% (30/43) of the studies examined PGHD independently, 30% (13/43) combined PGHD with other data sources. Most of these studies used machine learning algorithms to perform their analysis. The themes identified through this review include predicting diabetes or obesity, deriving factors that contribute to diabetes or obesity, obtaining insights from social media or web-based forums, predicting glycemia, improving adherence and outcomes, analyzing sedentary behaviors, deriving behavior patterns, discovering clinical correlations from behaviors, and developing design principles. CONCLUSIONS: The increased volume and availability of PGHD have the potential to derive analytical insights into the health behaviors of people living with T2D. From the literature, we determined that analytics can predict outcomes and identify granular behavior patterns from PGHD. This review determined the broad range of insights that can be examined through PGHD, which constitutes a unique source of data for these applications that would not be possible through the use of other data sources.

Examining the relationships among adolescent health behaviours, prefrontal function, and academic achievement using fNIRS.

Several adolescent health behaviours have been hypothesized to improve academic performance via their beneficial impact on cognitive control and functional aspects of the prefrontal cortex (PFC). The primary objective of this study is to examine the association between lifestyle behaviours and academic performance in a sample of adolescents, and to examine the extent to which activity within the PFC and behavioural indices of inhibition may mediate this relationship. Sixty-seven adolescents underwent two study sessions five days apart. Sleep and physical activity were measured using wrist-mounted accelerometry; eating habits, substance use and academic achievement were measured by self-report. Prefrontal function was quantified by Multi-Source Interference Task (MSIT) performance, and task-related activity via functional near-infrared spectroscopy (fNIRS). Higher levels of physical activity predicted higher MSIT accuracy scores (ß = .321, ρ = 0.019) as well as greater activation within the right dlPFC (b = .008, SE = .004, ρ = .0322). Frequency of fast-food consumption and substance use were negatively associated with MSIT accuracy scores (ß = -0.307, ρ = .023) and Math grades (b = -3.702, SE = 1.563, ρ = .022), respectively. Overall, the results of this study highlight the importance of lifestyle behaviours as predictors of prefrontal function and academic achievement in youth.