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

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

Setup of the testing facilities at the Clinical Skills and Patient Simulation Center at the University of North Carolina School of Medicine, where the simulator room and observation room can be seen. (TIF 9461 kb

Improving Public Health Surveillance methods via Smart Home technologies

ObjectiveThe objective of this study is to explore individual, household and population-level health indicators collected in the home via smart thermostats. The study’s approach is to (a) identify if it is possible to isolate specific user behaviours using the motion and thermostat sensor data, and (b) develop Remote Monitoring of healthy behaviours at population level. Furthermore, this study is interested in identifying if observed patterns will suffer variations. As a result, it will be possible to understand human behaviours and consequently understand lifestyle habits of a person or a group of people.IntroductionPublic health surveillance relies on surveys and/or self-reported data collection, both of which require manpower, time commitment, and financial resources from public health agencies and participants. The survey results can quickly become outdated due to fast-paced changes in our society. The health habits of Canadians have rapidly evolved with technology and research indicates we are becoming a sedentary society, thus the levels of physical activity (PA) are very important population level health indicators. We will present a novel method to gather data at a granular level in near real-time, with minimal effort from participants. Simple thermostats are found in nearly every house in Canada, and smart thermostats enable efficient temperature adjustment, saving energy costs by adjusting according to human activity. Thermostats are ubiquitous in Canadian homes and the current expansion of smart thermostats make them an ideal data source over traditional methods. Utilizing technology that can be deployed at a population level will enable vast granular data collection beyond capabilities of traditional surveys. In this project UbiLab1 is exploring the use of the zero-effort technology using sensor data collected by smart thermostats and other associated sensors to develop an innovative health surveillance platform and monitor an individual’s health at the household level as well as health indicators at population level. Utilizing the smart wi-fi thermostat, we able to report on PA, sedentary behaviour, and sleep patterns at the household level. The thermostat and remote sensors (RS) contain temperature and motion sensors, which can be used to monitor activity in the home (i.e. lack of travel indicates sedentary behaviour), as well as sleep characteristics. This is beneficial as no action is required from participants, allowing individuals to go about their lives unperturbed. This powerful system will be able to deliver real-time health insights to public health professionals.MethodsZero-effort-technologies2 represent the future of ambient assisted living (AAL), in which sensors gather data generated by the person without conscious effort by the user. Such data could be integrated with other technologies to give the system the ability to tackle unsolved remote monitoring issues challenged the traditional data collection method barriers. For example, when the RS is placed in the bedroom, they can provide insights on sleep duration and quality. This addresses the challenges of declining participant engagement, low response rates in surveys and focus groups, and technical barriers to wearable technology. This eliminates recall bias, common when asking participants to quantify the amount of PA and types of behaviours they engaged in. Using the motion data, we can quantify the amount of PA in the home to determine individual levels of PA. The UbiLab partnered with ecobee3, a Canadian smart wi-fi thermostat company, leveraging data from over 10,000 households in North-America collected through the Donate Your Data (DYD)4 program. A small pilot study (n = 8) was done to validate the use of motion sensor readings of movement between rooms through a cross comparison with Fitbit5 step data. And the DYD dataset was analyzed for patterns using Python6, pandas7, Elasticsearch8, and Kibana8. This method will enable the delivery of personalized insights to monitor individual- and population-level health behaviours.ResultsPhysical Activity, Sedentary Behaviour and Sleep (PASS) indicators9 are measured through surveys (i.e. Canadian Health Measures Survey and Canadian Community Housing Survey) administered by Statistics Canada. Using this technology public health agencies will enable to collect novel health indicators, monitor health in real-time and deliver health insights to Canadians to increase health literacy. A positive association between Fitbit and ecobee data was found (Spearman’s Correlation coefficient = 0.7, p > 0.001) from 380 person hours from the pilot study. Indicators (sleep, interrupted sleep, daily indoor activity, sedentary) based on the PASS Indicators Framework from the Public Health Agency of Canada (PHAC)2 were measured using DYD data. Single occupant ecobee households in Canada averaged 7.2 hours of sleep in 24-hours, 2.1 hours of interrupted sleep, were active for 85 minutes daily, and spent 4.44 hours being sedentary. Recently, we have improved data collection adding Fitbit Charge 2 HRs, to capture sleep and heart rate not previously possible with the Fitbit Zip. Adding more sensors functionality is crucial for algorithm modifications, this includes collecting additional data via the Samsung SmartThings Hub10; presence, light usage, and luminance. ecobee is sharing participants and data from their own study, increasing variability within data. We have improved our data storage and analysis process, moving the big data architecture from python to Elasticsearch for real-time data streaming and analysis. We are also actively collaborating with PHAC and improving our algorithm and analysis process using their feedback.ConclusionsThis is a key opportunity to innovate traditional data collection methods, empowering patients through education and leveraging technology infrastructures to enable healthcare and policy decisions to be made with relevant and real-time data. Lessons learned at the individual and community health levels will be shared with community members and researchers. Implications include understanding short-term impacts with minimal effort and new health policies at the community level. Increased awareness and improvement can help to better physical activity, sleep and sedentary behaviour which may lead to improvements in overall health and wellbeing.References1. Waterloo U of. Ubilab. https://uwaterloo.ca/ubiquitous-health-technology-lab/.2. Public Health Agency of Canada - Canada.ca. https://www.canada.ca/en/public-health.html. Accessed October 26, 2018.3. ecobee | Smart Home Technology |. https://www.ecobee.com/. Accessed October 26, 2018.4. Donate your Data | Smart WiFi Thermostats by ecobee. https://www.ecobee.com/donateyourdata/. Accessed September 21, 2017.5. Fitbit Official Site for Activity Trackers & More. https://www.fitbit.com/en-ca/home. Accessed September 21, 2017.6. Welcome to Python.org. https://www.python.org/. Accessed November 22, 2017.7. Python Data Analysis Library — pandas: Python Data Analysis Library. https://pandas.pydata.org/. Accessed January 14, 2018.8. Elasticsearch. https://www.elastic.co/. Accessed October 26, 2018.9. Physical Activity, Sedentary Behaviour and Sleep (PASS) Indicator Framework for surveillance - Canada.ca. https://www.canada.ca/en/services/health/monitoring-surveillance/physical-activity-sedentary-behaviour-sleep.html. Accessed January 14, 2018.10. Samsung. Samsung Smart thing hub. 2018. https://www.smartthings.com/products/smartthings-hub.

Health Monitoring Using Smart Home Technologies: Scoping Review

BackgroundThe 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. ObjectiveThis scoping review aims to provide insight into this evolving field of research by surveying the current technologies and applications for in-home health monitoring. MethodsPeer-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. ResultsMost 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. ConclusionsThis 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

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

BackgroundActive 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. ObjectiveThis 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. MethodsA 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. ResultsThe 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. ConclusionsBetter 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

Cardiovascular reactions during exposure to persuasion principles

To optimize effectiveness of persuasive technology, understanding also psychophysiological processes of persuasion is crucial. The current research explored cardiovascular reactions to persuasive messages using four persuasion principles proposed by Cialdini (authority, scarcity, consensus, and commitment) in a laboratory experiment. The study had a randomized within-subject design. Participants (N = 56) were presented with 4 × 14 persuasive messages while cardiovascular reactions were measured with electrocardiography. Findings showed significantly different cardiovascular arousal regarding inter-beat interval and standard deviations of normal-to-normal heart rate peaks during persuasive principles compared to baseline or startle reflex. Results show no relation between cardiovascular arousal and self-reported susceptibility to persuasion. However, during the presentation of authority-based persuasion messages, data of the first stimulus condition showed a negative correlation between self-reported susceptibility and inter-beat interval reactivity. This explorative study advances our knowledge of psychophysiological processes underlying persuasion and suggested that at least certain persuasive principles may relate to physiological changes