ORCID Profile
0000-0001-7499-6718
Current Organisation
Canadian Red Cross Society
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Publisher: JMIR Publications Inc.
Date: 16-02-2022
Abstract: he 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. his scoping review aims to provide insight into this evolving field of research by surveying the current technologies and applications for in-home health monitoring. eer-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. ost 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. his 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.
Publisher: JMIR Publications Inc.
Date: 04-2022
DOI: 10.2196/28811
Abstract: Sleep behavior and time spent at home are important determinants of human health. Research on sleep patterns has traditionally relied on self-reported data. Not only does this methodology suffer from bias but the population-level data collection is also time-consuming. Advances in smart home technology and the Internet of Things have the potential to overcome these challenges in behavioral monitoring. The objective of this study is to demonstrate the use of smart home thermostat data to evaluate household sleep patterns and the time spent at home and how these behaviors are influenced by different weekdays and seasonal variations. From the 2018 ecobee Donate your Data data set, 481 North American households were selected based on having at least 300 days of data available, equipped with ≥6 sensors, and having a maximum of 4 occupants. Daily sleep cycles were identified based on sensor activation and used to quantify sleep time, wake-up time, sleep duration, and time spent at home. Each household’s record was ided into different subsets based on seasonal, weekday, and seasonal weekday scales. Our results demonstrate that sleep parameters (sleep time, wake-up time, and sleep duration) were significantly influenced by the weekdays. The sleep time on Fridays and Saturdays is greater than that on Mondays, Wednesdays, and Thursdays (n=450 P .001 odds ratio [OR] 1.8, 95% CI 1.5-3). There is significant sleep duration difference between Fridays and Saturdays and the rest of the week (n=450 P .001 OR 1.8, 95% CI 1.4-2). Consequently, the wake-up time is significantly changing between weekends and weekdays (n=450 P .001 OR 5.6, 95% CI 4.3-6.3). The results also indicate that households spent more time at home on Sundays than on the other weekdays (n=445 P .001 OR 2.06, 95% CI 1.64-2.5). Although no significant association is found between sleep parameters and seasonal variation, the time spent at home in the winter is significantly greater than that in summer (n=455 P .001 OR 1.6, 95% CI 1.3-2.3). These results are in accordance with existing literature. This is the first study to use smart home thermostat data to monitor sleep parameters and time spent at home and their dependence on weekday, seasonal, and seasonal weekday variations at the population level. These results provide evidence of the potential of using Internet of Things data to help public health officials understand variations in sleep indicators caused by global events (eg, pandemics and climate change).
Publisher: Office of Academic Resources, Chulalongkorn University - DIGITAL COMMONS JOURNALS
Date: 08-01-2021
Abstract: Throughout history, pandemics have played a significant role in reshaping human civilizations through mortalities, morbidities, economic losses and other catastrophic consequences. The present COVID-19 pandemic has brought the world to its knees resulting in overstretched healthcare systems, increased health inequalities and disruptions to people’s right to health including life-saving routine immunization programs across the world. This is a commentary paper. Immunization remains one of the most successful, safe, cost-effective and proven fundamental disease prevention measures in the history of public health. However, the COVID-19 pandemic has effectively thrown the world's immunization practices out of gear, depriving approximately 80 million infants, in rich and poor countries alike, at risk of triggering a resurgence of vaccine-preventable diseases such as diphtheria, measles and polio. It is estimated that each COVID-19 death averted by suspending immunization sessions in Africa could lead to 29-347 future deaths due to other diseases including measles, yellow fever, polio, meningitis, pneumonia and diarrhoea. The value of implementing robust immunization policies cannot be underestimated. Risks associated with postponing immunization services and the fact that COVID-19 is now an integral part of human civilization have resulted in several countries making special efforts to continue their immunization services. However, critical precautionary measures are warranted to prevent COVID-19 among healthcare service providers, facilitators, caregivers and children during the immunization sessions.
Publisher: Frontiers Media SA
Date: 08-03-2022
DOI: 10.3389/FPUBH.2022.820750
Abstract: Almost all low- and middle-income countries (LMICs) have instated a program to control and manage non-communicable diseases (NCDs). Population screening is an integral component of this strategy and requires a substantial chunk of investment. Therefore, testing the screening program for economic along with clinical effectiveness is essential. There is significant proof of the benefits of incorporating economic evidence in health decision-making globally, although evidence from LMICs in NCD prevention is scanty. This systematic review aims to consolidate and synthesize economic evidence of screening programs for cardiovascular diseases (CVD) and diabetes from LMICs. The study protocol is registered on PROSPERO (CRD42021275806). The review includes articles from English and Chinese languages. An initial search retrieved a total of 2,644 potentially relevant publications. Finally, 15 articles (13 English and 2 Chinese reports) were included and scrutinized in detail. We found 6 economic evaluations of interventions targeting cardiovascular diseases, 5 evaluations of diabetes interventions, and 4 were combined interventions, i.e., screening of diabetes and cardiovascular diseases. The study showcases numerous innovative screening programs that have been piloted, such as using mobile technology for screening, integrating non-communicable disease screening with existing communicable disease screening programs, and using community health workers for screening. Our review reveals that context is of utmost importance while considering any intervention, i.e., depending on the available resources, cost-effectiveness may vary—screening programs can be made universal or targeted just for the high-risk population.
Publisher: JMIR Publications Inc.
Date: 06-2021
DOI: 10.2196/28961
Abstract: Following the onset of the COVID-19 pandemic, digital contact tracing apps have become prevalent worldwide in a coordinated effort to curb the spread of COVID-19. However, their uptake has been low and slow due to privacy concerns, the lack of trust and motivational affordances, and their minimalist design. The objective of this article is to present a protocol for a systematic review of the main factors, including facilitators and barriers, that influence the adoption of contact tracing apps. We searched seven databases, namely, Scopus, CINAHL, PubMed (MEDLINE), IEEE Xplore Digital Library, Association for Computing Machinery (ACM) Digital Library, Web of Science, and Google Scholar, for relevant publications between October 30, 2020, and January 31, 2021. Three authors were involved in removing duplicates, screening, and selection of relevant articles according to the PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-analysis Protocols) guidelines. Altogether, we retrieved 777 articles from the seven databases. As of May 14, 2021, we have completed the screening process and arrived at 13 eligible articles to be included in the systematic review. We hope to elicit, summarize, and report the main findings in the systematic review article by the end of August 2021. We expect to uncover facilitators and barriers related to app utility, data security, ease of use, and persuasive design that are deemed important to adoption of contact tracing apps. The findings of the systematic review will help researchers to uncover the gaps in the adoption of contact tracing apps, and decision makers and designers to focus on the principal adoption factors necessary to create better and more effective contact tracing apps. DERR1-10.2196/28961
Publisher: MDPI AG
Date: 11-10-2019
Abstract: The purpose of this descriptive research paper is to initiate discussions on the use of innovative technologies and their potential to support the research and development of pan-Canadian monitoring and surveillance activities associated with environmental impacts on health and within the health system. Its primary aim is to provide a review of disruptive technologies and their current uses in the environment and in healthcare. Drawing on extensive experience in population-level surveillance through the use of technology, knowledge from prior projects in the field, and conducting a review of the technologies, this paper is meant to serve as the initial steps toward a better understanding of the research area. In doing so, we hope to be able to better assess which technologies might best be leveraged to advance this unique intersection of health and environment. This paper first outlines the current use of technologies at the intersection of public health and the environment, in particular, Artificial Intelligence (AI), Blockchain, and the Internet of Things (IoT). The paper provides a description for each of these technologies, along with a summary of their current applications, and a description of the challenges one might face with adopting them. Thereafter, a high-level reference architecture, that addresses the challenges of the described technologies and could potentially be incorporated into the pan-Canadian surveillance system, is conceived and presented.
Publisher: JMIR Publications Inc.
Date: 29-06-2020
Abstract: dvances in technology will impact the field of human factors as new solutions change how we plan, share knowledge, perceive and act on real world problems. Human machine interaction will become more seamless until we are unable to differentiate between the human and the machine introducing issues of trust and privacy. Technological advancement has created big data and now we are able to tackle large problems with data for better evidence-based solutions and policy measures. his paper discusses the use of human factors methods when developing solutions that use artificial intelligence, including machine learning and deep learning, to tackle challenges for social good, especially related to health. e review relevant literature and present areas and ex le use cases. he potential uses for artificial intelligence applied with human factors are discussed in four areas which impact human health and wellbeing: precision medicine, independent living, public health and the environment. e hope to inspire future work in this field with a better understanding of how human factors can be applied to AI-based solutions. We make the case for the inclusion of HF experts on erse project teams.
Publisher: JMIR Publications Inc.
Date: 08-06-2020
Abstract: ne of the main concerns of public health surveillance is to preserve the physical and mental health of older adults while supporting their independence and privacy. On the other hand, to better assist those in iduals with essential health care services in the event of an emergency, their regular activities should be monitored. Internet of Things (IoT) sensors may be employed to track the sequence of activities of in iduals via ambient sensors, providing real-time insights on daily activity patterns and easy access to the data through the connected ecosystem. Previous surveys to identify the regular activity patterns of older adults were deficient in the limited number of participants, short period of activity tracking, and high reliance on predefined normal activity. he objective of this study was to overcome the aforementioned challenges by performing a pilot study to evaluate the utilization of large-scale data from smart home thermostats that collect the motion status of in iduals for every 5-minute interval over a long period of time. rom a large-scale dataset, we selected a group of 30 households who met the inclusion criteria (having at least 8 sensors, being connected to the system for at least 355 days in 2018, and having up to 4 occupants). The indoor activity patterns were captured through motion sensors. We used the unsupervised, time-based, deep neural-network architecture long short-term memory-variational autoencoder to identify the regular activity pattern for each household on 2 time scales: annual and weekday. The results were validated using 2019 records. The area under the curve as well as loss in 2018 were compatible with the 2019 schedule. Daily abnormal behaviors were identified based on deviation from the regular activity model. he utilization of this approach not only enabled us to identify the regular activity pattern for each household but also provided other insights by assessing sleep behavior using the sleep time and wake-up time. We could also compare the average time in iduals spent at home for the different days of the week. From our study s le, there was a significant difference in the time in iduals spent indoors during the weekend versus on weekdays. his approach could enhance in idual health monitoring as well as public health surveillance. It provides a potentially nonobtrusive tool to assist public health officials and governments in policy development and emergency personnel in the event of an emergency by measuring indoor behavior while preserving privacy and using existing commercially available thermostat equipment.
Publisher: JMIR Publications Inc.
Date: 08-10-2021
Abstract: he climate crisis is one of the biggest challenges of our time. Technological innovations particularly, persuasive technology have been identified as a veritable tool for effecting behaviour change in the climate-change domain. However, there is limited work on the synthesis of the findings of the existing literature on persuasive technology and climate-change interventions. Therefore, we aim to conduct a systematic review based on the PRISMA-P protocol to examine how persuasive technologies have been used hitherto as a motivational tool to address the problem of climate change and foster behaviour change. he objectives of this study are to explore, how effective is persuasive technology in fostering behaviour change aimed at reducing climate change, what persuasive strategies are being employed to promote positive behaviours aimed at reducing climate change, what behaviour theories are being employed in developing persuasive technologies aimed at reducing climate change, what are the behavioural outcomes targeted by persuasive technologies aimed at reducing climate change and what are the study methodologies being employed in persuasive technology/climate change research? copus, PubMed (MEDLINE), IEEE Xplore Digital Library, ACM Digital Library, Web of Science, and ProQuest databases were searched between October 30, 2020, and November 20, 2020, using specific keywords related to “persuasive technology” and "climate change." For the articles to be included in the systematic review, they must have been peer-reviewed user studies that evaluated the effectiveness of persuasive technology designs, prototypes, or implementations that promoted positive behaviours to reduce climate change. ummarized findings of the eligible studies will be tabulated under specific themes as described in the objectives of the study. The findings will relate to the effectiveness of persuasive technologies, persuasive strategies employed in technical solutions for climate change, a range of applications of behavioural theories for climate change, targeted behavioural outcomes, etc. he systematic review will help uncover empirical findings on behavioural outcomes related to climate-change persuasive interventions such as adoption intention, attitude, compliance with environmental guidelines, and adherence to pro-environmental behaviours.
Publisher: Frontiers Media SA
Date: 03-05-2022
DOI: 10.3389/FDGTH.2022.862466
Abstract: The emergence of new variants of COVID-19 causing breakthrough infections and the endemic potential of the coronavirus are an indication that digital contact tracing apps (CTAs) may continue to be useful for the long haul. However, the uptake of these apps in many countries around the world has been low due to several factors militating against their adoption and usage. In this systematic review, we set out to uncover the key factors that facilitate or militate against the adoption of CTAs, which researchers, designers and other stakeholders should focus on in future iterations to increase their adoption and effectiveness in curbing the spread of COVID-19. Seven databases, including PubMed, CINAHL, Scopus, Web of Service, IEEE Xplore, ACM Digital Library, and Google Scholar, were searched between October 30 and January 31, 2020. A total of 777 articles were retrieved from the databases, with 13 of them included in the systematic review after screening. The criteria for including articles in the systematic review were that they could be user studies from any country around the world, must be peer-reviewed, written in English, and focused on the perception and adoption of COVID-19 contact tracing and/or exposure notification apps. Other criteria included user study design could be quantitative, qualitative, or mixed, and must have been conducted during the COVID-19 pandemic, which began in the early part of 2020. Three researchers searched seven databases (three by the first author, and two each by the second and third authors) and stored the retrieved articles in a collaborative Mendeley reference management system online. After the removal of duplicates, each researcher independently screened one third of the articles based on title/abstract. Thereafter, all three researchers collectively screened articles that were in the borderline prior to undergoing a full-text review. Then, each of the three researchers conducted a full-text review of one-third of the eligible articles to decide the final articles to be included in the systematic review. Next, all three researchers went through the full text of each borderline article to determine their appropriateness and relevance. Finally, each researcher extracted the required data from one-third of the included articles into a collaborative Google spreadsheet and the first author utilized the data to write the review. This review identified 13 relevant articles, which found 56 factors that may positively or negatively impact the adoption of CTAs. The identified factors were thematically grouped into ten categories: privacy and trust, app utility, facilitating conditions, social-cognitive factors, ethical concerns, perceived technology threats, perceived health threats, technology familiarity, persuasive design, and socio-demographic factors. Of the 56 factors, privacy concern turned out to be the most frequent factor of CTA adoption (12/13), followed by perceived benefit (7/13), perceived trust (6/13), and perceived data security risk (6/13). In the structural equation models presented by the authors of the included articles, a subset of the 56 elicited factors (e.g., perceived benefit and privacy concern) explains 16 to 77% of the variance of users' intention to download, install, or use CTAs to curb the spread of COVID-19. Potential adoption rates of CTA range from 19% (in Australia) to 75% (in France, Italy, Germany, United Kingdom, and United States). Moreover, actual adoption rates range from 37% (in Australia) to 50% (in Germany). Finally, most of the studies were carried out in Europe (66.7%), followed by North America (13.3%), and Australia, Asia, and South America (6.7% each). The results suggest that future CTA iterations should give priority to privacy protection through minimal data collection and transparency, improving contact tracing benefits (personal and social), and fostering trust through laudable gestures such as delegating contact tracing to public health authorities, making source code publicly available and stating who will access user data, when, how, and what it will be used for. Moreover, the results suggest that data security and tailored persuasive design, involving reward, self-monitoring, and social-location monitoring features, have the potential of improving CTA adoption. Hence, in addition to addressing issues relating to utility, privacy, trust, and data security, we recommend the integration of persuasive features into future designs of CTAs to improve their motivational appeal, adoption, and the user experience. www.crd.york.ac.uk rospero/display_record.php?ID=CRD42021259080 PROSPERO, identifier CRD42021259080.
Publisher: MDPI AG
Date: 26-11-2021
DOI: 10.3390/HEALTHCARE9121637
Abstract: Community awareness regarding stroke signs, risk factors, and actions that help reduce the risk and complications of stroke is poorly addressed, as it is thought to be the best approach to control and prevent stroke. Aim: To establish the awareness of stroke and its management among high school and college students using an educational intervention. A questionnaire was administered to students from five high schools and four colleges with different areas of focus, (arts, science and commerce), types (public, semi-public and private), and economic locations before and after an educational lecture on stroke. The lecture covered the following elements: stroke definition, signs, risk factors, actions, time window for thrombolytic therapy, and types of rehabilitation interventions. This study included 1036 participants, of whom 36.3% were male and 56.4% were high school students, and the mean age was 17.15 ± 1.29 (15–22) years. Before the lecture, 147 participants were unaware of a single sign of stroke, and 124 did not know the risk factors. After the intervention, 439 participants knew four signs of stroke, and 196 knew 12 risk factors. Female students had better knowledge about stroke signs (odds ratio (OR), 3.08 95% confidence interval (95% CI), 2.15–4.43). Hypertension (52.7%) and weakness (59.85%) were the most known signs and risk factors. The proportion of students who selected traditional medicine as the mode of treatment decreased from 34.75% to 8.59% after the lecture. Other rehabilitation methods (e.g., physical therapy, occupational therapy, speech therapy and counseling) were chosen by more than 80% of the students. The results of the current study showed that the awareness on stroke risk factors and management among the school and college students can be significantly improved with regular educational interventions, and therefore stroke can be prevented to some extent.
Publisher: Informa UK Limited
Date: 11-10-2021
Publisher: JMIR Publications Inc.
Date: 13-04-2023
DOI: 10.2196/37347
Abstract: 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. 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. 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. 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. 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.
Publisher: JMIR Publications Inc.
Date: 13-11-2020
DOI: 10.2196/21209
Abstract: One of the main concerns of public health surveillance is to preserve the physical and mental health of older adults while supporting their independence and privacy. On the other hand, to better assist those in iduals with essential health care services in the event of an emergency, their regular activities should be monitored. Internet of Things (IoT) sensors may be employed to track the sequence of activities of in iduals via ambient sensors, providing real-time insights on daily activity patterns and easy access to the data through the connected ecosystem. Previous surveys to identify the regular activity patterns of older adults were deficient in the limited number of participants, short period of activity tracking, and high reliance on predefined normal activity. The objective of this study was to overcome the aforementioned challenges by performing a pilot study to evaluate the utilization of large-scale data from smart home thermostats that collect the motion status of in iduals for every 5-minute interval over a long period of time. From a large-scale dataset, we selected a group of 30 households who met the inclusion criteria (having at least 8 sensors, being connected to the system for at least 355 days in 2018, and having up to 4 occupants). The indoor activity patterns were captured through motion sensors. We used the unsupervised, time-based, deep neural-network architecture long short-term memory-variational autoencoder to identify the regular activity pattern for each household on 2 time scales: annual and weekday. The results were validated using 2019 records. The area under the curve as well as loss in 2018 were compatible with the 2019 schedule. Daily abnormal behaviors were identified based on deviation from the regular activity model. The utilization of this approach not only enabled us to identify the regular activity pattern for each household but also provided other insights by assessing sleep behavior using the sleep time and wake-up time. We could also compare the average time in iduals spent at home for the different days of the week. From our study s le, there was a significant difference in the time in iduals spent indoors during the weekend versus on weekdays. This approach could enhance in idual health monitoring as well as public health surveillance. It provides a potentially nonobtrusive tool to assist public health officials and governments in policy development and emergency personnel in the event of an emergency by measuring indoor behavior while preserving privacy and using existing commercially available thermostat equipment.
Publisher: JMIR Publications Inc.
Date: 22-04-2023
Abstract: he political will of politicians to act in the best interests of the populace's health, or lack thereof, has a significant impact on disease outcomes. The partisan ide that exists in the United States of America influenced the implementation and adherence to public health policies enacted during the COVID-19 pandemic. he objective of this study is to use zero-effort technology to analyze the impact of politically driven social restrictions during the pandemic on population-level sleep duration, a critical modifiable risk factor. or this study, 4405 households were selected from different political-leaning cities in California (Democratic) and Texas (Republic) using the USA dataset from the DYD (Donate your Data) initiative of ecobee, a smart thermostat company. The data were stratified based on time, geolocation, and political affiliation, and filtered into two categories: "Before" the pandemic (March 2019 to Feb 2020) and "During" the pandemic (March 2020 to Feb 2021). The average sleep duration was quantified by identifying the daily sleep cycles through sensor activation. verall, the findings show a significant decrease in the average sleep duration at the population level after the onset of the COVID-19 pandemic. Cities under Democratic governance at the time of the pandemic consistently showed a decrease in sleep duration “During” the pandemic compared to “Before” the pandemic. The impact of the pandemic in Republican cities on sleep duration was much more variable. olitical affiliation, as it pertains to the implementation and adherence to public health policies during the COVID-19 pandemic, can impact population health indicators such as sleep. The use of IoT data and innovative analytics represents a novel method to objectively monitor, promote, and improve population behaviours. These zero-effort technologies have the potential to provide real-time insights into public health and drive health policy.
Publisher: JMIR Publications Inc.
Date: 08-06-2020
Abstract: dvances in technology have made the development of remote patient monitoring possible in recent years. However, there is still room for innovation in the types of technologies that are developed, used, and implemented. The smart thermostat solutions provided in this study can expand beyond typically defined features and be used for improved holistic health monitoring purposes. he aim of this study is to validate the hypothesis that remote motion sensors could be used to quantify and track an in idual’s movements around the house. On the basis of our results, the next step would be to determine if using remote motion sensors could be a novel data collection method compared with the national census-level surveys administered by governmental bodies. The results will be used to inform a more extensive implementation study of similar smart home technologies to gather data for machine learning algorithms and to build upon pattern recognition and comprehensive health monitoring. e conducted a pilot study with a s le size of 8 to validate the use of remote motion sensors to quantify movement in the house. A large database containing data from smart home thermostats was analyzed to compare the following indicators sleep, physical activity, and sedentary behavior. These indicators were developed by the Public Health Agency of Canada and are collected through traditional survey methods. he results showed a significant Spearman rank correlation coefficient of 0.8 ( i P& /i .001), which indicates a positive linear association between the total number of sensors activated and the total number of indoor steps traveled by study participants. In addition, the indicators of sleep, physical activity, and sedentary behavior were all found to be highly comparable with those attained by the Public Health Agency of Canada. he findings demonstrate that remote motion sensors data from a smart thermostat solution are a viable option when compared with traditional survey data collection methods for health data collection and are also a form of zero-effort technology that can be used to monitor the activity levels and nature of activity of occupants within the home. >
Publisher: JMIR Publications Inc.
Date: 16-03-2021
Abstract: leep behavior and time spent at home are important determinants of human health. Research on sleep patterns has traditionally relied on self-reported data. Not only does this methodology suffer from bias but the population-level data collection is also time-consuming. Advances in smart home technology and the Internet of Things have the potential to overcome these challenges in behavioral monitoring. he objective of this study is to demonstrate the use of smart home thermostat data to evaluate household sleep patterns and the time spent at home and how these behaviors are influenced by different weekdays and seasonal variations. rom the 2018 ecobee i Donate your Data /i data set, 481 North American households were selected based on having at least 300 days of data available, equipped with ≥6 sensors, and having a maximum of 4 occupants. Daily sleep cycles were identified based on sensor activation and used to quantify sleep time, wake-up time, sleep duration, and time spent at home. Each household’s record was ided into different subsets based on seasonal, weekday, and seasonal weekday scales. ur results demonstrate that sleep parameters (sleep time, wake-up time, and sleep duration) were significantly influenced by the weekdays. The sleep time on Fridays and Saturdays is greater than that on Mondays, Wednesdays, and Thursdays (n=450 i P /i & .001 odds ratio [OR] 1.8, 95% CI 1.5-3). There is significant sleep duration difference between Fridays and Saturdays and the rest of the week (n=450 i P /i & .001 OR 1.8, 95% CI 1.4-2). Consequently, the wake-up time is significantly changing between weekends and weekdays (n=450 i P /i & .001 OR 5.6, 95% CI 4.3-6.3). The results also indicate that households spent more time at home on Sundays than on the other weekdays (n=445 i P /i & .001 OR 2.06, 95% CI 1.64-2.5). Although no significant association is found between sleep parameters and seasonal variation, the time spent at home in the winter is significantly greater than that in summer (n=455 i P /i & .001 OR 1.6, 95% CI 1.3-2.3). These results are in accordance with existing literature. his is the first study to use smart home thermostat data to monitor sleep parameters and time spent at home and their dependence on weekday, seasonal, and seasonal weekday variations at the population level. These results provide evidence of the potential of using Internet of Things data to help public health officials understand variations in sleep indicators caused by global events (eg, pandemics and climate change).
Publisher: Frontiers Media SA
Date: 03-12-2021
DOI: 10.3389/FPUBH.2021.756675
Abstract: Recent advances in technology have led to the rise of new-age data sources (e.g., Internet of Things (IoT), wearables, social media, and mobile health). IoT is becoming ubiquitous, and data generation is accelerating globally. Other health research domains have used IoT as a data source, but its potential has not been thoroughly explored and utilized systematically in public health surveillance. This article summarizes the existing literature on the use of IoT as a data source for surveillance. It presents the shortcomings of current data sources and how NextGen data sources, including the large-scale applications of IoT, can meet the needs of surveillance. The opportunities and challenges of using these modern data sources in public health surveillance are also explored. These IoT data ecosystems are being generated with minimal effort by the device users and benefit from high granularity, objectivity, and validity. Advances in computing are now bringing IoT-based surveillance into the realm of possibility. The potential advantages of IoT data include high-frequency, high volume, zero effort data collection methods, with a potential to have syndromic surveillance. In contrast, the critical challenges to mainstream this data source within surveillance systems are the huge volume and variety of data, fusing data from multiple devices to produce a unified result, and the lack of multidisciplinary professionals to understand the domain and analyze the domain data accordingly.
Publisher: JMIR Publications Inc.
Date: 20-11-2020
DOI: 10.2196/21016
Abstract: Advances in technology have made the development of remote patient monitoring possible in recent years. However, there is still room for innovation in the types of technologies that are developed, used, and implemented. The smart thermostat solutions provided in this study can expand beyond typically defined features and be used for improved holistic health monitoring purposes. The aim of this study is to validate the hypothesis that remote motion sensors could be used to quantify and track an in idual’s movements around the house. On the basis of our results, the next step would be to determine if using remote motion sensors could be a novel data collection method compared with the national census-level surveys administered by governmental bodies. The results will be used to inform a more extensive implementation study of similar smart home technologies to gather data for machine learning algorithms and to build upon pattern recognition and comprehensive health monitoring. We conducted a pilot study with a s le size of 8 to validate the use of remote motion sensors to quantify movement in the house. A large database containing data from smart home thermostats was analyzed to compare the following indicators sleep, physical activity, and sedentary behavior. These indicators were developed by the Public Health Agency of Canada and are collected through traditional survey methods. The results showed a significant Spearman rank correlation coefficient of 0.8 (P .001), which indicates a positive linear association between the total number of sensors activated and the total number of indoor steps traveled by study participants. In addition, the indicators of sleep, physical activity, and sedentary behavior were all found to be highly comparable with those attained by the Public Health Agency of Canada. The findings demonstrate that remote motion sensors data from a smart thermostat solution are a viable option when compared with traditional survey data collection methods for health data collection and are also a form of zero-effort technology that can be used to monitor the activity levels and nature of activity of occupants within the home.
Publisher: JMIR Publications Inc.
Date: 22-03-2021
Abstract: ollowing the onset of the COVID-19 pandemic, digital contact tracing apps have become prevalent worldwide in a coordinated effort to curb the spread of COVID-19. However, their uptake has been low and slow due to privacy concerns, the lack of trust and motivational affordances, and their minimalist design. he objective of this article is to present a protocol for a systematic review of the main factors, including facilitators and barriers, that influence the adoption of contact tracing apps. e searched seven databases, namely, Scopus, CINAHL, PubMed (MEDLINE), IEEE Xplore Digital Library, Association for Computing Machinery (ACM) Digital Library, Web of Science, and Google Scholar, for relevant publications between October 30, 2020, and January 31, 2021. Three authors were involved in removing duplicates, screening, and selection of relevant articles according to the PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-analysis Protocols) guidelines. ltogether, we retrieved 777 articles from the seven databases. As of May 14, 2021, we have completed the screening process and arrived at 13 eligible articles to be included in the systematic review. We hope to elicit, summarize, and report the main findings in the systematic review article by the end of August 2021. We expect to uncover facilitators and barriers related to app utility, data security, ease of use, and persuasive design that are deemed important to adoption of contact tracing apps. he findings of the systematic review will help researchers to uncover the gaps in the adoption of contact tracing apps, and decision makers and designers to focus on the principal adoption factors necessary to create better and more effective contact tracing apps. ERR1-10.2196/28961
Publisher: Springer Science and Business Media LLC
Date: 14-11-2015
Publisher: ACM
Date: 27-06-2022
Publisher: Elsevier BV
Date: 04-2023
Publisher: Elsevier BV
Date: 03-2022
Location: India
Location: India
Location: India
No related grants have been discovered for Kirti Sundar Sahu.