Array ( [0] => Array ( [ArticleId] => 481 [Create_Time] => 2023-02-23 [zipUrl] => https://www.explorationpub.com/uploads/zip/202312/20231228034020.zip [xmlUrl] => https://www.explorationpub.com/uploads/Article/A10111/10111.xml [pdfUrl] => https://www.explorationpub.com/uploads/Article/A10111/10111.pdf [coverUrl] => https://www.explorationpub.com/uploads/Article/A10111/10111_cover.png [JournalsId] => 14 [Title] => Exploration of Digital Health Technologies [Abstract] => [AbstractComplete] => [Names] => Atanas G. Atanasov [CName] => [Doi] => 10.37349/edht.2023.00001 [Published] => November 01, 2023 [Viewed] => 2293 [Downloaded] => 151 [Subject] => Editorial [Year] => 2023 [CiteUrl] => https://api.crossref.org/works/10.37349/edht.2023.00001 [Inline] => 1 [Type] => 1 [Issue] => 1 [Topic] => 0 [TitleAbbr] => Explor Digit Health Technol. [Pages] => 2023;1:1–3 [Recommend] => 0 [Keywords] => [DetailTitle] => [DetailUrl] => [Id] => 10111 [ris] => https://www.explorationpub.com/uploads/Article/A10111/742d8cd669c9a6d680977273ef1b5c03.ris [bib] => https://www.explorationpub.com/uploads/Article/A10111/20eefc691a84930c1d5f0bf2630c81b1.bib [ens] => [Cited] => 4 [Cited_Time] => 2024-05-27 [CitethisArticle] => Atanasov AG. Exploration of Digital Health Technologies. Explor Digit Health Technol. 2023;1:1–3. https://doi.org/10.37349/edht.2023.00001 [Jindex] => 0 [CEmail] => [Ris_Time] => 2023-02-22 08:03:34 [Bib_Time] => 2023-02-22 08:03:34 [KeysWordContens] => Exploration of Digital Health Technologies,,,Atanas G. Atanasov [PublishedText] => Published [IsEdit] => 0 [AccountId] => 46 [Zh] => 1 [AuthorsName] => Atanas G. Atanasov ) [1] => Array ( [ArticleId] => 596 [Create_Time] => 2023-05-31 [zipUrl] => https://www.explorationpub.com/uploads/zip/202405/20240509033917.zip [xmlUrl] => https://www.explorationpub.com/uploads/Article/A10112/10112.xml [pdfUrl] => https://www.explorationpub.com/uploads/Article/A10112/10112.pdf [coverUrl] => https://www.explorationpub.com/uploads/Article/A10112/10112_cover.png [JournalsId] => 14 [Title] => Turbulence at Twitter with leadership change: implications for health research and science communication [Abstract] => Twitter has been an invaluable social media platform for scientists to share research and host discourse among academics and the public. The change of ownership at Twitter has changed how scientists [AbstractComplete] =>

Twitter has been an invaluable social media platform for scientists to share research and host discourse among academics and the public. The change of ownership at Twitter has changed how scientists interact with the platform and has led some to worry about its future. This article discusses the current changes at Twitter and what implications these may have for future health research and communication.

[Names] => Ronan Lordan, Hari Prasad Devkota [CName] => RonanLordan, [Doi] => 10.37349/edht.2023.00002 [Published] => November 01, 2023 [Viewed] => 3031 [Downloaded] => 203 [Subject] => Perspective [Year] => 2023 [CiteUrl] => https://api.crossref.org/works/10.37349/edht.2023.00002 [Inline] => 1 [Type] => 1 [Issue] => 1 [Topic] => 143 [TitleAbbr] => Explor Digit Health Technol. [Pages] => 2023;1:4–10 [Recommend] => 1 [Keywords] => Twitter, social media, science communication, mastodon, misinformation, leadership [DetailTitle] => Social Media Applications in Biomedical Research [DetailUrl] => https://www.explorationpub.com/Journals/edht/Special_Issues/143 [Id] => 10112 [ris] => https://www.explorationpub.com/uploads/Article/A10112/3de16bb4415242dad0503b7a14c82a95.ris [bib] => https://www.explorationpub.com/uploads/Article/A10112/febe494888d9429a56e66eb150242b47.bib [ens] => [Cited] => 2 [Cited_Time] => 2024-05-27 [CitethisArticle] => Lordan R, Devkota HP. Turbulence at Twitter with leadership change: implications for health research and science communication. Explor Digit Health Technol. 2023;1:4–10. https://doi.org/10.37349/edht.2023.00002 [Jindex] => 0 [CEmail] => ronan.lordan@pennmedicine.upenn.edu, [Ris_Time] => 2023-05-30 07:58:41 [Bib_Time] => 2023-05-30 07:58:41 [KeysWordContens] => Turbulence at Twitter with leadership change: implications for health research and science communication, Twitter, social media, science communication, mastodon, misinformation, leadership, Twitter has been an invaluable social media platform for scientists to share research and host discourse among academics and the public. The change of ownership at Twitter has changed how scientists interact with the platform and has led some to worry about its future. This article discusses the current changes at Twitter and what implications these may have for future health research and communication. ,Ronan Lordan, Hari Prasad Devkota [PublishedText] => Published [IsEdit] => 0 [AccountId] => 38 [Zh] => 1 [AuthorsName] => Ronan Lordan, Hari Prasad Devkota ) [2] => Array ( [ArticleId] => 666 [Create_Time] => 2023-08-07 [zipUrl] => https://www.explorationpub.com/uploads/zip/202405/20240509055917.zip [xmlUrl] => https://www.explorationpub.com/uploads/Article/A10113/10113.xml [pdfUrl] => https://www.explorationpub.com/uploads/Article/A10113/10113.pdf [coverUrl] => https://www.explorationpub.com/uploads/Article/A10113/10113_cover.png [JournalsId] => 14 [Title] => Harnessing the untapped potential of digital twin technology in digital public health interventions [Abstract] => Digital technologies have garnered more attention in this epoch of public health emergencies like coronavirus disease 2019 (COVID-19) and monkeypox (mpox). Digital twin (DT) is the virtual cyberneti [AbstractComplete] =>

Digital technologies have garnered more attention in this epoch of public health emergencies like coronavirus disease 2019 (COVID-19) and monkeypox (mpox). Digital twin (DT) is the virtual cybernetic equivalent of a physical object (e.g., a device, a human, a community) used to better understand the complexity of the latter and predict, prevent, monitor, and optimize real-world outcomes. The possible use cases of DT systems in public health ranging from mass vaccination planning to understanding disease transmission patterns have been discussed. Despite potential applications in healthcare, several economic, social, and ethical challenges might hinder the universal implementation of DT. Nevertheless, devising appropriate policies, reinforcing good governance, and launching multinational collaborative efforts ascertain early espousal of DT technology.

[Names] => Salman Khan ... ArunSundar MohanaSundaram [CName] => ArunSundarMohanaSundaram, [Doi] => 10.37349/edht.2023.00003 [Published] => November 01, 2023 [Viewed] => 1597 [Downloaded] => 146 [Subject] => Letter to the Editor [Year] => 2023 [CiteUrl] => https://api.crossref.org/works/10.37349/edht.2023.00003 [Inline] => 1 [Type] => 1 [Issue] => 1 [Topic] => 143 [TitleAbbr] => Explor Digit Health Technol. [Pages] => 2023;1:11–16 [Recommend] => 0 [Keywords] => Digital public health, digital twins, public health emergencies, healthcare, digital technologies, epidemiology, remote health, smart health city [DetailTitle] => Social Media Applications in Biomedical Research [DetailUrl] => https://www.explorationpub.com/Journals/edht/Special_Issues/143 [Id] => 10113 [ris] => https://www.explorationpub.com/uploads/Article/A10113/b314839f60ce0e41d68f9a4bff544c90.ris [bib] => https://www.explorationpub.com/uploads/Article/A10113/6019c325c9b5517fd76473514eb46f64.bib [ens] => [Cited] => 2 [Cited_Time] => 2024-05-27 [CitethisArticle] => Khan S, Kandukuri DK, Subramaniyan EU, MohanaSundaram A. Harnessing the untapped potential of digital twin technology in digital public health interventions. Explor Digit Health Technol. 2023;1:11–6. https://doi.org/10.37349/edht.2023.00003 [Jindex] => 0 [CEmail] => arun.laureate@gmail.com, [Ris_Time] => 2023-07-31 03:22:57 [Bib_Time] => 2023-07-31 03:22:57 [KeysWordContens] => Harnessing the untapped potential of digital twin technology in digital public health interventions, Digital public health, digital twins, public health emergencies, healthcare, digital technologies, epidemiology, remote health, smart health city, Digital technologies have garnered more attention in this epoch of public health emergencies like coronavirus disease 2019 (COVID-19) and monkeypox (mpox). Digital twin (DT) is the virtual cybernetic equivalent of a physical object (e.g., a device, a human, a community) used to better understand the complexity of the latter and predict, prevent, monitor, and optimize real-world outcomes. The possible use cases of DT systems in public health ranging from mass vaccination planning to understanding disease transmission patterns have been discussed. Despite potential applications in healthcare, several economic, social, and ethical challenges might hinder the universal implementation of DT. Nevertheless, devising appropriate policies, reinforcing good governance, and launching multinational collaborative efforts ascertain early espousal of DT technology. ,Salman Khan ... ArunSundar MohanaSundaram [PublishedText] => Published [IsEdit] => 0 [AccountId] => 72 [Zh] => 1 [AuthorsName] => Salman Khan, Dilip Kumar Kandukuri, Elakeya Udhaya Subramaniyan, ArunSundar MohanaSundaram ) [3] => Array ( [ArticleId] => 826 [Create_Time] => 2023-09-27 [zipUrl] => https://www.explorationpub.com/uploads/zip/202312/20231228013537.zip [xmlUrl] => https://www.explorationpub.com/uploads/Article/A10114/10114.xml [pdfUrl] => https://www.explorationpub.com/uploads/Article/A10114/10114.pdf [coverUrl] => https://www.explorationpub.com/uploads/Article/A10114/10114_cover.png [JournalsId] => 14 [Title] => Envisioning urban environments resilient to vector-borne diseases: a protocol to study dengue in Vietnam [Abstract] => Transmitted primarily by Aedes aegypti (Ae. aegypti) and Aedes albopictus (Ae. albopictus), arboviral diseases pose a major global public health threat. Dengue, chikungunya, and Zika are increasingl [AbstractComplete] =>

Transmitted primarily by Aedes aegypti (Ae. aegypti) and Aedes albopictus (Ae. albopictus), arboviral diseases pose a major global public health threat. Dengue, chikungunya, and zika are increasingly prevalent in Southeast Asia. Among other arboviruses, dengue and zika are becoming more common in Central and South America. Given human encroachment into previously uninhabited, often deforested areas, to provide new housing in regions of population expansion, conceptualizing built urban environments in a novel way is urgently needed to safeguard against the growing climate change-driven threat of vector-borne diseases. By understanding the spread from a One Health perspective, enhanced control and prevention can be achieved. This is particularly important considering that climate change is likely to significantly impact the persistence of ponded water where mosquitoes breed due to increasing temperature and shifting rainfall patterns with regard to magnitude, duration, frequency, and season. Models can incorporate aquatic mosquito stages and adult spatial dynamics when habitats are heterogeneously available, thereby including dispersal and susceptible-exposed-infected-recovered (SEIR) epidemiology. Coupled with human population distribution (density, locations), atmospheric conditions (air temperature, precipitation), and hydrological conditions (soil moisture distribution, ponding persistence in topographic depressions), modeling has improved predictive ability for infection rates. However, it has not informed interventional approaches from an urban environment perspective which considers the role of ponds/lakes that support green spaces, the density of population that enables rapid spread of disease, and varying micro-habitats for various mosquito stages under climate change. Here, for an example of dengue in Vietnam, a preventive and predictive approach to design resilient urban environments is proposed, which uses data from rapidly expanding metropolitan communities to learn continually. This protocol deploys computational approaches including simulation and machine learning/artificial intelligence, underpinned by surveillance and medical data for validation and adaptive learning. Its application may best inform urban planning in low-middle income countries in tropical zones where arboviral pathogens are prevalent.

[Names] => Praveen Kumar ... Andrew W. Taylor-Robinson [CName] => [Doi] => 10.37349/edht.2023.00004 [Published] => November 01, 2023 [Viewed] => 1452 [Downloaded] => 126 [Subject] => Protocol [Year] => 2023 [CiteUrl] => https://api.crossref.org/works/10.37349/edht.2023.00004 [Inline] => 1 [Type] => 1 [Issue] => 1 [Topic] => 0 [TitleAbbr] => Explor Digit Health Technol. [Pages] => 2023;1:17–27 [Recommend] => 0 [Keywords] => Vector-borne disease, arbovirus, dengue, mosquito, urban environment, climate change, digital twin, Vietnam [DetailTitle] => [DetailUrl] => [Id] => 10114 [ris] => https://www.explorationpub.com/uploads/Article/A10114/f5ac4eae615673291903ad7b185a4c1d.ris [bib] => https://www.explorationpub.com/uploads/Article/A10114/59b76fc7af10d5885c285ea8e2f094ef.bib [ens] => [Cited] => 0 [Cited_Time] => [CitethisArticle] => Kumar P, Nguyen TH, Le PVV, Yan J, Zhao L, Allan BF, et al. Envisioning urban environments resilient to vector-borne diseases: a protocol to study dengue in Vietnam. Explor Digit Health Technol. 2023;1:17–27. https://doi.org/10.37349/edht.2023.00004 [Jindex] => 0 [CEmail] => [Ris_Time] => 2023-11-01 06:28:28 [Bib_Time] => 2023-11-01 06:28:28 [KeysWordContens] => Envisioning urban environments resilient to vector-borne diseases: a protocol to study dengue in Vietnam, Vector-borne disease, arbovirus, dengue, mosquito, urban environment, climate change, digital twin, Vietnam, Transmitted primarily by Aedes aegypti (Ae. aegypti) and Aedes albopictus (Ae. albopictus), arboviral diseases pose a major global public health threat. Dengue, chikungunya, and zika are increasingly prevalent in Southeast Asia. Among other arboviruses, dengue and zika are becoming more common in Central and South America. Given human encroachment into previously uninhabited, often deforested areas, to provide new housing in regions of population expansion, conceptualizing built urban environments in a novel way is urgently needed to safeguard against the growing climate change-driven threat of vector-borne diseases. By understanding the spread from a One Health perspective, enhanced control and prevention can be achieved. This is particularly important considering that climate change is likely to significantly impact the persistence of ponded water where mosquitoes breed due to increasing temperature and shifting rainfall patterns with regard to magnitude, duration, frequency, and season. Models can incorporate aquatic mosquito stages and adult spatial dynamics when habitats are heterogeneously available, thereby including dispersal and susceptible-exposed-infected-recovered (SEIR) epidemiology. Coupled with human population distribution (density, locations), atmospheric conditions (air temperature, precipitation), and hydrological conditions (soil moisture distribution, ponding persistence in topographic depressions), modeling has improved predictive ability for infection rates. However, it has not informed interventional approaches from an urban environment perspective which considers the role of ponds/lakes that support green spaces, the density of population that enables rapid spread of disease, and varying micro-habitats for various mosquito stages under climate change. Here, for an example of dengue in Vietnam, a preventive and predictive approach to design resilient urban environments is proposed, which uses data from rapidly expanding metropolitan communities to learn continually. This protocol deploys computational approaches including simulation and machine learning/artificial intelligence, underpinned by surveillance and medical data for validation and adaptive learning. Its application may best inform urban planning in low-middle income countries in tropical zones where arboviral pathogens are prevalent. ,Praveen Kumar ... Andrew W. Taylor-Robinson [PublishedText] => Published [IsEdit] => 1 [AccountId] => 86 [Zh] => 1 [AuthorsName] => Praveen Kumar, Thanh H. Nguyen, Phong V.V. Le, Jinhui Yan, Lei Zhao, Brian F. Allan, Andrew W. Taylor-Robinson ) [4] => Array ( [ArticleId] => 947 [Create_Time] => 2023-11-28 [zipUrl] => https://www.explorationpub.com/uploads/zip/202311/20231128060919.zip [xmlUrl] => https://www.explorationpub.com/uploads/Article/A10115/10115.xml [pdfUrl] => https://www.explorationpub.com/uploads/Article/A10115/10115.pdf [coverUrl] => https://www.explorationpub.com/uploads/Article/A10115/10115_cover.png [JournalsId] => 14 [Title] => Science communication on X (formerly Twitter): A picture is worth a thousand characters? [Abstract] => X (formerly Twitter), a microblogging social media platform, is being used by scientists and researchers to disseminate their research findings and promote the visibility of their work to the public [AbstractComplete] =>

X (formerly Twitter), a microblogging social media platform, is being used by scientists and researchers to disseminate their research findings and promote the visibility of their work to the public. Tweets can be posted with text messages, images, hyperlinks, or a combination of these features. Importantly, for the majority of users, the text must be limited to 280 characters. In this perspective, this study aimed to observe if adding an image is able to increase outreach for scientific communication on X. Therefore, the characteristics of tweets posted with the hashtag #SciComm (short for science communication) for a period of one year (28 May 2020 to 28 May 2021) were analyzed with the X analytics tool Symplur Signals. The conducted analysis revealed that when a science communication (#SciComm-containing) tweet is accompanied by an image added by the user, there is on average a 529% increase in the number of retweets, and adding a hyperlink is similarly effective in increasing the number of retweets. However, combining both an image and hyperlink in the same tweet did not yield an additive effect. Hence, for increased visibility, researchers may consider adding images or hyperlinks (e.g., to research publications or popular science articles) while communicating science to the public on X.

[Names] => Himel Mondal ... Harald Willschke [CName] => [Doi] => 10.37349/edht.2023.00005 [Published] => November 28, 2023 [Viewed] => 1470 [Downloaded] => 120 [Subject] => Perspective [Year] => 2023 [CiteUrl] => https://api.crossref.org/works/10.37349/edht.2023.00005 [Inline] => 1 [Type] => 1 [Issue] => 1 [Topic] => 0 [TitleAbbr] => Explor Digit Health Technol. [Pages] => 2023;1:28–34 [Recommend] => 0 [Keywords] => Social media, science communication, social media research [DetailTitle] => [DetailUrl] => [Id] => 10115 [ris] => https://www.explorationpub.com/uploads/Article/A10115/5c83e3307f587a9c3569535aeccfffe2.ris [bib] => https://www.explorationpub.com/uploads/Article/A10115/ad30518b3981bb064efe9c1d36d22653.bib [ens] => [Cited] => 0 [Cited_Time] => [CitethisArticle] => Mondal H, Atanasov AG, Eibensteiner F, Hribersek M, Brandstätter S, Matin M, et al. Science communication on X (formerly Twitter): A picture is worth a thousand characters? Explor Digit Health Technol. 2023;1:28–34. https://doi.org/10.37349/edht.2023.00005 [Jindex] => 0 [CEmail] => [Ris_Time] => 2023-11-28 06:09:19 [Bib_Time] => 2023-11-28 06:09:19 [KeysWordContens] => Science communication on X (formerly Twitter): A picture is worth a thousand characters?, Social media, science communication, social media research, X (formerly Twitter), a microblogging social media platform, is being used by scientists and researchers to disseminate their research findings and promote the visibility of their work to the public. Tweets can be posted with text messages, images, hyperlinks, or a combination of these features. Importantly, for the majority of users, the text must be limited to 280 characters. In this perspective, this study aimed to observe if adding an image is able to increase outreach for scientific communication on X. Therefore, the characteristics of tweets posted with the hashtag #SciComm (short for science communication) for a period of one year (28 May 2020 to 28 May 2021) were analyzed with the X analytics tool Symplur Signals. The conducted analysis revealed that when a science communication (#SciComm-containing) tweet is accompanied by an image added by the user, there is on average a 529% increase in the number of retweets, and adding a hyperlink is similarly effective in increasing the number of retweets. However, combining both an image and hyperlink in the same tweet did not yield an additive effect. Hence, for increased visibility, researchers may consider adding images or hyperlinks (e.g., to research publications or popular science articles) while communicating science to the public on X. ,Himel Mondal ... Harald Willschke [PublishedText] => Published [IsEdit] => 1 [AccountId] => 86 [Zh] => 1 [AuthorsName] => Himel Mondal, Atanas G. Atanasov, Fabian Eibensteiner, Mojca Hribersek, Stefan Brandstätter, Maima Matin, Ronan Lordan, Maria Kletecka-Pulker, Harald Willschke ) [5] => Array ( [ArticleId] => 1070 [Create_Time] => 2024-01-11 [zipUrl] => https://www.explorationpub.com/uploads/zip/202402/20240226085856.zip [xmlUrl] => https://www.explorationpub.com/uploads/Article/A10116/10116.xml [pdfUrl] => https://www.explorationpub.com/uploads/Article/A10116/10116.pdf [coverUrl] => https://www.explorationpub.com/uploads/Article/A10116/10116_cover.png [JournalsId] => 14 [Title] => Warning: Artificial intelligence chatbots can generate inaccurate medical and scientific information and references [Abstract] => The use of generative artificial intelligence (AI) chatbots, such as ChatGPT and YouChat, has increased enormously since their release in late 2022. Concerns have been raised over the potential of c [AbstractComplete] =>

The use of generative artificial intelligence (AI) chatbots, such as ChatGPT and YouChat, has increased enormously since their release in late 2022. Concerns have been raised over the potential of chatbots to facilitate cheating in education settings, including essay writing and exams. In addition, multiple publishers have updated their editorial policies to prohibit chatbot authorship on publications. This article highlights another potentially concerning issue; the strong propensity of chatbots in response to queries requesting medical and scientific information and its underlying references, to generate plausible looking but inaccurate responses, with the chatbots also generating nonexistent citations. As an example, a number of queries were generated and, using two popular chatbots, demonstrated that both generated inaccurate outputs. The authors thus urge extreme caution, because unwitting application of inconsistent and potentially inaccurate medical information could have adverse outcomes.

[Names] => Catherine L. Clelland ... James D. Clelland [CName] => [Doi] => 10.37349/edht.2024.00006 [Published] => January 10, 2024 [Viewed] => 2003 [Downloaded] => 166 [Subject] => Letter to the Editor [Year] => 2024 [CiteUrl] => https://api.crossref.org/works/10.37349/edht.2024.00006 [Inline] => 1 [Type] => 1 [Issue] => 1 [Topic] => 0 [TitleAbbr] => Explor Digit Health Technol. [Pages] => 2024;2:1–6 [Recommend] => 0 [Keywords] => ChatGPT, YouChat, artificial intelligence, inaccurate information [DetailTitle] => [DetailUrl] => [Id] => 10116 [ris] => https://www.explorationpub.com/uploads/Article/A10116/2df6ccae06f9c5f08ea67f2bf1d5deca.ris [bib] => https://www.explorationpub.com/uploads/Article/A10116/1ae7a669a65285f68938dfd08375494e.bib [ens] => [Cited] => 0 [Cited_Time] => [CitethisArticle] => Clelland CL, Moss S, Clelland JD. Warning: Artificial intelligence chatbots can generate inaccurate medical and scientific information and references. Explor Digit Health Technol. 2024;2:1–6. https://doi.org/10.37349/edht.2024.00006 [Jindex] => 0 [CEmail] => [Ris_Time] => 2024-02-26 08:58:56 [Bib_Time] => 2024-02-26 08:58:56 [KeysWordContens] => Warning: Artificial intelligence chatbots can generate inaccurate medical and scientific information and references, ChatGPT, YouChat, artificial intelligence, inaccurate information, The use of generative artificial intelligence (AI) chatbots, such as ChatGPT and YouChat, has increased enormously since their release in late 2022. Concerns have been raised over the potential of chatbots to facilitate cheating in education settings, including essay writing and exams. In addition, multiple publishers have updated their editorial policies to prohibit chatbot authorship on publications. This article highlights another potentially concerning issue; the strong propensity of chatbots in response to queries requesting medical and scientific information and its underlying references, to generate plausible looking but inaccurate responses, with the chatbots also generating nonexistent citations. As an example, a number of queries were generated and, using two popular chatbots, demonstrated that both generated inaccurate outputs. The authors thus urge extreme caution, because unwitting application of inconsistent and potentially inaccurate medical information could have adverse outcomes. ,Catherine L. Clelland ... James D. Clelland [PublishedText] => Published [IsEdit] => 1 [AccountId] => 86 [Zh] => 1 [AuthorsName] => Catherine L. Clelland, Stuart Moss, James D. Clelland ) [6] => Array ( [ArticleId] => 1159 [Create_Time] => 2024-02-28 [zipUrl] => https://www.explorationpub.com/uploads/zip/202405/20240509055845.zip [xmlUrl] => https://www.explorationpub.com/uploads/Article/A10117/10117.xml [pdfUrl] => https://www.explorationpub.com/uploads/Article/A10117/10117.pdf [coverUrl] => https://www.explorationpub.com/uploads/Article/A10117/10117_cover.png [JournalsId] => 14 [Title] => Functionality and feasibility of cognitive function training via mobile health application among youth at risk for psychosis [Abstract] => Aim: Mobile health applications (MHAs) have been rapidly designed and urgently need evaluation. Existing evaluation methods, such as platform, development, and subjective overall user observation [AbstractComplete] =>

Aim:

Mobile health applications (MHAs) have been rapidly designed and urgently need evaluation. Existing evaluation methods, such as platform, development, and subjective overall user observations, are mostly based on application (app) design. This study aimed to evaluate the functionality and feasibility of an MHA to train cognitive function in youth at clinical high risk (CHR) for psychosis with a tool that allows a comprehensive user experience evaluation of mobile apps from multiple dimensions.

Methods:

Eighty participants with CHR for psychosis were recruited and randomly assigned to the intervention and the group control. Participants in the intervention group used the Specific Memory Attention Resource and Training (SMART) app for three months. MHA’s functionality and feasibility were measured by the mobile app rating scale (MARS) and qualitative tools.

Results:

Participants in the SMART group report that the form and design of this app are simple to operate, and the content is trustworthy. They reported improvement in cognitive function and more motivation to seek help to improve their cognitive function. They also pointed out areas of improvement.

Conclusions:

SMART usability and functionality were measured by a multidimensional tool. It shows promise in improving CHR memory and attention and demonstrates appropriate usability and functionality.

[Names] => Huijun Li ... Jijun Wang [CName] => HongmeiChi, [Doi] => 10.37349/edht.2024.00007 [Published] => February 27, 2024 [Viewed] => 1167 [Downloaded] => 121 [Subject] => Original Article [Year] => 2024 [CiteUrl] => https://api.crossref.org/works/10.37349/edht.2024.00007 [Inline] => 1 [Type] => 1 [Issue] => 1 [Topic] => 0 [TitleAbbr] => Explor Digit Health Technol. [Pages] => 2024;2:7–19 [Recommend] => 0 [Keywords] => User interface design, mobile health application, serious games, mental health, user willingness [DetailTitle] => [DetailUrl] => [Id] => 10117 [ris] => https://www.explorationpub.com/uploads/Article/A10117/616c9791640091c5b3ca7acf2f1b5ead.ris [bib] => https://www.explorationpub.com/uploads/Article/A10117/0dbae8b8de0f953f87202fd7dbc58cd8.bib [ens] => [Cited] => 0 [Cited_Time] => [CitethisArticle] => Li H, Yang S, Chi H, Xu L, Zhang T, Bao F, et al. Functionality and feasibility of cognitive function training via mobile health application among youth at risk for psychosis. Explor Digit Health Technol. 2024;2:7–19. https://doi.org/10.37349/edht.2024.00007 [Jindex] => 0 [CEmail] => hongmei.chi@famu.edu, [Ris_Time] => 2024-02-27 07:50:11 [Bib_Time] => 2024-02-27 07:50:11 [KeysWordContens] => Functionality and feasibility of cognitive function training via mobile health application among youth at risk for psychosis, User interface design, mobile health application, serious games, mental health, user willingness, Aim: Mobile health applications (MHAs) have been rapidly designed and urgently need evaluation. Existing evaluation methods, such as platform, development, and subjective overall user observations, are mostly based on application (app) design. This study aimed to evaluate the functionality and feasibility of an MHA to train cognitive function in youth at clinical high risk (CHR) for psychosis with a tool that allows a comprehensive user experience evaluation of mobile apps from multiple dimensions. Methods: Eighty participants with CHR for psychosis were recruited and randomly assigned to the intervention and the group control. Participants in the intervention group used the Specific Memory Attention Resource and Training (SMART) app for three months. MHA’s functionality and feasibility were measured by the mobile app rating scale (MARS) and qualitative tools. Results: Participants in the SMART group report that the form and design of this app are simple to operate, and the content is trustworthy. They reported improvement in cognitive function and more motivation to seek help to improve their cognitive function. They also pointed out areas of improvement. Conclusions: SMART usability and functionality were measured by a multidimensional tool. It shows promise in improving CHR memory and attention and demonstrates appropriate usability and functionality. ,Huijun Li ... Jijun Wang [PublishedText] => Published [IsEdit] => 0 [AccountId] => 86 [Zh] => 1 [AuthorsName] => Huijun Li, Shunwen Yang, Hongmei Chi, Lihua Xu, Tianhong Zhang, Feng Bao, William S. Stone, Jijun Wang ) [7] => Array ( [ArticleId] => 1160 [Create_Time] => 2024-02-28 [zipUrl] => https://www.explorationpub.com/uploads/zip/202402/20240228065426.zip [xmlUrl] => https://www.explorationpub.com/uploads/Article/A10118/10118.xml [pdfUrl] => https://www.explorationpub.com/uploads/Article/A10118/10118.pdf [coverUrl] => https://www.explorationpub.com/uploads/Article/A10118/10118_cover.png [JournalsId] => 14 [Title] => System for classifying antibody concentration against severe acute respiratory syndrome coronavirus 2 S1 spike antigen with automatic quick response generation for integration with health passports [Abstract] => Aim: After the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic and the realization of mass vaccination against the virus, the availability of a reliable, rapid, and easy-to- [AbstractComplete] =>

Aim:

After the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic and the realization of mass vaccination against the virus, the availability of a reliable, rapid, and easy-to-use system for registering the individual anti-S1 antibody titer could facilitate the personalized assessment of the need for booster vaccine doses and the reduction of social distancing and other measures.

Methods:

The biosensor system is based on immobilized engineered SK-N-SH neuroblastoma cells, bearing the S1 protein, and it can detect immunoglobulin G (IgG) antibodies against the SARS-CoV-2 S1 spike antigen. A disposable electrode strip bearing the engineered mammalian cells is connected to a customized read-out potentiometric device with real-time data transmission to a wireless fidelity (WiFi)-connected smartphone. Blood samples from past-infected individuals and individuals vaccinated against SARS-CoV-2 were used for validation.

Results:

In the present study, a smartphone application (app), capable of analyzing data regarding the levels of anti-S1 antibodies in blood is introduced. The app works in conjunction with a portable, ultra-rapid, and sensitive biosensor transmitting real-time measurements to the smartphone. Both historical and current individual data can be encoded by using the app, resulting in a widely accepted quick response (QR) code, which can then be constantly updated to match a person’s status.

Conclusions:

This novel system could be utilized for the eventual development of a coronavirus disease 2019 (COVID-19) electronic passport, which could be further employed to improve the population-wide, cross-country surveillance of vaccination efficiency, as well as facilitate the implementation of cross-border digital health services in a user-friendly and secure way.

[Names] => Apostolos Apostolakis ... Spyridon Kintzios [CName] => [Doi] => 10.37349/edht.2024.00008 [Published] => February 28, 2024 [Viewed] => 1374 [Downloaded] => 129 [Subject] => Original Article [Year] => 2024 [CiteUrl] => https://api.crossref.org/works/10.37349/edht.2024.00008 [Inline] => 1 [Type] => 1 [Issue] => 1 [Topic] => 160 [TitleAbbr] => Explor Digit Health Technol. [Pages] => 2024;2:20–29 [Recommend] => 0 [Keywords] => Antibody screening, biosensor, health passport, quick response code, severe acute respiratory syndrome coronavirus 2, smartphone, surveillance, vaccination [DetailTitle] => Biosensors for Bioactive Molecules [DetailUrl] => https://www.explorationpub.com/Journals/edht/Special_Issues/160 [Id] => 10118 [ris] => https://www.explorationpub.com/uploads/Article/A10118/e6cafbb2d6580adedd646028f103885a.ris [bib] => https://www.explorationpub.com/uploads/Article/A10118/05096ede3a22a645ba9665a3edf0392f.bib [ens] => [Cited] => 1 [Cited_Time] => 2024-05-27 [CitethisArticle] => Apostolakis A, Barmpakos D, Mavrikou S, Papaionannou GM, Tsekouras V, Hatziagapiou K, et al. System for classifying antibody concentration against severe acute respiratory syndrome coronavirus 2 S1 spike antigen with automatic quick response generation for integration with health passports. Explor Digit Health Technol. 2024;2:20–9. https://doi.org/10.37349/edht.2024.00008 [Jindex] => 0 [CEmail] => [Ris_Time] => 2024-02-28 06:54:26 [Bib_Time] => 2024-02-28 06:54:26 [KeysWordContens] => System for classifying antibody concentration against severe acute respiratory syndrome coronavirus 2 S1 spike antigen with automatic quick response generation for integration with health passports, Antibody screening, biosensor, health passport, quick response code, severe acute respiratory syndrome coronavirus 2, smartphone, surveillance, vaccination, Aim: After the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic and the realization of mass vaccination against the virus, the availability of a reliable, rapid, and easy-to-use system for registering the individual anti-S1 antibody titer could facilitate the personalized assessment of the need for booster vaccine doses and the reduction of social distancing and other measures. Methods: The biosensor system is based on immobilized engineered SK-N-SH neuroblastoma cells, bearing the S1 protein, and it can detect immunoglobulin G (IgG) antibodies against the SARS-CoV-2 S1 spike antigen. A disposable electrode strip bearing the engineered mammalian cells is connected to a customized read-out potentiometric device with real-time data transmission to a wireless fidelity (WiFi)-connected smartphone. Blood samples from past-infected individuals and individuals vaccinated against SARS-CoV-2 were used for validation. Results: In the present study, a smartphone application (app), capable of analyzing data regarding the levels of anti-S1 antibodies in blood is introduced. The app works in conjunction with a portable, ultra-rapid, and sensitive biosensor transmitting real-time measurements to the smartphone. Both historical and current individual data can be encoded by using the app, resulting in a widely accepted quick response (QR) code, which can then be constantly updated to match a person’s status. Conclusions: This novel system could be utilized for the eventual development of a coronavirus disease 2019 (COVID-19) electronic passport, which could be further employed to improve the population-wide, cross-country surveillance of vaccination efficiency, as well as facilitate the implementation of cross-border digital health services in a user-friendly and secure way. ,Apostolos Apostolakis ... Spyridon Kintzios [PublishedText] => Published [IsEdit] => 1 [AccountId] => 86 [Zh] => 1 [AuthorsName] => Apostolos Apostolakis, Dimitris Barmpakos, Sofia Mavrikou, George Marios Papaionannou, Vasileios Tsekouras, Kyriaki Hatziagapiou, Eleni Koniari, Maroula Tritzali, Athanasios Michos, George P. Chrousos, Christina Kanaka-Gantenbein, Grigoris Kaltsas, Spyrid ) [8] => Array ( [ArticleId] => 1166 [Create_Time] => 2024-02-29 [zipUrl] => https://www.explorationpub.com/uploads/zip/202402/20240229063232.zip [xmlUrl] => https://www.explorationpub.com/uploads/Article/A10119/10119.xml [pdfUrl] => https://www.explorationpub.com/uploads/Article/A10119/10119.pdf [coverUrl] => https://www.explorationpub.com/uploads/Article/A10119/10119_cover.png [JournalsId] => 14 [Title] => Use of responsible artificial intelligence to predict health insurance claims in the USA using machine learning algorithms [Abstract] => Aim: This study investigates the potential of artificial intelligence (AI) in revolutionizing healthcare insurance claim processing in the USA. It aims to determine the most effective machine lea [AbstractComplete] =>

Aim:

This study investigates the potential of artificial intelligence (AI) in revolutionizing healthcare insurance claim processing in the USA. It aims to determine the most effective machine learning (ML) model for predicting health insurance claims, leading to cost savings for insurance companies.

Methods:

Six ML algorithms were used to predict health insurance claims, and their performance was evaluated using various metrics. The algorithms examined include support vector machine (SVM), decision tree (DT), random forest (RF), linear regression (LR), extreme gradient boosting (XGBoost), and k-nearest neighbors (KNN). The research involves a performance assessment that encompasses key metrics. Additionally, a feature importance analysis is conducted to illuminate the critical variables that exert influence on the prediction of insurance claims.

Results:

The findings demonstrate that the XGBoost and RF models outperformed the other algorithms, displaying the highest R-squared values of 79% and 77% and the lowest prediction errors. The feature importance analysis underscores the pivotal role of variables such as smoking habits, body mass index (BMI), and blood pressure levels in the domain of insurance claim prediction. These results emphasize the degree to which these variables should be included in the formulation of insurance policies and pricing strategies.

Conclusions:

This study supports the transformative potential of AI, with specific emphasis on the XGBoost model, in extending the precision and efficiency of healthcare insurance claim processing. The identification of key variables and the mitigation of prediction errors not only signal the potential for substantial cost savings but also affirm the potential to integrate AI into healthcare insurance processes. This research supports the value of the utilization of AI as an emerging tool for process optimization and data-informed decision-making within the healthcare insurance domain.

[Names] => Ashrafe Alam, Victor R. Prybutok [CName] => [Doi] => 10.37349/edht.2024.00009 [Published] => February 28, 2024 [Viewed] => 1384 [Downloaded] => 138 [Subject] => Original Article [Year] => 2024 [CiteUrl] => https://api.crossref.org/works/10.37349/edht.2024.00009 [Inline] => 1 [Type] => 1 [Issue] => 1 [Topic] => 220 [TitleAbbr] => Explor Digit Health Technol. [Pages] => 2024;2:30–45 [Recommend] => 0 [Keywords] => Insurance claim, responsible artificial intelligence, machine learning [DetailTitle] => Data-informed Decision Making in Healthcare [DetailUrl] => https://www.explorationpub.com/Journals/edht/Special_Issues/220 [Id] => 10119 [ris] => https://www.explorationpub.com/uploads/Article/A10119/0c283611186ab42d816631bbab6176d4.ris [bib] => https://www.explorationpub.com/uploads/Article/A10119/28ca665c6bf48282de8b97af238d7212.bib [ens] => [Cited] => 1 [Cited_Time] => 2024-05-27 [CitethisArticle] => Alam A, Prybutok VR. Use of responsible artificial intelligence to predict health insurance claims in the USA using machine learning algorithms. Explor Digit Health Technol. 2024;2:30–45. https://doi.org/10.37349/edht.2024.00009 [Jindex] => 0 [CEmail] => [Ris_Time] => 2024-02-29 06:32:33 [Bib_Time] => 2024-02-29 06:32:33 [KeysWordContens] => Use of responsible artificial intelligence to predict health insurance claims in the USA using machine learning algorithms, Insurance claim, responsible artificial intelligence, machine learning, Aim: This study investigates the potential of artificial intelligence (AI) in revolutionizing healthcare insurance claim processing in the USA. It aims to determine the most effective machine learning (ML) model for predicting health insurance claims, leading to cost savings for insurance companies. Methods: Six ML algorithms were used to predict health insurance claims, and their performance was evaluated using various metrics. The algorithms examined include support vector machine (SVM), decision tree (DT), random forest (RF), linear regression (LR), extreme gradient boosting (XGBoost), and k-nearest neighbors (KNN). The research involves a performance assessment that encompasses key metrics. Additionally, a feature importance analysis is conducted to illuminate the critical variables that exert influence on the prediction of insurance claims. Results: The findings demonstrate that the XGBoost and RF models outperformed the other algorithms, displaying the highest R-squared values of 79% and 77% and the lowest prediction errors. The feature importance analysis underscores the pivotal role of variables such as smoking habits, body mass index (BMI), and blood pressure levels in the domain of insurance claim prediction. These results emphasize the degree to which these variables should be included in the formulation of insurance policies and pricing strategies. Conclusions: This study supports the transformative potential of AI, with specific emphasis on the XGBoost model, in extending the precision and efficiency of healthcare insurance claim processing. The identification of key variables and the mitigation of prediction errors not only signal the potential for substantial cost savings but also affirm the potential to integrate AI into healthcare insurance processes. This research supports the value of the utilization of AI as an emerging tool for process optimization and data-informed decision-making within the healthcare insurance domain. ,Ashrafe Alam, Victor R. Prybutok [PublishedText] => Published [IsEdit] => 1 [AccountId] => 86 [Zh] => 1 [AuthorsName] => Ashrafe Alam, Victor R. Prybutok ) [9] => Array ( [ArticleId] => 1183 [Create_Time] => 2024-03-28 [zipUrl] => https://www.explorationpub.com/uploads/zip/202404/20240425014037.zip [xmlUrl] => https://www.explorationpub.com/uploads/Article/A101110/101110.xml [pdfUrl] => https://www.explorationpub.com/uploads/Article/A101110/101110.pdf [coverUrl] => https://www.explorationpub.com/uploads/Article/A101110/101110_cover.png [JournalsId] => 14 [Title] => Breathing tech: digital health innovations for managing asthma-related psychological dimensions [Abstract] => The paper aimed to provide a comprehensive overview of the use of digital health technologies in the assessment, treatment, and self-management of psychological and psychopathological factors associ [AbstractComplete] =>

The paper aimed to provide a comprehensive overview of the use of digital health technologies in the assessment, treatment, and self-management of psychological and psychopathological factors associated with asthma. A collection of research articles and systematic reviews related to asthma, including topics such as outdoor air pollution, early life wheezing illnesses, atopic dermatitis, digital interventions for asthma self-management, psychiatric disorders and asthma, family influences on pediatric asthma, and the use of mobile health (mHealth) applications for asthma management, were analyzed. Eight selected studies were reviewed to assess the potential of digital health technologies in improving asthma psychological-related factors management and treatment outcomes. The reviewed studies suggest that electronic health (eHealth) interventions, mixed reality tools, mHealth technology-enhanced nurse-guided interventions, and smartphone applications integrating Bluetooth-enabled sensors for asthma inhalers can significantly improve symptom self-management, quality of life, and mental health outcomes, especially in children and adolescents with asthma (JMIR Pediatr Parent. 2019;2:e12427. doi: 10.2196/12427; Cochrane Database Syst Rev. 2018;8:CD012489. doi: 10.1002/14651858.CD012489.pub2; Int J Environ Res Public Health. 2020;17:7750. doi: 10.3390/ijerph17217750; J Med Internet Res. 2017;19:e113. doi: 10.2196/jmir.6994; J Med Internet Res. 2021;23:e25472. doi: 10.2196/25472; Ann Allergy Asthma Immunol. 2015;114:341–2.E2. doi: 10.1016/j.anai.2014.12.017; J Med Internet Res. 2022;24:e38030. doi: 10.2196/38030; Int J Qual Methods. 2021;20:16094069211008333. doi: 10.1177/16094069211008333). However, further research is needed to determine their effectiveness and feasibility in different populations and settings. Tailored interventions that address the specific needs and preferences of patients with asthma and associated psychological factors are crucial for ensuring sustained and equitable use of these technologies. The manuscript emphasizes the importance of addressing psychological factors in the management and treatment of asthma and call for continued research and development in this area.

[Names] => Mirko Casu, Pasquale Caponnetto [CName] => [Doi] => 10.37349/edht.2024.00010 [Published] => March 28, 2024 [Viewed] => 1304 [Downloaded] => 124 [Subject] => Review [Year] => 2024 [CiteUrl] => https://api.crossref.org/works/10.37349/edht.2024.00010 [Inline] => 1 [Type] => 1 [Issue] => 2 [Topic] => 238 [TitleAbbr] => Explor Digit Health Technol. [Pages] => 2024;2:46–58 [Recommend] => 0 [Keywords] => Asthma, electronic health, mobile applications, digital health interventions, mixed reality, depression, anxiety, clinical psychology [DetailTitle] => Telepsychiatry in Low-and Middle-income Countries: an Update [DetailUrl] => https://www.explorationpub.com/Journals/edht/Special_Issues/238 [Id] => 101110 [ris] => https://www.explorationpub.com/uploads/Article/A101110/ea6a97120f6b2952b68639bb3be1e92e.ris [bib] => https://www.explorationpub.com/uploads/Article/A101110/ca9f7a03f40fd4bdd4f92fbb34c64632.bib [ens] => [Cited] => 0 [Cited_Time] => [CitethisArticle] => Casu M, Caponnetto P. Breathing tech: digital health innovations for managing asthma-related psychological dimensions. Explor Digit Health Technol. 2024;2:46–58. https://doi.org/10.37349/edht.2024.00010 [Jindex] => 0 [CEmail] => [Ris_Time] => 2024-04-25 01:45:36 [Bib_Time] => 2024-04-25 01:45:36 [KeysWordContens] => Breathing tech: digital health innovations for managing asthma-related psychological dimensions, Asthma, electronic health, mobile applications, digital health interventions, mixed reality, depression, anxiety, clinical psychology, The paper aimed to provide a comprehensive overview of the use of digital health technologies in the assessment, treatment, and self-management of psychological and psychopathological factors associated with asthma. A collection of research articles and systematic reviews related to asthma, including topics such as outdoor air pollution, early life wheezing illnesses, atopic dermatitis, digital interventions for asthma self-management, psychiatric disorders and asthma, family influences on pediatric asthma, and the use of mobile health (mHealth) applications for asthma management, were analyzed. Eight selected studies were reviewed to assess the potential of digital health technologies in improving asthma psychological-related factors management and treatment outcomes. The reviewed studies suggest that electronic health (eHealth) interventions, mixed reality tools, mHealth technology-enhanced nurse-guided interventions, and smartphone applications integrating Bluetooth-enabled sensors for asthma inhalers can significantly improve symptom self-management, quality of life, and mental health outcomes, especially in children and adolescents with asthma (JMIR Pediatr Parent. 2019;2:e12427. doi: 10.2196/12427; Cochrane Database Syst Rev. 2018;8:CD012489. doi: 10.1002/14651858.CD012489.pub2; Int J Environ Res Public Health. 2020;17:7750. doi: 10.3390/ijerph17217750; J Med Internet Res. 2017;19:e113. doi: 10.2196/jmir.6994; J Med Internet Res. 2021;23:e25472. doi: 10.2196/25472; Ann Allergy Asthma Immunol. 2015;114:341–2.E2. doi: 10.1016/j.anai.2014.12.017; J Med Internet Res. 2022;24:e38030. doi: 10.2196/38030; Int J Qual Methods. 2021;20:16094069211008333. doi: 10.1177/16094069211008333). However, further research is needed to determine their effectiveness and feasibility in different populations and settings. Tailored interventions that address the specific needs and preferences of patients with asthma and associated psychological factors are crucial for ensuring sustained and equitable use of these technologies. The manuscript emphasizes the importance of addressing psychological factors in the management and treatment of asthma and call for continued research and development in this area. ,Mirko Casu, Pasquale Caponnetto [PublishedText] => Published [IsEdit] => 0 [AccountId] => 56 [Zh] => 1 [AuthorsName] => Mirko Casu, Pasquale Caponnetto ) [10] => Array ( [ArticleId] => 1207 [Create_Time] => 2024-04-08 [zipUrl] => https://www.explorationpub.com/uploads/zip/202405/20240506081652.zip [xmlUrl] => https://www.explorationpub.com/uploads/Article/A101111/101111.xml [pdfUrl] => https://www.explorationpub.com/uploads/Article/A101111/101111.pdf [coverUrl] => https://www.explorationpub.com/uploads/Article/A101111/101111_cover.png [JournalsId] => 14 [Title] => Identifying children’s environmental health risks, needs, misconceptions, and opportunities for research translation using social media [Abstract] => As part of the Advancing Science, Practice, Programming, and Policy in Research Translation for Children’s Environmental Health (ASP3IRE) center, machine learning, geographic information systems ( [AbstractComplete] =>

As part of the Advancing Science, Practice, Programming, and Policy in Research Translation for Children’s Environmental Health (ASP3IRE) center, machine learning, geographic information systems (GIS), and natural language processing to analyze more than 650 million posts related to children’s environmental health are being used. Using preliminary analyses as examples, this commentary discusses the potential opportunities, benefits, challenges, and limitations of children’s health social media analytics. Social media contains large volumes of contextually rich data that describe children’s health risks and needs, characteristics of homes and childcare locations important to environmental exposures, and parent and childcare provider perceptions, awareness of, and misconceptions about children’s environmental health. Twenty five million unique conversations mentioning children, with likes, views, and replies from more than 33 million X (formerly Twitter) users were identified. Many of these posts can be linked to traditional environmental and health data. However, social media analytics have several challenges and limitations. Challenges include a need for interdisciplinary collaborations, selectivity and sensitivity of analytical methods, the dynamic, evolving communication methods and platform preferences of social media users, and operational policies. Limitations include data availability, generalizability, and self-report bias. Social media analytics has significant potential to contribute to children’s environmental health research and translation.

[Names] => Andrew Larkin ... Perry Hystad [CName] => AndrewLarkin, [Doi] => 10.37349/edht.2024.00011 [Published] => April 08, 2024 [Viewed] => 1252 [Downloaded] => 110 [Subject] => Commentary [Year] => 2024 [CiteUrl] => https://api.crossref.org/works/10.37349/edht.2024.00011 [Inline] => 1 [Type] => 1 [Issue] => 2 [Topic] => 143 [TitleAbbr] => Explor Digit Health Technol. [Pages] => 2024;2:59–66 [Recommend] => 0 [Keywords] => Social media, environment, children, health [DetailTitle] => Social Media Applications in Biomedical Research [DetailUrl] => https://www.explorationpub.com/Journals/edht/Special_Issues/143 [Id] => 101111 [ris] => https://www.explorationpub.com/uploads/Article/A101111/e4230ab0f4fae0c87cd0a5fbebc359c8.ris [bib] => https://www.explorationpub.com/uploads/Article/A101111/106e53f1459fe73858e519f986a256e3.bib [ens] => [Cited] => 0 [Cited_Time] => [CitethisArticle] => Larkin A, MacDonald M, Jackson D, Kile ML, Hystad P. Identifying children’s environmental health risks, needs, misconceptions, and opportunities for research translation using social media. Explor Digit Health Technol. 2024;2:59–66. https://doi.org/10.37349/edht.2024.00011 [Jindex] => 0 [CEmail] => larkinan@oregonstate.edu, [Ris_Time] => 2024-04-09 00:43:54 [Bib_Time] => 2024-04-09 00:43:54 [KeysWordContens] => Identifying children’s environmental health risks, needs, misconceptions, and opportunities for research translation using social media, Social media, environment, children, health, As part of the Advancing Science, Practice, Programming, and Policy in Research Translation for Children’s Environmental Health (ASP3IRE) center, machine learning, geographic information systems (GIS), and natural language processing to analyze more than 650 million posts related to children’s environmental health are being used. Using preliminary analyses as examples, this commentary discusses the potential opportunities, benefits, challenges, and limitations of children’s health social media analytics. Social media contains large volumes of contextually rich data that describe children’s health risks and needs, characteristics of homes and childcare locations important to environmental exposures, and parent and childcare provider perceptions, awareness of, and misconceptions about children’s environmental health. Twenty five million unique conversations mentioning children, with likes, views, and replies from more than 33 million X (formerly Twitter) users were identified. Many of these posts can be linked to traditional environmental and health data. However, social media analytics have several challenges and limitations. Challenges include a need for interdisciplinary collaborations, selectivity and sensitivity of analytical methods, the dynamic, evolving communication methods and platform preferences of social media users, and operational policies. Limitations include data availability, generalizability, and self-report bias. Social media analytics has significant potential to contribute to children’s environmental health research and translation. ,Andrew Larkin ... Perry Hystad [PublishedText] => Published [IsEdit] => 0 [AccountId] => 86 [Zh] => 1 [AuthorsName] => Andrew Larkin, Megan MacDonald, Dixie Jackson, Molly L. Kile, Perry Hystad ) [11] => Array ( [ArticleId] => 1215 [Create_Time] => 2024-04-12 [zipUrl] => https://www.explorationpub.com/uploads/zip/202404/20240429072506.zip [xmlUrl] => https://www.explorationpub.com/uploads/Article/A101112/101112.xml [pdfUrl] => https://www.explorationpub.com/uploads/Article/A101112/101112.pdf [coverUrl] => https://www.explorationpub.com/uploads/Article/A101112/101112_cover.png [JournalsId] => 14 [Title] => Data science techniques to gain novel insights into quality of care: a scoping review of long-term care for older adults [Abstract] => Background: The increase in powerful computers and technological devices as well as new forms of data analysis such as machine learning have resulted in the widespread availability of data scienc [AbstractComplete] =>

Background:

The increase in powerful computers and technological devices as well as new forms of data analysis such as machine learning have resulted in the widespread availability of data science in healthcare. However, its role in organizations providing long-term care (LTC) for older people LTC for older adults has yet to be systematically synthesized. This analysis provides a state-of-the-art overview of 1) data science techniques that are used with data accumulated in LTC and for what specific purposes and, 2) the results of these techniques in researching the study objectives at hand.

Methods:

A scoping review based on guidelines of the Joanna Briggs Institute. PubMed and Cumulative Index to Nursing and Allied Health Literature (CINAHL) were searched using keywords related to data science techniques and LTC. The screening and selection process was carried out by two authors and was not limited by any research design or publication date. A narrative synthesis was conducted based on the two aims.

Results:

The search strategy yielded 1,488 studies: 27 studies were included of which the majority were conducted in the US and in a nursing home setting. Text-mining/natural language processing (NLP) and support vector machines (SVMs) were the most deployed methods; accuracy was the most used metric. These techniques were primarily utilized for researching specific adverse outcomes including the identification of risk factors for falls and the prediction of frailty. All studies concluded that these techniques are valuable for their specific purposes.

Discussion:

This review reveals the limited use of data science techniques on data accumulated in or by LTC facilities. The low number of included articles in this review indicate the need for strategies aimed at the effective utilization of data with data science techniques and evidence of their practical benefits. There is a need for a wider adoption of these techniques in order to exploit data to their full potential and, consequently, improve the quality of care in LTC by making data-informed decisions.

[Names] => Ard Hendriks ... Sil Aarts [CName] => [Doi] => 10.37349/edht.2024.00012 [Published] => April 12, 2024 [Viewed] => 1252 [Downloaded] => 109 [Subject] => Systematic Review [Year] => 2024 [CiteUrl] => https://api.crossref.org/works/10.37349/edht.2024.00012 [Inline] => 1 [Type] => 1 [Issue] => 2 [Topic] => 220 [TitleAbbr] => Explor Digit Health Technol. [Pages] => 2024;2:67–85 [Recommend] => 0 [Keywords] => Data science, long-term care, analyzing methods, big data, big data analytics, older adults, nursing homes [DetailTitle] => Data-informed Decision Making in Healthcare [DetailUrl] => https://www.explorationpub.com/Journals/edht/Special_Issues/220 [Id] => 101112 [ris] => https://www.explorationpub.com/uploads/Article/A101112/f5ef1a2a9312d488d254d7b88036eae5.ris [bib] => https://www.explorationpub.com/uploads/Article/A101112/91cdad581a8b721f81988f6ac4bd4cb9.bib [ens] => [Cited] => 0 [Cited_Time] => [CitethisArticle] => Hendriks A, Hacking C, Verbeek H, Aarts S. Data science techniques to gain novel insights into quality of care: a scoping review of long-term care for older adults. Explor Digit Health Technol. 2024;2:67–85. https://doi.org/10.37349/edht.2024.00012 [Jindex] => 0 [CEmail] => [Ris_Time] => 2024-04-07 03:52:49 [Bib_Time] => 2024-04-07 03:52:49 [KeysWordContens] => Data science techniques to gain novel insights into quality of care: a scoping review of long-term care for older adults, Data science, long-term care, analyzing methods, big data, big data analytics, older adults, nursing homes, Background: The increase in powerful computers and technological devices as well as new forms of data analysis such as machine learning have resulted in the widespread availability of data science in healthcare. However, its role in organizations providing long-term care (LTC) for older people LTC for older adults has yet to be systematically synthesized. This analysis provides a state-of-the-art overview of 1) data science techniques that are used with data accumulated in LTC and for what specific purposes and, 2) the results of these techniques in researching the study objectives at hand. Methods: A scoping review based on guidelines of the Joanna Briggs Institute. PubMed and Cumulative Index to Nursing and Allied Health Literature (CINAHL) were searched using keywords related to data science techniques and LTC. The screening and selection process was carried out by two authors and was not limited by any research design or publication date. A narrative synthesis was conducted based on the two aims. Results: The search strategy yielded 1,488 studies: 27 studies were included of which the majority were conducted in the US and in a nursing home setting. Text-mining/natural language processing (NLP) and support vector machines (SVMs) were the most deployed methods; accuracy was the most used metric. These techniques were primarily utilized for researching specific adverse outcomes including the identification of risk factors for falls and the prediction of frailty. All studies concluded that these techniques are valuable for their specific purposes. Discussion: This review reveals the limited use of data science techniques on data accumulated in or by LTC facilities. The low number of included articles in this review indicate the need for strategies aimed at the effective utilization of data with data science techniques and evidence of their practical benefits. There is a need for a wider adoption of these techniques in order to exploit data to their full potential and, consequently, improve the quality of care in LTC by making data-informed decisions. ,Ard Hendriks ... Sil Aarts [PublishedText] => Published [IsEdit] => 0 [AccountId] => 86 [Zh] => 0 [AuthorsName] => Ard Hendriks, Coen Hacking, Hilde Verbeek, Sil Aarts ) [12] => Array ( [ArticleId] => 1235 [Create_Time] => 2024-04-22 [zipUrl] => https://www.explorationpub.com/uploads/zip/202405/20240509034036.zip [xmlUrl] => https://www.explorationpub.com/uploads/Article/A101113/101113.xml [pdfUrl] => https://www.explorationpub.com/uploads/Article/A101113/101113.pdf [coverUrl] => https://www.explorationpub.com/uploads/Article/A101113/101113_cover.png [JournalsId] => 14 [Title] => Digital health and mobile health: a bibliometric analysis of the 100 most cited papers and their contributing authors [Abstract] => Aim: This study aimed to identify and analyze the top 100 most cited digital health and mobile health (m-health) publications. It could aid researchers in the identification of promising new rese [AbstractComplete] =>

Aim:

This study aimed to identify and analyze the top 100 most cited digital health and mobile health (m-health) publications. It could aid researchers in the identification of promising new research avenues, additionally supporting the establishment of international scientific collaboration between interdisciplinary research groups with demonstrated achievements in the area of interest.

Methods:

On 30th August, 2023, the Web of Science Core Collection (WOSCC) electronic database was queried to identify the top 100 most cited digital health papers with a comprehensive search string. From the initial search, 106 papers were identified. After screening for relevance, six papers were excluded, resulting in the final list of the top 100 papers. The basic bibliographic data was directly extracted from WOSCC using its “Analyze” and “Create Citation Report” functions. The complete records of the top 100 papers were downloaded and imported into a bibliometric software called VOSviewer (version 1.6.19) to generate an author keyword map and author collaboration map.

Results:

The top 100 papers on digital health received a total of 49,653 citations. Over half of them (n = 55) were published during 2013–2017. Among these 100 papers, 59 were original articles, 36 were reviews, 4 were editorial materials, and 1 was a proceeding paper. All papers were written in English. The University of London and the University of California system were the most represented affiliations. The USA and the UK were the most represented countries. The Journal of Medical Internet Research was the most represented journal. Several diseases and health conditions were identified as a focus of these works, including anxiety, depression, diabetes mellitus, cardiovascular diseases, and coronavirus disease 2019 (COVID-19).

Conclusions:

The findings underscore key areas of focus in the field and prominent contributors, providing a roadmap for future research in digital and m-health.

[Names] => Andy Wai Kan Yeung ... Atanas G. Atanasov [CName] => Andy Wai KanYeung,Atanas G.Atanasov, [Doi] => 10.37349/edht.2024.00013 [Published] => April 22, 2024 [Viewed] => 2045 [Downloaded] => 182 [Subject] => Original Article [Year] => 2024 [CiteUrl] => https://api.crossref.org/works/10.37349/edht.2024.00013 [Inline] => 1 [Type] => 1 [Issue] => 2 [Topic] => 0 [TitleAbbr] => Explor Digit Health Technol. [Pages] => 2024;2:86–100 [Recommend] => 0 [Keywords] => Digital health, bibliometric, anxiety, depression, diabetes mellitus, cardiovascular diseases, coronavirus disease 2019 [DetailTitle] => [DetailUrl] => [Id] => 101113 [ris] => https://www.explorationpub.com/uploads/Article/A101113/569b8d83f835e7259d26c2098bbc3f95.ris [bib] => https://www.explorationpub.com/uploads/Article/A101113/d99cdf9cf7f1e7a2bd8500f1c13d299f.bib [ens] => [Cited] => 0 [Cited_Time] => [CitethisArticle] => Yeung AWK, Litvinova O, Bragazzi NL, Khader Y, Rahman MM, Said Z, et al. Digital health and mobile health: a bibliometric analysis of the 100 most cited papers and their contributing authors. Explor Digit Health Technol. 2024;2:86–100. https://doi.org/10.37349/edht.2024.00013 [Jindex] => 0 [CEmail] => ndyeung@hku.hk,atanas.atanasov@dhps.lbg.ac.at, [Ris_Time] => 2024-04-19 07:26:20 [Bib_Time] => 2024-04-19 07:26:20 [KeysWordContens] => Digital health and mobile health: a bibliometric analysis of the 100 most cited papers and their contributing authors, Digital health, bibliometric, anxiety, depression, diabetes mellitus, cardiovascular diseases, coronavirus disease 2019, Aim: This study aimed to identify and analyze the top 100 most cited digital health and mobile health (m-health) publications. It could aid researchers in the identification of promising new research avenues, additionally supporting the establishment of international scientific collaboration between interdisciplinary research groups with demonstrated achievements in the area of interest. Methods: On 30th August, 2023, the Web of Science Core Collection (WOSCC) electronic database was queried to identify the top 100 most cited digital health papers with a comprehensive search string. From the initial search, 106 papers were identified. After screening for relevance, six papers were excluded, resulting in the final list of the top 100 papers. The basic bibliographic data was directly extracted from WOSCC using its “Analyze” and “Create Citation Report” functions. The complete records of the top 100 papers were downloaded and imported into a bibliometric software called VOSviewer (version 1.6.19) to generate an author keyword map and author collaboration map. Results: The top 100 papers on digital health received a total of 49,653 citations. Over half of them (n = 55) were published during 2013–2017. Among these 100 papers, 59 were original articles, 36 were reviews, 4 were editorial materials, and 1 was a proceeding paper. All papers were written in English. The University of London and the University of California system were the most represented affiliations. The USA and the UK were the most represented countries. The Journal of Medical Internet Research was the most represented journal. Several diseases and health conditions were identified as a focus of these works, including anxiety, depression, diabetes mellitus, cardiovascular diseases, and coronavirus disease 2019 (COVID-19). Conclusions: The findings underscore key areas of focus in the field and prominent contributors, providing a roadmap for future research in digital and m-health. ,Andy Wai Kan Yeung ... Atanas G. Atanasov [PublishedText] => Published [IsEdit] => 0 [AccountId] => 38 [Zh] => 0 [AuthorsName] => Andy Wai Kan Yeung, Olena Litvinova, Nicola Luigi Bragazzi, Yousef Khader, Md. Mostafizur Rahman, Zafar Said, Robert S. H. Istepanian, Anastasios Koulaouzidis, Adeyemi Oladapo Aremu, James M. Flanagan, Navid Rabiee, Sheikh Mohammed Shariful Islam, Devesh ) )