Mobile Health Technology (Mhealth) in Combating COVID-19 Pandemic: Use, Challenges and Recommendations

Odeyemi, Olubunmi Emmanuel; Williams, Shekinah Yobuh; Adeyemi, Shalom Oluwafunke; Dada, Olaoluwa Ezekiel; Adesina, Miracle Ayomikun; Akintayo, Abiodun David

doi:10.30935/ejmets/8572

Cited by 13 publications

(5 citation statements)

References 2 publications

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“…Remote Learning for Healthcare Professionals: Mobile learning can support the training and education of healthcare professionals who are at the forefront of combating COVID-19 [46] . Online courses, webinars, and mobile apps can provide access to training on topics such as infection control, telemedicine, and contact tracing, ensuring that healthcare workers are equipped with the latest knowledge and skills.…”

Section: Mobile Learning Helps Covid-19 Preventionmentioning

confidence: 99%

Mobile Learning for COVID-19 Prevention

Wang

2024

EAI Endorsed Trans e-Learn

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In recent years, due to the explosion of COVID-19, people's expectation for accessing personalized learning resources anytime and anywhere has become stronger. The features of m-learning such as accessibility and personalization greatly satisfy people's needs and are therefore widely used. In this paper, a study was conducted to investigate and analyze how m-learning can help prevent COVID-19. The study shows that m-learning can help disseminate outbreak-related messages and provide people with personalized knowledge, so that it can enhance public health and community safety. While there are still many challenges, m-learning remains a valuable tool for preventing and mitigating the spread of COVID-19 globally, and provides a solid reference for deepening m-learning development in the future.

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Section: Mobile Learning Helps Covid-19 Preventionmentioning

confidence: 99%

Mobile Learning for COVID-19 Prevention

Wang

2024

EAI Endorsed Trans e-Learn

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“…The COVID-19 pandemic induced innovation in many technological sectors leading to the development of a variety of means to combat the global outbreak such as vaccines, bio-sensors facilitating the diagnosis at the point of care, 3D printed ventilators and a wealth of mobile applications. More specifically, leveraging the latest trends on mobile health technologies, several applications have been implemented to fight COVID-19 with the aim of creating awareness, collecting suitable data for health survey and surveillance, reducing person-to-person contacts, offering telemedicine services, tracking COVID-19 contacts, supporting healthcare professionals in decision making, facilitating communication and collaboration among healthcare providers and serving as a means towards coordinating emergency response and transport 1 . Further to the above, Artificial Intelligence (AI) and Machine Learning (ML) have played an important role in the response to the COVID-19 related challenges through accelerating the research and treatment while offering remarkable solutions to the diagnosis taking into consideration several biomedical data such as X-rays, Computer Tomography (CT) scans, electrocardiogram and audio recordings 2 , 3 .…”

Section: Background and Summarymentioning

confidence: 99%

The smarty4covid dataset and knowledge base as a framework for interpretable physiological audio data analysis

Zarkogianni,

Dervakos,

Filandrianos

et al. 2023

Sci Data

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Harnessing the power of Artificial Intelligence (AI) and m-health towards detecting new bio-markers indicative of the onset and progress of respiratory abnormalities/conditions has greatly attracted the scientific and research interest especially during COVID-19 pandemic. The smarty4covid dataset contains audio signals of cough (4,676), regular breathing (4,665), deep breathing (4,695) and voice (4,291) as recorded by means of mobile devices following a crowd-sourcing approach. Other self reported information is also included (e.g. COVID-19 virus tests), thus providing a comprehensive dataset for the development of COVID-19 risk detection models. The smarty4covid dataset is released in the form of a web-ontology language (OWL) knowledge base enabling data consolidation from other relevant datasets, complex queries and reasoning. It has been utilized towards the development of models able to: (i) extract clinically informative respiratory indicators from regular breathing records, and (ii) identify cough, breath and voice segments in crowd-sourced audio recordings. A new framework utilizing the smarty4covid OWL knowledge base towards generating counterfactual explanations in opaque AI-based COVID-19 risk detection models is proposed and validated.

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“…This technology assists in facilitating the continuity, maintenance of standard practice and accessibility patient care, and strengthens the healthcare. Williams et al [31] showed that in order to effectively restrain the extent and effect of the current global pandemic, concerted and drastic efforts must be put in place by health workers, researchers, and governments to combat the virus. The authors suggested that mobile health technology could offer great opportunity as a tool to create more awareness, information dissemination, smart tracking of affected persons, remote monitoring of patients, and virtual screening.…”

Section: Specific Authors That Worked On Covid-19 Pandemicmentioning

confidence: 99%