Investigating the Links Between Funding, Scholarly Production, and Public Health Determinants in IoMT Research

Investigating the Links Between Funding, Scholarly Production, and Public Health Determinants in IoMT Research | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Investigating the Links Between Funding, Scholarly Production, and Public Health Determinants in IoMT Research Peter Kokol, Bojan Žlahtič, Helena Blažun Vošner, Jernej Završnik This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6892735/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The Internet of Medical Things (IoMT) represents a transformative technology that connects medical devices, sensors, and healthcare systems to enable real-time monitoring, data sharing, and advanced decision-making in healthcare. While the technical and clinical potential of IoMT has been researched extensively, the scale and scope of research funding and their influence on research literature production patterns and country health determinants remain unknown. The study presented in this paper covers this gap by employing triangulation of quantitative and qualitative approaches. The results reveal a positive trend IoMT in research literature production. The funded research exhibits higher publication rates in high-impact journals but, unlike in many other research fields, is not regionally concentrated in countries with stronger healthcare systems and higher R&D expenditures, showing that IOMT can increasingly contribute to improving healthcare systems and outcomes even with the least investments. Thematic analysis shows that both funded and non-funded are associated with similar themes; however, founded research is more focused on recent research trends like artificial intelligence applications in healthcare. Finally, our study revealed the positive association between the number of funded papers and health determinants, suggesting that IoMT research funding might contribute to improved healthcare delivery. Internet of Medical Things Research funding Synthetic Knowledge Synthesis Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction New health paradigms advocate the idea that human health is a public good and that the health system's primary goal is to effectively and efficiently manage this good [ 1 ]. To achieve this goal, modern health professionals should benefit from significant advancements in medical technology and treatment methods, including innovations in diagnostics, medical imaging, and preventive health care. Remote health status monitoring and therapeutic techniques enabled by the digital and agile health revolution [ 2 – 5 ]. Researchers and healthcare innovators hope that emerging technologies will significantly enhance the efficiency and capacity of health systems, helping them to better meet the healthcare needs of populations worldwide [ 6 ]. One of the promising technologies that can be widely used in healthcare for all of the above purposes is the Internet of Things (IoT) [ 7 ]. While the Internet mainly connects people, IoT connects not only people but different entities in the physical world, including people and objects. The IoT has made remarkable progress across many fields, including medicine, where IoT is called the Internet of Medical Things (IoMT) [ 8 – 10 ]. The IoMT refers to the interconnected network of medical devices, sensors, software applications, and healthcare systems that communicate through the internet, enabling real-time data sharing, remote monitoring, and enhanced decision-making [ 11 ], supporting the patient-centric approach [ 12 ]. Research funding is believed to be a catalyst for the science and technology progress. As global increase in research expenditure has been noted in last years, analysing the effectiveness or return of research funding has become an important topic of interest [ 13 ]. In addition, to being an indicator of a research area importance, impact, maturity and visibility, funding information might be used to identify funding possibilities, research patterns, and most probable topics and themes to be funded [ 14 ]. While IoMT emergence has been researched from various aspects and viewpoints, the scope and extent of research funding have not yet been analysed. The aim of this paper is to fill this gap and answer the following research questions [ 15 ]: Which are the most productive funding agencies and countries associated with IoMT research? What are the prevailing research literature patterns regarding funded entities, funding trends, most prolific source titles where funded papers are published, and most prolific funded research themes? What is the impact of funded research on the country's health determinants? 2. Materials and Methods Scopus (Elsevier, The Netherlands) was used as the bibliographic database due to the fact that Scopus is regarded as the largest scientific bibliographic database of the reviewed research literature. In addition to providing powerful analytics services, Scopus also enables 20,000 records to be exported simultaneously, enabling more effective and efficient bibliometric analyses. Another strength of Scopus is its completeness in covering funding information [ 14 , 16 ]. The search query was formed by analysis and synthesis search strategies used in published IoT/IoMT review papers. The search was performed on September 8th, 2024. Zipf's law was used to calculate the number of relevant keywords to be used in Synthetic Knowledge Synthesis (SKS). The following search string was used for funded publications (FPs): TITLE-ABS-KEY("Internet of Medical Things") and FUND-SPONSOR(a* or b* or c* or d* or e* or f* or g* or h* or i* or j* or k* or l* or m* or n* or o* or p* or q* or r* or s* or t* or u* or v* or z* or x* or y* or w* or 1* or 2* or 3* or 4* or 5* or 6* or 7* or 8* or 9* or 0*) and TITLE-ABS-KEY("Internet of Medical Things") and NOT FUND-SPONSOR(a* or b* or c* or d* or e* or f* or g* or h* or i* or j* or k* or l* or m* or n* or o* or p* or q* or r* or s* or t* or u* or v* or z* or x* or y* or w* or 1* or 2* or 3* or 4* or 5* or 6* or 7* or 8* or 9* or 0*) for non-funded publications (NFPs). Equally structured search string, but with different search terms were used to calculate the percentage of FPs in the fields of Information and Communication Technologies (ICT) and Medicine and Health. First two research questions were naswered using Scopus’s built-in functionality and the Bibliometrics software [ 17 ]. The country ranks in Medicine, and Computer networks and communications research were obtained from Scimago [ 18 ]. Country determinants used in our study are Health systems ranking 2023 [ 19 ], Health Expenditure as % of GDP − 2021/22, Current R&D Expenditure as % of GDP − 2021/22[ 20 ] CR&D Expenditure as % of GDP − 2021/22 [ 20 ] and Bloomberg Global Health Index (BGHI) [ 21 ]. BGHI accounts for a variety of factors that contribute to the populational health of countries. It is based on the premise that the residents of developed countries tend to be healthier due to a higher quality of life, lower pollution rates, better infrastructure, access to quality healthcare, education, better nutrition, and good-paying jobs. On the contrary, residents of other developing countries frequently lack adequate access to these same benefits. The associations between bibliometric dimensions and country determinants were analysed by a framework developed by Kokol et al [ 22 ]. Thematic analysis was performed by Synthetic Knowledge Synthesis (SKS) [ 23 ]. 3. Results The search resulted in 1978 NFPs and 842 FPs. FPs were written by 2995 authors coming from 74 countries and were published in 337 source titles. FPs contained 2443 author keywords. NFPs were written by 5366 authors coming from 106 countries and were published in 905 source titles. NFPs contained 4241 author keywords. The percentage of FPs was 29.8%, which is higher than in related disciplines (ICT (25.5%) and Medicine and Health (20.9%)), but lower than in IoT use in preventive health (37.0%) [ 7 ]. 3.1. Spatial bibliometric dimensions of IoMT research and country determinants The publication types are shown in Table 1 . Comparison between the share of different types of publications between NFPs and FPs show that perceptually more original articles and reviews are funded than nonfunded. In all other types percentages of NFPs are prevailing. It is interesting to note that none of FPs was retracted or updated with errata. Table 1 Publication types Document Type Number of NFPs % of NFPs Number of FPs % of FPs Article 910 46.0% 602 71.5% Conference Paper 563 28.5% 171 20.3% Book Chapter 300 15.2% 9 1.1% Review 86 4.3% 57 6.8% Conference Review 43 2.2% 0 0.0% Book 33 1.7% 0 0.0% Editorial 17 0.9% 1 0.1% Erratum 11 0.6% 0 0.0% Retracted 7 0.4% 0 0.0% Letter 3 0.2% 0 0.0% Note 3 0.2% 1 0.1% Short Survey 2 0.1% 1 0.1% Figure 1 shows a steady growth in the number of papers in the domain of IoMT, for both NFPs and FPs, from 2016 to 2024. The slight decline observed in 2024 can be attributed to the timing of data collection, which occurred in September 2024. Early in the observed period, there was a significant discrepancy between the number of NFPs and FPs, as can be observed for the year 2017. However, a spike in 2019 brought the distribution closer to parity, with FPs accounting for 44.4% of total publications. After this peak in 2019, the proportion of FPs gradually decreased, stabilizing around 30% in subsequent years. Most of the original titles publishing research on IoMT are ranked in the top two quartiles according to SCIMago JCR (Table 2 ). Scimago JCR ranges from 0.15 to 4.42. Five source titles published, both FPs and NFPs, IEEE Access being the only journal publishing more FPs than NFPs. FPs were published only in journals, whereas NFPs were also published as book chapters. In general, FPs were published in higher-ranked source titles according to H-Index and JCR queries; however, the average JCR was slightly higher for NFP publishing source titles mainly due to the very high JCR of IEEE Transaction on Industrial Informatics. Table 2 Most prolific source titles publishing FPs and NFPs SOURCE TITLE Number of FPs SJR Quarter H-index SOURCE TITLE Number of NFPs SJR Quarter H-index IEEE Access 70 0.96 1 242 IEEE Access 59 0.96 1 242 Sensors 52 0.79 1 245 Lecture Notes In Networks And Systems 58 0.17 4 36 IEEE Internet Of Things Journal 43 3.38 1 179 IEEE Internet Of Things Journal 57 3.38 1 179 Electronics Switzerland 24 0.64 2 83 IEEE Journal Of Biomedical And Health Informatics 40 1.96 1 156 IEEE Journal Of Biomedical And Health Informatics 20 1.96 1 156 Communications In Computer And Information Science 27 0.2 4 69 Future Generation Computer Systems 19 1.95 1 164 Internet Of Things 24 1.64 1 52 Computers Materials And Continua 17 0.46 2 57 Electronics Switzerland 22 0.64 2 83 Applied Sciences MDPI 14 0.51 2 130 IEEE Transactions On Industrial Informatics 21 4.42 1 193 Computer Communications 13 1.40 1 128 Lecture Notes In Electrical Engineering 20 0.15 4 45 Information Sciences 12 2.24 1 227 Sensors 20 0.79 1 245 Average 1.43 1.3 161.1 Average 1.431 2 130 Table 4 reveals that the country productivity ranks of both NFPs, and FPs differs from the country productivity ranks in subjects Medicine and Computer networks and communications. Three most productive countries in above two subjects are still among 10 top productive countries, but top productive countries like Germany, Japan, France and Canada are missing. On the other hand, countries which don’t belong to top 30 most productive countries like Saudi Arabia, Pakistan and Malaysia are among top 10 productive countries in IoMT research. Eight countries are in both NFP and FP list, however the rankings differ slightly, India and United States being the most productive regarding NFPs and China and Saudi Arabia regarding FPs. Australia and Iraq are only ranked among top 10 NFPs countries and South Korea and Egypt only among FPs countries. The average percentage of funded papers in top 10 countries is 44.2% with South Korea having the largest percentage of funded papers (69.3%) and India the smallest percentage (20.0%). According to Organisation for Economic Co-operation and Development (OECD) [ 24 ] the average health spending related to GPD in 2022 in OECD countries is about 9.2%.Thereafter it is surprising to observe that only three countries in the top 10 NFPs or FPs countries are above that limit. Similarly, the most of top ranked countries in Health and Health systems ranking like Singapore, Japan, Taiwan, Scandinavian Countries, etc. are also not in the below lists. It seems that countries with low health expenditures and not so well ranked health systems are investing in the research in IoMT as a possible technology to improve health services. Similar observation can be made regarding the R&D expenditures. According to OECD [ 20 ] an appropriate spending in percentages of GPD is above 2% and only four of top 10 countries reached that limit. Data reveals that countries highly above or highly below 2% expenditure in R&D GPD are the most productive regarding both NFPs and FPs. A comparison of country determinants between the most productive countries in the NFP and FP lists reveals that the countries on the FP list rank higher on average in all three rankings, have a higher percentage of investments in healthcare and research and higher Global Health Index. Table 3 Most productive countries and their country determinants Funded/Nonfunded publication COUNTRY/TERRITORY Number of NFPs Scimago rank in subject Medicine Scimago rank in sub-subject Computer networks and communications Health systems ranking 2023 [ 19 ] Current Health Expenditure as % of GDP − 2021/22 Current R&D Expenditure as % of GDP − 2021/22 [ 20 ] BGHI NFP India 849 11 3 112 3.28 0.65 61.3 NFP United States 213 1 2 69 16.57 3.45 79.5 NFP China 192 2 1 5 5.38 2.43 46.3 NFP Saudi Arabia 145 35 27 56 5.97 0.46 77.2 NFP Pakistan 112 40 30 124 2.91 0.16 61.5 NFP United Kingdom 93 3 6 34 11.34 2.91 88.8 NFP Australia 73 9 13 21 10.54 3.25 90.9 NFP Iraq 69 65 50 115 5.25 0.04 62.8 NFP Italy 63 6 8 17 9.00 1.45 91.5 NFP Malaysia 62 42 16 42 4.38 0.59 84.2 AVERAGE 21.4 15.6 59.5 7.462 1.539 74.4 FP China 280 (57.1%) 2 1 5 5.38 2.43 46.3 FP Saudi Arabia 176 (52.1) 35 27 56 5.97 0.46 77.2 FP India 171 (20.0%) 11 3 112 3.28 0.65 61.3 FP United States 127 (36.3%) 1 2 69 16.57 3.45 79.5 FP South Korea 88 (69.3%) 14 9 3 9.72 4.93 94.3 FP Pakistan 75 (38.9%) 40 30 124 2.91 0.16 61.5 FP United Kingdom 66 (40.2%) 3 6 34 11.34 2.91 88.8 FP Italy 49 (42.2%) 6 8 17 9 1.45 91.5 FP Egypt 44 (48.4%) 33 36 107 4.61 1.02 64.6 FP Malaysia 42 (37.5%) 42 16 42 4.38 0.59 84.2 AVERAGE 14 10.75 52.5 8.02125 2.055 74.9 3.2. Thematic analysis According to Zipf bibliometric law [ 25 ], 65 most popular author keywords from NFPs and 49 from FPs were taken into VOSViewer analysis. The research landscapes are shown in Figs. 3 and 4 . SKS performed on those two landscapes resulted in the themes presented in Table 5 . SKS analysis of FPs and NFPs author keywords landscapes did not show big thematic differences except two cases. Namely, FPs are more concerned with using AI IoMT applications in e-health and telemedicine, while NFPs are more concerned with using IoMT in pandemic management. Other differences are mainly in the fact that author keywords representing semantically similar topics are differently related, whether funded or unfunded, appear in different topics or differ in popularity. Table 5 Themes derived by using SKS FPs Themes Representative topics identified in prominent publications NFPs themes Representative topics identified in prominent publications IoMT and AI use in e-health and telemedicine ECG monitoring [ 26 ], e-health patient monitoring [ 27 ], elderly healthcare [ 28 ], Accident and emergency detection in One digital health [ 29 ] Role of IoMT in pandemic management Point of care testing of infectious diseases [ 30 ], Cognitive IoMT for pandemic management [ 31 ], Pandemic forecasting [ 32 ], Covid-19 management by federated learning [ 33 ] Privacy in federated learning Skin diseases [ 34 ], Smart healthcare [ 35 ], ECG classification [ 36 ], Misbehaviour detection [ 37 ], Heart disease diagnosing [ 38 ] Privacy and security within federated learning Privacy preservation with fraud enabled blockchain [ 39 ]. Privacy preservation in smart healthcare [ 40 ], Intrusion detection [ 41 ], Privacy sensitive federated learning [ 42 ] Security in smart health care Blockchain industrial secure encryption in healthcare [ 43 ], Hybrid authentication for digital healthcare [ 44 ], Threat detection in IoMT networks [ 45 ], Secure intelligent biosensors [ 46 ] Machine learning detection of cybersecurity treads on IoMT applications Cybersecurity of healthcare 5.0 systems using federated learning [ 47 , 48 ]. Tree classifier based intrusion detection in IoMT [ 49 ]. Multilayer perceptron optimisation for cybersecurity [ 50 ] Secure big data analysis in healthcare security threats, vulnerabilities, and counter measures [ 51 ], Blockchain, Blockchain assisted big data management [ 52 ], Healthcare in Smart Cities [ 53 ] Big data analysis of data from wearable sensors for eHealth Ambient assisted living [ 54 ], edge-stream computing for real time analysis of wearable data [ 55 ], big and wearable data in gynaecology [ 56 ], Big data based Smart Health Monitoring [ 57 ] Advanced machine learning and data security in accessing data from wearables and sensors Secure wearable ultrasound system [ 58 ], Privacy preserving federated learning [ 59 ], Robust zero watermarking for federated learning [ 60 ], Scalable transferable federated learning in classification of healthcare IoMT data [ 61 ] Advanced machine learning Remote patient monitoring [ 62 , 63 ]. Lung tumour diagnosing [ 64 ], Digitalization [ 65 ] 3.3. IoMT Impact: Bloomberg Global Health Index in relation to the number of funded published papers on IoMT Figure 6 represents the BGHI in association with the number of papers published per 1M residents. The association is not strong; however, the trend line (dotted line in Fig. 2) shows that the BGHI is increasing with the number of published FPs. The graph is very scattered in the area of less than 0.4 FPs/1M residents, but most of the countries on right of that limit have in general higher BGHI than 80. On the other hand, countries with the lowest BGHIs are all on the left side of the 0.4 FPs/1M residents limit. From the bibliometric point of view the 0.4 FPs/1M residents limit also indicates the point after which the number of FPs do not affect the BGHI in a significant manner. Nevertheless the above patterns might indicate that the investments in IoMT research have positive effect on populational health status. Most of the most prolific funding agencies (Table 6 ) come from China, South Korea, Saudi Arabia, the US, and the EU, which are also among the most productive countries (Table 3 ). Except for Saudi Arabia, those countries spend a respectable share of GPD on health and research. Table 6 The most prolific funding agencies National Natural Science Foundation of China, China 167 National Science Foundation, USA 45 National Research Foundation of Korea," Korea 40 National Key Research and Development Program of China, China 36 King Saud University, Saudi Arabia 35 Ministry of Science and Technology of the People's Republic of China, China 33 Deanship of Scientific Research, King Saud University, Saudi Arabia 32 European Commission, EU 29 Ministry of Science, ICT and Future Planning, South Korea 25 Fundamental Research Funds for the Central Universities, China 24 4. Discussion Our study revealed that the proportion of funded projects (FPs) in the Internet of Medical Things (IoMT) research literature was higher than in the fields of Medicine, Health, and Information and Communication Technology (ICT), yet lower than in the Internet of Things (IoT) research focused on preventive health. This variation in the percentage of FPs can be attributed to several key factors. Firstly, the interdisciplinary nature of IoMT, which merges healthcare and technological innovation, makes it a significant area of interest for both public and private funding agencies. Its potential to revolutionize patient care and healthcare systems drives increased funding compared to more traditional research areas like Medicine, Health, and ICT. IoMT offers the promise of groundbreaking advancements, particularly in diagnostics, treatment, and patient monitoring, making it a high-priority area for innovation-driven investment. In contrast, IoT-based preventive health research, which emphasizes early detection and cost-effective management of chronic diseases, attracts even greater funding. Preventive health solutions using IoT technologies are often viewed as long-term strategies with the potential to reduce healthcare costs and improve population health outcomes, thereby garnering substantial support from stakeholders invested in sustainable healthcare solutions [ 66 ]. These approaches are particularly attractive to funders as they help alleviate the burdens on healthcare systems and align with public health initiatives aimed at reducing the prevalence of non-communicable diseases (NCDs). Additionally, IoMT, as an emerging field, often requires substantial technological investment [ 67 ], making it more likely to attract funding compared to more established fields like general Medicine or ICT research. Furthermore, funding agencies may prioritize preventive health due to its proven effectiveness in reducing the incidence of chronic diseases, thereby highlighting the significance of IoT applications in this area [ 68 ]. The higher percentage of funded original articles and reviews compared to non-funded ones can be attributed to the primary goals of funding, which focus on supporting innovation and original research. Such research often begins with a synthesis of existing knowledge, typically presented in review articles, laying the groundwork for subsequent innovation. The trend in IoMT research literature production has been positive, reflecting the broader upward trends in literature production across most modern scientific and research fields [ 69 ]. In contrast to some other studies [68–70], our analysis did not reveal a regional concentration in research and literature production. The most productive countries in both non-funded projects (NFPs) and funded projects (FPs) include not only well-developed and wealthy nations but also developing countries with less successful economies and healthcare systems. This may be because IoMT-based health solutions can help mitigate workforce shortages in healthcare, address the effects of climate and demographic changes, and improve access to healthcare in remote areas of larger, less developed countries more efficiently than traditional methods [20,71–73]. The lack of regional concentration in IoMT research may also explain why the ranking of the 10 most productive countries in this field differs from their rankings in more general research areas. Our study also revealed that up to a certain threshold (0.4 FPs per 1 million residents), the BGHI is increasing with the number of funded papers, suggesting that IoMT research funding may contribute to improved healthcare delivery. This finding aligns with other studies that have analyzed the association between research grants and health indices [74–76]. The funding patterns identified in our study can assist researchers in pinpointing suitable research themes for funding, identifying funding institutions, and locating productive countries for potential research collaborations. Additionally, these findings may be valuable to research managers, funding body administrators, government decision-makers, and policymakers. This article has both strengths and limitations. One strength is the use of SKS, a well-established knowledge synthesis method that allowed for a comprehensive thematic analysis of IoT research. Another key strength is that our study is the first to thoroughly examine funding patterns and the impact of funding in IoMT research. However, a major limitation is the reliance on a single database, which may have excluded some literature, particularly studies published on various preprint platforms. 5. Conclusions The trend of the IoMT research literature production regarding both funded and nonfunded papers is positive, however the percentage of funded papers is decreasing, but seems to stabilise in 2022. The percentage of funding is higher than in healthcare or medicine in general but lower than in ICT related disciplines. Contrary to some others, our study didn’t reveal regionally research concentration and literature production, meaning that even less developed, and less “rich” countries produce comparable amount of publications. Similarly, government spending in health and research and the health system rank didn’t seem to be associated with research literature production or funding. Nevertheless, funded papers seem to be published in slightly higher ranked journals, and more funded papers are affiliated to scientifically higher ranked countries and country with better country determinants. The research funding expressed with the number funding papers per capita, shows a positive trend with the BGHI. Declarations Author Contributions: All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Peter Kokol, ], The first draft of the manuscript was written by Peter Kokol and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Conflicts of Interest: The authors have no relevant financial or non-financial interests to disclose. Funding: No funding has been received for research presented in this paper Ethics, Consent to Participate, and Consent to Publish declarations : not applicable. Clinical trial number: not applicable.’ Declaration : he datasets used and/or analysed during the current study available from the corresponding author on reasonable request References Heimburg DV, Prilleltensky I, Ness O, Ytterhus B. From public health to public good: Toward universal wellbeing. Scand J Public Health. 2022;50:1062–70. https://doi.org/10.1177/14034948221124670 . Awad A, Trenfield SJ, Pollard TD, et al. Connected healthcare: Improving patient care using digital health technologies. Adv Drug Deliv Rev. 2021;178:113958. https://doi.org/10.1016/j.addr.2021.113958 . Satam H, Joshi K, Mangrolia U, et al. Next-Generation Sequencing Technology: Current Trends and Advancements. Biology. 2023;12:997. https://doi.org/10.3390/biology12070997 . Kokol P, Blažun Vošner H, Kokol M, Završnik J. Role of Agile in Digital Public Health Transformation. Front Public Health. 2022;10:899874. https://doi.org/10.3389/fpubh.2022.899874 . Kokol P. Agile Software Development in Healthcare: A Synthetic Scoping Review. Appl Sci. 2022;12:9462. https://doi.org/10.3390/app12199462 . Khang A, Rana G, Tailor RK, Abdullayev V. Data-Centric AI Solutions and Emerging Technologies in the Healthcare Ecosystem. CRC; 2023. Šajnović U, Vošner HB, Završnik J, et al. Internet of Things and Big Data Analytics in Preventive Healthcare: A Synthetic Review. Electronics. 2024;13:3642. https://doi.org/10.3390/electronics13183642 . El-Saleh AA, Sheikh AM, Albreem MAM, Honnurvali MS. The Internet of Medical Things (IoMT): opportunities and challenges. Wirel Netw. 2024. https://doi.org/10.1007/s11276-024-03764-8 . Osama M, Ateya AA, Sayed MS et al. (2023) Internet of Medical Things and Healthcare 4.0: Trends, Requirements, Challenges, and Research Directions. Sensors 23:. https://doi.org/10.3390/s23177435 Razdan S, Sharma S. Internet of Medical Things (IoMT): Overview, Emerging Technologies, and Case Studies. IETE Tech Rev. 2022;39:775–88. https://doi.org/10.1080/02564602.2021.1927863 . Manickam P, Mariappan SA, Murugesan SM, et al. Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare. Biosensors. 2022;12:562. https://doi.org/10.3390/bios12080562 . Shah V, Khang A. Internet of Medical Things (IoMT) Driving the Digital Transformation of the Healthcare Sector. Data-Centric AI Solutions and Emerging Technologies in the Healthcare Ecosystem. CRC; 2023. Ou G, Zhao K, Zuo R, Wu J. Effects of research funding on the academic impact and societal visibility of scientific research. J Informetrics. 2024;18:101592. https://doi.org/10.1016/j.joi.2024.101592 . Kokol P. Discrepancies among Scopus and Web of Science, coverage of funding information in medical journal articles: a follow-up study. J Med Libr Association. 2023;111:703–9. https://doi.org/10.5195/jmla.2023.1513 . Kokol P, Žlahtic B, Šajnovič U, et al. Funding in IoMT research. Observations Based on Synthetic Knowledge Synthesis; 2024. Kokol P, Blažun Vošner H. Discrepancies among Scopus, Web of Science, and PubMed coverage of funding information in medical journal articles. J Med Libr Assoc. 2018;106:81–6. https://doi.org/10.5195/jmla.2018.181 . Aria M, Cuccurullo C. bibliometrix: An R-tool for comprehensive science mapping analysis. J Informetrics. 2017;11:959–75. https://doi.org/10.1016/j.joi.2017.08.007 . (2023) Scimago Journal & Country Rank. https://www.scimagojr.com/ . Accessed 26 Jan 2023. Health. and health system ranking of countries worldwide in 2023. In: Statista. https://www.statista.com/statistics/1376359/health-and-health-system-ranking-of-countries-worldwide/ . Accessed 17 Aug 2024. World Development Indicators | DataBank. https://databank.worldbank.org/source/world-development-indicators/Series/GB.XPD.RSDV.GD.ZS# . Accessed 17 Aug 2024. Healthiest C. 2024. https://worldpopulationreview.com/country-rankings/healthiest-countries . Accessed 23 Aug 2024. Kokol P, Železnik D, Završnik J, Blažun Vošner H. Nursing Research Literature Production in Terms of the Scope of Country and Health Determinants: A Bibliometric Study. J Nurs Scholarsh. 2019;51:590–8. https://doi.org/10.1111/jnu.12500 . Kokol P. Synthetic Knowledge Synthesis in Hospital Libraries. J Hosp Librariansh. 2023;0:1–8. https://doi.org/10.1080/15323269.2023.2291282 . Health. In: OECD. https://www.oecd.org/en/topics/policy-areas/health.html . Accessed 17 Aug 2024. Zipf GK. The psycho-biology of language. Oxford, England: Houghton, Mifflin; 1935. Scrugli MA, Loi D, Raffo L, Meloni P. An Adaptive Cognitive Sensor Node for ECG Monitoring in the Internet of Medical Things. IEEE Access. 2022;10:1688–705. https://doi.org/10.1109/ACCESS.2021.3136793 . Ahila A, Dahan F, Alroobaea R, et al. A smart IoMT based architecture for E-healthcare patient monitoring system using artificial intelligence algorithms. Front Physiol. 2023;14. https://doi.org/10.3389/fphys.2023.1125952 . Ghosh S, Ghosh SK. FEEL: FEderated LEarning Framework for ELderly Healthcare Using Edge-IoMT. IEEE Trans Comput Social Syst. 2023;10:1800–9. https://doi.org/10.1109/TCSS.2022.3233300 . Haghi M, Benis A, Deserno TM. Accident & Emergency Informatics and One Digital Health. Yearb Med Inform. 2022;31:40–6. https://doi.org/10.1055/s-0042-1742506 . Jain S, Nehra M, Kumar R, et al. Internet of medical things (IoMT)-integrated biosensors for point-of-care testing of infectious diseases. Biosens Bioelectron. 2021;179:113074. https://doi.org/10.1016/j.bios.2021.113074 . Swayamsiddha S, Mohanty C. Application of cognitive Internet of Medical Things for COVID-19 pandemic. Diabetes Metabolic Syndrome: Clin Res Reviews. 2020;14:911–5. https://doi.org/10.1016/j.dsx.2020.06.014 . Shoeibi A, Khodatars M, Jafari M, et al. Automated detection and forecasting of COVID-19 using deep learning techniques: A review. Neurocomputing. 2024;577:127317. https://doi.org/10.1016/j.neucom.2024.127317 . Samuel O, Omojo AB, Onuja AM, et al. IoMT: A COVID-19 Healthcare System Driven by Federated Learning and Blockchain. IEEE J Biomedical Health Inf. 2023;27:823–34. https://doi.org/10.1109/JBHI.2022.3143576 . Hossen MN, Panneerselvam V, Koundal D, et al. Federated Machine Learning for Detection of Skin Diseases and Enhancement of Internet of Medical Things (IoMT) Security. IEEE J Biomedical Health Inf. 2023;27:835–41. https://doi.org/10.1109/JBHI.2022.3149288 . Nguyen DC, Pham Q-V, Pathirana PN, et al. Federated Learning for Smart Healthcare: A Survey. ACM-CSUR. 2022;55. https://doi.org/10.1145/3501296 . Cai W, Gao M, Jiang Y, et al. Hierarchical Domain Adaptation Projective Dictionary Pair Learning Model for EEG Classification in IoMT Systems. IEEE Trans Comput Social Syst. 2023;10:1559–67. https://doi.org/10.1109/TCSS.2022.3176656 . Rahmadika S, Astillo PV, Choudhary G, et al. Blockchain-Based Privacy Preservation Scheme for Misbehavior Detection in Lightweight IoMT Devices. IEEE J Biomedical Health Inf. 2023;27:710–21. https://doi.org/10.1109/JBHI.2022.3187037 . Yaqoob MM, Nazir M, Yousafzai A, et al. Modified Artificial Bee Colony Based Feature Optimized Federated Learning for Heart Disease Diagnosis in Healthcare. Appl Sci. 2022;12:12080. https://doi.org/10.3390/app122312080 . Lakhan A, Mohammed MA, Nedoma J, et al. Federated-Learning Based Privacy Preservation and Fraud-Enabled Blockchain IoMT System for Healthcare. IEEE J Biomedical Health Inf. 2023;27:664–72. https://doi.org/10.1109/JBHI.2022.3165945 . Ali M, Naeem F, Tariq M, Kaddoum G. Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey. IEEE J Biomedical Health Inf. 2023;27:778–89. https://doi.org/10.1109/JBHI.2022.3181823 . Si-Ahmed A, Al-Garadi MA, Boustia N. Survey of Machine Learning based intrusion detection methods for Internet of Medical Things. Appl Soft Comput. 2023;140:110227. https://doi.org/10.1016/j.asoc.2023.110227 . Aouedi O, Sacco A, Piamrat K, Marchetto G. Handling Privacy-Sensitive Medical Data With Federated Learning: Challenges and Future Directions. IEEE J Biomedical Health Inf. 2023;27:790–803. https://doi.org/10.1109/JBHI.2022.3185673 . Ali A, Almaiah MA, Hajjej F, et al. An Industrial IoT-Based Blockchain-Enabled Secure Searchable Encryption Approach for Healthcare Systems Using Neural Network. Sensors. 2022;22. https://doi.org/10.3390/s22020572 . Almaiah MA, Hajjej F, Ali A, et al. An AI-Enabled Hybrid Lightweight Authentication Model for Digital Healthcare Using Industrial Internet of Things Cyber‐Physical Systems. Sensors. 2022;22. https://doi.org/10.3390/s22041448 . Khan IA, Moustafa N, Razzak I, et al. XSRU-IoMT: Explainable simple recurrent units for threat detection in Internet of Medical Things networks. Future Generation Comput Syst. 2022;127:181–93. https://doi.org/10.1016/j.future.2021.09.010 . Chaudhary V, Khanna V, Ahmed Awan HT, et al. Towards hospital-on-chip supported by 2D MXenes-based 5th generation intelligent biosensors. Biosens Bioelectron. 2023;220. https://doi.org/10.1016/j.bios.2022.114847 . Rehman A, Abbas S, Khan MA, et al. A secure healthcare 5.0 system based on blockchain technology entangled with federated learning technique. Comput Biol Med. 2022;150:106019. https://doi.org/10.1016/j.compbiomed.2022.106019 . Khan IA, Razzak I, Pi D, et al. Fed-Inforce-Fusion: A federated reinforcement-based fusion model for security and privacy protection of IoMT networks against cyber-attacks. Inform Fusion. 2024;101:102002. https://doi.org/10.1016/j.inffus.2023.102002 . Gupta K, Sharma DK, Datta Gupta K, Kumar A. A tree classifier based network intrusion detection model for Internet of Medical Things. Comput Electr Eng. 2022;102:108158. https://doi.org/10.1016/j.compeleceng.2022.108158 . Firat Kilincer I, Ertam F, Sengur A, et al. Automated detection of cybersecurity attacks in healthcare systems with recursive feature elimination and multilayer perceptron optimization. Biocybernetics Biomedical Eng. 2023;43:30–41. https://doi.org/10.1016/j.bbe.2022.11.005 . Hasan MK, Ghazal TM, Saeed RA, et al. A review on security threats, vulnerabilities, and counter measures of 5G enabled Internet-of-Medical-Things. IET Commun. 2022;16:421–32. https://doi.org/10.1049/cmu2.12301 . Abbas A, Alroobaea R, Krichen M, et al. Blockchain-assisted secured data management framework for health information analysis based on Internet of Medical Things. Personal Uniquit Comput. 2024;28:59–72. https://doi.org/10.1007/s00779-021-01583-8 . Mishra P, Singh G. (2023) Internet of Medical Things Healthcare for Sustainable Smart Cities: Current Status and Future Prospects. Applied Sciences (Switzerland) 13:. https://doi.org/10.3390/app13158869 Syed L, Jabeen S, Alsaeedi SM A. Smart healthcare framework for ambient assisted living using IoMT and big data analytics techniques. Future Generation Comput Syst. 2019;101:136–51. https://doi.org/10.1016/j.future.2019.06.004 . AlShorman O, AlShorman B, Al-khassaweneh M, Alkahtani F. A review of internet of medical things (IoMT) - based remote health monitoring through wearable sensors: a case study for diabetic patients. Indonesian J Electr Eng Comput Sci. 2020;20:414–22. https://doi.org/10.11591/ijeecs.v20.i1.pp414-422 . Khamisy-Farah R, Furstenau LB, Kong JD, et al. Gynecology Meets Big Data in the Disruptive Innovation Medical Era: State-of-Art and Future Prospects. Int J Environ Res Public Health. 2021;18:5058. https://doi.org/10.3390/ijerph18105058 . Big Data-Based Smart Health Monitoring System. Using Deep Ensemble Learning | IEEE Journals & Magazine | IEEE Xplore. https://ieeexplore-ieee-org.ezproxy.lib.ukm.si/document/10286815 . Accessed 17 Sep 2024. Lin M, Zhang Z, Gao X, et al. A fully integrated wearable ultrasound system to monitor deep tissues in moving subjects. Nat Biotechnol. 2024;42:448–57. https://doi.org/10.1038/s41587-023-01800-0 . Wang R, Lai J, Zhang Z, et al. Privacy-Preserving Federated Learning for Internet of Medical Things Under Edge Computing. IEEE J Biomedical Health Inf. 2023;27:854–65. https://doi.org/10.1109/JBHI.2022.3157725 . Han B, Jhaveri RH, Wang H, et al. Application of Robust Zero-Watermarking Scheme Based on Federated Learning for Securing the Healthcare Data. IEEE J Biomedical Health Inf. 2023;27:804–13. https://doi.org/10.1109/JBHI.2021.3123936 . Sun L, Wu J. A Scalable and Transferable Federated Learning System for Classifying Healthcare Sensor Data. IEEE J Biomedical Health Inf. 2023;27:866–77. https://doi.org/10.1109/JBHI.2022.3171402 . Abidi MH, Umer U, Mian SH, Al-Ahmari A. Big Data-Based Smart Health Monitoring System: Using Deep Ensemble Learning. IEEE Access. 2023;11:114880–903. https://doi.org/10.1109/ACCESS.2023.3325323 . Raj EFI. (2022) Data-driven modern health care systems with the internet of medical things combined with big data and machine learning. In: AI-Enabled IoT for Smart Health Care Systems. pp 123–145. Chen Z. (2024) Lung Tumor Diagnosis Technology Based on 6G Wireless Network Sensors and Big Data Analysis. Wireless Pers Commun. https://doi.org/10.1007/s11277-024-11215-y Chatterjee R, Ray R, Dash SR, Jena OP. Conceptualizing Tomorrow’s Healthcare Through Digitization. Computational Intelligence and Healthcare Informatics. Ltd: Wiley; 2021. pp. 359–76. Singh S, Sharma S, Bhadula S, Mohan S. Industry 4.0 Internet of Medical Things Enabled Cost Effective Secure Smart Patient Care Medicine Pouch. In: Nayyar A, Naved M, Rameshwar R, editors. New Horizons for Industry 4.0 in Modern Business. Cham: Springer International Publishing; 2023. pp. 149–70. Ajagbe SA, Awotunde JB, Adesina AO, et al. Internet of Medical Things (IoMT): Applications, Challenges, and Prospects in a Data-Driven Technology. In: Chakraborty C, Khosravi MR, editors. Intelligent Healthcare. Singapore: Springer Nature Singapore; 2022. pp. 299–319. Robles SC. A Public Health Framework for Chronic Disease Prevention and Control. Food Nutr Bull. 2004;25:194–9. https://doi.org/10.1177/156482650402500213 . Abis S, Veldkamp L. The Changing Economics of Knowledge Production. Rev Financial Stud. 2024;37:89–118. https://doi.org/10.1093/rfs/hhad059 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6892735","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":475044190,"identity":"d8aeecf9-f254-42a0-b910-40bf2283a183","order_by":0,"name":"Peter Kokol","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYPCCBAYJBubGByAmG2HVzDAtjM0GJGtpkyDKRebs/QcfVzCkyUm2N7ZV8+Yw2PMR0mLZc5jZ8AxDjrE0z8G227zbGBLbCGkxuJHMJtnAUJE4TyIRrCWBoF8M7j9m/wnTUgzUYk9Yyw1mNsYGhpzE2UAtzEAtjAQdZtmTbCzZYJBmLNlzsFly7jYJwn4xZz/48GNDRbKcxPHmgx/ebrOxl28g5DAkEgSIiBoDwkpGwSgYBaNgxAMA6+U2oV2vOQMAAAAASUVORK5CYII=","orcid":"","institution":"University of Maribor","correspondingAuthor":true,"prefix":"","firstName":"Peter","middleName":"","lastName":"Kokol","suffix":""},{"id":475044191,"identity":"448edd45-f43c-44f0-b09a-98f8d9b0a93e","order_by":1,"name":"Bojan Žlahtič","email":"","orcid":"","institution":"University of Maribor","correspondingAuthor":false,"prefix":"","firstName":"Bojan","middleName":"","lastName":"Žlahtič","suffix":""},{"id":475044192,"identity":"43886669-1f53-4b63-ba06-946497eab2be","order_by":2,"name":"Helena Blažun Vošner","email":"","orcid":"","institution":"Alma Mater Europaea University","correspondingAuthor":false,"prefix":"","firstName":"Helena","middleName":"Blažun","lastName":"Vošner","suffix":""},{"id":475044193,"identity":"b1e09431-fd4f-41ce-8998-7771749aa59d","order_by":3,"name":"Jernej Završnik","email":"","orcid":"","institution":"Alma Mater Europaea University","correspondingAuthor":false,"prefix":"","firstName":"Jernej","middleName":"","lastName":"Završnik","suffix":""}],"badges":[],"createdAt":"2025-06-14 08:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6892735/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6892735/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85577591,"identity":"76651515-2130-442a-a554-6c8b4f849636","added_by":"auto","created_at":"2025-06-27 19:46:12","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":153819,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 1. \u003c/strong\u003eThe literature production dynamics of FPs and NFPs in IoMT research.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6892735/v1/897d8ccc25570fbb29df511f.jpg"},{"id":85577594,"identity":"f346115d-3f43-455b-8593-c7deb2b40b7e","added_by":"auto","created_at":"2025-06-27 19:46:12","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":203770,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 4\u003c/strong\u003e. The FPs author keywords landscape\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6892735/v1/cf412d11b169e34e6b036568.jpg"},{"id":85577760,"identity":"25f1fd7a-755a-4cb2-bf2b-6eb2444eee84","added_by":"auto","created_at":"2025-06-27 19:54:12","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":236917,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 5\u003c/strong\u003e. The NFPs author keywords landscape\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6892735/v1/61386ee71310e5d2e50fedf0.jpg"},{"id":85577759,"identity":"32e20efb-766c-4b84-9380-5e58eac6809b","added_by":"auto","created_at":"2025-06-27 19:54:12","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":105166,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 6\u003c/strong\u003e. The BGHI 2024 in association with the number of published FPs per capita on 1M residents\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6892735/v1/870bfa1c14fa9fa5d6eab87d.jpg"},{"id":92573740,"identity":"65bf92f9-4918-489d-98b1-d468a982d91a","added_by":"auto","created_at":"2025-10-01 08:09:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1700928,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6892735/v1/0efeb2fd-1ce5-4ec3-b586-851072ad948c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigating the Links Between Funding, Scholarly Production, and Public Health Determinants in IoMT Research","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNew health paradigms advocate the idea that human health is a public good and that the health system's primary goal is to effectively and efficiently manage this good [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. To achieve this goal, modern health professionals should benefit from significant advancements in medical technology and treatment methods, including innovations in diagnostics, medical imaging, and preventive health care. Remote health status monitoring and therapeutic techniques enabled by the digital and agile health revolution [\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Researchers and healthcare innovators hope that emerging technologies will significantly enhance the efficiency and capacity of health systems, helping them to better meet the healthcare needs of populations worldwide [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. One of the promising technologies that can be widely used in healthcare for all of the above purposes is the Internet of Things (IoT) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. While the Internet mainly connects people, IoT connects not only people but different entities in the physical world, including people and objects. The IoT has made remarkable progress across many fields, including medicine, where IoT is called the Internet of Medical Things (IoMT) [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The IoMT refers to the interconnected network of medical devices, sensors, software applications, and healthcare systems that communicate through the internet, enabling real-time data sharing, remote monitoring, and enhanced decision-making [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], supporting the patient-centric approach [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eResearch funding is believed to be a catalyst for the science and technology progress. As global increase in research expenditure has been noted in last years, analysing the effectiveness or return of research funding has become an important topic of interest [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In addition, to being an indicator of a research area importance, impact, maturity and visibility, funding information might be used to identify funding possibilities, research patterns, and most probable topics and themes to be funded [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. While IoMT emergence has been researched from various aspects and viewpoints, the scope and extent of research funding have not yet been analysed. The aim of this paper is to fill this gap and answer the following research questions [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eWhich are the most productive funding agencies and countries associated with IoMT research?\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWhat are the prevailing research literature patterns regarding funded entities, funding trends, most prolific source titles where funded papers are published, and most prolific funded research themes?\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWhat is the impact of funded research on the country's health determinants?\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003eScopus (Elsevier, The Netherlands) was used as the bibliographic database due to the fact that Scopus is regarded as the largest scientific bibliographic database of the reviewed research literature. In addition to providing powerful analytics services, Scopus also enables 20,000 records to be exported simultaneously, enabling more effective and efficient bibliometric analyses. Another strength of Scopus is its completeness in covering funding information [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The search query was formed by analysis and synthesis search strategies used in published IoT/IoMT review papers. The search was performed on September 8th, 2024. Zipf's law was used to calculate the number of relevant keywords to be used in Synthetic Knowledge Synthesis (SKS). The following search string was used for funded publications (FPs):\u003c/p\u003e \u003cp\u003eTITLE-ABS-KEY(\"Internet of Medical Things\") and FUND-SPONSOR(a* or b* or c* or d* or e* or f* or g* or h* or i* or j* or k* or l* or m* or n* or o* or p* or q* or r* or s* or t* or u* or v* or z* or x* or y* or w* or 1* or 2* or 3* or 4* or 5* or 6* or 7* or 8* or 9* or 0*)\u003c/p\u003e \u003cp\u003eand\u003c/p\u003e \u003cp\u003eTITLE-ABS-KEY(\"Internet of Medical Things\") and NOT FUND-SPONSOR(a* or b* or c* or d* or e* or f* or g* or h* or i* or j* or k* or l* or m* or n* or o* or p* or q* or r* or s* or t* or u* or v* or z* or x* or y* or w* or 1* or 2* or 3* or 4* or 5* or 6* or 7* or 8* or 9* or 0*)\u003c/p\u003e \u003cp\u003efor non-funded publications (NFPs).\u003c/p\u003e \u003cp\u003eEqually structured search string, but with different search terms were used to calculate the percentage of FPs in the fields of Information and Communication Technologies (ICT) and Medicine and Health.\u003c/p\u003e \u003cp\u003eFirst two research questions were naswered using Scopus\u0026rsquo;s built-in functionality and the Bibliometrics software [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The country ranks in Medicine, and Computer networks and communications research were obtained from Scimago [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Country determinants used in our study are Health systems ranking 2023 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], Health Expenditure as % of GDP \u0026minus;\u0026thinsp;2021/22, Current R\u0026amp;D Expenditure as % of GDP \u0026minus;\u0026thinsp;2021/22[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] CR\u0026amp;D Expenditure as % of GDP \u0026minus;\u0026thinsp;2021/22 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and Bloomberg Global Health Index (BGHI) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. BGHI accounts for a variety of factors that contribute to the populational health of countries. It is based on the premise that the residents of developed countries tend to be healthier due to a higher quality of life, lower pollution rates, better infrastructure, access to quality healthcare, education, better nutrition, and good-paying jobs. On the contrary, residents of other developing countries frequently lack adequate access to these same benefits. The associations between bibliometric dimensions and country determinants were analysed by a framework developed by Kokol et al [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThematic analysis was performed by Synthetic Knowledge Synthesis (SKS) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe search resulted in 1978 NFPs and 842 FPs. FPs were written by 2995 authors coming from 74 countries and were published in 337 source titles. FPs contained 2443 author keywords. NFPs were written by 5366 authors coming from 106 countries and were published in 905 source titles. NFPs contained 4241 author keywords. The percentage of FPs was 29.8%, which is higher than in related disciplines (ICT (25.5%) and Medicine and Health (20.9%)), but lower than in IoT use in preventive health (37.0%) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Spatial bibliometric dimensions of IoMT research and country determinants\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe publication types are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Comparison between the share of different types of publications between NFPs and FPs show that perceptually more original articles and reviews are funded than nonfunded. In all other types percentages of NFPs are prevailing. It is interesting to note that none of FPs was retracted or updated with errata.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePublication types\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDocument Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of NFPs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% of NFPs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of FPs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e% of FPs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArticle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConference Paper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBook Chapter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReview\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConference Review\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBook\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEditorial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eErratum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetracted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLetter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNote\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShort Survey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows a steady growth in the number of papers in the domain of IoMT, for both NFPs and FPs, from 2016 to 2024. The slight decline observed in 2024 can be attributed to the timing of data collection, which occurred in September 2024. Early in the observed period, there was a significant discrepancy between the number of NFPs and FPs, as can be observed for the year 2017. However, a spike in 2019 brought the distribution closer to parity, with FPs accounting for 44.4% of total publications. After this peak in 2019, the proportion of FPs gradually decreased, stabilizing around 30% in subsequent years.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMost of the original titles publishing research on IoMT are ranked in the top two quartiles according to SCIMago JCR (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Scimago JCR ranges from 0.15 to 4.42. Five source titles published, both FPs and NFPs, IEEE Access being the only journal publishing more FPs than NFPs. FPs were published only in journals, whereas NFPs were also published as book chapters. In general, FPs were published in higher-ranked source titles according to H-Index and JCR queries; however, the average JCR was slightly higher for NFP publishing source titles mainly due to the very high JCR of IEEE Transaction on Industrial Informatics.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMost prolific source titles publishing FPs and NFPs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOURCE TITLE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of FPs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSJR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuarter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH-index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSOURCE TITLE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNumber of NFPs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSJR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eQuarter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eH-index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIEEE Access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIEEE Access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e242\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLecture Notes In Networks And Systems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIEEE Internet Of Things Journal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIEEE Internet Of Things Journal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectronics Switzerland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIEEE Journal Of Biomedical And Health Informatics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIEEE Journal Of Biomedical And Health Informatics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCommunications In Computer And Information Science\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFuture Generation Computer Systems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInternet Of Things\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComputers Materials And Continua\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eElectronics Switzerland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApplied Sciences MDPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIEEE Transactions On Industrial Informatics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComputer Communications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLecture Notes In Electrical Engineering\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInformation Sciences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSensors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e161.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTable\u0026nbsp;4\u003c/b\u003e reveals that the country productivity ranks of both NFPs, and FPs differs from the country productivity ranks in subjects Medicine and Computer networks and communications. Three most productive countries in above two subjects are still among 10 top productive countries, but top productive countries like Germany, Japan, France and Canada are missing. On the other hand, countries which don\u0026rsquo;t belong to top 30 most productive countries like Saudi Arabia, Pakistan and Malaysia are among top 10 productive countries in IoMT research. Eight countries are in both NFP and FP list, however the rankings differ slightly, India and United States being the most productive regarding NFPs and China and Saudi Arabia regarding FPs. Australia and Iraq are only ranked among top 10 NFPs countries and South Korea and Egypt only among FPs countries. The average percentage of funded papers in top 10 countries is 44.2% with South Korea having the largest percentage of funded papers (69.3%) and India the smallest percentage (20.0%).\u003c/p\u003e \u003cp\u003eAccording to Organisation for Economic Co-operation and Development (OECD) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] the average health spending related to GPD in 2022 in OECD countries is about 9.2%.Thereafter it is surprising to observe that only three countries in the top 10 NFPs or FPs countries are above that limit. Similarly, the most of top ranked countries in Health and Health systems ranking like Singapore, Japan, Taiwan, Scandinavian Countries, etc. are also not in the below lists. It seems that countries with low health expenditures and not so well ranked health systems are investing in the research in IoMT as a possible technology to improve health services. Similar observation can be made regarding the R\u0026amp;D expenditures. According to OECD [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] an appropriate spending in percentages of GPD is above 2% and only four of top 10 countries reached that limit. Data reveals that countries highly above or highly below 2% expenditure in R\u0026amp;D GPD are the most productive regarding both NFPs and FPs. A comparison of country determinants between the most productive countries in the NFP and FP lists reveals that the countries on the FP list rank higher on average in all three rankings, have a higher percentage of investments in healthcare and research and higher Global Health Index.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMost productive countries and their country determinants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunded/Nonfunded publication\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOUNTRY/TERRITORY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of NFPs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eScimago rank in subject Medicine\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eScimago rank in sub-subject Computer networks and communications\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHealth systems ranking 2023 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCurrent Health Expenditure as % of GDP \u0026minus;\u0026thinsp;2021/22\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCurrent R\u0026amp;D Expenditure as % of GDP \u0026minus;\u0026thinsp;2021/22 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eBGHI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e61.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e79.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e46.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSaudi Arabia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e77.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePakistan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e61.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited Kingdom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e88.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e90.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIraq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e62.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e91.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMalaysia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e84.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAVERAGE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e74.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e280 (57.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e46.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSaudi Arabia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e176 (52.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e77.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e171 (20.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e61.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127 (36.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e79.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouth Korea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88 (69.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e94.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePakistan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75 (38.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e61.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited Kingdom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (40.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e88.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (42.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e91.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEgypt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (48.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e64.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMalaysia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e84.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAVERAGE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.02125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e74.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Thematic analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAccording to Zipf bibliometric law [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], 65 most popular author keywords from NFPs and 49 from FPs were taken into VOSViewer analysis. The research landscapes are shown in Figs.\u0026nbsp;3 and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003e. SKS performed on those two landscapes resulted in the themes presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e. SKS analysis of FPs and NFPs author keywords landscapes did not show big thematic differences except two cases. Namely, FPs are more concerned with using AI IoMT applications in e-health and telemedicine, while NFPs are more concerned with using IoMT in pandemic management. Other differences are mainly in the fact that author keywords representing semantically similar topics are differently related, whether funded or unfunded, appear in different topics or differ in popularity.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThemes derived by using SKS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPs Themes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRepresentative topics identified in prominent publications\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNFPs themes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRepresentative topics identified in prominent publications\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIoMT and AI use in e-health and telemedicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eECG monitoring [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], e-health patient monitoring [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], elderly healthcare [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], Accident and emergency detection in One digital health [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRole of IoMT in pandemic management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePoint of care testing of infectious diseases [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], Cognitive IoMT for pandemic management [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], Pandemic forecasting [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], Covid-19 management by federated learning [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivacy in federated learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSkin diseases [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], Smart healthcare [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], ECG classification [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], Misbehaviour detection [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], Heart disease diagnosing [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrivacy and security within federated learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePrivacy preservation with fraud enabled blockchain [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Privacy preservation in smart healthcare [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], Intrusion detection [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], Privacy sensitive federated learning [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecurity in smart health care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlockchain industrial secure encryption in healthcare [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], Hybrid authentication for digital healthcare [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], Threat detection in IoMT networks [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], Secure intelligent biosensors [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMachine learning detection of cybersecurity treads on IoMT applications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCybersecurity of healthcare 5.0 systems using federated learning [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Tree classifier based intrusion detection in IoMT [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Multilayer perceptron optimisation for cybersecurity [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecure big data analysis in healthcare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esecurity threats, vulnerabilities, and counter measures [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], Blockchain, Blockchain assisted big data management [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], Healthcare in Smart Cities [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBig data analysis of data from wearable sensors for eHealth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAmbient assisted living [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], edge-stream computing for real time analysis of wearable data [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], big and wearable data in gynaecology [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], Big data based Smart Health Monitoring [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvanced machine learning and data security in accessing data from wearables and sensors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecure wearable ultrasound system [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], Privacy preserving federated learning [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], Robust zero watermarking for federated learning [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], Scalable transferable federated learning in classification of healthcare IoMT data [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdvanced machine learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRemote patient monitoring [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Lung tumour diagnosing [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], Digitalization [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cem\u003e3.3. IoMT Impact: Bloomberg Global Health Index in relation to the number of funded published papers on IoMT\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e6\u003c/span\u003e represents the BGHI in association with the number of papers published per 1M residents. The association is not strong; however, the trend line (dotted line in Fig.\u0026nbsp;2) shows that the BGHI is increasing with the number of published FPs. The graph is very scattered in the area of less than 0.4 FPs/1M residents, but most of the countries on right of that limit have in general higher BGHI than 80. On the other hand, countries with the lowest BGHIs are all on the left side of the 0.4 FPs/1M residents limit. From the bibliometric point of view the 0.4 FPs/1M residents limit also indicates the point after which the number of FPs do not affect the BGHI in a significant manner. Nevertheless the above patterns might indicate that the investments in IoMT research have positive effect on populational health status.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMost of the most prolific funding agencies (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e) come from China, South Korea, Saudi Arabia, the US, and the EU, which are also among the most productive countries (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Except for Saudi Arabia, those countries spend a respectable share of GPD on health and research.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe most prolific funding agencies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNational Natural Science Foundation of China, China\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNational Science Foundation, USA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNational Research Foundation of Korea,\" Korea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNational Key Research and Development Program of China, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKing Saud University, Saudi Arabia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinistry of Science and Technology of the People's Republic of China, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeanship of Scientific Research, King Saud University, Saudi Arabia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEuropean Commission, EU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinistry of Science, ICT and Future Planning, South Korea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFundamental Research Funds for the Central Universities, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur study revealed that the proportion of funded projects (FPs) in the Internet of Medical Things (IoMT) research literature was higher than in the fields of Medicine, Health, and Information and Communication Technology (ICT), yet lower than in the Internet of Things (IoT) research focused on preventive health. This variation in the percentage of FPs can be attributed to several key factors. Firstly, the interdisciplinary nature of IoMT, which merges healthcare and technological innovation, makes it a significant area of interest for both public and private funding agencies. Its potential to revolutionize patient care and healthcare systems drives increased funding compared to more traditional research areas like Medicine, Health, and ICT. IoMT offers the promise of groundbreaking advancements, particularly in diagnostics, treatment, and patient monitoring, making it a high-priority area for innovation-driven investment. In contrast, IoT-based preventive health research, which emphasizes early detection and cost-effective management of chronic diseases, attracts even greater funding. Preventive health solutions using IoT technologies are often viewed as long-term strategies with the potential to reduce healthcare costs and improve population health outcomes, thereby garnering substantial support from stakeholders invested in sustainable healthcare solutions [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. These approaches are particularly attractive to funders as they help alleviate the burdens on healthcare systems and align with public health initiatives aimed at reducing the prevalence of non-communicable diseases (NCDs). Additionally, IoMT, as an emerging field, often requires substantial technological investment [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], making it more likely to attract funding compared to more established fields like general Medicine or ICT research. Furthermore, funding agencies may prioritize preventive health due to its proven effectiveness in reducing the incidence of chronic diseases, thereby highlighting the significance of IoT applications in this area [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe higher percentage of funded original articles and reviews compared to non-funded ones can be attributed to the primary goals of funding, which focus on supporting innovation and original research. Such research often begins with a synthesis of existing knowledge, typically presented in review articles, laying the groundwork for subsequent innovation.\u003c/p\u003e \u003cp\u003eThe trend in IoMT research literature production has been positive, reflecting the broader upward trends in literature production across most modern scientific and research fields [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn contrast to some other studies [68\u0026ndash;70], our analysis did not reveal a regional concentration in research and literature production. The most productive countries in both non-funded projects (NFPs) and funded projects (FPs) include not only well-developed and wealthy nations but also developing countries with less successful economies and healthcare systems. This may be because IoMT-based health solutions can help mitigate workforce shortages in healthcare, address the effects of climate and demographic changes, and improve access to healthcare in remote areas of larger, less developed countries more efficiently than traditional methods [20,71\u0026ndash;73]. The lack of regional concentration in IoMT research may also explain why the ranking of the 10 most productive countries in this field differs from their rankings in more general research areas.\u003c/p\u003e \u003cp\u003eOur study also revealed that up to a certain threshold (0.4 FPs per 1\u0026nbsp;million residents), the BGHI is increasing with the number of funded papers, suggesting that IoMT research funding may contribute to improved healthcare delivery. This finding aligns with other studies that have analyzed the association between research grants and health indices [74\u0026ndash;76].\u003c/p\u003e \u003cp\u003eThe funding patterns identified in our study can assist researchers in pinpointing suitable research themes for funding, identifying funding institutions, and locating productive countries for potential research collaborations. Additionally, these findings may be valuable to research managers, funding body administrators, government decision-makers, and policymakers.\u003c/p\u003e \u003cp\u003eThis article has both strengths and limitations. One strength is the use of SKS, a well-established knowledge synthesis method that allowed for a comprehensive thematic analysis of IoT research. Another key strength is that our study is the first to thoroughly examine funding patterns and the impact of funding in IoMT research. However, a major limitation is the reliance on a single database, which may have excluded some literature, particularly studies published on various preprint platforms.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe trend of the IoMT research literature production regarding both funded and nonfunded papers is positive, however the percentage of funded papers is decreasing, but seems to stabilise in 2022. The percentage of funding is higher than in healthcare or medicine in general but lower than in ICT related disciplines. Contrary to some others, our study didn\u0026rsquo;t reveal regionally research concentration and literature production, meaning that even less developed, and less \u0026ldquo;rich\u0026rdquo; countries produce comparable amount of publications. Similarly, government spending in health and research and the health system rank didn\u0026rsquo;t seem to be associated with research literature production or funding. Nevertheless, funded papers seem to be published in slightly higher ranked journals, and more funded papers are affiliated to scientifically higher ranked countries and country with better country determinants. The research funding expressed with the number funding papers per capita, shows a positive trend with the BGHI.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Peter Kokol, ], The first draft of the manuscript was written by Peter Kokol and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e No funding has been received for research presented in this paper\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics, Consent to Participate, and Consent to Publish declarations\u003c/strong\u003e: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e not applicable.’\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration\u003c/strong\u003e: he datasets used and/or analysed during the current study available from the corresponding author on reasonable request\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHeimburg DV, Prilleltensky I, Ness O, Ytterhus B. From public health to public good: Toward universal wellbeing. Scand J Public Health. 2022;50:1062\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/14034948221124670\u003c/span\u003e\u003cspan address=\"10.1177/14034948221124670\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAwad A, Trenfield SJ, Pollard TD, et al. Connected healthcare: Improving patient care using digital health technologies. Adv Drug Deliv Rev. 2021;178:113958. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.addr.2021.113958\u003c/span\u003e\u003cspan address=\"10.1016/j.addr.2021.113958\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSatam H, Joshi K, Mangrolia U, et al. Next-Generation Sequencing Technology: Current Trends and Advancements. Biology. 2023;12:997. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/biology12070997\u003c/span\u003e\u003cspan address=\"10.3390/biology12070997\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKokol P, Blažun Vošner H, Kokol M, Završnik J. Role of Agile in Digital Public Health Transformation. Front Public Health. 2022;10:899874. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpubh.2022.899874\u003c/span\u003e\u003cspan address=\"10.3389/fpubh.2022.899874\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKokol P. Agile Software Development in Healthcare: A Synthetic Scoping Review. Appl Sci. 2022;12:9462. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/app12199462\u003c/span\u003e\u003cspan address=\"10.3390/app12199462\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhang A, Rana G, Tailor RK, Abdullayev V. Data-Centric AI Solutions and Emerging Technologies in the Healthcare Ecosystem. CRC; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eŠajnović U, Vošner HB, Završnik J, et al. Internet of Things and Big Data Analytics in Preventive Healthcare: A Synthetic Review. Electronics. 2024;13:3642. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/electronics13183642\u003c/span\u003e\u003cspan address=\"10.3390/electronics13183642\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEl-Saleh AA, Sheikh AM, Albreem MAM, Honnurvali MS. The Internet of Medical Things (IoMT): opportunities and challenges. Wirel Netw. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11276-024-03764-8\u003c/span\u003e\u003cspan address=\"10.1007/s11276-024-03764-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOsama M, Ateya AA, Sayed MS et al. (2023) Internet of Medical Things and Healthcare 4.0: Trends, Requirements, Challenges, and Research Directions. Sensors 23:. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/s23177435\u003c/span\u003e\u003cspan address=\"10.3390/s23177435\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRazdan S, Sharma S. Internet of Medical Things (IoMT): Overview, Emerging Technologies, and Case Studies. IETE Tech Rev. 2022;39:775\u0026ndash;88. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/02564602.2021.1927863\u003c/span\u003e\u003cspan address=\"10.1080/02564602.2021.1927863\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManickam P, Mariappan SA, Murugesan SM, et al. Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare. Biosensors. 2022;12:562. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/bios12080562\u003c/span\u003e\u003cspan address=\"10.3390/bios12080562\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShah V, Khang A. Internet of Medical Things (IoMT) Driving the Digital Transformation of the Healthcare Sector. Data-Centric AI Solutions and Emerging Technologies in the Healthcare Ecosystem. CRC; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOu G, Zhao K, Zuo R, Wu J. Effects of research funding on the academic impact and societal visibility of scientific research. J Informetrics. 2024;18:101592. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.joi.2024.101592\u003c/span\u003e\u003cspan address=\"10.1016/j.joi.2024.101592\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKokol P. Discrepancies among Scopus and Web of Science, coverage of funding information in medical journal articles: a follow-up study. J Med Libr Association. 2023;111:703\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5195/jmla.2023.1513\u003c/span\u003e\u003cspan address=\"10.5195/jmla.2023.1513\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKokol P, Žlahtic B, Šajnovič U, et al. Funding in IoMT research. Observations Based on Synthetic Knowledge Synthesis; 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKokol P, Blažun Vošner H. Discrepancies among Scopus, Web of Science, and PubMed coverage of funding information in medical journal articles. J Med Libr Assoc. 2018;106:81\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5195/jmla.2018.181\u003c/span\u003e\u003cspan address=\"10.5195/jmla.2018.181\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAria M, Cuccurullo C. bibliometrix: An R-tool for comprehensive science mapping analysis. J Informetrics. 2017;11:959\u0026ndash;75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.joi.2017.08.007\u003c/span\u003e\u003cspan address=\"10.1016/j.joi.2017.08.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e(2023) Scimago Journal \u0026amp; Country Rank. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.scimagojr.com/\u003c/span\u003e\u003cspan address=\"https://www.scimagojr.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 26 Jan 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHealth. and health system ranking of countries worldwide in 2023. In: Statista. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.statista.com/statistics/1376359/health-and-health-system-ranking-of-countries-worldwide/\u003c/span\u003e\u003cspan address=\"https://www.statista.com/statistics/1376359/health-and-health-system-ranking-of-countries-worldwide/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 17 Aug 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Development Indicators | DataBank. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://databank.worldbank.org/source/world-development-indicators/Series/GB.XPD.RSDV.GD.ZS#\u003c/span\u003e\u003cspan address=\"https://databank.worldbank.org/source/world-development-indicators/Series/GB.XPD.RSDV.GD.ZS#\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 17 Aug 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHealthiest C. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://worldpopulationreview.com/country-rankings/healthiest-countries\u003c/span\u003e\u003cspan address=\"https://worldpopulationreview.com/country-rankings/healthiest-countries\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 23 Aug 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKokol P, Železnik D, Završnik J, Blažun Vošner H. Nursing Research Literature Production in Terms of the Scope of Country and Health Determinants: A Bibliometric Study. J Nurs Scholarsh. 2019;51:590\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jnu.12500\u003c/span\u003e\u003cspan address=\"10.1111/jnu.12500\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKokol P. Synthetic Knowledge Synthesis in Hospital Libraries. J Hosp Librariansh. 2023;0:1\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/15323269.2023.2291282\u003c/span\u003e\u003cspan address=\"10.1080/15323269.2023.2291282\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHealth. In: OECD. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.oecd.org/en/topics/policy-areas/health.html\u003c/span\u003e\u003cspan address=\"https://www.oecd.org/en/topics/policy-areas/health.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 17 Aug 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZipf GK. The psycho-biology of language. Oxford, England: Houghton, Mifflin; 1935.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScrugli MA, Loi D, Raffo L, Meloni P. An Adaptive Cognitive Sensor Node for ECG Monitoring in the Internet of Medical Things. IEEE Access. 2022;10:1688\u0026ndash;705. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/ACCESS.2021.3136793\u003c/span\u003e\u003cspan address=\"10.1109/ACCESS.2021.3136793\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhila A, Dahan F, Alroobaea R, et al. A smart IoMT based architecture for E-healthcare patient monitoring system using artificial intelligence algorithms. Front Physiol. 2023;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fphys.2023.1125952\u003c/span\u003e\u003cspan address=\"10.3389/fphys.2023.1125952\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhosh S, Ghosh SK. FEEL: FEderated LEarning Framework for ELderly Healthcare Using Edge-IoMT. IEEE Trans Comput Social Syst. 2023;10:1800\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/TCSS.2022.3233300\u003c/span\u003e\u003cspan address=\"10.1109/TCSS.2022.3233300\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaghi M, Benis A, Deserno TM. Accident \u0026amp; Emergency Informatics and One Digital Health. Yearb Med Inform. 2022;31:40\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1055/s-0042-1742506\u003c/span\u003e\u003cspan address=\"10.1055/s-0042-1742506\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJain S, Nehra M, Kumar R, et al. Internet of medical things (IoMT)-integrated biosensors for point-of-care testing of infectious diseases. Biosens Bioelectron. 2021;179:113074. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bios.2021.113074\u003c/span\u003e\u003cspan address=\"10.1016/j.bios.2021.113074\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSwayamsiddha S, Mohanty C. Application of cognitive Internet of Medical Things for COVID-19 pandemic. Diabetes Metabolic Syndrome: Clin Res Reviews. 2020;14:911\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.dsx.2020.06.014\u003c/span\u003e\u003cspan address=\"10.1016/j.dsx.2020.06.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShoeibi A, Khodatars M, Jafari M, et al. Automated detection and forecasting of COVID-19 using deep learning techniques: A review. Neurocomputing. 2024;577:127317. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neucom.2024.127317\u003c/span\u003e\u003cspan address=\"10.1016/j.neucom.2024.127317\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSamuel O, Omojo AB, Onuja AM, et al. IoMT: A COVID-19 Healthcare System Driven by Federated Learning and Blockchain. IEEE J Biomedical Health Inf. 2023;27:823\u0026ndash;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/JBHI.2022.3143576\u003c/span\u003e\u003cspan address=\"10.1109/JBHI.2022.3143576\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHossen MN, Panneerselvam V, Koundal D, et al. Federated Machine Learning for Detection of Skin Diseases and Enhancement of Internet of Medical Things (IoMT) Security. IEEE J Biomedical Health Inf. 2023;27:835\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/JBHI.2022.3149288\u003c/span\u003e\u003cspan address=\"10.1109/JBHI.2022.3149288\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguyen DC, Pham Q-V, Pathirana PN, et al. Federated Learning for Smart Healthcare: A Survey. ACM-CSUR. 2022;55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1145/3501296\u003c/span\u003e\u003cspan address=\"10.1145/3501296\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCai W, Gao M, Jiang Y, et al. Hierarchical Domain Adaptation Projective Dictionary Pair Learning Model for EEG Classification in IoMT Systems. IEEE Trans Comput Social Syst. 2023;10:1559\u0026ndash;67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/TCSS.2022.3176656\u003c/span\u003e\u003cspan address=\"10.1109/TCSS.2022.3176656\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahmadika S, Astillo PV, Choudhary G, et al. Blockchain-Based Privacy Preservation Scheme for Misbehavior Detection in Lightweight IoMT Devices. IEEE J Biomedical Health Inf. 2023;27:710\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/JBHI.2022.3187037\u003c/span\u003e\u003cspan address=\"10.1109/JBHI.2022.3187037\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYaqoob MM, Nazir M, Yousafzai A, et al. Modified Artificial Bee Colony Based Feature Optimized Federated Learning for Heart Disease Diagnosis in Healthcare. Appl Sci. 2022;12:12080. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/app122312080\u003c/span\u003e\u003cspan address=\"10.3390/app122312080\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLakhan A, Mohammed MA, Nedoma J, et al. Federated-Learning Based Privacy Preservation and Fraud-Enabled Blockchain IoMT System for Healthcare. IEEE J Biomedical Health Inf. 2023;27:664\u0026ndash;72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/JBHI.2022.3165945\u003c/span\u003e\u003cspan address=\"10.1109/JBHI.2022.3165945\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAli M, Naeem F, Tariq M, Kaddoum G. Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey. IEEE J Biomedical Health Inf. 2023;27:778\u0026ndash;89. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/JBHI.2022.3181823\u003c/span\u003e\u003cspan address=\"10.1109/JBHI.2022.3181823\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSi-Ahmed A, Al-Garadi MA, Boustia N. Survey of Machine Learning based intrusion detection methods for Internet of Medical Things. Appl Soft Comput. 2023;140:110227. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.asoc.2023.110227\u003c/span\u003e\u003cspan address=\"10.1016/j.asoc.2023.110227\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAouedi O, Sacco A, Piamrat K, Marchetto G. Handling Privacy-Sensitive Medical Data With Federated Learning: Challenges and Future Directions. IEEE J Biomedical Health Inf. 2023;27:790\u0026ndash;803. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/JBHI.2022.3185673\u003c/span\u003e\u003cspan address=\"10.1109/JBHI.2022.3185673\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAli A, Almaiah MA, Hajjej F, et al. An Industrial IoT-Based Blockchain-Enabled Secure Searchable Encryption Approach for Healthcare Systems Using Neural Network. Sensors. 2022;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/s22020572\u003c/span\u003e\u003cspan address=\"10.3390/s22020572\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlmaiah MA, Hajjej F, Ali A, et al. An AI-Enabled Hybrid Lightweight Authentication Model for Digital Healthcare Using Industrial Internet of Things Cyber‐Physical Systems. Sensors. 2022;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/s22041448\u003c/span\u003e\u003cspan address=\"10.3390/s22041448\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan IA, Moustafa N, Razzak I, et al. XSRU-IoMT: Explainable simple recurrent units for threat detection in Internet of Medical Things networks. Future Generation Comput Syst. 2022;127:181\u0026ndash;93. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.future.2021.09.010\u003c/span\u003e\u003cspan address=\"10.1016/j.future.2021.09.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChaudhary V, Khanna V, Ahmed Awan HT, et al. Towards hospital-on-chip supported by 2D MXenes-based 5th generation intelligent biosensors. Biosens Bioelectron. 2023;220. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bios.2022.114847\u003c/span\u003e\u003cspan address=\"10.1016/j.bios.2022.114847\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRehman A, Abbas S, Khan MA, et al. A secure healthcare 5.0 system based on blockchain technology entangled with federated learning technique. Comput Biol Med. 2022;150:106019. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.compbiomed.2022.106019\u003c/span\u003e\u003cspan address=\"10.1016/j.compbiomed.2022.106019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan IA, Razzak I, Pi D, et al. Fed-Inforce-Fusion: A federated reinforcement-based fusion model for security and privacy protection of IoMT networks against cyber-attacks. Inform Fusion. 2024;101:102002. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.inffus.2023.102002\u003c/span\u003e\u003cspan address=\"10.1016/j.inffus.2023.102002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta K, Sharma DK, Datta Gupta K, Kumar A. A tree classifier based network intrusion detection model for Internet of Medical Things. Comput Electr Eng. 2022;102:108158. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.compeleceng.2022.108158\u003c/span\u003e\u003cspan address=\"10.1016/j.compeleceng.2022.108158\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFirat Kilincer I, Ertam F, Sengur A, et al. Automated detection of cybersecurity attacks in healthcare systems with recursive feature elimination and multilayer perceptron optimization. Biocybernetics Biomedical Eng. 2023;43:30\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bbe.2022.11.005\u003c/span\u003e\u003cspan address=\"10.1016/j.bbe.2022.11.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHasan MK, Ghazal TM, Saeed RA, et al. A review on security threats, vulnerabilities, and counter measures of 5G enabled Internet-of-Medical-Things. IET Commun. 2022;16:421\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1049/cmu2.12301\u003c/span\u003e\u003cspan address=\"10.1049/cmu2.12301\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbbas A, Alroobaea R, Krichen M, et al. Blockchain-assisted secured data management framework for health information analysis based on Internet of Medical Things. Personal Uniquit Comput. 2024;28:59\u0026ndash;72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00779-021-01583-8\u003c/span\u003e\u003cspan address=\"10.1007/s00779-021-01583-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMishra P, Singh G. (2023) Internet of Medical Things Healthcare for Sustainable Smart Cities: Current Status and Future Prospects. Applied Sciences (Switzerland) 13:. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/app13158869\u003c/span\u003e\u003cspan address=\"10.3390/app13158869\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSyed L, Jabeen S, Alsaeedi SM A. Smart healthcare framework for ambient assisted living using IoMT and big data analytics techniques. Future Generation Comput Syst. 2019;101:136\u0026ndash;51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.future.2019.06.004\u003c/span\u003e\u003cspan address=\"10.1016/j.future.2019.06.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlShorman O, AlShorman B, Al-khassaweneh M, Alkahtani F. A review of internet of medical things (IoMT) - based remote health monitoring through wearable sensors: a case study for diabetic patients. Indonesian J Electr Eng Comput Sci. 2020;20:414\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.11591/ijeecs.v20.i1.pp414-422\u003c/span\u003e\u003cspan address=\"10.11591/ijeecs.v20.i1.pp414-422\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhamisy-Farah R, Furstenau LB, Kong JD, et al. Gynecology Meets Big Data in the Disruptive Innovation Medical Era: State-of-Art and Future Prospects. Int J Environ Res Public Health. 2021;18:5058. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph18105058\u003c/span\u003e\u003cspan address=\"10.3390/ijerph18105058\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBig Data-Based Smart Health Monitoring System. Using Deep Ensemble Learning | IEEE Journals \u0026amp; Magazine | IEEE Xplore. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ieeexplore-ieee-org.ezproxy.lib.ukm.si/document/10286815\u003c/span\u003e\u003cspan address=\"https://ieeexplore-ieee-org.ezproxy.lib.ukm.si/document/10286815\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 17 Sep 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin M, Zhang Z, Gao X, et al. A fully integrated wearable ultrasound system to monitor deep tissues in moving subjects. Nat Biotechnol. 2024;42:448\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41587-023-01800-0\u003c/span\u003e\u003cspan address=\"10.1038/s41587-023-01800-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang R, Lai J, Zhang Z, et al. Privacy-Preserving Federated Learning for Internet of Medical Things Under Edge Computing. IEEE J Biomedical Health Inf. 2023;27:854\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/JBHI.2022.3157725\u003c/span\u003e\u003cspan address=\"10.1109/JBHI.2022.3157725\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan B, Jhaveri RH, Wang H, et al. Application of Robust Zero-Watermarking Scheme Based on Federated Learning for Securing the Healthcare Data. IEEE J Biomedical Health Inf. 2023;27:804\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/JBHI.2021.3123936\u003c/span\u003e\u003cspan address=\"10.1109/JBHI.2021.3123936\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun L, Wu J. A Scalable and Transferable Federated Learning System for Classifying Healthcare Sensor Data. IEEE J Biomedical Health Inf. 2023;27:866\u0026ndash;77. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/JBHI.2022.3171402\u003c/span\u003e\u003cspan address=\"10.1109/JBHI.2022.3171402\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbidi MH, Umer U, Mian SH, Al-Ahmari A. Big Data-Based Smart Health Monitoring System: Using Deep Ensemble Learning. IEEE Access. 2023;11:114880\u0026ndash;903. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/ACCESS.2023.3325323\u003c/span\u003e\u003cspan address=\"10.1109/ACCESS.2023.3325323\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaj EFI. (2022) Data-driven modern health care systems with the internet of medical things combined with big data and machine learning. In: AI-Enabled IoT for Smart Health Care Systems. pp 123\u0026ndash;145.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Z. (2024) Lung Tumor Diagnosis Technology Based on 6G Wireless Network Sensors and Big Data Analysis. Wireless Pers Commun. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11277-024-11215-y\u003c/span\u003e\u003cspan address=\"10.1007/s11277-024-11215-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChatterjee R, Ray R, Dash SR, Jena OP. Conceptualizing Tomorrow\u0026rsquo;s Healthcare Through Digitization. Computational Intelligence and Healthcare Informatics. Ltd: Wiley; 2021. pp. 359\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh S, Sharma S, Bhadula S, Mohan S. Industry 4.0 Internet of Medical Things Enabled Cost Effective Secure Smart Patient Care Medicine Pouch. In: Nayyar A, Naved M, Rameshwar R, editors. New Horizons for Industry 4.0 in Modern Business. Cham: Springer International Publishing; 2023. pp. 149\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAjagbe SA, Awotunde JB, Adesina AO, et al. Internet of Medical Things (IoMT): Applications, Challenges, and Prospects in a Data-Driven Technology. In: Chakraborty C, Khosravi MR, editors. Intelligent Healthcare. Singapore: Springer Nature Singapore; 2022. pp. 299\u0026ndash;319.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRobles SC. A Public Health Framework for Chronic Disease Prevention and Control. Food Nutr Bull. 2004;25:194\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/156482650402500213\u003c/span\u003e\u003cspan address=\"10.1177/156482650402500213\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbis S, Veldkamp L. The Changing Economics of Knowledge Production. Rev Financial Stud. 2024;37:89\u0026ndash;118. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/rfs/hhad059\u003c/span\u003e\u003cspan address=\"10.1093/rfs/hhad059\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Internet of Medical Things, Research funding, Synthetic Knowledge Synthesis","lastPublishedDoi":"10.21203/rs.3.rs-6892735/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6892735/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Internet of Medical Things (IoMT) represents a transformative technology that connects medical devices, sensors, and healthcare systems to enable real-time monitoring, data sharing, and advanced decision-making in healthcare. While the technical and clinical potential of IoMT has been researched extensively, the scale and scope of research funding and their influence on research literature production patterns and country health determinants remain unknown. The study presented in this paper covers this gap by employing triangulation of quantitative and qualitative approaches. The results reveal a positive trend IoMT in research literature production. The funded research exhibits higher publication rates in high-impact journals but, unlike in many other research fields, is not regionally concentrated in countries with stronger healthcare systems and higher R\u0026amp;D expenditures, showing that IOMT can increasingly contribute to improving healthcare systems and outcomes even with the least investments. Thematic analysis shows that both funded and non-funded are associated with similar themes; however, founded research is more focused on recent research trends like artificial intelligence applications in healthcare. Finally, our study revealed the positive association between the number of funded papers and health determinants, suggesting that IoMT research funding might contribute to improved healthcare delivery.\u003c/p\u003e","manuscriptTitle":"Investigating the Links Between Funding, Scholarly Production, and Public Health Determinants in IoMT Research","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-27 19:46:07","doi":"10.21203/rs.3.rs-6892735/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ed1a112e-7d11-4035-adcd-83b495e9feb8","owner":[],"postedDate":"June 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-01T08:08:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-27 19:46:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6892735","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6892735","identity":"rs-6892735","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}