Learning to rank query expansion terms for COVID-19 scholarly search.

OBJECTIVE: With the onset of the Coronavirus Disease 2019 (COVID-19) pandemic, there has been a surge in the number of publicly available biomedical information sources, which makes it an increasingly challenging research goal to retrieve a relevant text to a topic of interest. In this paper, we propose a Contextual Query Expansion framework based on the clinical Domain knowledge (CQED) for formalizing an effective search over PubMed to retrieve relevant COVID-19 scholarly articles to a given information need. MATERIALS AND METHODS: For the sake of training and evaluation, we use the widely adopted TREC-COVID benchmark. Given a query, the proposed framework utilizes a contextual and a domain-specific neural language model to generate a set of candidate query expansion terms that enrich the original query. Moreover, the framework includes a multi-head attention mechanism that is trained alongside a learning-to-rank model for re-ranking the list of generated expansion candidate terms. The original query and the top-ranked expansion terms are posed to the PubMed search engine for retrieving relevant scholarly articles to an information need. The framework, CQED, can have four different variations, depending upon the learning path adopted for training and re-ranking the candidate expansion terms. RESULTS: The model drastically improves the search performance, when compared to the original query. The performance improvement in comparison to the original query, in terms of RECALL@1000 is 190.85% and in terms of NDCG@1000 is 343.55%. Additionally, the model outperforms all existing state-of-the-art baselines. In terms of P@10, the model that has been optimized based on Precision outperforms all baselines (0.7987). On the other hand, in terms of NDCG@10 (0.7986), MAP (0.3450) and bpref (0.4900), the CQED model that has been optimized based on an average of all retrieval measures outperforms all the baselines. CONCLUSION: The proposed model successfully expands queries posed to PubMed, and improves search performance, as compared to all existing baselines. A success/failure analysis shows that the model improved the search performance of each of the evaluated queries. Moreover, an ablation study depicted that if ranking of generated candidate terms is not conducted, the overall performance decreases. For future work, we would like to explore the application of the presented query expansion framework in conducting technology-assisted Systematic Literature Reviews (SLR).

User Compliance With the Health Emergency and Disaster Management System: Systematic Literature Review.

BACKGROUND: Health-related hazards have a detrimental impact on society. The health emergency and disaster management system (Health EDMS), such as a contact-tracing application, is used to respond to and cope with health-related hazards. User compliance with Health EDMS warnings is key to its success. However, it was reported that user compliance with such a system remains low. OBJECTIVE: Through a systematic literature review, this study aims to identify the theories and corresponding factors that explain user compliance with the warning message provided by Health EDMS. METHODS: The systematic literature review was conducted using Preferred Reporting Items for Systematic reviews and Meta-Analyses 2020 guidelines. The search was performed using the online databases Scopus, ScienceDirect, ProQuest, IEEE, and PubMed, for English journal papers published between January 2000 and February 2022. RESULTS: A total of 14 papers were selected for the review based on our inclusion and exclusion criteria. Previous research adopted 6 theories when examining user compliance, and central to the research was Health EDMS. To better understand Health EDMS, based on the literature reviewed, we mapped the activities and features of Health EDMS with the key stakeholders involved. We identified features that require involvement from individual users, which are surveillance and monitoring features and medical care and logistic assistance features. We then proposed a framework showing the individual, technological, and social influencing factors of the use of these features, which in turn affects compliance with the warning message from Health EDMS. CONCLUSIONS: Research on the Health EDMS topic increased rapidly in 2021 due to the COVID-19 pandemic. An in-depth understanding of Health EDMS and user compliance before designing the system is essential for governments and developers to increase the effectiveness of Health EDMS. Through a systematic literature review, this study proposed a research framework and identified research gaps for future research on this topic.

The New Version of the ANDDigest Tool with Improved AI-Based Short Names Recognition.

The body of scientific literature continues to grow annually. Over 1.5 million abstracts of biomedical publications were added to the PubMed database in 2021. Therefore, developing cognitive systems that provide a specialized search for information in scientific publications based on subject area ontology and modern artificial intelligence methods is urgently needed. We previously developed a web-based information retrieval system, ANDDigest, designed to search and analyze information in the PubMed database using a customized domain ontology. This paper presents an improved ANDDigest version that uses fine-tuned PubMedBERT classifiers to enhance the quality of short name recognition for molecular-genetics entities in PubMed abstracts on eight biological object types: cell components, diseases, side effects, genes, proteins, pathways, drugs, and metabolites. This approach increased average short name recognition accuracy by 13%.

The ascendancy of research in acronyms related to COVID-19 displayed on a growth-share matrix (GSM): Bibliometric analysis.

BACKGROUND: The acronym COVID, which stands for coronavirus disease, has become one of the most infamous acronyms in the world since 2020. An analysis of acronyms in health and medical journals has previously found that acronyms have become more common in titles and abstracts over time (e.g., DNA and human immunodeficiency virus are the most common acronyms). However, the trends in acronyms related to COVID remain unclear. It is necessary to verify whether the dramatic rise in COVID-related research can be observed by visualizations. The purpose of this study was to display the acronym trends in comparison through the use of temporal graphs and to verify that the COVID acronym has a significant edge over the other 2 in terms of research dominance. METHODS: An analysis of the 30 most frequently used acronyms related to COVID in PubMed since 1950 was carried out using 4 graphs to conduct this bibliometric analysis, including line charts, temporal bar graphs (TBGs), temporal heatmaps (THM), and growth-share matrices (GSM). The absolute advantage coefficient (AAC) was used to measure the dominance strength for COVID acronym since 2020. COVID's AAC trend was expected to decline over time. RESULTS: This study found that COVID, DNA, and human immunodeficiency virus have been the most frequently observed research acronyms since 2020, followed by computed tomography and World Health Organization; although there is no ideal method for displaying acronym trends over time, researchers can utilize the GSM to complement traditional line charts, TBGs, and THMs, as shown in this study; and COVID has a significant edge over the other 2 in terms of research dominance by ACC (≥0.67), but COVID's AAC trend has declined (e.g., AACs 0.83, 0.80, and 0.69) since 2020. CONCLUSIONS: It is recommended that the GSM complement traditional line charts, TBGs, and THMs in trend analysis, rather than being restricted to acronyms in future research. This research provides readers with the AAC to understand how research dominates its counterparts, which will be useful for future bibliometric analyses.

EvidenceMap: a three-level knowledge representation for medical evidence computation and comprehension.

OBJECTIVE: To develop a computable representation for medical evidence and to contribute a gold standard dataset of annotated randomized controlled trial (RCT) abstracts, along with a natural language processing (NLP) pipeline for transforming free-text RCT evidence in PubMed into the structured representation. MATERIALS AND METHODS: Our representation, EvidenceMap, consists of 3 levels of abstraction: Medical Evidence Entity, Proposition and Map, to represent the hierarchical structure of medical evidence composition. Randomly selected RCT abstracts were annotated following EvidenceMap based on the consensus of 2 independent annotators to train an NLP pipeline. Via a user study, we measured how the EvidenceMap improved evidence comprehension and analyzed its representative capacity by comparing the evidence annotation with EvidenceMap representation and without following any specific guidelines. RESULTS: Two corpora including 229 disease-agnostic and 80 COVID-19 RCT abstracts were annotated, yielding 12 725 entities and 1602 propositions. EvidenceMap saves users 51.9% of the time compared to reading raw-text abstracts. Most evidence elements identified during the freeform annotation were successfully represented by EvidenceMap, and users gave the enrollment, study design, and study Results sections mean 5-scale Likert ratings of 4.85, 4.70, and 4.20, respectively. The end-to-end evaluations of the pipeline show that the evidence proposition formulation achieves F1 scores of 0.84 and 0.86 in the adjusted random index score. CONCLUSIONS: EvidenceMap extends the participant, intervention, comparator, and outcome framework into 3 levels of abstraction for transforming free-text evidence from the clinical literature into a computable structure. It can be used as an interoperable format for better evidence retrieval and synthesis and an interpretable representation to efficiently comprehend RCT findings.

Leveraging Digital Technology to Support Pregnant and Early Parenting Women in Recovery from Addictive Substances: A Scoping Review.

Little is known about digital health interventions used to support treatment for pregnant and early parenting women (PEPW) with substance use disorders (SUD). METHODS: Guided by the Arksey and O'Malley's Scoping Review Framework, empirical studies were identified within the CINAHL, PsycInfo, PubMed, and ProQuest databases using subject headings and free-text keywords. Studies were selected based on a priori inclusion/exclusion criteria, and data extraction and descriptive analysis were performed. RESULTS: A total of 27 original studies and 30 articles were included. Varying study designs were used, including several feasibility and acceptability studies. However, efficacious findings on abstinence and other clinically important outcomes were reported in several studies. Most studies focused on digital interventions for pregnant women (89.7%), suggesting a dearth of research on how digital technologies may support early parenting women with SUD. No studies included PEPW family members or involved PEPW women in the intervention design. CONCLUSIONS: The science of digital interventions to support treatment for PEPW is in an early stage, but feasibility and efficacy results are promising. Future research should explore community-based participatory partnerships with PEPW to develop or tailor digital interventions and include family or external support systems to engage in the intervention alongside PEPW.

PreprintMatch: A tool for preprint to publication detection shows global inequities in scientific publication.

Preprints, versions of scientific manuscripts that precede peer review, are growing in popularity. They offer an opportunity to democratize and accelerate research, as they have no publication costs or a lengthy peer review process. Preprints are often later published in peer-reviewed venues, but these publications and the original preprints are frequently not linked in any way. To this end, we developed a tool, PreprintMatch, to find matches between preprints and their corresponding published papers, if they exist. This tool outperforms existing techniques to match preprints and papers, both on matching performance and speed. PreprintMatch was applied to search for matches between preprints (from bioRxiv and medRxiv), and PubMed. The preliminary nature of preprints offers a unique perspective into scientific projects at a relatively early stage, and with better matching between preprint and paper, we explored questions related to research inequity. We found that preprints from low income countries are published as peer-reviewed papers at a lower rate than high income countries (39.6% and 61.1%, respectively), and our data is consistent with previous work that cite a lack of resources, lack of stability, and policy choices to explain this discrepancy. Preprints from low income countries were also found to be published quicker (178 vs 203 days) and with less title, abstract, and author similarity to the published version compared to high income countries. Low income countries add more authors from the preprint to the published version than high income countries (0.42 authors vs 0.32, respectively), a practice that is significantly more frequent in China compared to similar countries. Finally, we find that some publishers publish work with authors from lower income countries more frequently than others.

[COVID-19 and mental health services in Europe]. / COVID-19 e servizi di salute mentale in Europa.

The area of mental health is directly affected by the pandemic and its consequences, for various reasons: 1-the pandemic triggered a global lockdown, with dramatic socioeconomic and therefore psychosocial implications; 2-mental health services, which treat by definition a fragile population from the psychological, biological and social points of view, have a complex organizational frame, and it was expected that this would be affected (or overwhelmed) by the pandemic; 3-mental health services should, at least in theory, be able to help guide public health policies when these involve a significant modification of individual behaviour. It was conducted a narrative review of the publications produced by European researchers in the period February-June 2020 and indexed in PubMed. A total of 34 papers were analyzed, which document the profound clinical, organizational and procedural changes introduced in mental health services following this exceptional and largely unforeseen planetary event.Among the main innovations recorded everywhere, the strong push towards the use of telemedicine techniques should be mentioned: however, these require an adequate critical evaluation, which highlights their possibilities, limits, advantages and disadvantages instead of simple triumphalist judgments. Furthermore, should be emphasized the scarcity of quantitative studies conducted in this period and the absence of studies aimed, for example, at exploring the consequences of prolonged and forced face-to-face contact between patients and family members with a high index of "expressed emotions".

[The year 2022 placed in perspective by hospital internists]. / Année 2022 mise en perspective par les internistes hospitaliers.

According to PubMed statistics when writing this review, the year 2022 is expected to mark the first dip in the number of articles published in relation to the Covid-19 pandemic. This review, without any mention to Sars-CoV-2, highlight this transition and addresses many topics in internal medicine: gastroenterology, cardiology, endocrinology, respiratory medicine, infectious diseases and venous access. Each year, the chief residents of the internal medicine ward in Lausanne university hospital (CHUV) in Switzerland meet up to share their readings: here is a selection of ten articles that have caught our attention, summarized and commented for you, which should change our daily practice.

D'après les statistiques PubMed au moment de la rédaction de cette revue, l'année 2022 devrait marquer le premier infléchissement du nombre d'articles publiés en relation avec la pandémie de Covid-19. Cette revue d'articles, sans écho au Sars-CoV-2, souligne cette transition et aborde de nombreux sujets de la médecine interne : gastroentérologie, cardiologie, endocrinologie, pneumologie, infectiologie et accès veineux. Chaque année, les cheffes et chefs de clinique du Service de médecine interne du CHUV se réunissent pour partager leurs lectures : voici une sélection de dix articles ayant retenu notre attention, revus et commentés pour vous, et qui devraient faire évoluer notre pratique quotidienne.

Decrease of cancer diagnosis during COVID-19 pandemic: a systematic review and meta-analysis.

Many health services, including cancer care, have been affected by the COVID-19 epidemic. This study aimed at providing a systematic review of the impact of the epidemic on cancer diagnostic tests and diagnosis worldwide. In our systematic review and meta-analysis, databases such as Pubmed, Proquest and Scopus were searched comprehensively for articles published between January 1st, 2020 and December 12th, 2021. Observational studies and articles that reported data from single clinics and population registries comparing the number of cancer diagnostic tests and/or diagnosis performed before and during the pandemic, were included. Two pairs of independent reviewers extracted data from the selected studies. The weighted average of the percentage variation was calculated and compared between pandemic and pre-pandemic periods. Stratified analysis was performed by geographic area, time interval and study setting. The review was registered on PROSPERO (ID: CRD42022314314). The review comprised 61 articles, whose results referred to the period January-October 2020. We found an overall decrease of - 37.3% for diagnostic tests and - 27.0% for cancer diagnosis during the pandemic. For both outcomes we identified a U-shaped temporal trend, with an almost complete recovery for the number of cancer diagnosis after May 2020. We also analyzed differences by geographic area and screening setting. We provided a summary estimate of the decrease in cancer diagnosis and diagnostic tests, during the first phase of the COVID-19 pandemic. The delay in cancer diagnosis could lead to an increase in the number of avoidable cancer deaths. Further research is needed to assess the impact of the pandemic measures on cancer treatment and mortality.

Risk of SARS-CoV-2 reinfection: a systematic review and meta-analysis.

This meta-analysis aims to synthesize global evidence on the risk of reinfection among people previously infected with SARS-CoV-2. We systematically searched PubMed, Scopus, Embase and Web of Science as of April 5, 2021. We conducted: (1) meta-analysis of cohort studies containing data sufficient for calculating the incidence rate of SARS-CoV-2 reinfection; (2) systematic review of case reports with confirmed SARS-CoV-2 reinfection cases. The reinfection incidence was pooled by zero-inflated beta distribution. The hazard ratio (HR) between reinfection incidence among previously infected individuals and new infection incidence among infection-naïve individuals was calculated using random-effects models. Of 906 records retrieved and reviewed, 11 studies and 11 case reports were included in the meta-analysis and the systematic review, respectively. The pooled SARS-CoV-2 reinfection incidence rate was 0.70 (standard deviation [SD] 0.33) per 10,000 person-days. The incidence of reinfection was lower than the incidence of new infection (HR = 0.12, 95% confidence interval 0.09-0.17). Our meta-analysis of studies conducted prior to the emergency of the more transmissible Omicron variant showed that people with a prior SARS-CoV-2 infection could be re-infected, and they have a lower risk of infection than those without prior infection. Continuing reviews are needed as the reinfection risk may change due to the rapid evolution of SARS-CoV-2 variants.

Relationship between self-efficacy, adversity quotient, COVID-19-related stress and academic performance among the undergraduate students: A protocol for a systematic review.

BACKGROUND: This systematic review aims to review research manuscripts during the COVID-19 pandemic that focus on the relationship between self-efficacy, adversity quotient, COVID-19-related stress and academic performance on a range of undergraduate student. METHODS: The authors will perform comprehensive searches of published studies in electronic databases such as PMC, PubMed, Scopus, Cochrane Library and Web of Science by using the following search terms: 'self-efficacy' AND 'adversity quotient' AND 'stress' AND 'academic performance' AND 'student' AND 'COVID-19 pandemic'. Only full-text articles in English language are included. Two reviewers will independently conduct the article selection, data extraction, and quality assessment. Any possible disagreement will be resolved by discussion, and one arbitrator (NA) will adjudicate unresolved disagreements. RESULTS: This review will provide an updated overview of investigating the relationship between self-efficacy, adversity quotient, COVID-19-related stress and academic performance on a range of undergraduate student during the COVID-19 pandemic. Ultimately, based on this systematic review, we will recommend the direction for future research. CONCLUSION: The result of the study may help the researchers to find an updated overview of various studies in related topic. ETHICS AND DISSEMINATION: Data from published studies will be used. Therefore, ethical approval is not required prior to this systematic review. The results will be published in a peer-reviewed journal.

regCOVID: Tracking publications of registered COVID-19 studies.

BACKGROUND: In response to the COVID-19 pandemic many clinical studies have been initiated leading to the need for efficient ways to track and analyze study results. We expanded our previous project that tracked registered COVID-19 clinical studies to also track result articles generated from these studies. Our objective was to develop a data science approach to identify and analyze all publications linked to COVID-19 clinical studies and generate a prioritized list of publications for efficient understanding of the state of COVID-19 clinical research. METHODS: We conducted searches of ClinicalTrials.gov and PubMed to identify articles linked to COVID-19 studies, and developed criteria based on the trial phase, intervention, location, and record recency to develop a prioritized list of result publications. RESULTS: The performed searchers resulted in 1 022 articles linked to 565 interventional trials (17.8% of all 3 167 COVID-19 interventional trials as of 31 January 2022). 609 publications were identified via abstract-link in PubMed and 413 via registry-link in ClinicalTrials.gov, with 27 articles linked from both sources. Of the 565 trials publishing at least one article, 197 (34.9%) had multiple linked publications. An attention score was assigned to each publication to develop a prioritized list of all publications linked to COVID-19 trials and 83 publications were identified that are result articles from late phase (Phase 3) trials with at least one US site and multiple study record updates. For COVID-19 vaccine trials, 108 linked result articles for 64 trials (14.7% of 436 total COVID-19 vaccine trials) were found. CONCLUSIONS: Our method allows for the efficient identification of important COVID-19 articles that report results of registered clinical trials and are connected via a structured article-trial link. Our data science methodology also allows for consistent and as needed data updates and is generalizable to other conditions of interest.

Sports and Health, Second Edition.

The International Journal of Environmental Research and Public Health (IJERPH) has increased its publications of scientific papers related to exercise; a search of Pubmed (on 22 June 2022) using IJERPH and exercise as keywords showed 1788 entries for 2021 compared to 80 entries in 2016 [...].

Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations.

The coronavirus disease 2019 (COVID-19) pandemic has been severely impacting global society since December 2019. The related findings such as vaccine and drug development have been reported in biomedical literature-at a rate of about 10 000 articles on COVID-19 per month. Such rapid growth significantly challenges manual curation and interpretation. For instance, LitCovid is a literature database of COVID-19-related articles in PubMed, which has accumulated more than 200 000 articles with millions of accesses each month by users worldwide. One primary curation task is to assign up to eight topics (e.g. Diagnosis and Treatment) to the articles in LitCovid. The annotated topics have been widely used for navigating the COVID literature, rapidly locating articles of interest and other downstream studies. However, annotating the topics has been the bottleneck of manual curation. Despite the continuing advances in biomedical text-mining methods, few have been dedicated to topic annotations in COVID-19 literature. To close the gap, we organized the BioCreative LitCovid track to call for a community effort to tackle automated topic annotation for COVID-19 literature. The BioCreative LitCovid dataset-consisting of over 30 000 articles with manually reviewed topics-was created for training and testing. It is one of the largest multi-label classification datasets in biomedical scientific literature. Nineteen teams worldwide participated and made 80 submissions in total. Most teams used hybrid systems based on transformers. The highest performing submissions achieved 0.8875, 0.9181 and 0.9394 for macro-F1-score, micro-F1-score and instance-based F1-score, respectively. Notably, these scores are substantially higher (e.g. 12%, higher for macro F1-score) than the corresponding scores of the state-of-art multi-label classification method. The level of participation and results demonstrate a successful track and help close the gap between dataset curation and method development. The dataset is publicly available via https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/ for benchmarking and further development. Database URL https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/.

Reliability of citations of medRxiv preprints in articles published on COVID-19 in the world leading medical journals.

INTRODUCTION: Preprints have been widely cited during the COVID-19 pandemics, even in the major medical journals. However, since subsequent publication of preprint is not always mentioned in preprint repositories, some may be inappropriately cited or quoted. Our objectives were to assess the reliability of preprint citations in articles on COVID-19, to the rate of publication of preprints cited in these articles and to compare, if relevant, the content of the preprints to their published version. METHODS: Articles published on COVID in 2020 in the BMJ, The Lancet, the JAMA and the NEJM were manually screened to identify all articles citing at least one preprint from medRxiv. We searched PubMed, Google and Google Scholar to assess if the preprint had been published in a peer-reviewed journal, and when. Published articles were screened to assess if the title, data or conclusions were identical to the preprint version. RESULTS: Among the 205 research articles on COVID published by the four major medical journals in 2020, 60 (29.3%) cited at least one medRxiv preprint. Among the 182 preprints cited, 124 were published in a peer-reviewed journal, with 51 (41.1%) before the citing article was published online and 73 (58.9%) later. There were differences in the title, the data or the conclusion between the preprint cited and the published version for nearly half of them. MedRxiv did not mentioned the publication for 53 (42.7%) of preprints. CONCLUSIONS: More than a quarter of preprints citations were inappropriate since preprints were in fact already published at the time of publication of the citing article, often with a different content. Authors and editors should check the accuracy of the citations and of the quotations of preprints before publishing manuscripts that cite them.

A BERT-based ensemble learning approach for the BioCreative VII challenges: full-text chemical identification and multi-label classification in PubMed articles.

In this research, we explored various state-of-the-art biomedical-specific pre-trained Bidirectional Encoder Representations from Transformers (BERT) models for the National Library of Medicine - Chemistry (NLM CHEM) and LitCovid tracks in the BioCreative VII Challenge, and propose a BERT-based ensemble learning approach to integrate the advantages of various models to improve the system's performance. The experimental results of the NLM-CHEM track demonstrate that our method can achieve remarkable performance, with F1-scores of 85% and 91.8% in strict and approximate evaluations, respectively. Moreover, the proposed Medical Subject Headings identifier (MeSH ID) normalization algorithm is effective in entity normalization, which achieved a F1-score of about 80% in both strict and approximate evaluations. For the LitCovid track, the proposed method is also effective in detecting topics in the Coronavirus disease 2019 (COVID-19) literature, which outperformed the compared methods and achieve state-of-the-art performance in the LitCovid corpus. Database URL: https://www.ncbi.nlm.nih.gov/research/coronavirus/.

PHILM2Web: A high-throughput database of macromolecular host-pathogen interactions on the Web.

During infection, the pathogen's entry into the host organism, breaching the host immune defense, spread and multiplication are frequently mediated by multiple interactions between the host and pathogen proteins. Systematic studying of host-pathogen interactions (HPIs) is a challenging task for both experimental and computational approaches and is critically dependent on the previously obtained knowledge about these interactions found in the biomedical literature. While several HPI databases exist that manually filter HPI protein-protein interactions from the generic databases and curated experimental interactomic studies, no comprehensive database on HPIs obtained from the biomedical literature is currently available. Here, we introduce a high-throughput literature-mining platform for extracting HPI data that includes the most comprehensive to date collection of HPIs obtained from the PubMed abstracts. Our HPI data portal, PHILM2Web (Pathogen-Host Interactions by Literature Mining on the Web), integrates an automatically generated database of interactions extracted by PHILM, our high-precision HPI literature-mining algorithm. Currently, the database contains 23 581 generic HPIs between 157 host and 403 pathogen organisms from 11 609 abstracts. The interactions were obtained from processing 608 972 PubMed abstracts, each containing mentions of at least one host and one pathogen organisms. In response to the coronavirus disease 2019 (COVID-19) pandemic, we also utilized PHILM to process 25 796 PubMed abstracts obtained by the same query as the COVID-19 Open Research Dataset. This COVID-19 processing batch resulted in 257 HPIs between 19 host and 31 pathogen organisms from 167 abstracts. The access to the entire HPI dataset is available via a searchable PHILM2Web interface; scientists can also download the entire database in bulk for offline processing. Database URL: http://philm2web.live.

Bibliometric Analysis of Literature on Physical Activity and COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic induced a sudden surge in COVID-19 related publications. This bibliometric analysis aimed to analyze literature on physical activity and COVID-19 published in the PubMed database. The search terms ((physical activity [MeSH Terms] OR physical inactivity [MeSH Terms]) AND COVID-19 [MeSH Terms]) were applied to obtain publications from the inception of PubMed to February 2022. The analyses included the year of publication, type of publication, and origin of publication by country, region, and country income. The research areas were analyzed for research articles and systematic reviews. Of 1268 articles, 143 articles were excluded, and 1125 articles were analyzed. A total of 709 articles (63.02%) were published in 2021. A majority of publications were research articles (n = 678, 60.27%). The USA (n = 176, 15.64%), countries in the European Region (n = 496, 44.09%), and high-income countries (n = 861, 76.53%) were dominant publishing countries. Of 699 research articles and systematic reviews, surveillance and trends of physical activity were the main research area, followed by health outcomes, and correlates and determinants of physical activity. There is a wide gap in publication productivity in the field of physical activity and health during the pandemic among different countries' economic statuses.