As of June 2024, the most popular database management system (DBMS) worldwide was Oracle, with a ranking score of *******; MySQL and Microsoft SQL server rounded out the top three. Although the database management industry contains some of the largest companies in the tech industry, such as Microsoft, Oracle and IBM, a number of free and open-source DBMSs such as PostgreSQL and MariaDB remain competitive. Database Management Systems As the name implies, DBMSs provide a platform through which developers can organize, update, and control large databases. Given the business world’s growing focus on big data and data analytics, knowledge of SQL programming languages has become an important asset for software developers around the world, and database management skills are seen as highly desirable. In addition to providing developers with the tools needed to operate databases, DBMS are also integral to the way that consumers access information through applications, which further illustrates the importance of the software.

Approximately ** percent of the surveyed software companies in Russia mentioned PostgreSQL, making it the most popular database management system (DBMS) in the period between February and May 2022. MS SQL and MySQL followed, having been mentioned by ** percent and ** percent of respondents, respectively.

As of December 2022, relational database management systems (RDBMS) were the most popular type of DBMS, accounting for a ** percent popularity share. The most popular RDBMS in the world has been reported as Oracle, while MySQL and Microsoft SQL server rounded out the top three.

The most used database by companies worldwide in 2022 was Amazon Web Services. Additionally, ** percent of respondents used Google Cloud Platform.

John Ioannidis and co-authors [1] created a publicly available database of top-cited scientists in the world. This database, intended to address the misuse of citation metrics, has generated a lot of interest among the scientific community, institutions, and media. Many institutions used this as a yardstick to assess the quality of researchers. At the same time, some people look at this list with skepticism citing problems with the methodology used. Two separate databases are created based on career-long and, single recent year impact. This database is created using Scopus data from Elsevier[1-3]. The Scientists included in this database are classified into 22 scientific fields and 174 sub-fields. The parameters considered for this analysis are total citations from 1996 to 2022 (nc9622), h index in 2022 (h22), c-score, and world rank based on c-score (Rank ns). Citations without self-cites are considered in all cases (indicated as ns). In the case of a single-year case, citations during 2022 (nc2222) instead of Nc9622 are considered.

To evaluate the robustness of c-score-based ranking, I have done a detailed analysis of the matrix parameters of the last 25 years (1998-2022) of Nobel laureates of Physics, chemistry, and medicine, and compared them with the top 100 rank holders in the list. The latest career-long and single-year-based databases (2022) were used for this analysis. The details of the analysis are presented below: Though the article says the selection is based on the top 100,000 scientists by c-score (with and without self-citations) or a percentile rank of 2% or above in the sub-field, the actual career-based ranking list has 204644 names[1]. The single-year database contains 210199 names. So, the list published contains ~ the top 4% of scientists. In the career-based rank list, for the person with the lowest rank of 4809825, the nc9622, h22, and c-score were 41, 3, and 1.3632, respectively. Whereas for the person with the No.1 rank in the list, the nc9622, h22, and c-score were 345061, 264, and 5.5927, respectively. Three people on the list had less than 100 citations during 96-2022, 1155 people had an h22 less than 10, and 6 people had a C-score less than 2.
In the single year-based rank list, for the person with the lowest rank (6547764), the nc2222, h22, and c-score were 1, 1, and 0. 6, respectively. Whereas for the person with the No.1 rank, the nc9622, h22, and c-score were 34582, 68, and 5.3368, respectively. 4463 people on the list had less than 100 citations in 2022, 71512 people had an h22 less than 10, and 313 people had a C-score less than 2. The entry of many authors having single digit H index and a very meager total number of citations indicates serious shortcomings of the c-score-based ranking methodology. These results indicate shortcomings in the ranking methodology.

The global in-memory database market size was valued at USD 10.5643 billion in 2025 and is projected to grow at a compound annual growth rate (CAGR) of 16.19% during the forecast period (2025-2033). The growth of the market is attributed to the increasing adoption of in-memory databases in various industries to improve data processing speed and performance. In-memory databases store data in the computer's main memory (RAM) instead of on a physical disk, which allows for faster data access and retrieval. Key market drivers include the growing volume of data, the need for real-time data analysis, and the increasing adoption of cloud computing. The growing volume of data, often referred to as "big data," is a significant factor driving market growth. The need for real-time data analysis is another key driver, as in-memory databases can provide faster data access than traditional databases. The increasing adoption of cloud computing is also driving market growth, as cloud-based in-memory databases offer scalability and flexibility. Recent developments include: March 2023: SAP revealed SAP Datasphere, the company's next-gen data management system. It gives customers easy access to business-ready data across the data landscape. SAP also announced strategic agreements with top data and AI companies, including Collibra NV, Confluent Inc., Databricks Inc., and DataRobot Inc., to improve SAP Datasphere and allow organizations to build a unified data architecture that securely combines SAP software data and non-SAP data., June 2023: IBM has released a new tool to aid corporations in monitoring their carbon footprint pollution across cloud services and improve their sustainability as they move to hybrid and multi-cloud environments. The IBM Cloud Carbon Calculator, an AI-powered dashboard, is now available to everyone. It can help clients access emissions data for various IBM Cloud tasks, such as AI, high-performance computing (HPC), and financial services., SingleStoreDB for December 2022 was announced last year by IBM and SingleStore. With IBM introducing SingleStoreDB as a solution, businesses are now moving forward in their strategic relationship to deliver the quickest, most scalable data platform that supports data-intensive programs. For Azure, AWS, and Microsoft Azure marketplace, IBM has released SingleStoreDB as a service., In April 2022, McObject issued the eXtremeDB/rt database management system (DBMS) for Green Hills Software’s Integrity RTOS. The first-ever commercial off-the-shelf (COTS) real-time DBMS satisfying basic criteria of temporal and deterministic consistency in data is known as eXtremeDB/rt. It was initially conceived and built as an integrated in-memory database system for embedded systems., November 2022: Redis, provider of real-time in-memory databases, and Amazon Web Services have formed a multi-year strategic alliance. It is a networked open-source NoSQL system that stores data on disk for durability before moving it to DRAM as required. As such, it can be used as a message broker cache, streaming engine, or database., December 2022: The largest Indian stock exchange, National Stock Exchange, opted for Raima Database Manager (RDM) Workgroup 12.0 In-Memory System as its foundational component for upcoming versions of its trading platform front-end called National Exchange for Automated Trading (NEAT)., On January 13th, 2021, Oracle launched Oracle Database 21c – the latest version of the world’s leading converged database available on Oracle Cloud with the Always Free tier of Oracle Autonomous Database included. It includes more than two hundred new features, according to Oracle’s press release, including immutable blockchain tables; In-Database JavaScript; native JSON binary data type; AutoML for in-database machine learning (ML); persistent memory store; enhancements, including improvements regarding graph processing performance that support sharding, multitenant, and security., Stanford engineers have developed a new chip to increase the efficiency of AI computing in August 2022. Stanford engineers have created a more efficient and flexible AI chip that could bring the power of AI into tiny edge devices., In-Memory Database Market Segmentation,

Relational

NoSQL

NewSQL

,

Online Analytical Processing (OLAP)

Online Transaction Processing (OLTP)

,

Transaction

Reporting

Analytics

,

North America

US

Canada

Europe

Germany

France

UK

Italy

Spain

Rest of Europe

Asia-Pacific

China

Japan

India

Australia

South Korea

Australia

Rest of Asia-Pacific

Rest of the World

Middle East

Africa

Latin America

, . Potential restraints include: Security And Data Privacy Concerns 26.

In 2023, over ** percent of surveyed software developers worldwide reported using PostgreSQL, the highest share of any database technology. Other popular database tools among developers included MySQL and SQLite.

PurposeThis study aimed to summarize the characteristics of the 100 most-cited articles on adult spinal deformity (ASD) and to analyze past and current research hotspots and trends.MethodsLiterature searches (from inception to 28 April 2022) using Web of Science databases were conducted to identify ASD-related articles. The top 100 most-cited articles were collected for further analysis. Meanwhile, author keywords from articles published in the last 5 years were selected for further analysis.ResultsThe top 100 most-cited articles on ASD were selected from 3,354 papers. The publication year ranged from 1979 to 2017, and all papers were written in English. The citation count among them ranged from 100 to 1,145, and the mean citation number was 215.2. The foremost productive first author was Schwab F. University of Washington had the largest number of publications. The United States of America had the largest number of published articles (n = 84) in this field. Spine was the most popular journal. Complications were the most studied themes. The visualization analysis of author keywords from the literature in the recent 5 years showed that complications, sagittal plane parameters, and surgical techniques are still the research hotspots, and minimally invasive surgery will continue to develop rapidly.ConclusionBased on a comparative analysis of the results of bibliometric and visualization, complications and sagittal plane parameters are still the major topics of research at present and even later, and minimally invasive surgery has a growth trend in this field of ASD.

Market Overview: The global in-memory database (IMDB) market is poised for substantial growth, with a projected CAGR of 19.00% from 2025 to 2033. The market size, valued at XX million in 2025, is attributed to the increasing adoption of IMDBs in various industries, including telecommunications, BFSI, logistics, retail, entertainment, and healthcare. Key drivers behind this growth include the need for real-time data processing, improved performance, and the rise of big data and analytics. Market Dynamics: The IMDB market is influenced by several trends and challenges. The growing adoption of cloud-based IMDB solutions is a key trend, as it provides flexibility and cost-effectiveness. However, security concerns and latency issues associated with cloud-based deployments pose challenges. Additionally, the increasing demand for high-performance computing and the need for faster data processing are driving the development of advanced IMDB technologies. The market is fragmented, with established players such as IBM, Oracle, and Microsoft competing alongside emerging startups like VoltDB and MemSQL. Regional variations in market maturity and adoption rates are also observed, with North America leading the way in terms of market penetration. Recent developments include: May 2022: IBM and SAP announced the extension of their collaboration as IBM embarks on a corporate transformation initiative to optimize its business operations using RISE and SAP S/4HANA Cloud. To execute work for over 1,000 legal entities in more than 120 countries and multiple IBM companies supporting hardware, software, consulting, and finance, IBM said it is transferring to SAP S/4HANA, SAP's most recent ERP system, as part of the extended relationship. The replacement for SAP R/3 and SAP ERP, SAP S/4HANA, is SAP's ERP system for large businesses. It is intended to work optimally with SAP's in-memory database, SAP HANA., November 2022: Redis, a provider of real-time in-memory databases, and Amazon Web Services have announced a multi-year strategic alliance. Redis is a networked, open-source NoSQL system that stores data on disk for durability before moving it to DRAM as necessary. It can function as a streaming engine, message broker, database, or cache. The business claims that when Redis is used as a database, apps may instantly search across tens of millions of rows of customer data to locate information specific to one particular customer. A managed database-as-a-service product on AWS is called the real-time Redis Enterprise Cloud., December 2022: The National Stock Exchange, the largest stock exchange in India, chose the Raima Database Manager (RDM) Workgroup 12.0 in-memory system as a foundational component for the next iterations of its trading platform front-end, the National Exchange for Automated Trading (NEAT).. Key drivers for this market are: Decreasing Hardware Cost, Increasing Penetration Of Trends Like Big Data And IOT; Increase In The Volume Of Data Generated And Shift Of Enterprise Operations. Potential restraints include: Resilience In Integration With VLDB'S. Notable trends are: Telecommunication End-User Industry to Hold Significant Market Share.

Communities of practice (CoPs) are defined as "groups of people who share a concern, a set of problems, or a passion about a topic, and who deepen their knowledge and expertise by interacting on an ongoing basis". They are an effective form of knowledge management that have been successfully used in the business sector and increasingly so in healthcare. In May 2023 the electronic databases MEDLINE and EMBASE were systematically searched for primary research studies on CoPs published between 1st January 1950 and 31st December 2022. PRISMA guidelines were followed. The following search terms were used: community/communities of practice AND (healthcare OR medicine OR patient/s). The database search picked up 2009 studies for screening. Of these, 50 papers met the inclusion criteria. The most common aim of CoPs was to directly improve a clinical outcome, with 19 studies aiming to achieve this. In terms of outcomes, qualitative outcomes were the most common measure used in 21 studies. Only 11 of the studies with a quantitative element had the appropriate statistical methodology to report significance. Of the 9 studies that showed a statistically significant effect, 5 showed improvements in hospital-based provision of services such as discharge planning or rehabilitation services. 2 of the studies showed improvements in primary-care, such as management of hepatitis C, and 2 studies showed improvements in direct clinical outcomes, such as central line infections. CoPs in healthcare are aimed at improving clinical outcomes and have been shown to be effective. There is still progress to be made and a need for further studies with more rigorous methodologies, such as RCTs, to provide further support of the causality of CoPs on outcomes.

The study of the patterns and evolution of international migration often requires high-frequency data on migration flows on a global scale. However, the presently existing databases force a researcher to choose between the frequency of the data and its geographical scale. Yearly data exist but only for a small subset of countries, while most others are only covered every 5 to 10 years. To fill in the gaps in the coverage, the vast majority of databases use some imputation method. Gaps in the stock of migrants are often filled by combining information on migrants based on their country of birth with data based on nationality or using ‘model’ countries and propensity methods. Gaps in the data on the flow of migrants, on the other hand, are often filled by taking the difference in the stock, which the ’demographic accounting’ methods then adjust for demographic evolutions.

This database aims to fill this gap by providing a global, yearly, bilateral database on the stock of migrants according to their country of birth. This database contains close to 2.9 million observations on over 56,000 country pairs from 1960 to 2022, a tenfold increase relative to the second-largest database. In addition, it also produces an estimate of the net flow of migrants. For a subset of countries –over 8,000 country pairs and half a million observations– we also have lower-bound estimates of the gross in- and outflow.

This database was constructed using a novel approach to estimating the most likely values of missing migration stocks and flows. Specifically, we use a Bayesian state-space model to combine the information from multiple datasets on both stocks and flows into a single estimate. Like the demographic accounting technique, the state-space model is built on the demographic relationship between migrant stocks, flows, births and deaths. The most crucial difference is that the state-space model combines the information from multiple databases, including those covering migrant stocks, net flows, and gross flows.

More details on the construction can currently be found in the UNU-CRIS working paper: Standaert, Samuel and Rayp, Glenn (2022) "Where Did They Come From, Where Did They Go? Bridging the Gaps in Migration Data" UNU-CRIS working paper 22.04. Bruges.

https://cris.unu.edu/where-did-they-come-where-did-they-go-bridging-gaps-migration-data

By June 2021, Databricks received 1.9 billion U.S. dollars of funding. Importantly, cloud database companies offer specialized services via public cloud. By the end of 2022, 75 percent of databases are expected to be moved to the cloud. Additionally, spending on database software is forecast to amount to more than 100 billion U.S. dollars by 2025.

This dataset contains information regarding patented methods for the production of hydrogen via methane pyrolysis.

The processes described in scientific literature can be divided into three categories: thermal, plasma and catalytic decomposition. [1] The same categories are found in the patent literature.

The most popular and currently used methods for making hydrogen are coal gasification, steam methane reforming (SMR) and water electrolysis.

Methane pyrolysis can be seen as a suitable alternative and environmentally friendly technology; It consists in the thermal decomposition of methane, with the formation of solid carbon (instead of CO/CO₂) as reaction by-product.

The catalytic methane pyrolysis has been widely investigated. Iron and carbon-based (carbon black, graphite, carbon nanotubes) catalysts are considered the best candidates for industrial implementation.[2]

The following patent databases used have been used for data mining:

- Espacenet (a free of charge database provided by the European Patent Office), accessed on Sept. 10, 2022

- Orbit Intelligence (FamPat database) (a fee-based platform provided by Questel), accessed on Sept. 10, 2022

The file titled “Methane pyrolysis _ Espacenet” contains information related to Title, Inventors, Applicants, Publication number, Earliest priority, IPC, CPC, Publication date, Earliest publication, and Family number.

The file titled “Methane pyrolysis _ Orbit” contains information related to Priority numbers, Application numbers, Publication numbers, Priority dates, Application dates, Publication dates, Title, Abstract, and Current assignees.

Patent searches were carried out by means of keywords and classification/indexing codes. [3, 4]

Both IPC (International Patent Classification) and CPC (Cooperative Patent Classification) systems were used.

Communities of practice (CoPs) are defined as "groups of people who share a concern, a set of problems, or a passion about a topic, and who deepen their knowledge and expertise by interacting on an ongoing basis". They are an effective form of knowledge management that have been successfully used in the business sector and increasingly so in healthcare. In May 2023 the electronic databases MEDLINE and EMBASE were systematically searched for primary research studies on CoPs published between 1st January 1950 and 31st December 2022. PRISMA guidelines were followed. The following search terms were used: community/communities of practice AND (healthcare OR medicine OR patient/s). The database search picked up 2009 studies for screening. Of these, 50 papers met the inclusion criteria. The most common aim of CoPs was to directly improve a clinical outcome, with 19 studies aiming to achieve this. In terms of outcomes, qualitative outcomes were the most common measure used in 21 studies. Only 11 of the studies with a quantitative element had the appropriate statistical methodology to report significance. Of the 9 studies that showed a statistically significant effect, 5 showed improvements in hospital-based provision of services such as discharge planning or rehabilitation services. 2 of the studies showed improvements in primary-care, such as management of hepatitis C, and 2 studies showed improvements in direct clinical outcomes, such as central line infections. CoPs in healthcare are aimed at improving clinical outcomes and have been shown to be effective. There is still progress to be made and a need for further studies with more rigorous methodologies, such as RCTs, to provide further support of the causality of CoPs on outcomes.

Communities of practice (CoPs) are defined as "groups of people who share a concern, a set of problems, or a passion about a topic, and who deepen their knowledge and expertise by interacting on an ongoing basis". They are an effective form of knowledge management that have been successfully used in the business sector and increasingly so in healthcare. In May 2023 the electronic databases MEDLINE and EMBASE were systematically searched for primary research studies on CoPs published between 1st January 1950 and 31st December 2022. PRISMA guidelines were followed. The following search terms were used: community/communities of practice AND (healthcare OR medicine OR patient/s). The database search picked up 2009 studies for screening. Of these, 50 papers met the inclusion criteria. The most common aim of CoPs was to directly improve a clinical outcome, with 19 studies aiming to achieve this. In terms of outcomes, qualitative outcomes were the most common measure used in 21 studies. Only 11 of the studies with a quantitative element had the appropriate statistical methodology to report significance. Of the 9 studies that showed a statistically significant effect, 5 showed improvements in hospital-based provision of services such as discharge planning or rehabilitation services. 2 of the studies showed improvements in primary-care, such as management of hepatitis C, and 2 studies showed improvements in direct clinical outcomes, such as central line infections. CoPs in healthcare are aimed at improving clinical outcomes and have been shown to be effective. There is still progress to be made and a need for further studies with more rigorous methodologies, such as RCTs, to provide further support of the causality of CoPs on outcomes.

[Note: Integrated as part of FoodData Central, April 2019.] The USDA National Nutrient Database for Standard Reference (SR) is the major source of food composition data in the United States and provides the foundation for most food composition databases in the public and private sectors. This is the last release of the database in its current format. SR-Legacy will continue its preeminent role as a stand-alone food composition resource and will be available in the new modernized system currently under development. SR-Legacy contains data on 7,793 food items and up to 150 food components that were reported in SR28 (2015), with selected corrections and updates. This release supersedes all previous releases. Resources in this dataset:Resource Title: USDA National Nutrient Database for Standard Reference, Legacy Release. File Name: SR-Leg_DB.zipResource Description: Locally stored copy - The USDA National Nutrient Database for Standard Reference as a relational database using AcessResource Title: USDA National Nutrient Database for Standard Reference, Legacy Release. File Name: SR-Leg_ASC.zipResource Description: ASCII files containing the data of the USDA National Nutrient Database for Standard Reference, Legacy Release.Resource Title: USDA National Nutrient Database for Standard Reference, Legacy Release. File Name: SR-Leg_ASC.zipResource Description: Locally stored copy - ASCII files containing the data of the USDA National Nutrient Database for Standard Reference, Legacy Release.

The Non-adoption, Abandonment, Scale-up, Spread, Sustainability (NASSS) framework (2017) was established as an evidence-based, theory-informed tool to predict and evaluate the success of implementing health and care technologies. While the NASSS is gaining popularity, its use has not been systematically described. Literature reviews on the applications of popular implementation frameworks, such as the RE-AIM and the CFIR, have enabled their advancement in implementation science. Similarly, we sought to advance the science of implementation and application of theories, models, and frameworks (TMFs) in research by exploring the application of the NASSS in the five years since its inception. We aim to understand the characteristics of studies that used the NASSS, how it was used, and the lessons learned from its application. We conducted a scoping review following the JBI methodology. On December 20, 2022, we searched the following databases: Ovid MEDLINE, EMBASE, PsychINFO, CINAHL, Scopus, Web of Science, and LISTA. We used typologies and frameworks to characterize evidence to address our aim. This review included 57 studies that were qualitative (n=28), mixed/multi-methods (n=13), case studies (n=6), observational (n=3), experimental (n=3), and other designs (e.g., quality improvement) (n=4). The four most common types of digital applications being implemented were telemedicine/virtual care (n=24), personal health devices (n=10), digital interventions such as internet Cognitive Behavioural Therapies (n=10), and knowledge generation applications (n=9). Studies used the NASSS to inform study design (n=9), data collection (n=35), analysis (n=41), data presentation (n=33), and interpretation (n=39). Most studies applied the NASSS retrospectively to implementation (n=33). The remainder applied the NASSS prospectively (n=15) or concurrently (n=8) with implementation. We also collated reported barriers and enablers to implementation. We found the most reported barriers fell within the Organization and Adopter System domains, and the most frequently reported enablers fell within the Value Proposition domain. Eighteen studies highlighted the NASSS as a valuable and practical resource, particularly for unravelling complexities, comprehending implementation context, understanding contextual relevance in implementing health technology, and recognizing its adaptable nature to cater to researchers’ requirements. Most studies used the NASSS retrospectively, which may be attributed to the framework’s novelty. However, this finding highlights the need for prospective and concurrent application of the NASSS within the implementation process. In addition, almost all included studies reported multiple domains as barriers and enablers to implementation, indicating that implementation is a highly complex process that requires careful preparation to ensure implementation success. Finally, we identified a need for better reporting when using the NASSS in implementation research to contribute to the collective knowledge in the field.

BackgroundGiven the increased availability of data sources such as hospital information systems, electronic health records, and health-related registries, a novel approach is required to develop artificial intelligence-based decision support that can assist clinicians in their diagnostic decision-making and shorten rare disease patients’ diagnostic odyssey. The aim is to identify key challenges in the process of mapping European rare disease databases, relevant to ML-based screening technologies in terms of organizational, FAIR and legal principles.MethodsA scoping review was conducted based on the PRISMA-ScR checklist. The primary article search was conducted in three electronic databases (MEDLINE/Pubmed, Scopus, and Web of Science) and a secondary search was performed in Google scholar and on the organizations’ websites. Each step of this review was carried out independently by two researchers. A charting form for relevant study analysis was developed and used to categorize data and identify data items in three domains – organizational, FAIR and legal.ResultsAt the end of the screening process, 73 studies were eligible for review based on inclusion and exclusion criteria with more than 60% (n = 46) of the research published in the last 5 years and originated only from EU/EEA countries. Over the ten-year period (2013–2022), there is a clear cycling trend in the publications, with a peak of challenges reporting every four years. Within this trend, the following dynamic was identified: except for 2016, organizational challenges dominated the articles published up to 2018; legal challenges were the most frequently discussed topic from 2018 to 2022. The following distribution of the data items by domains was observed – (1) organizational (n = 36): data accessibility and sharing (20.2%); long-term sustainability (18.2%); governance, planning and design (17.2%); lack of harmonization and standardization (17.2%); quality of data collection (16.2%); and privacy risks and small sample size (11.1%); (2) FAIR (n = 15): findable (17.9%); accessible sustainability (25.0%); interoperable (39.3%); and reusable (17.9%); and (3) legal (n = 33): data protection by all means (34.4%); data management and ownership (22.9%); research under GDPR and member state law (20.8%); trust and transparency (13.5%); and digitalization of health (8.3%). We observed a specific pattern repeated in all domains during the process of data charting and data item identification – in addition to the outlined challenges, good practices, guidelines, and recommendations were also discussed. The proportion of publications addressing only good practices, guidelines, and recommendations for overcoming challenges when mapping RD databases in at least one domain was calculated to be 47.9% (n = 35).ConclusionDespite the opportunities provided by innovation – automation, electronic health records, hospital-based information systems, biobanks, rare disease registries and European Reference Networks – the results of the current scoping review demonstrate a diversity of the challenges that must still be addressed, with immediate actions on ensuring better governance of rare disease registries, implementing FAIR principles, and enhancing the EU legal framework.

Physical literacy is a multidimensional construct that has been defined and interpreted in various ways, one of the most common being “the motivation, confidence, physical competence, knowledge and understanding to maintain physical activity throughout the life course”. Although its improvement can positively affect many behavioral, psychological, social, and physical variables, debate remains over an appropriate method of collecting empirical physical literacy data. This systematic review sought to identify and critically evaluate all primary studies (published and unpublished, regardless of design or language) that assessed physical literacy in adults or have proposed measurement criteria. Relevant studies were identified by searching four databases (Pubmed, SportDiscus, APA PsycINFO, Web of Science), scanning reference lists of included articles, and manual cross-referencing of bibliographies cited in prior reviews. The final search was concluded on July 15, 2022. Thirty-one studies, published from 2016 to 2022, were analyzed. We found seven instruments measuring physical literacy in adults, of which six were questionnaires. The Perceived Physical Literacy Instrument was the first developed for adults and the most adopted. The included studies approached physical literacy definition in two ways: by pre-defining domains and assessing them discretely (through pre-validated or self-constructed instruments) and by defining domains as sub-scales after factorial analyses. We found a fair use of objective and subjective measures to assess different domains. The wide use of instruments developed for other purposes in combined assessments suggests the need for further instrument development and the potential oversimplification of the holistic concept, which may not result in a better understanding of physical literacy. Quality and usability characteristics of measurements were generally insufficiently reported. This lack of data makes it impossible to compare and make robust conclusions. We could not identify if any of the existing physical literacy assessments for adults is appropriate for large-scale/epidemiological studies.

Physical literacy is a multidimensional construct that has been defined and interpreted in various ways, one of the most common being “the motivation, confidence, physical competence, knowledge and understanding to maintain physical activity throughout the life course”. Although its improvement can positively affect many behavioral, psychological, social, and physical variables, debate remains over an appropriate method of collecting empirical physical literacy data. This systematic review sought to identify and critically evaluate all primary studies (published and unpublished, regardless of design or language) that assessed physical literacy in adults or have proposed measurement criteria. Relevant studies were identified by searching four databases (Pubmed, SportDiscus, APA PsycINFO, Web of Science), scanning reference lists of included articles, and manual cross-referencing of bibliographies cited in prior reviews. The final search was concluded on July 15, 2022. Thirty-one studies, published from 2016 to 2022, were analyzed. We found seven instruments measuring physical literacy in adults, of which six were questionnaires. The Perceived Physical Literacy Instrument was the first developed for adults and the most adopted. The included studies approached physical literacy definition in two ways: by pre-defining domains and assessing them discretely (through pre-validated or self-constructed instruments) and by defining domains as sub-scales after factorial analyses. We found a fair use of objective and subjective measures to assess different domains. The wide use of instruments developed for other purposes in combined assessments suggests the need for further instrument development and the potential oversimplification of the holistic concept, which may not result in a better understanding of physical literacy. Quality and usability characteristics of measurements were generally insufficiently reported. This lack of data makes it impossible to compare and make robust conclusions. We could not identify if any of the existing physical literacy assessments for adults is appropriate for large-scale/epidemiological studies.