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How to Recognize Empirical Journal Articles

Definition of an empirical study:  An empirical research article reports the results of a study that uses data derived from actual observation or experimentation. Empirical research articles are examples of primary research.

Parts of a standard empirical research article:  (articles will not necessary use the exact terms listed below.)

• Abstract  ... A paragraph length description of what the study includes.
• Introduction ...Includes a statement of the hypotheses for the research and a review of other research on the topic.
• Who are participants
• Design of the study
• What the participants did
• What measures were used
• Results ...Describes the outcomes of the measures of the study.
• Discussion ...Contains the interpretations and implications of the study.
• References ...Contains citation information on the material cited in the report. (also called bibliography or works cited)

Characteristics of an Empirical Article:

• Empirical articles will include charts, graphs, or statistical analysis.
• Empirical research articles are usually substantial, maybe from 8-30 pages long.
• There is always a bibliography found at the end of the article.

Type of publications that publish empirical studies:

• Empirical research articles are published in scholarly or academic journals
• These journals are also called “peer-reviewed,” or “refereed” publications.

Examples of such publications include:

• American Educational Research Journal
• Computers & Education
• Journal of Educational Psychology

Databases that contain empirical research:  (selected list only)

• List of other useful databases by subject area

This page is adapted from Eric Karkhoff's  Sociology Research Guide: Identify Empirical Articles page (Cal State Fullerton Pollak Library).

Sample Empirical Articles

Roschelle, J., Feng, M., Murphy, R. F., & Mason, C. A. (2016). Online Mathematics Homework Increases Student Achievement. AERA Open .  ( L INK TO ARTICLE )

Lester, J., Yamanaka, A., & Struthers, B. (2016). Gender microaggressions and learning environments: The role of physical space in teaching pedagogy and communication.  Community College Journal of Research and Practice , 40(11), 909-926. ( LINK TO ARTICLE )

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Penn State University Libraries

Empirical research in the social sciences and education.

• What is Empirical Research and How to Read It
• Finding Empirical Research in Library Databases
• Designing Empirical Research
• Ethics, Cultural Responsiveness, and Anti-Racism in Research
• Citing, Writing, and Presenting Your Work

Contact the Librarian at your campus for more help!

Introduction: What is Empirical Research?

Empirical research is based on observed and measured phenomena and derives knowledge from actual experience rather than from theory or belief. 

How do you know if a study is empirical? Read the subheadings within the article, book, or report and look for a description of the research "methodology."  Ask yourself: Could I recreate this study and test these results?

Key characteristics to look for:

• Specific research questions to be answered
• Definition of the population, behavior, or   phenomena being studied
• Description of the process used to study this population or phenomena, including selection criteria, controls, and testing instruments (such as surveys)

Another hint: some scholarly journals use a specific layout, called the "IMRaD" format, to communicate empirical research findings. Such articles typically have 4 components:

• Introduction : sometimes called "literature review" -- what is currently known about the topic -- usually includes a theoretical framework and/or discussion of previous studies
• Methodology: sometimes called "research design" -- how to recreate the study -- usually describes the population, research process, and analytical tools used in the present study
• Results : sometimes called "findings" -- what was learned through the study -- usually appears as statistical data or as substantial quotations from research participants
• Discussion : sometimes called "conclusion" or "implications" -- why the study is important -- usually describes how the research results influence professional practices or future studies

Reading and Evaluating Scholarly Materials

Reading research can be a challenge. However, the tutorials and videos below can help. They explain what scholarly articles look like, how to read them, and how to evaluate them:

• CRAAP Checklist A frequently-used checklist that helps you examine the currency, relevance, authority, accuracy, and purpose of an information source.
• IF I APPLY A newer model of evaluating sources which encourages you to think about your own biases as a reader, as well as concerns about the item you are reading.
• Credo Video: How to Read Scholarly Materials (4 min.)
• Credo Tutorial: How to Read Scholarly Materials
• Credo Tutorial: Evaluating Information
• Credo Video: Evaluating Statistics (4 min.)
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Introduction to Empirical Research

Databases for finding empirical research, guided search, google scholar, examples of empirical research, sources and further reading.

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Your Librarian

• Introductory Video This video covers what empirical research is, what kinds of questions and methods empirical researchers use, and some tips for finding empirical research articles in your discipline.

• Guided Search: Finding Empirical Research Articles This is a hands-on tutorial that will allow you to use your own search terms to find resources.

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Identify Empirical Research Articles

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What is a Literature Review--YouTube

Literature Review Guides

• How to write a literature review
• The Literature Review:a few steps on conducting it Permission granted from Writing at the University of Toronto
• Six steps for writing a literature review This blog, written by Tanya Golash-Bozal PhD an Associate Professor of Sociology at the University of California at Merced, offers a very nice and simple advice on how to write a literature review from the point of view of an experience professional.
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• Literature Reviews Otago Polytechnic in New Zealand produced this guide and in my opinion, it is one of the best. NOTE: Except where otherwise noted, content on this site is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. However, all images and Otago Polytechnic videos are copyrighted.

What Are Empirical Articles?

As a student at the University of La Verne, faculty may instruct you to read and analyze empirical articles when writing a research paper, a senior or master's project, or a doctoral dissertation. How can you recognize an empirical article in an academic discipline? An empirical research article is an article which reports research based on actual observations or experiments. The research may use quantitative research methods, which generate numerical data and seek to establish causal relationships between two or more variables.(1) Empirical research articles may use qualitative research methods, which objectively and critically analyze behaviors, beliefs, feelings, or values with few or no numerical data available for analysis.(2)

How can I determine if I have found an empirical article?

When looking at an article or the abstract of an article, here are some guidelines to use to decide if an article is an empirical article.

• Is the article published in an academic, scholarly, or professional journal? Popular magazines such as Business Week or Newsweek do not publish empirical research articles; academic journals such as Business Communication Quarterly or Journal of Psychology may publish empirical articles. Some professional journals, such as JAMA: Journal of the American Medical Association publish empirical research. Other professional journals, such as Coach & Athletic Director publish articles of professional interest, but they do not publish research articles.
• Does the abstract of the article mention a study, an observation, an analysis or a number of participants or subjects? Was data collected, a survey or questionnaire administered, an assessment or measurement used, an interview conducted? All of these terms indicate possible methodologies used in empirical research.
• Introduction -The introduction provides a very brief summary of the research.
• Methodology -The method section describes how the research was conducted, including who the participants were, the design of the study, what the participants did, and what measures were used.
• Results -The results section describes the outcomes of the measures of the study.
• Discussion -The discussion section contains the interpretations and implications of the study.
• Conclusion -
• References -A reference section contains information about the articles and books cited in the report and should be substantial.
• How long is the article? An empirical article is usually substantial; it is normally seven or more pages long.

When in doubt if an article is an empirical research article, share the article citation and abstract with your professor or a librarian so that we can help you become better at recognizing the differences between empirical research and other types of scholarly articles.

How can I search for empirical research articles using the electronic databases available through Wilson Library?

• A quick and somewhat superficial way to look for empirical research is to type your search terms into the database's search boxes, then type STUDY OR STUDIES in the final search box to look for studies on your topic area. Be certain to use the ability to limit your search to scholarly/professional journals if that is available on the database. Evaluate the results of your search using the guidelines above to determine if any of the articles are empirical research articles.
• In EbscoHost databases, such as Education Source , on the Advanced Search page you should see a PUBLICATION TYPE field; highlight the appropriate entry. Empirical research may not be the term used; look for a term that may be a synonym for empirical research. ERIC uses REPORTS-RESEARCH. Also find the field for INTENDED AUDIENCE and highlight RESEARCHER. PsycArticles and Psycinfo include a field for METHODOLOGY where you can highlight EMPIRICAL STUDY. National Criminal Justice Reference Service Abstracts has a field for DOCUMENT TYPE; highlight STUDIES/RESEARCH REPORTS. Then evaluate the articles you find using the guidelines above to determine if an article is empirical.
• In ProQuest databases, such as ProQuest Psychology Journals , on the Advanced Search page look under MORE SEARCH OPTIONS and click on the pull down menu for DOCUMENT TYPE and highlight an appropriate type, such as REPORT or EVIDENCE BASED. Also look for the SOURCE TYPE field and highlight SCHOLARLY JOURNALS. Evaluate the search results using the guidelines to determine if an article is empirical.
• Pub Med Central , Sage Premier , Science Direct , Wiley Interscience , and Wiley Interscience Humanities and Social Sciences consist of scholarly and professional journals which publish primarily empirical articles. After conducting a subject search in these databases, evaluate the items you find by using the guidelines above for deciding if an article is empirical.
• "Quantitative research" A Dictionary of Nursing. Oxford University Press, 2008. Oxford Reference Online. Oxford University Press. University of La Verne. 25 August 2009
• "Qualitative analysis" A Dictionary of Public Health. Ed. John M. Last, Oxford University Press, 2007. Oxford Reference Online . Oxford University Press. University of La Verne. 25 August 2009

Empirical Articles:Tips on Database Searching

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Psychology Research Guide

• What are Empirical Articles?
• Developing a Topic
• Finding Sources
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Citation Resources

The most often used citation style for Psychology is:

DOIs and URLs

Follow these instructions from the American Psychological Association to correctly include DOIs and URLs in your list of references.

DOIs & URLs in APA Style

What are empirical articles?

In psychology, empirical research articles are peer-reviewed and report on new research that answers one or more specific questions. Empirical research is based on measurable observation and experimentation. When reading an empirical article, think about what research question is being asked or what experiment is being conducted.

How are they organized?

Empirical research articles in psychology typically follow APA style guidelines. They are organized into the following major sections:

• Abstract (An abstract provides a summary of the study.)
• Introduction (This section includes a literature review.)
• Method (How was the experiment or study conducted?)
• Results (What are the findings?)
• Discussion (What did the researchers conclude? What are the implications of the research?)

How should I read an empirical article?

Because empirical research articles follow a particular format, you can dip into the article at multiple points and move around in a non-linear way. First, think about why you're reading the article. Are you looking for specific information or trying to get research ideas? Are you reading it for a course or for your own knowledge? Next, read the abstract to get an overview of the study. Skim the first and last paragraphs of the introduction and results to deepen your understanding of the article as a whole. Read the methods section to understand how the study was organized and conducted. Review the charts, graphs, and statistics to understand the analyses. Skim the remaining pieces of the article, before going back and reading it more closely from the beginning to the end. Don't forget that the references can be a great source for additional empirical articles.

Don't confuse empirical articles with other types of articles.

Be careful not to confuse an empirical article with other types of articles that you might find when researching a psychology topic. Meta-analyis, systematic reviews, scoping reviews, and other types of literature reviews are not empirical articles. Instead, these articles are designed to analyze and summarize the existing literature on a topic. Similarly, don't confuse book reviews with empirical articles. These types of articles might be helpful in your research, but they are not empirical research articles.

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PSYC 301: Intro to Research Methods

• Advanced Search Strategies
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Finding Empirical Research

Empirical research is published in books and in scholarly, peer-reviewed journals. PsycInfo  offers straightforward ways to identify empirical research, unlike most other databases.

Finding Empirical Research in PsycInfo

• PsycInfo Choose "Advanced Search" Scroll down the page to "Methodology," and choose "Empirical Study" Type your keywords into the search boxes Choose other limits, such as publication date, if needed Click on the "Search" button

Slideshow showing how to find empirical research in APA PsycInfo

Video of finding empirical articles in psycinfo.

• Searching for Peer-Reviewed Empirical Articles (YouTube Video) Created by the APA

What is Empirical Research?

Empirical research  is based on observed and measured phenomena and derives knowledge from actual experience rather than from theory or belief. 

How do you know if a study is empirical? Read the subheadings within the article, book, or report and look for a description of the research "methodology." Ask yourself: Could I recreate this study and test these results?

Key characteristics to look for:

• Specific research questions  to be answered
• Definition of the  population, behavior, or   phenomena  being studied
• Description of the  process  used to study this population or phenomena, including selection criteria, controls, and testing instruments (such as surveys)

Another hint: some scholarly journals use a specific layout, called the "IMRaD" format, to communicate empirical research findings. Such articles typically have 4 components:

• Introduction : sometimes called "literature review" -- what is currently known about the topic -- usually includes a theoretical framework and/or discussion of previous studies
• Methodology:  sometimes called "research design" -- how to recreate the study -- usually describes the population, research process, and analytical tools
• Results : sometimes called "findings"  --  what was learned through the study -- usually appears as statistical data or as substantial quotations from research participants
• Discussion : sometimes called "conclusion" or "implications" -- why the study is important -- usually describes how the research results influence professional practices or future studies

Adapted from PennState University Libraries, Empirical Research in the Social Sciences and Education

Using PsycInfo

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• Introduction & Help
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Finding Empirical Research Articles

• Introduction
• Methods or Methodology
• Results or Findings

The method for finding empirical research articles varies depending upon the database* being used. 

1. The PsycARTICLES and PsycInfo databases (both from the APA) includes a Methodology filter that can be used to identify empirical studies. Look for the filter on the Advanced Search screen. To see a list and description of all of the of methodology filter options in PsycARTICLES and PsycInfo visit the  APA Databases Methodology Field Values page .

2. When using databases that do not provide a methodology filter—including ProQuest Psychology Journals and Academic Search Complete—experiment with using keywords to retrieve articles on your topic that contain empirical research. For example:

• empirical research
• empirical study
• quantitative study
• qualitative study
• longitudinal study
• observation
• questionnaire
• methodology
• participants

Qualitative research can be challenging to find as these methodologies are not always well-indexed in the databases. Here are some suggested keywords for retrieving articles that include qualitative research.

• qualitative
• ethnograph*
• observation*
• "case study”
• "focus group"
• "phenomenological research"
• "conversation analysis"

*Recommended databases are listed on the  Databases: Find Journal Articles page of this guide.

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Psychology Research: Finding Empirical Articles

• Finding Empirical Articles
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• What is Peer Review?
• Primary versus Secondary Resources
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Search Tips

• Empirical Study in PsycINFO & PsycARTICLES
• Common Limiters
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• PsycINFO PsycINFO, American Psychological Association's (APA) resource for abstracts of scholarly journal articles, book chapters, books, and dissertations, is the largest resource devoted to peer-reviewed literature in behavioral science, psychology, and mental health. Part of the Database Offerings in GALILEO, Georgia’s Virtual Library.
• PsycARTICLES Full-text collection of psychology articles in general psychology and specialized basic, applied, clinical, and theoretical research in psychology. Part of the Database Offerings in GALILEO, Georgia’s Virtual Library.

PsycINFO and PsycARTICLES are two APA databases that include a limiter for Empirical Study. When you limit your search to Empirical Study, all the search results will be empirical studies. 

The  APA defines  an empirical study as a "Study based on facts, systematic observation, or experiment, rather than theory or general philosophical principle." An empirical research article reports on the results of research that uses data collected from observation or experiment. Empirical research articles are primary research articles.

Empirical Studies:

Type your search terms into the search boxes., on the advanced search screen, scroll down to methodology, and select empirical study., click search..

PsycINFO 

PsycARTICLES

As you search in the database, you can select certain options, known as limiters, to make your search results fit with your research needs or the instructions in your assignment. Often, professors provide guidelines on the type of publication or when the article was published.

Common Limiters 

Full text .

All your search results will be Full text. 

Peer Reviewed 

 Search results will only include articles that are Peer Reviewed. 

 Search results published within a certain date range.

The EBSCOhost databases have several features to help manage your research results. 

Create a free account in EBSCOhost to save item records or searches. 

Go into any EBSCOhost database

Click on Sign In

Click on Create a new Account

Fill in the information

You do not have to use your BlazeVIEW username or password, this is an entirely separate system

Your password must be "strong" or the system will not accept it

Once you are logged into an EBSCOhost database, you will see a small yellow icon that reads "My"  

Save item records in your folder.

When you find a book record, article record, etc. that you want to keep...

Click on the blue folder icon to add it to your folder

The blue folder icon is available on the search results page and in the item's record  

Warning!  If you add a record to your folder but you are not logged in, it will disappear when you close the browser. Be sure you are logged in!

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Identifying Empirical Research Articles

• Identifying Empirical Articles
• Searching for Empirical Research Articles

Where to find empirical research articles

Finding empirical research.

When searching for empirical research, it can be helpful to use terms that relate to the method used in empirical research in addition to keywords that describe your topic. For example: 

• (generalized anxiety  AND  treatment*)  AND  (randomized clinical trial*  OR  clinical trial*)

You might also try using terms related to the type of instrument used:

• (generalized anxiety  AND  intervention*)  AND  (survey  OR  questionnaire)

You can also narrow your results to peer-review . Usually databases have a peer-review check box that you can select. To learn more about peer review, see our related guide:

• Understand Peer Review

Searching by Methodology

Some databases give you the option to do an advanced search by  methodology, where you can choose "empirical study" as a type. Here's an example from PsycInfo: 

Other filters includes things like document type, age group, population, language, and target audience. You can use these to narrow your search and get more relevant results.

Databasics: How to Filter by Methodology in ProQuest's PsycInfo + PsycArticles

Part of our Databasics YouTube series, this short video shows you how to limit by methodology in ProQuest's PsycInfo + PsycArticles database.

Attribution

Information in this guide adapted from Boston College Libraries' guide to " Finding Empirical Research "; Brandeis Library's " Finding Empirical Studies "; and CSUSM's " How do I know if a research article is empirical? "

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Identify Empirical Research Articles

• What is empirical research?
• Finding empirical research in library databases
• Research design
• Need additional help?

Getting started

According to the APA , empirical research is defined as the following: "Study based on facts, systematic observation, or experiment, rather than theory or general philosophical principle." Empirical research articles are generally located in scholarly, peer-reviewed journals and often follow a specific layout known as IMRaD: 1) Introduction - This provides a theoretical framework and might discuss previous studies related to the topic at hand. 2) Methodology - This describes the analytical tools used, research process, and the populations included. 3) Results - Sometimes this is referred to as findings, and it typically includes statistical data.  4) Discussion - This can also be known as the conclusion to the study, this usually describes what was learned and how the results can impact future practices.

In addition to IMRaD, it's important to see a conclusion and references that can back up the author's claims.

Characteristics to look for

In addition to the IMRaD format mentioned above, empirical research articles contain several key characteristics for identification purposes:

• The length of empirical research is often substantial, usually eight to thirty pages long.
• You should see data of some kind, this includes graphs, charts, or some kind of statistical analysis.
• There is always a bibliography found at the end of the article.

Publications

Empirical research articles can be found in scholarly or academic journals. These types of journals are often referred to as "peer-reviewed" publications; this means qualified members of an academic discipline review and evaluate an academic paper's suitability in order to be published. 

The CRAAP Checklist should be utilized to help you examine the currency, relevancy, authority, accuracy, and purpose of an information resource. This checklist was developed by California State University's Meriam Library . 

This page has been adapted from the Sociology Research Guide: Identify Empirical Articles at Cal State Fullerton Pollak Library.

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Empirical Research in the Social Sciences and Education

What is empirical research.

• Finding Empirical Research
• Designing Empirical Research
• Ethics & Anti-Racism in Research
• Citing, Writing, and Presenting Your Work

Academic Services Librarian | Research, Education, & Engagement

Gratitude to Penn State

Thank you to librarians at Penn State for serving as the inspiration for this library guide

An empirical research article is a primary source where the authors reported on experiments or observations that they conducted. Their research includes their observed and measured data that they derived from an actual experiment rather than theory or belief. 

How do you know if you are reading an empirical article? Ask yourself: "What did the authors actually do?" or "How could this study be re-created?"

Key characteristics to look for:

• Specific research questions  to be answered
• Definition of the  population, behavior, or phenomena  being studied
• Description of the  process or methodology  used to study this population or phenomena, including selection criteria, controls, and testing instruments (example: surveys, questionnaires, etc)
• You can readily describe what the  authors actually did 

Layout of Empirical Articles

Scholarly journals sometimes use a specific layout for empirical articles, called the "IMRaD" format, to communicate empirical research findings. There are four main components:

• Introduction : aka "literature review". This section summarizes what is known about the topic at the time of the article's publication. It brings the reader up-to-speed on the research and usually includes a theoretical framework 
• Methodology : aka "research design". This section describes exactly how the study was done. It describes the population, research process, and analytical tools
• Results : aka "findings". This section describes what was learned in the study. It usually contains statistical data or substantial quotes from research participants
• Discussion : aka "conclusion" or "implications". This section explains why the study is important, and also describes the limitations of the study. While research results can influence professional practices and future studies, it's important for the researchers to clarify if specific aspects of the study should limit its use. For example, a study using undergraduate students at a small, western, private college can not be extrapolated to include  all  undergraduates. 
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Experimental (Empirical) Research Articles

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How Can I Find Experimental (Empirical) Articles?

Many of the recommended databases in this research guide contain scholarly experimental articles (also known as empirical articles or research studies or primary research). Search in databases like: 

• APA PsycInfo ​
• ScienceDirect

Because those databases are rich in scholarly experimental articles, any well-structured search that you enter will retrieve experimental/empirical articles. These searches, for example, will retrieve many experimental/empirical articles:

• caffeine AND "reaction time"
• aging AND ("cognitive function" OR "cognitive ability")
• "child development" AND play

Experimental (Empirical) Articles: How Will I Know One When I See One?

Scholarly experimental articles  to conduct and publish an experiment, an author or team of authors designs an experiment, gathers data, then analyzes the data and discusses the results of the experiment. a published experiment or research study will therefore  look  very different from other types of articles (newspaper stories, magazine articles, essays, etc.) found in our library databases..

In fact, newspapers, magazines, and websites written by journalists report on psychology research all the time, summarizing published experiments in non-technical language for the general public. Although that kind of article can be interesting to read (and can even lead you to look up the original experiment published by the researchers themselves),  to write a research paper about a psychology topic, you should, generally, use experimental articles written by researchers. The following guidelines will help you recognize an experimental article, written by the researchers themselves and published in a scholarly journal.

Structure of a Experimental Article Typically, an experimental article has the following sections:

• The author summarizes her article
• The author discusses the general background of her research topic; often, she will present a literature review, that is, summarize what other experts have written on this particular research topic
• The author describes the experiment she designed and conducted
• The author presents the data she gathered during her experiment
• The author offers ideas about the importance and implications of her research findings, and speculates on future directions that similar research might take
• The author gives a References list of sources she used in her paper

Look for articles structured in that way--they will be experimental/empirical articles. ​

Also, experimental/empirical articles are written in very formal, technical language (even the titles of the articles sound complicated!) and will usually contain numerical data presented in tables. 

As noted above, when you search in a database like APA PsycInfo, it's really easy to find experimental/empirical articles, once you know what you're looking for. Just in case, though, here is a shortcut that might help:

First, do your keyword search, for example:

In the results screen, on the left-hand side, scroll down until you see "Methodology." You can use that menu to refine your search by limiting the articles to empirical studies only:

You can learn learn more about advanced search techniques in APA PsycInfo here . 

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Empirical Research: Definition, Methods, Types and Examples

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Empirical research: Definition

Empirical research: origin, quantitative research methods, qualitative research methods, steps for conducting empirical research, empirical research methodology cycle, advantages of empirical research, disadvantages of empirical research, why is there a need for empirical research.

Empirical research is defined as any research where conclusions of the study is strictly drawn from concretely empirical evidence, and therefore “verifiable” evidence.

This empirical evidence can be gathered using quantitative market research and  qualitative market research  methods.

For example: A research is being conducted to find out if listening to happy music in the workplace while working may promote creativity? An experiment is conducted by using a music website survey on a set of audience who are exposed to happy music and another set who are not listening to music at all, and the subjects are then observed. The results derived from such a research will give empirical evidence if it does promote creativity or not.

LEARN ABOUT: Behavioral Research

You must have heard the quote” I will not believe it unless I see it”. This came from the ancient empiricists, a fundamental understanding that powered the emergence of medieval science during the renaissance period and laid the foundation of modern science, as we know it today. The word itself has its roots in greek. It is derived from the greek word empeirikos which means “experienced”.

In today’s world, the word empirical refers to collection of data using evidence that is collected through observation or experience or by using calibrated scientific instruments. All of the above origins have one thing in common which is dependence of observation and experiments to collect data and test them to come up with conclusions.

LEARN ABOUT: Causal Research

Types and methodologies of empirical research

Empirical research can be conducted and analysed using qualitative or quantitative methods.

• Quantitative research : Quantitative research methods are used to gather information through numerical data. It is used to quantify opinions, behaviors or other defined variables . These are predetermined and are in a more structured format. Some of the commonly used methods are survey, longitudinal studies, polls, etc
• Qualitative research:   Qualitative research methods are used to gather non numerical data.  It is used to find meanings, opinions, or the underlying reasons from its subjects. These methods are unstructured or semi structured. The sample size for such a research is usually small and it is a conversational type of method to provide more insight or in-depth information about the problem Some of the most popular forms of methods are focus groups, experiments, interviews, etc.

Data collected from these will need to be analysed. Empirical evidence can also be analysed either quantitatively and qualitatively. Using this, the researcher can answer empirical questions which have to be clearly defined and answerable with the findings he has got. The type of research design used will vary depending on the field in which it is going to be used. Many of them might choose to do a collective research involving quantitative and qualitative method to better answer questions which cannot be studied in a laboratory setting.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

Quantitative research methods aid in analyzing the empirical evidence gathered. By using these a researcher can find out if his hypothesis is supported or not.

• Survey research: Survey research generally involves a large audience to collect a large amount of data. This is a quantitative method having a predetermined set of closed questions which are pretty easy to answer. Because of the simplicity of such a method, high responses are achieved. It is one of the most commonly used methods for all kinds of research in today’s world.

Previously, surveys were taken face to face only with maybe a recorder. However, with advancement in technology and for ease, new mediums such as emails , or social media have emerged.

For example: Depletion of energy resources is a growing concern and hence there is a need for awareness about renewable energy. According to recent studies, fossil fuels still account for around 80% of energy consumption in the United States. Even though there is a rise in the use of green energy every year, there are certain parameters because of which the general population is still not opting for green energy. In order to understand why, a survey can be conducted to gather opinions of the general population about green energy and the factors that influence their choice of switching to renewable energy. Such a survey can help institutions or governing bodies to promote appropriate awareness and incentive schemes to push the use of greener energy.

Learn more: Renewable Energy Survey Template Descriptive Research vs Correlational Research

• Experimental research: In experimental research , an experiment is set up and a hypothesis is tested by creating a situation in which one of the variable is manipulated. This is also used to check cause and effect. It is tested to see what happens to the independent variable if the other one is removed or altered. The process for such a method is usually proposing a hypothesis, experimenting on it, analyzing the findings and reporting the findings to understand if it supports the theory or not.

For example: A particular product company is trying to find what is the reason for them to not be able to capture the market. So the organisation makes changes in each one of the processes like manufacturing, marketing, sales and operations. Through the experiment they understand that sales training directly impacts the market coverage for their product. If the person is trained well, then the product will have better coverage.

• Correlational research: Correlational research is used to find relation between two set of variables . Regression analysis is generally used to predict outcomes of such a method. It can be positive, negative or neutral correlation.

LEARN ABOUT: Level of Analysis

For example: Higher educated individuals will get higher paying jobs. This means higher education enables the individual to high paying job and less education will lead to lower paying jobs.

• Longitudinal study: Longitudinal study is used to understand the traits or behavior of a subject under observation after repeatedly testing the subject over a period of time. Data collected from such a method can be qualitative or quantitative in nature.

For example: A research to find out benefits of exercise. The target is asked to exercise everyday for a particular period of time and the results show higher endurance, stamina, and muscle growth. This supports the fact that exercise benefits an individual body.

• Cross sectional: Cross sectional study is an observational type of method, in which a set of audience is observed at a given point in time. In this type, the set of people are chosen in a fashion which depicts similarity in all the variables except the one which is being researched. This type does not enable the researcher to establish a cause and effect relationship as it is not observed for a continuous time period. It is majorly used by healthcare sector or the retail industry.

For example: A medical study to find the prevalence of under-nutrition disorders in kids of a given population. This will involve looking at a wide range of parameters like age, ethnicity, location, incomes  and social backgrounds. If a significant number of kids coming from poor families show under-nutrition disorders, the researcher can further investigate into it. Usually a cross sectional study is followed by a longitudinal study to find out the exact reason.

• Causal-Comparative research : This method is based on comparison. It is mainly used to find out cause-effect relationship between two variables or even multiple variables.

For example: A researcher measured the productivity of employees in a company which gave breaks to the employees during work and compared that to the employees of the company which did not give breaks at all.

LEARN ABOUT: Action Research

Some research questions need to be analysed qualitatively, as quantitative methods are not applicable there. In many cases, in-depth information is needed or a researcher may need to observe a target audience behavior, hence the results needed are in a descriptive analysis form. Qualitative research results will be descriptive rather than predictive. It enables the researcher to build or support theories for future potential quantitative research. In such a situation qualitative research methods are used to derive a conclusion to support the theory or hypothesis being studied.

LEARN ABOUT: Qualitative Interview

• Case study: Case study method is used to find more information through carefully analyzing existing cases. It is very often used for business research or to gather empirical evidence for investigation purpose. It is a method to investigate a problem within its real life context through existing cases. The researcher has to carefully analyse making sure the parameter and variables in the existing case are the same as to the case that is being investigated. Using the findings from the case study, conclusions can be drawn regarding the topic that is being studied.

For example: A report mentioning the solution provided by a company to its client. The challenges they faced during initiation and deployment, the findings of the case and solutions they offered for the problems. Such case studies are used by most companies as it forms an empirical evidence for the company to promote in order to get more business.

• Observational method:   Observational method is a process to observe and gather data from its target. Since it is a qualitative method it is time consuming and very personal. It can be said that observational research method is a part of ethnographic research which is also used to gather empirical evidence. This is usually a qualitative form of research, however in some cases it can be quantitative as well depending on what is being studied.

For example: setting up a research to observe a particular animal in the rain-forests of amazon. Such a research usually take a lot of time as observation has to be done for a set amount of time to study patterns or behavior of the subject. Another example used widely nowadays is to observe people shopping in a mall to figure out buying behavior of consumers.

• One-on-one interview: Such a method is purely qualitative and one of the most widely used. The reason being it enables a researcher get precise meaningful data if the right questions are asked. It is a conversational method where in-depth data can be gathered depending on where the conversation leads.

For example: A one-on-one interview with the finance minister to gather data on financial policies of the country and its implications on the public.

• Focus groups: Focus groups are used when a researcher wants to find answers to why, what and how questions. A small group is generally chosen for such a method and it is not necessary to interact with the group in person. A moderator is generally needed in case the group is being addressed in person. This is widely used by product companies to collect data about their brands and the product.

For example: A mobile phone manufacturer wanting to have a feedback on the dimensions of one of their models which is yet to be launched. Such studies help the company meet the demand of the customer and position their model appropriately in the market.

• Text analysis: Text analysis method is a little new compared to the other types. Such a method is used to analyse social life by going through images or words used by the individual. In today’s world, with social media playing a major part of everyone’s life, such a method enables the research to follow the pattern that relates to his study.

For example: A lot of companies ask for feedback from the customer in detail mentioning how satisfied are they with their customer support team. Such data enables the researcher to take appropriate decisions to make their support team better.

Sometimes a combination of the methods is also needed for some questions that cannot be answered using only one type of method especially when a researcher needs to gain a complete understanding of complex subject matter.

We recently published a blog that talks about examples of qualitative data in education ; why don’t you check it out for more ideas?

Since empirical research is based on observation and capturing experiences, it is important to plan the steps to conduct the experiment and how to analyse it. This will enable the researcher to resolve problems or obstacles which can occur during the experiment.

Step #1: Define the purpose of the research

This is the step where the researcher has to answer questions like what exactly do I want to find out? What is the problem statement? Are there any issues in terms of the availability of knowledge, data, time or resources. Will this research be more beneficial than what it will cost.

Before going ahead, a researcher has to clearly define his purpose for the research and set up a plan to carry out further tasks.

Step #2 : Supporting theories and relevant literature

The researcher needs to find out if there are theories which can be linked to his research problem . He has to figure out if any theory can help him support his findings. All kind of relevant literature will help the researcher to find if there are others who have researched this before, or what are the problems faced during this research. The researcher will also have to set up assumptions and also find out if there is any history regarding his research problem

Step #3: Creation of Hypothesis and measurement

Before beginning the actual research he needs to provide himself a working hypothesis or guess what will be the probable result. Researcher has to set up variables, decide the environment for the research and find out how can he relate between the variables.

Researcher will also need to define the units of measurements, tolerable degree for errors, and find out if the measurement chosen will be acceptable by others.

Step #4: Methodology, research design and data collection

In this step, the researcher has to define a strategy for conducting his research. He has to set up experiments to collect data which will enable him to propose the hypothesis. The researcher will decide whether he will need experimental or non experimental method for conducting the research. The type of research design will vary depending on the field in which the research is being conducted. Last but not the least, the researcher will have to find out parameters that will affect the validity of the research design. Data collection will need to be done by choosing appropriate samples depending on the research question. To carry out the research, he can use one of the many sampling techniques. Once data collection is complete, researcher will have empirical data which needs to be analysed.

LEARN ABOUT: Best Data Collection Tools

Step #5: Data Analysis and result

Data analysis can be done in two ways, qualitatively and quantitatively. Researcher will need to find out what qualitative method or quantitative method will be needed or will he need a combination of both. Depending on the unit of analysis of his data, he will know if his hypothesis is supported or rejected. Analyzing this data is the most important part to support his hypothesis.

Step #6: Conclusion

A report will need to be made with the findings of the research. The researcher can give the theories and literature that support his research. He can make suggestions or recommendations for further research on his topic.

A.D. de Groot, a famous dutch psychologist and a chess expert conducted some of the most notable experiments using chess in the 1940’s. During his study, he came up with a cycle which is consistent and now widely used to conduct empirical research. It consists of 5 phases with each phase being as important as the next one. The empirical cycle captures the process of coming up with hypothesis about how certain subjects work or behave and then testing these hypothesis against empirical data in a systematic and rigorous approach. It can be said that it characterizes the deductive approach to science. Following is the empirical cycle.

• Observation: At this phase an idea is sparked for proposing a hypothesis. During this phase empirical data is gathered using observation. For example: a particular species of flower bloom in a different color only during a specific season.
• Induction: Inductive reasoning is then carried out to form a general conclusion from the data gathered through observation. For example: As stated above it is observed that the species of flower blooms in a different color during a specific season. A researcher may ask a question “does the temperature in the season cause the color change in the flower?” He can assume that is the case, however it is a mere conjecture and hence an experiment needs to be set up to support this hypothesis. So he tags a few set of flowers kept at a different temperature and observes if they still change the color?
• Deduction: This phase helps the researcher to deduce a conclusion out of his experiment. This has to be based on logic and rationality to come up with specific unbiased results.For example: In the experiment, if the tagged flowers in a different temperature environment do not change the color then it can be concluded that temperature plays a role in changing the color of the bloom.
• Testing: This phase involves the researcher to return to empirical methods to put his hypothesis to the test. The researcher now needs to make sense of his data and hence needs to use statistical analysis plans to determine the temperature and bloom color relationship. If the researcher finds out that most flowers bloom a different color when exposed to the certain temperature and the others do not when the temperature is different, he has found support to his hypothesis. Please note this not proof but just a support to his hypothesis.
• Evaluation: This phase is generally forgotten by most but is an important one to keep gaining knowledge. During this phase the researcher puts forth the data he has collected, the support argument and his conclusion. The researcher also states the limitations for the experiment and his hypothesis and suggests tips for others to pick it up and continue a more in-depth research for others in the future. LEARN MORE: Population vs Sample

LEARN MORE: Population vs Sample

There is a reason why empirical research is one of the most widely used method. There are a few advantages associated with it. Following are a few of them.

• It is used to authenticate traditional research through various experiments and observations.
• This research methodology makes the research being conducted more competent and authentic.
• It enables a researcher understand the dynamic changes that can happen and change his strategy accordingly.
• The level of control in such a research is high so the researcher can control multiple variables.
• It plays a vital role in increasing internal validity .

Even though empirical research makes the research more competent and authentic, it does have a few disadvantages. Following are a few of them.

• Such a research needs patience as it can be very time consuming. The researcher has to collect data from multiple sources and the parameters involved are quite a few, which will lead to a time consuming research.
• Most of the time, a researcher will need to conduct research at different locations or in different environments, this can lead to an expensive affair.
• There are a few rules in which experiments can be performed and hence permissions are needed. Many a times, it is very difficult to get certain permissions to carry out different methods of this research.
• Collection of data can be a problem sometimes, as it has to be collected from a variety of sources through different methods.

LEARN ABOUT:  Social Communication Questionnaire

Empirical research is important in today’s world because most people believe in something only that they can see, hear or experience. It is used to validate multiple hypothesis and increase human knowledge and continue doing it to keep advancing in various fields.

For example: Pharmaceutical companies use empirical research to try out a specific drug on controlled groups or random groups to study the effect and cause. This way, they prove certain theories they had proposed for the specific drug. Such research is very important as sometimes it can lead to finding a cure for a disease that has existed for many years. It is useful in science and many other fields like history, social sciences, business, etc.

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With the advancement in today’s world, empirical research has become critical and a norm in many fields to support their hypothesis and gain more knowledge. The methods mentioned above are very useful for carrying out such research. However, a number of new methods will keep coming up as the nature of new investigative questions keeps getting unique or changing.

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Open access

• Published: 22 December 2022

A systematic review of high impact empirical studies in STEM education

• Yeping Li 1 ,
• Yu Xiao 1 ,
• Ke Wang 2 ,
• Nan Zhang 3 , 4 ,
• Yali Pang 5 ,
• Ruilin Wang 6 ,
• Chunxia Qi 7 ,
• Zhiqiang Yuan 8 ,
• Jianxing Xu 9 ,
• Sandra B. Nite 1 &
• Jon R. Star 10  

International Journal of STEM Education volume  9 , Article number:  72 ( 2022 ) Cite this article

11k Accesses

13 Citations

33 Altmetric

Metrics details

The formation of an academic field is evidenced by many factors, including the growth of relevant research articles and the increasing impact of highly cited publications. Building upon recent scoping reviews of journal publications in STEM education, this study aimed to provide a systematic review of high impact empirical studies in STEM education to gain insights into the development of STEM education research paradigms. Through a search of the Web of Science core database, we identified the top 100 most-cited empirical studies focusing on STEM education that were published in journals from 2000 to 2021 and examined them in terms of various aspects, including the journals where they were published, disciplinary content coverage, research topics and methods, and authorship’s nationality/region and profession. The results show that STEM education continues to gain more exposure and varied disciplinary content with an increasing number of high impact empirical studies published in journals in various STEM disciplines. High impact research articles were mainly authored by researchers in the West, especially the United States, and indicate possible “hot” topics within the broader field of STEM education. Our analysis also revealed the increased participation and contributions from researchers in diverse fields who are working to formulate research agendas in STEM education and the nature of STEM education scholarship.

Introduction

Two recent reviews of research publications, the first examining articles in the International Journal of STEM Education (IJSTEM) and the second looking at an expanded scope of 36 journals, examined how scholarship in science, technology, engineering, and mathematics (STEM) education has developed over the years (Li et al., 2019 , 2020a ). Although these two reviews differed in multiple ways (e.g., the number of journals covered, the time period of article publications, and article selection), they shared the common purpose of providing an overview of the status and trends in STEM education research. The selection of journal publications in these two reviews thus emphasized the coverage and inclusion of all relevant publications but did not consider publication impact. Given that the development of a vibrant field depends not only on the number of research outputs and its growth over the years but also the existence and influence of some high impact research articles, here we aimed to identify and examine those high impact research publications in STEM education in this review.

Learning from existing reviews of STEM education research

Existing reviews of STEM education have provided valuable insights about STEM education scholarship development over the years. In addition to the two reviews mentioned above, there are many other research reviews on different aspects of STEM education. For example, Chomphuphra et al. ( 2019 ) reviewed 56 journal articles published from 2007 to 2017 covering three popular topics: innovation for STEM learning, professional development, and gender gap and career in STEM. They identified and selected these journal articles through searching the Scopus database and two additional journals in STEM education that were not indexed in Scopus at that time. Several other reviews have been conducted and published with a focus on specific topics, such as the assessment of the learning assistant model (Barrasso & Spilios, 2021 ), STEM education in early childhood (Wan et al., 2021 ), and research on individuals' STEM identity (Simpson & Bouhafa, 2020 ). All of these reviews helped in summarizing and synthesizing what we can learn from research on different topics related to STEM education.

Given the on-going rapid expansion of interest in STEM education, the number of research reviews in STEM education research has also been growing rapidly over the years. For example, there were only one or two research reviews published yearly in IJSTEM just a few years ago (Li, 2019 ). However, the situation started to change quickly over the past several years (Li & Xiao, 2022 ). Table 1 provides a summary list of research reviews published in IJSTEM in 2020 and 2021. The journal published a total of five research reviews in 2020 (8%, out of 59 publications), which then increased to seven in 2021 (12%, out of 59 publications).

Taking a closer look at these research reviews, we noticed that three reviews were conducted with a broad perspective to examine research and trends in STEM education (Li et al., 2020a , 2020b ) or STEAM (science, technology, engineering, arts, and mathematics) education (Marin-Marin et al., 2021 ). Relatively large numbers of publications/projects were reviewed in these studies to provide a general overview of research development and trends. The other nine reviews focused on research on specific topics or aspects in STEM education. These results suggest that, with the availability of a rapidly accumulating number of studies in STEM education, researchers have started to go beyond general research trends to examine and summarize research development on specific topics. Moreover, across these 12 reviews, researchers used many different approaches to search multiple data sources (often with specified search terms) to identify and select articles, including journal publications, research reports, conference papers, or dissertations. It appears that researchers have been creative in developing and using specific approaches to select and review publications that are pertinent to their topics. At the same time, however, none of these reviews were designed and conducted to identify and review high impact research articles that had notable influences on the development of STEM education scholarship.

The importance of examining high impact empirical research publications in STEM education

STEM education differs from many other fields, as STEM itself is not a discipline. There are diverse perspectives about the disciplinarity of STEM and STEM education (e.g., Erduran, 2020 ; Li et al., 2020a ; Takeuchi et al., 2020 ; Tytler, 2020 ). The complexity and ambiguity in viewing and examining STEM and STEM education presents challenges as well as opportunities for researchers to explore and specify what and how they do in ways different from and/or connected with traditional education in the individual disciplines of science, technology, engineering, and mathematics.

Although the field of STEM education is still in an early stage of its development, STEM education has experienced tremendous growth over the past decade. This field has evolved from traditional individual discipline-based education in STEM fields to multi- and interdisciplinary education in STEM. The development of STEM education has been supported by multiple factors, including research funding (Li et al., 2020b ) and the growth of research publications (Li et al., 2020a ). High impact publications play a very large role in the growth of the field, as they are read and cited frequently by others and serve to shape the development of scholarship in the field more than other publications.

Among high impact research publications, we can identify several different types of articles, including empirical studies, research reviews, and conceptual or theoretical papers. Research reviews and conceptual/theoretical papers are very valuable, as they synthesize existing research on a specific topic and/or provide new perspective(s) and direction(s), but they are typically not empirical studies. Review articles aim to provide a summary of the current state of the research in the field or on a particular topic, and they help readers to gain an overview about a topic, key issues and publications. Thus, they are more about what has been published in the literature about a topic and less about reporting new empirical evidence about a topic. Similarly, theoretical or conceptual papers tend to draw on existing research to advance theory or propose new perspectives. In contrast, empirical studies require the use and analysis of empirical data to provide empirical evidence. While reporting original research has been typical in empirical studies in education, these studies can also be secondary analyses of empirical data that test hypotheses not considered or addressed in previous studies. Empirical studies are generally published in academic, peer-reviewed journals and consist of distinct sections that reflect the stages in the research process. With the aim to gain insights about research development in STEM education, we thus decided to focus here on empirical studies in STEM education. Examining and reviewing high impact empirical research publications can help provide us a better understanding about emerging trends in STEM education in terms of research topics, methods, and possible directions in the future.

Considerations in identifying and selecting high impact empirical research publications

Publishing as a way of disseminating and sharing knowledge has many types of outlets, including journals, books, and conference proceedings. Different publishing outlets have different advantages in reaching out to readers. Researchers may search different data sources to identify and select publications to review, as indicated in Table 1 . At the same time, journal publications are commonly chosen and viewed as one of the most important outlets valued by the research community for knowledge dissemination and exchange. Specifically, there are two important advantages in terms of evaluating the quality and impact of journal publications over other formats. First, journal publications typically go through a rigorous peer-review process to ensure the quality of manuscripts for publication acceptance based on certain criteria. In educational research, some common criteria being used include “Standards for Reporting on Empirical Social Science Research in AERA Publications” (AERA, 2006 ), “Standards for Reporting on Humanities-Oriented Research in AERA Publications” (AERA, 2009 ), and “Scientific Research in Education” (NRC, 2002 ). Although the peer-review process is also employed in assessing and selecting proposals or papers for publication acceptance in other formats such as books and conference proceedings, the peer-review process employed by journals (esp. those reputable and top journals in a field) tends to be more rigorous and selective than other publication formats. Second, the impact of journals and their publications has frequently been evaluated by peers and different indexing services for inclusion, such as Clarivate’s Social Sciences Citation Index (SSCI) and Elsevier’s Scopus. The citation information collected and evaluated by indexing services provides another important measure about the quality and impact of selected journals and their publications. Based on these considerations, we decided to select and review those journal publications that can be identified as having high citations to gain an overview of their impact on the research development of STEM education.

Focusing on the selection and review of journal publications with high citations has also been used by many other scholars. For example, Martín‐Páez et al. ( 2019 ) conducted a literature review to examine how STEM education is conceptualized, used, and implemented in educational studies. To ensure the quality of published articles for review, they searched and selected journal articles published in the 2013–2018 period from the Web of Science (WoS) database only. Likewise, Akçayır and Akçayır ( 2017 ) conducted a systematic literature review on augmented reality used in educational settings. They used keywords to search all SSCI-indexed journals from WoS database to identify and select published articles, given that WoS provides easy access to search SSCI indexed articles. In addition to the method of searching the WoS database, some researchers used other approaches to identify and select published articles with high citations. For example, some researchers may search different databases to identify and select articles for reviews, such as Scopus (Chomphuphra et al., 2019 ) and Google (Godin et al., 2015 ). In comparison, however, the WoS core database is more selective than many others, including Scopus. The WoS is the world’s leading scientific citation search and analytical information platform (Li et al., 2018 ), and has its own independent and thorough editorial process to ensure journal quality together with the most comprehensive and complete citation network ( https://clarivate.com/webofsciencegroup/solutions/webofscience-ssci/ ). Its core database has been commonly used as a reliable indexing database with close attention to high standard research publications with a peer-review process and is thus used in many research review studies (e.g., Akçayır & Akçayır, 2017 ; Li et al., 2018 ; Marín-Marín et al., 2021 ; Martín‐Páez et al., 2019 ).

It should be noted that some researchers have used a different approach to identify and select high impact publications other than focusing on article citations. This alternative approach is to identify leading journals from specific fields first and then select relevant articles from these journals. For example, Brown ( 2012 ) identified and selected eight important journals in each STEM discipline after consulting with university faculty and K-12 teachers. Once these journals were selected, Brown then located 60 articles that authors self-identified as connected to STEM education from over 1100 articles published between January 1, 2007 and October 1, 2010. However, as there was no well-established journal in STEM education until just a few years ago (Li et al., 2020a ), the approach used by Brown may be less useful for identifying high impact publications in the field of STEM education. In fact, researchers in STEM education have been publishing their high-quality articles in many different journals, especially those well-established journals with an impact factor. Thus, this approach will not help ensure the selection of high impact articles in STEM education, even though they were selected from well-recognized journals rooted in each of STEM disciplines.

In summary, we searched the WoS core database to identify and select high impact empirical research articles in STEM education as those highly cited articles published in journals indexed and collected in the WoS.

Current review

Similar to previous research reviews (e.g., Li et al., 2020a ), we need to specify the scope of the current review with specific considerations of the following two issues:

What time period should be considered?

How should we identify and select highly cited research publications in STEM education?

Time period

As discussed in a previous review (Li et al., 2020a ), the acronym STEM did not exist until the early 2000s. The existence of the acronym has helped to focus attention on and efforts in STEM education. Thus, consistent with the determination of the time period used in the previous review on examining the status and trends in STEM education, we decided to select articles starting from the year 2000. At the same time, we can use the acronym of STEM as an identifier in locating journal articles in a way as done before (Li et al., 2020a ) and also by others (e.g., Brown, 2012 ; Mizell & Brown, 2016 ). We chose the end of 2021 as the end of the time period for publication search and inclusion.

Searching and identifying highly cited empirical research journal publications in STEM education

To identify and select journal articles in STEM education from the WoS core database, we decided to use the common approach of keyword searches as used in many other reviews (e.g., Gladstone & Cimpian, 2021 ; Winterer et al., 2020 ). Li et al. ( 2020a ) also noted the complexity and ambiguity of identifying publications in STEM education. Thus, we planned to identify and select publications in STEM education as those self-identified by authors. As mentioned above, we then used the acronym STEM (or STEAM) as key terms in our search for publications in STEM education.

Different from the previous review on research status and trends in STEM education (Li et al., 2020a ), the current review aimed to identify and select high impact journal articles but not coverage. Thus, we decided to define and limit the scope of high impact empirical research journal publications as the top 100 most-cited empirical research journal publications obtained from the WoS core database.

Research questions

Li et al. ( 2020a ) showed that STEM education articles have been published in many different journals, especially with the limited journal choices available in STEM education. Given a broader range of journals and a longer period of time to be covered in this review, we can thus gain some insights through examining multiple aspects of the top 100 most-cited empirical studies, including journals in which these empirical studies were published, publication years, disciplinary content coverage, research topics and methods. In addition, recent reviews suggested the value of examining possible trends in the authorship and school level focus (Li, 2022 ; Li & Xiao, 2022 ). Taken together, we are interested in addressing the following six research questions:

What are the top 100 most-cited empirical STEM education research journal publications?

What are the distributions and patterns of the top 100 most-cited empirical research publications in different journals?

What is the disciplinary content coverage of the top 100 most-cited empirical research journal publications and possible trends?

What are research topics and methods of the top 100 most-cited empirical research journal publications?

What are the corresponding authors’ nationalities/regions and professions?

What are school level foci of the top 100 most-cited empirical research journal publications over the years?

Based on the above discussion, we carried out the following steps for this systematic review to address these research questions.

Searching and identifying the top 100 most-cited empirical research journal publications in STEM education

Figure  1 provides a summary of the article search and selection process that was used for this review. The process started with a search of the WoS core database on September 12, 2022 under the field of “topic” (covering title, abstract, author keywords, and keywords plus), using the search terms: “STEM” OR “STEAM” OR “science, technology, engineering, and mathematics”. Because there are many different categories in the WoS database, we then specified the publication search using the four WoS categories listed under “education”: “Education Educational Research,” “Education Scientific Disciplines,” “Psychology Educational,” and “Education Special.” The time period of publication search was further specified as starting from 2000 to 2021.

Flowchart of publication search, identification, and selection process

The search returned 9275 publications under “Education Educational Research,” 2161 under “Education Scientific Disciplines,” 247 under “Psychology Educational,” and 15 under “Education Special.” The combined list of all publications was then placed in descending order in terms of citation counts up to the search date of Sept. 12, 2022, and each publication record was screened one-by-one by three researchers using the inclusion or exclusion criteria (see Table 2 ). At times when the publication record listed was not detailed enough, we searched and obtained the full article to screen and check to determine its eligibility. The process ended after identifying and selecting the top 100 most-cited empirical research journal publications.

Data analysis

To address research question 3, we categorized all 100 publications in terms of the number of STEM disciplines covered in a study. Two general categories were used for this review: publications within a single discipline of STEM vs. those with multi- or inter-disciplines of STEM. In contrast to the detailed classifications used in a previous review (Li et al., 2020a ), this simplified classification can help reveal overall trends in disciplinary content coverage and approach reflected in high impact empirical research in STEM education.

To examine research topics, we used the same list of topics from previous reviews (Li & Xiao, 2022 ; Li et al., 2020a ). The following list contains the seven topic categories (TCs) that were used to classify and examine all 100 publications identified and selected from the search in this study.

TC1: Teaching, teacher, and teacher education in STEM (including both pre-service and in-service teacher education) in K-12 education;

TC2: Teacher and teaching in STEM (including faculty development, etc.) at post-secondary level;

TC3: STEM learner, learning, and learning environment in K-12 education;

TC4: STEM learner, learning, and learning environments (excluding pre-service teacher education) at post-secondary level;

TC5: Policy, curriculum, evaluation, and assessment in STEM (including literature reviews about a field in general);

TC6: Culture, social, and gender issues in STEM education;

TC7: History, epistemology, and perspectives about STEM and STEM education.

To examine research methods, we coded all publications in terms of the following methodological categories: (1) qualitative methods, (2) quantitative methods, and (3) mixed methods. We assigned each publication to only one research topic and one method, following the process used in the previous reviews (Li et al., 2019 , 2020a ). When there was more than one topic or method that could have been used for a publication, a final decision was made in choosing and assigning the primary topic and/or method after discussion.

To address research question 5, we examined the corresponding author’s (or the first author, if no specific indication was given about the corresponding author) nationality/region and profession. Many publications in STEM education have joint authorship but may contain limited information about different co-authors. Focusing on the corresponding author’s nationality/region is a feasible approach as we learned from a previous research review (Li et al., 2020a ). For the corresponding author’s profession, we used the same two general categories from the recent reviews (Li, 2022 ; Li & Xiao, 2022 ): “education” and “STEM+” that differentiate a corresponding author’s profession in education/educational research vs. disciplines and fields other than education. If a publication’s corresponding author was listed as affiliated with multiple departments/institutions, the first department/institution affiliation was chosen and used to identify the author’s nationality/region and profession.

To answer research question 6, we adopted the three categories from recent research reviews: K-12, postsecondary, and general (Li, 2022 ; Li & Xiao, 2022 ). The use of these school level categories helped reveal the distribution of STEM education research interests and development over the school level span. While the first two categories are self-explanatory, the “general” category is for those empirical research publications on questions or issues either pertinent to all school levels or that cross the boundary of K-12 school and college.

Results and discussion

The following sections are structured to report findings as corresponding to each of the six research questions.

Top 100 most-cited empirical research articles from 2000 to 2021

Figure  2 shows the distribution of the top 100 most-cited empirical research journal publications in STEM education over the years 2000–2021. As the majority of these publications (72 out of 100, 72%) were published between 2011 and 2016, the results suggest that publications typically need about 5–10 years to accumulate high enough citations for inclusion. Research articles published more than 10 years ago would likely become out-of-dated, unless those studies have been recognized as classic in the field. Some recent publications (6 publications, 2018–2019) emerged with high citations could suggest the emergency of interesting ‘hot’ topics in the field.

Distribution of the top 100 most-cited empirical research publications over the years (Note: all 100 of these most-cited publications were published in the years 2005-2019.)

To have a more fine-grained sense of these highly cited research articles, we took a more detailed look at the top ten most-cited publications from the search (see Table 3 ). These ten most-cited publications were published between 2005 and 2014, with an average of 337 citations and a range of 238–820 citations per article. Only two of the top ten articles were published before 2010; both gained very impressive citations over the years (820 citations for the article published in 2009 and 289 citations for the other published in 2005). The on-going high citations of these two research articles are clear indication of their impact and importance in the field.

Table 3 also shows that the top ten list of most-cited empirical research articles were published in six different journals, with the majority of these journals focusing on general educational research or educational psychology. The importance of STEM education research was clearly recognized with high impact publications in these well-established journals. At the same time, the results imply the rapid development of STEM education research in its early stages and the value of examining possible trends in journals that published high impact articles in STEM education over time.

Moreover, we noticed that all of these top ten articles had corresponding authors who were from the U.S., with the exception of one by researchers in the U.K. This result is consistent with what we learned from previous reviews of STEM education research publications (Li et al., 2019 , 2020a ). About 75% of STEM-related journal publications were typically contributed by U.S. scholars, either in this journal’s publications from 2014 to 2018 (Li et al., 2019 ) or publications from 36 journals from 2000 to 2018 (Li et al., 2020a ). It is not surprising that all of these high impact research publications from 2005 to 2014 were contributed by researchers in the West, especially the United States. (Below we report more about the corresponding authorship of the 100 high impact research publications beyond the top 10 that are reported here.)

Distributions and patterns of highly cited publications across different journals

Forty-five journals were identified as publishing these top 100 most-cited articles. Table 4 shows that the majority (26) of these journals focus on general educational research or educational psychology, publishing 52 of the top 100 most-cited articles. Fourteen journals with titles specifying a single discipline of STEM published 38 of these top 100 articles, three journals with two specified STEM disciplines in their titles published seven of these articles, one journal with three specified STEM disciplines published one article, and one journal specifying all four STEM disciplines published two articles. Among these 45 journals, 36 journals are indexed in SSCI, with the remaining nine journals indexed in ESCI (Emerging Sources Citation Index). These are clearly all reputable and well-established journals, with 36 established before 2000 and 9 established in or after 2000. Only three journals in the list are Open Access (OA) journals, and they were all established after 2000. The results suggest that researchers have been publishing high impact STEM education research articles in a wide range of well-established traditional journals, with the majority in general educational research or educational psychology with a long publishing history. It further confirms that the importance of STEM education research has been well-recognized in educational research or educational psychology as noted above. At the same time, the results imply that the history of STEM education itself has been too brief to establish its own top journals and identity except only one in STEM education (IJSTEM) (Li et al., 2020a ).

Among these 45 journals listed in Table 4 , we classified them into two general categories: general education research journals (26, all without mention of a discipline of STEM in a journal’s title) and those (19) with one or more STEM disciplines specified in a journal’s title. Figure  3 presents the distributions of these top 100 articles in these two general categories over the years. Among 49 articles published before 2014, the majority (31, 63%) of these articles were published in journals on general educational research or educational psychology. However, starting in 2014, a new trend emerged with more of these highly cited articles (30 out of 51, 59%) published in journals with STEM discipline(s) specified. The result suggests a possible shift of developing and gaining disciplinary content consciousness in STEM education research publications.

Trend of the top 100 most-cited articles published in journals without vs. with subject discipline(s) of STEM specified. (Note: 0 = journals without STEM discipline specified, 1 = journals with STEM discipline(s) specified.)

As a further examination of the distribution of publications in journals specified with STEM discipline(s), Fig.  4 shows the distributions of these highly cited articles in different journal categories over the years. It is clear that these highly cited articles were typically published in journals on general educational research or educational psychology before 2014. However, things started to change since 2014, with these highly cited articles published in more diverse journals including those with STEM discipline(s) specified in the journal titles. The journals that include only a single discipline of STEM have been more popular than others among those journals that specify one or more STEM disciplines. The result is not surprising as journals specified with a single discipline of STEM are more common, often with a long publishing history and support from well-established professional societies of education on a single discipline of STEM. This trend suggests that the importance of STEM education has also gained increasing recognition from professional societies that used to focus on a single discipline of STEM.

Distribution of highly cited research articles across different journal categories over the years. (Note: 0 = journals without STEM discipline specified, 1 = journals with a single discipline of STEM specified, 2 = journals with two disciplines of STEM specified, 3/4 = journals with 3 or 4 disciplines of STEM specified.)

To glimpse into those recent changes, we took a closer look at the six articles published in 2018 and 2019 as examples (see Table 5 ). All of these articles have been highly cited in just 3 or 4 years, with an average of 102 citations (range, 75–144) per article. Across these six articles, the majority were published in journals whose titles specified one or more STEM disciplines: three in journals with a single discipline of STEM specified, one in a journal on STEM education, and two in journals on general educational research. At the same time, these recent publications are not specifically on any single discipline of STEM, but multi- and interdisciplinary STEM education.

Disciplinary content coverage

The search of STEM education publications from the WoS core database relied on several keywords that the authors used to self-identify their research on STEM education. After coding and categorizing all top 100 publications, 25 research publications were found as focusing on a single discipline of STEM and 75 publications on multi- and interdisciplinary STEM education. The majority of these 100 most-cited empirical studies, in their focus on multi- and interdisciplinary STEM education, reflects the overall focus in STEM education, a trend consistent with what was learned from a previous review of journal publications in STEM education (Li et al., 2020a ).

Among the 25 research articles on a single discipline of STEM, the majority of these articles (56%, 14 out of 25) focused on science, 5 articles on technology, 4 articles on mathematics, and 2 articles on engineering. The result suggests that of the four STEM disciplines, arguably “science” is the broadest category and so it is not surprising that the number of publications on science is the most prevalent. Indeed, the result is also consistent with what we can learn from Table 4 . Among the 14 journals specifying a single STEM discipline that published 38 of the top 100 articles, seven journals focus on “science” that published 27 of these 38 articles.

To examine possible trends over time, Fig.  5 shows the distribution of these 100 articles across these two disciplinary content coverage categories over the years. For each of the publishing years from 2005 to 2019, there were always more high citation empirical publications on multi- and interdisciplinary STEM education than high citation publications focusing on a single discipline of STEM. Moreover, there were no high citation publications on a single discipline of STEM before 2011 or after 2017 that made the cut for inclusion in the top 100 list. These results suggest an overall trend of on-going emphasis on multi- and interdisciplinary research in STEM education, which can be further verified by what we learned from the six recent publications in Table 5 .

Publication distribution by disciplinary content coverage over the years. (Note: S = single discipline of STEM, M = multiple disciplines of STEM.)

Research topics and methods

Table 6 presents the distribution of all 100 highly cited publications classified in terms of the seven topic categories (TCs) over the years. Overall, all seven TCs have publications that were on the top 100 high citation publication list. There were clearly the most publications on TC6 (culture, social, and gender issues in STEM education), followed by publications on TC4 (STEM learner, learning, and learning environments at post-secondary level). The large number of publications with high citations in these two categories suggest possible evolution of research interests and topics in the field of STEM education. Taking a closer look at the six recent publications in Table 5 , it is clear that culture, social, and gender issues were the focus in these recent publications, with the exception of one publication on assessment. This result presents a picture that appears somewhat different from what we learned from previous research reviews that did not focus exclusively on high impact publications from the WoS database (Li & Xiao, 2022 ; Li et al., 2020a ).

Looking at the distribution of these publications within each of the seven TCs, “culture, social, and gender issues in STEM education” (TC6) is a topic area that consistently has some highly cited research publications in almost each of the publishing years. “STEM learner, learning, and learning environments at post-secondary level” (TC4) also has some consistent and on-going research interest with highly cited publications making the list in most of these publishing years. In contrast, publication distributions in the rest of the TCs did not present clearly notable patterns over the years.

Figure  6 shows the number of publications distributed over the years by research methods in these empirical studies. The use of quantitative methods (71) is dominant overall and is especially prevalent among these most-cited publications in the years from 2005 to 2019, a result consistent with what we learned from a previous research review (Li et al., 2020a ). Across these three methodological classifications, qualitative methods were used in 20 empirical studies, and mixed methods were used in only 9 empirical studies. Comparatively, there were many more articles published between 2010 and 2016 that used quantitative methods than the other two methods. However, there were somehow less dramatic differences in method use among empirical studies published either before 2010 or after 2016. As the use of different methods can help reveal ways of collecting and analyzing data to provide empirical evidence, it would be interesting to learn more about possible development and use of research methods in STEM education in the future as a new empirical research paradigm.

Publication distribution in terms of research methods over the years. (Note: 1 = qualitative, 2 = quantitative, 3 = mixed.)

Corresponding author’s nationality/region and profession Footnote 1

Examining the corresponding author’s nationality/region helps reveal the international diversity in research engagement and scholarly contribution to STEM education. Figure  7 indicates 87 highly cited publications (87%, out of 100 publications) with the corresponding author from the United States, followed by 6 publications (6%) contributed by researchers in the U.K., and the remaining 7 publications with the corresponding author from seven other countries/regions (i.e., one publication for each country/region). The results show some international diversity in terms of the number of country/region represented, but with a clear dominance of research contributions from the West especially the United States. The result echoes what we learned above about the corresponding author’s nationality/region for the top ten most-cited articles (see Table 3 ).

Distribution of corresponding author’s nationality/region of the top 100 articles

Recent reviews of journal publications in IJSTEM suggest a trend of increasing diversity in research contributions from many more different countries/regions (Li, 2022 ; Li & Xiao, 2022 ). We would not be surprised if the list of top 100 most-cited empirical research publications contained more contributions from other countries/regions in the future.

After coding the corresponding author’s profession in these top 100 articles, we found that similar numbers of publications had corresponding authors who were researchers in education (49) and STEM+ (51). This result is consistent with what we learned from the corresponding authors’ profession distribution in recent publications in IJSTEM (Li, 2022 ). The diversity in contributing to STEM education scholarship from researchers with various disciplinary training is evident.

To examine possible trends in the corresponding authors’ profession over time, Fig.  8 shows the distributions of these publications in the two profession categories over the years. It is interesting to note that researchers in education typically served as the corresponding authors for more articles published before 2014: 31 articles by researchers in education and 18 articles by researchers in STEM+ for a total of 49 published before 2014. However, a new trend has emerged since 2014, with many more researchers in STEM+ serving as the corresponding authors for these highly cited research articles: 18 articles by researchers in education and 33 articles by researchers in STEM+ for a total of 51 published since 2014.

Distribution of publications by corresponding author’s profession over the years. (Note: 1 = education, 2 = STEM+)

This trend is consistent with what we learned above about the increased number of these publications in journals specified with STEM discipline(s) since 2014 (see Figs. 3 and 4 ). We see an increasing number of researchers in STEM+ fields contributing and publishing empirical research articles in many journals associated with STEM discipline(s) since 2014, resulted in an increase in citations from professional communities while furthering the development of STEM education scholarship. The result is also consistent with what we learned from the authorship development of publications in IJSTEM over the years (Li & Xiao, 2022 ), an increasing trend of having STEM education scholarship contributions from diverse STEM+ fields.

Publications by school level over the years

With an increasing trend of contributions from researchers in diverse STEM+ fields, the identification of school level can help reveal where these high impact research publications focus on issues in STEM education. The coding results show that the majority (63) of these 100 most-cited articles focused on issues at the postsecondary level, 30 articles on issues at the K-12 school level, and 7 articles in the category of “general.”

Figure  9 presents the distributions of these highly cited publications across these three school categories over the years. It is interesting to note that high impact publications on issues at the postsecondary level outnumbered those in other two categories in almost every of these publishing years. As educational issues in K-12 school level were typically attended to by researchers in education, the increasing number of contributions from researchers in diverse STEM+ fields likely pushed the number of citations on publications that fit their interests more at the postsecondary level. The result is consistent with a growing trend in IJSTEM publications on STEM education at the post-secondary level revealed in a recent review (Li & Xiao, 2022 ).

Distribution of highly cited publications by school level focus and year. (Note: 1 = K-12 school level, 2 = Post-secondary level, 3 = General.)

We also noticed that almost no articles in the category of “general” before 2011 and after 2015 made to the list of top 100 most-cited publications. This result suggests that high impact empirical research in STEM education was conducted more at the school level rather than on issues across the boundary of K-12 school and college. With an increasing number of publications in the “general” category noted in recent review of IJSTEM publications (Li & Xiao, 2022 ), it would be interesting to learn more about cross-school boundary development of STEM education scholarship in the future.

Concluding remarks

This systematic review of high impact empirical studies in STEM education explores the top 100 most-cited research articles from the WoS database as published in journals from 2000 to 2021. These articles were published in a wide range of 45 reputable and well-established journals, typically with a long publishing history. These publications present an overall emphasis more on multi- and interdisciplinary STEM education rather than a single discipline of STEM, with an increasing trend of publishing in journals whose title specified one or more STEM discipline(s). Before 2014, 37% (18 out of 49) of these most-cited articles were published in journals whose title specified with a STEM discipline(s). In contrast, 59% (30 out of 51) articles were published in such journals since 2014, and even more so with 67% of the six articles published in 2018 and 2019. This trend is further elevated with two of those high impact articles recently published in this journal, International Journal of STEM Education . There appears a growing sense of developing disciplinary content consciousness and identity in STEM education.

Consistent with our previous reviews (Li et al., 2019 , 2020a ), the vast majority of these highly cited STEM research publications were contributed by authors from the West, especially the United States where STEM and STEAM education originated. Although there were contributions from eight other countries/regions in these top 100 publications, the diversity of international engagement and contribution was limited. Our results also provide an explanation of what may become “hot” topics among these highly cited articles. In particular, the topic of “culture, social, and gender issues in STEM education” is quite prevalent among those highly cited research publications, followed by the topic area of “STEM learner, learning, and learning environments at post-secondary level.” In comparison, topics related to disciplinary content integration in STEM teaching and learning and STEM teacher training have not yet emerged as “hot” among these highly cited empirical studies. Given that an increasing trend of diversity was noted from a review of recent publications in IJSTEM (Li, 2022 ), we would not be surprised if there will be more high impact research publications contributed by researchers from many other countries/regions on diverse topics in the future.

As STEM education does not have a long history, there will be many challenges and opportunities for new development in STEM education. One important dimension is research method. Among the top 100 most-cited empirical studies, quantitative methods were used as the dominant approach, followed by qualitative methods and then mixed methods. This is not surprising as research in multidisciplinary STEM education may require the use and analysis of data across different disciplines, more frequently in large quantitative data than in other data formats. However, when research questions evolve in the future, it would be interesting to learn more about method development and use in STEM education as a new research paradigm.

We started this review with the intention of gaining insights into the development of STEM education scholarship beyond what we learned about publication growth in STEM education from prior reviews. Indeed, this systematic review provided us with the opportunity to learn about possible trends and gaps in different aspects as discussed above. At the same time, we can learn even more by making connections across these different aspects. One important question in STEM education is to understand the nature of STEM education scholarship and to find ways of developing STEM education scholarship. However, STEM is not a discipline by itself, which suggests possible fundamental differences between STEM education scholarship and scholarship typically defined and classified for a single discipline of STEM. With the increasing participation and contributions from researchers in diverse STEM+ fields as we learned from this review, there is a good possibility that the nature of STEM education scholarship will be collectively formulated with numerous contributions from diverse scholars. Continuing analyses of high impact publications is an important and interesting topic that can yield more insights in the years to come.

Availability of data and materials

The data and materials used and analyzed for the report were obtained through searching the Web of Science database, and related journal information are available directly from these journals’ websites.

Our analysis found that the vast majority (94%) of these top 100 articles had the same researcher to serve as the first author and the corresponding author. There are 10 articles that had more than one corresponding authors, and we chose the first corresponding author as listed in our coding.

Abbreviations

Association for computing machinery  AERA

American Educational Research Association

Cell biology education

Emerging Sources Citation Index

Institute of electrical and electronics engineers

International Journal of STEM Education

Kindergarten-Grade 12

National Research Council 

Social Sciences Citation Index

Science, technology, engineering, and mathematics

Disciplines or fields other than education, including those commonly considered under the STEM umbrella plus some others

Science, technology, engineering, arts, and mathematics

Topic category

Web of Science

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• Published: 03 May 2024

Exploring the effects of AI literacy in teacher learning: an empirical study

• Hua Du 1   na1 ,
• Yanchao Sun 1   na1 ,
• Haozhe Jiang 2 ,
• A. Y. M. Atiquil Islam   ORCID: orcid.org/0000-0002-5430-8057 3 , 4 &
• Xiaoqing Gu 3  

Humanities and Social Sciences Communications volume  11 , Article number:  559 ( 2024 ) Cite this article

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As most practitioners (including teachers) do not know how AI functions and cannot make full use of AI in education, there is an urgent need to investigate teachers’ intentions to learn AI and related determinants so as to promote their AI learning. This study collected survey data from a total of 318 K-12 teachers from sixteen provinces or municipalities in China. A two-step structural equation modeling approach was performed to analyze the data. Our findings show that K-12 teachers’ perceptions of the use of AI for social good and self-efficacy in learning AI are two direct determinants of behavioral intentions to learn AI, while awareness of AI ethics and AI literacy are two indirect ones. AI literacy has a direct impact on perceptions of the use of AI for social good, self-efficacy in learning AI and awareness of AI ethics and has an indirect impact on behavioral intentions to learn AI. This study represents one of the earliest attempts to empirically examine the power of AI literacy and explore the determinants of behavioral intentions to learn AI among K-12 teachers. Our findings can theoretically and practically contribute to the virgin field of K-12 teachers’ AI learning.

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Introduction.

Recently, Artificial Intelligence (AI) has been actively applied in a variety of educational scenarios, which creates numerous promising opportunities for educational innovations (Cheng et al. 2020 ; Hwang et al. 2020 ; Wang et al. 2020 ). Meanwhile, as AI in education is regarded as a “highly technology-dependent and cross-disciplinary field” (Hwang et al. 2020 , p. 1), most practitioners (including teachers) do not know how AI functions and cannot make full use of AI in education (Celik et al. 2022 ; Chounta et al. 2021 ; Chiu and Chai 2020 ; Hwang et al. 2020 ). Actually, developing and implementing effective AI-based learning activities has been considered very challenging in practice (Hwang et al. 2020 ). This highlights the critical importance of enhancing teachers’ professional learning regarding AI. However, as Lindner and Berges ( 2020 ) have claimed, in the field of AI, there is a dearth of investigations focusing on K-12 teacher education. Furthermore, a recent study showed that many K-12 teachers in China were anxious about the complex algorithms or codes of AI and reluctant to learn AI (Li and Gu 2021 ). Therefore, it is necessary to investigate K-12 teachers’ intentions to learn AI and related determinants so as to promote their AI learning, which is the prerequisite for effective teaching with AI.

The Theory of Reasoned Action (TRA) (Fishbein and Ajzen 1975 ), Theory of Planned Behavior (TPB) (Ajzen 1985 , 1991 ) and technology acceptance model (TAM) (Davis 1989 ; Davis et al. 1989 ) is widely used to predict people’s behavioral intentions to use new technologies. However, these models have seldom been applied in the domain of AI education (Nazaretsky et al. 2022 ), and the construct of behavioral intentions to learn AI has scarcely been explored (Chai et al. 2021 ), especially that of teachers. Furthermore, when explaining individuals’ behavioral intentions, traditional theories (i.e., TRA, TPB, TAM) neglect some perspectives (e.g., Akman and Turhan 2017 ; Scherer and Teo 2019 ; Ursavaş et al. 2019 ; Zhao et al. 2021 ), including literacy (Teeroovengadum et al. 2017 ; Nazaretsky et al. 2022 ) and ethical awareness (Akman and Turhan 2017 ). In the context of AI education, the two perspectives are highly valued. Firstly, AI literacy has been considered as an indispensable capability that everybody needs to have in response to the AI-powered world during the twenty-first century (Ng et al. 2021 , 2022 ). It is argued that individuals with higher levels of AI literacy are less likely to be fearful of AI applications (Chai et al. 2021 ). As AI literacy is frequently emphasized in K-12 schools (Chai et al. 2021 ; Ng et al. 2021 , 2022 ), it is meaningful to take AI literacy into account when predicting teachers’ and students’ behavioral intentions to learn or use AI. Secondly, due to its novelty and complexity, the risk of using AI has become a momentous issue, and numerous global institutions have thus called for attention on AI ethics (Borenstein and Howard 2021 ; Lin et al. 2021 ; Qin et al. 2020 ; Richards and Dignum 2019 ; Shih et al. 2021 ). If individuals do not trust in existing AI ethics guidelines, they would not be eager to learn or use AI (Qin et al. 2020 ). Akman and Turhan ( 2017 ) highlighted that exploring the complex relationship between people’s ethical concerns and their behavioral intentions can help understand their decision-making process of learning and using new technologies. As AI ethics is incorporated in K-12 teaching (Lin et al. 2021 ; Shih et al. 2021 ), it is meaningful to examine the often-neglected perspective of ethical awareness when assessing K-12 teachers’ behavioral intentions to learn AI.

Motivated by these gaps, our aim to explore the antecedents of K-12 teachers’ intentions to learn AI. To this end, we propose and validate a model, which integrates AI literacy and awareness of AI ethics with TRA and TPB. Our larger goals include using our findings as a foundation for further investigation in the arena of K-12 teachers’ AI learning, which is still in its infancy, and for future practical design and implementation of professional teacher programs focusing on AI.

Literature review and hypotheses development

Behavioral intentions to learn ai.

TRA, proposed by Fishbein and Ajzen ( 1975 ), posits that the variable of people’s actual behavior can be accurately and immediately determined by their behavioral intentions to perform that behavior. The central factor of TRA, behavioral intentions , is defined as people’s belief about their future willingness to perform a certain action (Fishbein and Ajzen 1975 ). Ajzen ( 1991 ) further explained that behavioral intentions are “indications of how hard people are willing to try, of how much of an effort they are planning to exert, in order to perform the behavior” (p. 181). Nowadays, the factor behavioral intentions is widely used in various fields (e.g., Bin et al. 2020 ; Davis 1989 ; Davis et al. 1989 ; Kyndt et al. 2011 ; LaCaille 2013 ). For instance, Davis ( 1989 ) and Davis et al. ( 1989 ) proposed in the TAM that a user’s behavioral intentions to use a new system directly determine his or her adoption of the system. For another instance, the variable intentions to learn is recognized as “the proximal determinant of participation in learning activities” (Kyndt et al. 2011 , p. 214).

As AI is a brand of new, updated and advanced technologies, many teachers feel that they do not have enough knowledge and skills to use or even teach it well in practice (Celik et al. 2022 ; Chounta et al. 2021 ; Chiu and Chai 2020 ; Hwang et al. 2020 ). To address this issue and equip teachers with required knowledge and skills, it is important to increase teachers’ readiness to learn AI. However, As Chai et al. ( 2021 ) claimed, the factor behavioral intentions to learn , undergirded by TRA, has yet to be fully discussed and thoroughly investigated in AI education. This study operationally conceptualizes behavioral intentions to learn AI as people’s belief about their future willingness to learn AI. The term behavioral intentions to learn AI describes K–12 teachers’ belief about their future willingness to learn what constitutes AI and how to apply AI in their teaching (Chai et al. 2021 ). If teachers have higher behavioral intentions to learn AI, they are more likely to engage in different kinds of professional learning activities involving AI knowledge and skills.

Perceptions of the use of AI for social good (PAIS)

The initial TRA suggests that people’s attitudes towards a certain behavior can significantly predict their related behavioral intentions (Fishbein and Ajzen 1975 ). Specifically, attitudes toward a certain behavior refer to “the degree to which a person has a favorable or unfavorable evaluation or appraisal of the behavior in question” (Azjen 1991 , p. 188). These kinds of attitudes are generally produced when people judge the outcomes of the behavior (Fishbein and Ajzen 1975 ). People can have positive attitudes toward a behavior if they consider its outcomes beneficial (Fishbein and Ajzen 1975 ).

The fair use of AI can cause different outcomes, which may benefit not only the users themselves, but also the society. Recently, there have been growing calls for the application of AI in the domain of social good (Cowls et al. 2021 ; Floridi et al. 2021 ; Tomašev et al. 2020 ). The term AI for social good has also been introduced to describe the phenomenon of “leveraging AI technologies to deliver socially beneficial outcomes” (Cowls et al. 2021 , p. 111). Educational researchers have recommended that the idea of AI for social good should be incorporated into K-12 school curriculum (e.g., Chiu and Chai 2020 ; Lin and Van Brummelen 2021 ). In doing so, teachers and students can have positive attitudes towards AI learning as they realize that using AI can be of great benefit to others and society (Chai et al. 2021 ). Actually, Chai et al. ( 2021 ) claimed that the factor, PAIS , is one important but often-neglected facet of attitudes towards AI learning. Furthermore, considering the impact of attitudes on behavioral intentions (Fishbein and Ajzen 1975 ), it could be assumed that if people can perceive the benefit of the use of AI to the society, they will be extrinsically motivated and have strong behavioral intentions to learn AI. However, to the best of our knowledge, this relationship has never been certified among teachers. We hypothesize:

H1 : K-12 teachers’ PAIS will directly influence their behavioral intentions to learn AI.

Self-efficacy in learning AI

Ajzen ( 1985 , 1991 ) added perceived behavioral control to TRA and proposed the TPB. TPB complements that perceived behavioral control is also an important determinant of behavioral intentions (Ajzen 1985 , 1991 ), which describes “people’s perception of the ease or difficulty of performing the behavior of interest” (Ajzen 1991 , p. 183). Particularly, Ajzen ( 1991 ) pointed out that perceived behavioral control “is most compatible with Bandura’s ( 1977 , 1982 ) concept of perceived self-efficacy” (p. 184). In his social cognitive theory, Bandura ( 1982 ) proposed that self-efficacy “is concerned with judgments of how well one can execute courses of action required to deal with prospective situations” (p. 122).

Previous studies have confirmed the impact of self-efficacy on behavioral intentions to learn (e.g., Evans et al. 2020 ; Lin et al. 2018 ; Kumar et al. 2020 ). For instance, Kumar et al. ( 2020 ) substantiated the direct influence of mobile learning self-efficacy on mobile learning intentions. Based on TPB, this study operationally conceptualizes teachers’ self-efficacy in learning AI as their perception of the ease or difficulty of learning and understanding the basic knowledge or concepts of AI, and further hypothesizes:

H2 : K-12 teachers’ self-efficacy in learning AI will directly influence their behavioral intentions to learn AI.

Several prior studies have indicated the two predictors of behavioral intentions derived from TPB, namely attitudes towards the behavior and perceived behavioral control (i.e., self-efficacy), are significantly correlated (e.g., Coban and Atasoy 2019 ; Kao et al. 2020 ; Yada et al. 2018 ). For instance, it was found that teachers’ attitudes towards inclusive education were significantly influenced by their self-efficacy in the use of inclusive practices (Yada et al. 2018 ). However, as very few studies applies TPB in AI education (Chai et al. 2021 ), the relationship between self-efficacy in learning AI and attitudes towards the use of AI has seldom been examined, especially among teachers. Considering that PAIS is one important facet of attitudes towards the use of AI, we hypothesize:

H3 : K-12 teachers’ self-efficacy in learning AI will directly influence their PAIS.

AI literacy

Beyond the TPB, Fishbein and Ajzen ( 2010 ) also noted that epistemic factors could be the antecedents of attitudinal and control beliefs, which may consequently predict behavioral intentions. Specifically, epistemic factors usually describe people’s conceptions about knowledge or knowing in a certain domain or field (Hofer and Pintrich 1997 ). In AI education, AI literacy is a critical epistemic factor (Chai et al. 2021 ), which encapsulates people’s knowledge and understanding of AI concepts and application (Chai et al. 2021 ; Lin et al. 2021 ; Ng et al. 2021 ). Chai et al. ( 2021 ) defined AI literate as people who “know what constitutes AI and know how to apply AI to different problems” (p. 90). Long and Magerko ( 2020 ) provided a more comprehensive definition of AI literacy: “a set of competencies that enables individuals to critically evaluate AI technologies; communicate and collaborate effectively with AI; and use AI as a tool online, at home, and in the workplace” (p. 598). Although AI episteme is not explicitly emphasized in their definition, Long and Magerko ( 2020 ) noted that literacy is historically associated with people’s access to knowledge and suggested that knowledge of AI is an important component of AI literacy.

Previous studies have verified the impact of literacy on attitudes towards a certain behavior (e.g., Jan 2018 ; Nam and Park 2016 ) and self-efficacy (e.g., Khan and Idris 2019 ; Prior et al. 2016 ). Nevertheless, to the best of our knowledge, such effects have never been explored in research on teachers’ AI learning. Based on Fishbein and Ajzen’s ( 2010 ) notes, we hypothesize:

H4 : K-12 teachers’ AI literacy will directly influence their PAIS.

H5 : K-12 teachers’ AI literacy will directly influence their self-efficacy in learning AI.

Furthermore, considering the hypothetical impact of K-12 teachers’ AI literacy on their PAIS, self-efficacy and behavioral intentions to learn AI, the following indirect effects are formulated:

H6 : K-12 teachers’ AI literacy will indirectly influence their behavioral intentions to learn AI mediated by PAIS.

H7 : K-12 teachers’ AI literacy will indirectly influence their behavioral intentions to learn AI mediated by self-efficacy.

Awareness of AI ethics

When it comes to the appropriate learning and use of AI, ethics is a critical issue that can never be ignored (Borenstein and Howard 2021 ; Lin et al. 2021 ; Qin et al. 2020 ; Richards and Dignum 2019 ; Shih et al. 2021 ). Actually, the uncertainty and risk of AI has aroused wide public concerns (Jobin et al. 2019 ; Qin et al. 2020 ). To respond to these concerns, a large number of ethical principles have been developed to promote the proper understanding and use of AI (Jobin et al. 2019 ; Richards and Dignum 2019 ). Among them, transparency, responsibility, justice and sustainability are four widely-emphasized core AI ethical principles (Lin et al. 2021 ).

The term awareness describes people’s attention, concern (mindful or heedful) and sensitivity regarding a certain issue or action (Sudarmadi et al. 2001 ). Lin et al. ( 2021 ) and Shih et al. ( 2021 ) pointed out that there was a strong positive link between awareness of AI ethics and AI literacy. Actually, according to Long and Magerko’s ( 2020 ) definition, individuals with AI literacy are able to critically evaluate AI. Therefore, they may pay close attention to and be concerned about the risk of AI, and then be aware of the AI ethical issues. Additionally, as the AI literate usually have a good knowledge and understanding of AI (Chai et al. 2021 ; Lin et al. 2021 ; Ng et al. 2021 ), they can also know and understand the potential risk, limitation and uncertainty of AI, and thus realize the ethical aspects of AI. However, the direct impact of awareness of AI ethics on AI literacy has rarely been confirmed among K-12 teachers. We hypothesize:

H8 : K-12 teachers’ AI literacy will directly influence their awareness of AI ethics.

Awareness can play a vital role in attitude formation (Potas et al. 2022 ; Shuhaiber and Mashal 2019 ; Sweldens et al. 2014 ) Footnote 1 . For instance, in the educational technology field, Potas et al. ( 2022 ) found that adolescents’ awareness of technology addiction directly affected their attitudes towards it. Hence, it is reasonable to assume that individuals’ awareness of AI ethics may influence their attitudes towards the use of AI. However, to the best of our knowledge, this effect has never been confirmed. Considering that the factor, PAIS, is one of the most important attitudes towards the use of AI (Chai et al. 2021 ), we hypothesize:

H9 : K-12 teachers’ awareness of AI ethics will directly influence their PAIS.

Furthermore, considering the hypothetical impact of AI literacy on awareness of AI ethics, PAIS and behavioral intentions to learn AI, the following indirect impact is formulated:

H10 : K-12 teachers’ AI literacy will indirectly influence their PAIS mediated by awareness of AI ethics.

H11 : K-12 teachers’ awareness of AI ethics will indirectly influence their behavioral intentions to learn AI mediated by the use of AI for social good.

Based on the aforementioned justifications, the conceptual research model is proposed (see Fig. 1 ).

Note. BI behavioral intentions to learn AI, PAIS perceptions of the use of AI for social good, SE self-efficacy in learning AI, AIL AI literacy, AAIE awareness of AI ethics.

Participants and procedure

A total of 318 K-12 teachers from sixteen provinces or municipalities in China participated in our study voluntarily. Table 1 shows the profile of our participant teachers. As for the recruitment process, we first targeted 300 as our sample size based on Hu and Bentler’s ( 1999 ) recommendations for structural equation modeling analysis. Then, we randomly selected around fifty K-12 partner schools in different regions of China. Next, we randomly selected around eight teachers in each partner school and sent an open recruitment letter as well as an online questionnaire link to them by email. Teachers interested in our study could complete the questionnaire with mobile phones or computers. We finally received 339 responses, and 21 of them were removed due to incompleteness. Ethics approval was obtained before we distributed the questionnaires.

All teachers in our K–12 partner schools used one AI-based product in their teaching named Zhixue, which was developed by iFLYTEK. This AI-based product had three main functions. Firstly, it could record and analyze teachers’ teaching language and behaviors automatically and further help teachers assess and improve their teaching performance. Secondly, it could automatically generate teaching materials and resources and further help teachers complete their lesson plans. Thirdly, it could assist teachers in evaluating students’ coursework. It is also one of the most widely used AI-based products in Chinese K–12 schools currently.

Data collection tool

The scales of the four sub-dimensions (i.e., transparency, responsibility, justice, sustainability) of awareness of AI ethics were adapted from Lin et al. ( 2021 ) and Shih et al. ( 2021 ). Each sub-dimension was assessed by three items. The scales of AI literacy, self-efficacy in learning AI, PAIS and behavioral intention to learn AI were adapted from Chai et al. ( 2021 ), respectively containing four items, four items, five items, and four items. After modifying some statements of the five original scales to suit our study whose participants were teachers, we followed the forward- and back-translation step to develop its Chinese version. During the translation process, language experts were consulted. After that, a pilot test was conducted. Some items were revised according to the results of the exploratory factor analysis. Lastly, we finalized the formal questionnaire. It was seven-point Likert-type, where 1 represents strongly disagree and 7 represents strong agree. The validity and reliability of our formal questionnaire is presented in the Results section.

Data analysis

Following Anderson and Gerbing’s ( 1988 ) guide, a two-step structural equation modeling analysis was performed using AMOS 21. First, we conducted the confirmatory factor analysis (CFA) to validate our measurement model. The validation process is shown in the Results section. Second, we estimated the structural model to test the research hypotheses and detect the direct and indirect effects of AI literacy on other constructs.

Our conceptual research model has four sub-dimensions of awareness of AI ethics, which require to us to firstly validate the first-order factors and the second-order factor of measurement models using CFA before integrating and testing all factors of the conceptual model. Our results of CFA were compared with the suggested fit statistics (Byrne 2010 , p. 80) such as “chi-square ( χ 2 )/degree of freedom (<5), root mean square error of approximation (RMSEA < 0.1), Tucker–Lewis Index (TLI > 0.90) and comparative fit index (CFI > 0.90)”. The four-factor measurement model of transparency, responsibility, justice and sustainability is considered to be the first-order factors consisted of 12 indicators ( χ 2  = 157.223; df = 48; p  = 0.000; RMSEA = 0.085; CFI = 0.965; and TLI = 0.952) and the second-order factor of awareness of AI ethics ( χ 2  = 176.537; df = 50; p  = 0.000; RMSEA = 0.089; CFI = 0.960; and TLI = 0.947) were valid. Then, we interrelated our second-order factor of awareness of AI ethics (AAIE) with other factors such as behavioral intentions to learn AI (BI), perceptions of the use of AI for social good (PAIS), self-efficacy in learning AI (SE), and AI literacy (AIL) as indicated in Fig. 2 , and we excluded three problematic items (SE2, SG5 and BI1) to validate our modified measurement model ( χ 2  = 856.382; df = 285; p  = 0.000; RMSEA = 0.080; CFI = 0.927; and TLI = 0.917) through convergent and discriminant validity.

The figure presents the modified measurement model and its constructs.

Table 2 shows how gradually fit indices confirmed the required values after excluding highly correlated items one at a time.

The significant values of double headed arrows in Fig. 2 contains covariances among the factors, which are smaller than the square root of average variance extracted (AVEs) (Fornell and Larcker, 1981 ). Additionally, the composite reliability (CR > 0.70) and AVE (>0.50) scores for all the dimensions of our revised model exceeded Hair et al.’s ( 2010 ) recommendations due to the significant loadings ranging from 0.73 to 0.95. Above all, the results ensure the convergent and discriminant validity of our model and allow us to test the predicted hypotheses (see Tables 3 and 4 ).

To estimate our conceptual research model, we constructed 11 hypotheses and found all of them are valid based on their path coefficients ( β ), critical ratio (CR > 1.96) and p values. Our conceptual model’s validity was also adequately analyzed based on its fit indices ( χ 2  = 958.646; df = 366; p  = 0.000; RMSEA = 0.071; CFI = 0.925; and TLI = 0.917). Figure 3 confirms that K-12 teachers’ PAIS had a direct influence on their behavioral intentions (BI) to learn AI ( β  = 0.62, p  = 0.000, CR = 9.037). K-12 teachers’ self-efficacy (SE) in learning AI had direct influence on their behavioral intentions to learn AI ( β  = 0.29, p  = 0.000, CR = 4.557) and PAIS ( β  = 0.55, p  = 0.000, CR = 7.625). On the other hand, K-12 teachers’ AI literacy (AIL) had a direct influence on their PAIS ( β  = 0.18, p  = 0.016, CR = 2.406), self-efficacy in learning AI ( β  = 0.77, p  = .000, CR = 12.238) and awareness of AI ethics ( β  = 0.62, p  = 0.000, CR = 10.063). K-12 teachers’ awareness of AI ethics (AAIE) also had a direct influence on their PAIS ( β  = 0.22, p  = 0.000, CR = 4.249). Additionally, we tested the control variables of Gender, School Stage, Age, School District, Education Background, and Major to examine their impacts on the constructs of the structural model. The results showed that among the control variables, only age ( β  = 0.24, p  = 0.000, CR = 5.102) and major ( β  = −0.13, p  = 0.006, CR = −2.733) have a significant impact on AAIE, while school district ( β  = 0.08, p  = 0.029, CR = 2.177) has a significant impact on PAIS. Interestingly, the results with control variables found that our proposed model is valid and robust, and the constructed hypotheses remain statistically significant. To produce a clear path diagram of the structural model, we excluded insignificant control variables from the structural model (see Fig. 3 ).

The figure indicates the causal relationships among the constructs.

Furthermore, through the Sobel test (Sobel 1982 ), we found that K-12 teachers’ AI literacy had an indirect influence on their behavioral intentions to learn AI mediated by PAIS ( χ 2  = 2.779; p  = 0.002) and self-efficacy ( χ 2  = 4.066; p  = 0.000). Teachers’ AI literacy also had an indirect influence on their PAIS mediated by awareness of AI ethics ( χ 2  = 4.210; p  = 0.000). Lastly, K-12 teachers’ awareness of AI ethics had an indirect influence on their behavioral intentions to learn AI mediated by the use of AI for social good ( χ 2  = 3.835; p  = 0.000). We have summarized the accepted hypotheses and the variances of mediating and endogenous variables in Table 5 .

This study proposes an empirically-based model for K-12 teachers to illustrate the power of AI literacy and exhibit the antecedents of behavioral intentions to learn AI. Our model is based on a mixed of theoretical backgrounds, including: (1) Fishbein and Ajzen ( 1975 ) TRA, (2) Ajzen’s ( 1985 , 1991 ) TPB, (3) Fishbein and Ajzen’s ( 2010 ) conceptualization regarding the impact of epistemic factors on attitudinal and control beliefs, (4) Lin et al.’s ( 2021 ) and Shih et al.’s ( 2021 ) conceptualization regarding the link between awareness of AI ethics and AI literacy, and (5) Potas et al.’s ( 2022 ), Shuhaiber and Mashal’s ( 2019 ) and Sweldens et al.’s ( 2014 ) conceptualization regarding the role of awareness played in attitude formation. This study is among the first to integrate AI literacy and awareness of AI ethics with TRA and TPB to predict K-12 teachers’ behavioral intentions to learn AI. Our findings theoretically and practically contribute to the limited knowledge of K-12 teachers’ AI learning as follows.

Firstly, in light of TRA and TPB, which has seldom been used in the domain of AI education (Chai et al. 2021 ), this study confirms that K-12 teachers’ PAIS and self-efficacy in learning AI are the direct determinants of their behavioral intentions to learn AI. Our findings support prior research showing the impact of attitudes (e.g., Gjicali and Lipnevich 2021 ; Norwich and Duncan 1990 ; Zhu et al. 2020 ) and self-efficacy (e.g., Evans et al. 2020 ; Lin et al. 2018 ; Kumar et al. 2020 ) on behavioral intentions to learn in the context of K-12 teachers’ AI learning.

Secondly, for the first time, this study successfully incorporates two important perspectives (i.e., literacy and ethical awareness) into TRA and TPB. Our study is the first to articulate that AI literacy and awareness of AI ethics are two indirect predictors of behavioral intentions to learn AI. On the one hand, our findings are in line with previous studies indicating the impact of literacy on attitudes towards a certain behavior (e.g., Jan 2018 ; Nam and Park 2016 ) and self-efficacy (e.g., Khan and Idris 2019 ; Prior et al. 2016 ) in AI education. On the other hand, our findings confirm Lin et al.’s ( 2021 ) and Shih et al.’s ( 2021 ) standpoint that awareness of AI ethics and AI literacy are positively linked. More importantly, as there is some dispute about the role of awareness played in attitude formation (Sweldens et al. 2014 ), our findings can help better understand the relationships between awareness and attitude by detecting the direct effect of awareness of AI ethics on AI attitudes.

Our findings elucidate that AI literacy has a direct impact on PAIS, self-efficacy in learning AI and awareness of AI ethics and has an indirect impact on behavioral intentions to learn AI. This impact has rarely been examined before among K-12 teachers, especially the indirect relationship between AI literacy and behavioral intentions to learn AI. Our model shows the significance of AI literacy in K-12 teachers’ AI learning. Therefore, throughout K-12 teachers’ professional learning, empowering their AI literacy should be the central element.

Fourthly, our study detects the significant effects of K–12 teachers’ age and major on AAIE, and school district on PAIS. On the one hand, our study supports Wilford and Wakunuma’s ( 2014 ) findings that age and major could impact individuals’ ethical awareness of technologies. As they pointed out, old people and information systems professionals could respectively better understand the ethical issues than young people and other professionals (Wilford and Wakunuma 2014 ). On the other hand, the impact of K–12 teachers’ school district on PAIS has never been reported before. As Tena-Meza et al. ( 2022 ) pointed out, many people in marginalized, low-income and rural communities did not have access to AI technologies. Therefore, urban teachers have more chances to witness and experience the socially beneficial outcomes that AI technologies deliver than rural teachers. This may be the reason why there are significant differences between urban and rural teachers’ PAIS.

This study provides insights into the practical design and implementation of professional teacher programs focusing on AI. To enhance K-12 teachers’ behavioral intentions to learn AI, professional teacher programs should help eliminate their anxiety about the complexity and uncertainty of AI and enhance their self-efficacy in learning AI. Professional teacher programs can also help teachers understand the idea of AI for social good and encourage teachers to make full use of AI to help students or others. In addition, we recommend that professional teacher programs should include AI ethics as part of their core content. Teachers should fully realize and understand AI ethical principles (e.g., transparency, responsibility, justice and sustainability). Besides, professional teacher programs cannot emphasize the significance of AI literacy too much. In response to the upcoming AI era, teachers have to thoroughly know and understand the basic concepts and knowledge of AI through their professional learning. Only in this way can they have a fruitful and successful teaching life in the AI-powered educational world.

This study represents one of the earliest attempts to empirically examine the power of AI literacy and explore the determinants of behavioral intentions to learn AI among K-12 teachers. Our findings demonstrate that K-12 teachers’ AI literacy can directly influence their PAIS and self-efficacy in learning AI, which are the immediate antecedents of behavioral intentions to learn AI. Meanwhile, K-12 teachers’ AI literacy also directly impacts their awareness of AI ethics, which will influence PAIS and further influence behavioral intentions to learn AI. In summary, this study shows that PAIS and self-efficacy in learning AI are two direct determinants of behavioral intentions to learn AI, while awareness of AI ethics and AI literacy are two indirect ones. Notably, AI literacy, as the only exogenous variable in the model, has a direct impact on the other three mediating variables (i.e., PAIS, self-efficacy in learning AI and awareness of AI ethics), and an indirect impact on the endogenous variable (behavioral intentions to learn AI). Most importantly, 75% of the variance in K-12 teachers’ behavioral intentions to learn AI can be accounted for by using the four predictive variables, indicating the strong explanation power of our model.

For the first time, this study successfully incorporates two important perspectives (i.e., literacy and ethical awareness) into TRA and TPB, and thus expands TRA and TPB. Moreover, this study can theoretically contribute to the virgin field of K–12 teachers’ AI learning in the following aspects. Firstly, our study is the first to articulate that K–12 teachers’ AI literacy and awareness of AI ethics are two indirect predictors of their behavioral intentions to learn AI. Secondly, our study detects the direct effect of K–12 teachers’ awareness of AI ethics on AI attitudes. It should be noted that there is some dispute about the impact of awareness on attitudes in the existing literature (Sweldens et al. 2014 ). Thirdly, our model empirically shows the significance of AI literacy in K–12 teachers’ AI learning by revealing the direct impact of AI literacy on PAIS, self-efficacy in learning AI and awareness of AI ethics, and the indirect impact on behavioral intentions to learn AI. This impact has rarely been examined before among K–12 teachers. Fourthly, our study detects the significant effects of K–12 teachers’ age and major on AAIE, and school district on PAIS. These effects have seldom been reported before. Last but not least, our study contributes to the understanding of the antecedents of the construct K–12 teachers’ behavioral intentions to learn AI , which has yet to be discussed and thoroughly investigated in the literature of AI education (Chai et al. 2021 ).

Finally, it is necessary to acknowledge four limitations. First of all, due to limited time and funding, a few regions in China are not covered in our study. Therefore, a certain amount of caution is still needed when generalizing our findings. Meanwhile, considering the huge development gap between coastal and inland provinces in China (Jiang et al. 2021 ; Jiang et al. 2024 ), it should be acknowledged that teachers in different regions may have different AI resources, knowledge and learning opportunities. In the future, regional comparisons are needed to understand the differences of behavioral intentions to learn AI among teachers in different regions. Secondly, this is a quantitative study that relies on teachers’ self-reported data. Nowadays, self-reported data is widely used in educational studies although it may be subjective (Fryer and Dinsmore 2020 ). As prior studies claimed that self-report may be the only viable manner in which to explore individuals’ self-efficacy (Fryer and Dinsmore 2020 ; Zimmerman 2000 ), we only collected teachers’ self-reported data. We recognize that the use of subjective data is one limitation of our study. Future studies can collect data from more sources to increase the robustness of our findings. Thirdly, although our model has shown strong explanation power, it does not mean that the model cannot be further improved. Based on our model, future studies can include more perspectives (e.g., subjective norms) to further enhance its explanation power. Fourthly, three items (i.e., SE2, SG5 and BI1) in the original instrument cannot be validated in our study. According to Wolf et al. ( 2021 ), the validity of the items may vary in different cultural contexts as people in different cultural contexts may understand the meaning of the items differently. Despite this, the actual reasons why these three items cannot be validated needs further investigation.

Data availability

The datasets generated and/or analyzed during this study are not publicly available due to general data protection regulations, but are available from the corresponding authors on reasonable request.

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Acknowledgements

This work is supported by the Education Programs of the National Social Science Fund of China (Project No: BCA210091). HD and YS have equally contributed to this article, and they should be considered as first authors.

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These authors contributed equally: Hua Du, Yanchao Sun.

Authors and Affiliations

Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, Zhejiang, China

Hua Du & Yanchao Sun

College of Education, Zhejiang University, Hangzhou, China

Haozhe Jiang

Department of Education Information Technology, East China Normal University, Shanghai, China

A. Y. M. Atiquil Islam & Xiaoqing Gu

School of Teacher Education, Jiangsu University, Zhenjiang, Jiangsu, China

A. Y. M. Atiquil Islam

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Contributions

Hua Du and Yanchao Sun: investigation, data collection and analysis, writing—original draft, supervision, funding acquisition. Haozhe Jiang and A.Y.M Atiquil Islam: conceptualization, methodology, writing—original draft, writing—reviewing and editing. Xiaoqing Gu: writing—reviewing and editing. All the authors approved the submitted version.

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This research was approved by the Committee for Human Research of East China Normal University (No. 20192006). The procedures used in this study adhere to the tenets of the Declaration of Helsinki.

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Du, H., Sun, Y., Jiang, H. et al. Exploring the effects of AI literacy in teacher learning: an empirical study. Humanit Soc Sci Commun 11 , 559 (2024). https://doi.org/10.1057/s41599-024-03101-6

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A Peek Inside the Brains of ‘Super-Agers’

New research explores why some octogenarians have exceptional memories.

By Dana G. Smith

When it comes to aging, we tend to assume that cognition gets worse as we get older. Our thoughts may slow down or become confused, or we may start to forget things, like the name of our high school English teacher or what we meant to buy at the grocery store.

But that’s not the case for everyone.

For a little over a decade, scientists have been studying a subset of people they call “super-agers.” These individuals are age 80 and up, but they have the memory ability of a person 20 to 30 years younger.

Most research on aging and memory focuses on the other side of the equation — people who develop dementia in their later years. But, “if we’re constantly talking about what’s going wrong in aging, it’s not capturing the full spectrum of what’s happening in the older adult population,” said Emily Rogalski, a professor of neurology at the University of Chicago, who published one of the first studies on super-agers in 2012.

A paper published Monday in the Journal of Neuroscience helps shed light on what’s so special about the brains of super-agers. The biggest takeaway, in combination with a companion study that came out last year on the same group of individuals, is that their brains have less atrophy than their peers’ do.

The research was conducted on 119 octogenarians from Spain: 64 super-agers and 55 older adults with normal memory abilities for their age. The participants completed multiple tests assessing their memory, motor and verbal skills; underwent brain scans and blood draws; and answered questions about their lifestyle and behaviors.

The scientists found that the super-agers had more volume in areas of the brain important for memory, most notably the hippocampus and entorhinal cortex. They also had better preserved connectivity between regions in the front of the brain that are involved in cognition. Both the super-agers and the control group showed minimal signs of Alzheimer’s disease in their brains.

“By having two groups that have low levels of Alzheimer’s markers, but striking cognitive differences and striking differences in their brain, then we’re really speaking to a resistance to age-related decline,” said Dr. Bryan Strange, a professor of clinical neuroscience at the Polytechnic University of Madrid, who led the studies.

These findings are backed up by Dr. Rogalski’s research , initially conducted when she was at Northwestern University, which showed that super-agers’ brains looked more like 50- or 60-year-olds’ brains than their 80-year-old peers. When followed over several years, the super-agers’ brains atrophied at a slower rate than average.

No precise numbers exist on how many super-agers there are among us, but Dr. Rogalski said they’re “relatively rare,” noting that “far less than 10 percent” of the people she sees end up meeting the criteria.

But when you meet a super-ager, you know it, Dr. Strange said. “They are really quite energetic people, you can see. Motivated, on the ball, elderly individuals.”

Experts don’t know how someone becomes a super-ager, though there were a few differences in health and lifestyle behaviors between the two groups in the Spanish study. Most notably, the super-agers had slightly better physical health, both in terms of blood pressure and glucose metabolism, and they performed better on a test of mobility . The super-agers didn’t report doing more exercise at their current age than the typical older adults, but they were more active in middle age. They also reported better mental health .

But overall, Dr. Strange said, there were a lot of similarities between the super-agers and the regular agers. “There are a lot of things that are not particularly striking about them,” he said. And, he added, “we see some surprising omissions, things that you would expect to be associated with super-agers that weren’t really there.” For example, there were no differences between the groups in terms of their diets, the amount of sleep they got, their professional backgrounds or their alcohol and tobacco use.

The behaviors of some of the Chicago super-agers were similarly a surprise. Some exercised regularly, but some never had; some stuck to a Mediterranean diet, others subsisted off TV dinners; and a few of them still smoked cigarettes. However, one consistency among the group was that they tended to have strong social relationships , Dr. Rogalski said.

“In an ideal world, you’d find out that, like, all the super-agers, you know, ate six tomatoes every day and that was the key,” said Tessa Harrison, an assistant project scientist at the University of California, Berkeley, who collaborated with Dr. Rogalski on the first Chicago super-ager study.

Instead, Dr. Harrison continued, super-agers probably have “some sort of lucky predisposition or some resistance mechanism in the brain that’s on the molecular level that we don’t understand yet,” possibly related to their genes.

While there isn’t a recipe for becoming a super-ager, scientists do know that, in general , eating healthily, staying physically active, getting enough sleep and maintaining social connections are important for healthy brain aging.

Dana G. Smith is a Times reporter covering personal health, particularly aging and brain health. More about Dana G. Smith

A Guide to Aging Well

Looking to grow old gracefully we can help..

The “car key conversation,” when it’s time for an aging driver to hit the brakes, can be painful for families to navigate . Experts say there are ways to have it with empathy and care.

Calorie restriction and intermittent fasting both increase longevity in animals, aging experts say. Here’s what that means for you .

Researchers are investigating how our biology changes as we grow older — and whether there are ways to stop it .

You need more than strength to age well — you also need power. Here’s how to measure how much power you have  and here’s how to increase yours .

Ignore the hyperbaric chambers and infrared light: These are the evidence-backed secrets to aging well .

Your body’s need for fuel shifts as you get older. Your eating habits should shift , too.

People who think positively about getting older often live longer, healthier lives. These tips can help you reconsider your perspective .

• Open access
• Published: 09 May 2024

Diabetes, life course and childhood socioeconomic conditions: an empirical assessment for Mexico

• Marina Gonzalez-Samano 1 &
• Hector J. Villarreal 1  

BMC Public Health volume  24 , Article number:  1274 ( 2024 ) Cite this article

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Demographic and epidemiological dynamics characterized by lower fertility rates and longer life expectancy, as well as higher prevalence of non-communicable diseases such as diabetes, represent important challenges for policy makers around the World. We investigate the risk factors that influence the diagnosis of diabetes in the Mexican population aged 50 years and over, including childhood poverty.

This work employs a probabilistic regression model with information from the Mexican Health and Aging Study (MHAS) of 2012 and 2018. Our results are consistent with the existing literature and should raise strong concerns. The findings suggest that risk factors that favor the diagnosis of diabetes in adulthood are: age, family antecedents of diabetes, obesity, and socioeconomic conditions during both adulthood and childhood.

Conclusions

Poverty conditions before the age 10, with inter-temporal poverty implications, are associated with a higher probability of being diagnosed with diabetes when older and pose extraordinary policy challenges.

Peer Review reports

One of the major public health concerns worldwide is the negative consequences that the demographic (with its epidemiological) transition could bring. This demographic transition is driven by increasing levels of life expectancy (caused by technological innovation and scientific breakthroughs in many cases) and decreasing fertility rates. While during the 20th century, the main health concerns were related to infectious and parasitic diseases, at the present time, non-communicable diseases (NCDs), such as diabetes, constitute a harsh burden in terms of economic and social impact. NCDs most commonly affect the health of adults and the elderly. The economic and social costs associated with NCDs increase sharply with age. These patterns have implications for economic growth, poverty-reduction efforts and social welfare [ 1 ].

Mexico’s demographic trends are reflecting a significant shift over the past decades, much like those observed globally. In 1950, the fertility rate stood at 6.7 children per woman, and the proportion of the population aged 60 or over was about 2%. Since the 1970s, there has been a considerable decrease in fertility rates; by 2017, it had dropped to 2.2 children per woman [ 2 ]. Even more pressing, according to CONAPO Mexico had a total fertility rate of 1.91 during 2023 [ 3 ]. Alongside the declining fertility, the aging population is becoming a more prominent feature in Mexico’s demographic profile. In 2017, individuals aged 60 and over constituted around 10% of the population. Forecasts for 2050 project that this figure will more than double, with those 60 and over representing 25% of the total population. These trends suggest substantial changes in Mexico’s population structure, with implications for policy-making in areas such as healthcare, pensions, and workforce development [ 2 ].

Regarding NCDs, in 2017 13% of the Mexican adult population suffered from diabetes, which is twice the Organisation for Economic Cooperation and Development (OECD) average and it is also the highest rate among its members. Some of the risk factors associated with this disease are being overweight or obese, unhealthy diets and sedentary lifestyles. In 2017 72.5% of the Mexican population was overweight or obese [ 4 ] and the country had the highest OECD rate of hospital admissions for diabetes. During the period of 2012 to 2017, the number of hospital admissions for amputations related to this condition, increased by more than 10%, which suggests a deterioration in quality and control of diabetes treatments [ 4 ]. Moreover, it is estimated that diabetes prevalence will continue with its upward trend; forecasts anticipate that in 2030 there will be around 17.2 million people in Mexico with this condition [ 5 ].

Despite the increasing proportion of older people, most of the research regarding the effects of socioeconomic conditions on health focuses on economically active populations. Those which do consider older people, do not investigate length factors such as childhood conditions [ 6 , 7 ]. In this sense, the Social Determinants of Health (SDH) throughout the Life Course approach provide a framework to ponder and direct the design of public policies on population aging and health [ 8 , 9 ]. They focus on well-being and the quality of life of populations from a multi-factorial perspective [ 10 , 11 , 12 ].

In this study, we explore the impact of childhood and adulthood conditions and other demographic and health aspects on diabetes among older people. The literature has proposed several mechanisms through which the mentioned drivers could operate. In general, these approaches imply that satisfactory socioeconomic outcomes for adults may relatively atone for poor socioeconomic conditions in early childhood [ 13 , 14 , 15 , 16 ].

Poverty conditions during the first years of life have critical implications, and yet children are twice as likely to live in poverty as adults [ 16 , 17 ]. On the other hand, poverty is known to be closely linked to NCDs such as diabetes. According to [ 13 ], NCDs are expected to obstruct poverty reduction efforts in low and middle-income countries (LMICs) by increasing costs associated with health care. Moreover, the costs resulting from NCDs such as diabetes could deplete household incomes rapidly and impulse millions of people into poverty [ 16 ].

The United Nations Children’s Fund (UNICEF) has highlighted the consequences of what it describes as the “invisible epidemic”: non-communicable diseases. NCDs are the leading cause of death worldwide, accounting for 71% or 41 million of the annual deaths globally. The majority (85%) of NCD deaths among people under 70 years of age occur in low and middle-income countries [ 17 ].

According the World Health Organization (WHO), SDH are non-medical factors that influence health outcomes, such as the circumstances in which people are born, grow, work, live, and age, and the broader set of forces and systems that shape the conditions of daily life Footnote 1 .

These forces include economic policies and systems, development agendas, social norms and policies, and political systems [ 11 , 18 ]. In this regard, SDH have an important influence on health inequities in countries of all income levels. Health and disease follow a social gradient, that is, the lower the socioeconomic status, a lesser health is expected [ 11 , 18 ].

On the other hand, the Life Course perspective distinguishes the opportunity to inhibit and control illnesses at key phases of life from preconception to pregnancy, infancy, childhood, adolescence, and through adulthood. This does not follow the health model where an individual is healthy until disease occurs, the trajectory is determined earlier in life. Evidence suggests that age related mortality and morbidity can be anticipated in early life with factors such as maternal diet [ 19 ] and body composition, low childhood intelligence, and negative childhood experiences acting as antecedents of late-life diseases [ 13 ].

The consequential diversity in the capacities and health needs of older people is not accidental. They are rooted in events throughout the life course and SDH that can often be modified, hence opening intervention opportunities. This framework is central in the proposed “Healthy Aging”. According to WHO [ 20 ], Healthy Aging is “the process of developing and maintaining the functional ability that enables well-being in older age”.

In this way, the Life Course and SDH approaches allow to better distinguish how social differences in health are perpetuated and propagated, and how they can be diminished or assuaged through generations. Several research efforts suggest that age related mortality and morbidity can be predicted in early life with aspects such as maternal nutrition, low childhood intelligence, difficult childhood experiences acting as antecedents of late-life diseases [ 13 ]. The Life Course acknowledges the contribution of earlier life conditions on adult health outcomes [ 15 , 21 ]. In addition, SDH have an important influence on inequality and, therefore, on people’s well-being and quality of life [ 22 ]. Trends in health literacy across life are also influenced by various SDH such as income, educational level, gender and ethnicity [ 23 ].

Finally, though the research that links early life conditions and health outcomes in adulthood is scarce in low and middle-income countries, our study aims to address the gaps in knowledge regarding the impact of childhood socioeconomic conditions on long-term health outcomes, including the prevalence of non-communicable diseases in LMICs. We specifically focus on the incidence of diabetes in Mexico. Advocating for early-life targeted interventions, we highlight the critical need to address the root causes of NCDs to reduce their impact on the most vulnerable groups. Utilizing data from the Mexican Health and Aging Study (MHAS), which provides comprehensive health, demographic, and socioeconomic information on individuals aged 50 and older, as well as details on their childhoods (before the age of 10) and family health backgrounds [ 24 ], our research emphasizes the importance of developing targeted interventions on early life course stages.

Health, childhood and adulthood conditions

Multiple studies highlight that childhood experiences can influence patterns of disease, aging, and mortality later in life [ 10 , 11 , 16 , 20 , 25 ]. The conditions in health and its social determinants accumulate over the life course. This process initiates with pregnancy and early childhood, continues throughout school years and the transition to working life and later in retirement. The main priority should be for countries to ensure a good start in life during childhood. This requires at least adequate social and health protection for women, plus affordable good early childhood education and care systems for infants [ 11 ].

However, demonstrating links between childhood health conditions and adult development and health is complex. Frequently, researchers do not have the data necessary to distinguish the health effects of changes in living standards or environmental conditions with respect to childhood illnesses [ 26 ]. A study conducted in Sweden, concluded that reduced early exposure to diseases is related to increases in life expectancy. Additionally, research with data from two surveys of Latin America countries found associations between early life conditions and disabilities later in life. In this sense, the study suggests that older people who were born and raised in times of poor nutrition and a higher risk of exposure to infectious diseases, were more likely to have some disability. In a survey in Puerto Rico, it was observed that the probability of being disabled was greater than 64% for people who grew up in poor conditions than for those who grew up in good conditions. Another survey that considered seven urban centers in Latin America found that the probability of disability was 43% higher for those with disadvantaged backgrounds, than for those with favorable ones [ 26 ].

Recent studies have focused on childhood circumstances to explain later life outcomes [ 12 , 27 , 28 , 29 , 30 , 31 ]. These research findings have shown the importance of considering socioeconomic aspects during childhood, including child poverty from a multidimensional perspective [ 12 ], as a determinant of health status of adults and health disparities. When disadvantaged as children, irreversible effects on health show-up frequently. One clear example is the association of socioeconomic aspects during childhood with type 2 diabetes and obesity in adulthood [ 32 , 33 ].

The future development of children is linked to present socioeconomic levels and social mobility in adulthood [ 27 ]. Some studies [ 28 , 34 , 35 ] indicate that the effects of childhood exposure to lower socioeconomic status or conditions of poverty on health in old age may persist independently of upward social mobility in adulthood. Hence, children who grow up in poverty are more likely to present health problems during adulthood, while those who did not grow up in poverty have a higher probability of remaining healthy.

Another important consideration regards developmental mismatches [ 36 ]. Their article emphasizes how developmental and evolutionary mismatches impact the risk of diseases like diabetes. There could be a disparity between the early life environment and the one encountered in adulthood, turning adaptations that were once beneficial into risk factors for non-communicable diseases. High-calorie diets and sedentary lifestyles could trigger diabetes prevalence.

If these connections between early life and health in old age can be established firmly, it is expected that aging people in low and middle-income countries have another disadvantage regarding elders in developed countries, including a higher risk of developing health problems in old age and frequently multiple NCDs [ 26 ]. Under this context, the effective management of NCDs such as diabetes is crucial, and childhood living standards would be a variable to ponder [ 26 , 37 ]. Work related to the Life Course approach has emphasized the importance of considering socioeconomic aspects during childhood, including poverty [ 12 ] as a determinant of adult health status and its disparities [ 28 , 29 , 30 , 31 ].

Data and methods

Data source.

The Mexican Health and Aging Study (MHAS) is a national longitudinal survey of adults aged 50 years and over in Mexico. The baseline survey has national, urban, and rural representation of adults born in 1951 or earlier. It was conducted in 2001 with follow-up interviews in 2003, 2012, 2015, 2018 and 2021 [ 38 ]. New samples of adults were added in 2012 and 2018 to refresh the panel. The survey includes information on health measures (self-reports of conditions and functional status), background (education and childhood living conditions), family demographics, and economic measures. The MHAS (Mexican Health and Aging Study) is partly sponsored by the National Institutes of Health/National Institute on Aging (grant number NIH R01AG018016) in the United States and the Instituto Nacional de Estadística y Geografía (INEGI) in Mexico. Data files and documentation are public use and available at www.MHASweb.org .

In this research, the analysis was based on data from the survey conducted in 2018 (it was the most recent when the project started, later the 2021 survey became available). The study focused exclusively on participants who were aged 50 or older at the time of the 2018 survey. To minimize response bias, the study included only observations from direct interviewees, excluding proxy respondents, and particularly those who completed the section of the questionnaire pertaining to “Childhood Characteristics before the age of 10 years” Footnote 2 . Furthermore, to expand the sample size, individuals who first joined the survey during the 2012 cycle were identified, utilizing data from both the 2012 and 2018 surveys [ 39 ]. After locating the same individuals in both datasets, responses related to childhood conditions from the 2012 survey were extracted and integrated into the 2018 dataset. Biases in the samples were not found. This approach resulted in a total sample size of 8,082 observations.

In addition, we selected a suite of predictor variables to provide a comprehensive examination of the demographic, socioeconomic, and health-related characteristics within our sample (Table 1 ). The cohort consists of 8,082 participants with males exhibiting a marginally higher mean age (58.3 years) compared to females (56.7 years). In terms of educational achievement, males attained a slightly higher level of schooling, averaging 8.3 years, as opposed to 7.6 years for females.

Regarding the spatial distribution of the study population reveals that 1,717 individuals reside in areas with 2,500 inhabitants or fewer, indicating a rural setting, while the majority, 6,365 individuals, are found in regions with more than 2,500 inhabitants, suggesting an urban setting. Among the subjects, a significant number of males (23%) are located in the former, rural settings, which is higher than their female counterparts (19.7%). The data on living arrangements indicate notable gender differences, with 86% of males cohabiting with partners against 68.8% of females. The state of being single-a term here encompassing a spectrum of prior marital experiences but currently not cohabiting-is observed in 31.2% of females and 14% of males. The socioeconomic dimension is gauged using “proxy variables” such as the absence of poverty in adulthood and presence of childhood poverty, both of which are evenly represented across genders. Health-related self-reporting data reveals that females have a higher incidence of diagnosed diabetes (24.4%) compared to males (20.1%), and a larger percentage of females (26.6%) manage their diabetes with insulin. The propensity for medication use to control diabetes is high among both sexes, though more pronounced in females (91.5%) relative to males (85.3%). Additionally, obesity rates, determined by a Body Mass Index Footnote 3 of 30 or greater, are substantially elevated in females (34.8%) versus males (24.6%). Furthermore, a familial history of diabetes is slightly more prevalent in females, affecting 32.6% with diabetic mothers and 20% with diabetic fathers.

There is a serious concern about self-reporting medical conditions, to what extent this information is reliable. For [ 40 , 41 ] the validity and high accuracy of self-reported diagnosis of diabetes mellitus has been confirmed by previous research, and previous studies using WHO data have also used this question to evaluate diabetes mellitus [ 42 , 43 ].

For the survey employed in this paper, [ 44 ] confirm a correspondence between self-reported and objective measures. Nonetheless, [ 45 ] warn about true prevalence and this kind of reporting. In addition, the implications of relying on diagnosed diabetes, rather than total diabetes prevalence, include the potential under-representation of the condition’s true prevalence due to undiagnosed cases. Since the study’s analysis is based on self-reported data from the Mexican Health and Aging Study, it might not capture those individuals who are unaware of their condition [ 45 ]. The existence of statistical biases could be a potential limitation in the analysis.

Equally or even more troublesome is the problem of recalling conditions during childhood. While some factors (depression among others) can produce limited recalling [ 46 ], specific conditions are well recalled, if not their details and timing [ 47 ].

Regarding the age distribution, the sample is mostly concentrated in three groups: 67.6% for individuals between 50 and 59 years of age, followed by 29.6% for those between 60 and 69 years of age, and 2.5% for those between 70 and 79 years of age. On average, the educational level for women is 7.6 years of schooling while for men it is 8.2 years, which suggests an incomplete level of secondary education for both. On the other hand, from the total number of women in the sample (4,368), 24% of them indicated the presence of diabetes, and 20% of men in the sample (3,714) reported this condition. In addition, around 68% of women with diabetes reported being overweight or obese, for men this percentage was 69%. Meanwhile, 71.4% women with diabetes reported parental history of diabetes, for men this percentage was 68%. The next subsections describe the construction and identification of the key dependent and independent variables.

Dependent variable

The dependent variable is binary, which refers to the individual’s diagnosis of diabetes. This variable was taken from section C of the basic questionnaire of the MHAS 2018. The question is as follows: Has a doctor or medical professional ever told you that you have diabetes? If the answer is “yes” it was assigned a value of 1 and if the answer was “no”, a 0. The absence of answers was left empty, non-imputed. Regarding the individuals who reported being diagnosed with diabetes, 94.2% were taking medication or using insulin injections or pumps, and / or following a special diet to manage diabetes, without statistical differences when interchanging the samples.

Independent variables

For the explanatory variables of the model, sociodemographic, socioeconomic (“proxy” Footnote 4 of poverty in childhood and non-poverty in old age) Footnote 5 , and geographical variables were considered, as well as other variables related the parents of the interviewees. Given the difficulty of constructing a robust variable that reflects respondents’ income, internet access was considered as a proxy variable that would allow to ascertain the poverty status of the individual in old age. Several tests were performed for robustness Footnote 6 .

Internet access in Mexico is more common among relative well-off Mexicans than it was among the poorest sector of the population. Thus, according to [ 49 , 50 ], 7 out of 10 individuals from the highest income segment were internet users, while for the lowest income deciles, this was only 2 out of 10. Furthermore, a low level of schooling was related to internet access opportunities. Therefore, people who only received primary education were 4 times less likely to use the internet in Mexico.

Additionally, for the variable of poverty during childhood, a proxy was considered which corresponds to the answer of the question “Before you were 10 years old, did your home have an indoor toilet?” Footnote 7 , United Nations Children’s Fund (UNICEF) collaborators [ 12 ], pointed out that the severe deprivation of sanitation facilities has critical long-term effects on various aspects of an infant. In this regard, UNICEF highlights the crucial importance of eradicating severe sanitation deprivation as a method to eradicate absolute child poverty, emphasizing that sanitation facilities should be a priority for children.

Statistical analysis

Linear Probability Models (LPM) define the probability:

They assume (require) that: i) \(Pr(Y=1 \mid X)\) is an increasing function in X for \(\beta _{0}>0\) , and ii) \(0 \le Pr(Y=1 \mid X) \le 1 \forall X\) .

This implies a cumulative distribution function that guarantees that for any value of the parameters of X , probabilities are well-defined, with values in the interval [0, 1].

The dependent variable to be explained is binary (diabetes diagnosis is 1 if the person has been diagnosed with diabetes and 0 for the person who has not been diagnosed with diabetes). Hence, a special class of regression models (with limited dependent variable), is considered. There are two probability models with these characteristics frequently used: the Logit model, and the Probit model. In relation to this, [ 48 ] points out that, theoretically, both models are very similar. A potential advantage of Probit models is they could feed other related inquiries. For example, when testing selection via Inverse Mill’s Ratios.

The Probit model is expressed as:

In the Probit model with multiple regressors, \(X_1,X_2,\ldots ,X_k\) , \(\phi (.)\) the cumulative standard normal distribution function is \(\phi (Z)=P(X\le z)\) , \(Z\sim N(0,1)\) .

Therefore, in ( 2 ) \(P(Y=1 \mid X_1,X_2,\ldots ,X_k )\) means the probability that an event occurs given the values of other explanatory variables, where Z is distributed as a standard normal \(Z\sim N(0,1)\) . While a series of tests could be performed in the model, two are critical for this investigation: the linearity between the independent variables and the underlying latent variable, and the normality of errors.

In ( 2 ), the coefficient \(\beta _{1}\) represents the change in z associated to a unit of change in \(X_1\) . It is then observed that, although the effect of z on a change is linear, the link between z and the dependent variable Y is not linear since \(\phi\) is a non-linear function of X . Therefore, the coefficients of X do not have a simple interpretation. In that sense, marginal effects must be calculated. Considering that in the linear regression model, the slope coefficient measures the change in the average value of the returned variable, due to a unit of change in the value of the regressor, maintaining the other variables constant. In these models, the slope coefficient directly measures the change in the probability of an event occurring, as a result of a unit change in the value of the regressor, holding all other variables constant, a discussion can be found at [ 51 ]. The \(\beta\) parameters are frequently estimated by maximum likelihood. The likelihood function is the joint probability distribution of the data treated as a function of the unknown coefficients Footnote 8 .

The maximum likelihood function is the conditional density of \(Y_1,\ldots ,Y_k\) given \(X_1,\ldots ,X_k\) as a function of the unknown parameters \(\beta\) . Thus, the Maximum Likelihood Estimation (MLE) is the value of the parameters \(\beta\) that maximizes the maximum likelihood function. Hence, the MLE is the value of \(\beta\) that best describes the distribution of the data. In this regard and in large samples, the MLE is consistent, normally distributed, and efficient (it has the lowest variance among all the estimators). The \(\beta\) is solved by numerical methods. The resulting \({\hat{\beta }}\) is consistent, normally distributed, and asymptotically efficient.

A Probit model is proposed as follows. The dependent variable is diagnosed diabetes in adulthood correlated to several independent variables: sex, age, marital status, locality size, a dummy variable (to identify observations sourced from the 2012 survey wave, which is focused on childhood-related questions), obesity condition (Body Mass Index \(\ge\) 30), family history of diabetes, childhood poverty, no poverty in adulthood and the interaction of childhood poverty and no poverty in adulthood.

The variables should have analogous probability distributions and behave mutually independent. If errors violate the assumptions, the estimated values would be biased and inconsistent. Therefore, estimated values will also be shown with the Linear Probability Model.

In this type of model, \(y_i\) is a latent dependent variable that takes values of 1 if the person has been diagnosed with diabetes, that is, if individual i has a certain characteristic or quality and 0 otherwise; X is a set of explanatory variables that are assumed to be strictly exogenous, which implies that \(Cov\left[ x_i,\varepsilon _j\right] =0\ \forall\) the i individuals. In addition, the error term \(\varepsilon\) is assumed to be i . i . d . In this way, the probability of an event occurring given a set of explanatory variables is obtained:

In ( 1 ) G is a function that strictly takes values between 0 and 1, \(0<G(z)<1\) , for all real numbers z . As noted at the beginning of this section, in the Probit model, G represents a standardized normal cumulative distribution function given by:

Finally, to know the effects of the changes in the explanatory variables on the probability of the event occurring, a partial derivative can show that:

The term \(g\left( z\right)\) corresponds to a probability density function. Since the Probit model \(G\left( .\right)\) is a strictly positive cumulative distribution function, \(g\left( z\right) >0\ \forall \ z\) , the sign of the partial effect is the same as that of \(\beta _j\) .

This section reviews the factors associated with the probability of being diagnosed with diabetes for men and women and discusses their significance. Table  2 summarizes the main results of the Probit model.

Sociodemographic

Marginal effects on the dependent variable show that the age of individuals is highly significant with a positive correlation. This suggests that age is a factor leading to a higher probability (1%) of obtaining a diagnosis of diabetes, which could imply that as the person ages, the likelihood of developing diabetes increases. This result is consistent with studies conducted on the age-related decline in mitochondrial function, which in turn contributes to insulin resistance in old age. These conditions may foster the development of glucose intolerance and type 2 diabetes [ 53 , 54 ].

In addition, the outcomes indicate that women have an associated probability increase of 4% of suffering from this disease compared to men Footnote 9 . Regarding the differences by marital status, women and men living in a couple have a higher probability of being diagnosed with diabetes. In a study for Mexico using MHAS 2012, [ 45 ] found that being a woman and being married are significantly associated with a higher likelihood of self-reported diabetes Footnote 10 .

On the other hand, the results by size of locality suggest that individuals residing in urban areas have a non-negligible higher probability of suffering from diabetes compared to people living in rural locations. This is in line with the phenomenon of “nutritional transition”, which initially occurred in high-income countries and later in low-income countries, first in urban areas and then in rural areas [ 56 , 57 ]. For Mexico, [ 58 ] despite the prevalence of diabetes presents heterogeneous patterns, this condition is strongly greater in urban areas compared with rural areas.

Health and lifestyle

The results suggest a significant positive effect on the probability of diagnosis of diabetes for the individuals in the sample when the father and/or mother have this condition. In the case of a mother with diabetes, the associated probability of diabetes is 13%, while for a father with diabetes, it is 12%. Additionally, obesity is an important risk factor in the diagnosis of diabetes, the linked marginal effect of this comorbidity in the diagnosis of diabetes is 4%. In this regard, no significant differences were found by sex or locality size Footnote 11 .

Socioeconomic

The findings indicate a lower probability that individuals are diagnosed with diabetes if during adulthood they are not poor (-5%). On the other hand, from the interaction of the variables poverty in childhood and non-poverty in old age, a considerable positive effect is observed. This suggests that when the individual was poor in childhood, despite no longer poor in adulthood, the probability associated with the diagnosis of diabetes is positive and significant. Thus, it is possible that conditions of poverty in childhood influence the development of this disease later in life Footnote 12 . While this is a correlation, the fact that an interaction of socioeconomic characteristics has bigger linear effect than a key biological characteristic (obesity) is non trivial, and reinforces the importance of life course analysis.

Social mobility, defined as the change in an individual’s socioeconomic status relative to their parents or over their lifetime, is a crucial metric for assessing equal opportunity-a measure of whether people have the same chances to achieve success regardless of their initial socioeconomic position. Our study aligns with the broader evidence [ 65 , 66 ], suggesting that those from disadvantaged backgrounds often face significant barriers to socioeconomic advancement Footnote 13 .

A compelling finding of this paper, refers how poverty conditions during childhood remain an important risk factor associated with the greater probability of being diagnosed with diabetes during adulthood in Mexico. Despite these circumstances do not determine the diagnosis of diabetes in older adults, they have a strong correlation with the ailment. On the other hand, even when individuals have not experienced poverty during childhood, but it occurs during adulthood, the probability associated with the diagnosis of diabetes increases. Not surprisingly, the probability of being diagnosed with diabetes scales when the person was poor in both stages. These effects are persistent for men and women, although for women the associated probability was higher than for men. Likewise, there is a positive and high correlation of the parents’ history of diabetes and the obesity condition on the probability of developing this disease. Biological aspects could be present, but also modifiable factors, with the generational transmission of elements related to lifestyle (eating habits and physical activity). Similarly, people who live with a partner have a higher associated probability of being diagnosed with diabetes. The literature suggests that this is due to the tendency of individuals to select spouses based on the preference for similar phenotype characteristics and the convergence of their behaviors and lifestyle. Moreover, these issues have been exacerbated by urbanization processes and by the “food transition” Footnote 14 that has made processed and ultra-processed products more and more accessible. Such products are characterized by being high in fat, salt, and sugar. Regarding the effect of the size of the locality on the probability of being diagnosed with diabetes, the results show differences for people residing in rural and urban areas. In urban localities, the associated probability is higher compared to rural ones. Likewise, aging is an important factor that affects the probability of suffering from diabetes: as the individual ages, the probability of developing this disease increases.

In terms of the analysis and empirical strategy used, the findings show valuable relationships. Aligned with efforts to improve the accuracy and reliability of health data by combining biomarkers and objective measurements with self-reported data [ 70 ], biomarkers in the survey were employed. These biomarkers were used for diabetes (the dependent variable) and obesity condition (as one of the independent variables) in the model of Results  section. The results are consistent with the previous findings (See Appendix ).

There is ample space for additional work and get over the limitations of this work. For example, being MHAS a longitudinal survey, an econometric model can be developed in order to explore (test) causal relationships among the extensive set of variables. Also self-reporting could present different types of biases. While the use of biomarkers was an important robustness test, calculating bounds and checking selection biases would be valuable. Moreover, the survey also captures information related to social protection variables and social programs transfers, which could be useful for testing policies.

Given the interconnection of childhood conditions and the importance of these in the development of adult capacities and their success in their future life, they should be considered within the design and formulation of public policies and programs. The policies should focus and prioritize objectives of reducing the inequality gaps and pre-existing poverty in the country. Adopting measures to reduce inequalities in the social sphere is essential to protect future generations. In this sense, it is important to act on the Social Determinants of Health throughout the course of life in a broader social and economic context. Acting on the SDH would improve prospects for health and generate considerable social benefits that would allow people to achieve their capabilities and reduce the intergenerational perpetuation of inequalities. Thus, the SDH together with the Life Course approach, provide a sensible framework to identify risk clusters that can be broken in periods of effective interventions (e.g. childhood), as well as to improve the design of public policies on population aging and health, from a perspective focused on the well-being and quality of life of the Mexican population.

In this way, and to face the demographic transition and the diabetes epidemic in Mexico, comprehensive public policies that consider interventions from childhood will be required to reduce inequality and poverty. For some years now, the WHO has emphasized the importance and role of the inclusion of long-term care policies and programs focused on older adults. The forecasts in case of untimely acting indicate a significant negative effect on the social, economic and health structures for the coming years.

Finally, despite the increase of older population, much of the research on the effects of socioeconomic conditions on health is concentrated in economically active populations, and those ignore older people, and pay restricted attention to long term factors such as childhood conditions. The results presented in this document contribute to studies on population aging and public health. Evidence is found with respect to health determinants in a demographic group that is growing rapidly and not sufficiently considered.

Availability of data and materials

Data files and documentation are public use and available at www.MHASweb.org . Data and code used during the current study are available from the corresponding author on reasonable request.

Social Determinants of Health. Retrieved from https://www.who.int/health-topics/social-determinants-of-health#tab=tab_1 . Accessed on January 22, 2024.

Given the survey design, people responding the childhood questionnaire are new participants.

A Body Mass Index (BMI) was constructed considering the variables of height and weight reported in the MHAS 2018 survey (C6: “What is your current weight in kilograms?”, C67: “What is your height without shoes in meters?”). For adults, the World Health Organization (WHO) defines overweight as a BMI of 25 or higher, and obesity as a BMI of 30 or higher. BMI was calculated by dividing a person’s weight in kilograms by the square of their height in meters (kilograms/m 2 ).This information is available at: https://www.who.int/es/news-room/fact-sheets/detail/obesity-and-overweight , accessed on January 10, 2024.

In this context, the term “proxy”, was employed to describe variables that serve as stand-ins for factors that are not directly observable within our dataset. As noted by [ 48 ].

Numerous variables that could reflect household income were tested, but since they were self-reported and not part of the survey’s core, there is a large number of missing values.

We thank one referee for her suggestions regarding education years.

This question is found in section J.18 of the basic questionnaire and corresponds to the question “Does this home have ... internet?” If the person answers “yes”, that means that they have internet service and were assigned a value of 1, and 0 if the person does not have this service.

There is an interesting possibility of comparing the linear marginal effects with direct estimations from a Logit model (risk differences), [ 52 ]. We thank a referee for pointing this out.

This is consistent with what was stated in Aging in Mexico: The Most Vulnerable Adults of the MHAS Newsletter: May 20-2, 2020, which indicates that women are more likely to report diabetes than men. Retrieved from http://www.enasem.org/images/ENASEM-20-2-Aging_In_Mexico_AdutosMasVulnerables_2020.pdf . Accessed on February 10, 2024.

Furthermore, Danish researchers found a connection between the Body Mass Index of one spouse and the other spouse’s risk of developing type 2 diabetes. According to this study, spouses tend to be similar in terms of body weight, as people often tend to marry someone similar to themselves and share dietary and exercise habits when living together [ 55 ].

It has long been known that type 2 diabetes is, in part, hereditary. Family studies have revealed that first-degree relatives of people with type 2 diabetes are approximately 3 times more likely to develop the disease than people without a positive family history of the disease [ 59 , 60 , 61 ]. Likewise, in a study for Mexico, [ 62 ] point out that obesity and a history of type 2 diabetes in parents and genes play an important role in the development of type 2 diabetes. Furthermore, [ 63 ], points out that the frequency of diabetes mellitus also varies between different races and ethnicities.

This is consistent with the research by [ 64 ] who find that the conditions in which the person lived at the age of 10 affect health in old age.

According to [ 67 ] in a regional analysis on the degree of social mobility in Mexico, it indicates that social mobility is higher than the national average in the North and Central North regions, similar to the national average in the Central region, and lower than the average in the South region. In particular, it notes that children of poor parents made above-average progress if they grew up in the northern region, and less than average progress if they grew up in the southern region.

The country’s food environment has been transformed; it is becoming easier to access unhealthy products. In this sense, for the last 40 years, important changes have been observed in the Mexican diet, mainly from fresh and unprocessed foods to processed and ultra-processed products with a high content of sugar, salt, and fat. Marrón-Ponce et al. [ 68 ], point out that in 2016 around 23.1% of the energy in the Mexican population’s diet came from ultra-processed products, even though the WHO recommendations suggest that at most, this percentage should present between 5 and 10% of total energy per day. In addition, Mexico is the worldwide largest consumer of sugary beverages; its consumption represents approximately 10% of the total daily energy intake in adults and children and constitutes 70% of the total added sugar in the diet [ 69 ].

The study incorporates biomarkers to evaluate health conditions related to diabetes and obesity. Glycosylated hemoglobin results are employed as an indicator of diabetes [ 71 ], with a value equal to or exceeding 6.5% signifying a positive diagnosis (coded as “1”), while values below this threshold are coded as “0”, indicating the absence of the condition. Concurrently, Body Mass Index (BMI) is calculated from weight and height measurements to determine obesity, with a BMI of 30 or more classified as obese. These biomarkers provide quantifiable and reliable means of assessing the presence of these two critical health issues within the study’s population.

Abbreviations

Mexican Health and Aging Study

non-communicable diseases

Social Determinants of Health

World Health Organization

Organisation for Economic Cooperation and Development

National Institute of Statistics and Geography

United Nations Children’s Fund

Low and middle-income countries

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MGS (Marina Gonzalez-Samano) contributed to the design of the study and the final document with guidance and conceptual insights from HJV (Hector J. Villarreal). MGS and HJV carried out the search, analysed the documents and wrote the first draft of the article. All authors were involved in the conception of the research, revisions and editing of the article. All authors read and approved the final manuscript.

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For robustness testing a model specification was employed where self-reported diabetes and obesity measures are substituted with biomarkers obtained from the MHAS 2012. Table 3 summarizes the main results of the Probit model.

The analytical results from Table  2 (Model 1), and those derived from the utilization of biomarkers in Table 3 (Model 2) exhibit a considerable likeness, especially in the context of diabetes and obesity indicators. Notably, there is a significant reduction in the sample size when biomarkers Footnote 15 are introduced, which might account for the increased standard errors observed in Table 3. Consequently, certain variables such as: being “woman”, “living with a couple” and “residing in an urban locality”, have lost statistical significance in the biomarker analysis. Despite these differences, the general conclusions derived from this specification remain consistent with those presented in Model 1 (Table  2 ). Moreover, the linear effect of the interaction effect of poverty in childhood with no poverty in adulthood is bigger with the biomarker specification. Nonetheless, the larger confidence intervals need to be considered.

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Gonzalez-Samano, M., Villarreal, H. Diabetes, life course and childhood socioeconomic conditions: an empirical assessment for Mexico . BMC Public Health 24 , 1274 (2024). https://doi.org/10.1186/s12889-024-18767-5

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REVIEW article

Multi-dimensional impact of covid-19 on active mobility in urban china: a scoping review of empirical knowledge.

• 1 Department of Sociology, Tianjin University of Technology, Tianjin, China
• 2 Department of Sociology, Nankai University, Tianjin, China
• 3 Beijing Federation of Trade Unions Cadre College, Beijing, China

Active mobility, such as cycling and walking, is assuming a growing significance in the daily lives of urban residents in China due to its positive impact on health and the environment. The impact of the COVID-19 epidemic has elicited significant changes in behaviors, perceptions, and intellectual viewpoints in this domain, potentially altering residents’ physical activities in the long-term. This scoping review seeks to delve into the multi-dimensional influence of the epidemic on active mobility in urban China. A thorough investigation of English and Chinese studies up to January 2024 was conducted, drawing from articles in Web of Science and the Chinese National Knowledge Infrastructure. Only empirical studies providing knowledge into this subject were selected in the review, which comprised 20 studies in total. This review indicates that the influence of COVID-19 on active urban mobility in China has exhibited contradictory outcomes in terms of behavior. Besides, the experiences during the epidemic have significantly shaped citizens’ attitudes and understanding of active mobility. The repercussions of the epidemic and the ensuing restrictions exacerbate the existing challenges faced by women, particularly those who are married, the older adult, and individuals with low incomes. The results exhibit both resemblances and idiosyncrasies when juxtaposed with prior research conducted in different nations. This analysis also offers valuable insights for improving active mobility across individual, organizational, and socio-political realms. The current state of empirical understanding in this field underscores the need for further research endeavors employing diverse methodological approaches and increased emphasis on the transformations anticipated in the post-epidemic era.

1 Introduction

Active mobility, or saying active transportation, is the transport of people or goods, through non-motorized means, based on human physical activity ( 1 ), and its best-known forms are walking and cycling. Because of its healthy and environmental benefits, it is an important component of sustainable mobilities ( 2 , 3 ). In recent years, active mobility has attracted increasing concern all over the world, especially in cities suffering from increasingly serious transportation, health, and environmental issues ( 4 , 5 ). Urban China is facing an obesity and chronic disease epidemic along with traffic congestion and air pollution due to rapid urbanization, population aging, and unhealthy lifestyles ( 6 ). That’s why promoting active mobility is becoming a common governmental response in pursuing sustainable urban development. This is reflected in the implementation of a series of measures represented by urban greenways, pedestrian streets, bicycle-friendly streets, and various kinds of shared bicycles in many cities ( 7 – 9 ).

The outbreak of COVID-19 and its associated constraints have had notable repercussions on the everyday travel patterns of affected citizens ( 10 ). This transformation is particularly pronounced within urban areas of China. Leveraging insights gained from the SARS outbreak of 2002–2003, the Chinese authorities have enforced rigorous protocols to limit the movement of their populace in light of COVID-19. Chinese residents witnessed some of the most pronounced and significant alterations in their daily travel routines amidst the epidemic ( 11 – 13 ). A series of empirical studies have discovered the negative relationship between COVID-19, encompassing anti-epidemic policies and measures, and the physical activity levels as well as physical health of Chinese citizens ( 14 – 16 ). Studies in the realm of daily active mobility have revealed that the epidemic has a direct negative impact on the quantity, regularity, distance, and inclination of individuals to engage in active transportation ( 17 – 19 ).

Underlying diminished levels of active mobility, however, some other studies have unveiled a multitude of circumstances. One such instance pertains to the adoption of alternative means of transportation, notably bike sharing, by individuals faced with disruptions in public transit and taxi services during the epidemic. This shift has opened a window of opportunity to encourage more individuals to embrace active travel and to promote greater awareness of the health advantages associated with active transportation ( 20 ). Besides, the epidemic has prompted numerous individuals to contemplate the health and environmental advantages of increasing their walking or cycling activities. This alteration in perspective may influence their attitudes toward everyday commuting practices ( 21 , 22 ). Additionally, the impact of the epidemic on active travel, which entails daily mobility undertaken by diverse individuals for multifaceted reasons, is intricately shaped by sociodemographic variables such as gender, age, income, and other related factors ( 23 , 24 ). The epidemic might also bring new variations in travel inequalities in affected cities ( 25 , 26 ). To the best of our understanding, however, a comprehensive evaluation of existing empirical knowledge about how and in what dimensions that COVID-19 impacted Chinese citizens’ daily active mobility is currently lacking in scholarly literature.

The void requiring attention arises from the enduring impact of COVID-19, which represents an extraordinary lived encounter for all individuals impacted over an extended duration. This shared collective experience is poised to exert a profound influence on the resurgence and advancement of urban active mobility in the aftermath of the epidemic ( 27 ). Consequently, a comprehensive evaluation of the diverse impacts of COVID-19 on active mobility in Chinese cities is vital for advancing sustainable and healthy urban mobility practices in the aftermath of the health crisis. To address this research need, we conducted a scoping review to explore the existing empirical evidence regarding the multi-dimensional impact of COVID-19 on urban active mobility in China.

The primary objective of this paper is to examine the empirical knowledge available on the impact of COVID-19 on urban active transportation in China. To this end, we have utilized the scoping review approach as our methodology. A scoping review is considered an apt tool when the studies being reviewed are heterogeneous, making it impossible to conduct a systematic review or meta-analysis on specific research questions ( 28 ). Through the scoping review approach, we aim to provide a comprehensive overview of the research scope, results, and gaps in existing studies ( 29 , 30 ). Specifically, the study examines the following research questions: (1) What behavioral, attitudinal, intellectual, and situational changes have been discovered and analyzed in relation to Chinese urban active transportation? (2) How were these changes discovered and analyzed? (3) How did they emerge and evolve? (4) What insights do current findings provide for our understanding of Chinese active transportation in the post-epidemic era?

2.1 Data sources and search strategy

Our inquiry included an extensive review of studies pertaining to the effects of COVID-19 and associated anti-epidemic measures on active transportation in China. Our research scope encompassed empirical studies published in both English and Chinese. On January 29, 2024, we conducted our search for studies published prior to this date.

We conducted a thorough investigation of English language research by utilizing Web of Science (WoS), a comprehensive database that incorporates a variety of indexes such as Science Citation Index Expanded, Social Sciences Citation Index, Arts & Humanities Citation Index, Emerging Sources Citation Index journals and so on. It includes all the concerned disciplines of this review, like public health, transportation, urban studies, environmental science and so on. Our search was constrained to peer-reviewed empirical studies that were relevant to our topic. To limit our search further, we used an array of keywords, including COVID-19 and terms related to active transportation, such as active mobilities, active transportation, active travel, cycling, bike, bicycle, walking, pedestrian, and location (China). We specifically focused on walking and cycling, as these two modes are basic constitutions of active transportation in current China. To ensure an exhaustive search, we did not restrict the categories of studied participants.

Our investigation consisted of conducting a thorough exploration within the Chinese National Knowledge Infrastructure (CNKI) database for empirical studies written in the Chinese language. CNKI is the most widely used and the most authoritative academic database in China ( 31 ). Our initial search of relevant keywords translated into Chinese yielded much fewer studies than those available in the English language. Consequently, we adopted a more inclusive approach to our Chinese retrieval to enhance our search results. We incorporated all relevant studies from a search of COVID-19 (“ Yiqing ” / “ Xinguan ”) accompanied by bike/bicycle/cycling (“ Zixingche ”), or walking/pedestrians (“ Buxing ”). Since most of the articles in CNKI were location-specific to China, we refrained from utilizing any location-based keywords. The search and review procedures show in Figure 1 .

Figure 1 . Search and review procedures.

2.2 Inclusion and exclusion criteria

This review incorporated empirical studies examining active mobilities in China following the emergence of COVID-19. These modifications included changes in Chinese residents’ travel behavior, perceptions, and awareness, as well as modifications in transport-linked infrastructures and built environments, both movable and immovable. Qualitative, quantitative, or hybrid empirical data collection and analysis methods were employed in these studies, with study subjects consisting of Chinese residents with some sort of visible or hidden travel alterations. Given the considerable economic and socio-political disparities between mainland China and Taiwan, Hong Kong, and Macao, this review excluded research carried out in these areas. Master’s theses, doctoral dissertations, and conference papers were also omitted from the review because most of them were not peer-reviewed, and it was challenging to assess their research quality.

2.3 Review process

Initially, the authors conducted a thorough review of the titles, keywords, and abstracts of the articles that were selected. Only studies that met the criteria for inclusion received further screening. The authors then undertook a full-text review of the selected studies to establish the final list of included and excluded studies, a process that was conducted independently by two of the authors. If any disagreement between the authors arose, a discussion ensued until a consensus was reached. A detailed depiction of the review procedures was provided in Figure 1 . The search yielded a total of 606 research articles in English, of which 580 studies were excluded based on the inclusion criteria after the title, abstract, and keywords screening. A further 10 studies were rejected after the full-text review as they did not meet the required eligibility criteria. Therefore, 16 studies in English qualified for inclusion after the full-text review. Additionally, a total of 237 articles in Chinese were initially identified, of which 228 studies were excluded after the title, abstract, and keywords screening. After a full-text review, four studies in Chinese met the eligibility criteria for inclusion while five studies were excluded. Thus, a total of 20 studies (16 in English and four in Chinese) were included in this scoping review.

We conducted a coding analysis on the selected articles. Studied travel mode (cycling and/or walking), research subject population, study location, research method, covered pandemic period (for the case city/cities), socioeconomic indicators, and active transportation-relevant behavior, attitude, knowledge, and material/facilities changes were coded. Three authors conducted independent coding, with any discrepancies being thoroughly discussed and resolved through consensus after consultation.

2.4 Limitation of method

The research relies on data sources from SSCI and CNKI. While prior studies have validated the relative representativeness and reliability of these databases for conducting systematic reviews in each language ( 31 , 32 ), it is acknowledged that they do not encompass all studies pertinent to the field of active mobility. Consequently, this research overlooks studies published in journals not indexed by these databases. Furthermore, the study’s emphasis on credibility is restricted to published peer-reviewed research, excluding other forms such as academic dissertations, working papers, and research reports. These methodological constraints represent areas for improvement in future research endeavors.

3.1 Characteristics of existing research

Of all the 20 studies, six studies focused on walking only ( 18 , 21 , 33 – 36 ), eight focused on cycling only ( 17 , 22 , 24 , 37 – 41 ), and another six studies included both cycling and walking ( 23 , 42 – 46 ). Bike-sharing is a concerned research topic that five studies focused only on it ( 17 , 22 , 24 , 37 , 41 ), three of which were about dock-less bike-sharing (DBS) ( 17 , 22 , 37 ), one about station-based bike-sharing (SBBS) ( 24 ), and one about both kinds ( 41 ). 13 studies focused on general urban residents ( 17 , 22 – 24 , 37 – 45 ), while another of seven studied specific research populations, like youths ( 33 , 35 , 36 , 46 ), middle-aged and older adult people ( 18 , 21 ) and disabled people ( 34 ). The earliest study was published in October 2020.

A survey by questionnaire was the most used method as 13 studies adopted this method ( 18 , 22 , 23 , 33 – 36 , 38 , 40 , 43 – 46 ), with the sample size range from 127 ( 22 ) to 10,082 ( 35 ). Secondary analysis of existing data is another commonly used research method based on data collected from relevant companies ( n  = 4) ( 17 , 24 , 37 , 41 ). There was one study that collected and analyzed citizens’ daily steps via a smartphone application ( 18 ). Only one research study utilized a qualitative research approach through the execution of in-person interviews ( 21 ).

Regarding the location of the studies, of the 20 studies, three were national surveys that did not focus on a certain case city ( 23 , 35 , 46 ), while the other 17 were conducted each in a single city ( Figure 2 ). There were 11 studies that were conducted in cities in the eastern region with well-developed economies, such as Beijing ( n  = 4) ( 36 , 38 , 43 , 45 ), Shanghai ( n  = 1) ( 37 ), Nanjing ( n  = 3) ( 22 , 24 , 41 ), Zhongshan ( n  = 1) ( 44 ), Guangzhou ( n  = 1) ( 39 ) and Shenzhen ( n  = 1) ( 40 ). Wuhan, the city in the middle region that endured the initial outbreak of the epidemic in China, attracted two studies’ attention ( 17 , 42 ). Changsha and Taiyuan are another two middle region cities that each got one study’s attention ( 18 , 34 ). There was one study conducted in Kunming, a city located in the western region ( 21 ). Another one study was implemented in Dalian ( 33 ), which was in the northeast region.

Figure 2 . Locations of the studied case cities. This figure is made by the authors based on the open-source map provided by National Database for Geographical Names of China. The division of four regions (west, east, middle, northeast) was based on the classification standard promulgated by the government ( https://www.stats.gov.cn/zt_18555/zthd/sjtjr/dejtjkfr/tjkp/202302/t20230216_1909741.htm , accessed 2 April 2024).

In terms of covered epidemic phases of the empirical studies, due to different cities experiencing different periods and durations of the epidemic, we did not adopt a unified time division. Relatively, we divide the epidemic experience of each into three basic phases: pre-pandemic (before the outbreak of COVID-19, generally before the end of 2019), outbreak phase (the initial and most severe stage of the epidemic in the case city, which was also the most strict stage of epidemic control; for national studies, from January 20th to March 10th, 2020 as defined by State Council of China), and the recovery phase (the “new normal” period after the outbreak). In fact, there should be a post-epidemic era that regards the period after the actual cancelation of epidemic control in China on December 7th, 2022, marked by the release of the “New Ten Rules” of COVID-19. But as all current studies were empirically conducted before this phase, we mainly divided all studies into the above three categories. As changes caused by the epidemic were the key concern, 17 out of 20 studies covered more than one period to conduct certain comparisons. Of the 17 studies, seven covered both pre-epidemic and outbreak phases ( 18 , 22 – 24 , 36 , 38 , 41 ), three covered outbreak and recovery phases ( 42 , 44 , 45 ), two compared pre-epidemic and recovery phases ( 34 , 37 ), and five covered all pre-epidemic, outbreak, and recovery phases ( 17 , 35 , 39 , 40 , 46 ).

Of the 20 studies, 16 involved findings and explorations on unequal active mobilities among different populations in the context of the epidemic. The two most concerned demographic factors were gender ( n  = 13) ( 18 , 21 – 24 , 33 , 35 , 36 , 40 , 41 , 44 – 46 ) and age ( n  = 9) ( 18 , 21 – 24 , 40 , 41 , 44 , 45 ), followed by income ( n  = 3) ( 23 , 40 , 42 ), and one study focused on visually impaired people ( 34 ). Regarding the language, four were published in Chinese. The other 16 were in English. The characteristics of the reviewed empirical studies were shown in Table 1 .

Table 1 . Characteristics of empirical studies about the impact of COVID-19 on urban active transportation in China.

3.2 Studies on behavioral changes in active mobility

The scholarly investigation revealed distinct behavioral shifts in active transportation within China across various phases of the epidemic, as evidenced by all 20 studies. These scholarly inquiries unveiled a paradoxical phenomenon in the realm of Chinese urban active mobility induced by the COVID-19 outbreak. While a reduction in the overall volume, distance, and frequency of active travel among citizens was commonly observed due to apprehensions surrounding the epidemic and implementation of lockdown measures, there was a simultaneous elevation in the utilization and prominence of active mobility during this crisis period.

Of all the 20 studies eight analyzed the changes in walking behaviors in the outbreak period. At the national level, citizens saying they never walked during the epidemic increased by 15.5% ( 23 ). Daily steps of Changsha residents aged≥40 years dropped significantly, as their mean daily steps dropped from 8,097 to 5,440 and the prevalence of low daily steps increased from 3 to 18.5% ( 18 ). In Zhongshan, residents mainly travel short distances during the outbreak, with walking being the main mode of transportation ( 44 ). For the youths, significant decreases were also generally observed in their daily walking behaviors ( 35 , 46 ). For university students in Beijing, the epidemic produced a 24.36% reduction in total weekly minutes of walking ( 36 ). Nevertheless, for university students in Dalian, no correlation was found between the impact of the COVID-19 pandemic and walking behavior ( 33 ), which is contrary to the finding in Beijing.

Regarding cycling behavioral change in the outbreak period, seven studies brought us interesting findings under the travel decline preface. On the one hand, it is obvious that cycling decrease significantly because of the epidemic. For instance, in Nanjing, the number of station-based bike-sharing (SBBS) users have been hit hard by the outbreak ( 22 , 24 ), and SBBS trips in this city fell by 72% ( 41 ). SBBS trips at all stations declined, and most stations experienced a drop of around 70%. The trips of another kind of sharing bicycle – dock-less bike-sharing (DBS) – in this city even fell by 82% ( 41 ). In Wuhan, a significant decrease in bike-sharing trips also emerged during the outbreak ( 17 ).

On the other hand, studies also pointed out the positive functions of cycling and its relative increase in the proportion of daily travel during the epidemic. During the epidemic, especially in the outbreak phase, cycling played a key role in replacing bus and subway ( 40 ). Before the epidemic, the complementary role of cycling was more important than its substitutive role, but during the epidemic, the substitution role of cycling for public transit was enhanced. For instance, the daily trips of Nanjing Metro fell by 95%, which is much more than the drop in bike-sharing trips in this city. The average travel distance of SBBS increased by 32%, and the average travel distance of DBS increased by 16% ( 41 ). Furthermore, while the overall travel duration and travel distance of DBS users decreased after the epidemic, the trip frequency of them increased as the travel duration increased ( 22 ). And no evident decline in the proportion of commuters in this city was found (from 36.6 to 34.8%) ( 24 ). In Beijing, the proportion of bike-sharing increased by 0.89% ( 38 ). The user base of DBS in Shanghai increased during the epidemic ( 37 ).

The function of cycling also changed during the epidemic. Before the outbreak, the main function of cycling is commuting. It could be proved by obvious morning and evening cycling peaks between 7 am–9 am and 5 pm–7 pm in cities before the epidemic in Wuhan, which, however, were replaced by a unimodal structure with cycling activities concentrated between 11 am and 2 pm during the outbreak phase ( 17 ). This change indicated that the purposes of cycling were more diverse, and cycling played a more important role in residents’ daily life and travel. This can also be supported by the relatively low decrease in independent cycling trips compared with cycling and public transit cooperation trips in Nanjing, which reflected that the major role of cycling changed from cooperating with public transport for commuting to independently providing more categories of mobility services ( 41 ). Cycling was more adopted for shopping, scenery, health care, and other travel demands ( 17 , 24 , 41 ).

Regarding active transportation changes in the recovery phase, two studies explored walking changes in this stage: one study pointed out that the proportion of walking in all travel within a district in Beijing was 19% ( 43 ); another one study pointed out that for the visually impaired people in Taiyuan, despite the absolute amount of walking decreased, the proportion of walking in their daily travel increased from 35.6% (pre-epidemic) to 42.8% (during the epidemic). There were five studies analyzing cycling changes in this period. In general, due to the advantage of cycling in allowing for social distance and being more useful for relatively long-distance travel, the recovery of it is significant and quick. For instance, in Wuhan, the recovery rate of passenger volume reached 39.80% by June 2020, while the recovery rate of sharing bicycles came to 104.31%, which returned to the level before the epidemic. And by October 2020, the recovery rate of total passenger volume reached 71.40%, while the recovery rate of sharing bicycles became 260.64% year on year ( 17 ). In Shanghai, by June 2020, the number of rush-hours sharing bicycle users increased by 2%, and the number of rush-hour rides increased by 4%, compared with those in October 2019 ( 37 ). In Guangzhou, the share of trips by owned bicycles in all urban daily travels in 2021 is 11.7%, higher than that (9.18%) in 2019 before the outbreak, and for sharing bicycles, the change is from 3.43% in 2019 to 6.01% in 2021 ( 39 ). The substantiation function of cycling for relatively short distance travel was also experienced by many citizens in Shenzhen ( 40 ), which may bring in more cycling in the post-epidemic era. Therefore, it seems that urban cycling might enjoy an increase in the recovery phase and even in the post-epidemic era. Nevertheless, this prediction may not be applicable to SBBS. As in Nanjing, the monthly usage of SBBS continued to decline and did not return to pre-epidemic volumes, and scholars inferred that people may become less willing to use shared modes of transportation in the post-pandemic era ( 24 ). The brief summary of behavioral changes in active mobility during the epidemic shows in Table 2 .

Table 2 . Summary of behavioral changes in active mobility during the epidemic.

3.3 Studies on changes in attitudinal and knowledge-based dimensions of active mobility

Exploring the shifts in public perception and understanding of active transportation in light of the COVID-19 epidemic can yield valuable insights into the latent and enduring impacts of the outbreak. Among the 20 studies reviewed, half specifically examined this aspect. While an inherent negative correlation exists between pandemics and travel patterns, it is noteworthy that enhanced awareness of the epidemic tended to foster a more favorable attitude toward walking and cycling among residents.

As COVID-19 is mainly transmitted through the air, most citizens believed that keeping a safe distance from others is an effective means to avoid infection ( 22 ), which made active travel modes more favored during the epidemic period. At the national level, walking and cycling were perceived as low probability of contracting COVID-19 by Chinese citizens in the outbreak and recovery phases ( 23 ). In Wuhan, after the outbreak, citizens tended to make various preparations for non-motorized travel, and with the upgrading of epidemic control measures, citizens’ walking and riding maximum willingness distances were both continuously increasing ( 42 ). In Shanghai, after experiencing the outbreak of the epidemic, the enhanced user base improves people’s confidence about the long survival of the bike-sharing industry, and citizens showed increasing positive value of bike-sharing as a complement to public transit in the upcoming post-epidemic era ( 37 ). But the epidemic also brought certain negative experience in the active travel of some groups of people, which affected their attitude regarding active travel. For instance, for visually impaired people in Tainyuan, the unwillingness to travel using blind track increased from 26% (pre-epidemic) to 41.8% (during the epidemic). This may partially because that visually impaired people need assistance or support from others while walking crossing the road, and the lockdown during COVID-19 caused their dissatisfaction ( 34 ).

It is significant that the relationship between attitude, knowledge, and behavior regarding active travel was moderated by the severity of the epidemic. For instance, during the outbreak phase in Beijing, the more citizens understood epidemic prevention policies, the greater the likelihood of choosing to walk, but this kind of positive relation appeared to weaken in the recovery phase ( 45 ). Similarly, for the university students in Dalian, although there was a generally positive attitude among them, the impact of the epidemic was negatively correlated with the walking attitude, which had a significant impact on students’ walking behavior on weekends ( 33 ). In Beijing, among all kinds of sharing mobilities, bike sharing was perceived as the second safe but the second to last comfort by citizens, and when the perceived epidemic severity was relatively low, citizens’ preference for cycling significantly increased ( 38 ).

For some citizens, especially the older adult, the role of active mobility as an activity by itself was more important during the epidemic. After the outbreak, because all public spaces were closed, walking almost offered the only opportunity for many older citizens to interact with non-family members ( 21 ). Active mobility served as an important, if not the only, part of social life for many older adult citizens, so it’s understandable that they show resistance to travel restrictions.

3.4 Inequalities in active mobility during the epidemic

A series of studies explored transportation-related social equity among different demographic groups due to the COVID-19 epidemic ( Figure 3 ). Gender is the most concerned dimension of existing studies in exploring the inequalities in active transportation during the epidemic. Out of 20 studies, 13 found changes in this dimension. In general, the outbreak accelerated more decline in the daily active travel of females compared with males, which was reflected in various behavioral changes. In Nanjing, before the outbreak, the SBBS trips of females and males are roughly equal, but the female trip proportion fell from 47 to 43% after the outbreak ( 41 ), and there was also a significant decline in the proportion of female SBBS commuters ( 24 ). In Changsha, females were associated with a higher prevalence of frequent low daily steps, which were more pronounced during the epidemic period ( 18 ). In both Beijing and Zhongshan, after the outbreak, males were more willing to conduct cycling compared with females ( 44 , 45 ).

Figure 3 . Analyzed inequality dimensions in active mobility in urban China during the epidemic.

The reason that females were more likely to be affected by the epidemic might not be because they were more afraid of the epidemic, as gender had no significant impact on their attitudes regarding active travel ( 33 ). It might be because during the epidemic more females had to stay at home to care for the children who studied at home as schools were closed ( 41 ). This was partially proved from the reverse side as that for those young and unmarried females, like university students, the epidemic caused less decrease in physical activity compared with males ( 36 ), although the young females even conducted more moderate−/vigorous-intensity housework during the epidemic ( 35 ). Besides, as females generally had relatively low participation in cycling and used it more as the complement of public transport ( 22 ), the huge decline in public transportation travel during the epidemic may also contribute to the low active mobility of females.

Age is another important dimension regarding the active travel inequality issue, as nine out of 20 studies explored this dimension. The relationship between age and daily active travel is a little complex. For the youths, as schools were all closed during the epidemic, it is understandable that their daily travel dropped significantly and heavily ( 24 , 41 ). For the older adult, however, the impact of the epidemic was multi-dimensional. On the one hand, for the health concern, the absolute daily active travel of the older adult decreased, which is consistent with the overall situation of the whole society. For instance, in Changsha, the daily steps of Changsha elder residents dropped significantly during the outbreak ( 18 ). Nevertheless, some studies discovered that the aged depended more on active mobility and conducted relatively more active travel compared with the young citizens. For instance, during the outbreak in Nanjing, the proportion of SBBS trips of older adult users increased, and their trip amount was least affected by the pandemic ( 41 ). Similarly, in Zhongshan, older citizens were more likely to choose walking for travel after the outbreak ( 44 ). In Kunming, most of the older citizens routinely performed outdoor physical activities in the first month of the outbreak, whilst only much fewer young people went out regularly during that time ( 21 ). However, there are other studies with different findings. For instance, for the users of DBS in Nanjing, the variable of age had a significantly negative effect on cycling after the epidemic, as the older adult were more sensitive to the possibility of infection risk ( 22 ).

This phenomenon reflected the dual dilemma of the older adult in modern digital society as a partial result of the digital divide ( 47 ). First, as the older adult were generally less technology-savvy and were less engaged in Cyber-society, the fill of their various material and emotional needs relied more on offline face-to-face interactions. So, during the epidemic, unlike the young people who could conduct shop, work, socialize, and entertainment at home, the older adult had to go out. Especially those elder people who did not live with their children. Second, the epidemic greatly promoted the “digital reform” of the Chinese urban transportation system, and a smartphone is a necessary device for using various transportation modes, especially public transit ( 21 ). Therefore, for most elder residents, it was very inconvenient and even unfeasible to use other travel modes besides walking and cycling.

There were three studies exploring the differences in active travel between groups of different economic statuses. There was a significant positive correlation between monthly income and the choice of active mobility during the epidemic. For instance, a study in Beijing found that after the outbreak, the higher the monthly income of travelers, the stronger their willingness to conduct cycling for daily travel ( 45 ). In Zhongshan, during the outbreak period, citizens with higher economic status showed more willingness to choose walking rather than electric bicycles ( 44 ). Similarly, in Shenzhen, those citizens who tend to ride bicycles for commuting during the epidemic have less income than average ( 40 ). It might be because the upper-middle income groups could work from home and enjoy higher economic resilience to stop working, so they mainly conducted short daily travel during the epidemic ( 42 ). This is partly proved by the fact that coming into the recovery phase, the relationship between economic conditions and the conduct of active mobility turned negative ( 44 ).

Besides the above three dimensions, other dimensions like education and residential area were also concerned by some studies. For instance, a research investigation revealed that individuals possessing a university degree or higher exhibit a reduced incidence of consistently low daily step counts in the period preceding an epidemic, in contrast to those with a high school education or less. However, this disparity was not observed during the epidemic phase ( 18 ). Cycling in the urban area of Nanjing was less impacted by COVID-19, and the suburban area was relatively more impacted ( 41 ). In the recovery phase in Wuhan, however, more cycling neighborhoods emerged in suburban areas, and the percentage of ridership in denser areas tends to decrease, which showed a trend toward decentralization and localization ( 33 ).

4 Discussion and applications to further research

The onset and persistence of the COVID-19 outbreak have had a profound impact on the daily routines and engagements of Chinese citizens. Among the various aspects affected, physical activity stands out as one of the most significantly influenced areas ( 36 , 46 ). Research has revealed a marked reduction in physical activity levels among Chinese individuals due to the outbreak, leading to adverse effects on their overall well-being ( 12 , 14 ). Unlike indoor exercises focused on health benefits ( 16 ), active mobility involves outdoor movements for diverse purposes and has been particularly impacted by the outbreak compared to other forms of physical activity. A comprehensive examination of the shifts in Chinese citizens’ active mobility patterns during the outbreak not only enables a deeper understanding of the societal repercussions of the crisis but also offers valuable insights on promoting sustainable active mobility practices in the post-epidemic era. Based on this concern, this systematic scoping review addresses the impact of COVID-19 on active mobility in China through an empirical lens. Following rigorous screening procedures, 20 empirical studies were meticulously chosen from a pool of 606 English articles and 237 Chinese articles. A comprehensive analysis of these studies reveals significant insights into the subject matter.

First, the impact of COVID-19 on Chinese urban activity exhibited paradoxical effects on the behavioral dimension, which is consistent with scholars’ analysis of the complexity of the epidemic impact ( 19 , 27 , 48 ). While the epidemic led to a decrease in active mobility in urban areas, with reduced frequencies and distances covered in walking and cycling trips, there was also an observed increase in the adoption of active mobility practices among Chinese citizens. The proportion of active mobility in daily travel rose during the outbreak phase, with individuals engaging in active travel for a variety of purposes more frequently than before the epidemic. Bike-sharing, as a form of active mobility in the digital age, emerged as a significant mode of urban transportation during the epidemic, with an increase in the proportion of bike-sharing trips, distance traveled per trip, and diversity of trip purposes. The recovery rate of bike-sharing trips surpassed that of other transportation modes, hinting at a potential rise in the acceptance and use of bike-sharing among Chinese citizens in the post-epidemic era.

Second, the experience amid the epidemic significantly influenced Chinese citizens’ attitudes and understanding of active mobility. Throughout this period, citizens in China not only acknowledged the practical significance ( 49 , 50 ) of active mobility, but also recognized it as a form of physical and social engagement. Active travel transcended its traditional role of transporting individuals and goods from one point to another, providing travellers with physical, social, and emotional benefits, particularly benefiting certain demographics like the older adult. This suggests that the epidemic presented an opportunity to transition toward the “new mobilities paradigm” and foster a more supportive environment for active mobility development. However, it is important to note that negative connotations toward mobility emerged during the epidemic, particularly regarding “non-essential travel” (a term commonly used in China by the government during the epidemic). The evolving attitudes and knowledge pertaining to active mobility among Chinese citizens may have enduring and profound implications, surpassing short-term behavioral changes.

Third, the influences of the epidemic and associated restrictions tend to compound and exacerbate the existing disadvantages faced by (married) women, the older adult, and low-income individuals in daily active transportation. Women, especially the married ones may experience reduced mobility due to the uneven distribution of childcare duties within couples. The older adult, on the other hand, encounter challenges related to the digital divide and transportation disparities, leading to increased active travel during the epidemic phase but inconvenience and exclusion during the recovery period. Low-income groups, with limited economic resilience, are compelled to undertake medium to long-distance travels even amidst the outbreak, resulting in comparatively lower levels of active transportation compared to their higher-income counterparts. Therefore, a nuanced understanding of active travel patterns during the pandemic and beyond is crucial, rather than simply advocating for increased active mobility as a solution. It is essential to recognize that the promotion of active travel does not automatically translate to enhanced travel equality across different socioeconomic groups.

The extant literature on active mobility in urban China exhibits both similarities and distinctions when compared to related studies in other nations. Notably, the research conducted in urban China has confirmed the simultaneous effects of COVID-19 on active mobility observed in other countries, like in the US ( 51 – 53 ), Iran ( 54 ), Greece ( 55 ), Australia ( 56 ), Bangladesh ( 57 ), Serbia ( 58 ), South Korea ( 59 ), and so on. This is evidenced by a reduction in the total amount of active travel, coupled with an increase in the proportion of active travel utilized in citizens’ daily travel patterns. The observation underscores a second commonality, namely the emergence of a potential window of opportunity to advocate for increased active mobility due to the epidemic as discovered by studies in Europe ( 60 , 61 ), the US ( 62 ) and Latin America ( 63 , 64 ). This situation may prompt citizens to acknowledge and prioritize the health and environmental advantages associated with active mobility. Furthermore, research conducted in urban China validates the altered landscape of active mobility equality resulting from the epidemic, particularly affecting vulnerable demographics such as females, the older adult, and individuals with lower incomes ( 56 , 64 – 66 ).

The review also identifies unique characteristics in urban China that have not been observed in other nations. First, due to enduring stringent measures over an extended period, Chinese residents have demonstrated a notably more substantial and favorable shift in their perception toward active mobility. This is evidenced by the swift resurgence in active mobility levels and the wider range of purposes for active transportation during the epidemic, a trend distinct from the developments seen in South Korea ( 59 ), Hungary ( 67 ), Canada ( 66 ) and Latin America countries ( 63 ). Second, the significance of cycling, particularly the utilization of shared bicycles like DBS, stands out in China amidst the epidemic. Unlike the limited impact observed on SBBS as noted in studies across different nations ( 24 , 67 ), the epidemic has had a predominantly positive and enduring effect on DBS ( 17 , 41 ). This underscores the considerable promise of such forms of active mobility in the global landscape post-epidemic. Third, the impact of epidemics on gender dynamics in active mobility in China underscores the precarious position of married women with children. This is different with the reduction in the gender gap discovered in France ( 65 ) and Canada ( 66 ). It may reveal the disparities in the allocation of family duties within dual-income households in contemporary urban China, necessitating further empirical investigations. Finally, studies conducted in other countries have underscored that the significance of the enduring favorable impact for active mobility provided by the epidemic-induced opportunity window can yield, contingent upon the provision of suitable preconditions ( 60 , 61 , 67 ). This underscores the necessity for the implementation of policies, regulations, and infrastructures that promote active mobility even amidst the epidemic ( 62 , 64 ). Nevertheless, there has been a notable dearth of such initiatives in urban China, even during the epidemic recovery phase. Consequently, it is imperative to undertake additional active mobility initiatives across various levels in the contemporary post-epidemic period to capitalize on the opportunity window before it eludes.

This analysis enriches our understanding of the collective experience of Chinese residents throughout the epidemic period and presents valuable perspectives for advancing active mobility in the post-epidemic era. On an individual level, the first-hand encounters during the epidemic serve as tangible assets for advocating and sustaining increased mobility in the future. Many individuals are likely to consider walking and cycling as viable and health-conscious options for their daily commuting needs. Additionally, the increased integration of smart technology with active mobility can be attributed in part to the impact of COVID-19. The utilization of shared bicycles, health-related mobile applications, and virtual reality tools within the realm of physical activity have assumed a growing significance in the daily routines of Chinese citizens amid the epidemic ( 13 , 15 ). As we transition into the post-pandemic digital age, individuals can explore a variety of innovative technologies for enhancing their active mobility practices, such as utilizing smartphones for route planning, monitoring health metrics, and assessing traffic conditions to enhance the efficiency and safety of their travel experiences. At the organizational level, businesses and entities have the capacity to facilitate active mobility by implementing strategies such as flexible work hours and telecommuting policies. Moreover, organizations can enhance active transportation by offering amenities like bicycle parking facilities and financial assistance.

On a socio-political front, urban administrators should prioritize the development of bicycle lanes and pedestrian pathways to establish a secure and accessible environment for active transport. Additionally, drawing from past health crises, government bodies and academic institutions are encouraged to advocate for the advantages of active transportation through various channels such as media campaigns and community engagements to bolster public engagement and awareness. Through incentives like tax benefits, financial aid, and other supportive measures, governmental bodies can motivate individuals to adopt eco-friendly modes of travel. Lastly, it is imperative for policymakers and stakeholders to address the needs of marginalized demographics including the older adult, women, individuals with disabilities, and low-income populations by implementing structural reforms to mitigate disparities in the realm of active mobility.

The analysis of contemporary scholarly literature also provides valuable insights into potential directions for future research endeavors. Initially, the predominance of quantitative methodologies in existing studies suggests the potential for more comprehensive qualitative research to enhance our comprehension of various transformations and their enduring consequences within this domain. Subsequently, the prevalence of single-case studies underscores the need for further validation and enhancement of their findings through comparative analyses. Additionally, the examination of a broader range of social variables is warranted to address disparities in active travel during the post-epidemic period. Notably, the absence of studies focusing on the genuine “post-epidemic” era in China, commencing in late 2022, highlights a critical gap in the literature. The purported “post-epidemic” periods in current studies pertain to the recovery period, characterized by the persistence of certain anti-epidemic measures. Encouraging subsequent investigations that can shed light on emerging developments and transformations following the complete relaxation of restrictions will be instrumental in advancing our understanding of promoting active, healthy, and sustainable mobilities in the forthcoming era.

Author contributions

SD: Conceptualization, Formal analysis, Methodology, Writing – original draft, Writing – review & editing. HT: Methodology, Writing – review & editing. HG: Visualization, Writing – review & editing.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by the National Social Science Found of China [Grant number 20CSH045].

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: active mobility, physical activity, COVID-19, post-epidemic era, China

Citation: Du S, Tan H and Gao H (2024) Multi-dimensional impact of COVID-19 on active mobility in urban China: a scoping review of empirical knowledge. Front. Public Health . 12:1398340. doi: 10.3389/fpubh.2024.1398340

Received: 09 March 2024; Accepted: 29 April 2024; Published: 09 May 2024.

Reviewed by:

Copyright © 2024 Du, Tan and Gao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Hongze Tan, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

• Open access
• Published: 09 May 2024

Analysis of factors influencing hospitalization cost of patients with distal radius fractures: an empirical study based on public traditional Chinese medicine hospitals in two cities, China

• Mengen Chen 1 , 2   na1 ,
• Jingyu Yang 3 , 4   na1 ,
• Haojia Hou 5   na1 ,
• Baozhu Zheng 6 ,
• Shiji Xia 2 ,
• Yuhan Wang 2 ,
• Jing Yu 2 ,
• Guoping Wu 2 ,
• Henong Sun 2 ,
• Xuan Jia 2 ,
• Hao Ning 2 ,
• Hui Chang 2 ,
• Xiaoxi Zhang 1 , 2 , 7 ,
• Youshu Yuan 1 , 2 &
• Zhiwei Wang 2 , 8  

BMC Health Services Research volume  24 , Article number:  605 ( 2024 ) Cite this article

Metrics details

Distal radius fractures (DRFs) have become a public health problem for all countries, bringing a heavier economic burden of disease globally, with China’s disease economic burden being even more acute due to the trend of an aging population. This study aimed to explore the influencing factors of hospitalization cost of patients with DRFs in traditional Chinese medicine (TCM a ) hospitals to provide a scientific basis for controlling hospitalization cost.

With 1306 cases of DRFs patients hospitalized in 15 public TCM a hospitals in two cities of Gansu Province in China from January 2017 to 2022 as the study object, the influencing factors of hospitalization cost were studied in depth gradually through univariate analysis, multiple linear regression, and path model.

Hospitalization cost of patients with DRFs is mainly affected by the length of stay, surgery and operation, hospital levels, payment methods of medical insurance, use of TCM a preparations, complications and comorbidities, and clinical pathways. The length of stay is the most critical factor influencing the hospitalization cost, and the longer the length of stay, the higher the hospitalization cost.

Conclusions

TCM a hospitals should actively take advantage of TCM b diagnostic modalities and therapeutic methods to ensure the efficacy of treatment and effectively reduce the length of stay at the same time, to lower hospitalization cost. It is also necessary to further deepen the reform of the medical insurance payment methods and strengthen the construction of the hierarchical diagnosis and treatment system, to make the patients receive reasonable reimbursement for medical expenses, thus effectively alleviating the economic burden of the disease in the patients with DRFs.

Peer Review reports

Introduction

DRFs are defined as a fracture within 3 cm from the articular surface of the distal radius [ 1 ], which is a relatively common type of fracture, most commonly seen in elderly women and children, whereas the occurrence of young adults is traumatic violence greater [ 2 , 3 ]. A fracture of the distal radius may be described as a Colles, Smith, Barton, or Hutchinson fracture depending on the characteristics of the injury [ 2 , 4 ]. In addition to these four commonly used fracture names, some specially named fractures have been deferred in clinical work, such as chauffeur’s fracture, die-punch fracture, and so on [ 5 , 6 ].

Patients with DRFs account for approximately one-sixth of all fractures in the United States emergency departments, with an annual incidence of more than 640,000 cases [ 7 ], costing roughly $170 million in 2007 in Medicare reimbursement alone [ 8 ], and the incidence of DRFs in the United States is expected to continue to rise based on the evidence from existing studies [ 9 , 10 ]. In addition to the U.S., available studies also show that the incidence of DRFs in countries and regions such as the United Kingdom, Finland, Sweden, and Norway will continue to increase over the coming period [ 11 , 12 , 13 , 14 ]. In China, DRFs account for about 20% of emergency fractures and 75% of forearm fractures, and the number of distal radius fracture patients may exceed 200 million by 2025 with the aging of China’s population [ 15 , 16 , 17 ]. DRFs have become a public health problem that places a heavy economic burden on people around the world, with fewer studies of DRFs in TCM a hospitals being conducted to alleviate this problem.

TCM a is the main component of Chinese medicine with a long history of development and is characterized by Chinese cultural connotations and local characteristics. In the process of its development, TCM b has gradually formed characteristic therapies and methods for the treatment of some diseases, and these diseases are named ‘TCM b advantageous diseases’. DRFs is one of the advantageous diseases in TCM b , treated as one of the key specialties in TCM a hospitals. The treatment of DRFs in TCM a hospitals can be broadly divided into two categories, one is the non-surgical treatment through reduction maneuvers and splinting, which is also the conservative treatment actively adopted by TCM a hospitals, and the other is the surgical treatment through the fixation of the bone position using plate or stent to gradually achieve the healing of the injury [ 1 , 18 , 19 , 20 , 21 ]. When the patient’s fracture condition is not serious, the TCM a hospitals tend to promote the conservative treatment without secondary surgery of removing the plate, which generates less consumption of medical resources, supplementing with Chinese medicine to cooperate with the treatment can significantly improve the speed of recovery, reduce the length of stay, and ultimately reduce the hospitalization cost of the patient effectively.

The Chinese government is currently pushing forward the reform of medical insurance payment methods to improve the quality of medical services as well as to control medical costs, and the TCM b advantageous diseases will be taken as the main target and direction of the preliminary reform in the exploration of the payment reform of TCM a hospitals. The TCM b advantageous diseases of DRFs is a common type of fracture in various countries with a high incidence of disease, analyzing and researching the factors affecting its hospitalization cost has great significance for health economics and public health, especially in the context of the Chinese government’s policy of comprehensively implementing the reform of the diagnosis related groups (DRG) or diagnosis-intervention packet (DIP) medical insurance payment methods with the TCM b advantageous disease of DRFs as a pioneer of the medical insurance reform field [ 22 ]. Exploring the influencing factors of hospitalization cost of TCM b advantageous diseases such as DRFs in TCM a hospitals, can provide thoughts for the Chinese health insurance authorities to promote the reform of the payment methods for controlling medical costs in TCM a hospitals, and at the same time inspire cost control of general hospitals (Western medicine) with optimization of their treatment modalities.

Study design and population

The study data was obtained from the Health Statistics and Information Center of the Gansu Province Health and Wellness Commission. All information on patients hospitalized in 15 TCM a hospitals in Qingyang City and Tianshui City from January 2017 to June 2022 was extracted from the center’s province-wide big data platform for universal health and was cleaned and screened by the corresponding inclusion and exclusion criteria. Our inclusion criteria was western medical diagnosis code S52.500x001 (ICD-10), exclusion criteria were logical errors or missing data that could not be adjusted or supplemented based on the data, as well as patients with the length of stay greater than 90, and 1306 valid cases were included finally (Fig. S 1 ).

Data processing

The endogenous variables in this study were length of stay and hospitalization cost, mainly because existing studies have shown a significant correlation between hospitalization cost and length of stay [ 23 , 24 , 25 , 26 ], which also facilitated the subsequent comprehensive and systematic analysis of the influencing factors of hospitalization cost. The exogenous variables included patients’ basic information, medical situation, and treatment modality. Patients’ basic information included gender, ethnicity, age, marital status, complications and comorbidities, etc., indicators of medical situation included visit times, payment methods of medical insurance, hospital levels, route of admission, and treatment modalities included clinical pathways, types of treatment, use of TCM b preparations, use of TCM b diagnostic and therapeutic equipment, use of TCM b diagnostic and therapeutic techniques, diagnosis and treatment based on TCM b evidence, and surgeries and operations. In particular, since the raw data of length of stay and hospitalization cost did not obey normal distribution, the logarithm of the two data was used as the dependent variable in the regression analyses with the log-transformed data approximated a normal distribution. Further clarification, the analysis using log-transformed data aimed to explore correlations of variables theoretically, whereas the actual comparison of variances used the raw data transformed by the EXP [log(x)] function. The details of the coding and assignment processing of each variable are shown in Table S 1 .

Statistical analysis

Before formal statistical analysis, hospitalization cost was adjusted according to the CPI (Consumer Price Index, CPI) of Healthcare in Gansu Province from 2017 to 2022 to reduce study bias, with 2016 as the base period. Statistical analysis methods in our study mainly involve univariate analysis, multiple linear regression, and path model. Mann–Whitney U rank sum test was used in the univariate analysis when the independent variable is dichotomous, and the Kruskal–Wallis H test for a multi-categorical variable. The independent variables for multiple linear regression were selected from statistically significant variables in the univariate analysis, and regression models were built using the logarithm of length of stay and hospitalization cost as the dependent variables. It is worth mentioning that the covariate “Cities” was included in the regression analysis to minimize the bias caused by the differences in economic and social development between Qingyang City and Tianshui City. Path analysis used the logarithm of hospitalization cost as the dependent variable, the length of stay as the mediator variable, and significant independent variables from the multiple linear regression models as the input variables to comprehensively analyze the factors affecting hospitalization cost. The univariate analysis and multiple linear regression models were performed using SPSS 26.0, and the path model was developed using AMOS 24.0. The test level for the above statistical analysis was α = 0.05.

Univariate analysis

In univariate analysis of length of stay and hospitalization cost in patients with DRFs, we found the patient’s length of stay is associated with gender, age, marital status, visit times, payment methods of medical insurance, hospital levels, admission routes, types of treatment, clinical pathways, use of TCM a preparations, use of TCM b diagnostic and therapeutic equipment, diagnosis and treatment based on TCM b evidence, complications and comorbidities, and surgeries and operations ( P  < 0.05), and the patient’s hospitalization cost is associated with age, marital status, visit times, payment methods of medical insurance, hospital levels, admission routes, types of treatment, clinical pathways, use of TCM a preparations, diagnosis and treatment based on TCM b evidence, complications and comorbidities, surgeries and operations, and length of stay ( P  < 0.05) (Table  1 ).

Multiple linear regression

Multiple linear regression models were established with the log-transformed values of length of stay and hospitalization cost as the dependent variables, with statistically significant in the univariate analysis as the independent variables ( P  < 0.05) (Table  2 ).

From the results of multiple linear regression, we found the length of stay is mainly affected by the patient’s gender, age (45–60), marital status (married), payment methods of medical insurance (UEBMI, others), hospital levels, TCM b and Western medical treatment, diagnosis and treatment based on TCM b evidence, complications and comorbidities, and surgeries and operations, with the regression equation of the patient’s length of stay: Y 1  = 0.816–0.058*X 1  + 0.052*X 3–1  + 0.047*X 4–1 –0.117*X 6–1 –0.115*X 6–3  + 0.140*X 7  + 0.075*X 9–1 –0.047*X 15 –0.176*X 16 ( F  = 19.437, P  < 0.001, R 2  = 0.250). Hospitalization cost is mainly affected by the patient’s marital status (married, others), hospital levels, clinical pathways (Western medicine pathway, no pathway), use of TCM a preparations, diagnosis and treatment based on TCM b evidence, complications and comorbidities, surgeries and operations, and length of stay, with the regression equation of the patient’s hospitalization cost: Y 2  = 2.852 + 0.086*X 4–1  + 0.111*X 4–2  + 0.230*X 7 –0.235*X 10–1 –0.081*X 10–2 –0.092 *X 11  + 0.055*X 14 –0.045*X 15 –0.283*X 16  + 0.823* Y 1 ( F  = 113.156, P  < 0.001, R 2  = 0.649). The VIF (Variance inflation factor, VIF) values for each variable in the regressions analysis of length of stay and hospitalization cost are close to or less than 10, meaning there is no collinearity in either model. Moreover, the residual statistical coefficient of hospitalization cost is \(0.592\left({P}_{e}=\sqrt{{1-R}^{2}}\right)\) , less than the standardized coefficient of Y 1 , indicating there may be other factors indirectly affecting hospitalization cost, and a comprehensive analysis of the impact of hospitalization cost should be developed by establishing a path model.

Based on the multiple linear regression results of length of stay and hospitalization cost, statistically significant independent variables were included as input variables, and a path model was developed with length of stay as the mediator variable and hospitalization cost as the dependent variable (Fig.  1 ).

Path diagram of influencing factors of hospitalization cost of DRFs patients

From the path model analysis results, we could get the specific decomposition effect of factors affecting the hospitalization cost of patients with DRFs, and we also could further quantitatively rank the influencing factors, the specific results are shown in Table  3 . It should be stated in advance that the direct path coefficient of the independent variable on the dependent variable is equal to the standardized regression coefficient, and the indirect path coefficient of the independent variable on the dependent variable through the mediator is equal to the product of direct path coefficient of the independent variable on the mediator and direct path coefficient of the mediator on the dependent variable, and the total path coefficient is the sum of the direct path coefficient and the indirect path coefficient.

By using the above calculation method, the effect size of the factors affecting the hospitalization cost of DRFs patients could be derived, and the ranking results of the degree of influence for each factor on the hospitalization cost as follows: length of stay, surgeries and operations, hospital levels, use of TCM a preparations, marital status (married), payment methods of medical insurance (others), complications and comorbidities, marital status (others), clinical pathway (no pathway), diagnosis and treatment based on TCM b evidence, TCM b and Western medical treatment, gender, age (45–60), clinical pathway (Western medicine), and payment methods of medical insurance (URBMI).

As shown by univariate analysis, the hospitalization cost of inpatients with DRFs, an advantageous disease of Chinese medicine in TCM a hospitals, was mainly related to inpatients’ age, marital status, visit times, payment methods of medical insurance, hospital levels, admission routes, types of treatment, clinical pathways, use of TCM a preparations, diagnosis and treatment based on TCM b evidence, complications and comorbidities, surgeries and operations, and length of stay. The hospitalization cost of patients of age (45–60) with DRFs was higher than age (< 45 or > 60), and the hospitalization cost of unmarried patients was lower than the married or other marital status. Besides, different hospitalized patients with different methods of payment for health insurance will also have an impact on their hospitalization cost, and the UEBMI was the highest, followed by the UEBMI and other health insurance, and the lowest was UEBMI, a key point to consider is that China’s township peasants’ income is lower than urban workers, and their level of health care consumption and ability are also weaker. Furthermore, the hospitalization cost through other admission routes was higher than emergency and outpatient care because patients admitted through others may have a more severe disease profile, resulting in higher consumption of medical services and resources. For example, patients admitted in the form of transfer may be transferred to higher-level hospitals because their conditions are too severe to be effectively treated in lower-level hospitals, and the medical costs the patients face in higher-level hospitals for the same diseases will be higher, as verified in our and others’ studies [ 27 , 28 , 29 ]. What’s more, different types of treatment for patients in TCM a hospitals also led to different hospitalization cost, with the cost of pure TCM b treatment being significantly higher than combined TCM b and Western medicine treatment or independent Western medicine treatment, inconsistent with the results of some studies [ 30 , 31 ], probably because the therapeutic effect of pure TCM b treatment is relatively slow to appear, and the long treatment course leads to high cost, and the sample hospitals are TCM a hospitals with mostly predominantly TCM b treatment programs, making TCM a cost higher. Of note, the hospitalization cost of patients without diagnosis and treatment based on TCM b evidence was higher than those had, mainly because diagnosis and treatment based on TCM b evidence can reduce the patient’s rehabilitation course by improving treatment efficacy and optimizing the treatment plan, resulting in relatively less hospitalization cost, consistent with the studies conducted by Shou Wujing [ 32 ], Wang Shihua [ 33 ], et al. In addition, hospitalization cost was lower for patients using TCM a preparations than for those who did not, higher for patients with complications and comorbidities than for those without, and higher for patients undergoing surgery and operations than for non-surgical patients, mostly in correlation with the content of healthcare services and the consumption of healthcare resources, specifically, the use of TCM a preparations speeds up the process of recovery and reduces the length of stay, and the complications and comorbidities, as well as surgeries and operations, increase the difficulty in treating the disease and generate more healthcare resources to be used.

In our study, by further combining the results of multiple linear regression and path model analysis, we found the inpatient hospitalization cost of DRFs with TCM b advantageous diseases in TCM a hospitals is related to length of stay, surgeries and operations, hospital levels, use of TCM a preparations, payment methods of medical insurance (others), marital status (married), complications and comorbidities, marital status (other), clinical pathways (no pathway), payment methods of medical insurance (URBMI), age (45–60), clinical pathways (Western medicine), gender, and diagnosis and treatment based on TCM b evidence, and the length of stay was the key influencing factor, similar to some scholars’ studies [ 34 , 35 , 36 , 37 ]. Simply put, the longer the length of stay, the relatively more healthcare resources are used by the hospitals, resulting in higher hospitalization cost. Additionally, hospitalization cost and length of stay were lower for female patients than for males, patients’ age (45–60) and marital status (married, other) were associated with higher hospitalization cost and length of stay, and patients would have lower hospitalization cost and length of stay if their payment methods of medical insurance are URBMI and others. Through the analysis of our models, it can also be concluded that the higher the level of the hospital, the more serious the complications and comorbidities with surgeries and operations performed, the higher hospitalization cost and the longer length of stay will be for DRFs patients, and the patients who are adopting TCM b pathway, using TCM a preparations, and not undergoing diagnosis and treatment based on TCM b evidence would face a greater economic burden of the disease.

From the point of cost control for dominant diseases (TCM b advantageous diseases) in TCM a hospitals, firstly, the length of stay of patients should be minimized on the premise of ensuring the efficacy of life-saving treatment. Secondly, the rate of hospital surgery should be controlled, and the fractures that can be treated conservatively with Chinese medicine should be actively adopted [ 38 , 39 ]. Thirdly, the levels of TCM a hospitals, as one of the main factors influencing hospitalization cost, should receive further attention. Accordingly, the local authorities should continue to promote the construction of a hierarchical diagnosis and treatment system for TCM a medical institutions, and regulate the conditions and severity of patients that should be treated in TCM a hospitals of all levels reasonably, to avoid the admission of patients with lower levels of illnesses into higher-level hospitals as much as possible, and to alleviate the financial burden of illness on both the patients and the health insurance fund [ 40 , 41 ]. As the main body of medical cost control, TCM a hospitals should actively guide patients to use TCM a preparations and carry out diagnosis and treatment based on TCM b evidence, the use of TCM a preparations can enable patients to get higher-value rehabilitation, and evidence-based care will enable patients to get higher-quality diagnostic and therapeutic services, both of which are conducive to the reduction of the length of stay and the realization of lower cost control. Of greater concern, the selection of clinical pathways for DRFs patients hospitalized in TCM a hospitals should be based on the actual situation of the patient’s condition, and not be considered unilaterally only from the perspective of cost control, but be combined with the comprehensive consideration of patients’ treatment needs and treatment cost, with the main principle of the patients’ effective medical treatment and relatively low cost being adhered to.

Limitations

Our study was based on hospitalized patients with DRFs in TCM a hospitals in Tianshui City and Qingyang City. On the one hand, the valid samples obtained were relatively small due to the quality of the cases and other reasons, so the study was not broadly representative. On the other hand, our study mainly focused on the TCM a hospitals themselves and did not incorporate the Western medicine hospitals for comparative study, making the study object too homogeneous, and it will be necessary to further optimize the content and form of the study and expand the study object and topic. In addition, the database lacks complete information on the occupations and household incomes of patients with DRFs, which may have an impact on the deeper refinement of our study.

Our study indicates the main influencing factors of hospitalization cost are the length of stay, surgeries and operations, hospital levels, use of TCM a preparations, payment methods of medical insurance, and complications and comorbidities, with the length of stay being the primary influencing factor. China’s government medical and health reform should pay particular attention to the length of stay of patients with TCM b advantageous diseases, encourage TCM a hospitals to try to take DRG or DIP as the main health insurance payment method, and advocate TCM a doctors to adopt non-surgical TCM b specialty therapies under the circumstance ensuring the efficacy of the treatment, to reduce the length of stay, increase health insurance reimbursement and lower the hospitalization cost as much as possible.

Availability of data and materials

Datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Distal radius fractures

Traditional Chinese medicine (TCM a for ‘Traditional Chinese Medicine’, TCM b for ‘diagnosis and treatment-based evidence’)

Diagnosis related groups

Diagnosis-intervention packet

Urban employee basic medical insurance

Urban residents’ basic medical insurance

New cooperative medical scheme

Variance inflation factor

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Acknowledgements

The authors would like to acknowledge the Gansu Provincial Health and Wellness Commission for data support for our study.

This study was supported by the State Administration of Traditional Chinese Medicine (SACM) (Grant NO. ZYZB-2023-435 and NO. GZY-FJS-2022-045).

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Mengen Chen, Jingyu Yang and Haojia Hou contributed equally to this work.

Authors and Affiliations

School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 102400, China

Mengen Chen, Xiaoxi Zhang & Youshu Yuan

School of Management, Beijing University of Chinese Medicine, Beijing, 102400, China

Mengen Chen, Shiji Xia, Yuhan Wang, Jing Yu, Guoping Wu, Henong Sun, Xuan Jia, Hao Ning, Hui Chang, Xiaoxi Zhang, Youshu Yuan & Zhiwei Wang

School of Health Management, Gansu University of Chinese Medicine, Lanzhou, 730000, China

Jingyu Yang

School of Public Health, Lanzhou University, Lanzhou, 730000, China

School of Public Health, Gansu University of Chinese Medicine, Lanzhou, 730000, China

School of Stomatology, Capital Medical University, Beijing, 100050, China

Baozhu Zheng

Guang’anmen Hospital, China Academy of Chinese Medicine Sciences, Beijing, 100053, China

Xiaoxi Zhang

National Institute of Chinese Medicine Development and Strategy, Beijing, 102400, China

Zhiwei Wang

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MC HH, BZ, and JYY participated in data collection and collation. MC, XZ, YY, HN, HC, and ZW participated in the method design, analyzed data, and drafted the initial manuscript. GW, HS, XJ, SX, JY and YW participated in text-checking correction and helped to draft the manuscript. ZW and JYY oversaw and provided input on all aspects of manuscript writing and the final analytical plan. All the authors read and approved the final manuscript.

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Correspondence to Zhiwei Wang .

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Additional file 1: supplemental fig. s1..

Flowchart illustrating patients selection. Supplemental Table S1. Classification and assignment of variables.

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Chen, M., Yang, J., Hou, H. et al. Analysis of factors influencing hospitalization cost of patients with distal radius fractures: an empirical study based on public traditional Chinese medicine hospitals in two cities, China. BMC Health Serv Res 24 , 605 (2024). https://doi.org/10.1186/s12913-024-10953-w

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Received : 29 December 2023

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DOI : https://doi.org/10.1186/s12913-024-10953-w

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