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Tables in Research Paper – Types, Creating Guide and Examples

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Tables in Research Paper

Definition:

In Research Papers , Tables are a way of presenting data and information in a structured format. Tables can be used to summarize large amounts of data or to highlight important findings. They are often used in scientific or technical papers to display experimental results, statistical analyses, or other quantitative information.

Importance of Tables in Research Paper

Tables are an important component of a research paper as they provide a clear and concise presentation of data, statistics, and other information that support the research findings . Here are some reasons why tables are important in a research paper:

• Visual Representation : Tables provide a visual representation of data that is easy to understand and interpret. They help readers to quickly grasp the main points of the research findings and draw their own conclusions.
• Organize Data : Tables help to organize large amounts of data in a systematic and structured manner. This makes it easier for readers to identify patterns and trends in the data.
• Clarity and Accuracy : Tables allow researchers to present data in a clear and accurate manner. They can include precise numbers, percentages, and other information that may be difficult to convey in written form.
• Comparison: Tables allow for easy comparison between different data sets or groups. This makes it easier to identify similarities and differences, and to draw meaningful conclusions from the data.
• Efficiency: Tables allow for a more efficient use of space in the research paper. They can convey a large amount of information in a compact and concise format, which saves space and makes the research paper more readable.

Types of Tables in Research Paper

Most common Types of Tables in Research Paper are as follows:

• Descriptive tables : These tables provide a summary of the data collected in the study. They are usually used to present basic descriptive statistics such as means, medians, standard deviations, and frequencies.
• Comparative tables : These tables are used to compare the results of different groups or variables. They may be used to show the differences between two or more groups or to compare the results of different variables.
• Correlation tables: These tables are used to show the relationships between variables. They may show the correlation coefficients between variables, or they may show the results of regression analyses.
• Longitudinal tables : These tables are used to show changes in variables over time. They may show the results of repeated measures analyses or longitudinal regression analyses.
• Qualitative tables: These tables are used to summarize qualitative data such as interview transcripts or open-ended survey responses. They may present themes or categories that emerged from the data.

How to Create Tables in Research Paper

Here are the steps to create tables in a research paper:

• Plan your table: Determine the purpose of the table and the type of information you want to include. Consider the layout and format that will best convey your information.
• Choose a table format : Decide on the type of table you want to create. Common table formats include basic tables, summary tables, comparison tables, and correlation tables.
• Choose a software program : Use a spreadsheet program like Microsoft Excel or Google Sheets to create your table. These programs allow you to easily enter and manipulate data, format the table, and export it for use in your research paper.
• Input data: Enter your data into the spreadsheet program. Make sure to label each row and column clearly.
• Format the table : Apply formatting options such as font, font size, font color, cell borders, and shading to make your table more visually appealing and easier to read.
• Insert the table into your paper: Copy and paste the table into your research paper. Make sure to place the table in the appropriate location and refer to it in the text of your paper.
• Label the table: Give the table a descriptive title that clearly and accurately summarizes the contents of the table. Also, include a number and a caption that explains the table in more detail.
• Check for accuracy: Review the table for accuracy and make any necessary changes before submitting your research paper.

Examples of Tables in Research Paper

Examples of Tables in the Research Paper are as follows:

Table 1: Demographic Characteristics of Study Participants

This table shows the demographic characteristics of 200 participants in a research study. The table includes information about age, gender, and education level. The mean age of the participants was 35.2 years with a standard deviation of 8.6 years, and the age range was between 21 and 57 years. The table also shows that 46% of the participants were male and 54% were female. In terms of education, 10% of the participants had less than a high school education, 30% were high school graduates, 35% had some college education, and 25% had a bachelor’s degree or higher.

Table 2: Summary of Key Findings

This table summarizes the key findings of a study comparing three different groups on a particular variable. The table shows the mean score, standard deviation, t-value, and p-value for each group. The asterisk next to the t-value for Group 1 indicates that the difference between Group 1 and the other groups was statistically significant at p < 0.01, while the differences between Group 2 and Group 3 were not statistically significant.

Purpose of Tables in Research Paper

The primary purposes of including tables in a research paper are:

• To present data: Tables are an effective way to present large amounts of data in a clear and organized manner. Researchers can use tables to present numerical data, survey results, or other types of data that are difficult to represent in text.
• To summarize data: Tables can be used to summarize large amounts of data into a concise and easy-to-read format. Researchers can use tables to summarize the key findings of their research, such as descriptive statistics or the results of regression analyses.
• To compare data : Tables can be used to compare data across different variables or groups. Researchers can use tables to compare the characteristics of different study populations or to compare the results of different studies on the same topic.
• To enhance the readability of the paper: Tables can help to break up long sections of text and make the paper more visually appealing. By presenting data in a table, researchers can help readers to quickly identify the most important information and understand the key findings of the study.

Advantages of Tables in Research Paper

Some of the advantages of using tables in research papers include:

• Clarity : Tables can present data in a way that is easy to read and understand. They can help readers to quickly and easily identify patterns, trends, and relationships in the data.
• Efficiency: Tables can save space and reduce the need for lengthy explanations or descriptions of the data in the main body of the paper. This can make the paper more concise and easier to read.
• Organization: Tables can help to organize large amounts of data in a logical and meaningful way. This can help to reduce confusion and make it easier for readers to navigate the data.
• Comparison : Tables can be useful for comparing data across different groups, variables, or time periods. This can help to highlight similarities, differences, and changes over time.
• Visualization : Tables can also be used to visually represent data, making it easier for readers to see patterns and trends. This can be particularly useful when the data is complex or difficult to understand.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Literature Review Basics

• What is a Literature Review?
• Synthesizing Research
• Using Research & Synthesis Tables
• Additional Resources

About the Research and Synthesis Tables

Research Tables and Synthesis Tables are useful tools for organizing and analyzing your research as you assemble your literature review. They represent two different parts of the review process: assembling relevant information and synthesizing it. Use a Research table to compile the main info you need about the items you find in your research -- it's a great thing to have on hand as you take notes on what you read! Then, once you've assembled your research, use the Synthesis table to start charting the similarities/differences and major themes among your collected items.

We've included an Excel file with templates for you to use below; the examples pictured on this page are snapshots from that file.

• Research and Synthesis Table Templates This Excel workbook includes simple templates for creating research tables and synthesis tables. Feel free to download and use!

Using the Research Table

This is an example of a  research table,  in which you provide a basic description of the most important features of the studies, articles, and other items you discover in your research. The table identifies each item according to its author/date of publication, its purpose or thesis, what type of work it is (systematic review, clinical trial, etc.), the level of evidence it represents (which tells you a lot about its impact on the field of study), and its major findings. Your job, when you assemble this information, is to develop a snapshot of what the research shows about the topic of your research question and assess its value (both for the purpose of your work and for general knowledge in the field).

Think of your work on the research table as the foundational step for your analysis of the literature, in which you assemble the information you'll be analyzing and lay the groundwork for thinking about what it means and how it can be used.

Using the Synthesis Table

This is an example of a  synthesis table  or  synthesis matrix , in which you organize and analyze your research by listing each source and indicating whether a given finding or result occurred in a particular study or article ( each row lists an individual source, and each finding has its own column, in which X = yes, blank = no). You can also add or alter the columns to look for shared study populations, sort by level of evidence or source type, etc. The key here is to use the table to provide a simple representation of what the research has found (or not found, as the case may be). Think of a synthesis table as a tool for making comparisons, identifying trends, and locating gaps in the literature.

How do I know which findings to use, or how many to include?  Your research question tells you which findings are of interest in your research, so work from your research question to decide what needs to go in each Finding header, and how many findings are necessary. The number is up to you; again, you can alter this table by adding or deleting columns to match what you're actually looking for in your analysis. You should also, of course, be guided by what's actually present in the material your research turns up!

• << Previous: Synthesizing Research
• Next: Additional Resources >>
• Last Updated: Sep 26, 2023 12:06 PM
• URL: https://usi.libguides.com/literature-review-basics

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14 Modern Study Table Design Ideas for a Perfect Study or Workspace – 2024

27 Mar 2023, Read Time : 9 Min

There are numerous ways a study table can be designed, but it is necessary to design it in such a way that it proves to be functional and also turns out to look quite pretty and stunning. A study table can be quite an important part of one’s house as it allows you to study, read, and work efficiently. It also helps you be more productive. A study table or a work table has become one of the major necessities that a person needs to have in their homes now, thanks to the post-Covid work-from-home culture. 

Here are a few ways in which you can design some pretty, sturdy, and functional study tables in your house.

Study Table Designs: Things to Remember

There are a few things that you must keep in mind while choosing a study table for your study. Some of the major things have been explained below. 

Check Your Requirements:

One of the basic things that you need to remember about study table design is that you should be aware of what your requirements are. It is of no use to spend an exorbitant amount of money on features, sizes, colours and shapes that you will never use. The study table should have everything that you need, but should still be simple enough so that it looks elegant and good.

Check the Space:

A lot of modern study table designs nowadays try to focus on minimalism and try to keep things as simple as possible. This is a great aesthetic choice, however, you should always consider the space available while designing a study table. Study table designs for small rooms will differ a lot from study table designs for larger rooms. Choose a design according to the space so that you can use it to its fullest. 

Understand the Flooring:

Different floors and rooms require different furniture. Marble tiles and wooden tiles are generally the best choices for any study room as they are not only sturdy and durable, but they also look chic and suit the ‘professional’ vibe of a study room or study area. You can also use other tiles such as mosaic and ceramic for a different and unique look. 

Storage is Necessary:

Make Most of the Variety of Materials: 

Wooden Study Tables:

Wooden study tables are iconic. Wood is one of the most used materials for designer study tables as well as simple study table designs. They look classy, are quite durable, and can work with almost any decor and colour scheme. 

Glass Study Tables:

Marble-top Study Table:

Metal study desks: 

Various Types of Study Tables and Designs

Many different types and styles of study table designs are available in the market. The design is not only dependent on the material and colour of the table, but also the shape, utility, functions, features, and much more. 

Here are a few types of study table designs that are commonly found in the market.

Study Table With Built-in Bookshelf Design: 

Study/Computer Table:

As mentioned earlier, most work nowadays happens with the help of computers. If you use a desktop system you need enough space to store all the accessories such as CPU, monitor, printer, etc., and also need good and accessible space for input devices such as mice and keyboards. A computer table can provide you with a seamless experience in dealing with computers and can help you increase your productivity.

Wall-Mounted Study Table Design:

If you are low on space and want to set up your study in a smaller room, then a wall-mounted study table design is perfect for you. There are many wall-mounted study table designs to choose from. These are sleek, stylish, and highly functional. They can be folded to save space and use it judiciously. Wall-mounted study tables can be suitable as study table designs for adults as well as children study table designs. 

Writing Desk:  

Corner Desk Design: 

Executive Desk: 

Modern Study Table Designs: 

Credenza desks and Tables: 

Credenza tables are perfect for people who have got a lot of things and would like to store them away at their work desks. These are generally made of wood, but can be made of other materials as well. They are shaped like cabinets and are normally used in kitchens and dining rooms, but this does not mean that you cannot use them in your study! Credenza-like tables are the best study table with storage designs especially if you want to use your space creatively. 

Simple Wooden Study Desk:

Nothing can beat the class and simplicity of a simple wooden study desk. The wooden study table design is a classic design that suits almost all interiors. These are sturdy, look good, and are functional, making them an asset for your study.

Storage Shelves Study Desk:

Study tables with storage designs are all rage currently. These desks have ample space for you to store your books and other equipment when you don’t need them, enabling you to keep your desktop clutter-free.

L Shape Study Table Design:

L-shaped study tables are functional and provide a lot of space for you to work on. They also fit perfectly in a corner and look amazing next to windows.

Study Table with Wardrobes:

A wardrobe with a study table design is perfect for smaller rooms where you need to use every inch of space with care. These desks combine the functionality of a wardrobe with a table, making them ideal for homes where function is always preferred over aesthetics.

Folding Desk Design: 

Designer study tables such as folding desks are suitable for people who want to use their space in the best way possible. You can fold these desks and then store them away when you don’t need them so that you can use the area for other work.

Creating a Study Table Space

People can get distracted often while working or studying especially if the atmosphere and the table are not conducive to studying. It is, therefore, necessary to set up a proper and dedicated study space for yourself so that you will stay focused on your work. Here are a few tips that you can use to create a study table space for yourself in your house.

• Create a Space Without Distractions: Choose a space where you won’t be distracted from time to time and can focus freely on your work.
• Find a good desk and chair: It is necessary to find a comfortable chair and desk that matches your style and the decor of your house.
• Adequate lighting: It is necessary to have proper lighting in your study room so that you can work without straining your eyes.
• Arrange Supplies: Arrange your supplies in such a way that they are easily accessible.
• Clock: You can also have a small clock on your desk to keep a track of your time.
• Reduced clutter: Keep things organised and declutter your desk from time to time.
• Personalise: Personalise your space in such a way that it feels like home to you and that you can work for longer durations without getting tired or bored. 

There are many different designs of study tables available in the market, but it is up to you and your needs that can help you decide which table to choose. Do remember to seek a balance between aesthetics and functionality so that you end up with a good and comfortable desk.

Mannika Mitra

Mannika Mitra brings a wealth of experience to her role as Digital Content and Marketing Manager at Orientbell Tiles, having been associated with the company for the past 5 years. With a total of 12 years in the industry, Mannika holds an Arts degree from Delhi University and a Post-Graduate Diploma in Journalism and Mass Communication. Her journey has seen her excel as a digital producer at esteemed news agencies like ANI, NDTV, and Hindustan Times.

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How to Read a Research Table

The tables in this section present the research findings that drive many recommendations and standards of practice related to breast cancer.

Research tables are useful for presenting data. They show a lot of information in a simple format, but they can be hard to understand if you don’t work with them every day.

Here, we describe some basic concepts that may help you read and understand research tables. The sample table below gives examples.

The numbered table items are described below. You will see many of these items in all of the tables.

Sample table – Alcohol and breast cancer risk

Selection criteria.

Studies vary in how well they help answer scientific questions. When reviewing the research on a topic, it’s important to recognize “good” studies. Good studies are well-designed.

Most scientific reviews set standards for the studies they include. These standards are called “selection criteria” and are listed for each table in this section. These selection criteria help make sure well-designed studies are included in the table.

Types of studies

The types of studies (for example, randomized controlled trial, prospective cohort, case-control) included in each table are listed in the selection criteria.

Learn about the strengths and weaknesses of different types of research studies .

Selection criteria for most tables include the minimum number of cases of breast cancer or participants for the studies in the table.

Large studies have more statistical power than small studies. This means the results from large studies are less likely to be due to chance than results from small studies.

The power of large numbers

You can see the power of large numbers if you think about flipping a coin. Say you are trying to figure out whether a coin is fixed so that it lands on “heads” more than “tails.” A fair coin would land on heads half the time. So, you want to test whether the coin lands on heads more than half of the time.

If you flip the coin twice and get 2 heads, you don’t have a lot of evidence. It wouldn’t be surprising to flip a fair coin and get 2 heads in a row. With 2 coin flips, you can’t be sure whether you have a fair coin or not. Even 3 or 4 heads in a row wouldn’t be surprising for a fair coin.

If, however, you flipped the coin 20 times and got mostly heads, you would start to think the coin might be fixed.

With an increasing number of observations, you have more evidence on which to base your conclusions. So, you have more confidence in your conclusions. It’s a similar idea in research.

Example of study size in breast cancer research

Say you’re interested in finding out whether or not alcohol use increases the risk of breast cancer.

If there are only a few cases of breast cancer among the alcohol drinkers and the non-drinkers, you won’t have much confidence drawing conclusions.

If, however, there are hundreds of breast cancer cases, it’s easier to draw firm conclusions about a link between alcohol and breast cancer. With more evidence, you have more confidence in your findings.

The importance of study design and study quality

Study design (the type of research study) and study quality are also important. For example, a small, well-designed study may be better than a large, poorly-designed study. However, when all else is equal, a larger number of people in a study means the study is better able to answer research questions.

Learn about different types of research studies .

The studies

The first column (from the left) lists either the name of the study or the name of the first author of the published article.

Below each table, there’s a reference list so you can find the original published articles.

Sometimes, a table will report the results of only one analysis. This can occur for a few reasons. Either there’s only one study that meets the selection criteria or there’s a report that combines data from many studies into one large analysis.

Study population

The second column describes the people in each study.

• For randomized controlled trials, the study population is the total number of people who were randomized at the start of the study to either the treatment (or intervention) group or the control group.
• For prospective cohort studies, the study population is the number of people at the start of the study (baseline cohort).
• For case-control studies, the study population is the number of cases and the number of controls.

In some tables, more details on the people in the study are included. 

Length of follow-up

Randomized controlled trials and prospective cohort studies follow people forward in time to see who will have the outcome of interest (such as breast cancer).

For these studies, one column shows the length of follow-up time. This is the number or months or years people in the study were followed.

Because case-control studies don’t follow people forward in time, there are no data on follow-up time for these studies.

Tables that focus on cumulative risk may also show the length of follow-up. These tables give the length of time, or age range, used to compute cumulative risk (for example, the cumulative risk of breast cancer up to age 70).

Learn more about cumulative risk . 

Other information

Some tables have columns with other information on the study population or the topic being studied. For example, the table Exercise and Breast Cancer Risk has a column with the comparisons of exercise used in the studies.

This extra information gives more details about the studies and shows how the studies are similar to (and different from) each other.

Studies on the same topic can differ in important ways. They may define “high” and “low” levels of a risk factor differently. Studies may look at outcomes among women of different ages or menopausal status.

These differences are important to keep in mind when you review the findings in a table. They may help explain differences in study findings. 

Understanding the numbers

All of the information in the tables is important, but the main purpose of the tables is to present the numbers that show the risk, survival or other measures for each topic. These numbers are shown in the remaining columns of the tables.

The headings of the columns tell you what the numbers represent. For example:

• What is the outcome of interest? Is it breast cancer? Is it 5-year survival? Is it breast cancer recurrence?
• Are groups being compared to each other? If so, what groups are being compared?

Relative risks

Most often, findings are reported as relative risks. A relative risk shows how much higher, how much lower or whether there’s no difference in risk for people with a certain risk factor compared to the risk in people without the factor.

A relative risk compares 2 absolute risks.

• The numerator (the top number in a fraction) is the absolute risk among people with the risk factor.
• The denominator (the bottom number) is the absolute risk among those without the risk factor.

The absolute risk of those with the factor divided by the absolute risk of those without the factor gives the relative risk. 

The confidence interval around a relative risk helps show whether or not the relative risk is statistically significant (whether or not the finding is likely due to chance).

Learn more about confidence intervals .

Example of relative risk

Say a study shows women who don’t exercise (inactive women) have a 25 percent increase in breast cancer risk compared to women who do exercise (active women).

This statistic is a relative risk (the relative risk is 1.25). It means the inactive women were 25 percent more likely to develop breast cancer than women who exercised.

Learn more about relative risk .

Confidence intervals

A 95 percent confidence interval (95% CI) around a risk measure means there’s a 95 percent chance the “true” measure falls within the interval.

Because there’s random error in studies, and study populations are only samples of much larger populations, a single study doesn’t give the “one” correct answer. There’s always a range of likely answers. A single study gives a “best estimate” along with a 95 % CI of a likely range.

Most scientific studies report risk measures, such as relative risks, odds ratios and averages, with 95% CI.

Confidence intervals and statistical significance

For relative risks and odds ratios, a 95% CI that includes the number 1.0 means there’s no link between an exposure (such as a risk factor or a treatment) and an outcome (such as breast cancer or survival).

When this happens, the results are not statistically significant. This means any link between the exposure and outcome is likely due to chance.

If a 95% CI does not include 1.0, the results are statistically significant. This means there’s likely a true link between an exposure and an outcome.

Examples of confidence intervals

A few examples from the sample table above may help explain statistical significance.

The EPIC study found a relative risk of breast cancer of 1.07, with a 95% CI of 0.96 to 1.19. In the table, you will see 1.07 (0.96-1.19).

Women in the EPIC study who drank 1-2 drinks per day had a 7 percent higher risk of breast cancer than women who did not drink alcohol. The 95% CI of 0.96 to 1.19 includes 1.0. This means these results are not statistically significant and the increased risk of breast cancer is likely due to chance.

The Million Women’s Study found a relative risk of breast cancer of 1.13 with a 95% CI of 1.10 to 1.16. This is shown as 1.13 (1.10-1.16) in the table.

Women in the Million Women’s Study who drank 1-2 drinks per day had a 13 percent higher risk of breast cancer than women who did not drink alcohol. In this case, the 95% CI of 1.10 to 1.16 does not include 1.0. So, these results are statistically significant and suggest there’s likely a true link between alcohol and breast cancer.

For any topic, it’s important to look at the findings as a whole. In the sample table above, most studies show a statistically significant increase in risk among women who drink alcohol compared to women who don’t drink alcohol. Thus, the findings as a whole suggest alcohol increases the risk of breast cancer.

Summary relative risks

Summary relative risks from meta-analyses.

A meta-analysis takes relative risks reported in different studies and “averages” them to come up with a single, summary measure. Findings from a meta-analysis can give stronger conclusions than findings from a single study.

Summary relative risks from pooled analyses

A pooled analysis uses data from multiple studies to give a summary measure. It combines the data from each person in each of the studies into one large data set and analyses the data as if it were one big study. A pooled analysis is almost always better than a meta-analysis.

In a meta-analysis, researchers combine the results from different studies. In a pooled analysis, researchers combine the individual data from the different studies. This usually gives more statistical power than a meta-analyses. More statistical power means it’s more likely the results are not simply due to chance.

Cumulative risk

Sometimes, study findings are presented as a cumulative risk (risk up to a certain age). This risk is often shown as a percentage.

A cumulative risk may show the risk of breast cancer for a certain group of people up to a certain age. Say the cumulative risk up to age 70 for women with a risk factor is 20 percent. This means by age 70, 20 percent of the women (or 1 in 5) with the risk factor will get breast cancer.

Lifetime risk is a cumulative risk. It shows the risk of getting breast cancer during your lifetime (or up to a certain age). Women in the U.S. have a 13 percent lifetime risk of getting breast cancer. This means 1 in 8 women in the U.S. will get breast cancer during their lifetime.

Learn more about lifetime risk .

Sensitivity and specificity

Some tables show study findings on the sensitivity and specificity of screening tests. These measures describe the quality of a breast cancer screening test.

• Sensitivity  shows how well the screening test shows who truly has breast cancer. A sensitivity of 90 percent means 90 percent of people tested who truly have breast cancer are correctly identified as having cancer.
• Specificity  shows how well the screening test shows who truly does not have breast cancer. A specificity of 90 percent means 90 percent of the people who do not have breast cancer are correctly identified as not having cancer.

The goals of any screening test are:

• To correctly identify everyone who has a certain disease (100 percent sensitivity)
• To correctly identify everyone who does not have the disease (100 percent specificity)

A perfect test would correctly identify everyone with no mistakes. There would be no:

• False negatives (when people who have the disease are missed by the test)
• False positives (when healthy people are incorrectly shown to have the disease)

No screening test has perfect (100 percent) sensitivity and perfect (100 percent) specificity. There’s always a trade-off between the two. When a test gains sensitivity, it loses some specificity.

Learn more about sensitivity and specificity .

Finding studies

You may want more detail about a study than is given in the summary table. To help you find this information, the references for all the studies in a table are listed below the table.

Each reference includes the:

• Authors of the study article
• Title of the article
• Year the article was published
• Title and specific issue of the medical journal where the article appeared

PubMed , the National Library of Medicine’s search engine, is a good source for finding summaries of science and medical journal articles (called abstracts).

For some abstracts, PubMed also has links to the full text articles. Most medical journals have websites and offer their articles either for free or for a fee.

If you live near a university with a medical school or public health school, you may be able to go to the school’s medical library to get a copy of an article. Local public libraries may not carry medical journals, but they may be able to find a copy of an article from another source.

More information on research studies

If you’re interested in learning more about health research, a basic epidemiology textbook may be a good place to start. The National Cancer Institute also has information on epidemiology studies and randomized controlled trials.

Updated 07/25/22

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Top 6 Modular Study Table Designs: Maximize Learning

In today’s fast-paced world, having a dedicated space for studying is essential for productive learning. A modular study table offers a versatile and efficient solution that caters to the needs of students and professionals alike. This article explores the various benefits and innovative designs of modular study tables , providing valuable insights for creating an ideal study environment.

Table of Contents

What is a Modular Study Table?

A modular study table is a versatile and customizable furniture piece specifically designed for creating an efficient and productive study environment. Unlike traditional study tables, modular study tables offer flexibility in terms of design, functionality, and storage options . They consist of various interchangeable modules and accessories that can be easily assembled, rearranged, or removed to meet individual preferences and needs. These tables prioritize ergonomics, space optimization, and organization, providing users with a personalized and comfortable workspace for studying, working, or engaging in other activities.

Benefits of Modular Study Tables

Modular study tables provide numerous advantages over their traditional counterparts. Here are six key benefits to consider:

• Ergonomic Design :

Prioritizing your comfort, these tables promote good posture and reduce strain on your body. Adjustable heights, ergonomic chairs , and proper lighting ensure you can study comfortably for extended periods.

Space Optimization :

Limited space is no longer a problem. Customizable modules and attachments allow you to tailor the tables to your specific needs, incorporating shelves, drawers, or book racks to keep your study materials organized and within reach.

• Ample Storage Solutions :

Stay organized and focused with built-in compartments and drawers. These storage options help you keep your books, stationery, and study materials neatly arranged, creating a clean and tidy workspace.

• Efficient Cable Management :

Say goodbye to tangled cables. These tables feature built-in cable management systems , keeping wires organized and minimizing distractions caused by messy cables.

• Adjustable Height and Flexibility :

Personalize your study experience with adjustable heights that cater to your comfort level. Whether you’re tall or short, these tables can be customized to ensure a comfortable and ergonomic study environment.

• Aesthetics and Style :

Enhance your study space with contemporary designs that complement any interior style. From sleek and minimalistic to vibrant and colourful, these tables add visual appeal and inspiration to your study area.

Popular Modular Study Table Designs

Modular study tables come in various designs to suit different preferences and spatial constraints. 

Here are some popular modular study table designs:

Compact Foldable Study Tables

Ideal for small spaces, the compact foldable study table can be easily folded and stored when not in use. It offers a minimalist design with a sleek finish, making it suitable for contemporary interiors.

L-Shaped Study Tables

The L-shaped study table provides ample workspace, perfect for those who require extra room for books, laptops, or other study materials. It offers a modern and stylish design while also optimizing corner spaces effectively.

Wall-Mounted Study Tables

For those with limited floor space, a wall-mounted study table is an excellent choice. It can be installed at a convenient height, and when not in use, it can be folded up against the wall, creating more space in the room.

Standing Study Tables

Promoting a healthy study routine, standing tables allow users to alternate between sitting and standing positions. These tables provide height adjustability and encourage better blood circulation, keeping the body active during long study sessions.

Multi-Level Study Tables

A multi-level study table offers separate tiers or shelves for different study materials. This design provides easy accessibility to books, stationery, and electronic devices, ensuring an organized and clutter-free workspace.

Corner Study Tables

Utilizing corner spaces efficiently, corner study tables offer a triangular design that fits perfectly in corners. They provide ample desktop space and storage options while maximizing the available room area.

How to Set Up a Modular Study Table

Setting up a modular study table is a straightforward process. Follow these steps to create an organized and functional study space:

• Choose a suitable location with adequate lighting and minimal distractions.
• Assemble the study table according to the manufacturer’s instructions.
• Customize the table by attaching modules and accessories as per your requirements.
• Arrange your study materials, stationery, and books in the designated storage compartments.
• Ensure that cables are managed and organized using the built-in cable management system.
• Adjust the table height and chair position for optimal ergonomics.
• Personalize the study area with inspirational quotes, plants, or other decorative elements.
• Keep the study table clean and clutter-free by maintaining a regular cleaning routine.

Tips for Creating a Productive Study Environment

To maximize productivity while using modular study table designs, consider the following tips:

• Establish a daily study routine and stick to it.
• Minimize distractions by turning off notifications on electronic devices.
• Keep essential study materials within arm’s reach.
• Take regular breaks to avoid mental fatigue.
• Use a timer or productivity apps to manage study sessions effectively.
• Experiment with different lighting options to find the most comfortable ambience.
• Incorporate ergonomic accessories like wrist rests and footrests for added comfort.
• Stay hydrated and maintain a healthy diet to support cognitive function.
• Seek a quiet and peaceful study environment free from excessive noise.

Modular study table designs provide a wide array of options to create a productive and organized learning space. With their ergonomic features, space optimization, and storage solutions, modular study tables enhance comfort and efficiency during study sessions. Whether it’s a compact foldable table for small spaces or an L-shaped table for extra workspace, the versatility of modular designs caters to diverse needs. By setting up modular tables and implementing the mentioned tips, you can create an ideal study environment that fosters focus, organization, and productivity.

Can I customize a modular study table according to my specific requirements? 

Yes, It offers customization options to cater to individual needs. You can add or remove modules and accessories based on your preferences.

Are modular study table designs suitable for children?

Yes, modular study table designs can be suitable for children. They can be adjusted to different heights, allowing children to have an ergonomic and comfortable study setup.

Do modular study tables require professional installation? 

Most study tables come with simple assembly instructions, allowing users to set them up without professional assistance. However, if you prefer professional installation, you can hire a handyman.

Can a modular study table be used for other purposes besides studying? 

Yes, a modular study table is versatile and can be used for various purposes. They can serve as workstations, crafting tables, or even as a home office setup.

What are the benefits of modular study table designs?

Modular study table designs provide benefits such as space optimization, customization, efficient storage solutions, ergonomic features, and flexibility in adjusting height and layout.

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Your Guide to Creating Effective Tables and Figures in Research Papers

Research papers are full of data and other information that needs to be effectively illustrated and organized. Without a clear presentation of a study's data, the information will not reach the intended audience and could easily be misunderstood. Clarity of thought and purpose is essential for any kind of research. Using tables and figures to present findings and other data in a research paper can be effective ways to communicate that information to the chosen audience.

When manuscripts are screened, tables and figures can give reviewers and publication editors a quick overview of the findings and key information. After the research paper is published or accepted as a final dissertation, tables and figures will offer the same opportunity for other interested readers. While some readers may not read the entire paper, the tables and figures have the chance to still get the most important parts of your research across to those readers.

However, tables and figures are only valuable within a research paper if they are succinct and informative. Just about any audience—from scientists to the general public—should be able to identify key pieces of information in well-placed and well-organized tables. Figures can help to illustrate ideas and data visually. It is important to remember that tables and figures should not simply be repetitions of data presented in the text. They are not a vehicle for superfluous or repetitious information. Stay focused, stay organized, and you will be able to use tables and figures effectively in your research papers. The following key rules for using tables and figures in research papers will help you do just that.

Check style guides and journal requirements

The first step in deciding how you want to use tables and figures in your research paper is to review the requirements outlined by your chosen style guide or the submission requirements for the journal or publication you will be submitting to. For example, JMIR Publications states that for readability purposes, we encourage authors to include no more than 5 tables and no more than 8 figures per article. They continue to outline that tables should not go beyond the 1-inch margin of a portrait-orientation 8.5"x11" page using 12pt font or they may not be able to be included in your main manuscript because of our PDF sizing.

Consider the reviewers that will be examining your research paper for consistency, clarity, and applicability to a specific publication. If your chosen publication usually has shorter articles with supplemental information provided elsewhere, then you will want to keep the number of tables and figures to a minimum.

According to the Purdue Online Writing Lab (Purdue OWL), the American Psychological Association (APA) states that Data in a table that would require only two or fewer columns and rows should be presented in the text. More complex data is better presented in tabular format. You can avoid unnecessary tables by reviewing the data and deciding if it is simple enough to be included in the text. There is a balance, and the APA guideline above gives a good standard cutoff point for text versus table. Finally, when deciding if you should include a table or a figure, ask yourself is it necessary. Are you including it because you think you should or because you think it will look more professional, or are you including it because it is necessary to articulate the data? Only include tables or figures if they are necessary to articulate the data.

Table formatting

Creating tables is not as difficult as it once was. Most word processing programs have functions that allow you to simply select how many rows and columns you want, and then it builds the structure for you. Whether you create a table in LaTeX , Microsoft Word , Microsoft Excel , or Google Sheets , there are some key features that you will want to include. Tables generally include a legend, title, column titles, and the body of the table.

When deciding what the title of the table should be, think about how you would describe the table's contents in one sentence. There isn't a set length for table titles, and it varies depending on the discipline of the research, but it does need to be specific and clear what the table is presenting. Think of this as a concise topic sentence of the table.

Column titles should be designed in such a way that they simplify the contents of the table. Readers will generally skim the column titles first before getting into the data to prepare their minds for what they are about to see. While the text introducing the table will give a brief overview of what data is being presented, the column titles break that information down into easier-to-understand parts. The Purdue OWL gives a good example of what a table format could look like:

When deciding what your column titles should be, consider the width of the column itself when the data is entered. The heading should be as close to the length of the data as possible. This can be accomplished using standard abbreviations. When using symbols for the data, such as the percentage "%" symbol, place the symbol in the heading, and then you will not use the symbol in each entry, because it is already indicated in the column title.

For the body of the table, consistency is key. Use the same number of decimal places for numbers, keep the alignment the same throughout the table data, and maintain the same unit of measurement throughout each column. When information is changed within the same column, the reader can become confused, and your data may be considered inaccurate.

Figures in research papers

Figures can be of many different graphical types, including bar graphs, scatterplots, maps, photos, and more. Compared to tables, figures have a lot more variation and personalization. Depending on the discipline, figures take different forms. Sometimes a photograph is the best choice if you're illustrating spatial relationships or data hiding techniques in images. Sometimes a map is best to illustrate locations that have specific characteristics in an economic study. Carefully consider your reader's perspective and what detail you want them to see.

As with tables, your figures should be numbered sequentially and follow the same guidelines for titles and labels. Depending on your chosen style guide, keep the figure or figure placeholder as close to the text introducing it as possible. Similar to the figure title, any captions should be succinct and clear, and they should be placed directly under the figure.

Using the wrong kind of figure is a common mistake that can affect a reader's experience with your research paper. Carefully consider what type of figure will best describe your point. For example, if you are describing levels of decomposition of different kinds of paper at a certain point in time, then a scatter plot would not be the appropriate depiction of that data; a bar graph would allow you to accurately show decomposition levels of each kind of paper at time "t." The Writing Center of the University of North Carolina at Chapel Hill has a good example of a bar graph offering easy-to-understand information:

If you have taken a figure from another source, such as from a presentation available online, then you will need to make sure to always cite the source. If you've modified the figure in any way, then you will need to say that you adapted the figure from that source. Plagiarism can still happen with figures – and even tables – so be sure to include a citation if needed.

Using the tips above, you can take your research data and give your reader or reviewer a clear perspective on your findings. As The Writing Center recommends, Consider the best way to communicate information to your audience, especially if you plan to use data in the form of numbers, words, or images that will help you construct and support your argument. If you can summarize the data in a couple of sentences, then don't try and expand that information into an unnecessary table or figure. Trying to use a table or figure in such cases only lengthens the paper and can make the tables and figures meaningless instead of informative.

Carefully choose your table and figure style so that they will serve as quick and clear references for your reader to see patterns, relationships, and trends you have discovered in your research. For additional assistance with formatting and requirements, be sure to review your publication or style guide's instructions to ensure success in the review and submission process.

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• Volume 24, Issue 2
• Five tips for developing useful literature summary tables for writing review articles
• Article Text
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• http://orcid.org/0000-0003-0157-5319 Ahtisham Younas 1 , 2 ,
• http://orcid.org/0000-0002-7839-8130 Parveen Ali 3 , 4
• 1 Memorial University of Newfoundland , St John's , Newfoundland , Canada
• 2 Swat College of Nursing , Pakistan
• 3 School of Nursing and Midwifery , University of Sheffield , Sheffield , South Yorkshire , UK
• 4 Sheffield University Interpersonal Violence Research Group , Sheffield University , Sheffield , UK
• Correspondence to Ahtisham Younas, Memorial University of Newfoundland, St John's, NL A1C 5C4, Canada; ay6133{at}mun.ca

https://doi.org/10.1136/ebnurs-2021-103417

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Introduction

Literature reviews offer a critical synthesis of empirical and theoretical literature to assess the strength of evidence, develop guidelines for practice and policymaking, and identify areas for future research. 1 It is often essential and usually the first task in any research endeavour, particularly in masters or doctoral level education. For effective data extraction and rigorous synthesis in reviews, the use of literature summary tables is of utmost importance. A literature summary table provides a synopsis of an included article. It succinctly presents its purpose, methods, findings and other relevant information pertinent to the review. The aim of developing these literature summary tables is to provide the reader with the information at one glance. Since there are multiple types of reviews (eg, systematic, integrative, scoping, critical and mixed methods) with distinct purposes and techniques, 2 there could be various approaches for developing literature summary tables making it a complex task specialty for the novice researchers or reviewers. Here, we offer five tips for authors of the review articles, relevant to all types of reviews, for creating useful and relevant literature summary tables. We also provide examples from our published reviews to illustrate how useful literature summary tables can be developed and what sort of information should be provided.

Tip 1: provide detailed information about frameworks and methods

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Tabular literature summaries from a scoping review. Source: Rasheed et al . 3

The provision of information about conceptual and theoretical frameworks and methods is useful for several reasons. First, in quantitative (reviews synthesising the results of quantitative studies) and mixed reviews (reviews synthesising the results of both qualitative and quantitative studies to address a mixed review question), it allows the readers to assess the congruence of the core findings and methods with the adapted framework and tested assumptions. In qualitative reviews (reviews synthesising results of qualitative studies), this information is beneficial for readers to recognise the underlying philosophical and paradigmatic stance of the authors of the included articles. For example, imagine the authors of an article, included in a review, used phenomenological inquiry for their research. In that case, the review authors and the readers of the review need to know what kind of (transcendental or hermeneutic) philosophical stance guided the inquiry. Review authors should, therefore, include the philosophical stance in their literature summary for the particular article. Second, information about frameworks and methods enables review authors and readers to judge the quality of the research, which allows for discerning the strengths and limitations of the article. For example, if authors of an included article intended to develop a new scale and test its psychometric properties. To achieve this aim, they used a convenience sample of 150 participants and performed exploratory (EFA) and confirmatory factor analysis (CFA) on the same sample. Such an approach would indicate a flawed methodology because EFA and CFA should not be conducted on the same sample. The review authors must include this information in their summary table. Omitting this information from a summary could lead to the inclusion of a flawed article in the review, thereby jeopardising the review’s rigour.

Tip 2: include strengths and limitations for each article

Critical appraisal of individual articles included in a review is crucial for increasing the rigour of the review. Despite using various templates for critical appraisal, authors often do not provide detailed information about each reviewed article’s strengths and limitations. Merely noting the quality score based on standardised critical appraisal templates is not adequate because the readers should be able to identify the reasons for assigning a weak or moderate rating. Many recent critical appraisal checklists (eg, Mixed Methods Appraisal Tool) discourage review authors from assigning a quality score and recommend noting the main strengths and limitations of included studies. It is also vital that methodological and conceptual limitations and strengths of the articles included in the review are provided because not all review articles include empirical research papers. Rather some review synthesises the theoretical aspects of articles. Providing information about conceptual limitations is also important for readers to judge the quality of foundations of the research. For example, if you included a mixed-methods study in the review, reporting the methodological and conceptual limitations about ‘integration’ is critical for evaluating the study’s strength. Suppose the authors only collected qualitative and quantitative data and did not state the intent and timing of integration. In that case, the strength of the study is weak. Integration only occurred at the levels of data collection. However, integration may not have occurred at the analysis, interpretation and reporting levels.

Tip 3: write conceptual contribution of each reviewed article

While reading and evaluating review papers, we have observed that many review authors only provide core results of the article included in a review and do not explain the conceptual contribution offered by the included article. We refer to conceptual contribution as a description of how the article’s key results contribute towards the development of potential codes, themes or subthemes, or emerging patterns that are reported as the review findings. For example, the authors of a review article noted that one of the research articles included in their review demonstrated the usefulness of case studies and reflective logs as strategies for fostering compassion in nursing students. The conceptual contribution of this research article could be that experiential learning is one way to teach compassion to nursing students, as supported by case studies and reflective logs. This conceptual contribution of the article should be mentioned in the literature summary table. Delineating each reviewed article’s conceptual contribution is particularly beneficial in qualitative reviews, mixed-methods reviews, and critical reviews that often focus on developing models and describing or explaining various phenomena. Figure 2 offers an example of a literature summary table. 4

Tabular literature summaries from a critical review. Source: Younas and Maddigan. 4

Tip 4: compose potential themes from each article during summary writing

While developing literature summary tables, many authors use themes or subthemes reported in the given articles as the key results of their own review. Such an approach prevents the review authors from understanding the article’s conceptual contribution, developing rigorous synthesis and drawing reasonable interpretations of results from an individual article. Ultimately, it affects the generation of novel review findings. For example, one of the articles about women’s healthcare-seeking behaviours in developing countries reported a theme ‘social-cultural determinants of health as precursors of delays’. Instead of using this theme as one of the review findings, the reviewers should read and interpret beyond the given description in an article, compare and contrast themes, findings from one article with findings and themes from another article to find similarities and differences and to understand and explain bigger picture for their readers. Therefore, while developing literature summary tables, think twice before using the predeveloped themes. Including your themes in the summary tables (see figure 1 ) demonstrates to the readers that a robust method of data extraction and synthesis has been followed.

Tip 5: create your personalised template for literature summaries

Often templates are available for data extraction and development of literature summary tables. The available templates may be in the form of a table, chart or a structured framework that extracts some essential information about every article. The commonly used information may include authors, purpose, methods, key results and quality scores. While extracting all relevant information is important, such templates should be tailored to meet the needs of the individuals’ review. For example, for a review about the effectiveness of healthcare interventions, a literature summary table must include information about the intervention, its type, content timing, duration, setting, effectiveness, negative consequences, and receivers and implementers’ experiences of its usage. Similarly, literature summary tables for articles included in a meta-synthesis must include information about the participants’ characteristics, research context and conceptual contribution of each reviewed article so as to help the reader make an informed decision about the usefulness or lack of usefulness of the individual article in the review and the whole review.

In conclusion, narrative or systematic reviews are almost always conducted as a part of any educational project (thesis or dissertation) or academic or clinical research. Literature reviews are the foundation of research on a given topic. Robust and high-quality reviews play an instrumental role in guiding research, practice and policymaking. However, the quality of reviews is also contingent on rigorous data extraction and synthesis, which require developing literature summaries. We have outlined five tips that could enhance the quality of the data extraction and synthesis process by developing useful literature summaries.

• Aromataris E ,
• Rasheed SP ,

Twitter @Ahtisham04, @parveenazamali

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Patient consent for publication Not required.

Provenance and peer review Not commissioned; externally peer reviewed.

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How to make a scientific table | Step-by-step and Formatting

It’s time to learn how to make a scientific table to increase the readability and attractiveness of your research paper.

When writing a research paper, there is frequently a massive quantity of data that must be incorporated to meet the research’s purpose. Instead of stuffing your research paper with all this information, you can employ visual assets to make it simpler to read and use to your advantage to make it more appealing to readers.

In this Mind The Graph article, you will learn how to make a scientific table properly, to attract readers and improve understandability.

What is a scientific table and what are its purposes?

Tables are typically used to organize data that is too extensive or nuanced to properly convey in the text, allowing the reader to quickly see and comprehend the findings. Tables can be used to summarize information, explain variables, or organize and present surveys. They can be used to highlight trends or patterns in data and to make research more readable by separating numerical data from text. Tables, although full, should not be overly convoluted.

Tables can only display numerical values and text in columns and rows. Any other type of illustration, such as a chart, graph, photograph, drawing, and so on is called a figure.

If you’re not sure whether to use tables or figures in your research, see How to Include Figures in a Research Paper to find out.

Table formatting

This section teaches you all you need to know on how to make a scientific table to include in your research paper. The proper table format is extremely basic and straightforward to accomplish, here’s a simple guideline to help you:

• Number: If you have more than one table, number them sequentially (Table 1, Table 2…).
• Referencing: Each table must be referred to in the text with a capital T: “as seen in Table 1”.
• Title: Make sure the title corresponds to the topic of the table. Tables should have a precise, informative title that serves as an explanation for the table. Titles can be short or long depending on their subject.
• Column headings: Headings must be helpful and clear when representing the type of data provided. The reader’s attention is drawn progressively from the headline to the column title. A solid collection of column headings will help the reader understand what the table is about immediately.
• Table body: This is the major section of the table that contains numerical or textual data. Make your table such that the elements read from top to bottom, not across.
• Needed information: Make sure to include units, error values and number of samples, as well as explain whatever abbreviation or symbol is used in tables. 
• Lines: Limit the use of lines, only use what’s necessary. 

Steps to make an effective scientific table

Now that you understand the fundamentals of how to make a scientific table , consider the following ideas and best practices for creating the most effective tables for your research work:

• If your study includes both a table and a graph, avoid including the same information in both.
• Do not duplicate information from a table in a text.
• Make your table aesthetically appealing and easy to read by leaving enough space between columns and rows and using a basic yet effective structure.
• If your table has a lot of information, consider categorizing it and dividing it into columns.
• Consider merging tables with repeated information or deleting those that may not be essential.
• Use footnotes to highlight important information for any of the cells. Use an alphabetical footnote marker if your table contains numerical data. 
• Cite the reference if the table you’re displaying contains data from prior research to avoid plagiarism.

Make scientifically accurate infographics in minutes

Aside from adding tables to make your research paper more precise and appealing, consider using infographics, Mind the Graph is a simple tool for creating excellent scientific infographics that may help you solidify and improve the authority of your research.

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Effective Use of Tables and Figures in Research Papers

Research papers are often based on copious amounts of data that can be summarized and easily read through tables and graphs. When writing a research paper , it is important for data to be presented to the reader in a visually appealing way. The data in figures and tables, however, should not be a repetition of the data found in the text. There are many ways of presenting data in tables and figures, governed by a few simple rules. An APA research paper and MLA research paper both require tables and figures, but the rules around them are different. When writing a research paper, the importance of tables and figures cannot be underestimated. How do you know if you need a table or figure? The rule of thumb is that if you cannot present your data in one or two sentences, then you need a table .

Using Tables

Tables are easily created using programs such as Excel. Tables and figures in scientific papers are wonderful ways of presenting data. Effective data presentation in research papers requires understanding your reader and the elements that comprise a table. Tables have several elements, including the legend, column titles, and body. As with academic writing, it is also just as important to structure tables so that readers can easily understand them. Tables that are disorganized or otherwise confusing will make the reader lose interest in your work.

• Title: Tables should have a clear, descriptive title, which functions as the “topic sentence” of the table. The titles can be lengthy or short, depending on the discipline.
• Column Titles: The goal of these title headings is to simplify the table. The reader’s attention moves from the title to the column title sequentially. A good set of column titles will allow the reader to quickly grasp what the table is about.
• Table Body: This is the main area of the table where numerical or textual data is located. Construct your table so that elements read from up to down, and not across.

Related: Done organizing your research data effectively in tables? Check out this post on tips for citing tables in your manuscript now!

The placement of figures and tables should be at the center of the page. It should be properly referenced and ordered in the number that it appears in the text. In addition, tables should be set apart from the text. Text wrapping should not be used. Sometimes, tables and figures are presented after the references in selected journals.

Using Figures

Figures can take many forms, such as bar graphs, frequency histograms, scatterplots, drawings, maps, etc. When using figures in a research paper, always think of your reader. What is the easiest figure for your reader to understand? How can you present the data in the simplest and most effective way? For instance, a photograph may be the best choice if you want your reader to understand spatial relationships.

• Figure Captions: Figures should be numbered and have descriptive titles or captions. The captions should be succinct enough to understand at the first glance. Captions are placed under the figure and are left justified.
• Image: Choose an image that is simple and easily understandable. Consider the size, resolution, and the image’s overall visual attractiveness.
• Additional Information: Illustrations in manuscripts are numbered separately from tables. Include any information that the reader needs to understand your figure, such as legends.

Common Errors in Research Papers

Effective data presentation in research papers requires understanding the common errors that make data presentation ineffective. These common mistakes include using the wrong type of figure for the data. For instance, using a scatterplot instead of a bar graph for showing levels of hydration is a mistake. Another common mistake is that some authors tend to italicize the table number. Remember, only the table title should be italicized .  Another common mistake is failing to attribute the table. If the table/figure is from another source, simply put “ Note. Adapted from…” underneath the table. This should help avoid any issues with plagiarism.

Using tables and figures in research papers is essential for the paper’s readability. The reader is given a chance to understand data through visual content. When writing a research paper, these elements should be considered as part of good research writing. APA research papers, MLA research papers, and other manuscripts require visual content if the data is too complex or voluminous. The importance of tables and graphs is underscored by the main purpose of writing, and that is to be understood.

Frequently Asked Questions

"Consider the following points when creating figures for research papers: Determine purpose: Clarify the message or information to be conveyed. Choose figure type: Select the appropriate type for data representation. Prepare and organize data: Collect and arrange accurate and relevant data. Select software: Use suitable software for figure creation and editing. Design figure: Focus on clarity, labeling, and visual elements. Create the figure: Plot data or generate the figure using the chosen software. Label and annotate: Clearly identify and explain all elements in the figure. Review and revise: Verify accuracy, coherence, and alignment with the paper. Format and export: Adjust format to meet publication guidelines and export as suitable file."

"To create tables for a research paper, follow these steps: 1) Determine the purpose and information to be conveyed. 2) Plan the layout, including rows, columns, and headings. 3) Use spreadsheet software like Excel to design and format the table. 4) Input accurate data into cells, aligning it logically. 5) Include column and row headers for context. 6) Format the table for readability using consistent styles. 7) Add a descriptive title and caption to summarize and provide context. 8) Number and reference the table in the paper. 9) Review and revise for accuracy and clarity before finalizing."

"Including figures in a research paper enhances clarity and visual appeal. Follow these steps: Determine the need for figures based on data trends or to explain complex processes. Choose the right type of figure, such as graphs, charts, or images, to convey your message effectively. Create or obtain the figure, properly citing the source if needed. Number and caption each figure, providing concise and informative descriptions. Place figures logically in the paper and reference them in the text. Format and label figures clearly for better understanding. Provide detailed figure captions to aid comprehension. Cite the source for non-original figures or images. Review and revise figures for accuracy and consistency."

"Research papers use various types of tables to present data: Descriptive tables: Summarize main data characteristics, often presenting demographic information. Frequency tables: Display distribution of categorical variables, showing counts or percentages in different categories. Cross-tabulation tables: Explore relationships between categorical variables by presenting joint frequencies or percentages. Summary statistics tables: Present key statistics (mean, standard deviation, etc.) for numerical variables. Comparative tables: Compare different groups or conditions, displaying key statistics side by side. Correlation or regression tables: Display results of statistical analyses, such as coefficients and p-values. Longitudinal or time-series tables: Show data collected over multiple time points with columns for periods and rows for variables/subjects. Data matrix tables: Present raw data or matrices, common in experimental psychology or biology. Label tables clearly, include titles, and use footnotes or captions for explanations."

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Getting started with tables

Hazel inskip.

MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton General Hospital, Southampton, SO16 6YD UK

Georgia Ntani

Leo westbury, chiara di gravio, stefania d’angelo, camille parsons, janis baird, associated data.

Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.

Tables are often overlooked by many readers of papers who tend to focus on the text. Good tables tell much of the story of a paper and give a richer insight into the details of the study participants and the main research findings. Being confident in reading tables and constructing clear tables are important skills for researchers to master.

Common forms of tables were considered, along with the standard statistics used in them. Papers in the Archives of Public Health published during 2015 and 2016 were hand-searched for examples to illustrate the points being made. Presentation of graphs and figures were not considered as they are outside the scope of the paper.

Basic statistical concepts are outlined to aid understanding of each of the tables presented. The first table in many papers gives an overview of the study population and its characteristics, usually giving numbers and percentages of the study population in different categories (e.g. by sex, educational attainment, smoking status) and summaries of measured characteristics (continuous variables) of the participants (e.g. age, height, body mass index). Tables giving the results of the analyses follow; these often include summaries of characteristics in different groups of participants, as well as relationships between the outcome under study and the exposure of interest. For continuous outcome data, results are often expressed as differences between means, or regression or correlation coefficients. Ratio/relative measures (e.g. relative risks, odds ratios) are usually used for binary outcome measures that take one of two values for each study participants (e.g. dead versus alive, obese versus non-obese). Tables come in many forms, but various standard types are described here.

Clear tables provide much of the important detail in a paper and researchers are encouraged to read and construct them with care.

Tables are an important component of any research paper. Yet, anecdotally, many people say that they find tables difficult to understand so focus only on the text when reading a paper. However, tables provide a much richer sense of a study population and the results than can be described in the text. The tables and text complement each other in that the text outlines the main findings, while the detail is contained in the tables; the text should refer to each table at the appropriate place(s) in the paper. We aim to give some insights into reading tables for those who find them challenging, and to assist those preparing tables in deciding what they need to put into them. Producing clear, informative tables increases the likelihood of papers being published and read. Good graphs and figures can often provide a more accessible presentation of study findings than tables. They can add to the understanding of the findings considerably, but they can rarely contain as much detail as a table. Choosing when to present a graph or figure and when to present a table needs careful consideration but this article focuses only on the presentation of tables.

We provide a general description of tables and statistics commonly used when presenting data, followed by specific examples. No two papers will present the tables in the same way, so we can only give some general insights. The statistical approaches are described briefly but cannot be explained fully; the reader is referred to various books on the topic [ 1 – 6 ].

Presentation of tables

The title (or legend) of a table should enable the reader to understand its content, so a clear, concise description of the contents of the table is required. The specific details needed for the title will vary according to the type of table. For example, titles for tables of characteristics should give details of the study population being summarised and indicate whether separate columns are presented for particular characteristics, such as sex. For tables of main findings, the title should include the details of the type of statistics presented or the analytical method. Ideally the table title should enable the table to be examined and understood without reference to the rest of the article, and so information on study, time and place needs to be included. Footnotes may be required to amplify particular points, but should be kept to a minimum. Often they will be used to explain abbreviations or symbols used in the table or to list confounding factors for which adjustment has been made in the analysis.

Clear headings for rows and columns are also required and the format of the table needs careful consideration, not least in regard to the appropriateness and number of rows and columns included within the table. Generally it is better to present tables with more rows than columns; it is usually easier to read down a table than across it, and page sizes currently in use are longer than they are wide. Very large tables can be hard to absorb and make the reader’s work more onerous, but can be useful for those who require extra detail. Getting the balance right needs care.

Types of tables

Many research articles present a summary of the characteristics of the study population in the first table. The purpose of these tables is to provide information on the key characteristics of the study participants, and allow the reader to assess the generalisability of the findings. Typically, age and sex will be presented along with various characteristics pertinent to the study in question, for example smoking prevalence, socio-economic position, educational attainment, height, and body mass index. A single summary column may be presented or perhaps more than one column split according to major characteristics such as sex (i.e. separate columns for males and females) or, for trials, the intervention and control groups.

Subsequent tables generally present details of the associations identified in the main analyses. Sometimes these include results that are unadjusted or ‘crude’ (i.e. don’t take account of other variables that might influence the association) often followed by results from adjusted models taking account of other factors.

Other types of tables occur in some papers. For example, systematic review papers contain tables giving the inclusion and exclusion criteria for the review as well as tables that summarise the characteristics and results of each study included in the review; such tables can be extremely large if the review covers many studies. Qualitative studies often provide tables describing the characteristics of the study participants in a more narrative format than is used for quantitative studies. This paper however, focuses on tables that present numerical data.

Statistics commonly presented in tables

The main summary statistics provided within a table depend on the type of outcome under investigation in the study. If the variable is continuous (i.e. can take any numerical value, between a minimum and a maximum, such as blood pressure, height, birth weight), then means and standard deviations (SD) tend to be given when the distribution is symmetrical, and particularly when it follows the classical bell shaped curve known as a Normal or Gaussian distribution (see Fig.  1a ). The mean is the usual arithmetic average and the SD is an indication of the spread of the values. Roughly speaking, the SD is about a quarter of the difference between the largest and the smallest value excluding 5% of values at the extreme ends. So, if the mean is 100 and the SD is 20 we would expect 95% of the values in our data to be between about 60 (i.e. 100–2×20) and 140 (100 + 2×40).

Distribution of heights and weights of young women from the Southampton Women’s Survey [ 7 ]. a Shows the height distribution, which is symmetrical and generally follows a standard normal distribution, while b shows weight, which is skewed to the right

The median and inter-quartile range (IQR) are usually provided when the data are not symmetrical as in Fig.  1b , which gives an example of data that are skewed, such that if the values are plotted in a histogram there are many values at one end of the distribution but fewer at the other end [ 7 ]. If all the values of the variable were listed in order, the median would be the middle value and the IQR would be the values a quarter and three-quarters of the way through the list. Sometimes the lower value of the IQR is labelled Q1 (quartile 1), the median is Q2, and the upper value is Q3. For categorical variables, frequencies and percentages are used.

Common statistics for associations between continuous outcomes include differences in means, regression coefficients and correlation coefficients. For these statistics, values of zero indicate no association between the exposure and outcome of interest. A correlation coefficient of 0 indicates no association, while a value of 1 or −1 would indicate perfect positive or negative correlation; values outside the range −1 to 1 are not possible. Regression coefficients can take any positive or negative value depending on the units of measurement of the exposure and outcome.

For binary outcome measures that only take two possible values (e.g. diseased versus not, dead versus alive, obese versus not obese) the results are commonly presented in the form of relative measures. These include any measure with the word ‘relative’ or ‘ratio’ in their name, such as odds ratios, relative risks, prevalence ratios, incidence rate ratios and hazard ratios. All are interpreted in much the same way: values above 1 indicate an elevated risk of the outcome associated with the exposure under study, whereas below 1 implies a protective effect. No association between the outcome and exposure is apparent if the ratio is 1.

Typically in results tables, 95% confidence intervals (95% CIs) and/or p -values will be presented. A 95% CI around a result indicates that, in the absence of bias, there is a 95% probability that the interval includes the true value of the result in the wider population from which the study participants were drawn. It also gives an indication of how precisely the study team has been able to estimate the result (whether it is a regression coefficient, a ratio/relative measure or any of the summary measures mentioned above). The wider the 95% CI, the less precise is our estimate of the result. Wide 95% CIs tend to arise from small studies and hence the drive for larger studies to give greater precision and certainty about the findings.

If a 95% CI around a result for a continuous variable (difference in means, regression or correlation coefficient) includes 0 then it is unlikely that there is a real association between exposure and outcome whereas, for a binary outcome, a real association is unlikely if the 95% CI around a relative measure, such as a hazard or odds ratio, includes 1.

The p -value is the probability that the finding we have observed could have occurred by chance, and therefore there is no identifiable association between the exposure of interest and the outcome measure in the wider population. If the p -value is very small, then we are more convinced that we have found an association that is not explained by chance (though it may be due to bias or confounding in our study). Traditionally a p -value of less than 0.05 (sometimes expressed as 5%) has been considered as ‘statistically significant’ but this is an arbitrary value and the smaller the p -value the less likely the result is simply due to chance [ 8 ].

Frequently, data within tables are presented with 95% CIs but without p -values or vice versa. If the 95% CI includes 0 (for a continuous outcome measure) or 1 (for a binary outcome), then generally the p -value will be greater than 0.05, whereas if it does not include 0 or 1 respectively, then the p -value will be less than 0.05 [ 9 ]. Generally, 95% CIs are more informative than p -values; providing both may affect the readability of a table and so preference should generally be given to 95% CIs. Sometimes, rather than giving exact p-values, they are indicated by symbols that are explained in a footnote; commonly one star (*) indicates p  < 0.05, two stars (**) indicates p  < 0.01.

Results in tables can only be interpreted if the units of measurement are clearly given. For example, mean or median age could be in days, weeks, months or years if infants and children are being considered, and 365, 52, 12 or 1 for a mean age of 1 year could all be presented, as long the unit of measurement is provided. Standard deviations should be quoted in the same units as the mean to which they refer. Relative measures, such as odds ratios, and correlation coefficients do not have units of measurement, but for regression coefficients the unit of measurement of the outcome variable is required, and also of the exposure variable if it is continuous.

The examples are all drawn from recent articles in Archives of Public Health. They were chosen to represent a variety of types of tables seen in research publications.

Tables of characteristics

The table of characteristics in Table  1 is from a study assessing knowledge and practice in relation to tuberculosis control among in Ethiopian health workers [ 10 ]. The authors have presented the characteristics of the health workers who participated in the study. Summary statistics are based on categories of the characteristics, so numbers (frequencies) in each category and the percentages of the total study population within each category are presented for each characteristic. From this, the reader can see that:

• the study population is quite young, as only around 10% are more than 40 years old;
• the majority are female;
• more than half are nurses;
• about half were educated to degree level or above.

Table of study population characteristics from a paper on the assessment of knowledge and practice in relation to tuberculosis control in health workers in Ethiopia [ 10 ]. Socio demographic characteristics of the study population in public health facilities, Addis Ababa, 2014

OPD outpatient department; TB Tuberculosis.

a Midwife, radiology, physiotherapy; b MCH, delivery,EPI, FP, physiotherapy

The table of characteristics in Table  2 is from a study of the relationship between distorted body image and lifestyle in adolescents in Japan [ 11 ]. Here the presentation is split into separate columns for boys and girls. The first four characteristics are continuous variables, not split into categories but, instead, presented as means, with the SDs given in brackets. The three characteristics in the lower part of the table are categorical variables and, similar to Table  1 , the frequency/numbers and percentages in each category are presented. The p -values indicate that boys and girls differ on some of the characteristics, notably height, self-perceived weight status and body image perception.

Table of study population characteristics from a paper on the relationship between distorted body image and lifestyle in adolescents in Japan [ 11 ]. Characteristics of study participants by sex (Japan; 2005–2009)

Data are expressed as numbers (%), values are means (standard deviation). The unpaired t- test and chi-squad test were used to compare characteristics between boys and girls

In Table  3 , considerable detail is given for continuous variables in the table. This comes from an article describing the relationship between mid-upper-arm circumference (MUAC) and weight changes in young children admitted to hospital with severe acute malnutrition from three countries [ 12 ]. For each country, the categorical characteristic of sex is presented as in the previous two examples, but more detail is given for the continuous variables of age, MUAC and height. The mean is provided as in Table  2 , though without a standard deviation, but we are also given the minimum value, the 25th percentile (labelled Q1 – for quartile 1), the median (the middle value), the 75th percentile (labelled Q2, here though correctly it should be Q3 – see above) and the maximum value. The table shows:

• Ethiopian children in this study were older and taller than those from the other two countries but their MUAC measurements tended to be smaller;
• in Bangladesh, disproportionally more females than males were admitted for treatment compared with the other two countries.

Table of study population characteristics from a paper describing the relationship between mid-upper-arm circumference (MUAC) and weight changes in young children [ 12 ]. Characteristics of study population at admission

It is unusual to present as much detail on continuous characteristics as is given in Table  3 . Usually, for each characteristic, either (a) mean and SD or (b) median and IQR would be given, but not both.

Tables of results – summary findings

Many results tables are simple summaries and look similar to tables presenting characteristics, as described above. Sometimes the initial table of characteristics includes some basic comparisons that indicate the main results of the study. Table  4 shows part of a large table of characteristics for a study of risk factors for acute lower respiratory infections (ALRI) among young children in Rwanda [ 13 ]. In addition to presenting the numbers of children in each category of a variety of characteristics, it also shows the percentage in each category among those who suffered ALRI in the previous two weeks, and provides p- values for the differences between the categories among those who did and did not suffer from ALRI. Thus only 2.9% of older children (24–59 months) within the study suffered from ALRI, compared with about 5% in the two youngest categories. The p -value of 0.001, well below 0.05, indicates that this difference is statistically significant. The other finding of some interest is that children who took vitamin A supplements appeared to be less likely to suffer from ALRI than those who did not, but the p -value of 0.04 is close to 0.05 so not as remarkable a finding as for the difference between the age groups.

Part of a table of basic results from a study of risk factors for acute lower respiratory infections (ALRI) among young children in Rwanda [ 13 ]. Bivariate analysis of factors associated with acute lower respiratory infection among children under five in Rwanda, RDHS 2010

Table  5 shows a summary table of average life expectancy in British Columbia by socioeconomic status [ 14 ]. The average life expectancy at birth and the associated 95% CIs are given according to level of socio-economic status for the total population (column 1), followed by males and females separately. The study is large so the 95% CIs are quite narrow, and the table indicates that there are considerable differences in life expectancy between the three socioeconomic groups, with the lowest category having the poorest life expectancy. The gap in life expectancy between the lowest and highest category is more than three years, as shown in the final row.

Summary table of average life expectancy in British Columbia by socioeconomic status [ 14 ]. British Columbia regional average life expectancy at birth by regional socioeconomic status, 2007–2011

SES Socioeconomic status, LE 0 Life expectancy at birth, CI Confidence interval

Tables of results – continuous outcomes

Continuous outcome measures can be analysed in a variety of ways, depending on the purpose of the study and whether the measure of the exposure is continuous, categorical or binary.

Table  6 shows an example of correlation coefficients indicating the degree of association between the exposure of interest (cognitive test scores) and the outcome measure (academic performance) [ 15 ]. No confidence intervals are presented, but the results show that almost all the particular cognitive test scores are statistically significantly associated ( p -value < 0.05) with the two measures of academic performance. Note that this table is an example of where a footnote is used to give information about the p-values. Not surprisingly, all the correlations are positive; one would expect that as cognitive score increase so too would academic performance. The numbers labelled “N” give the number of children who contributed data to each correlation coefficient.

Correlation coefficients from a study assessing the association between cognitive function and academic performance in Ethiopia [ 15 ]. Correlation between cognitive fuinction test and academic performance among school aged children in Goba Town, South east Ethiopia, May 2014

*Statistically significant at p <0.05, **Statistically significant a p >0.01

Table  7 is quite a complex table, but one that bears examination. It presents regression coefficients from an analysis of pregnancy exposure to nitrogen dioxide (NO 2 ) and birth weight of the baby in a large study of four areas in Norway; more than 17,000 women-baby pairs contributed to the complete crude analysis [ 16 ]. Regression coefficients are presented and labelled “Beta”, the usual name for such coefficients, though the Greek letter β, B or b are sometimes used. They are interpreted as follows: for one unit increase in the exposure variable then the outcome measure increases by the amount of the regression coefficient. Regression coefficients of zero indicate no association. In this table, the Beta in the top left of the table indicates that as NO 2 exposure of the mother increases by 1 unit (a ‘unit’ in this analysis is 10 μg/m 3 , see the footnote in the table, which gives the units of measurement used for the regression coefficients: grams per 10 μg/m 3 NO 2 ) then the birth weight of her baby decreases (because the Beta is negative) by 37.9 g. The 95% CI does not include zero and the p -value is small (<0.001) implying that the association is not due solely to chance.

Table of regression coefficients for the relationship between exposure to NO 2 in pregnancy and birth weight [ 16 ]. Main and stratified analysis of association between pregnancy exposure to NO 2 and birth weight

Effect estimate in grams per 10μg/m 3 NO 2

GA gestational age, LMP last menstrual period

a Model 1 adjusted for: maternal education, birth season, sex of child, maternal age, maternal marital status, maternal smoking during pregnancy, maternal height

b Model 2 adjusted for: maternal education, birth season, sex of child, maternal age, maternal marital status, maternal smoking during pregnancy, maternal height, area

c Model 3 adjusted for: maternal education, birth season, sex of child, maternal age, maternal marital status, maternal smoking during pregnancy, maternal height, parity, maternal weight, in stratified analysis the corresponding stratification variable is not included in the adjusment

However, reading across the columns of the table gives a different story. The successive sets of columns include adjustment for increasing numbers of factors that might affect the association. While model 1 still indicates a negative association between NO 2 and birth weight that is highly significant ( p  < 0.001), models 2 and 3 do not. Inclusion of adjustment for parity or area and maternal weight has reduced the association such that the Betas have shrunk in magnitude to be closer to 0, with 95% CIs including 0 and p -values >0.05.

The table has multiple rows, with each one providing information on a different subset of the data, so the numbers in the analyses are all smaller than in the first row. The second row restricts the analysis to women who did not move address during pregnancy, an important consideration in estimating NO 2 exposure from home addresses. The third row restricts the analysis to those whose gestational age was based on the last menstrual period. These second two rows present ‘sensitivity analyses’, performed to check that the results were not due to potential biases resulting from women moving house or having uncertain gestational ages. The remaining rows in the table present stratified analyses, with results given for each category of various variables of interest, namely geographical area, maternal smoking, parity, baby’s sex, mother’s educational level and season of birth. Only one row of this table has a statistically significant result for models 2 and 3, namely babies born in spring, but this finding is not discussed in the paper. Note the gap in the table in the model 2 column as it is not possible to adjust for area (one of the adjustment factors in model 2) when the analysis is being presented for each area separately.

Tables of results – binary outcomes

Table  8 presents results from a study assessing whether children’s eating styles are associated with having a waist-hip ratio greater or equal to 0.5 (the latter being the outcome variable expressed in binary form – ≥0.5 versus <0.5) [ 17 ]. Results for boys and girls are presented separately, along with the number of children in each of the eating style categories. The main results are presented as crude and adjusted odds ratios (ORs). The adjusted ORs take account of age, exercise, skipping breakfast and having a snack after dinner, all of these being variables thought to affect the association between eating style and waist-hip ratio. Looking at the crude OR column, the value of 2.04 in the first row indicates that, among boys, those who report eating quickly have around twice the odds of having a high waist-hip ratio than those who do not eat quickly (not eating quickly is the baseline category, with an odds ratio given as 1.00). The 95% CI for the crude OR for eating quickly is 1.31 – 3.18. This interval does not include 1, indicating that the elevated OR for eating quickly is unlikely to be a chance finding and that there is a 95% probability that the range of 1.31 – 3.18 includes the true OR. The p -value is 0.002, considerably smaller than 0.05, indicating that this finding is ‘statistically significant’. The other ORs can be considered in the same way, but note that, for both boys and girls, the ORs for eating until full are greater than 1 but their 95% CIs include 1 and the p- values are considerably greater than 0.05, so not ‘statistically significant’, indicating chance findings.

Results table from a study assessing whether children’s eating styles are associated with having a waist-hip ratio ≥0.5 or not [ 17 ]. Crude and adjusted odds ratios of eating quickly or eating until full for waist-to-height ratio (WHtr) ≥ 0.5

OR odds ratio; CI confidence interval

Adjusted for age, exercise, skipping breakfast, and snack after dinner

The final columns present the ORs after adjustment for various additional factors, along with their 95% CIs and p -values. The ORs given here differ little from the crude ORs in the table, indicating that the adjustment has not had much effect, so the conclusions from examining the crude ORs are unaltered. It thus appears that eating quickly is strongly associated with a greater waist-hip ratio, but that eating until full is not.

Summary tables of characteristics describe the study population and set the study in context. The main findings can be presented in different ways and choice of presentation is determined by the nature of the variables under study. Scrutiny of tables allows the reader to acquire much more information about the study and a richer insight than if the text only is examined. Constructing clear tables that communicate the nature of the study population and the key results is important in the preparation of papers; good tables can assist the reader enormously as well as increasing the chance of the paper being published.

Acknowledgement

Not applicable.

The work was funded by the UK Medical Research Council which funds the work of the MRC Lifecourse Epidemiology Unit where the authors work. The funding body had no role in the design and conduct of the work, or in the writing the manuscript.

Availability of data and materials

Authors’ contributions.

HI conceived the idea for the paper in discussion with JB. HI wrote the first draft and all other authors commented on successive versions and contributed ideas to improve content, clarity and flow of the paper. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Ethics approval and consent to participate, abbreviations, contributor information.

Hazel Inskip, Email: ku.ca.notos.crm@imh .

Georgia Ntani, Email: ku.ca.notos.crm@ng .

Leo Westbury, Email: ku.ca.notos.crm@wl .

Chiara Di Gravio, Email: ku.ca.notos.crm@gdc .

Stefania D’Angelo, Email: ku.ca.notos.crm@ds .

Camille Parsons, Email: ku.ca.notos.crm@pc .

Janis Baird, Email: ku.ca.notos.crm@bj .

Who is in this study, anyway? Guidelines for a useful Table 1

Affiliations.

• 1 Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, USA. Electronic address: [email protected].
• 2 Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, USA.
• 3 Harborview Injury Prevention & Research Center, University of Washington, Seattle, WA, USA; Department of Epidemiology, University of Washington, Seattle, WA, USA.
• 4 Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Philadelphia, PA, USA.
• PMID: 31229583
• PMCID: PMC6773463
• DOI: 10.1016/j.jclinepi.2019.06.011

Objective: Epidemiologic and clinical research papers often describe the study sample in the first table. If well-executed, this "Table 1" can illuminate potential threats to internal and external validity. However, little guidance exists on best practices for designing a Table 1, especially for complex study designs and analyses. We aimed to summarize and extend the literature related to reporting descriptive statistics.

Study design and setting: In consultation with existing guidelines, we synthesized and developed reporting recommendations driven by study design and focused on transparency related to potential threats to internal and external validity.

Results: We describe a basic structure for Table 1 and discuss simple modifications in terms of columns, rows, and cells to enhance a reader's ability to judge both internal and external validity. We further highlight several analytic complexities common in epidemiologic research (missing data, sample weights, clustered data, and interaction) and describe possible variations to Table 1 to maintain and add clarity about study validity in light of these issues. We discuss considerations and tradeoffs in Table 1 related to breadth and comprehensiveness vs. parsimony and reader-friendliness.

Conclusion: We anticipate that our work will guide authors considering layouts for Table 1, with attention to the reader's perspective.

Keywords: Clinical research; Descriptive statistics; Epidemiologic methods; External validity; Generalizability; Internal validity; Tables.

Copyright © 2019 Elsevier Inc. All rights reserved.

Publication types

• Research Support, N.I.H., Extramural
• Data Analysis*
• Documentation / methods
• Documentation / standards*
• Epidemiologic Research Design
• Guidelines as Topic*
• Publishing / standards*
• Reproducibility of Results
• Research Design

Grants and funding

• T32 AI114398/AI/NIAID NIH HHS/United States
• T32 HD057822/HD/NICHD NIH HHS/United States
• R01 AG049970/AG/NIA NIH HHS/United States
• T32 ES023772/ES/NIEHS NIH HHS/United States
• K99 LM012868/LM/NLM NIH HHS/United States
• T32 MH013043/MH/NIMH NIH HHS/United States

• Open access
• Published: 18 May 2024

Independent relationship between sleep apnea-specific hypoxic burden and glucolipid metabolism disorder: a cross-sectional study

• Chenyang Li 1 , 2   na1 ,
• Yu Peng 1 , 2   na1 ,
• Xiaoyue Zhu 1 , 2   na1 ,
• Yupu Liu 1 , 2 ,
• Jianyin Zou 1 , 2 ,
• Huaming Zhu 1 , 2 ,
• Xinyi Li 1 , 2 ,
• Hongliang Yi 1 , 2 ,
• Jian Guan 1 , 2 ,
• Xu Zhang 3 ,
• Huajun Xu 1 , 2 &
• Shankai Yin 1 , 2  

Respiratory Research volume  25 , Article number:  214 ( 2024 ) Cite this article

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Metrics details

Obstructive sleep apnea (OSA) is associated with abnormal glucose and lipid metabolism. However, whether there is an independent association between Sleep Apnea-Specific Hypoxic Burden (SASHB) and glycolipid metabolism disorders in patients with OSA is unknown.

We enrolled 2,173 participants with suspected OSA from January 2019 to July 2023 in this study. Polysomnographic variables, biochemical indicators, and physical measurements were collected from each participant. Multiple linear regression analyses were used to evaluate independent associations between SASHB, AHI, CT90 and glucose as well as lipid profile. Furthermore, logistic regressions were used to determine the odds ratios (ORs) for abnormal glucose and lipid metabolism across various SASHB, AHI, CT90 quartiles.

The SASHB was independently associated with fasting blood glucose (FBG) (β = 0.058, P  = 0.016), fasting insulin (FIN) (β = 0.073, P  < 0.001), homeostasis model assessment of insulin resistance (HOMA-IR) (β = 0.058, P  = 0.011), total cholesterol (TC) (β = 0.100, P  < 0.001), total triglycerides (TG) (β = 0.063, P  = 0.011), low-density lipoprotein cholesterol (LDL-C) (β = 0.075, P  = 0.003), apolipoprotein A-I (apoA-I) (β = 0.051, P  = 0.049), apolipoprotein B (apoB) (β = 0.136, P  < 0.001), apolipoprotein E (apoE) (β = 0.088, P  < 0.001) after adjustments for confounding factors. Furthermore, the ORs for hyperinsulinemia across the higher SASHB quartiles were 1.527, 1.545, and 2.024 respectively, compared with the lowest quartile ( P  < 0.001 for a linear trend); the ORs for hyper-total cholesterolemia across the higher SASHB quartiles were 1.762, 1.998, and 2.708, compared with the lowest quartile ( P  < 0.001 for a linear trend) and the ORs for hyper-LDL cholesterolemia across the higher SASHB quartiles were 1.663, 1.695, and 2.316, compared with the lowest quartile ( P  < 0.001 for a linear trend). Notably, the ORs for hyper-triglyceridemia{1.471, 1.773, 2.099} and abnormal HOMA-IR{1.510, 1.492, 1.937} maintained a consistent trend across the SASHB quartiles.

Conclusions

We found SASHB was independently associated with hyperinsulinemia, abnormal HOMA-IR, hyper-total cholesterolemia, hyper-triglyceridemia and hyper-LDL cholesterolemia in Chinese Han population. Further prospective studies are needed to confirm that SASHB can be used as a predictor of abnormal glycolipid metabolism disorders in patients with OSA.

Trial registration

ChiCTR1900025714 { http://www.chictr.org.cn/ }; Prospectively registered on 6 September 2019; China.

Introduction

Obstructive sleep apnea (OSA) is a highly prevalent sleep disordered breathing, with estimated prevalence rates of 17% in females and 34% in males within the general population, and increasingly with age and obesity [ 1 , 2 ]. OSA is characterized by repeated intermittent hypoxia (IH), frequent arousals, and daytime drowsiness. A significant association between OSA and the risk of metabolic syndrome was recently established and attracted considerable attention [ 3 , 4 ]. Dyslipidemia and abnormal glucose metabolism emerge as two primary components of metabolic disorders, and both of them are commonly intertwined with clinical outcomes associated with OSA [ 5 , 6 ]. Previous studies have confirmed that OSA is associated with dyslipidemia, abnormal glucose metabolism and such an association is mainly attributable to nocturnal IH and sleep fragmentation (SF) [ 7 , 8 , 9 , 10 ]. Previous rodent studies have suggested that IH can impair pancreatic islet beta-cell function, subsequently increasing fasting glucose levels and generating insulin resistance [ 11 , 12 ], thereby further exacerbating the development of diabetes [ 13 , 14 ].

In our previous study [ 15 ], we found Sleep Apnea-Specific Hypoxic Burden (SASHB), a new pulse oximetry (SpO 2 )-related index and defined as the sum of the areas under the baseline SpO 2 curves corresponding to respiratory events, has shown promise in identifying people at risk of OSA in Chinese Han population. In addition to quantifying the frequency of respiratory events, SASHB captures the depth and duration of hypoxemia associated with OSA. Series of studies have shown that higher SASHB in OSA were associated with higher risks of cardiovascular mortality, major cardiovascular event rates [ 16 ], blood pressure [ 17 ], stroke [ 18 ], heart failure [ 19 ], and chronic kidney disease in the clinical setting [ 20 ]. However, whether SASHB was independently associated with abnormal glucose and lipid metabolism remains unknown.

In order to clearly address such an association, we performed such comprehensive cross-sectional study. Furthermore, we calculated adjusted odds ratios (ORs) for different abnormal glucose and lipid metabolism categories among OSA patients, stratified by varying levels of SASHB.

Subjects and methods

Study design and population.

We enrolled 2,914 subjects with suspected OSA who underwent overnight polysomnography (PSG) in the sleep laboratory of Shanghai Jiao Tong University of Medicine Affiliated Sixth People’s Hospital from January 2019 to July 2023. The exclusion criteria were as follows: (1) history of OSA treatment; (2) age < 18 years; (3) severe systemic disease such as heart, liver, lung, and renal failure; (4) other non-OSA sleep disorders; (5) severe psychiatric disorders or malignancy; (6) administration of glucose-lowering or lipid-lowering medications; and (7) missing clinical PSG data. Ultimately, a total of 2,173 subjects met the inclusion criteria for this study. We learned about their general health status including habits such as smoking, alcohol consumption, and medication use through a comprehensive questionnaire. The recruitment flow chart is shown in Fig.  1 . This study was conducted in accordance with the Helsinki Declaration and was approved by the Ethics Review Committee of the Sixth People’s Hospital which is affiliated with the Medical College of Shanghai Jiaotong University (approval no. 2019-KY-050 [K]); the study was registered in the China Clinical Trials Registry (serial number ChiCTR1900025714). All participants gave written informed consent.

Screening flow chat of participants

Polysomnography and definitions

To obtain precise and objective sleep parameters, sleep was monitored in the sleep laboratory by overnight PSG (Alice-5, Alice-6; Respironics, Pittsburgh, Pennsylvania, USA). Bilateral electroencephalogram (EEG) channels (C3-M2 and C4-M1), bilateral electrooculogram (EOG), chin electromyogram (EMG), lowest oxygen saturation, chest and abdominal wall motion, airflow, and body position were recorded for all study participants. The scoring of respiratory events, oxygen desaturations adhered to the guidelines established by the American Academy of Sleep Medicine (AASM) in 2017 [ 21 ]. Scoring of micro-arousals followed the Rechtschaffen and Kales (R&K) rule [ 22 ]. A micro-arousal event was defined as an abrupt shift in EEG frequency, encompassing alpha, theta and/or frequencies > 16 Hz (excluding spindles) that lasted at least 3s, with at least 10s of stable sleep preceding the observed change. Additionally, scoring of arousal during rapid eye movement (REM) requires a concurrent increase in submental EMG lasting at least 1s. We operationally defined the microarousal index (MAI) as the tally of abrupt EEG frequency shifts, each lasting at least 3 s, per hour of recorded sleep. Moreover, the apnea hypopnea index (AHI) was quantified as the number of apnea and hypopnea events occurring per hour during the sleep period.

Biochemical indicators

For each study participant, a fasting blood sample was collected from the antecubital vein on the morning following the PSG evaluation. Fasting blood glucose (FBG) and fasting insulin (FIN), and serum lipid profiles, which included total cholesterol (TC), total triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C), apolipoprotein A-I (apoA-I), apolipoprotein B (apoB), apolipoprotein E (apoE) were measured for each participant. FBG levels were quantified using the H-7600 autoanalyzer (Hitachi, Tokyo, Japan), while FIN levels were determined through immunoradiological assays. Calibration of the analyzer and quality control operations were routinely carried out. The homeostasis model assessment of insulin resistance (HOMA-IR) was calculated to quantify insulin resistance using FIN and FBG as follows: HOMA-IR = FIN (uIU/mL)×FBG (mmol/L)/22.5 [ 23 ]. We defined FBG of 6.1 mmol/L or greater as hyperglycemia, FIN of 12.2 uIU/mL or greater as hyperinsulinemia, and HOMA-IR of 2.5 or greater as insulin resistance [ 23 , 24 , 25 ].

Serum lipid profiles were assessed in the hospital laboratory utilizing standard procedures. According to the US National Cholesterol Education Program Adult Treatment Panel III (NCEPIII) [ 26 ] and the Joint Committee for Developing Chinese Guidelines on the Prevention and Treatment of Dyslipidemia in Adults (JCDCG) [ 27 ], dyslipidemia in terms of TC, LDL-c, HDL-c and TG, were defined as TC levels > 5.17mmol/L, LDL-c levels≥3.37mmol/L, HDL-c levels < 1.03 mmol/L, and TG levels ≥ 1.7 mmol/L, separately.

Anthropometric measurements

All participants were instructed to wear light clothing and removed shoes. Height, weight, neck circumference (NC), waist circumference (WC), and hip circumference (HC) were measured with a meter ruler and weighing scale, respectively, following established procedures. Body Mass Index (BMI) was calculated as weight (kg)/height 2 (m 2 ); the neck height ratio (NHR) = NC/height and the waist-hip ratio (WHR) = WC/HC were also calculated. Daytime blood pressure (BP) was measured after at least 5 min of rest in a seated position employing a mercury sphygmomanometer, following the American Society of Hypertension Guidelines, and the mean of three measurements was recorded for each participant. Hypertension was defined as a systolic BP ≥ 140mmHg, a diastolic BP ≥ 90mmHg, or current use of antihypertensive medication [ 28 ].

The SASHB calculation flow

The SASHB was determined by assessing the respiratory event-associated area under the desaturation curve commencing from a pre-event baseline. Our SASHB calculations are based on those of Dr. Azarbarzin’s research team, but the methods are not identical, differing principally in the definition of the pre-event SpO 2 baseline level [ 15 ]. For each apnea or hypopnea event, the Azarbarzin team defined the pre-event baseline saturation as the maximum SpO 2 during the 100 s prior to the end of the event; in our study, the maximum value at the start point of the SpO 2 trend evident in the search window at the time of an apnea or hypopnea event served as the SpO 2 baseline level. The search window used to detect respiratory events is shown in Fig.  2 .

Calculation of SASHB for individual respiratory events corresponding to specific search window

The specific SASHB calculation process was: First, take the maximum value of the starting point of SpO 2 trend in the search window for each respiratory event as the SpO 2 baseline level; Second, calculate \({S}_{i}\) , thus the area of the similar triangle for which the SpO 2 level serves as the horizontal baseline in each search window, and the downward and upward trends in the SpO 2 , the other sides of the triangle; Third, S= \({\sum }_{i=1}^{n}{S}_{i}\) , which is the sum of all respiratory events in the similar triangle of the specific search window, where i is the number of apnea or hypopnea events during the night; Fourth, the total area is divided by the total night recording time to yield the SASHB in units of %min/h; Fifth, a SASHB of 40% min/h corresponds to a 4% reduction in SpO 2 below baseline for 10 min during every hour of sleep or a 5% reduction below baseline for 8 min every hour.

The calculation of SASHB relies on computer-based analysis rather than manual operation. In this study, the calculation of the SASHB index is rooted in laboratory test data, encompassing nasal airflow and blood oxygen saturation trend maps. We developed the operation program of SASHB by using MATLAB (MathWorks, R2018a, USA), and this software can realize the batch processing of the original SpO 2 data. A description of the quality control criteria for the SpO 2 trend graph (recording duration and artifact management) and details of the original calculation codes for SASHB were provided in the supplementary material.

Statistical analysis

Data are presented as mean values ± standard deviation (SD) for continuous variables and percentages for categorical variables. Descriptive statistics were computed across the quartiles of SASHB. Inter-group differences in descriptive statistics were examined using analysis of variance (ANOVA) for continuous variables and chi-square tests for categorical variables. A polynomial linear trend test was used to evaluate linear trends across SASHB quartiles for continuous variables, and a linear-by-linear association test was applied for dichotomous variables. The treatment of missing data in the dataset is done using the maximum likelihood estimate method, where missing values are estimated from the marginal distribution of the observations.

Stepwise multiple regression analyses were executed separately to explore the independent associations of SASHB, AHI, CT90 with glucose metabolism indicators (FBG, FIN, HOMA-IR) and lipid profiles including TC, LDL-c, HDL-c, TG, apoA-I, apoB and apoE. Binary logistic regression analyses were employed to determine risk factors for hyperglycemia, hyperinsulinemia, abnormal HOMA-IR, hyper-total cholesterolemia, hyper-LDL cholesterolemia, hypo-HDL cholesterolemia, and hypertriglyceridemia. Linear trends were assessed by examining the median SASHB value for each quartile and conducting the overall F-test for that value. Additionally, odds ratios (ORs) and 95% confidence intervals (CIs) were also computed. Importantly, the statistical analysis was preceded using collinearity diagnostics to eliminate potential multicollinearity among variables. The two steps of the collinearity analyses were: (1) a preliminary analysis using a Spearman correlation and (2) collinearity diagnostics to determine the selected covariates in the multivariate linear regression analyses. For detail, please see Supplementary (Tables S28 - S30 ). Following the collinearity diagnosis, Model 1 was adjusted for age, and BMI as continuous variables, as well as sex as categorical variables. Model 2 included the following covariates: age, BMI as a continuous variable; and sex, hypertension, smoking status, and alcohol consumption as categorical variables. Furthermore, Model 3 further added the MAI to Model 2. Of note, SASHB and CT90 were examined as both a classified variable according to its quartiles as well as a continuous one. These yielded similar conclusions, and we presented only the results employing the continuous variables in linear regression analyses and the quartile variables in logistic regression analyses for simplicity. All statistical analyses employed SPSS ver. 26.0 (SPSS Inc., Chicago, IL, USA). Statistical significance was defined as a bilateral p -value < 0.05.

Baseline characteristics and univariate analysis

In total, 2,173 patients with suspected OSA were enrolled in this study. Of these, 1634 were male and 539 were female. Participants were categorized by SASHB quartiles (≤20.84, 20.84–77.11, 77.11–214.53, and > 214.53). Demographic characteristics (age, height, BMI, NC, WC, HC, NHR, WHR) and sleep indices (AHI, MAI) differed significantly across SASHB quartiles (all P for trend < 0.001; Table  1 ); Continuous variables such as FBG, FIN, HOMA-IR, TC, TG, HDL-C, LDL-C, apoB and apoE were also differed across the SASHB quartiles (all P for trend < 0.001; Table  1 ). Specifically, a positive dose-response relationship was observed between SASHB and FBG, FIN, HOMA-IR, TC, LDL-c, and TG levels, while conversely, a negative dose-response relationship was observed between SASHB and HDL-c levels (Fig.  3 ). Furthermore, the prevalence rates of Hyperglycemia, Hyperinsulinemia, insulin resistance, Hyper-total cholesterolemia, Hypo-HDL cholesterolemia, Hyper-LDL cholesterolemia, and Hyper-triglyceridemia increased with the SASHB quartile from 14.90 to 33.70%, 24.50–50.20%, 30.60–60.30%, 17.90–36.40%, 42.20–57.70%, 15.80–30.20%, and 26.10–55.00%, respectively (linear trends, p  < 0.001) (Table  1 ).

Adjusted mean values of the glucose and lipid levels in model 1. (a) FBG - SASHB; (b) FIN - SASHB; (c) HOMA-IR - SASHB; (d) TC - SASHB; (e) TG - SASHB; (f) HDL - SASHB; (g) LDL - SASHB; (h) apoA-I - SASHB; (i) apoB - SASHB; and (j) apoE - SASHB. Abbreviations : The data were adjusted for age, body mass index (BMI), and sex. FBG: Fasting blood glucose; FIN: Fasting insulin; HOMA-IR: Homeostasis model assessment of insulin resistance; TC: Total cholesterol; TG: Total triglycerides; HDL-C: High-density lipoprotein cholesterol; LDL-C: Low-density lipoprotein cholesterol; apoA-I: apolipoprotein A-I; apoB: apolipoprotein B; apoE: apolipoprotein E; SASHB: Sleep Apnea-Specific Hypoxic Burden

Relationship between SASHB and glucose metabolism

After adjusting for age, gender, BMI, MAP, smoking status, alcohol consumption and MAI, the SASHB was found to be independently associated with FBG (β = 0.058, P  = 0.017), FIN (β = 0.073, P  < 0.001), and the HOMA-IR (β = 0.058, P  = 0.005) (Table S1 , Model 3, Figs.  4 and 5 ).

Adjusted mean values of the glucose and lipid levels in model 2. (a) FBG - SASHB; (b) FIN - SASHB; (c) HOMA-IR - SASHB; (d) TC - SASHB; (e) TG - SASHB; (f) HDL - SASHB; (g) LDL - SASHB; (h) apoA-I - SASHB; (i) apoB - SASHB; and (j) apoE - SASHB. Abbreviations : The data were adjusted for age, body mass index (BMI), sex, smoking status, mean artery pressure, and alcohol consumption. FBG: Fasting blood glucose; FIN: Fasting insulin; HOMA-IR: Homeostasis model assessment of insulin resistance; TC: Total cholesterol; TG: Total triglycerides; HDL-C: High-density lipoprotein cholesterol; LDL-C: Low-density lipoprotein cholesterol; apoA-I: apolipoprotein A-I; apoB: apolipoprotein B; apoE: apolipoprotein E; SASHB: Sleep Apnea-Specific Hypoxic Burden

Adjusted mean values of the glucose and lipid levels in model 3. (a) FBG - SASHB; (b) FIN - SASHB; (c) HOMA-IR - SASHB; (d) TC - SASHB; (e) TG - SASHB; (f) HDL - SASHB; (g) LDL - SASHB; (h) apoA-I - SASHB; (i) apoB - SASHB; and (j) apoE - SASHB. Abbreviations : The data were adjusted for age, body mass index (BMI), sex, smoking status, mean artery pressure, alcohol consumption and microarousal index. FBG: Fasting blood glucose; FIN: Fasting insulin; HOMA-IR: Homeostasis model assessment of insulin resistance; TC: Total cholesterol; TG: Total triglycerides; HDL-C: High-density lipoprotein cholesterol; LDL-C: Low-density lipoprotein cholesterol; apoA-I: apolipoprotein A-I; apoB: apolipoprotein B; apoE: apolipoprotein E; SASHB: Sleep Apnea-Specific Hypoxic Burden

As shown in Table S7 , after adjusting for age, gender, and BMI in Model 1, as well as accounting for the MAP, smoking status, and alcohol consumption in Model 2, logistic regression models were employed to assess the association between the SASHB and abnormal glucose metabolism (i.e., hyperglycemia, hyperinsulinemia, and an abnormal HOMA-IR). Upon the incorporation of the MAI into Model 2, the ORs (95% CI) for hyperinsulinemia and abnormal HOMA-IR remained significant in the linear trend test, with ORs (95% CI) for SASHB quartiles being 1 (reference), hyperinsulinemia{1.527 (1.077, 2.166), 1.545 (1.083, 2.204), and 2.024 (1.400, 2.926), respectively ( p  < 0.001 for a linear trend)}; abnormal HOMA-IR {1.510 (1.085, 2.104), 1.492(1.064, 2.092), 1.937(1.356, 2.767), respectively ( p  = 0.001 for a linear trend)}.

Relationship between SASHB and lipid profile

Upon adjusting for age, gender, BMI, MAP, smoking status, alcohol consumption and MAI, the SASHB was found to be independently associated with TC (β = 0.100, P  < 0.001), TG (β = 0.063, P  = 0.011), LDL-C (β = 0.075, P  = 0.003), apoB (β = 0.136, P  < 0.001) and apoE (β = 0.088, P  < 0.001) (Table S4 , Model 3, Figs.  4 and 5 ).

Logistic regression analyses revealed positive associations between all abnormal lipid metabolism ORs (95% CI) and increasing SASHB quartiles. These results persisted even after the MAI was incorporated into the model, with ORs (95% CI) of 1 (reference), 1.762 (1.243, 2.499), 1.998 (1.399, 2.856), and 2.708 (1.871, 3.919) ( P  < 0.001 for a linear trend) for Hyper-total cholesterolemia; ORs (95% CI) of 1 (reference), 1.663 (1.156, 2.392), 1.695 (1.164, 2.467), and 2.316 (1.574, 3.407) ( p  < 0.001 for a linear trend) for Hyper-LDL cholesterolemia; and ORs (95% CI) of 1 (reference), 1.471 (1.078, 2.007), 1.773 (1.293, 2.433), and 2.099 (1.505, 2.928) ( p  < 0.001 for a linear trend), respectively, for Hyper-triglyceridemia across SASHB quartiles (Table S7 , Model 3).

Relationship between AHI, CT90 and glycolipid metabolism

Logistic regression models were employed to assess the association between the AHI and abnormal glucose and lipid metabolism. Within Model 3, significant positive linear trends were observed for abnormal glucose and lipid metabolism (Hyperinsulinemia, HOMA-IR ≥ 2.5, Hyper-total cholesterolemia, Hyper-LDL cholesterolemia, Hyper-triglyceridemia) ORs and 95% CIs with increasing AHI quartiles (all P for trend≤0.001). Specific ORs (95% CI) are shown in the supplemental file Table S8 . Similarly, we analyzed CT90, Logistic regression models were employed to assess the association between the CT90 and abnormal glucose and lipid metabolism (Table S9 ). The results of the stepwise multiple linear regression (AHI, CT90) of glucose metabolism index in models 1, 2 and 3 are presented in the supplemental file (Table S2 , 3 , 5 , 6 ).

Association of AHI, CT90 and SASHB with glycolipid metabolism in male and female concentration

In the male subset, Logistic regression models were employed to assess the association between the SASHB and abnormal glucose and lipid metabolism. Within Model 3, significant positive linear trends were observed for abnormal glycolipid metabolism (hyperinsulinemia, abnormal HOMA-IR, Hyper-total cholesterolemia, Hyper-LDL cholesterolemia) ORs and 95% CIs with increasing SASHB quartiles (Table S16 ). The results of the AHI and CT90 are shown in supplemental file Tables  17 and 18 .

In the female subset, Logistic regression models were employed to assess the association between the SASHB and abnormal glucose and lipid metabolism. Within Model 3, significant positive linear trends were observed for abnormal glycolipid metabolism (hyperinsulinemia, Hyperinsulinemia, Hyper-triglyceridemia) ORs and 95% CIs with increasing SASHB quartiles (Table S25 ). The results of the AHI and CT90 are shown in supplemental file Tables  26 and 27 .

In the men’s and female’s subset, the results of the stepwise multiple linear regression (SASHB, AHI, CT90) of glucose metabolism index in models 1, 2 and 3 are presented in the supplemental file (Table S2 , 3, 5, 6).

The present study demonstrated independent associations between SASHB and abnormal glucose as well as lipid metabolism with substantial sample, objective PSG data, and rigorous multivariate adjustments. Our findings indicate a positive linear trend for the risk of hyperinsulinemia and abnormal HOMA-IR across SASHB quartiles in abnormal glucose metabolism after adjusting for multiple variables; In terms of abnormal lipid metabolism, we observed a positive linear trend for risk of Hyper-total cholesterolemia, Hyper-LDL cholesterolemia and Hyper-triglyceridemia across SASHB quartiles after adjusting for multiple variables.

Because SpO 2 is readily available from laboratory and home sleep studies, it makes sense to include the depth and duration of IH among the metrics that have predictive value for abnormalities in glycolipid metabolism. SASHB is a candidate metric designed to predict the likelihood of the occurrence of abnormalities in glycolipid metabolism by capturing the frequency of respiratory events, and the depth and duration of the hypoxia associated with them. Azarbarzin et al. [ 29 ] first explored the association between SASHB and cardiovascular mortality, this study suggested that SASHB can be considered as early warning, diagnosis and prevention of cardiovascular diseases in OSA patients. Blanchard et al. examined the relationship between SASHB and incidence of new cerebrovascular events, they found that SASHB presented a higher prognostic value of cerebrovascular events when compared with other sleep variables (HR = 1.28); they also noted that SASHB may be a robust risk factor for stroke stratification in OSA [ 30 ].

Previous studies have confirmed that SASHB is one of the important early warning indicators of risk for cardiovascular morbidity and mortality, and given that abnormalities in glucose and lipid metabolism are established risk factors for cardiovascular morbidity and mortality [ 31 ], there has been an ongoing effort to find potential factors associated with glucose and lipid homeostasis. The impact of SASHB, an important early warning indicator for assessing IH in patients with OSA, on glucose metabolism and lipid metabolism remains unknown. Our findings support an independent correlation between SASHB and a range of metabolic abnormalities, including hyperinsulinemia, abnormal HOMA-IR, Hyper-cholesterolemia, Hyper -LDL cholesterolemia and Hyper-triglyceridemia. IH stimulation has been found to result in reduced insulin sensitivity and impaired glucose tolerance, with potential mechanisms of influence including activation of the sympathetic and hypothalamic-pituitary-adrenal systems, with the release of catecholamines that reduce insulin receptor sensitivity and decrease tissue insulin-mediated glucose uptake, while stimulating gluconeogenesis [ 32 ]. A previous study also found that SF could induce abnormal glucose metabolism through increased activity of the hypothalamic–pituitary–adrenal axis, resulting in higher circulating cortisol concentrations [ 33 ]. In rodent studies, SF was also associated with the development of glucose intolerance and insulin resistance [ 34 , 35 ]. Sleep fragmentation is a stressor that causes the elevation of hormones such as adrenocorticotropic hormone and cortisol. These hormones play a role in lipolysis, which might affect lipid levels [ 36 , 37 ] A previous study [ 38 ] reported an association between sleep fragmentation and dyslipidemia in a population of approximately 700 OSA patients. However, the included subjects in that studies were non-consecutive, which could have induced selection bias. Studies performed in animal models showed that repeated arousal from sleep could cause impaired lipid levels [ 39 , 40 ].

Rodent studies demonstrated that sleep disruption and IH could lead to insulin resistance [ 12 , 35 ]. Many clinical studies exploring the relationship between OSA and glucose metabolism were performed, with inconsistent results. Some studies suggested a link between OSA and abnormal glucose metabolism prior to the manifestation of diabetes [ 41 , 42 ], and further demonstrated that two pathophysiological processes of OSA (SF and IH) could increase circulating glucose by decreasing insulin sensitivity and reducing glucose effectiveness [ 13 , 43 , 44 , 45 ]. A cross-sectional study recruiting 1,834 patients with suspected OSA demonstrated that SF was independently associated with hyperinsulinemia, whereas IH was associated with hyperglycemia, hyperinsulinemia, and abnormal HOMA-IR abnormalities [ 46 ]. An epidemiologic study of 2,686 patients with suspected OSA suggests that sleep fragmentation is strongly associated with high LDL cholesterolemia in patients with OSA. It is warranted to investigate the causal relationship between sleep fragmentation and dyslipidemia, as well as the underlying mechanisms of this association further in prospective cohort studies [ 47 ].

A case-control study [ 48 ] observed inflammatory markers (IL-6, IL-8, IL-17, IL-18, MIF, Hs CRP, TNF- \(\alpha\) , PAI-1 and leptin) were significantly associated with OSA as compared to those without OSAs. Fang Y et al. revealed the association between autoantibodies against inflammatory factors and OSA, and the combination of auto antibodies against CRP, IL-6, IL-8 and TNF- \(\alpha\) may function as novel biomarker for monitoring the presence of OSA [ 49 ]. It has also been found that IH can stimulate the expression of NF-ĸB [ 50 ], leading to an increase in the expression of downstream inflammatory mediators, such as tumor necrosis factor- \({\alpha }\) and interleukin-8, which results in damage to pancreatic islet cells and decreases insulin sensitivity in the liver, muscle, and adipose tissues, ultimately leading to disturbances in glucose metabolism in patients with OSA. It has also been suggested [ 51 ] that the increase in FFA levels may be mediated by hypoxia-induced up-regulation of adipose triglyceride lipase activators such as protein kinase A. OSA, in addition to affecting circulating levels of TC and FFA, also regulates lipid function through oxidative stress. oxidative stress to regulate lipid function. We speculate that the depth and duration of hypoxia may be a possible factor in the excitation of sympathetic nerve activity, expression of inflammatory mediators, and oxidative stress, although further prospective studies are needed to elucidate this potential relationship.

Combining the duration and depth of respiratory events and their associated desaturation may provide useful information for more precise identification and management of patients with OSA (precision medicine). For example, studies have shown that longer [ 52 , 53 ] and deeper [ 54 ] apneas and hypoventilation elicit a greater cardiovascular response than shorter and milder apneas and hypoventilation. The autonomous relationship between SASHB and abnormalities of glucose and lipid metabolism observed in this study highlights the need for improvement with a focus on IH when developing novel treatment strategies for OSA patients with comorbid abnormalities of glucose metabolism. For example, when considering oxygen therapy for OSA, the frequency, depth and duration of oxygen were all important. This integrated approach has the potential to improve the prognosis and outcome of OSA patients with comorbid glucose metabolism abnormalities.

The highlight of this study is that all OSA indices were collected by laboratory-based PSG monitoring rather than surrogate measures (e.g., witnessed apnea or portable PSG). Additionally, we meticulously excluded individuals undergoing treatment with hypoglycemic and lipid-lowering medications. Finally, the substantial sample size and adjustment for confounding factors enhance the accuracy and credibility of our findings. Despite these merits, our study carries several limitations that merit discussion in the interpretation of our results. First, the present report is limited by the fact that it was based on clinical samples and observational research, and could not provide the causative evidence. Second, diet and physical activity are two important factors that influence glucose and lipid metabolism. Although only residents in east China with roughly analogous lifestyles were enrolled, and we strictly excluded patients who had been treated with hypoglycemic and lipid-lowering drugs, not controlling these two confounding factors is a potential limitation of our study. Third, the non-community-based prospective design of our study is a limitation worth noting. Fourth, these data are valid only for the examined population. Fifth, Morphological changes (android obesity) also play a major role in explaining both the higher hypoxic burden and the dysmetabolisms. However, we did not have the classic morphological co-variates of the metabolic syndrome been correctly entered into the model. Sixth, currently the calculation of the SASHB is based on the identification of desaturating events from ventilatory traces and qualified respiratory events. Other oximetric techniques propose to do without the detection of respiratory events and are perhaps more interesting.

In conclusion, our study revealed a positive linear trend for risk of hyperinsulinemia and HOMA-IR across SASHB quartiles; Similarly, concerning abnormal lipid metabolism, we observed a positive linear trend for risk of Hyper-total cholesterolemia, Hyper-LDL cholesterolemia and Hyper-triglyceridemia across SASHB quartiles. These findings underscore the imperative need to delve deeper into the causal connection between hypoxic burden and abnormal glucose and lipid metabolism. Future research endeavors should focus on elucidating the underlying mechanisms of such an association through prospective cohort studies.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

Obstructive sleep apnea

• Sleep apnea-specific hypoxic burden

Fasting blood glucose

Fasting insulin

Total cholesterol

Total triglycerides

High-density lipoprotein cholesterol

Low-density lipoprotein cholesterol

Apolipoprotein A-I

Apolipoprotein B

Apolipoprotein E

Intermittent hypoxia

Polysomnography

Electrooculogram

Electromyogram

American Academy of Sleep Medicine

Rapid eye movement

Microarousal index

Apnea hypopnea index

Homeostasis model assessment of insulin resistance

Neck circumference, WC, waist circumference

Hip circumference

Blood pressure

Body mass index

Mean arterial pressure

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Acknowledgements

The authors acknowledge all the participants and survey staffs for their participation.

This study was funded by the Ministry of Science and Technology of the People’s Republic of China (STI2030-Major Projects 2021ZD0201900); Interdisciplinary Program of Shanghai Jiao Tong University (YG2023LC11); Shanghai Three Year Action Plan for Traditional Chinese Medicine ZY(2021–2023)-0205-04.

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Chenyang Li, Yu Peng and Xiaoyue Zhu contributed equally to this work.

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Department of Otolaryngology-Head and Neck Surgery & Center of Sleep Medicine, Shanghai Jiao Tong University School of Medicine Affiliated Sixth People’s Hospital, Shanghai, China

Chenyang Li, Yu Peng, Xiaoyue Zhu, Yupu Liu, Jianyin Zou, Huaming Zhu, Xinyi Li, Hongliang Yi, Jian Guan, Huajun Xu & Shankai Yin

Shanghai Key Laboratory of Sleep Disordered Breathing, Shanghai, China

School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, China

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Li, C., Peng, Y., Zhu, X. et al. Independent relationship between sleep apnea-specific hypoxic burden and glucolipid metabolism disorder: a cross-sectional study. Respir Res 25 , 214 (2024). https://doi.org/10.1186/s12931-024-02846-7

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Pharmacist clinics offer professional pharmaceutical services that can improve public health outcomes. However, primary healthcare staff in China face various barriers and challenges in implementing such clinics. To identify existing problems and provide recommendations for the implementation of pharmacist clinics, this study aims to assess the knowledge, attitudes, and practices of pharmacist clinics among primary healthcare providers.

A cross-sectional survey based on the Knowledge-Attitude-Practice (KAP) model, was conducted in community health centers (CHCs) and private hospitals in Shanghai, China in May, 2023. Descriptive analytics and the Pareto principle were used to multiple-answer questions. Chi-square test, Fisher’s exact test, and binary logistic regression models were employed to identify factors associated with the knowledge, attitudes, and practices of pharmacist clinics.

A total of 223 primary practitioners participated in the survey. Our study revealed that most of them had limited knowledge (60.1%, n  = 134) but a positive attitude (82.9%, n  = 185) towards pharmacist clinics, with only 17.0% ( n  = 38) having implemented them. The primary goal of pharmacist clinics was to provide comprehensive medication guidance (31.5%, n  = 200), with medication education (26.3%, n  = 202) being the primary service, and special populations (24.5%, n  = 153) identified as key recipients. Logistic regression analysis revealed that education, age, occupation, position, work seniority, and institution significantly influenced their perceptions. Practitioners with bachelor’s degrees, for instance, were more likely than those with less education to recognize the importance of pharmacist clinics in medication guidance (aOR: 7.130, 95%CI: 1.809–28.099, p -value = 0.005) and prescription reviews (aOR: 4.675, 95% CI: 1.548–14.112, p -value = 0.006). Additionally, practitioners expressed positive attitudes but low confidence, with only 33.3% ( n  = 74) feeling confident in implementation. The confidence levels of male practitioners surpassed those of female practitioners ( p -value = 0.037), and practitioners from community health centers (CHCs) exhibited higher confidence compared to their counterparts in private hospitals ( p -value = 0.008). Joint physician-pharmacist clinics (36.8%, n  = 82) through collaboration with medical institutions (52.0%, n  = 116) emerged as the favored modality. Daily sessions were preferred (38.5%, n  = 86), and both registration and pharmacy service fees were considered appropriate for payment (42.2%, n  = 94). The primary challenge identified was high outpatient workload (30.9%, n  = 69).

Conclusions

Although primary healthcare practitioners held positive attitudes towards pharmacist clinics, limited knowledge, low confidence, and high workload contributed to the scarcity of their implementation. Practitioners with diverse sociodemographic characteristics, such as education, age, and institution, showed varying perceptions and practices regarding pharmacist clinics.

Peer Review reports

Pharmacist clinics are specialized healthcare facilities that offer professional pharmaceutical services, such as medication therapy management, medication reconciliation, lifestyle counseling, and immunizations, for patients with chronic diseases or managing multiple drugs [ 1 ]. Through the provision of these services, pharmacist clinics aim to improve patient access to healthcare, optimize medication use, and improve overall public health outcomes.

Pharmacist clinics originated in the 1960s in the United States and have spread globally in recent decades [ 2 ], with a growing number of countries adopting this model of care. The World Health Organization (WHO) has recognized the importance of pharmacists in primary healthcare and encouraged the integration of pharmaceutical services into broader healthcare systems [ 3 ]. This integration facilitates the rational use of medication, thereby minimizing adverse drug events and medication errors, ultimately leading to better therapeutic outcomes. Moreover, pharmacist clinics offer medication guidance and education, which adjusts optimal medication dosage [ 4 ], enhances patient adherence [ 1 , 5 ], expands access to health care [ 6 ], and reduces treatment costs [ 7 ]. These clinics effectively bridge the communication gap between physicians and pharmacists [ 8 ], fostering interdisciplinary collaboration and integrated patient care [ 1 , 9 ].

The development of pharmacist clinics in China was initiated in the late 20th century, coinciding with the introduction of healthcare reforms by the Chinese government in the early 2000s. The release of “Opinions on Deepening the Reform of the Medical and Health System” [ 10 ] in 2009 highlighted the importance of pharmacist clinics and the crucial role of pharmacists in improving the quality and accessibility of healthcare services in primary settings. In 2020, the Chinese government released a guidance document titled “Opinions on Strengthening the Pharmaceutical Management of Medical Institutions and Promoting Rational Drug Use,” encouraging provinces to actively establish pharmacist clinics [ 11 ]. However, it wasn’t until 2021 that the General Office of the National Health Commission developed the “Guidelines for Pharmaceutical Outpatient Services in Medical Institutions” to standardize these pharmacist clinics [ 12 ]. Despite the progress made, primary medical staff in both developed and developing countries face various challenges, especially in developing countries [ 13 ], including a shortage of qualified pharmacists [ 14 , 15 ], limited recognition of pharmacists’ roles among healthcare professionals and the public [ 16 , 17 ], and the need for a more standardized approach to pharmaceutical care [ 18 ]. Additionally, these clinics are predominantly located in large general hospitals or specialized medical facilities, limiting their coverage to specific areas, such as antibiotics [ 19 ] and anticoagulants [ 20 ]. In rural areas, there is scarce awareness and discussion regarding the promotion of pharmacist clinics.

To date, most research on pharmacist clinics comes from countries like the United States, the UK, Canada, and Australia, focusing primarily on the outcomes of pharmacist interventions rather than the implementation challenges [ 1 , 4 , 21 , 22 , 23 , 24 ]. In China, only a few studies have assessed the current state of pharmacist clinics. Cai et al. [ 25 ], for instance, conducted a national survey revealing that just 10.03% of hospitals had pharmacist clinics. Wu et al. [ 26 ] investigated the establishment and operational details of pharmacist-managed clinics in Taiwan. However, there is no published research exploring optimal practices for setting up pharmacist clinics in China or identifying the barriers to establishing these clinics in primary healthcare settings. In this study, we aim to assess the awareness and understanding of pharmacist clinics among primary healthcare providers. We conducted a cross-sectional survey based on the Knowledge-Attitude-Practice (KAP) model to identify knowledge gaps and develop interventions to encourage interprofessional collaboration and enhance practice efficiency. The findings may also improve patient outcomes, healthcare delivery by streamlining the implementation process, and utilization of high-quality pharmaceutical services. Our ultimate goal was to overcome barriers to advancing pharmacist clinics within China’s healthcare system and offer insights for policymakers and healthcare authorities to integrate these clinics into primary healthcare settings, not only in China but potentially in other countries as well.

Survey instrument & selection criteria

Our study employed a structural equation model based on the Knowledge, Attitude, and Practice (KAP) theory [ 27 ] and relevant literature [ 28 , 29 , 30 , 31 ] to explore the relationships between various factors. Following the KAP principles, we developed a questionnaire consisting of 21 questions across three domains: (A) knowledge of pharmacist clinics, (B) attitudes towards pharmacist clinics, and (C) practices related to pharmacist clinics. Demographic information such as gender, age, education, occupation, position, seniority, department, and institution was collected through self-reporting.

The inclusion and exclusion criteria for the sampled respondents were as follows. Inclusion criteria: (1) Full-time primary healthcare practitioners attending a continuing education course at Minhang Hospital in Shanghai, China. This included physicians, pharmacists, nurses, and other primary healthcare practitioners. (2) Willingness to participate in the study and provide informed consent. Exclusion criteria: (1) Part-time employees or interns. (2) Non-medical staff. (3) Individuals who declined to sign the informed consent form.

Study population and data source

This study used data from a cross-sectional survey conducted in May, 2023, involving primary healthcare practitioners from 10 community health centers (CHCs) and 38 private hospitals in Shanghai, China. After excluding participants from secondary or tertiary hospitals ( n  = 9), nursing homes ( n  = 6), and other facilities such as welfare homes and school clinics ( n  = 9), a total of 223 eligible subjects were included.

Data collection

The sample size was optimized to range between 105 and 210, based on the recommended ratio of 5 to 10 respondents per item [ 32 , 33 ]. We also performed a pilot study in April, 2023 to ensure linguistic clarity and readability of the questionnaire. Twenty-six student volunteers from the School of Pharmacy at Fudan University were recruited to refine the questionnaire. Additionally, face-to-face interviews were conducted to further assess their understanding of the content. The final version was electronically distributed to participants during a continuing education course using a voluntary sampling approach. The full questionnaire is available in Supplementary Table 1 , and all data were anonymized.

Statistical analysis

Categorical variables were summarized using frequency counts (weighted percentage, %). The Chi-square test and Fisher’s exact test were used to assess differences in knowledge, attitude, and practice regarding pharmacist clinics across various sociodemographic characteristics. Descriptive analytics and the Pareto principle were applied to multiple-answer questions. In case of rejection of the null hypothesis, multiple pairwise comparisons would be conducted as confirmatory post hoc analysis using Bonferroni correction. Based on the univariate analysis results, we constructed binary logistic regression models to calculate adjusted odds ratios (aOR) and 95% confidence intervals (CI) to reveal factors associated with perceived goals, service scope, and target recipients of pharmacist clinics.

All statistical analyses were performed using IBM SPSS Statistics for Windows, Version 20.0 (IBM Corp., Armonk, NY, USA). A two-sided p -value < 0.05 was considered statistically significant.

Demographics

As presented in Table  1 , a total of 223 primary healthcare practitioners participated in the survey, with 41.3% ( n  = 92) being male and 76.2% ( n  = 170) under 45 years old. The majority (84.3%, n  = 188) were physicians, while the remaining were pharmacists. Regarding educational qualifications, 82.5% ( n  = 184) of respondents held a bachelor’s degree or below. Furthermore, 91.9% ( n  = 205) held mid-level or lower positions, and 56.1% ( n  = 125) reported professional tenures of less than 10 years. Of these 223 practitioners, 36.8% ( n  = 82) were from public institutions (community health centers), and 63.2% ( n  = 141) were from private hospitals.

Knowledge of pharmacist clinics

Of primary care practitioners, 84.8% ( n  = 189) recognized pharmacist clinics, with 24.7% ( n  = 55) having strong familiarity. Figure  1 a-c showed practitioners’ views on the goals, services, and target recipients of these clinics. The primary goal was to provide comprehensive medication guidance (31.5%, n  = 200), with medication education (26.3%, n  = 202) being the primary service, and special populations (24.5%, n  = 153) identified as key recipients. Logistic regression results revealed several significant influential factors (Table  2 ).

Pareto chart demonstrating respondents’ knowledge of pharmacist clinics

( a ) Perceived goals: A prescription reviews, B medication guidance, C time-saving, D conflict alleviation, E patient empowerment, F cost reduction, G role enhancement, H research, I training, and J no perceived value

( b ) Perceived service scope: A drug regimen adjustments, B medication reconciliation, C medication education on dosage, side effects, and interactions, D adherence interventions, E health promotion, F patient follow-ups

( c ) Perceived target recipients: A isolated/empty-nest patients, B special populations (e.g. elderly, children, pregnant, and liver/kidney-impaired), C economically disadvantaged patients, D patients suffering from adverse reactions, E patients needing test report interpretations, F frequent drug collectors (> 20 times/year), G patients with ≥ 2 chronic diseases, H patients with any chronic diseases, I patients on ≥ 5 medications, J high-risk drug users (e.g. psychotropic drugs, hormones, injections, and inhalants), K patients under contract with family physicians, and L all patients

Compared to those with less education, practitioners with bachelor’s degrees were more likely to see the role of pharmacist clinics in medication guidance (aOR: 7.130, 95%CI: 1.809–28.099, p -value = 0.005), prescription reviews (aOR: 4.675, 95% CI: 1.548–14.112, p -value = 0.006), and serving patients on high-risk drugs (aOR: 2.824, 95% CI: 1.090–7.316, p -value = 0.033).

Besides medication guidance (aOR: 7.303, 95%CI: 1.343–39.720, p -value = 0.021), practitioners with master’s or higher degrees preferred adherence interventions (aOR: 4.221, 95%CI: 1.339–13.300, p -value = 0.014), follow-up services (aOR: 3.125, 95%CI: 1.095–8.915, p -value = 0.033), and catering to patients with ≥ 2 chronic diseases (aOR: 6.401, 95%CI: 1.233–33.223, p -value = 0.027) or ≥ 5 medications (aOR: 3.987, 95%CI: 1.250-12.717, p -value = 0.019). Higher education was also inversely associated with emphasizing patients needing test report interpretations (aOR < 1, p -value < 0.05).

Younger practitioners, aged 18 to 30, considered pharmacist clinics as tools to mitigate physician-patient conflicts through improved communication compared to those aged ≥ 46 (aOR: 0.165, 95%CI: 0.028–0.988, p -value = 0.048).

Compared to physicians, pharmacists typically addressed all patients as recipients (aOR: 3.322, 95%CI: 1.031–10.703, p -value = 0.044), but were less likely to offer drug regimen adjustments (aOR: 0.210, 95%CI: 0.088-0.500, p -value < 0.001).

Junior and intermediate-level practitioners demonstrated a greater likelihood for follow-up services (aOR 1 : 5.832, 95%CI: 1.308–25.998, p -value = 0.021; aOR 2 : 3.99, 95%CI: 1.087–14.646, p -value = 0.037), and were less likely to target patients in need of test report interpretations (aOR 1 : 0.172, 95%CI: 0.038–0.781, p -value = 0.023; aOR 2 : 0.287, 95%CI: 0.082–0.997, p -value = 0.049) than their senior counterparts.

Work seniority

Practitioners with 10–19 years of work experience were significantly more likely to consider isolated/empty-nest patients as suitable recipients compared to those with < 5 years of experience (aOR: 3.328, 95%CI: 1.021–10.849, p -value = 0.046).

Institution

Practitioners from CHCs were more likely to view frequent drug collectors as suitable recipients compared to those from private hospitals (aOR: 0.359, 95%CI: 0.134–0.966, p -value = 0.043).

Attitude of pharmacist clinics

Necessity and confidence in implementing pharmacist clinics.

Table  3 showed that 82.9% ( n  = 185) of practitioners recognized the necessity of pharmacist clinics, but only 33.3% ( n  = 75) felt confident in their implementation. Male practitioners exhibited significantly higher confidence levels compared to female practitioners ( p  = 0.037), and practitioners from community health centers (CHCs) showed greater confidence relative to those practicing in private hospitals ( p  = 0.008).

Preferred mode of pharmacist clinics

As shown in Table  4 , the favored modality was found to be joint physician-pharmacist clinics (36.8%, n  = 82), through collaboration with medical institutions (52.0%, n  = 116). Daily sessions emerged as the preferred frequency ( n  = 86, 38.5%), with both registration and pharmacy service fees considered appropriate for payment (42.2%, n  = 94).

Furthermore, we explored the influence of different sociodemographic variables. Practitioners holding a master’s degree or higher demonstrated a preference for a clinic frequency of 2–4 times per week ( p -value = 0.015), along with acceptance of both registration and pharmacy service fees ( p -value < 0.001), compared to those with lower levels of education. Conversely, those with a junior college education or below were more willing to seek free services. Practitioners from CHCs exhibited a preference for weekly or 2–4 times per week clinics, whereas those from private hospitals favored daily or monthly sessions ( p -value < 0.001).

Practice of pharmacist clinics

As shown in Table  5 , there was a limited prevalence of pharmacist clinics within primary care institutions. Only 17.0% ( n  = 38) of practitioners reported the implementation of pharmacy clinics, mostly scheduled once a week (47.4%, n  = 18), with the primary challenge being a high outpatient workload (30.9%, n  = 69). Practitioners from CHCs demonstrated a significantly higher implementation frequency compared to those from private hospitals ( p -value < 0.001).

We further explored sociodemographic factors associated with challenges. Practitioners aged over 45 years ( P  = 0.020) and occupying senior/deputy senior positions ( p -value = 0.018) were more likely to consider the absence of fee collection mechanisms as the principal difficulty, as opposed to their younger counterparts and those in lower positions.

Our study aims to evaluate the perceptions, attitudes, and practices of primary healthcare practitioners regarding pharmacist clinics and to identify necessary changes. The findings unveiled a lack of knowledge and confidence among primary care providers, who are faced with barriers including high outpatient workloads and concerns related to professionalism. Collaborative models are preferred as they align with the current emphasis on multidisciplinary approaches in modern healthcare, which aim to achieve optimal population health [ 34 ]. Additionally, our findings highlight the impact of institution and gender on the perceptions of primary care providers.

In this study, more practitioners preferred joint physician-pharmacist clinics over traditional physician-led clinics (36.8%, n  = 82 vs. 24.2%, n  = 54), which is in line with a global focus on integrating pharmacists into the provision of patient-centered, coordinated, and comprehensive care [ 1 , 35 , 36 ]. Primary care physicians are in short supply, and studies unveiled that the shortage of primary care physicians has led to increased workloads and a greater demand for medication guidance services, especially among vulnerable patients aged 65 and above [ 37 , 38 , 39 , 40 ]. Our study showed the primary goals of pharmacist clinics were found to be prescription reviews (28.9%, n  = 183) and medication guidance (31.5%, n  = 200), which are critical in addressing concerns regarding poorly managed or duplicate prescriptions [ 41 , 42 ]. Integrating pharmaceutical services into primary care offers expedited access and convenience for patients, thereby releasing physicians to focus on more complex cases and reducing their workload [ 43 , 44 ]. These services also contribute to overall savings in healthcare and medication costs, as well as reduced general physician appointments, emergency department visits, and inappropriate drug use [ 45 , 46 ]. Our findings support the potential of pharmacist-led prescription reviews in reducing duplicate prescriptions [ 47 ], drug-related problems [ 48 ], and medication costs, without increasing physicians’ workload [ 49 ]. Moreover, pharmacist-led medication guidance provided to other professionals has been shown to reduce medication errors and inappropriate prescriptions compared to standard care [ 50 , 51 ]. The development of joint physician-pharmacist clinics may be an advantageous choice for the development of pharmacist clinics in the future.

Current evidence highlights the suboptimal quality of primary care in China [ 52 ], with previous research suggesting that inadequate education and training pose significant challenges in enhancing care quality [ 53 ]. Primary healthcare providers in China have reported being too busy for continued education, dissatisfaction with course content, and having unqualified supervisors [ 54 ]. This issue seems to be consistent in the United States [ 55 ], Canada [ 56 ], and Belgium [ 57 ]. Moreover, our study has identified high workload (30.9%, n  = 69) and insufficient professionalism (25.1%, n  = 56) as the top two challenges faced by pharmacist clinics. On the other hand, insufficient knowledge may contribute to negative attitudes [ 39 ].

In this study, a minority of practitioners (24.7%, n  = 55) demonstrated strong familiarity, and only 33.3% ( n  = 75) felt confident. While some global studies did not find a significant difference in clinical competence confidence between public and private practitioners [ 58 , 59 ], our study revealed that pharmacists from CHCs exhibited greater confidence in conducting pharmacist clinics compared to those from private hospitals, partially due to their greater exposure to training. Studies have also shown that community pharmacists, through enhanced training, can acquire expanded expertise and knowledge [ 60 , 61 ], leading to improved service quality in primary care [ 62 , 63 ]. Future efforts should focus on establishing a more efficient learning and continued education system for community practitioners in China [ 52 ].

Several impediments were identified by respondents, including limited patient volume (22.0%, n  = 49) and low staff motivation (6.3%, n  = 14). Despite the positive impact of pharmacists in outpatient settings on patient outcomes, the adoption of these services remains low [ 1 ]. Recent literature has highlighted public uncertainty about primary care specialties and skepticism regarding their capacity to deliver comprehensive care [ 64 ]. Evidence suggests a lack of awareness, demand, and utilization of community pharmacy services among patients [ 65 , 66 ]. Another barrier is the prevailing focus on quantity rather than quality of care, with job content and bonuses linked more to quantity than the quality of care delivered [ 52 , 67 ]. Financial conflicts over funding and the absence of fee collection may also hinder collaboration between pharmacists and other healthcare providers [ 43 , 68 ]. Additionally, the implementation of the zero-mark-up drug policy in China in 2011 caused a substantial decrease of about 40% in drug-related incomes [ 69 ]. Institutions responded by scaling back clinical care services to offset this profit loss [ 70 ], leading to an uptick in hospital visits for minor ailments and further burdening the healthcare system [ 53 ]. It is important to expand community pharmacy services by establishing reimbursement mechanisms to relieve the burden on general practice [ 71 ]. Countries like Australia, the UK, New Zealand, and Canada have established systems for pharmacist remuneration [ 72 ]. Payment models for pharmaceutical services typically include fee-for-service, where providers are compensated based on the services delivered (as seen in Australia, Canada, Belgium, and Japan), capitation, where providers receive a fixed amount per patient (as in the US, Thailand, and Denmark), and blended funding, which combines government and private payments (as in China, Australia, New Zealand, and Canada) [ 73 ]. Despite the existence of various payment models for pharmaceutical services, there is no standardized pricing for pharmacist clinics. Among 465 hospitals with pharmacist clinics, only 98 (21.08%) owned charging mechanisms [ 25 ]. Various studies have explored the willingness to pay (WTP) for pharmaceutical services in different countries. For instance, Porteous et al. [ 74 ] found a WTP of $69.19 for community practices in the UK. Tsao et al. [ 75 ] reported a WTP of $21.26 for medication therapy management in Canada, and in Brazil, the estimated WTP for comprehensive medication management was $17.75 [ 76 ].

Our findings also revealed gender-based disparities in the perceptions and implementation of pharmacist clinics. Female practitioners exhibited lower levels of confidence in conducting the clinics compared to males, consistent with previous research indicating that women in healthcare often perceive deficiencies in their abilities despite no differences in clinical performance between genders [ 77 ]. Additionally, female medical students reported higher levels of anxiety, stress, and self-doubt about their knowledge and performance [ 78 ]. However, in Australia and Ireland, females rated themselves higher than males in self-assessment tests [ 79 , 80 ]. Further investigations to explore potential confounding factors, such as cultural influences, may contribute to understanding these variations and better address the need to tailor pharmacist-managed clinic services based on institutional needs [ 81 ].

This research is geographically confined to Shanghai and solely captures the perspectives of practitioners, potentially limiting generalizability. Future studies should broaden their scope to encompass diverse practices and include patients’ perceptions. The cross-sectional design used in this study restricts the evaluation of cause-effect relationships, emphasizing the need for longitudinal investigations. Despite these limitations, to the best of the authors’ knowledge, this is the first quantitative study that has examined the knowledge, attitudes, and practice of practitioners regarding pharmacist clinics in primary settings based on real-world data in China. The identified challenges in conducting these clinics provide valuable insights for policymakers, researchers, and institutions in this field.

Although primary healthcare practitioners generally hold positive attitudes towards pharmacist clinics, limited knowledge and confidence, high workload, and other factors lead to the scarcity of such clinics. Practitioners with diverse sociodemographic backgrounds, especially those from different institutions and genders, exhibit varying perceptions of the forms of pharmacist clinics. Further exploration with lager samples from different regions and service recipients is necessary.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

We thank all the participants in this research.

This study received funding from the Shanghai Committee of Science and Technology (Grant No. 22YF1439800) and the Shanghai Municipal Health Commission (Grant No. 20194Y0234).

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Xinyue Zhang, Zhijia Tang and Yanxia Zhang contributed equally to this work.

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Minhang Hospital & Department of Clinical Pharmacy, School of Pharmacy, Fudan University, 170 Xinsong Road, Shanghai, 201199, P.R. China

Xinyue Zhang, Zhijia Tang, Yanxia Zhang, Wai Kei Tong, Qian Xia, Bing Han & Nan Guo

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ZT and YZ designed the research, developed the questionnaire; WT and QX collected the data; XZ and ZT performed the statistical analysis and wrote the manuscript; BH and NG critically reviewed the statistical analysis, work, and this report. All authors read and approved the final manuscript.

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Zhang, X., Tang, Z., Zhang, Y. et al. Knowledge, attitudes, and practices of primary healthcare practitioners regarding pharmacist clinics: a cross-sectional study in Shanghai. BMC Health Serv Res 24 , 677 (2024). https://doi.org/10.1186/s12913-024-11136-3

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Getting started with tables

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• Georgia Ntani 1 ,
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• Chiara Di Gravio 1 ,
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Tables are often overlooked by many readers of papers who tend to focus on the text. Good tables tell much of the story of a paper and give a richer insight into the details of the study participants and the main research findings. Being confident in reading tables and constructing clear tables are important skills for researchers to master.

Common forms of tables were considered, along with the standard statistics used in them. Papers in the Archives of Public Health published during 2015 and 2016 were hand-searched for examples to illustrate the points being made. Presentation of graphs and figures were not considered as they are outside the scope of the paper.

Basic statistical concepts are outlined to aid understanding of each of the tables presented. The first table in many papers gives an overview of the study population and its characteristics, usually giving numbers and percentages of the study population in different categories (e.g. by sex, educational attainment, smoking status) and summaries of measured characteristics (continuous variables) of the participants (e.g. age, height, body mass index). Tables giving the results of the analyses follow; these often include summaries of characteristics in different groups of participants, as well as relationships between the outcome under study and the exposure of interest. For continuous outcome data, results are often expressed as differences between means, or regression or correlation coefficients. Ratio/relative measures (e.g. relative risks, odds ratios) are usually used for binary outcome measures that take one of two values for each study participants (e.g. dead versus alive, obese versus non-obese). Tables come in many forms, but various standard types are described here.

Clear tables provide much of the important detail in a paper and researchers are encouraged to read and construct them with care.

Peer Review reports

Tables are an important component of any research paper. Yet, anecdotally, many people say that they find tables difficult to understand so focus only on the text when reading a paper. However, tables provide a much richer sense of a study population and the results than can be described in the text. The tables and text complement each other in that the text outlines the main findings, while the detail is contained in the tables; the text should refer to each table at the appropriate place(s) in the paper. We aim to give some insights into reading tables for those who find them challenging, and to assist those preparing tables in deciding what they need to put into them. Producing clear, informative tables increases the likelihood of papers being published and read. Good graphs and figures can often provide a more accessible presentation of study findings than tables. They can add to the understanding of the findings considerably, but they can rarely contain as much detail as a table. Choosing when to present a graph or figure and when to present a table needs careful consideration but this article focuses only on the presentation of tables.

We provide a general description of tables and statistics commonly used when presenting data, followed by specific examples. No two papers will present the tables in the same way, so we can only give some general insights. The statistical approaches are described briefly but cannot be explained fully; the reader is referred to various books on the topic [ 1 – 6 ].

Presentation of tables

The title (or legend) of a table should enable the reader to understand its content, so a clear, concise description of the contents of the table is required. The specific details needed for the title will vary according to the type of table. For example, titles for tables of characteristics should give details of the study population being summarised and indicate whether separate columns are presented for particular characteristics, such as sex. For tables of main findings, the title should include the details of the type of statistics presented or the analytical method. Ideally the table title should enable the table to be examined and understood without reference to the rest of the article, and so information on study, time and place needs to be included. Footnotes may be required to amplify particular points, but should be kept to a minimum. Often they will be used to explain abbreviations or symbols used in the table or to list confounding factors for which adjustment has been made in the analysis.

Clear headings for rows and columns are also required and the format of the table needs careful consideration, not least in regard to the appropriateness and number of rows and columns included within the table. Generally it is better to present tables with more rows than columns; it is usually easier to read down a table than across it, and page sizes currently in use are longer than they are wide. Very large tables can be hard to absorb and make the reader’s work more onerous, but can be useful for those who require extra detail. Getting the balance right needs care.

Types of tables

Many research articles present a summary of the characteristics of the study population in the first table. The purpose of these tables is to provide information on the key characteristics of the study participants, and allow the reader to assess the generalisability of the findings. Typically, age and sex will be presented along with various characteristics pertinent to the study in question, for example smoking prevalence, socio-economic position, educational attainment, height, and body mass index. A single summary column may be presented or perhaps more than one column split according to major characteristics such as sex (i.e. separate columns for males and females) or, for trials, the intervention and control groups.

Subsequent tables generally present details of the associations identified in the main analyses. Sometimes these include results that are unadjusted or ‘crude’ (i.e. don’t take account of other variables that might influence the association) often followed by results from adjusted models taking account of other factors.

Other types of tables occur in some papers. For example, systematic review papers contain tables giving the inclusion and exclusion criteria for the review as well as tables that summarise the characteristics and results of each study included in the review; such tables can be extremely large if the review covers many studies. Qualitative studies often provide tables describing the characteristics of the study participants in a more narrative format than is used for quantitative studies. This paper however, focuses on tables that present numerical data.

Statistics commonly presented in tables

The main summary statistics provided within a table depend on the type of outcome under investigation in the study. If the variable is continuous (i.e. can take any numerical value, between a minimum and a maximum, such as blood pressure, height, birth weight), then means and standard deviations (SD) tend to be given when the distribution is symmetrical, and particularly when it follows the classical bell shaped curve known as a Normal or Gaussian distribution (see Fig.  1a ). The mean is the usual arithmetic average and the SD is an indication of the spread of the values. Roughly speaking, the SD is about a quarter of the difference between the largest and the smallest value excluding 5% of values at the extreme ends. So, if the mean is 100 and the SD is 20 we would expect 95% of the values in our data to be between about 60 (i.e. 100–2×20) and 140 (100 + 2×40).

Distribution of heights and weights of young women from the Southampton Women’s Survey [ 7 ]. a Shows the height distribution, which is symmetrical and generally follows a standard normal distribution, while b shows weight, which is skewed to the right

The median and inter-quartile range (IQR) are usually provided when the data are not symmetrical as in Fig.  1b , which gives an example of data that are skewed, such that if the values are plotted in a histogram there are many values at one end of the distribution but fewer at the other end [ 7 ]. If all the values of the variable were listed in order, the median would be the middle value and the IQR would be the values a quarter and three-quarters of the way through the list. Sometimes the lower value of the IQR is labelled Q1 (quartile 1), the median is Q2, and the upper value is Q3. For categorical variables, frequencies and percentages are used.

Common statistics for associations between continuous outcomes include differences in means, regression coefficients and correlation coefficients. For these statistics, values of zero indicate no association between the exposure and outcome of interest. A correlation coefficient of 0 indicates no association, while a value of 1 or −1 would indicate perfect positive or negative correlation; values outside the range −1 to 1 are not possible. Regression coefficients can take any positive or negative value depending on the units of measurement of the exposure and outcome.

For binary outcome measures that only take two possible values (e.g. diseased versus not, dead versus alive, obese versus not obese) the results are commonly presented in the form of relative measures. These include any measure with the word ‘relative’ or ‘ratio’ in their name, such as odds ratios, relative risks, prevalence ratios, incidence rate ratios and hazard ratios. All are interpreted in much the same way: values above 1 indicate an elevated risk of the outcome associated with the exposure under study, whereas below 1 implies a protective effect. No association between the outcome and exposure is apparent if the ratio is 1.

Typically in results tables, 95% confidence intervals (95% CIs) and/or p -values will be presented. A 95% CI around a result indicates that, in the absence of bias, there is a 95% probability that the interval includes the true value of the result in the wider population from which the study participants were drawn. It also gives an indication of how precisely the study team has been able to estimate the result (whether it is a regression coefficient, a ratio/relative measure or any of the summary measures mentioned above). The wider the 95% CI, the less precise is our estimate of the result. Wide 95% CIs tend to arise from small studies and hence the drive for larger studies to give greater precision and certainty about the findings.

If a 95% CI around a result for a continuous variable (difference in means, regression or correlation coefficient) includes 0 then it is unlikely that there is a real association between exposure and outcome whereas, for a binary outcome, a real association is unlikely if the 95% CI around a relative measure, such as a hazard or odds ratio, includes 1.

The p -value is the probability that the finding we have observed could have occurred by chance, and therefore there is no identifiable association between the exposure of interest and the outcome measure in the wider population. If the p -value is very small, then we are more convinced that we have found an association that is not explained by chance (though it may be due to bias or confounding in our study). Traditionally a p -value of less than 0.05 (sometimes expressed as 5%) has been considered as ‘statistically significant’ but this is an arbitrary value and the smaller the p -value the less likely the result is simply due to chance [ 8 ].

Frequently, data within tables are presented with 95% CIs but without p -values or vice versa. If the 95% CI includes 0 (for a continuous outcome measure) or 1 (for a binary outcome), then generally the p -value will be greater than 0.05, whereas if it does not include 0 or 1 respectively, then the p -value will be less than 0.05 [ 9 ]. Generally, 95% CIs are more informative than p -values; providing both may affect the readability of a table and so preference should generally be given to 95% CIs. Sometimes, rather than giving exact p-values, they are indicated by symbols that are explained in a footnote; commonly one star (*) indicates p  < 0.05, two stars (**) indicates p  < 0.01.

Results in tables can only be interpreted if the units of measurement are clearly given. For example, mean or median age could be in days, weeks, months or years if infants and children are being considered, and 365, 52, 12 or 1 for a mean age of 1 year could all be presented, as long the unit of measurement is provided. Standard deviations should be quoted in the same units as the mean to which they refer. Relative measures, such as odds ratios, and correlation coefficients do not have units of measurement, but for regression coefficients the unit of measurement of the outcome variable is required, and also of the exposure variable if it is continuous.

The examples are all drawn from recent articles in Archives of Public Health. They were chosen to represent a variety of types of tables seen in research publications.

Tables of characteristics

The table of characteristics in Table  1 is from a study assessing knowledge and practice in relation to tuberculosis control among in Ethiopian health workers [ 10 ]. The authors have presented the characteristics of the health workers who participated in the study. Summary statistics are based on categories of the characteristics, so numbers (frequencies) in each category and the percentages of the total study population within each category are presented for each characteristic. From this, the reader can see that:

the study population is quite young, as only around 10% are more than 40 years old;

the majority are female;

more than half are nurses;

about half were educated to degree level or above.

The table of characteristics in Table  2 is from a study of the relationship between distorted body image and lifestyle in adolescents in Japan [ 11 ]. Here the presentation is split into separate columns for boys and girls. The first four characteristics are continuous variables, not split into categories but, instead, presented as means, with the SDs given in brackets. The three characteristics in the lower part of the table are categorical variables and, similar to Table  1 , the frequency/numbers and percentages in each category are presented. The p -values indicate that boys and girls differ on some of the characteristics, notably height, self-perceived weight status and body image perception.

In Table  3 , considerable detail is given for continuous variables in the table. This comes from an article describing the relationship between mid-upper-arm circumference (MUAC) and weight changes in young children admitted to hospital with severe acute malnutrition from three countries [ 12 ]. For each country, the categorical characteristic of sex is presented as in the previous two examples, but more detail is given for the continuous variables of age, MUAC and height. The mean is provided as in Table  2 , though without a standard deviation, but we are also given the minimum value, the 25th percentile (labelled Q1 – for quartile 1), the median (the middle value), the 75th percentile (labelled Q2, here though correctly it should be Q3 – see above) and the maximum value. The table shows:

Ethiopian children in this study were older and taller than those from the other two countries but their MUAC measurements tended to be smaller;

in Bangladesh, disproportionally more females than males were admitted for treatment compared with the other two countries.

It is unusual to present as much detail on continuous characteristics as is given in Table  3 . Usually, for each characteristic, either (a) mean and SD or (b) median and IQR would be given, but not both.

Tables of results – summary findings

Many results tables are simple summaries and look similar to tables presenting characteristics, as described above. Sometimes the initial table of characteristics includes some basic comparisons that indicate the main results of the study. Table  4 shows part of a large table of characteristics for a study of risk factors for acute lower respiratory infections (ALRI) among young children in Rwanda [ 13 ]. In addition to presenting the numbers of children in each category of a variety of characteristics, it also shows the percentage in each category among those who suffered ALRI in the previous two weeks, and provides p- values for the differences between the categories among those who did and did not suffer from ALRI. Thus only 2.9% of older children (24–59 months) within the study suffered from ALRI, compared with about 5% in the two youngest categories. The p -value of 0.001, well below 0.05, indicates that this difference is statistically significant. The other finding of some interest is that children who took vitamin A supplements appeared to be less likely to suffer from ALRI than those who did not, but the p -value of 0.04 is close to 0.05 so not as remarkable a finding as for the difference between the age groups.

Table  5 shows a summary table of average life expectancy in British Columbia by socioeconomic status [ 14 ]. The average life expectancy at birth and the associated 95% CIs are given according to level of socio-economic status for the total population (column 1), followed by males and females separately. The study is large so the 95% CIs are quite narrow, and the table indicates that there are considerable differences in life expectancy between the three socioeconomic groups, with the lowest category having the poorest life expectancy. The gap in life expectancy between the lowest and highest category is more than three years, as shown in the final row.

Tables of results – continuous outcomes

Continuous outcome measures can be analysed in a variety of ways, depending on the purpose of the study and whether the measure of the exposure is continuous, categorical or binary.

Table  6 shows an example of correlation coefficients indicating the degree of association between the exposure of interest (cognitive test scores) and the outcome measure (academic performance) [ 15 ]. No confidence intervals are presented, but the results show that almost all the particular cognitive test scores are statistically significantly associated ( p -value < 0.05) with the two measures of academic performance. Note that this table is an example of where a footnote is used to give information about the p-values. Not surprisingly, all the correlations are positive; one would expect that as cognitive score increase so too would academic performance. The numbers labelled “N” give the number of children who contributed data to each correlation coefficient.

Table  7 is quite a complex table, but one that bears examination. It presents regression coefficients from an analysis of pregnancy exposure to nitrogen dioxide (NO 2 ) and birth weight of the baby in a large study of four areas in Norway; more than 17,000 women-baby pairs contributed to the complete crude analysis [ 16 ]. Regression coefficients are presented and labelled “Beta”, the usual name for such coefficients, though the Greek letter β, B or b are sometimes used. They are interpreted as follows: for one unit increase in the exposure variable then the outcome measure increases by the amount of the regression coefficient. Regression coefficients of zero indicate no association. In this table, the Beta in the top left of the table indicates that as NO 2 exposure of the mother increases by 1 unit (a ‘unit’ in this analysis is 10 μg/m 3 , see the footnote in the table, which gives the units of measurement used for the regression coefficients: grams per 10 μg/m 3 NO 2 ) then the birth weight of her baby decreases (because the Beta is negative) by 37.9 g. The 95% CI does not include zero and the p -value is small (<0.001) implying that the association is not due solely to chance.

However, reading across the columns of the table gives a different story. The successive sets of columns include adjustment for increasing numbers of factors that might affect the association. While model 1 still indicates a negative association between NO 2 and birth weight that is highly significant ( p  < 0.001), models 2 and 3 do not. Inclusion of adjustment for parity or area and maternal weight has reduced the association such that the Betas have shrunk in magnitude to be closer to 0, with 95% CIs including 0 and p -values >0.05.

The table has multiple rows, with each one providing information on a different subset of the data, so the numbers in the analyses are all smaller than in the first row. The second row restricts the analysis to women who did not move address during pregnancy, an important consideration in estimating NO 2 exposure from home addresses. The third row restricts the analysis to those whose gestational age was based on the last menstrual period. These second two rows present ‘sensitivity analyses’, performed to check that the results were not due to potential biases resulting from women moving house or having uncertain gestational ages. The remaining rows in the table present stratified analyses, with results given for each category of various variables of interest, namely geographical area, maternal smoking, parity, baby’s sex, mother’s educational level and season of birth. Only one row of this table has a statistically significant result for models 2 and 3, namely babies born in spring, but this finding is not discussed in the paper. Note the gap in the table in the model 2 column as it is not possible to adjust for area (one of the adjustment factors in model 2) when the analysis is being presented for each area separately.

Tables of results – binary outcomes

Table  8 presents results from a study assessing whether children’s eating styles are associated with having a waist-hip ratio greater or equal to 0.5 (the latter being the outcome variable expressed in binary form – ≥0.5 versus <0.5) [ 17 ]. Results for boys and girls are presented separately, along with the number of children in each of the eating style categories. The main results are presented as crude and adjusted odds ratios (ORs). The adjusted ORs take account of age, exercise, skipping breakfast and having a snack after dinner, all of these being variables thought to affect the association between eating style and waist-hip ratio. Looking at the crude OR column, the value of 2.04 in the first row indicates that, among boys, those who report eating quickly have around twice the odds of having a high waist-hip ratio than those who do not eat quickly (not eating quickly is the baseline category, with an odds ratio given as 1.00). The 95% CI for the crude OR for eating quickly is 1.31 – 3.18. This interval does not include 1, indicating that the elevated OR for eating quickly is unlikely to be a chance finding and that there is a 95% probability that the range of 1.31 – 3.18 includes the true OR. The p -value is 0.002, considerably smaller than 0.05, indicating that this finding is ‘statistically significant’. The other ORs can be considered in the same way, but note that, for both boys and girls, the ORs for eating until full are greater than 1 but their 95% CIs include 1 and the p- values are considerably greater than 0.05, so not ‘statistically significant’, indicating chance findings.

The final columns present the ORs after adjustment for various additional factors, along with their 95% CIs and p -values. The ORs given here differ little from the crude ORs in the table, indicating that the adjustment has not had much effect, so the conclusions from examining the crude ORs are unaltered. It thus appears that eating quickly is strongly associated with a greater waist-hip ratio, but that eating until full is not.

Summary tables of characteristics describe the study population and set the study in context. The main findings can be presented in different ways and choice of presentation is determined by the nature of the variables under study. Scrutiny of tables allows the reader to acquire much more information about the study and a richer insight than if the text only is examined. Constructing clear tables that communicate the nature of the study population and the key results is important in the preparation of papers; good tables can assist the reader enormously as well as increasing the chance of the paper being published.

Abbreviations

Acute lower respiratory infections

Confidence interval

Mid-upper-arm circumference

• Inter-quartile range

Nitrogen dioxide

Quartile 1 (25th percentile)

Quartile 2 (50th percentile = median)

Quartile 3 (75th percentile)

• Standard deviation

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The work was funded by the UK Medical Research Council which funds the work of the MRC Lifecourse Epidemiology Unit where the authors work. The funding body had no role in the design and conduct of the work, or in the writing the manuscript.

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HI conceived the idea for the paper in discussion with JB. HI wrote the first draft and all other authors commented on successive versions and contributed ideas to improve content, clarity and flow of the paper. All authors read and approved the final manuscript.

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Epidemiological trends in psoriatic arthritis: a comprehensive population-based study

• Amir Haddad 1 , 5 ,
• Perach Chen Elkayam 2 ,
• Nili Stein 2 ,
• Ilan Feldhamer 3 ,
• Arnon Dov Cohen 3 , 4 ,
• Walid Saliba 2 , 5   na1 &
• Devy Zisman 1 , 5   na1  

Arthritis Research & Therapy volume  26 , Article number:  108 ( 2024 ) Cite this article

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Psoriatic arthritis (PsA) is a chronic, potentially debilitating inflammatory arthritis often associated with psoriasis. Understanding the epidemiology of PsA across diverse populations can provide valuable insights into its global burden and the role of genetic and environmental factors. This study aimed to estimate PsA’s temporal trends, prevalence, and incidence, while assessing variations in age, gender, and ethnicity in Israel from 2016 to 2022.

Data were sourced from the Clalit Health Services (CHS) database, covering over half of the Israeli population. Algorithm-based definitions for PsA and psoriasis cases were used. Demographic factors, including age, gender, socioeconomic status (SES), ethnicity, urban/rural residence, BMI, and smoking status, were analyzed. Standardized prevalence and incidence rates were calculated. Logistic regression analyses examined associations of sociodemographic variables with PsA.

In 2022, the prevalence of PsA was 0.221%, with an incidence rate of 13.54 per 100,000 population. This prevalence has tripled since 2006, reflecting a rising trend in PsA over time. Females exhibited a higher prevalence (1.15; 95%CI 1.09–1.21), and PsA was more common in Jewish individuals (1.58; 95%CI 1.45–1.71) those with higher SES (1.4; 95% CI 1.31, 1.5), and those with obesity (2.17; 95%CI 2.04–2.31).

Conclusions

This comprehensive population-based study pointed to an increase prevalence of PsA, emphasizing the rising healthcare demands and economic burden faced by this patient population. Further research is essential to delve into the factors driving these trends.

Psoriatic arthritis (PsA) is a chronic, potentially destructive inflammatory arthritis that is associated with comorbidities and affects individuals with psoriasis.

Investigating PsA’s epidemiology in diverse populations holds the potential to enhance our comprehension of the global disease burden. Moreover, given the significant influence of environmental and genetic factors on PsA susceptibility, exploring this condition’s epidemiology in ethnically varied populations across different geographic regions could shed light on the underlying mechanisms of the disease.

The reported prevalence of PsA worldwide ranges from 0.1 to 1% in the general population [ 1 , 2 ].

A 2018 systematic review and meta-analysis identified a PsA prevalence of 133 (95% CI, 107–164) per 100,000 individuals (or 0.13%) within the general population [ 3 ]. Considerable heterogeneity among the included studies was observed in the review, likely stemming from variations in geographic location, target demographics, research methodologies, genetic backgrounds, environmental and lifestyle factors. Additionally, differences in PsA case definitions contributed to the observed heterogeneity.

Most investigations into PsA prevalence trends indicate a rise in prevalence in recent years, whereas fewer studies addressing incidence trends over time yield inconsistent findings [ 4 ].

Scant data exists regarding PsA’s epidemiology in Middle Eastern populations representing various ethnic backgrounds [ 5 , 6 ]. In a prior study conducted by our group, the prevalence of PsA in the adult Israeli population in 2015 was 0.153%. This marked a doubling of the prevalence in this population over the preceding decade, while the incidence remained stable during the same period [ 7 ]. As the prevalence is a function of incidence and disease duration, this increase suggests a possibility of a genuine increase in disease duration that might be as a consequence of a combination of population aging or growth and improvement in disease treatment or a decrease in mortality over the years as was observed in a previous study on this population[ 8 ].

The objectives of our study encompassed estimating the temporal trends in PsA prevalence and incidence, as well as assessing variations among different age, gender, and ethnic subgroups in the general population of Israel spanning from 2016 to 2022.

Patient and data source

The study utilized data spanning from 2016 to 2022, sourced from the Clalit Health Services (CHS) database. CHS is one of the four nonprofit health organizations delivering both hospital-based and community-based healthcare services, encompassing medical treatments, diagnostic tests, and hospitalizations in the Israeli population. Israeli law mandates all residents to choose coverage with one of these health maintenance organizations, with CHS being the largest, serving approximately 4.7 million enrolees, constituting 52% of Israel’s total population. The demographic makeup, geographic distribution, and ethnic diversity of the population served by CHS closely resemble that of the broader Israeli population. The CHS database comprises comprehensive demographics and clinical data, incorporating sociodemographic information derived from Israel’s Central Bureau of Statistics and the National Insurance Institute (Social Security). The database is continually updated with information from pharmaceutical, medical, and administrative systems. It consolidates data from electronic medical records originating from primary care and specialist clinics, hospitals, pharmacies, and laboratories. A registry of chronic diseases diagnoses is compiled from these data sources. Diagnoses are captured in the registry by diagnosis-specific algorithms, employing International Classification of Diseases Ninth revision (ICD-9) code reading, laboratory test results and disease-specific drug usage. A record is kept of the data-sources and dates used to establish the diagnosis, with the earliest recorded date, from any source, considered to be the defining date of diagnosis. The CHS database has been instrumental in various epidemiological studies, including those in the field of psoriatic disease.

Study population

The study encompassed all individuals aged 18 years and above who were enrolled in CHS at any point between 2016 and 2022. Patients were tracked from their enrolment with CHS until their demise, departure from CHS, or the conclusion of the study in December 2022. The primary analysis pertaining to the global and subgroup prevalence of PsA involved the entire CHS population in 2022. Temporal trends in the prevalence and incidence rates of PsA from 2016 to 2022 were evaluated. For this analysis, the study population comprised all CHS members aged 18 years and older for each respective year. All data utilized in this study were rendered anonymous to the researchers. The study received approval from the research ethics board of Carmel Medical Centre.

Psoriatic arthritis and psoriasis case definition

A pilot study was conducted to validate an algorithm for identifying patients with PsA within the CHS database[ 9 ]. Briefly an algorithm that encompassed the following conditions was applied: (1) PsA diagnosis assigned by a rheumatologist at least once; (2) a permanent diagnosis code assigned by a primary care physician, combined with the use of synthetic or biologic disease-modifying antirheumatic drugs; or (3) a PsA code listed in a hospitalization discharge summary. This algorithm exhibited a positive predictive value, sensitivity, and specificity of 90.5%, 88.7%, and 88.1%, respectively.

The process for identifying patients with psoriasis in the CHS database has been detailed in earlier publications[ 10 , 11 ]. In essence, individuals were classified as having psoriasis if their medical records contained at least one documented diagnosis of psoriasis by a CHS dermatologist or if psoriasis was included in the diagnosis section of hospital discharge letters.

Additional information

The following variables were extracted from the database: age, gender, socioeconomic status (SES), ethnicity (Jewish or Arab), area of residence (urban vs. rural), body mass index (BMI), and smoking status. SES was categorized as low, middle, or high based on neighborhood socioeconomic scores established by the Israel Central Bureau of Statistics, representing a Z-score comparing the mean SES of the subject’s neighborhood with that of the overall neighborhood[ 12 ].

Statistical analysis

Crude and age- and sex-standardized annual prevalence and incidence rates (with corresponding 95% confidence intervals) for PsA were computed by dividing the number of PsA patients (aged 18 and over) by the total count of CHS enrollees (aged 18 and over) for the respective year within the 2016–2022 period. Disease onset was defined as the date of the first qualified health services contact for which a PsA diagnosis was documented. Patients with such initial contacts were considered incident PsA cases. Prevalent cases were carried forward from the date of their first PsA diagnosis until they met one of the following endpoints: death, departure from CHS, or the study’s conclusion in December 2022. Annual sex- and age-specific prevalence and incidence rates were determined for 10-year age groups and expressed as proportions (95% confidence intervals) using the 2006 Israeli population for direct age and sex standardization[ 13 ] so we could compare the numbers to our previous study that was conducted over the years 2006–2015. Furthermore, the crude prevalence of PsA (in 2022) was separately reported for the following subgroups: gender (males vs. females), ethnicity (Jewish vs. Arab), SES (high, moderate, low), area of residence (urban vs. rural), BMI category (underweight, normal, overweight, obese), and smoking status (smokers vs. non-smokers). Information on model covariates was drawn from the CHS database for the year 2022. Results were presented as odds ratios with 95% confidence intervals.

Among the 3,133,500 individuals aged 18 years and older registered in the CHS database in 2022, 6930 patients had a diagnosis of PsA; of those, 432 patients had a new diagnosis of PsA, resulting in an overall crude prevalence rate of 0.221% and incidence rate of 13.9 per 100,000 population (Table  1 ). Additionally, 42,046 patients had a diagnosis of psoriasis, resulting in an overall crude prevalence of 1.34% (95% CI 1.327%, 1.353%). The prevalence of PsA among patients with psoriasis (in 2022) was 16.48%.

The prevalence of PsA was slightly higher in females (0.233%) than in males (0.208%). The prevalence of PsA peaked in the sixth decade of life, at 0.440%. The age at diagnosis of PsA was higher than reported in previous cohorts from Europe and North America, since over half the patients were diagnosed after the age of 50 years (Fig.  1 ).

The distribution of age at diagnosis of psoriatic arthritis among all patients

Most patients with PsA had a recorded diagnosis of psoriasis (5671 patients [81.4%]). In the majority (3363 [59.3%]), the diagnosis of PsA was recorded following the diagnosis of psoriasis. In 1925 patients (33.95%), the diagnoses of PsA and psoriasis were recorded in the same year, and in the remaining 6.75% (383) of patients, the diagnosis of PsA preceded the diagnosis of psoriasis.

Temporal trend in the prevalence and incidence of PsA from 2016 to 2022

The crude prevalence of PsA has increased during the study period, from 0.165% in 2016 to 0.221% in 2022 (Table  2 ) and compared to 2006, it had tripled (the crude prevalence rate at that time was 0.073% as reported in a previous study). A similar increase was observed in the age- and sex-standardized PsA prevalence: from 0.148% in 2016 to 0.197% in 2022.

There was also a trend on increase in the crude incidence rate over the years 2016–2022, as it has increased from 12.98 up to the range of 17.75 and 17.51 in the years 2019 and 2020 respectively, then gradually decreased to 13.90 per 100,000 population in 2022 as well as an increasing trend in the the age- and sex-standardized incidence rates from 12.4 to 13.54%. The prevalence and incidence rates increased in both sexes over time. The temporal trends in sex-standardized prevalence and incidence of PsA by age groups are shown in Fig.  2 . A more pronounced increase in the prevalence of PsA was observed in the age group (51–60 years) in females.

Prevalence and incidence of psoriatic arthritis in Israel by age and sex group from 2016–2022

Factors associated with PsA

The prevalence of PsA was associated with several demographic and lifestyle factors, as shown in Table  3 . We found an association of female sex and PsA. PsA was more frequent in female individuals (prevalence 0.233%, adjusted OR of 1.15; 95% CI 1.09–1.21). An association was found between the level of SES and PsA. PsA was most frequent in people with a higher SES (high SES prevalence 0.286%, adjusted OR high vs. low SES (1.4; 95% CI 1.31, 1.5), followed by middle SES (moderate SES prevalence 0.243%, OR middle vs. low SES 1.17; 95% CI 1.10, 1.24) and lowest in the low SES (prevalence 0.169%). Additionally, the distribution of PsA varied by ethnicity, with higher prevalence in individuals of Jewish ethnicity compared with Arabs (adjusted OR 1.58, 95% CI 1.45–1.71). Moreover, PsA was more frequent in overweight (prevalence 0.277%; OR 1.66; 95% CI 1.56–1.77) and obese individuals (prevalence 0.353%; OR 2.17; 95% CI 2.04–2.31) than in people with normal weight (prevalence 0.170%). Lastly, smoking was also associated with PsA (prevalence 0.283%, adjusted OR of 1.42; 95% CI 1.35–1.49).

In this population-based study, we examined the the temporal trends in the prevalence and incidence of PsA in a large datsbase in Israel from 2016 to 2022. The age and sex standardized prevalence and incidence rates were calculated using the 2006 Israeli population to allow a direct comparison to the rates provided in the previous study. We observed that in 2022, the prevalence of PsA in the adult population in Israel was 0.221% with an incidence rate of 13.54 per 100,000 population. The reported prevalence of PsA in Israel has tripled since 2006, rising from 0.067% in 2006 to 0.221% in 2022. The global incidence rate has also relatively increased from 10.4 (95% CI 9.5–11.4) in 2006 to 15.5 (95%CI 14-16.8) in 2021 and 13.5 (95%CI 12.2–14.8) in 2022 and that PsA was more commonly found in individuals from specific groups, including females, Jewish ethnicity, higher SES, higher BMI and among smokers.

Most population-based epidemiological studies on PsA have focused on European populations, leaving a significant gap in our understanding the disease prevalence in other ethnic groups and geographic regions, particularly in Middle Eastern populations.

To the best our knowledge, this study stands as the largest-scale population-based research providing insights into PsA prevalence in a Middle Eastern population, encompassing two diverse subpopulations.

Our estimated PsA prevalence in Israel (0.221%) falls above the prevalence range reported in a systematic review and meta-analysis (0.13%) the general population [ 4 ], but aligns more closely with estimates in the United States (0.25%)[ 14 ], Ontario[ 15 ] and Northern European countries such as Sweden (0.25%)[ 16 ], Norway (0.19–0.67%)[ 17 , 18 ], the United Kingdom (0.19%)[ 5 ].

Discrepancies in prevalence estimates between studies are attributed to factors like differences in study design, case definitions (self-reported or database-derived), genetic backgrounds, environmental factors (including climate and infections), lifestyle (smoking, alcohol consumption, and obesity), and dietary habits (such as adherence to the Mediterranean diet and fish oil consumption).

Notably, the prevalence of PsA varies in other Mediterranean countries, ranging from 0.05% in Turkey[ 19 ], to 0.06–0.17% in Greece[ 20 , 21 ].

Fewer studies have explored the incidence of PsA. Our estimated incidence rate of cases per 100,000 population falls within the range of previous estimates in most European and US populations, which typically range from 6 to 35.9 per 100,000 [ 4 ].

Traditionally, the proportion of male and female PsA patients has been considered roughly equal. However, slight variations in sex proportions have been reported in different studies. [ 4 ] In our study we observed a slight female predominance (0.233% vs. 0.209%) and both sexes exhibited an increasing incidence over time.

The prevalence of PsA among patients with psoriasis in our study was 16.48%, compared to a pooled PsA prevalence of 19.7% (95% CI 18.5–20.9%) in patients with psoriasis in a 2019 systematic review and meta-analysis.[ 22 ].

Our study demonstrates an increasing trend in the crude and age-adjusted prevalence and incidence of PsA over the study period. These findings align with recent research from Europe and Asia that has reported a rise in the prevalence and incidence of psoriasis and PsA over time[ 23 , 24 , 25 ]. Possible factors contributing to this trend include increased disease awareness among physicians, possibly driven by the 2006 Classification Criteria for Psoriatic Arthritis (CASPAR) criteria, which heighlighted awarness and recognition of PsA and might have lead more rheumatologists to classify a disease as “PsA” rather than “spondyloarthritis”. Morever, the enhanced and increased utilization of use of an advaced and sensitive diagnostic modalities (e.g., ultrasound and magnetic resonance imaging- MRI) could have impacted PsA diagnosis. Additionally, both international and local educational initiatives among rheumatologist and primary care physicians as well as rheumatologist and dermatology specialists (Group for Research and Assessment of Psoriasis and psoriatic arthritis - GRAPPA) has led to an increase in disease awareness and more referral to a rheumatologist.

Other potential drivers for the rise in PsA prevalence could be the decrease in disease mortality over the past decades, that could be the as a result of improvement in disease management and treatments over the years. Additionally, the increased presence of known risk factors like obesity in Israel [ 26 , 27 ] and shifts in population demographics such as immigration of high-risk groups could have also impacted these trends.

Moreover, greater availability of effective medications can also lead to more individuals seeking medical advice and consequently being diagnosed with the disease.

Various demographic factors were found to be correlated with the occurrence of PsA including sex, ethnicity, SES, BMI and smoking status. The prevalence of PsA was higher in females, consistent with studies that showed higher incidence of PsA in females[ 22 , 28 ]. Nevertheless, the data on sex ratio also appears disparate. PsA was also nearly 1.6 times more prevalent in the Jewish population compared to Arabs, as well as among individual with higher SES. These disparities may be attributed to differences in genetics, environmental exposures, or healthcare utilization.

Furthermore, our findings confirm the association of obesity with a nearly twofold increase in PsA prevalence, aligning with the existing literature. [ 29 , 30 ]

Additionally, our study suggests an association of smoking with PsA (adjusted OR of 1.42; 95% CI 1.35–1.49). However, these results should be interpreted with cation as we didn’t account for psoriasis as a causal intermediate variable. Thus, smoking might indirectly elevate the risk of PsA by increasing the risk of psoriasis, potentially leading to spurious findings. Smoking is well-known risk factor for psoriasis; yet its link to the development of PsA has produced varying results in prior research [ 31 , 32 ].

Our study carries several limitations. We could not capture patients with PsA or psoriasis who did not seek medical attention or those who remained undiagnosed. Additionally, our case definition relies on diagnostic coding from electronic medical records based on physician diagnoses, which carries the risk of misclassification. Nonetheless, we minimized this risk by employing an algorithm with high accuracy that largely relied on specialist diagnoses.

A significant strength of our study is the use of a large, representative sample covering over half the Israeli population, allowing for broad generalization of our findings.

High-quality real-world data concerning the epidemiology of PsA are essential for understanding the disease’s societal burden. Our study, involving approximately 7,000 PsA patients, stands as one of the most extensive population-based studies to date, assessing PsA prevalence in Middle Eastern individuals. Based on this large data base the prevalence of PsA in the adult Israeli population in 2022 was 0.221% and it tripled since 2006, signalling increasing healthcare demands and economic burden among this patient population. Further research is warranted to continue monitoring this trend and uncover its underlying determinants.

Data availability

All data in this study is available upon reasonable request from the corresponding author.

Abbreviations

• Psoriatic arthritis

Clalit health services

International Classification of Diseases Ninth revision

Socioeconomic status

Body mass index

Magnetic resonance imaging

Classification Criteria for Psoriatic Arthritis

Group for Research and Assessment of Psoriasis and psoriatic arthritis

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Walid Saliba and Devy Zisman contributed equally to this work.

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Rheumatology Unit, Carmel Medical Center, 7 Michal Street, Haifa, Israel

Amir Haddad & Devy Zisman

Department of Epidemiology, Clalit health services, Haifa, Israel

Perach Chen Elkayam, Nili Stein & Walid Saliba

Chief Physician’s Office, Central Headquarters, Clalit Health Services, Tel Aviv, Israel

Ilan Feldhamer & Arnon Dov Cohen

Siaal Research Center for Family Medicine and Primary Care, Faculty of Health Sciences, Ben- Gurion University of the Negev, Beer-Sheba, Israel

Arnon Dov Cohen

Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel

Amir Haddad, Walid Saliba & Devy Zisman

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Haddad, A., Elkayam, P.C., Stein, N. et al. Epidemiological trends in psoriatic arthritis: a comprehensive population-based study. Arthritis Res Ther 26 , 108 (2024). https://doi.org/10.1186/s13075-024-03339-0

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How Informal Carers Support Video Consulting in Physiotherapy, Heart Failure, and Cancer: Qualitative Study Using Linguistic Ethnography

Authors of this article:

Original Paper

• Lucas Martinus Seuren 1, 2 , PhD   ; 
• Sara Shaw 1 , PhD  

1 Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom

2 Institute for Better Health, Trillium Health Partners, Mississauga, ON, Canada

Corresponding Author:

Lucas Martinus Seuren, PhD

Nuffield Department of Primary Care Health Sciences

University of Oxford

Radcliffe Observatory Quarter

Woodstock Road

Oxford, OX2 6GG

United Kingdom

Phone: 1 4372607372

Email: [email protected]

Background: Informal carers play an important role in the everyday care of patients and the delivery of health care services. They aid patients in transportation to and from appointments, and they provide assistance during the appointments (eg, answering questions on the patient’s behalf). Video consultations are often seen as a way of providing patients with easier access to care. However, few studies have considered how this affects the role of informal carers and how they are needed to make video consultations safe and feasible.

Objective: This study aims to identify how informal carers, usually friends or family who provide unpaid assistance, support patients and clinicians during video consultations.

Methods: We conducted an in-depth analysis of the communication in a sample of video consultations drawn from 7 clinical settings across 4 National Health Service Trusts in the United Kingdom. The data set consisted of 52 video consultation recordings (of patients with diabetes, gestational diabetes, cancer, heart failure, orthopedic problems, long-term pain, and neuromuscular rehabilitation) and interviews with all participants involved in these consultations. Using Linguistic Ethnography, which embeds detailed analysis of verbal and nonverbal communication in the context of the interaction, we examined the interactional, technological, and clinical work carers did to facilitate video consultations and help patients and clinicians overcome challenges of the remote and video-mediated context.

Results: Most patients (40/52, 77%) participated in the video consultation without support from an informal carer. Only 23% (12/52) of the consultations involved an informal carer. In addition to facilitating the clinical interaction (eg, answering questions on behalf of the patient), we identified 3 types of work that informal carers did: facilitating the use of technology; addressing problems when the patient could not hear or understand the clinician; and assisting with physical examinations, acting as the eyes, ears, and hands of the clinician. Carers often stayed in the background, monitoring the consultation to identify situations where they might be needed. In doing so, copresent carers reassured patients and helped them conduct the activities that make up a consultation. However, carers did not necessarily help patients solve all the challenges of a video consultation (eg, aiming the camera while laying hands on the patient during an examination). We compared cases where an informal carer was copresent with cases where the patient was alone, which showed that carers provided an important safety net, particularly for patients who were frail and experienced mobility difficulties.

Conclusions: Informal carers play a critical role in making video consultations safe and feasible, particularly for patients with limited technological experience or complex needs. Guidance and research on video consulting need to consider the availability and work done by informal carers and how they can be supported in providing patients access to digital health care services.

Introduction

Video consulting has become an established health care service model since the outbreak of the COVID-19 pandemic [ 1 ]. Video consultations have been shown to be safe and effective in a range of clinical settings [ 2 - 6 ]. Patients and clinicians have largely reported positive experiences, particularly in secondary and tertiary care [ 7 - 10 ], with some patients even preferring video consultations over face-to-face consultations, especially for follow-up appointments and where a trusted relationship with the provider is already in place [ 11 ]. Given the policy push for remote health care services to continue beyond the COVID-19 pandemic [ 12 - 14 ], it is clear that video consulting is here to stay. However, significant concerns remain around when video consulting is feasible and appropriate (eg, for which patients and in which clinical settings) [ 15 - 17 ]. Some patients still do not have access to the necessary technology (ie, smartphone, tablet, or computer and high-quality internet) [ 18 , 19 ] and they may also lack the experience, confidence, or ability to use it for a medical consultation [ 20 - 22 ]. In these situations, carers, either professional or informal (eg, family and friends), can provide assistance [ 23 , 24 ].

There is extensive literature on video consulting, documenting the benefits and challenges for patients and clinicians [ 5 ]. However, very few studies address how informal carers participate in video consultations [ 25 , 26 ]. This is surprising, given that informal carers play an important role in patient care. Informal carers, usually family or friends, “[provide] unpaid care and support to a family member, friend or neighbour who is disabled, has an illness or long-term condition, or who needs extra help as they grow older” [ 27 ]. In the United Kingdom, approximately 6 million people provide unpaid care, many of whom play a vital role in coordinating and supporting care received by the person they care for [ 28 , 29 ]. Therefore, it is important to understand the role they play in supporting and delivering video-consulting services.

Contemporary health care systems and policy makers have been pushing a transition to patient-centered or person-centered care, that is, care that is “respectful and responsive to individual preferences, needs and values” [ 30 ]. However, person-centered care has often been taken to only mean patient-centered care. Guidelines do not always address family or carers, and where they do, they merely highlight that practitioners must involve carers in patient care, for example, by asking them to clarify the patient’s wishes [ 31 ]. In other words, the focus in person-centered care is still on the patient. Nevertheless, informal carers play a central role in the delivery of care, supporting patients (to varying degrees and in varying situations) with their needs and care. Carers may deliver up to 90% of the care and support for patients in the community [ 32 ]. Therefore, it is potentially important for guidance on video consultations to take carers, and the support of carers, into account. Given that the work done by carers can cause a significant mental and physical strain [ 33 ], practitioners and policy makers need to consider the preferences, needs, and values of patients and carers.

Health communication research has shown that carers sometimes play an active role in making in-person consultations work: carers may speak on behalf of the patient (eg, to provide additional medical or other information for children or patients who lack capacity), alongside the patient (eg, when planning a next appointment), or with the patient (eg, to help them answer questions about their medication) [ 34 - 36 ]. However, having a carer copresent, that is, physically with the patient in the consultation, can be challenging as patients, clinicians, and carers report that they have trouble managing turn-taking [ 37 ]. This raises questions regarding when the carer is able to talk, what they can talk about, and how they can determine this.

Participation problems may be more pronounced in video consultations. From research outside the health care setting, we know that it can be difficult for carers to facilitate a conversation over video [ 38 ]. The camera restricts the field of view, and generally, only 1 person is visible at a time on each end [ 39 ]. The clinical context adds additional challenges, with participants having to manage new interactional skills (eg, how to begin a video consultation) and accomplish activities that are constrained by the lack of physical copresence (eg, conducting a physical examination) [ 5 , 40 , 41 ].

To date, only 1 study has investigated how the constraints of technology affect communication in health care where informal carers are copresent, focusing on postoperative cancer consultations in the Netherlands and showing that carers often remain offscreen and do not actively participate, and when they do, they mostly talk to the patient [ 42 ]. Several other studies have investigated how professional carers (eg, copresent nurses or primary care physicians) participate in video consultations, with a focus on how these professional Despite their crucial role in health care delivery, informal carers have not yet benefited from the advancements made in this field [ 29 ].

Overall, there is a need to understand how informal carers support video consultations when they are copresent with the patient. This study focuses on how informal carers support patients and clinicians during video consultations. Our focus is on the work (either interactional, clinical, or technological) that informal carers do to make video consultations work to provide key insights into how they affect the feasibility of video consulting. To support our analysis, we compared the consultations where informal carers provided support and the reflections of participants in subsequent interviews with consultations where patients were alone and the reflections of those participants.

Study Design

We conducted a qualitative, multimethods study using Linguistic Ethnography, which combines ethnographic approaches (ie, observation and interviews) with the close inspection of interactional data [ 43 ]. We used ethnography of communication [ 44 ] to guide our understanding of how the context of video consultations (eg, restricted visual field) may shape the ways in which patients, carers, and clinicians communicate over video. We combined this with conversation analysis, an inductive method that investigates the turn-by-turn construction of a conversation, to understand the communication practices (verbal and nonverbal) that make up a video consultation [ 45 ]. Combining these methods enabled us to show how the interactions in video consultations shape, and are shaped by, the wider sociocultural and clinical contexts (eg, established clinical routines, policy, and technology in use) [ 46 ].

For this study, we conducted secondary analysis of qualitative data that were previously collected for 3 separate studies on video consultations in different clinical settings across 4 National Health Service clinics in the United Kingdom (1 in Oxford and 3 in London):

• Supporting Consultations in Remote Physiotherapy (SCiP; 2021-2022) was funded by the National Institute for Health Research to investigate the feasibility and practicalities of video consultations for physiotherapy.
• Virtual Online Consultations: Advantages and Limitations (VOCAL; 2015-2017) was funded by the National Institute for Health Research and investigated (gestational) diabetes and cancer.
• Oxford Telehealth Qualitative Study (OTQS; 2015-2017) was funded by the Wellcome Trust to investigate the feasibility of video consulting in a specialist nurse service for patients with heart failure.

Data were chosen for convenience, having been collected as part of research studies that had already been conducted by members of the larger research team and available for secondary analysis [ 47 , 48 ].

Data Collection

We analyzed all 52 video recordings of video consultations that were recorded across the 3 studies. Data for VOCAL and OTQS were collected from 2015 to 2017 (refer to the study by Shaw et al [ 5 ] for an overview), and data for SCiP were collected from 2021 to 2022 (refer to the study by Seuren et al [ 47 ] for an overview).

In all 3 studies, recruitment was done based on convenience. For VOCAL and OTQS, which took place before the COVID-19 pandemic when video consulting was still relatively unfamiliar, patient participants were recruited in collaboration with clinical staff to ensure that patients were suitable to have a video consultation. The aim was to create a sample with a range of experiences with video consultations, “seeking maximum variety in clinical, ethnic and personal circumstances.” Patients were initially contacted by their clinician, after which the research team sent out an invitation letter [ 5 ]. For SCiP, data collection took place between August 2021 and March 2022, during the COVID-19 pandemic. Initially, clinicians reached out to any patient who had an upcoming appointment by video. Those who showed an interest in the study were subsequently contacted by a member of the research team to explain the details of the study [ 47 ]. For all studies, exclusion criteria were the inability to give informed consent and comorbidity preventing participation. For VOCAL and OTQS, additional exclusion criteria were no 3G internet access at home and lack of familiarity with technology [ 5 ].

Video consultations for VOCAL and OTQS were recorded using small digital camcorders (Sony Handycam DCR-SR72; Sony Corporation) and a handheld iPad (Apple Inc), combined with a commercially available screen-capture software tool (ACA Systems), which was run directly from an encrypted USB memory stick. Whenever feasible, both the patient’s and the clinician’s end of the consultation had been recorded, capturing the consultations and the context in which they took place. The total data set from VOCAL and OTQS consisted of 37 video recordings and transcripts of the video consultations (cancer: 12/37, 32%; diabetes: 12/37, 32%; heart failure: 7/37, 19%; and gestational diabetes: 6/37, 16%), 35 transcripts of semistructured interviews conducted with staff and 26 transcripts of semistructured interviews conducted with patients involved in these consultations ( Table 1 ) [ 5 ].

Video consultations for SCiP were recorded by the clinical team in the 2 National Health Service Trusts using the built-in recording option in Microsoft Teams (Microsoft Corp). The total data set consisted of 15 video recordings and transcripts of video consultations (neuromuscular rehabilitation: 5/15, 33%; long-term pain: 1/15, 7%; and orthopedics: 9/15, 60%), 15 transcripts of semistructured interviews with patients and 7 transcripts of semistructured interviews with clinicians involved in these consultations ( Table 2 ) [ 47 ].

a There was only one participant; hence, there is no IQR.

An initial exploration of the 52 recorded video consultations across all 3 studies showed that informal carers performed a range of seemingly vital tasks in some (but not all) video consultations (12/52, 23%; eg, holding the tablet and laying hands on the patient). This raised questions about the role of carers in video consultations. We collected all instances in our video data where carers were involved at any point during a video consultation and corresponding interview data in which participants in these video consultations reflected on the work carers do. As a routine practice in conversation analysis [ 49 , 50 ], we then built “collections” of similar cases [ 51 ], organizing the data based on the type of work done by carers. To further refine our analysis, we compared our findings with consultations where no carer was present (40/52, 77%), combining researcher observations of potentially risky situations (eg, an older patient nearly fell) with clinician reflections on these consultations to identify cases where the lack of a copresent carer might have negatively affected the quality of care. On the basis of these collections, we then analyzed the qualitative interviews deductively using thematic analysis [ 52 ]. Themes were identified based on our analysis of the consultations and used deductively to analyze the interviews. We examined how participants talked about the 3 key themes, aiming to discern whether participants’ reflections were in line with our findings of the consultations (eg, when and why do patients require assistance with technology) or whether they offered complementary (eg, additional work done by informal carers outside of the consultation) or even contradicting viewpoints (eg, informal carers not being able to offer support). Our analysis focused on the conversation analysis of the consultations, with supporting reflections from the participants.

As all data were selected for convenience, the consultations that involved a carer and those that did not involve a carer were not matched regarding, for example, clinical context, patient demographics, or type of technology used.

We transcribed all video consultations orthographically and subsequently used established conventions for verbal and nonverbal communication [ 53 , 54 ] for the data in our collections. This is a routine practice in conversation analysis and, for this paper, enabled us to track how and why carers assist in video consultations. In the Results section, we present simplified extracts from transcripts, providing orthographic transcripts complemented with notations for silence and overlapping talk to maintain legibility. We added screengrabs to allow readers to appreciate the context of consultations and how participants use their bodies and material objects (eg, how they move and hold a tablet). All interviews were transcribed orthographically. We extracted screengrabs using Adobe Premiere Pro 2023 (Adobe Inc), adding a video filter and facial blur to deidentify participants. Subsequently, we combined these screengrabs with the transcript in Adobe InDesign 2023 (Adobe Inc) and exported these at 600 dots per inch to generate the figures.

Ethical Considerations

All studies received ethics approval for detailed analysis of video recordings of video consultations and audio recordings of interviews. VOCAL was approved by the National Research Ethics Committee London-City Road and Hampstead in December 2014 (14/LO/1883), OTQS by the South Central-Berkshire Research Ethics Committee in September 2015 (15/SC/0553), and SCiP by the East Midland-Nottingham 1 Research Ethics Committee in April 2021 (21/EM/0082). All participating staff and patients provided their informed consent to be audio and video recorded during consultations and interviews and for the data to be used for research purposes, including secondary analysis.

Patients were initially approached by a member of their clinical care team. After signaling an interest in the study, the patient’s contact information was forwarded to a member of the research team. The author provided the patient with an information sheet to review. After providing an opportunity to ask questions, patients were asked if they wanted to participate, and if they agreed, they were asked to sign the consent form. For VOCAL and OTQS, patients provided consent during an in-person conversation with a member of the research team. For SCiP, to comply with infection control procedures during the COVID-19 pandemic, patients provided verbal consent during a video call. Participants did not receive compensation for participation in any of the 3 studies.

All transcriptions were anonymized by removing identifying data and replacing names with descriptions (eg, NAME, where someone’s name is used). Participants consented to the analysis of the raw (ie, recognizable) video data. For publication, video data were anonymized using a visual filter and blur effect in Adobe Premiere Pro 2023.

Main Findings

Of the 52 video consultations in our data, 12 (23%) involved a copresent carer: 8 (67%) with patients with cancer, 3 (25%) with patients with heart failure, and 1 (8%) with a patient consulting for physiotherapy. None of the patients with gestational diabetes had a copresent carer. In these 12 consultations, we identified three main types of work that carers performed: (1) facilitating the use of the technology, (2) helping the patient hear or understand what the clinician said, and (3) assisting the patient with and performing parts of the physical examination. Carers performed these tasks through the use of verbal and nonverbal communication strategies, as seen in the data extracts, screengrabs, and participants’ reported experiences in the following sections. Furthermore, we found that in 10% (5/52) of the consultations the patient did not have a carer copresent, but either the patient or clinician expressed concerns regarding safety during the consultation (eg, a patient saying, “I’m not sure if I’ll be able to get back up again”) or the clinician, during the interview afterward, commented that they felt they might have put the patient in an unsafe situation.

Facilitating the Use of Technology in Video Consultations

Informal carers facilitated the use of technology for video consultations in 2 ways: they provided patients access to the technology, and their presence and perceived expertise provided patients with confidence and reassurance for using the technology.

In our data, some patients (5/52, 10%) either did not have the technology or had never used it for video chat. Therefore, they relied on carers to set up, and sometimes provide, the technology. This facilitation involved activities such as the carer bringing a tablet for the patient to use, registering a Skype (Skype Technologies) account, adding the clinician as a contact on Skype or FaceTime (Apple Inc), talking to the clinician beforehand regarding any practicalities, and explaining to the patient what to expect from the video consultation. For the patients who lacked experience with video-mediated communication, carers provided a sense of reassurance if something went wrong or if there were difficulties. This was evident both in how the informal carers acted in the consultations and how they discussed their experiences during the interviews. An older patient explained before her oncology consultation that she only agreed to a video consultation if her husband would be there:

First uh, I was a bit uh, I said uh, if he’s here it’s fine. I haven’t got any problem.

Another older patient stated after her heart failure consultation that, while she could learn to use the technology, she relied on her daughter being there and would not have been able to do it on her own:

Patient: that’s what I really think, that for me,... it’s easy. Because I don’t have to sit here and think, what if I do something wrong? Carer: no Patient: for people, old people on their own, entirely different. Carer: yeah. it is entirely different. Patient: And I would not be able to do it on my own. ... I wouldn't have the confidence.

During consultations, we found that carers often facilitated the use of technology while being silent (ie, nonspeaking) and offscreen. This involved carers performing 3 types of background activities that allowed the patient to consult with the clinician via video: they handled the “preopening,” the work people do before they start a video consultation [ 55 ]; they handled the camera allowing the patient and clinician to adequately see each other; and they made sure that the patient and clinician could hear each other.

In 8 (67%) of the 12 consultations, carers took care of the “preopening” [ 55 ]: they set up the technology, logged in, and answered the call from the clinician when using a program such as Skype or FaceTime. Then, the patient took over when the consultation started.

In the example in Figure 1 , the patient had never used FaceTime before and did not own a video communication technology (eg, a smartphone or tablet). The carer brought a tablet with her, signed into FaceTime, and held it ready for use. When the clinician called via video, the carer explained to the patient that they would accept the call (line 1). Then, she swiped to answer, pointed out to the patient when the connection was established (line 7) and answered the video call with a "hello, conveying to the clinician that the connection had been established and they were ready [ 56 ]. The carer stayed out of the frame (refer to screengrab 2 in Figure 1 ) and hence out of the interaction [ 42 , 57 ], allowing the patient to conduct her consultation while still remaining available in the background.

In 7 (58%) of the 12 consultations, carers did additional background work that enabled patients to talk to their clinician. This included making sure that the patient and clinician could adequately see and hear each other, for example, by acting as a cameraperson: positioning the technology and framing the patient throughout the consultation [ 38 ] to maintain a “talking heads” configuration for the patient and clinician [ 39 ], a setup in which both participants are visible from the shoulders up. In 71% (5/7) of these consultations, the carer held a tablet or smartphone, moving this to frame the patient while remaining outside the frame themselves. In 29% (2/7) of these consultations, the patient used a desktop PC, so the carer moved the patient instead of the technology.

In the example in Figure 2 , from the start of an oncology consultation, the patient was at the left edge of the field of view of the camera and only half of her face was visible to the clinician. As soon as the physician told the patient to “move slightly” (line 3), the carer turned toward the patient and began to pull their chair. At the point where the physician completed his request (line 8), the patient was visible in the center of the screen. Our recording of the clinician’s end does not capture the screen. However, on the screen on the patient’s side, we can see that initially only the right half of her face is visible, and the carer then adjusts the chair so that the patient is centered and fully visible.

In 4 (33%) of the 12 consultations, the carers acted as a technological facilitator to ensure the audio and video were working. Carers did most of this work at the start of the consultation. This was the first point where participants could determine whether the sound and video were working. In the example in Figure 3 , the carer answered the clinician’s call when he appeared on screen by saying “hello” (line 1), but the clinician did not respond. The carer treated this silence as indicating a problem: she said “hello” again but this time with a more questioning intonation (a strong rising pitch on the “o”), a typical communication strategy for testing if someone can still hear [ 58 ].

After the clinician had said “hi” (line 4), he asked whether the patient (and carer) could hear him (line 8). The patient and carer confirmed (lines 9-10), and the carer checked whether the clinician could hear them. In other words, before the consultation began, the carer and clinician ensured that the technology was working and that the patient and clinician could see and hear each other. It was only when the physician had confirmed (line 14), that the consultation proceeded in a usual manner. At this point, the carer faded into the background.

Staying largely in the background (and so invisible to the clinician), carers typically maintained an active role, helping to address any problems (eg, lost connection or microphone on mute) that arose during the consultation. In these instances, carers temporarily became active participants while fixing the problem. In the example in Figure 4 , the physician asked the patient “how are you.” However, a technical disruption occurred and his turn was cut off after “ho.” After a few seconds of silence, the physician said, “what happened” (line 3), taking the lack of response by the patient as indicative of a problem. It was the carer who then switched to become an active coparticipant, asking if the physician could hear them (line 5). Once all parties had established that they could see and hear each other, the physician acknowledged (line 13) [ 59 ] and repeated the question.

Overall, carers in our data made video consultations feasible by facilitating the use of technology. Much of this work involved carers moving from being coparticipants to listening in the background, often unobservable to the clinician. They did this either by self-selecting to respond to a clinician’s question ( Figures 3 and 4 ) or by being selected by the clinician to answer a question. After responding, they would visually move out of the screen, or at least, no longer respond or take turns.

Making the Interaction Work in Video Consultations

Patients in our study occasionally had problems with hearing or understanding the clinician (eg, due to soft or distorted sound). Such problems happen routinely in any form of conversation [ 60 ], and people have a large array of repair strategies to fix them [ 61 , 62 ]. Normally, when trouble arises, recipients ask the speaker to repeat or clarify (part of) their turn (eg, by repeating the part of the turn they did hear or using exclamations such as “sorry,” “what,” or “huh” [ 63 - 65 ]). During in-person consultations, if patients have problems, they can ask the clinician to clarify [ 66 ].

In our video consultation data, we found that 25% (3/12) of the patients relied on their carer to help them hear or understand the clinician’s talk (in all 3 consultations, the quality of the call was problematic, eg, low volume and distortions). The example in Figure 5 illustrates how carers perform this type of interactional repair. In lines 1 to 4, the physician checked that the patient had seen one of his registrars the week before at an in-person consultation. At this point, the volume was low, making it hard to hear. Moving into the physician’s turn, the patient started squinting (refer to screengrab 1 in Figure 5 ), indicating she had a problem. When the physician finished his question, the patient remained silent for 700 milliseconds (a substantial amount of time, given the usual response time for face-to-face interaction being 0-200 milliseconds [ 67 ]), indicating difficulty [ 68 ]. Instead of answering, the patient turned to the carer (refer to screengrab 2 in Figure 5 ), softly asking “what?” (indicated with the degree symbols) and expecting the carer to perform an interactional repair on the physician’s question. The carer (offscreen) repeated the physician’s verification question in line 8. Once the patient could answer, she started to nod, turned her gaze toward the physician (line 9), and answered (line 11) loudly, thereby making clear her response was now directed to the physician.

While this was a brief interaction, the carer in this example played a crucial role in the successful communication between the physician and the patient. The patient mobilized the carer to help her hear what the physician said. Akin to an interpreter, the carer “animated” the physician’s talk [ 57 ]. Similar examples using indirect communication (eg, physically turning to the carer when something was unclear) were evident across our data set, where patients sought help from carers to enable repair and continuation of the interaction.

Making Physical Examination Possible in Video Consultations

The final area where carers made a vital contribution to video consultations was during physical examinations. The inability of clinicians to lay hands on the patient is one of the main concerns among clinicians and patients about video consultation [ 69 - 71 ]. Instead, patients have to describe and show their body and, where available, use their own devices such as oximeters (a device that people clip onto their finger to measure their blood oxygen saturation and heart rate) [ 40 , 72 ].

Carers supported remote physical examinations in 8 (67%) of the 12 video consultations in our data. This included helping to make the relevant parts of a patient’s body visible, acting as the clinician’s hands to perform tactile examinations and providing visual assessments, and assisting the patient with operating equipment such as blood pressure meters. Support was typically for patients who were frail, in cases where they were either unable to bend over (eg, due to the nature of their condition) or unable to move their tablet or laptop at the same time as moving their body (a complex sociotechnical task that was particularly challenging for those experiencing chronic illness) [ 72 ].

Figure 6 illustrates how carers can play a vital role in the feasibility of a physical examination. The patient had recently undergone surgery to remove a tumor and had complained to the physician about pain in her abdomen around the scar. The physician asked to examine the scar, requesting her to stand up (lines 1-3). The patient did not respond to this request. Instead, she waited for the carer to help out. After 1.3 seconds of silence, the physician made his request again, but at the same time, the carer said “hold on.” Then, the carer helped the patient lift her sweater and aimed the camera toward the scar, allowing the physician to perform a visual assessment (lines 16-19).

In the example in Figure 6 , the role of the carer was crucial for making the physical examination work. With limited physical capacity (and technological literacy), the patient was unable to hold the tablet and show the clinician her abdomen. It was only with the help of her carer that she could provide a sufficiently clear view for the physician to perform a visual assessment remotely.

At times, clinicians relied on a carer to lay hands on the patient on their behalf during video consultations. In the heart failure consultations—both routine follow-up consultations—the specialist nurse wanted to check whether the patients had fluid build-up (edema) in their legs and ankles by pressing their thumb on the patient’s leg and check whether this leaves an indentation. Carers played a vital role in performing these remote assessments, which involved patients who were frail, with restricted mobility and breathlessness, and for whom moving could cause severe discomfort [ 72 ]. Figure 7 illustrates an example in which the patient had just measured her blood oxygen saturation with an oximeter. Then, the nurse addressed the carer directly, announcing that she wanted to check the patient’s legs (lines 1-4). Depicting how the carer should hold her hand (lines 11-16) [ 72 , 73 ], she explained how to press (lines 18-19). The carer followed these instructions and pressed the patient’s legs several times. Using the camera on the back of the tablet, she not only performed the examination but also did so while simultaneously monitoring what the nurse could see (refer to screengrabs 2 to 4 in Figure 7 ). The carer’s presence meant that the nurse was able to make a good assessment of the patient’s legs, telling the carer that “you’re doing a good job, and I can see it really clearly on the screen.”

In total, 2 (4%) of the 52 cases in our wider data set of video consultations flagged questions regarding the safety of physical assessment where carers were not present. Figure 8 illustrates the example of a neuromuscular physiotherapy consultation with a patient with Charcot-Marie-Tooth disease (a neurological disorder that causes damage to the peripheral nerves leading to muscle weakness and atrophy), who struggled with walking and balance. At one point, the clinician asked the patient to stand up so that she could see her walk while holding onto a wall. The patient had to push herself from the bed, had difficulty standing up without losing her balance, and had to use both hands to help herself. In hindsight, the clinician acknowledged that this may have been too difficult.

We identified a similar case in our heart failure data, where an older patient raised her leg to the camera, allowing her nurse to assess whether there was any swelling ( Figure 9 ). The patient needed to stand and had to hold on to the chair in front of her to maintain her balance, but the uncomfortable position caused abdominal cramps and led her to drop her leg. This raised questions regarding safety while also placing limits on what was only a brief visual assessment.

Furthermore, carers helped some patients (2/12, 10%) operate measuring devices during examination. This was particularly relevant in remote heart failure consultations, in which all 7 patients needed to measure their oxygen saturation, blood pressure, and heart rate. All 7 patients were able to use the oximeter; however, operating a blood pressure meter proved challenging for 2 patients, both experiencing frailty. In both cases, the patient’s carer placed the cuff on their arm, held the monitor up to the screen to display the results, and adjusted positions so that the patient’s blood pressure measurements could be obtained from both sitting and standing positions ( Figure 10 ).

Remote physical examinations are complex sociotechnical tasks, involving (in our data) at least 3 people, multiple devices at both ends of the call, and a series of instructions and interactions conducted over a video consultation [ 72 ]. Hence, while carers were often needed to make physical examinations work, the assistance of a carer did not make them straightforward. In 1 consultation, video largely restricted the examination to a visual inspection. As the carer from Figure 6 reported after the consultation, with an in-person consultation, “[the patient] could probably explain more where it hurts and [the physician] could, you know, feel why it’s, you know, still tender.” Furthermore, doing the examination while making it visible to the clinician can be challenging for the carer [ 72 ]. This was summarized by the carer from Figure 1 as follows:

Well, look at me, fannying about just trying to get a picture of your leg. I mean it’s not a matchstick. I just could not picture. But it’s partly, because I’m holding it and I can’t see what I’m looking at.

Principal Findings

Our findings demonstrate that, for some video consultations with some patients, informal carers play an important role in supporting setting up and running a video consultation. While most patients (40/52, 77%) in our data completed the consultation on their own, informal carers were the linchpin that made the video consultation safe and feasible, especially when the patients lacked technological literacy or experienced high frailty. We demonstrated this using recordings and observations to show 3 types of work that carers perform. First, they help patients use video technology by setting up everything beforehand and acting as technological support, providing patients with the confidence to even commit to using video. Second, where patients struggle to hear or understand the clinician, carers perform the interactional repair work, repeating or clarifying the clinician’s words. Third, where physical assessments are needed, carers can lay hands on the patient’s or the clinician’s behalf or assist the patient with using the technology (either video technology or examination equipment). Even where patients seemed to manage on their own, patients performed maneuvers that put them at risk of falling, and this was not always clear to the remote clinician. Copresent carers provide an important safety net, making video consultations safe and feasible.

Comparison With Previous Research

There is an extensive body of research on the feasibility and acceptability of video consultations [ 6 - 10 ], which indicates that some patients may need assistance from carers [ 74 ]. However, to date, no study has investigated the work informal carers do to support video consultations. A total of 3 health communication studies have used robust methods for analyzing interaction to demonstrate how carers, whether professional or informal, can be involved in a consultation. Two (67%) of these 3 studies documented that nurses and general practitioners play an essential role in making physical examinations work when patients talk to a remote consultant [ 74 , 75 ]. One study on follow-up consultations after surgery showed that informal carers mostly act as bystanders: they remain invisible to the clinician and only occasionally facilitate the consultation [ 42 ].

Our study adds to this growing body of literature, demonstrating that informal carers may take a more active role than that of a bystander: in our data, they are attentive to the interaction, moving into and out of the field of view of the camera as needed; performing a range of technical, interactional, and clinical tasks; and taking a more active role depending on the needs of both the patient and clinician. Discrepancies between our findings and previous studies can be accounted for in many ways. First, as both the 3 previous studies and our study are qualitative in nature, they prioritize analytical depth, which mandates small patient samples that are not necessarily representative, and these are prone to bias in recruitment processes. Second, all studies have taken place in different geographical locations, at different points in time (eg, before or during the COVID-19 pandemic), and in different clinical settings, with patients with different sociodemographic backgrounds. While the methods may be transferable, more research is needed to appreciate to what extent the findings transfer.

The important role of carers is not limited to video consulting. For in-person consultations, research has shown that carers can be actively involved, talking about, alongside, or with the patients, to provide clinicians with relevant information [ 34 - 36 ]. Findings from our study extend this, demonstrating not only the other types of work that carers do to support video consultations but also how the technology shapes this work.

Videoconferencing technologies and the visual angle of webcams are designed for one-to-one conversations [ 39 ]. These aspects of technology add to the complexity of the interactional dynamics that already exist for triadic consultations (ie, involving a patient, clinician, and informal carer), where participants may struggle with turn-taking [ 34 , 35 ]. Because of this added complexity, video consultations have a continuously shifting participation framework (ie, the roles of patient, clinician, and carer as, for example, an active coparticipant of overhearer) [ 57 ], where carers move in and out of a variety of interactional and technical support roles. Depending on the situation (eg, the patient’s capacity and willingness to talk on their own behalf), carers may be expected to be more or less active coparticipants during consultations. Being offscreen makes carers less available for the clinician. They are more likely to act as overhearers [ 42 ], which can be beneficial in cases where patients wish to interact with the clinician themselves, but it may also be detrimental when patients need more continuous support. Therefore, our findings contribute to not only our appreciation of the important role of carers in the delivery of health care services but also the interactional organization of video consultations. Future research should investigate systematically how the affordances of the technology, particularly the camera’s field of view, affect the norms regarding participation, quality of care, and participant satisfaction.

Meaning of the Study

Our findings suggest that when considering the feasibility of video consultations, some important considerations need to be taken into account. Video consulting has often only been considered a suitable service model for patients with technological competence and experience, where the goal of the consultation is expected to be relatively straightforward (eg, sharing test results and routine follow-up). However, our study shows that this unnecessarily limits to application of video for 2 reasons. First, where patients have a lack of experience with or have anxiety around technology, informal carers can help overcome technological or interactional difficulties. Furthermore, they offer reassurance, making patients comfortable with doing a video consultation. Help may not be needed, but where it would be needed, it would be available [ 76 ]. Second, where the goal of the consultation is more complex (eg, involves a physical assessment), video can still be an appropriate option if the patient has adequate support. Assessments in a video consultation often require the patient to move the camera around to frame themselves in a way that they are adequately visible to the clinician while performing movements that may be difficult for them to do safely or using devices that they are not familiar with (eg, oximeters). Copresent carers can overcome some of these challenges, for example, by taking care of the camera or laying hands on the patient, where patients are comfortable with that.

Since the outbreak of the COVID-19 pandemic, video consultations have become a more routinely used service model. While many patients and providers are moving back to in-person delivery of (health) care, hybrid service models that involve remote options, including video consultation, are likely to constitute the new normal. However, despite the routinization of video-consulting services, clinicians still have limited evidence on when they are a feasible and safe option. While the literature is growing quickly and many organizations have proposed guidelines, these often ignore the role of informal carers. Further rollout of this new service model needs to consider not only what patients themselves can do but also what informal carers can do. Given the important role that informal carers have in health care management, particularly for certain groups of patients (eg, young children, patients with high frailty, or patients who lack capacity), it is logical to assume that their role can be transferred to video-consulting models. The additional work for carers will have to be weighed against the potential benefits for each specific clinical context and each individual patient.

The importance of carers for making some video consultations work raises important questions for those providing and supporting services. Not all patients will have access to an informal carer, and those who have may not always want a carer to be present during the consultation. A systematic review found that patients are not necessarily as involved during consultations where they are accompanied by a carer, and while most patients say they appreciate having someone with them, they want to be able to decide whether a carer will be present during the consultation [ 77 ]. Patients should feel comfortable asking for their carer to leave the room at any point during a consultation. However, this might put an unnecessary burden on the patient. It may be necessary for clinicians to create opportunities to talk to the patient privately.

Strengths and Limitations

Physiotherapy consultations in our data set were conducted during the COVID-19 pandemic, with heart failure and diabetes consultations conducted before the pandemic when video consulting was not a routine service model and few patients, carers, or clinicians had experience with it. Given the uptake and learning around video consultations during the COVID-19 pandemic, it is possible that patients involved in heart failure and cancer consultations needed more support with the technology than they would now. The prepandemic data were also likely to involve early-adopter clinicians who were supportive of video consultations as a new service model. Furthermore, participants in our data set used mainly Skype (Microsoft Corporation) and FaceTime (Apple Inc), whereas video consultations now often take place on dedicated platforms such as Teams (Microsoft Corporation), Attend Anywhere, or AccuRx. Some of these platforms affect the opening of video consultations, with patients expected to join a virtual waiting room before joining the consultation with their clinician. In addition, we focused on the positive experiences of patients and carers, without actively considering whether and when clinical staff are receptive to carer involvement. Despite these limitations, we anticipate that many of our findings are transferable to current video-consulting services. Our use of methods focused on interaction and communication has enabled us to demonstrate in detail the active role that carers played in some video consultations. While the exact role of carers may differ during and after the COVID-19 pandemic, it is highly likely that some patients (eg, older patients, those experiencing frailty, or those with multimorbidity) will continue to need assistance.

To our knowledge, this study is the first to use robust methods for analyzing communication in triadic video consultations (ie, among clinician, patient, and informal carer) across multiple clinical settings. Doing so has allowed us to show in detail not only that carers play a vital role in making video consulting work but also how they go about doing this. Our work adds to the existing literature by highlighting the interactional complexity of these consultations, demonstrating the sociotechnical nature of the work undertaken by informal carers, and underscoring the importance of focusing on the microlevel organization of consultations where carers are (and are not) involved [ 46 , 50 ]. Our work was exploratory in nature, relying on secondary analysis; future studies could investigate how the presence of carers affects the overall experience of patients and clinical staff with health care services, the patient-carer relationship, and the health outcomes for patients.

Conclusions

Video consulting remains a viable service option but depends on patient access to technology and their ability to use it. While many patients can manage a video consultation on their own, some (continue to) require assistance. In these circumstances, informal carers can play a unique, and often invisible, role in making video consultations work. To date, research and guidelines have not adequately considered the work of informal carers. This urgently needs addressing, not only to support the policy vision of the spread of video-consulting services but also to make visible and enable the informal carers (and the patients and clinicians they support) in this often vital role.

Acknowledgments

The authors are grateful to Marissa Bird and Joe Wherton for commenting on an earlier draft of this paper. The authors would like to thank Joe Wherton, Chrysanthi Papoutsi, Trish Greenhalgh, Christine A’Court, Gita Ramdharry, Anthony Gilbert, and Jackie Walumbe for their support in collecting the data used in this paper. This project was funded by UK Research and Innovation via the Economic and Social Research Council (ES/V010069/1), Wellcome Trust (WT104830MA), National Institute for Health Research (NIHR) Biomedical Research Centre (BRC-1215-20008), and the NIHR under its Research for Patient Benefit (PB-PG-1216-20012) and Policy Research Programme (grant NIHR202067). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.

Data Availability

The data sets generated and analyzed during this study are not publicly available due to confidentiality and sensitivity of the material but are available from the corresponding author on reasonable request.

Authors' Contributions

LMS was the principal investigator for this study and led data collection and formal analysis and wrote the first draft of the manuscript. SS provided supervision as a co–principal investigator and supported the review and editing of the manuscript. Both authors were involved in all aspects of the study design and funding acquisition and have reviewed and approved the final manuscript.

Conflicts of Interest

None declared.

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Abbreviations

Edited by T de Azevedo Cardoso; submitted 08.08.23; peer-reviewed by P Traulsen, G Gauhe, T Halkowski, S White; comments to author 06.10.23; revised version received 24.11.23; accepted 19.04.24; published 31.05.24.

©Lucas Martinus Seuren, Sara Shaw. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 31.05.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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• Published: 27 May 2024

Association between gut microbiota and anxiety disorders: a bidirectional two-sample mendelian randomization study

• Jianbing Li 1 ,
• Changhe Fan 1 ,
• Jiaqi Wang 2 ,
• Bulang Tang 2 ,
• Jiafan Cao 2 ,
• Xianzhe Hu 2 ,
• Xuan Zhao 2 &
• Caiqin Feng 1  

BMC Psychiatry volume  24 , Article number:  398 ( 2024 ) Cite this article

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There are many articles reporting that the component of intestinal microbiota implies a link to anxiety disorders (AD), and the brain-gut axis is also a hot topic in current research. However, the specific relevance between gut microbiota and AD is uncertain. We aimed to investigate causal relationship between gut microbiota and AD by using bidirectional Mendelian randomization (MR).

Genetic instrumental variable (IV) for the gut microbiota were obtained from a genome-wide association study (GWAS) involving 18,340 participants. Summary data for AD were derived from the GWAS and included 158,565 cases and 300,995 controls. We applied the inverse variance weighted (IVW) method as the main analysis. Cochran’s Q values was computed to evaluate the heterogeneity among IVs. Sensitivity analyses including intercept of MR-Egger method and MR-PRESSO analysis were used to test the horizontal pleiotropy.

We discovered 9 potential connections between bacterial traits on genus level and AD. Utilizing the IVW method, we identified 5 bacterial genera that exhibited a direct correlation with the risk of AD: genus Eubacteriumbrachygroup , genus Coprococcus3 , genus Enterorhabdus , genus Oxalobacter , genus Ruminiclostridium6 . Additionally, we found 4 bacterial genera that exhibited a negative association with AD: genus Blautia , genus Butyricicoccus , genus Erysipelotrichaceae-UCG003 and genus Parasutterella . The associations were confirmed by the sensitivity analyses.

Our study found a causal relation between parts of the gut microbiota and AD. Further randomized controlled trials are crucial to elucidate the positive effects of probiotics on AD and their particular protection systems.

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Introduction

Anxiety disorders (AD), being the prevailing mental disorders, have a substantial impact on individuals and society alike [ 1 ]. The core features of AD contain indiscriminate anxiety and fear or elusion of persistent and debilitating threats, resulting in substantial medical costs and a burdensome morbidity burden [ 1 , 2 ]. As one of the most popular mental illnesses among young individuals, AD are also the earliest-onset mental disorders [ 3 ]. Amidst the COVID-19 pandemic, there has been a significant surge in the occurrence of AD among children, adolescents, and young adults globally [ 4 ]. First-line treatments for AD include medication and psychotherapy [ 5 ]. However, medication treatments carry certain side effects and risks, such as dependence, cognitive impairment, and an increased risk of heart disease [ 6 ]. The majority of individuals suffering from AD lack access to efficacious treatment options, leaving them vulnerable to relapse [ 7 , 8 ].

Many studies have shown that the occurrence of AD is related to changes in intestinal flora [ 9 , 10 ]. In social anxiety disorder (SAD), there was an increase in the relative abundance of Anaeromassillibacillus and Gordonibacter genera, whereas healthy controls exhibited an enrichment of Parasuterella [ 11 ]. Another article found a reduction in Eubacterium rectale and Fecalibacterium , as well as an increase in Escherichia , Shigella , Fusobacterium , and Ruminococcus in patients with generalized anxiety disorder (GAD) [ 12 ]. In addition, there are numerous documents demonstrating an association between the gut microbiota and mental illness, and the modulation of the gut microbiota on the gut-brain axis has garnered significant attention, such as an elevation of Enterobacteriaceae and Desulfovibrio , and a reduction of Faecalibacterium in patients with AD [ 10 , 13 , 14 , 15 , 16 , 17 ]. In the aforementioned section, it was observed that the evidence exhibits complexities and disparities, as well as some contradictory results, potentially stemming from various confounding factors among different studies.

The previous studies examining the connection between gut microbiota and AD have predominantly relied on cross-sectional designs, which limits the ability to establish a causal relationship between these associations. Therefore, unraveling the causal mechanisms behind gut microbiota-derived AD not only enhances our understanding of their pathogenesis but also provides valuable guidance for implementing microbiota-directed interventions in clinical settings to address AD. Previous Mendelian randomization (MR) studies have primarily focused on investigating the causal relationship between oral microbiota abundance and AD, or between gut microbiota and other psychiatric disorders. A systematic MR study specifically examining the causal relationship between gut microbiota and AD is still lacking in the current literature. In light of this, it is imperative to unravel the causal link between the gut microbiota and AD.

MR is a statistical approach that infers a causal relationship with exposure to a result. It leverages genetic variations linked to the exposure as a proxy for the exposure itself, enabling the assessment of the association between the exposure and the outcome [ 18 ]. Due to the highly effective findings of large-scale genome-wide association study (GWAS) at the gut microbiota and disease level, MR analysis has been abroad used in many scenarios, such as between the oral microbiome and AD, relations between genetically determined metabolites and anxiety symptoms [ 19 , 20 ]. However, there are no specific studies on the causal relationship between gut microbiota and AD. In this research, we applied a bidirectional two-sample MR method to investigate causal relationship between the gut microbiota and AD.

Materials and methods

The assumptions and study design of mr.

MR is a methodology employed to assess causal associations between variables. In order to ensure the validity of MR analysis, 3 fundamental assumptions must be met: (i) the instrumental variable (IV) exhibits a strong link to the exposure factor, (ii) the IV remains unaffected by potential confounding factors., and (iii) the IV influences the result factor solely via the exposure factor [ 21 ]. By applying strict selection criteria, appropriate SNPs were selected as IV for conducting MR analysis on two independent samples. The main aim was to examine the causal relationship between gut microbiota and AD. Furthermore, this study adhered to the guidelines outlined in the Strengthening the Reporting of Observational Studies in Epidemiology-Mendelian Randomization (STROBE-MR) framework [ 22 ] (Fig.  1 ).

A flowchart illustrating the MR analysis process for the association between gut microbiota and AD

Data sources

The data on gut microbiota GWAS used in this study were obtained from an overall meta-analysis conducted by the MiBioGen consortium. The meta-analysis comprised a total of 18,340 individuals from 24 different groups. The alliance combines human whole-genome genotyping with fecal 16 S rRNA sequencing data to perform thorough research and analysis. The large-scale, multi-ethnic genome-wide meta-analysis provided valuable insights into the genetic influences on the gut microbiome composition [ 23 ]. The GWAS data on the gut microbiome can be integrated into MR studies to explore the causal relationship between genetic variations in the gut microbiome and phenotypic traits, providing valuable insights into the role of the microbiome in human health and disease.

As for the data on genetic variants linked to AD, they were sourced from the Medical Research Council Integrative Epidemiology Unit (MRC-IEU) consortium. The cases were defined as individuals who had sought medical attention for symptoms of nervousness, anxiety, or depression. The study population consisted of individuals of European descent, comprising both males and females, and the data were sourced from the year 2018. The dataset included a total of 158,565 cases and 300,995 controls. The diagnosis was based on self-report questionnaires. Detailed information regarding the data origins for this MR study can be found in Table  1 [ 24 , 25 ].

Selection of IV

The GWAS data of exposure contained a total of 5 taxonomic levels for 211 bacterial groups. The genus level is the smallest and most specific classification level. To accurately identify each pathogenic bacterial group, we focused our analysis only on the genus level, specifically examining 131 bacterial classifications. After excluding 12 unknown groups, a total of 119 bacterial genera were included in the study.

To fulfill the demands of MR studies, our initial step involved the SNPs that exhibited an intense association with the exposure factors. However, when employing a stringent threshold of ( P  < 5 × 10 − 8 ), we obtained a limited number of IVs. Consequently, we adjusted the threshold to ( P  < 1 × 10 − 5 ) to ensure the inclusion of more IVs, thereby enabling robust and reliable results. For the selection of IVs associated with AD in the reverse MR analysis, a heightened level of stringency was implemented by applying a P -value threshold of P  < 5 × 10 − 8 .

We utilized the F-statistic to further evaluate the instrument strength. The F-statistic was determined using the formula: F =  β 2 / SE 2 . This statistic provided an assessment of the overall instrument strength [ 26 ] (Fig.  2 ). An F-statistic exceeding 10 was considered indicative of an intense conjunction between the IV and the exposure. Besides P -value threshold, the F statistic in our analysis would provide additional information on the instrument strength beyond P -value.

Assumptions in MR studies: a brief overview

Statistical analysis

The primary methodology employed in MR analysis is the inverse variance weighting (IVW) method. This approach utilizes a meta-analysis technique to combine the Wald estimates connected to individual single nucleotide polymorphisms (SNPs), providing comprehensive estimate of the collective impact of gut microbiota on AD. A crucial assumption in MR is the absence of horizontal pleiotropy, where the IV has a direct impact on the outcome variable solely through the exposure factor, without any influence from through alternative pathways. When this assumption is satisfied, the IVW method can provide estimates that are consistent and estimates [ 27 ]. In cases where a causal relationship ( P  < 0.05) is established by the IVW method, two alternative approaches, namely MR-Egger and the weighted median approach, are utilized to supplement an enrich the IVW results. The MR-Egger method relaxes the assumption of a zero intercept, and it can estimate causal effects, even pleiotropy was presented in IVs. The intercept in the MR-Egger method can indicate the extent of horizontal pleiotropy [ 27 ]. These additional methods provide valuable insights and strengthen the overall analysis by considering potential biases and alternative causal pathways.

The weighted median method can return unbiased causal estimate when only 50% of SNPs are valid [ 28 ]. In this study, we employed a significance threshold of P  < 0.05 to determine statistical significance, and the assessment of causality was expressed through odds ratios (OR) and 95% confidence intervals (CI). In instances where causal relationships were established, unidentified taxa were excluded, and additional sensitivity analyses were performed to guarantee the stability of the consequences. The false discovery rate (FDR) is utilized to control for multiple testing and reduce the likelihood of false positive findings. All of the aforementioned analyses were performed utilizing the TwoSampleMR package (version 0.5.7) in R (version 4.3.0), providing a robust and standardized approach to MR analysis.

According to the criteria for IV selection, a total of 1,531 SNPs were identified and selected as IV associated with gut microbiota. The F-statistics for these IVs all exceed 10, suggesting that the estimated coefficients are improbable to be influenced by the bias caused by weak instruments. Supplementary Tables 1 and 2 provides detailed information about the selected IVs. None of the SNPs were involved in more than one of the association results in Fig.  3 .

The scatter plots depict the causal relationship between gut microbiota and AD

The majority of gut microbiota showed no significant correlation with AD. However, using the IVW method, we identified 9 bacterial features that were significantly associated with the risk of AD on genus level (Supplementary Table 3 ). We used 3 methods, IVW, weighted median and MR-Egger, and defined P  < 0.05 for IVW method screening as a positive result.

Among them, 4 bacterial genera are negatively correlated with AD, indicating that a higher genetically predicted a lower risk of for AD (Fig. 4 and Supplementary Table 4 ). They are: genus Blautia (OR = 0.9838, 95% CI, 0.9725–0.9952, P  = 0.0056), genus Butyricicoccus (OR = 0.9859, 95% CI, 0.9739–0.9981, P  = 0.0233), genus ErysipelotrichaceaeUCG003 (OR = 0.9914, 95% CI, 0.9833–0.9995, P  = 0.0381) and genus Parasutterella (OR = 0.9911, 95% CI, 0.9823–0.9999, P  = 0.0478). Supplementary Table 4 shows the completed data. In sensitivity analysis, MR-Egger, weighted median demonstrated consistent results, except for genus ErysipelotrichaceaeUCG003 , where the MR-Egger trend was in the contrary direction compared to IVW and weighted median.

The forest plot illustrates the connections between 9 bacterial genus traits and the likelihood of developing AD

Another 5 bacterial genera showed a positive correlation with AD, genus Eubacteriumbrachygroup (OR = 1.0068, 95% CI, 1.0010–1.0127, P  = 0.0225), genus Coprococcus3 (OR = 1.0164, 95% CI, 1.0046–1.0285, P  = 0.0065), genus Enterorhabdus (OR = 1.0117, 95% CI, 1.0027–1.0208, P  = 0.0108), genus Oxalobacter (OR = 1.0067, 95% CI, 1.0009–1.0125, P  = 0.0231) and genus Ruminiclostridium6 (OR = 1.0129, 95% CI, 1.0048–1.0212, P  = 0.0019) (Fig. 4 and Supplementary Table 4 ). In the MR-Egger method, the trends of genus Eubacteriumbrachygroup are different from those of the IVW and WM methods.

In horizontal pleiotropy analysis, we used the MR-Egger method and found P -value of the MR-intercept were all greater than 0.05. In addition, further MR PRESSO analysis was conducted, ruling out the existence of horizontal pleiotropy ( P  > 0.05) (Supplementary Tables 5 and 6 ). To assess the heterogeneity of gut microbiome IVs, we employed Cochran’s Q test statistics, which revealed no heterogeneity among the gut microbiome IVs ( P  > 0.05) (Supplementary Table 7 ).

Reverse MR analyses were conducted to examine the links between the 9 bacterial genera and AD. No significant statistical relationship was observed using the IVW method: genus Eubacteriumbrachygroup (OR = 1.4058, 95% CI, 0.4060–4.8674, P  = 0.5909), genus Blautia (OR = 0.9453, 95% CI, 0.5572–1.6038, P  = 0.8348), genus Butyricicoccus (OR = 0.9834, 95% CI, 0.5704–1.6952, P  = 0.9518), genus Coprococcus3 (OR = 0.8886, 95% CI, 0.5040–1.5667, P  = 0.6831), genus Enterorhabdus (OR = 1.0383, 95% CI, 0.4168–2.5868, P  = 0.9356), genus ErysipelotrichaceaeUCG003 (OR = 0.6593, 95% CI, 0.3556–1.2221, P  = 0.1858), genus Oxalobacter (OR = 1.2849, 95% CI, 0.4021–4.1051, P  = 0.6724), genus Parasutterella (OR = 0.7245, 95% CI, 0.3713–1.4136, P  = 0.3447), genus Ruminiclostridium6 (OR = 0.7095, 95% CI, 0.3825–1.3162, P  = 0.2764) (Supplementary Tables 8 and 9 ).

In the context of this study, we used two-sample MR studies to discover the link between AD and gut microbiota. Among the 9 bacterial genus we found, 4 bacteria were negatively correlated with AD and may have a positive effect on AD, and the other 5 bacteria were positively correlated with the occurrence of AD and may promote the development of AD.

Blautia stercoris MRx0006 has been shown to alleviate social dysfunctions, monotonous behaviors, and anxiety-like behaviors relevant to autism disorders in a mouse model. MRx0006 administration at the microbial level, as observed by Paromita Sen et al., resulted in a reduction in the abundance of Alistipes putredinis, which likely underlie the observed increase in expressions of oxytocin, arginine vasopressin, and their receptors, ultimately leading to improved behavioral outcomes [ 29 ]. Butyricicoccus was also inversely associated with AD in a cross-sectional study, which is consistent with our findings [ 12 ]. Approximately 70% of individuals with autism spectrum disorder (ASD) exhibit comorbid symptoms of anxiety, and the findings from a published article confirming the decreased relative abundance of ErysipelotrichaceaeUCG003 in ASD patients further support our research results indicating a negative correlation between ErysipelotrichaceaeUCG003 and AD [ 30 ]. In a study examining SAD, the control group exhibited higher levels of the positive bacteria Parasutterella compared to the anxiety group. The term “psychobiotics” has been coined to refer to these microbes that are associated with improved mood [ 11 ]. However, in a study by Yi Zhang et al., a psychological stress model was established in C57BL/6J mice, followed by fecal microbiota transplantation using samples from stressed (S) and non-stressed (NS) mice. The results showed an increased abundance of Parasutterella in S mice and mechanistic analysis suggested its potential involvement in negative regulation of metabolism. Despite this controversial finding, our study utilized MR to reveal a negative association between Parasutterella and anxiety disorders. However, further experimental investigations are required to elucidate the underlying molecular mechanisms [ 31 ].

Five bacterial genera positively linked to anxiety may indicate that they exacerbate anxiety, but they were less reported. In a study in which consuming prebiotics altered the microbiota of healthy adults, the prebiotics reduced Eubacteriumbrachygroup but did not significantly change biomarkers of stress or mental health symptoms [ 32 ]. In previous studies on AD cases, it has been found that individuals with AD have lower levels of Coprococcus [ 33 ]. However, in our study, we observed an increasing trend in Coprococcus3 , despite belonging to the same genus. This suggests that even within the same genus, the impact of different genus may vary. In contrast to our findings, Enterorhabdus exhibited a declining pattern in a mouse model of anxiety and depression induced by social defeat [ 34 ]. This observation highlights the influence of various factors on alterations in gut microbiota, which may diverge across different species.

Nevertheless, it is crucial to acknowledge that our study has certain limitations. First, the results of this analysis are limited to European populations and may not be generalizable to other populations. Secondly, we observed that the adjusted P -values remained relatively large after multiple test adjustment. The reduced statistical power resulting from the limited sample size may also constrain our ability to detect significant associations between variables. Finally, proving the direct impact of sample types on the outcomes is challenging. However, the selection of sample types is often constrained by the availability of suitable genetic instruments and relevant data sources. The dataset we utilized does not provide specific information on the dietary habits of the individuals or their other medical conditions. Therefore, further examination and validation are needed in the future.

In summary, utilizing large-scale GWAS analysis, MR studies have disclosed a causal relationship between gut microbiota and AD. Among these, 4 bacterial genera exhibited a negative correlation, while 5 bacteria genera showed a positive correlation with AD. However, further exploration of the mechanisms linking gut microbiota to AD requires the establishment of larger GWAS databases. Several gut bacteria have been identified to reduce the occurrence of anxiety, offering promising prospects for the treatment and precaution of AD. Subsequent research should prioritize the exploration of the underlying mechanisms and the development of targeted interventions based on these findings.

Data availability

The raw data analyzed during the current study were available in public databases including IEU database(ukb-b-6991) and MiBioGen database(https://mibiogen.gcc.rug.nl). The code and data related to this study are available from the corresponding author upon reasonable request.

Abbreviations

• Anxiety disorders
• Mendelian randomization

Instrumental variable(s)

Genome-wide association study

Medical Research Council Integrative Epidemiology Unit

Inverse variance weighting

Social anxiety disorder

Generalized anxiety disorder

Strengthening the Reporting of Observational Studies in Epidemiology-Mendelian Randomization

Single nucleotide polymorphism(s)

Odds ratios

Confidence intervals

Autism spectrum disorder

Major depressive disorder

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Acknowledgements

We express our gratitude to the hospital action teams, staff, and participants from the participating hospitals for their valuable support in data collection. Additionally, we extend our appreciation to our collaborators for their assistance throughout the process.

Program of Guangzhou Science and Technology Program Project (No. 202102010115) and Guangdong Yiyang Healthcare Charity Foundation (No. JZ2022001-3).

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Department of Psychiatry, Guangdong Second Provincial General Hospital, Guangzhou, 510317, PR China

Jianbing Li, Changhe Fan & Caiqin Feng

School of Pharmacy, Guangdong Pharmaceutical University, Guangzhou, 510006, China

Jiaqi Wang, Bulang Tang, Jiafan Cao, Xianzhe Hu & Xuan Zhao

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Contributions

CQF designed the research framework. JBL is responsible for data and analysis methods determination as well as manuscript writing. CHF assisted in conducting the literature review. JQW was responsible for manuscript writing. BLT and JFC performed the data statistical analysis. XZH and XZ were responsible for critical revisions.

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Correspondence to Caiqin Feng .

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Li, J., Fan, C., Wang, J. et al. Association between gut microbiota and anxiety disorders: a bidirectional two-sample mendelian randomization study. BMC Psychiatry 24 , 398 (2024). https://doi.org/10.1186/s12888-024-05824-x

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Received : 23 December 2023

Accepted : 09 May 2024

Published : 27 May 2024

DOI : https://doi.org/10.1186/s12888-024-05824-x

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• Gut microbiota
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