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what is the sample size in a research study

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Sample Size Determination: Definition, Formula, and Example

what is the sample size in a research study

Are you ready to survey your research target? Research surveys help you gain insights from your target audience. The data you collect gives you insights to meet customer needs, leading to increased sales and customer loyalty. Sample size calculation and determination are imperative to the researcher to determine the right number of respondents, keeping in mind the research study’s quality.

So, how should you do the sample size determination? How do you know who should get your survey? How do you decide on the number of the target audience?

Sending out too many surveys can be expensive without giving you a definitive advantage over a smaller sample. But if you send out too few, you won’t have enough data to draw accurate conclusions. 

Knowing how to calculate and determine the appropriate sample size accurately can give you an edge over your competitors. Let’s take a look at what a good sample includes. Also, let’s look at the sample size calculation formula so you can determine the perfect sample size for your next survey.

What is Sample Size?

‘Sample size’ is a market research term used for defining the number of individuals included in conducting research. Researchers choose their sample based on demographics, such as age, gender questions , or physical location. It can be vague or specific. 

For example, you may want to know what people within the 18-25 age range think of your product. Or, you may only require your sample to live in the United States, giving you a wide population range. The total number of individuals in a particular sample is the sample size.

What is sample size determination?

Sample size determination is the process of choosing the right number of observations or people from a larger group to use in a sample. The goal of figuring out the sample size is to ensure that the sample is big enough to give statistically valid results and accurate estimates of population parameters but small enough to be manageable and cost-effective.

In many research studies, getting information from every member of the population of interest is not possible or useful. Instead, researchers choose a sample of people or events that is representative of the whole to study. How accurate and precise the results are can depend a lot on the size of the sample.

Choosing the statistically significant sample size depends on a number of things, such as the size of the population, how precise you want your estimates to be, how confident you want to be in the results, how different the population is likely to be, and how much money and time you have for the study. Statistics are often used to figure out how big a sample should be for a certain type of study and research question.

Figuring out the sample size is important in ensuring that research findings and conclusions are valid and reliable.

Why do you need to determine the sample size?

Let’s say you are a market researcher in the US and want to send out a survey or questionnaire . The survey aims to understand your audience’s feelings toward a new cell phone you are about to launch. You want to know what people in the US think about the new product to predict the phone’s success or failure before launch.

Hypothetically, you choose the population of New York, which is 8.49 million. You use a sample size determination formula to select a sample of 500 individuals that fit into the consumer panel requirement. You can use the responses to help you determine how your audience will react to the new product.

However, determining a sample size requires more than just throwing your survey at as many people as possible. If your estimated sample sizes are too big, it could waste resources, time, and money. A sample size that’s too small doesn’t allow you to gain maximum insights, leading to inconclusive results.

LEARN ABOUT: Survey Sample Sizes

What are the terms used around the sample size?

Before we jump into sample size determination, let’s take a look at the terms you should know:


1. Population size: 

Population size is how many people fit your demographic. For example, you want to get information on doctors residing in North America. Your population size is the total number of doctors in North America. 

Don’t worry! Your population size doesn’t always have to be that big. Smaller population sizes can still give you accurate results as long as you know who you’re trying to represent.

2. Confidence level: 

The confidence level tells you how sure you can be that your data is accurate. It is expressed as a percentage and aligned to the confidence interval. For example, if your confidence level is 90%, your results will most likely be 90% accurate.

3. The margin of error (confidence interval): 

There’s no way to be 100% accurate when it comes to surveys. Confidence intervals tell you how far off from the population means you’re willing to allow your data to fall. 

A margin of error describes how close you can reasonably expect a survey result to fall relative to the real population value. Remember, if you need help with this information, use our margin of error calculator .

4. Standard deviation: 

Standard deviation is the measure of the dispersion of a data set from its mean. It measures the absolute variability of a distribution. The higher the dispersion or variability, the greater the standard deviation and the greater the magnitude of the deviation. 

For example, you have already sent out your survey. How much variance do you expect in your responses? That variation in response is the standard deviation.

Sample size calculation formula – sample size determination

With all the necessary terms defined, it’s time to learn how to determine sample size using a sample calculation formula.

Your confidence level corresponds to a Z-score. This is a constant value needed for this equation. Here are the z-scores for the most common confidence levels:

90% – Z Score = 1.645

95% – Z Score = 1.96

99% – Z Score = 2.576

If you choose a different confidence level, various online tools can help you find your score.

Necessary Sample Size = (Z-score)2 * StdDev*(1-StdDev) / (margin of error)2

Here is an example of how the math works, assuming you chose a 90% confidence level, .6 standard deviation, and a margin of error (confidence interval) of +/- 4%.

((1.64)2 x .6(.6)) / (.04)2

( 2.68x .0.36) / .0016

.9648 / .0016

603 respondents are needed, and that becomes your sample size.

Free Sample Size Calculator

How is a sample size determined?

Determining the right sample size for your survey is one of the most common questions researchers ask when they begin a market research study. Luckily, sample size determination isn’t as hard to calculate as you might remember from an old high school statistics class.

Before calculating your sample size, ensure you have these things in place:

Goals and objectives: 

What do you hope to do with the survey? Are you planning on projecting the results onto a whole demographic or population? Do you want to see what a specific group thinks? Are you trying to make a big decision or just setting a direction? 

Calculating sample size is critical if you’re projecting your survey results on a larger population. You’ll want to make sure that it’s balanced and reflects the community as a whole. The sample size isn’t as critical if you’re trying to get a feel for preferences. 

For example, you’re surveying homeowners across the US on the cost of cooling their homes in the summer. A homeowner in the South probably spends much more money cooling their home in the humid heat than someone in Denver, where the climate is dry and cool. 

For the most accurate results, you’ll need to get responses from people in all US areas and environments. If you only collect responses from one extreme, such as the warm South, your results will be skewed.

Precision level: 

How close do you want the survey results to mimic the true value if everyone responded? Again, if this survey determines how you’re going to spend millions of dollars, then your sample size determination should be exact. 

The more accurate you need to be, the larger the sample you want to have, and the more your sample will have to represent the overall population. If your population is small, say, 200 people, you may want to survey the entire population rather than cut it down with a sample.

Confidence level: 

Think of confidence from the perspective of risk. How much risk are you willing to take on? This is where your Confidence Interval numbers become important. How confident do you want to be — 98% confident, 95% confident? 

Understand that the confidence percentage you choose greatly impacts the number of completions you’ll need for accuracy. This can increase the survey’s length and how many responses you need, which means increased costs for your survey. 

Knowing the actual numbers and amounts behind percentages can help make more sense of your correct sample size needs vs. survey costs. 

For example, you want to be 99% confident. After using the sample size determination formula, you find you need to collect an additional 1000 respondents. 

This, in turn, means you’ll be paying for samples or keeping your survey running for an extra week or two. You have to determine if the increased accuracy is more important than the cost.

Population variability: 

What variability exists in your population? In other words, how similar or different is the population?

If you are surveying consumers on a broad topic, you may have lots of variations. You’ll need a larger sample size to get the most accurate picture of the population. 

However, if you’re surveying a population with similar characteristics, your variability will be less, and you can sample fewer people. More variability equals more samples, and less variability equals fewer samples. If you’re not sure, you can start with 50% variability.

Response rate: 

You want everyone to respond to your survey. Unfortunately, every survey comes with targeted respondents who either never open the study or drop out halfway. Your response rate will depend on your population’s engagement with your product, service organization, or brand. 

The higher the response rate, the higher your population’s engagement level. Your base sample size is the number of responses you must get for a successful survey.

Consider your audience: 

Besides the variability within your population, you need to ensure your sample doesn’t include people who won’t benefit from the results. One of the biggest mistakes you can make in sample size determination is forgetting to consider your actual audience. 

For example, you don’t want to send a survey asking about the quality of local apartment amenities to a group of homeowners.

Select your respondents

Focus on your survey’s objectives: 

You may start with general demographics and characteristics, but can you narrow those characteristics down even more? Narrowing down your audience makes getting a more accurate result from a small sample size easier. 

For example, you want to know how people will react to new automobile technology. Your current population includes anyone who owns a car in a particular market. 

However, you know your target audience is people who drive cars that are less than five years old. You can remove anyone with an older vehicle from your sample because they’re unlikely to purchase your product.

Once you know what you hope to gain from your survey and what variables exist within your population, you can decide how to calculate sample size. Using the formula for determining sample size is a great starting point to get accurate results. 

After calculating the sample size, you’ll want to find reliable customer survey software to help you accurately collect survey responses and turn them into analyzed reports.

LEARN MORE: Population vs Sample

In sample size determination, statistical analysis plan needs careful consideration of the level of significance, effect size, and sample size. 

Researchers must reconcile statistical significance with practical and ethical factors like practicality and cost. A well-designed study with a sufficient sample size can improve the odds of obtaining statistically significant results.

To meet the goal of your survey, you may have to try a few methods to increase the response rate, such as:

  • Increase the list of people who receive the survey.
  • To reach a wider audience, use multiple distribution channels, such as SMS, website, and email surveys.
  • Send reminders to survey participants to complete the survey.
  • Offer incentives for completing the survey, such as an entry into a prize drawing or a discount on the respondent’s next order.
  • Consider your survey structure and find ways to simplify your questions. The less work someone has to do to complete the survey, the more likely they will finish it. 
  • Longer surveys tend to have lower response rates due to the length of time it takes to complete the survey. In this case, you can reduce the number of questions in your survey to increase responses.  

QuestionPro’s sample size calculator makes it easy to find the right sample size for your research based on your desired level of confidence, your margin of error, and the size of the population.


Frequently Asked Questions (FAQ)

The four ways to determine sample size are: 1. Power analysis 2. Convenience sampling, 3. Random sampling , 4. Stratified sampling

The three factors that determine sample size are: 1. Effect size, 2. Level of significance 3. Power

Using statistical techniques like power analysis, the minimal detectable effect size, or the sample size formula while taking into account the study’s goals and practical limitations is the best way to calculate the sample size.

The sample size is important because it affects how precise and accurate the results of a study are and how well researchers can spot real effects or relationships between variables.

The sample size is the number of observations or study participants chosen to be representative of a larger group


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Determining sample size: how to make sure you get the correct sample size.

6 min read How many is enough? Over the years, researchers have grappled with the problem of finding the perfect sample size for statistically sound results. Here we shed light on some methods and tools for sample size determination.

What is sample size?

Sample size is a frequently-used term in statistics and  market research , and one that inevitably comes up whenever you’re  surveying  a large population of respondents. It relates to the way research is conducted on large populations.

Free eBook: 2022 Market Research Global Trends Report

So what is sampling, and why does sample size matter?

When you survey a large population of respondents, you’re interested in the entire group, but it’s not realistically possible to get answers or results from absolutely everyone. So you take a random sample of individuals which represents the population as a whole.

The size of the sample is very important for getting accurate, statistically significant results and running your study successfully.

  • If your sample is too small , you may include a disproportionate number of individuals which are outliers and anomalies. These skew the results and you don’t get a fair picture of the whole population.
  • If the sample is too big , the whole study becomes complex, expensive and time-consuming to run, and although the results are more accurate, the benefits don’t outweigh the costs.

If you’ve already worked out your variables you can get to the right sample size quickly with the online sample size calculator below:

Sample size calculator

If you want to start from scratch in determining the right sample size for your market research, let us walk you through the steps.

Learn how to determine sample size

To choose the correct sample size, you need to consider a few different factors that affect your research, and gain a basic understanding of the statistics involved. You’ll then be able to use a sample size formula to bring everything together and sample confidently, knowing that there is a high probability that your survey is statistically accurate.

The steps that follow are suitable for finding a sample size for continuous data – i.e. data that is counted numerically. It doesn’t apply to categorical data – i.e. put into categories like green, blue, male, female etc.

Stage 1: Consider your sample size variables

Before you can calculate a sample size, you need to determine a few things about the target population and the level of accuracy you need:

1. Population size

How many people are you talking about in total? To find this out, you need to be clear about who does and doesn’t fit into your group. For example, if you want to know about dog owners, you’ll include everyone who has at some point owned at least one dog. (You may include or exclude those who owned a dog in the past, depending on your research goals.) Don’t worry if you’re unable to calculate the exact number. It’s common to have an unknown number or an estimated range.

2. Margin of error (confidence interval)

Errors are inevitable – the question is how much error you’ll allow. The margin of error , AKA confidence interval, is expressed in terms of mean numbers. You can set how much difference you’ll allow between the mean number of your sample and the mean number of your population. If you’ve ever seen a political poll on the news, you’ve seen a confidence interval and how it’s expressed. It will look something like this: “68% of voters said yes to Proposition Z, with a margin of error of +/- 5%.”

3. Confidence level

This is a separate step to the similarly-named confidence interval in step 2. It deals with how confident you want to be that the actual mean falls within your margin of error. The most common confidence intervals are 90% confident, 95% confident, and 99% confident.

4. Standard deviation

This step asks you to estimate how much the responses you receive will vary from each other and from the mean number. A low standard deviation means that all the values will be clustered around the mean number, whereas a high standard deviation means they are spread out across a much wider range with very small and very large outlying figures. Since you haven’t yet run your survey, a safe choice is a standard deviation of .5 which will help make sure your sample size is large enough.

Stage 2: Calculate sample size

Now that you’ve got answers for steps 1 – 4, you’re ready to calculate the sample size you need. This can be done using an  online sample size calculator  or with paper and pencil.

1. Find your Z-score

Next, you need to turn your confidence level into a Z-score. Here are the Z-scores for the most common confidence levels:

  • 90% – Z Score = 1.645
  • 95% – Z Score = 1.96
  • 99% – Z Score = 2.576

If you chose a different confidence level, use this  Z-score table  (a resource owned and hosted by SJSU.edu) to find your score.

2. Use the sample size formula

Plug in your Z-score, standard of deviation, and confidence interval into the  sample size calculator  or use this sample size formula to work it out yourself:

Sample size formula graphic

This equation is for an unknown population size or a very large population size. If your population is smaller and known, just  use the sample size calculator.

What does that look like in practice?

Here’s a worked example, assuming you chose a 95% confidence level, .5 standard deviation, and a margin of error (confidence interval) of +/- 5%.

((1.96)2 x .5(.5)) / (.05)2

(3.8416 x .25) / .0025

.9604 / .0025

385 respondents are needed

Voila! You’ve just determined your sample size.

eBook: 2022 Market Research Global Trends Report

Related resources

Convenience sampling 15 min read, non-probability sampling 17 min read, probability sampling 8 min read, stratified random sampling 13 min read, simple random sampling 10 min read, sampling methods 10 min read, sampling and non-sampling errors 10 min read, request demo.

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How To Determine Sample Size for Quantitative Research

mrx glossary sample size

This blog post looks at how large a sample size should be for reliable, usable market research findings.

Table of Contents: 

What is sample size , why do you need to determine sample size , variables that impact sample size.

  • Determining sample size

The sample size of a quantitative study is the number of people who complete questionnaires in a research project. It is a representative sample of the target audience in which you are interested.

Back to Table of Contents

You need to determine how big of a sample size you need so that you can be sure the quantitative data you get from a survey is reflective of your target population as a whole - and so that the decisions you make based on the research have a firm foundation. Too big a sample and a project can be needlessly expensive and time-consuming. Too small a sample size, and you risk interviewing the wrong respondents - meaning ultimately you miss out on valuable insights.

There are a few variables to be aware of before working out the right sample size for your project.

Population size

The subject matter of your research will determine who your respondents are - chocolate eaters, dentists, homeowners, drivers, people who work in IT, etc. For your respective group of interest, the total of this target group (i.e. the number of chocolate eaters/homeowners/drivers that exist in the general population) will guide how many respondents you need to interview for reliable results in that field.

Ideally, you would use a random sample of people who fit within the group of people you’re interested in. Some of these people are easy to get hold of, while others aren‘t as easy. Some represent smaller groups of people in the population, so a small sample is inevitable. For example, if you’re interviewing chocolate eaters aged 5-99 you’ll have a larger sample size - and a much easier time sampling the population - than if you’re interviewing healthcare professionals who specialize in a niche branch of medicine.

Confidence interval (margin of error)

Confidence intervals, otherwise known as the margin of error, indicate the reliability of statistics that have been calculated by research; in other words, how certain you can be that the statistics are close to what they would be if it were possible to interview the entire population of the people you’re researching.

Confidence intervals are helpful since it would be impossible to interview all chocolate eaters in the US. However, statistics and research enable you to take a sample of that group and achieve results that reflect their opinions as a total population. Before starting a research project, you can decide how large a margin of error you will allow between the mean number of your sample and the mean number of its total population. The confidence interval is expressed as +/- a number, indicating the margin of error on either side of your statistic. For example, if 35% of chocolate eaters say that they eat chocolate for breakfast and your margin of error is 5, you’ll know that if you had asked the entire population, 30-40% of people would admit to eating chocolate at that time of day.

Confidence level

The confidence level indicates how probable it is that if you were to repeat your study multiple times with a random sample, you would get the same statistics and they would fall within the confidence interval every time.

In the example above, if you were to repeat the chocolate study over and over, you would have a certain level of confidence that those eating chocolate for breakfast would always fall within the 30-40% parameters. Most research studies have confidence intervals of 90% confident, 95% confident, or 99% confident. The number you choose will depend on whether you are happy to accept a broadly accurate set of data or whether the nature of your study demands one that is almost completely reliable.

Standard deviation

Standard deviation represents how much the results will vary from the mean number and from each other. A high standard deviation means that there is a wide range of responses to your research questions, while a low standard deviation indicates that responses are more similar to each other, clustered around the mean number. A standard deviation of 0.5 is a safe level to pick to ensure that the sample size is large enough.

Population variability 

If you already know anything about your target audience, you should have a feel for the degree to which their opinions vary. If you’re interviewing the entire population of a city, without any other criteria, their views are going to be wildly diverse so you’ll want to sample a high number of residents. If you’re honing in on a sample of chocolate breakfast eaters - there’s probably a limited number of reasons why that’s their meal of choice, so you can feel confident with a much smaller sample.

Project scope

The scope and objectives of the research will have an influence on how big the sample is. If the project aims to evaluate four different pieces of stimulus (an advert, a concept, a website, etc.) and each respondent is giving feedback on a single piece, then a higher number of respondents will need to be interviewed than if each respondent were evaluating all four; the same would be true when looking for reads on four different sub-audiences vs. not needing any sub-group data cuts.

Determining a good sample size for quantitative research

Sample size, as we’ve seen, is an important factor to consider in market research projects. Getting the sample size right will result in research findings you can use confidently when translating them into action. So now that you’ve thought about the subject of your research, the population that you’d like to interview, and how confident you want to be with the findings, how do you calculate the appropriate sample size?

There are many factors that can go into determining the sample size for a study, including z-scores, standard deviations, confidence levels, and margins of error. The great thing about quantilope is that your research consultants and data scientists are the experts in helping you land on the right target so you can focus on the actual study and the findings. 

To learn more about determining sample size for quantitative research, get in touch below: 

Get in touch to learn more about quantitative sample sizes!

Related posts, master the art of tracking with quantilope's certification course, van westendorp price sensitivity meter questions, quantilope & organic valley: understanding consumer values behind behaviors, quantilope & wire webinar: solving the research dilemma with ai.

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  • A Researcher’s Guide To Statistical Significance And Sample Size Calculations

Determining Sample Size: How Many Survey Participants Do You Need?


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How to calculate a statistically significant sample size in research, determining sample size for probability-based surveys and polling studies, determining sample size for controlled surveys, determining sample size for experiments, how to calculate sample size for simple experiments, an example sample size calculation for an a/b test, what if i don’t know what size difference to expect, part iii: sample size: how many participants do i need for a survey to be valid.

In the U.S., there is a Presidential election every four years. In election years, there is a steady stream of polls in the months leading up to the election announcing which candidates are up and which are down in the horse race of popular opinion.

If you have ever wondered what makes these polls accurate and how each poll decides how many voters to talk to, then you have thought like a researcher who seeks to know how many participants they need in order to obtain statistically significant survey results.

Statistically significant results are those in which the researchers have confidence their findings are not due to chance . Obtaining statistically significant results depends on the researchers’ sample size (how many people they gather data from) and the overall size of the population they wish to understand (voters in the U.S., for example).

Calculating sample sizes can be difficult even for expert researchers. Here, we show you how to calculate sample size for a variety of different research designs.

Before jumping into the details, it is worth noting that formal sample size calculations are often based on the premise that researchers are conducting a representative survey with probability-based sampling techniques. Probability-based sampling ensures that every member of the population being studied has an equal chance of participating in the study and respondents are selected at random.

For a variety of reasons, probability sampling is not feasible for most behavioral studies conducted in industry and academia . As a result, we outline the steps required to calculate sample sizes for probability-based surveys and then extend our discussion to calculating sample sizes for non-probability surveys (i.e., controlled samples) and experiments.

Determining how many people you need to sample in a survey study can be difficult. How difficult? Look at this formula for sample size.

what is the sample size in a research study

No one wants to work through something like that just to know how many people they should sample. Fortunately, there are several sample size calculators online that simplify knowing how many people to collect data from.

Even if you use a sample size calculator, however, you still need to know some important details about your study. Specifically, you need to know:

  • What is the population size in my research?

Population size is the total number of people in the group you are trying to study. If, for example, you were conducting a poll asking U.S. voters about Presidential candidates, then your population of interest would be everyone living in the U.S.—about 330 million people.

Determining the size of the population you’re interested in will often require some background research. For instance, if your company sells digital marketing services and you’re interested in surveying potential customers, it isn’t easy to determine the size of your population. Everyone who is currently engaged in digital marketing may be a potential customer. In situations like these, you can often use industry data or other information to arrive at a reasonable estimate for your population size.

  • What margin of error should you use?

Margin of error is a percentage that tells you how much the results from your sample may deviate from the views of the overall population. The smaller your margin of error, the closer your data reflect the opinion of the population at a given confidence level.

Generally speaking, the more people you gather data from the smaller your margin of error. However, because it is almost never feasible to collect data from everyone in the population, some margin of error is necessary in most studies.

  • What is your survey’s significance level?

The significance level  is a percentage that tells you how confident you can be that the true population value lies within your margin of error. So, for example, if you are asking people whether they support a candidate for President, the significance level tells you how likely it is that the level of support for the candidate in the population (i.e., people not in your sample) falls within the margin of error found in your sample.

Common significance levels in survey research are 90%, 95%, and 99%.

Once you know the values above, you can plug them into a sample size formula or more conveniently an online calculator to determine your sample size.

The table below displays the necessary sample size for different sized populations and margin of errors. As you can see, even when a population is large, researchers can often understand the entire group with about 1,000 respondents.

  • How Many People Should I Invite to My Study?

Sample size calculations tell you how many people you need to complete your survey. What they do not tell you, however, is how many people you need to invite to your survey. To find that number, you need to consider the response rate.

For example, if you are conducting a study of customer satisfaction and you know from previous experience that only about 30% of the people you contact will actually respond to your survey, then you can determine how many people you should invite to the survey to wind up with your desired sample size.

All you have to do is take the number of respondents you need, divide by your expected response rate, and multiple by 100. For example, if you need 500 customers to respond to your survey and you know the response rate is 30%, you should invite about 1,666 people to your study (500/30*100 = 1,666).

Sample size formulas are based on probability sampling techniques—methods that randomly select people from the population to participate in a survey. For most market surveys and academic studies, however, researchers do not use probability sampling methods. Instead they use a mix of convenience and purposive sampling methods that we refer to as controlled sampling .

When surveys and descriptive studies are based on controlled sampling methods, how should researchers calculate sample size?

When the study’s aim is to measure the frequency of something or to describe people’s behavior, we recommend following the calculations made for probability sampling. This often translates to a sample of about 1,000 to 2,000 people. When a study’s aim is to investigate a correlational relationship, however, we recommend sampling between 500 and 1,000 people. More participants in a study will always be better, but these numbers are a useful rule of thumb for researchers seeking to find out how many participants they need to sample.

If you look online, you will find many sources with information for calculating sample size when conducting a survey, but fewer resources for calculating sample size when conducting an experiment. Experiments involve randomly assigning people to different conditions and manipulating variables in order to determine a cause-and-effect relationship. The reason why sample size calculators for experiments are hard to find is simple: experiments are complex and sample size calculations depend on several factors.

The guidance we offer here is to help researchers calculate sample size for some of the simplest and most common experimental designs: t -tests, A/B tests, and chi square tests.

Many businesses today rely on A/B tests. Especially in the digital environment, A/B tests provide an efficient way to learn what kinds of features, messages, and displays cause people to spend more time or money on a website or an app.

For example, one common use of A/B testing is marketing emails. A marketing manager might create two versions of an email, randomly send one to half the company’s customers and randomly send the second to the other half of customers and then measure which email generates more sales.

In many cases , researchers may know they want to conduct an A/B test but be unsure how many people they need in their sample to obtain statistically significant results. In order to begin a sample size calculation, you need to know three things.

1. The significance level .

The significance level represents how sure you want to be that your results are not due to chance. A significance level of .05 is a good starting point, but you may adjust this number up or down depending on the aim of your study.

2. Your desired power.

Statistical tests are only useful when they have enough power to detect an effect if one actually exists. Most researchers aim for 80% power—meaning their tests are sensitive enough to detect an effect 8 out of 10 times if one exists.

3. The minimum effect size you are interested in.

The final piece of information you need is the minimum effect size, or difference between groups, you are interested in. Sometimes there may be a difference between groups, but if the difference is so small that it makes little practical difference to your business, it probably isn’t worth investigating. Determining the minimum effect size you are interested in requires some thought about your goals and the potential impact on your business. 

Once you have decided on the factors above, you can use a sample size calculator to determine how many people you need in each of your study’s conditions.

Let’s say a marketing team wants to test two different email campaigns. They set their significance level at .05 and their power at 80%. In addition, the team determines that the minimum response rate difference between groups that they are interested in is 7.5%. Plugging these numbers into an effect size calculator reveals that the team needs 693 people in each condition of their study, for a total of 1,386.

Sending an email out to 1,386 people who are already on your contact list doesn’t cost too much. But for many other studies, each respondent you recruit will cost money. For this reason, it is important to strongly consider what the minimum effect size of interest is when planning a study.    

When you don’t know what size difference to expect among groups, you can default to one of a few rules of thumb. First, use the effect size of minimum practical significance. By deciding what the minimum difference is between groups that would be meaningful, you can avoid spending resources investigating things that are likely to have little consequences for your business.

A second rule of thumb that is particularly relevant for researchers in academia is to assume an effect size of d = .4. A d = .4 is considered by some to be the smallest effect size that begins to have practical relevance . And fortunately, with this effect size and just two conditions, researchers need about 100 people per condition.

After you know how many people to recruit for your study, the next step is finding your participants. By using CloudResearch’s Prime Panels or MTurk Toolkit, you can gain access to more than 50 million people worldwide in addition to user-friendly tools designed to make running your study easy. We can help you find your sample regardless of what your study entails. Need people from a narrow demographic group? Looking to collect data from thousands of people? Do you need people who are willing to engage in a long or complicated study? Our team has the knowledge and expertise to match you with the right group of participants for your study. Get in touch with us today and learn what we can do for you.

Continue Reading: A Researcher’s Guide to Statistical Significance and Sample Size Calculations

what is the sample size in a research study

Part 1: What Does It Mean for Research to Be Statistically Significant?

what is the sample size in a research study

Part 2: How to Calculate Statistical Significance

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How to Determine Sample Size for a Research Study

Frankline kibuacha | apr. 06, 2021 | 3 min. read.

sample size research

This article will discuss considerations to put in place when determining your sample size and how to calculate the sample size.

Confidence Interval and Confidence Level

As we have noted before, when selecting a sample there are multiple factors that can impact the reliability and validity of results, including sampling and non-sampling errors . When thinking about sample size, the two measures of error that are almost always synonymous with sample sizes are the confidence interval and the confidence level.

Confidence Interval (Margin of Error)

Confidence intervals measure the degree of uncertainty or certainty in a sampling method and how much uncertainty there is with any particular statistic. In simple terms, the confidence interval tells you how confident you can be that the results from a study reflect what you would expect to find if it were possible to survey the entire population being studied. The confidence interval is usually a plus or minus (±) figure. For example, if your confidence interval is 6 and 60% percent of your sample picks an answer, you can be confident that if you had asked the entire population, between 54% (60-6) and 66% (60+6) would have picked that answer.

Confidence Level

The confidence level refers to the percentage of probability, or certainty that the confidence interval would contain the true population parameter when you draw a random sample many times. It is expressed as a percentage and represents how often the percentage of the population who would pick an answer lies within the confidence interval. For example, a 99% confidence level means that should you repeat an experiment or survey over and over again, 99 percent of the time, your results will match the results you get from a population.

The larger your sample size, the more confident you can be that their answers truly reflect the population. In other words, the larger your sample for a given confidence level, the smaller your confidence interval.

Standard Deviation

Another critical measure when determining the sample size is the standard deviation, which measures a data set’s distribution from its mean. In calculating the sample size, the standard deviation is useful in estimating how much the responses you receive will vary from each other and from the mean number, and the standard deviation of a sample can be used to approximate the standard deviation of a population.

The higher the distribution or variability, the greater the standard deviation and the greater the magnitude of the deviation. For example, once you have already sent out your survey, how much variance do you expect in your responses? That variation in responses is the standard deviation.

Population Size


As demonstrated through the calculation below, a sample size of about 385 will give you a sufficient sample size to draw assumptions of nearly any population size at the 95% confidence level with a 5% margin of error, which is why samples of 400 and 500 are often used in research. However, if you are looking to draw comparisons between different sub-groups, for example, provinces within a country, a larger sample size is required. GeoPoll typically recommends a sample size of 400 per country as the minimum viable sample for a research project, 800 per country for conducting a study with analysis by a second-level breakdown such as females versus males, and 1200+ per country for doing third-level breakdowns such as males aged 18-24 in Nairobi.

How to Calculate Sample Size

As we have defined all the necessary terms, let us briefly learn how to determine the sample size using a sample calculation formula known as Andrew Fisher’s Formula.

  • Determine the population size (if known).
  • Determine the confidence interval.
  • Determine the confidence level.
  • Determine the standard deviation ( a standard deviation of 0.5 is a safe choice where the figure is unknown )
  • Convert the confidence level into a Z-Score. This table shows the z-scores for the most common confidence levels:
  • Put these figures into the sample size formula to get your sample size.

sample size calculation

Here is an example calculation:

Say you choose to work with a 95% confidence level, a standard deviation of 0.5, and a confidence interval (margin of error) of ± 5%, you just need to substitute the values in the formula:

((1.96)2 x .5(.5)) / (.05)2

(3.8416 x .25) / .0025

.9604 / .0025

Your sample size should be 385.

Fortunately, there are several available online tools to help you with this calculation. Here’s an online sample calculator from Easy Calculation. Just put in the confidence level, population size, the confidence interval, and the perfect sample size is calculated for you.

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Open Access


Research Article

Cardiovascular health and cancer risk associated with plant based diets: An umbrella review

Roles Conceptualization, Data curation, Formal analysis, Writing – original draft

Affiliations Department of Biomedical and Neuromotor Science, Alma Mater Studiorum–University of Bologna, Bologna, Italy, Interdisciplinary Research Center for Health Science, Sant’Anna School of Advanced Studies, Pisa, Tuscany, Italy

ORCID logo

Roles Conceptualization, Formal analysis, Writing – review & editing

Affiliation Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom

Roles Conceptualization, Methodology, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Biomedical and Neuromotor Science, Alma Mater Studiorum–University of Bologna, Bologna, Italy

Roles Conceptualization, Supervision, Writing – review & editing

Affiliation Stanford Prevention Research Center, Stanford University School of Medicine, Stanford, CA, United States of America

Affiliation Department of Translational Medicine, University of Eastern Piedmont, (UNIUPO), Novara, Italy

Roles Conceptualization, Data curation, Writing – review & editing

Roles Conceptualization, Methodology, Supervision, Writing – review & editing

Affiliation IRCCS Istituto delle Scienze Neurologiche di Bologna, Programma Neurochirurgia Ipofisi—Pituitary Unit, Bologna, Italy

  • Angelo Capodici, 
  • Gabriele Mocciaro, 
  • Davide Gori, 
  • Matthew J. Landry, 
  • Alice Masini, 
  • Francesco Sanmarchi, 
  • Matteo Fiore, 
  • Angela Andrea Coa, 
  • Gisele Castagna, 


  • Published: May 15, 2024
  • https://doi.org/10.1371/journal.pone.0300711
  • Reader Comments

Table 1

Cardiovascular diseases (CVDs) and cancer are the two main leading causes of death and disability worldwide. Suboptimal diet, poor in vegetables, fruits, legumes and whole grain, and rich in processed and red meat, refined grains, and added sugars, is a primary modifiable risk factor. Based on health, economic and ethical concerns, plant-based diets have progressively widespread worldwide.

This umbrella review aims at assessing the impact of animal-free and animal-products-free diets (A/APFDs) on the risk factors associated with the development of cardiometabolic diseases, cancer and their related mortalities.

Data sources

PubMed and Scopus were searched for reviews, systematic reviews, and meta-analyses published from 1st January 2000 to 31st June 2023, written in English and involving human subjects of all ages. Primary studies and reviews/meta-analyses based on interventional trials which used A/APFDs as a therapy for people with metabolic diseases were excluded.

Data extraction

The umbrella review approach was applied for data extraction and analysis. The revised AMSTAR-R 11-item tool was applied to assess the quality of reviews/meta-analyses.

Overall, vegetarian and vegan diets are significantly associated with better lipid profile, glycemic control, body weight/BMI, inflammation, and lower risk of ischemic heart disease and cancer. Vegetarian diet is also associated with lower mortality from CVDs. On the other hand, no difference in the risk of developing gestational diabetes and hypertension were reported in pregnant women following vegetarian diets. Study quality was average. A key limitation is represented by the high heterogeneity of the study population in terms of sample size, demography, geographical origin, dietary patterns, and other lifestyle confounders.


Plant-based diets appear beneficial in reducing cardiometabolic risk factors, as well as CVDs, cancer risk and mortality. However, caution should be paid before broadly suggesting the adoption of A/AFPDs since the strength-of-evidence of study results is significantly limited by the large study heterogeneity alongside the potential risks associated with potentially restrictive regimens.

Citation: Capodici A, Mocciaro G, Gori D, Landry MJ, Masini A, Sanmarchi F, et al. (2024) Cardiovascular health and cancer risk associated with plant based diets: An umbrella review. PLoS ONE 19(5): e0300711. https://doi.org/10.1371/journal.pone.0300711

Editor: Melissa Orlandin Premaor, Federal University of Minas Gerais: Universidade Federal de Minas Gerais, BRAZIL

Received: January 8, 2024; Accepted: March 4, 2024; Published: May 15, 2024

Copyright: © 2024 Capodici et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.


Cardiovascular diseases (CVDs) and cancer currently represent the leading causes of death and disability worldwide. Studies performed on large cohorts worldwide have identified several modifiable and non-modifiable risk factors. Among them, robust evidence supports diet as a major modifiable risk factor [ 1 ].

A suboptimal diet, marked by insufficient consumption of fruits, vegetables, legumes, and whole grains, coupled with an excessive intake of meat (particularly red and processed), salt, refined grains and sugar, has been shown to notably elevate both mortality rates and disability-adjusted life years. Over time, these dietary choices have led to a concerning increase in health-related issues [ 1 , 2 ].

Additionally, the reduction of products of animal origin in favor of vegetarian ones has been suggested to reduce CVD and cancer risk [ 3 , 4 ]. Several major professional and scientific organizations encourage the adoption of vegetarian and vegan diets for the prevention and treatment of a range of chronic metabolic diseases such as atherosclerosis, type 2 diabetes, hypertension and obesity [ 5 , 6 ]. Ethical, environmental, and socio-economic concerns have contributed to the widespread growth of plant-based diets, particularly vegetarian and vegan options [ 7 – 9 ]. 2014 cross-national governmental survey estimated that approximately 75 million people around the globe deliberately followed a vegetarian diet, while an additional 1,45 million were obliged to because of socio-economic factors [ 10 , 11 ].

At the same time, study heterogeneity in terms of plant-based dietary regimens (from limitation of certain types to the total exclusion of animal products), their association with other lifestyle factors, patient demographic and geographical features, associated diseases, as well as study design and duration, significantly limit the assessment of the real benefits associated with animal-free and animal-products-free diets (A/APFDs). Finally, an increasing number of studies have highlighted the potential threatening consequences of chronic vitamin and mineral deficiencies induced by these diets (e.g., megaloblastic anemia due to vitamin B12 deficiency), especially more restrictive ones and in critical periods of life, like pregnancy and early childhood [ 5 ].

Based on these premises, our umbrella review aims at assessing the impact of animal-free and animal-products-free diets (A/APFDs) on the risk factors associated with the development of cardiometabolic diseases, cancer and their related mortalities in both the adult and the pediatric population, as well as pregnant women.

Search strategy

PubMed ( https://pubmed.ncbi.nlm.nih.gov/ ) and Scopus ( https://www.scopus.com/search/form.uri?display=basic#basic ) databases were searched for reviews, systematic reviews and meta-analyses published from 1st January 2000 to 31st June 2023. We considered only articles written in English, involving human subjects, with an available abstract, and answering to the following PICO question: P (population): people of all ages; I (intervention) and C (comparison): people adopting A/APFDs vs. omnivores; O (outcome): impact of A/APFD on health parameters associated with CVDs, metabolic disorders or cancer.

Articles not specifying the type of A/APFD regimen were excluded. If not detailed, the A/APFDs adopted by study participants was defined as “mixed diet”. Vegetarian diets limiting but not completely excluding certain types of meat/fish (i.e. pesco- or pollo-vegetarian diet) were excluded. Studies focusing on subjects with specific nutritional needs (i.e., athletes or military personnel) -except pregnant women-, or with known underlying chronic diseases (i.e., chronic kidney disease), as well as articles focusing on conditions/health parameters related to disorders different from CVDs or cancer, and, finally, reviews/meta-analyses including interventional studies assessing A/APFDs comparing it with pharmacological interventions were excluded.

Ad hoc literature search strings, made of a broad selection of terms related to A/APFDs, including PubMed MeSH-terms, free-text words and their combinations, combined by proper Boolean operators, were created to search PubMed database: ((vegetari* OR vegan OR Diet , Vegetarian[MH] OR fruitar* OR veganism OR raw-food* OR lacto-veget* OR ovo-vege* OR semi-veget* OR plant-based diet* OR vegetable-based diet* OR fruit-based diet* OR root-based diet OR juice-based diet OR non-meat eate* OR non-meat diet*) AND ((review[Publication Type]) OR (meta-analysis[Publication Type]))) AND (("2000/01/01"[Date—Publication] : "2023/06/31"[Date—Publication])) and Scopus database: ALL(vegetari* OR vegan OR Diet , Vegetarian OR fruitar* OR veganism OR raw-food* OR lacto-veget* OR ovo-vege* OR semi-veget* OR plant-based diet* OR vegetable-based diet* OR fruit-based diet* OR root-based diet OR juice-based diet OR non-meat eate* OR non-meat diet) AND SUBJAREA(MEDI OR NURS OR VETE OR DENT OR HEAL OR MULT) PUBYEAR > 1999 AND (LIMIT-TO (DOCTYPE , "re"))

Research design and study classification

An umbrella review approach [ 12 ] was applied to systematically assess the effect of A/APFDs on risk factors related to CVDs, metabolic disorders and cancer as derived from literature reviews, systematic reviews and meta-analyses ( Table 1 ).


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  • TIFF original image


Study selection

The list of articles identified by literature search was split into 5 equivalent parts, each assigned to a couple of readers (AC, DG, CW, ML, AM, FS, MF, AAC, GC and FG), who independently and blindly read the title and then the abstract of each article to define its pertinence. Papers included in the umbrella review had to focus on one/some of the following A/APFDs: vegans, lacto-vegetarians, ovo-vegetarians, lacto-ovo-vegetarians. No restriction was applied for age, gender, ethnicity, geographical origin, nor socio economic status. Primary studies, reviews/meta-analyses not written in English, or focusing on non-previously mentioned dietary regimens (including the Mediterranean diet) were excluded. Abstract meetings, editorials, letters to the editor, and study protocols were also excluded. To reduce study heterogeneity, at least in terms of dietary regimens, we excluded studies based on vegetarian regimens limiting but not avoiding fish or poultry, and prospective trials directly comparing A/AFPDs to pharmacological interventions.

In case of discordance between readers, we resorted to discussion amongst the authors to resolve it, based on the article’s abstract or, if not decisive, the full text. The study selection process is summarized in Fig 1 .



This review was registered on PROSPERO (Record ID: 372913 https://www.crd.york.ac.uk /prospero/display_record.php?RecordID=372913 ).

Quality literature analysis

Three raters (AC, DG, FS) independently and blindly assessed the quality of the systematic reviews and meta-analyses using the revised AMSTAR-R 11-item tool, developed by the PEROSH group [ 13 ]. In case of disagreement, the score of each item and the final decision were discussed among the three raters.

Data extraction and reporting

Ten investigators (AC, DG, GM, ML, AM, FS, MF, AAC, GC, FG) independently extracted data from eligible articles. Disagreements in data extraction were resolved by consensus. Using a predefined protocol and a Microsoft Excel sheet, the following data were extracted: first author’s affiliation country; type of review; type of diet; target population; number of aggregated participants; total cholesterol; HDL-cholesterol; LDL-cholesterol; triglycerides; apolipoprotein B; C-Reactive Protein (CRP); Body Mass Index (BMI); body weight; fasting glucose; glycosylated hemoglobin (HbA1c); systolic blood pressure; diastolic blood pressure; cardiac events (type; risk); cardiovascular diseases (type; risk); gestational diabetes; gestational hypertension; cancer (type; risk); death due to CVDs/cancer (risk). Data were reported as mean difference (MD), weighted mean difference (WMD), standardized mean difference (SMD), and 95%CI, while the estimated risk could be reported as relative risk (RR), odds ratio (OR), or hazard ratio (HR), according to the data reported by the study authors. Articles assessing the risk of gestational diabetes and hypertension, as well as risk of low birth weight, and their determinants were examined separately.

Results from studies focusing on both vegetarian and vegan diets were analyzed and reported separately if authors had stratified the results according to the type of diet. On the contrary, if data from vegan and vegetarian subjects were mixed, we arbitrarily considered all of them as “vegetarian”.

Group 1: Cardiovascular endpoints and risk factors

I. total cholesterol (tc)..

Eight studies examined the levels of total serum cholesterol (TC) in vegetarians. Two focused on the general population and included 5,561 [ 14 ] and 576 [ 15 ] respectively, and, based on data meta-analysis, found a significant reduction in TC among vegetarians and people who assumed plant-based proteins (MD: -1.56 mmol/L; 95%CI: −1.73, −1.39; and -0.11 mmol/L; 95%CI: −0.22, −0.01, respectively).

Data were confirmed by Wang et al. (N = 832 total; Ovolacto/lacto-vegetarians: 291) [ 16 ], showing a greater dietary effect in subjects with a BMI ranging from 18.5 to 25 kg/m 2 (mean TC reduction: −0.94 mmol/L; 95%CI: −1.33, −0.55), and from 25 to 30 kg/m 2 (−0.58 mmol/L; 95%CI: −0.89, −0.27), than in those with a BMI >30 kg/m 2 (−0.16 mmol/L; 95%CI: −0.30, −0.01), and by Xu et al. (N = 783) [ 17 ], reporting lower TC in overweight and obese people (WMD: −0.37 mmol/L; 95%CI: −0.52, −0.22) adopting a vegetarian diet.

Another systematic review by Elliott et al., including 27 randomized controlled trials on plant based vs. normal western diets [ 18 ], found lower TC levels in vegetarians. These results were in line with other two descriptive reviews, the first including 2,890 overweight/obese adults [ 19 ], the second 8,969 vegetarian children aged 0–18 years [ 20 ]. Furthermore, a meta-analysis by Liang et al. described significantly lower TC (from -0.36 to -0.24 mmol/L) in people adopting plant based diets vs. people adopting western habitual diets [ 21 ].

Moreover, the review and meta-analysis by Dinu et al. [ 14 ], based on 19 studies for a total of 1,272 adults, reported significantly lower levels of TC among vegans than in omnivores (WMD: −1.72 mmol/L; 95%CI: −1.93, −1.51).

II. High-density lipoprotein cholesterol (HDL-C).

Eight reviews focused on the effects of vegetarian diet on serum high-density lipoprotein cholesterol (HDL-C) levels. Six [ 15 , 17 , 18 , 21 – 23 ] found no significant difference between vegetarians and omnivores, when considering normal weight and overweight/obese people. On the contrary, the study by Dinu et al. [ 14 ], based on 51 studies, for a total of 6,194 vegetarian adults, reported a WMD −0.15 mmol/L (95%CI: −0.19, −0.11). Liang et al. [ 21 ] analyzed 4 studies and reported a pooled estimated MD of −0.10 mmol/L (95%CI: −0.14, −0.05; p<0.001) in vegetarian diet adopters vs. western diets adopters. Finally, Zhang et al. [ 22 ] did not find any statistically significant differences in HDL-C levels when assessing vegetarian diets compared to non-vegetarians; on the same note Dinu et al. [ 14 ], analyzing data from 15 studies, for a total of 1,175 adults, found no significant differences in HDL-C levels between vegans and people following other dietary regimens.

III. Low-density lipoprotein cholesterol (LDL-C).

Ten reviews summarized the effect of vegetarian diets on serum levels of low-density lipoprotein cholesterol (LDL-C). Seven [ 14 – 18 , 21 , 23 ] found significantly lower LDL-C levels associated with vegetarian diet, both in the general population and in diabetic patients. In particular, Elliot et al. [ 18 ], analyzing 43 observational and interventional studies, described lower LDL-C in people adopting plant based diets; a significant difference was reported by the study of Liang et al. [ 21 ] based on 68 studies (MD: -0.29 to -0.17), and similar to data by Lamberg et al. [ 15 ], based on 13 RCTs including for a total of 576 participants (MD: -0.14 mmol/L; 95%CI: -0.25, -0.02). The impact of vegetarian diet appeared even greater in overweight or obese people, according to the analysis by Xu et al. [ 17 ], based on 7 RCTs (N = 783; MD: -0.31 mmol/L; 95%CI: -0.46, -0.16). Two reviews [ 19 , 20 ] reported similar results in overweight/obese patients and children aged 0–18 years, but no meta-analyses were conducted. Wang et al. [ 16 ] reported a MD of −0.34 mmol/L (95%CI: −0.57, −0.11; p<0.001) in the general adult population. Ferdowsian et al. [ 23 ] reported an overall reduction of LDL-C associated with vegetarian diet, but no synthesis analyses were performed. Dinu et al. [ 14 ] analyzed 46 studies encompassing 5,583 vegetarians and found a WMD of -1.18 mmol/L (95%CI: -1.34, -1.01). Finally, Viguiliouk et al. [ 24 ] reported a MD of −0.12 mmol/L (95%CI: −0.20, −0.04) in 6 trials involving 602 diabetic patients.

Four reviews identified a significant reduction in LDL-C in vegans as compared to omnivores [ 14 , 19 , 23 , 25 ]. Benatar et al. [ 25 ] analyzed 31 studies, for a total of 3,355 healthy vegan adults and 53,393 non-vegan controls and found MD of -0.49 mmol/L (95%CI: -0.62, -0.36; p<0.0001). Ferdowsian et al. [ 23 ] reported a reduction of LDL-C in healthy vegans, and Ivanova et al. [ 19 ] in overweight patients, but no meta-analysis was performed. Finally, Dinu et al. [ 14 ] analyzed 13 studies, for a total of 728 healthy vegan adults, and found a significant LDL-C reduction (WMD: −1.27 mmol/L; 95%CI: −1.66, −0.88).

IV. Triglycerides (TG).

Seven systematic reviews [ 14 , 16 – 18 , 20 , 23 , 26 ] analyzed serum triglycerides (TG) in vegetarians vs. omnivores. Specifically, Wang et al. [ 16 ] described no differences between the two, with a pooled estimated effect of 0.04 mmol/L (95%CI: −0.05, 0.13; p = 0.4). Zhang et al. [ 26 ] analyzing 12 studies for a total of 1,300 subjects, found a MD of −1.28 mmol/L (95%CI; −2.14, −0.42). Schürmann et al. and Ferdowsian et al. [ 20 , 23 ] reported lower TG in vegetarians in both children and adults but did not perform data meta-analysis. Dinu et al. [ 14 ] analyzed 55 studies including 4,008 vegetarians and found a WMD of −0.63 mmol/L (95%CI: −0.97, −0.30; p = 0.02). Conversely, in the review by Elliott et al. [ 18 ] no differences were reported in triglycerides. Xu et al. [ 17 ] reported a significant increase of TG (WMD: 0.29 mmol/L; 95%CI: 0.11, 0.47) in vegetarians as compared to meat eaters.

The effect of vegan diet on TG remains debated as one review [ 23 ] reported significative changes in TGs (-0.14 mmol/L, CI -0.24 to -0.05), while another [ 14 ] did not find any differences between vegans and omnivores since, after having analyzed 13 studies for 483 vegans, they reported a WMD of -0.52 mmol/L (95%CI: -1.13; 0.09).

V. C-reactive protein (CRP).

Three studies reported lower C-reactive protein (CRP) levels in normal weight, overweight and obese vegetarians as compared to non-vegetarians. Craddock et al. and Menzel et al. reported a WMD of -0.61 mg/L (95%CI: -0.91, -0.32; p = 0.0001) [ 27 ]; -0.25 mg/L (95%CI: -0.49, 0; p = 0.05) [ 28 ], respectively.

Data derived from the analysis by Menzel et al. [ 28 ] in vegan subjects were in line with previously mentioned studies performed in vegetarians (WMD: -0.54 mg/L; 95%CI: -0.79, -0.28; p<0.0001).

Two reviews [ 29 , 30 ] focused on the effects of mixed vegetarian diets on CRP levels. The first [ 29 ] included 2,689 obese patients and found a WMD of -0.55 mg/L (95%CI: -0.78, -0.32; I 2 = 94.4%), while the other [ 30 ], based on 2,398 normal weight subjects found no significant differences between vegetarians and omnivores in the primary analysis; alas, when considering a minimum duration of two years vegetarianism they described lower CRP levels vs. omnivores (Hedges’ g = -0.29; 95%CI: -0.59, 0.01).

VI. Plant-based diets and lipids.

Three studies [ 23 , 26 , 31 ] assessed the lipid profile in people following plant-based diets (without differentiating among diet subtypes) in comparison with omnivores. All of them found significantly lower levels of TC, HDL-C and LDL-C in subjects following plant-based diets. Specifically, Yokoyama et al. [ 31 ] reported a WMD of −1.62 mmol/L (95%CI: −1.92, −1.32; p< 0.001; I 2 = 81.4) for TC, −1.27 mmol/L (95%CI: −1.55, −0.99; p< 0.001; I 2 = 83.3) for LDL-C, −0.2 mmol/L (95%CI: −0.26, −0.14; p< 0.001; I 2 = 49.7) for HDL-C, and −0.36 mmol/L; 95%CI: −0.78, 0.06; p = 0.092; I 2 = 83.0) for TG when considering observational studies, and of −0.69 mmol/L (95%CI: −0.99, −0.4; p<0.001; I 2 = 54.8) for TC, −0.69 mmol/L (95%CI: −0.98, −0.37; p<0.001; I 2 = 79.2) for LDL-C, −0.19 mmol/L (95%CI: −0.24, −0.14; p<0.001; I 2 = 8.5) for HDL-C, and a non-statistically significant increase of TG based on prospective cohort studies. Additionally, Zhang et al. [ 26 ] in their meta-analysis, including 1,300 subjects, found a SMD of -1.28 mmol/L in TG (95% CI -2.14 to -0.42).

Finally, Picasso et al. [ 32 ] did not find any differences in triglycerides for mixed vegetarian diets (MD: 0.04 mmol/L; 95%CI: -0.09, 0.28), but did find statistically significant differences in HDL-C (MD: -0.05 mmol/L; 95%CI: -0.07, -0.03).

VII. Blood pressure.

A . Systolic blood pressure (SBP) . Various studies found significantly lower mean levels of systolic blood pressure (SBP) levels in vegetarians compared to the general population [ 33 – 36 ]. Specifically, Gibbs et al. [ 33 ] reported a SMD of -5.47 mmHg (95%CI: -7.60, -3.34; p<0.00001) in ovo-lacto-vegetarians, as did Lee et al. [ 34 ] reporting a SMD of -1.75 mmHg (95%CI: -5.38, 1.88; p = 0.05); furthermore, they reported a SBP decreased by -2.66 mmHg (95%CI: -3.76, -1.55), in people adopting generic vegetarian diets. Moreover, Garbett et al. [ 35 ] reported a 33% lower prevalence of hypertension in vegetarians vs. nonvegetarians. On the contrary, Schwingshackl et al. [ 36 ], analyzing data from 67 clinical trials overall including 17,230 pre-hypertensive and hypertensive adult patients with a BMI between 23.6 and 45.4 kg/m 2 , followed for 3 to 48 months, did not find any significant reductions in SBP associated with vegetarian diet.

Four reviews investigated the differences in SBP between vegans and non-vegans. Benatar et al. and Lee et al. [ 25 , 34 ] reported significantly lower mean SBP levels in vegans vs. omnivores (MD: -2.56 mmHg; 95%CI: -4.66, -0.45; and WMD: -3.12 mmHg; 95%CI: -4.54, -1.70; p<0.001, respectively). On the other hand, Gibbs et al. [-1.30 mmHg (95%CI: -3.90,1.29)] and Lopez et al. (-1.33 mmHg; 95%CI: −3.50, 0.84; P = 0.230) [ 33 , 37 ] did not find any significant difference in mean SBP levels between vegans and omnivores.

Both reviews [ 32 , 38 ] focusing on SBP in mixed-plant-based dietary patterns found significantly lower levels in vegetarians than in omnivores. The meta-analysis by Picasso et al. [ 32 ], based on 4 RCTs did not find any differences, alas, analyzing 42 cross sectional studies, they described a MD of -4.18 mmHg (95%CI -5.57, -2.80; p<0.00001), in agreement with Yokoyama et al. [ 38 ], who reported a MD of -4.8 mmHg (95%CI: -6.6, -3.1; p<0.001; I 2 = 0) according to the 7 controlled trials, 6 of which being randomized (311 participants), included in the analysis, and of -6.9 mmHg (95%CI: -9.1, -4.7; p<0.001; I 2 = 91.4) based on the other 32 observational studies (21,604 participants).

B . Diastolic blood pressure (DBP) . Garbett et al. [ 35 ] reported reduced mean diastolic blood pressure (DBP) values in vegetarians vs. omnivores, confirmed by the analysis of Gibbs et al. [ 33 ] (WMD: –2.49 mmHg; 95%CI: –4.17, –0.80; p = 0.004; I 2 = 0%) in ovo-lacto-vegetarians, by Lee et al. [ 34 ] [WMD: -1.69 mmHg (95%CI: -2.97, -0.41; p<0.001)] who included 15 randomized controlled trials (N = 856) performed in vegetarians; and by Yokoyama et al. [ 38 ], who highlighted a MD -2.2 mmHg (95%CI: -3.5, -1.0; p<0.001; I 2 = 0%) and -4.7 mmHg (95%CI: -6.3, -3.1; p<0.001; I 2 = 92.6%) according to data from 7 controlled trials (N = 311) and 32 observational studies (N = 21,604), respectively. Conversely, Schwingshackl et al. [ 36 ] did not find significant differences between vegetarians and non-vegetarians.

Three reviews [ 25 , 34 , 37 ] examined the impact of vegan vs. non-vegan diet on DBP and described statistically significant reductions. Benatar et al. described reduction of DBP, corresponding to a MD of -1.33 mmHg (95%CI: -2.67, -0.02) [ 25 ]. Lee et al. described a reduction in DBP of a WMD of -1.92 mmHg (95%CI: -3.18, -0.66; p<0.001) [ 34 ]. Finally, Lopez et al. [ 37 ] described the same reduction amounting to WMD: -4.10 mmHg (95%CI: -8.14, -0.06).

Four studies agreed upon the lower mean DBP levels in subjects following mixed vegetarian diets as compared to omnivores [ 32 – 34 , 38 ], quantified as MD -3.03 mmHg (95%CI: -4.93, 1.13; p = 0.002) by Picasso et al. [ 32 ], and −2.2 mmHg (95%CI: −3.5, −1.0; p<0.001) and −4.7 mmHg (95%CI: −6.3, −3.1; p <0.001) by the analysis performed on clinical trials and observational studies, respectively, by Yokoyama et al. [ 38 ].

VIII. Body weight and body mass index (BMI).

Berkow et al. [ 39 ] identified 40 observational studies comparing weight status of vegetarians vs. non-vegetarians: 29 reported that weight/BMI of vegetarians of both genders, different ethnicities (i.e., African Americans, Nigerians, Caucasians and Asians), and from widely separated geographic areas, was significantly lower than that of non-vegetarians, while the other 11 did not find significant differences between the two groups. In female vegetarians, weight was 2.9 to 10.6 kg (6% to 17%) and BMI 2.7% to 15.0% lower than female non-vegetarians, while the weight of male vegetarians was 4.6 to 12.6 kg (8% to 17%) lower and the BMI 4.6% to 16.3% lower than that of male non-vegetarians. The review by Schürmann et al. [ 20 ], focusing on 8,969 children aged 0–18 years old found similar body weight in both vegetarian and vegan children as compared to omnivore ones. Dinu et al. [ 14 ] analyzed data from 71 studies (including 57,724 vegetarians and 199,230 omnivores) and identified a WMD BMI of -1.49 kg/m 2 (95%CI: -1,72, -1,25; p<0.0001) in vegetarians when compared to omnivores.

Barnard et al. [ 40 ] found a significant reduction in weight in pure ovolactovegetarians (−2.9 kg; 95% CI −4.1 to −1.6; P<0.0001), compared to non-vegetarians from control groups; furthermore, they found in vegans the mean effect was of -3.2 kg (95% CI: -4.0;-2.4, P: <0.0001); overall they included 490 subjects in their analysis, excluding subjects who did not complete the trials.

Benatar et al. [ 25 ]–including 12,619 vegans and 179,630 omnivores from 40 observation studies–and Dinu et al. [ 14 ]–based on 19 cross sectional studies, for a total of 8,376 vegans and 123,292 omnivores–reported the same exact result, with a mean lower BMI in vegans vs omnivores, equal to -1.72 kg/m 2 (95%CI: -2.30, -1.16) and -1.72 kg/m 2 (95%CI: -2.21,-1.22; p<0.0001), respectively. The meta-analysis by Long et al. [ 41 ], performed on 27 studies, reported a MD of -0.70 kg/m 2 (95%CI: -1.38, -0.01) for BMI in vegans vs. omnivores. A systematic review and meta-analysis by Agnoli et al. [ 42 ] found mean BMI to be lower in subjects adhering to mixed vegetarian diets as compared to omnivores. Additionally, Tran et al. [ 43 ] described weight reductions in clinically healthy patients, as well as in people who underwent vegetarian diets as a prescription, but no meta-analysis was performed.

Finally, Huang et al. [ 44 ] found significant differences in both vegans and vegetarians, who were found to have lost weight after having adopted the diet as a consequence of being assigned to the intervention group in their randomized studies. For vegetarians the WMD was -2.02 kg (95%CI: -2.80 to -1.23), when compared to mixed diets, and for vegans the WMD was -2.52 kg (95%CI: -3.02 to -1.98), when compared to vegetarians.

IX. Glucose metabolism.

Viguiliouk et al. [ 24 ] found a significant reduction in HbA1c (MD: −0.29%; 95%CI: −0.45, −0.12) and fasting glucose (MD: −0.56 mmol/L; 95%CI: −0.99, −0.13) in vegetarians vs. non-vegetarians.

The meta-analysis by Dinu et al. [ 14 ], reported for vegetarians (2256) vs omnivores (2192) WMD: -0.28 mmol/L (95%CI: -0.33, -0.23) in fasting blood glucose.

These findings were confirmed by Picasso et al. [ 32 ] who found a MD of -0.26 mmol/L (95% CI: -0.35, -0.17) in fasting glucose in mixed-vegetarian diets as compared to omnivores.

A meta-analysis by Long et al. [ 41 ], based of 27 cross sectional studies, showed a MD for homeostasis model assessment of insulin resistance -measured as HOMA-IR, a unitless measure ideally less than one- of -0.75 (95%CI: -1.08, -0.42), fasting plasma glucose in vegetarians who adhered also to an exercise intervention as compared to omnivores.

Lee & Park [ 45 ] reported a significantly lower diabetes risk (OR 0.73; 95%CI: 0.61, 0.87; p<0.001) in vegetarians vs. non-vegetarians, being the association stronger in studies conducted in the Western Pacific region and Europe/North America than in those from Southeast Asia.

Regarding vegans, the review by Benatar et al. [ 25 ] determined a mean reduction of 0.23 mmol/L (95%CI: -0.35, -0.10) of fasting blood glucose in vegans (N = 12,619) as compared to omnivores (N = 179,630). The finding was in line with Dinu et al. [ 14 ], who reported a WMD of -0.35 mmol/L (95%CI: -0.69, -0.02; p = 0.04) of fasting blood glucose in vegans (n = 83) than omnivores (n = 125).

A systematic review, finally, including 61 studies [ 42 ] found mean values of fasting plasma glucose, and T2D risk to be lower in subjects following mixed vegetarian diets as compared to omnivores.

X. Cardiovascular events.

Huang et al. [ 46 ] found a significantly lower risk of ischemic heart disease (IHD) (RR: 0.71; 95%CI: 0.56, 0.87), but no significant differences for cerebrovascular mortality between vegetarians and non-vegetarians. The review by Remde et al. [ 47 ] was not conclusive, as only a few studies showed a reduction of the risk of CVDs for vegetarians versus omnivores, while the others did not find any significant results.

Dybvik et al. [ 48 ] based on 13 cohort studies for a total of 844,175 participants (115,392 with CVDs, 30,377 with IHD and 14,419 with stroke) showed that the overall RR for vegetarians vs. nonvegetarians was 0.85 (95%CI: 0.79–0.92, I 2 = 68%; 8 studies) for CVD, 0.79 (95%CI: 0.71–0.88, I 2 = 67%; 8 studies) for IHD, 0.90 (95%CI: 0.77–1.05, I 2 = 61%; 12 studies) for total stroke, while the RR of IHD in vegans vs. omnivores was 0.82 (95%CI: 0.68–1.00, I 2 = 0%; 6 studies).

The meta-analysis by Kwok et al. [ 49 ], based on 8 studies including 183,321 subjects comparing vegetarians versus non-vegetarians. They identified a significant reduction of IHD in the Seventh Day Adventist (SDA) cohort, who primarily follow ovo-lacto-vegetarian diets, while other non-SDA vegetarian diets were associated only with a modest reduction of IHD risk, raising the concern that other lifestyle factors typical of SDA and, thus not generalizable to other groups, play a primary role on outcomes. IHD was significantly reduced in both genders (RR: 0.60; 95%CI: 0.43, 0.83), while the risk of death and cerebrovascular disease and cardiovascular mortality risk reduction was significantly reduced only in men. No significant differences were detected for the risk of cerebrovascular events.

The meta-analysis by Lu et al. [ 50 ] -657,433 participants from cohort studies- reported a lower incidence of total stroke among vegetarians vs. nonvegetarians (HR = 0.66; 95%CI = 0.45–0.95; I 2 = 54%), while no differences were identified for incident stroke.

The descriptive systematic review by Babalola et al. [ 3 ] reported that adherence to a plant-based diet was inversely related to heart failure risk and advantageous for the secondary prevention of CHD, particularly if started from adolescence. Another review by Agnoli et al. [ 42 ], confirmed a lower incidence of CVDs associated with mixed vegetarian diets as compared to omnivorous diets. Finally, Chhabra et al. [ 51 ] found that vegetarian diet, particularly if started in adolescence and associated with vitamin B intake, can reduce the risk of stroke.

Gan et al. [ 52 ] described a lower risk of CVDs (RR 0.84; 95% CI 0.79 to 0.89; p < 0.05) in high, vs. low, adherence plant based diets, but the same association was not confirmed for stroke (RR 0.87; 95% CI: 0.73, 1.03).

Group 2: Pregnancy outcomes

The meta-analysis by Foster et al. [ 53 ], performed on 6 observational studies, found significantly lower zinc levels in vegetarians than in meat eaters (-1.53 ± 0.44 mg/day; p = 0.001), but no association with pregnancy outcomes, specifically no increase in low children birth weight. The finding was confirmed by Tan et al. [ 54 ], who similarly reported no specific risks, but reported that Asian (India/Nepal) vegetarian mothers exhibited increased risks to deliver a baby with Low Birth Weight (RR: 1.33 [95%CI:1.01, 1.76, p =  0.04, I 2 = 0%]; nonetheless, the WMD of neonatal birth weight in five studies they analyzed suggested no difference between vegetarians and omnivores.

To our knowledge, no reviews/meta-analyses have assessed the risk of zinc deficiency and its association with functional outcomes in pregnancy in relation to mixed or vegan diets.

Group 3: Cancer

The meta-analysis by Parra-Soto et al. [ 55 ], based on 409,110 participants from the UK Biobank study (mean follow-up 10.6 years), found a lower risk of liver, pancreatic, lung, prostate, bladder, colorectal, melanoma, kidney, non-Hodgkin lymphoma and lymphatic cancer as well as overall cancer (HR ranging from 0.29 to 0.70) determined by non-adjusted models in vegetarians vs. omnivores; when adjusted for sociodemographic and lifestyle factors, multimorbidity and BMI, the associations remained statistically significant only for prostate cancer (HR 0.57; 95%CI: 0.43, 0.76), colorectal cancer (HR 0.73; 95%CI: 0.54, 0.99), and all cancers combined (HR 0.87; 95%CI 0.79, 0.96). When colorectal cancer was stratified according to subtypes, a lower risk was observed for colon (HR 0.69; 95%CI: 0.48, 0.99) and proximal colon (HR 0.43; 95%CI: 0.22, 0.82), but not for rectal or distal cancer.

Similarly, the analysis by Huang et al. [ 46 ], based on 7 studies for a total of 124,706 subjects, reported a significantly lower overall/total cancer incidence in vegetarians than non-vegetarians (RR 0.82; 95%CI: 0.67, 0.97).

Zhao et al. [ 56 ] found a lower risk of digestive system cancer in plant-based dieters (RR = 0.82, 95%CI: 0.78–0.86; p< 0.001) and in vegans (RR: 0.80; 95%CI: 0.74, 0.86; p<0.001) as compared to meat eaters.

Additionally, DeClercq et al. [ 57 ] reported a decreased risk of overall cancer and colorectal cancer, but inconsistent results for prostate cancer and breast cancer; this was substantiated by Godos et al. [ 58 ] found no significant differences in breast, colorectal, and prostate cancer risk between vegetarians and non-vegetarians.

The umbrella review by Gianfredi et al. [ 59 ], did describe a lower risk of pancreatic cancer associated with vegetarian diets.

Dinu et al. [ 14 ] reported a reduction in the risk of total cancer of 8% in vegetarians, and of 15% in vegans, as compared to omnivores. They described lower risk of cancer among vegetarians (RR 0.92; 95%CI 0.87, 0.98) and vegans (RR: 0.85; 95%CI: 0.75,0.95); nonetheless, they also described non-significant reduced risk of mortality from colorectal, breast, lung and prostate cancers. Regarding the latter, a meta-analysis by Gupta et al. [ 60 ] on prostate cancer risk found a decreased hazard ratio for the incidence of prostate cancer (HR: 0.69; 95%CI: 0.54–0.89, P<0.001) in vegetarians as compared to omnivores from the evidence coming from 3 studies. In the vegan population, similar results were observed from the only included study (HR: 0.65; 95%CI: 0.49–0.85; p<0.001).

Group 4: Death by cardiometabolic diseases and cancer

According to Huang et al. [ 46 ], the mortality from IHD (RR: 0.71; 95%CI: 0.56, 0.87), circulatory diseases (RR: 0.84; 95%CI: 0.54, 1.14) and cerebrovascular diseases (RR: 0.88; 95%CI: 0.70, 1.06) was significantly lower in vegetarians than in non-vegetarians.

The analysis by Dinu et al. [ 14 ] performed on 7 prospective studies, overall including 65,058 vegetarians, reported a 25% reduced mortality risk from ischemic heart diseases (RR 0.75; 95%CI: 0.68, 0.82; p<0.001), but no significant differences were found analyzing 5 cohort studies in terms of mortality from CVDs, cerebrovascular diseases, nor colorectal, breast, prostate, and lung cancer. Regarding vegans, they analyzed 6 cohort studies, and found no differences in all-cause mortality, but significant differences in cancer incidence (RR: 0.85; 95%CI: 0.75, 0.95), indicating a protective effect of vegan diets.

The literature search did not identify studies focusing on mortality risk for cardiometabolic and cancer diseases in vegans.

Quality of the included studies

The quality of the 48 reviews and meta-analyses included in this umbrella review was assessed through the AMSTAR-R tool. Results are reported in S1 Table . Overall, the average quality score was 28, corresponding to mean quality. However, 36 studies (75%) scored between 60% and 90% of the maximum obtainable score, and can, therefore, be considered of good/very good quality. The least satisfied item on the R-AMSTAR grid was #8 -scientific quality of included studies used to draw conclusions-, where as many as 19 studies (39.6%) failed to indicate the use of study-related quality analysis to make recommendations. This finding should be read in conjunction with the missing quality analysis in 15 studies (31.3%)–Item #7 scientific quality of included studies assessed and documented-. Item #10, regarding publication bias, was the second least met item, in which 18 studies (37.5%) did not perform any analysis on this type of bias. 16 studies (33.3%) lacked to indicate careful exclusion of duplicates (Item #2), but also the presence of conflict of interest (Item #11). This point is certainly another important piece to consider in the overall quality assessment of these articles. All these considerations give us a picture of a general low quality of the publications found, lowering the strength of evidence as well as the external validity of the results.

This umbrella review provides an update on the benefits associated with the adoption of A/AFPDs in reducing risk factors associated with the development of cardiometabolic diseases and cancer, considering both the adult and the pediatric population, as well as pregnant women.

Compared to omnivorous regimens, vegetarian and vegan diets appear to significantly improve the metabolic profile through the reduction of total and LDL cholesterol [ 14 – 21 , 23 , 25 ], fasting blood glucose and HbA1c [ 14 , 24 , 25 , 37 , 39 – 41 ], and are associated with lower body weight/BMI, as well as reduced levels of inflammation (evaluated by serum CRP levels [ 27 , 30 ]), while the effect on HDL cholesterol and triglycerides, systolic and diastolic blood pressure levels remains debated. A much more limited body of literature suggested vegetarian, but not vegan diets also reduce ApoB levels further improving the lipid profile [ 61 ].

It should be remarked that, in the majority of the cases, people adopting plant-based diets are more prone to engage in healthy lifestyles that include regular physical activity, reduction/avoidance of sugar-sweetened beverages, alcohol and tobacco, that, in association with previously mentioned modification of diet [ 62 ], lead to the reduction of the risk of ischemic heart disease and related mortality, and, to a lesser extent, of other CVDs.

The adoption of vegan diets is known to increase the risk of vitamin B-12 deficiency and consequent disorders–for which appropriate supplementation was recommended by a 2016 position paper of the Academy of Nutrition and Dietetics’ [ 5 ], but, apparently, does not modify the risk of pregnancy-induced hypertension nor gestational diabetes mellitus [ 53 , 54 ].

The three meta-analyses [ 46 , 55 , 57 ] that analyzed the overall risk of cancer incidence in any form concordantly showed a reduction in risk in vegetarians compared to omnivores. These general results were inconsistent in the stratified analyses for cancer types, which as expected involved smaller numbers of events and wider confidence intervals, especially for less prevalent types of cancers.

The stratified analyses in the different reviews did not show any significant difference for bladder, melanoma, kidney, lymphoma, liver, lung, or breast cancer. Conversely the three meta-analyses that addressed colorectal cancer [ 55 , 57 , 58 ] showed a decrease in risk in two out of three with one not showing a significant difference in vegetarians versus omnivores for the generic colorectal tract.

Interestingly, one review [ 55 ] showed how analysis with even more specific granularity could reveal significant differences in particular subsets of cancers, e.g., distal, and proximal colon. Also, another recent review found significant results for pancreatic cancer [ 59 ].

Our umbrella review seems consistent with other primary evidence that links the consumption of red processed meats to an increased risk of cancers of the gastro-intestinal tract [ 63 ]. The association certainly has two faces, because while a potential risk of cancer given by increased red meat consumption can be observed, the potential protective factor given by increased fruit and vegetable consumption, shown by other previous evidence, must also be considered [ 64 ].

It has also been described that vegetarians, in addition to reduced meat intake, ate less refined grains, added fats, sweets, snacks foods, and caloric beverages than did nonvegetarians and had increased consumption of a wide variety of plant foods [ 65 ]. Such a dietary pattern seems responsible for a reduction of hyperinsulinemia, one of the possible factors for colorectal cancer risk related to diet and food intake [ 66 , 67 ]. In the same manner, some research has suggested that insulin-like growth factors and its binding proteins may relate to cancer risk [ 68 , 69 ]. This dietary pattern should not be regarded as a universal principle, as varying tendencies have been observed among vegetarians and vegans in different studies. This pattern of consumption may potentially negate the anticipated beneficial effects of their diets.

Also, some protective patterns can be attributed to the effects of bioactive compounds of plant foods, these being primary sources of fiber, carotenoids, vitamins, minerals, and other compounds that have been associated with anti-cancer properties [ 70 , 71 ]. The protective patterns are likely attributed to the mechanistic actions of the many bioactives found in plant foods such as fiber, carotenoids, vitamins, and minerals with plausible anti-cancer properties. These ranged from epigenetic mechanisms [ 72 ], to immunoregulation, antioxidant and anti-inflammatory activity [ 73 , 74 ].

Finally, increased adiposity could be another pathway by which food intake is associated with these types of cancers. Since our umbrella review has demonstrated that vegetarian diets are associated with lower BMI, this might be another concurrent factor in the decreased risk for pancreatic and colorectal cancers in vegetarians.

Inflammatory biomarkers and adiposity play pivotal roles in the genesis of prostate cancer [ 75 , 76 ], hence the same etiological pathways might be hypothesized even for the increase of this type of cancer in people adopting an omnivorous diet.

The study presents several noteworthy strengths in its methodological approach and thematic focus. It has employed a rigorous and comprehensive search strategy involving two major databases, PubMed, and Scopus, spanning over two decades of research from 1 st January 2000 to 31 st June 2023, thereby ensuring a robust and exhaustive collection of pertinent literature. By utilizing an umbrella review, the research enables the synthesis of existing systematic reviews and meta-analyses, providing a higher level of evidence and summarizing a vast quantity of information. Furthermore, its alignment with current health concerns, specifically targeting cardiovascular diseases and cancer, makes the study highly relevant to ongoing public health challenges and positions it as a valuable resource for informing preventive measures and dietary guidelines. The deployment of blinded and independent assessments by multiple raters and investigators fortifies the research by minimizing bias and reinforcing the reliability of the selection, quality assessment, and data extraction processes. Quality assessment is standardized using the revised AMSTAR-R 11-item tool, and transparency is fostered through registration on PROSPERO, thus enhancing the credibility of the study. Lastly, the study’s detailed analysis and reporting, particularly the extraction of specific health measures such as cholesterol levels, glucose levels, blood pressure, and cancer risks, contribute to the comprehensiveness of the data synthesis, thereby underlining the overall integrity and significance of the research.

Main limitations to data analysis and interpretation are intrinsic to the original studies and consist in the wide heterogeneity in terms of sample size, demographic features, and geographical origin of included subjects, dietary patterns–not only in terms of quality, but, even more important and often neglected, quantity, distribution during the day, processing, cooking methods–and adherence, and other lifestyle confounders. In this regard, it is worth to mention that the impact of diet per se on the development of complex disorders (i.e. CVDs and cancer) and related mortality is extremely difficult to assess [ 71 ], especially in large populations, characterized by a highly heterogeneous lifestyle. It should also be considered the heterogeneity in dietary and lifestyle habits among countries, according to which the adoption of A/AFPDs could modify significantly habits in some countries, but not in others, and consequently have an extremely different impact on the risk of developing cardiometabolic disorders and cancer [ 25 ]. Furthermore, due to the nature of umbrella reviews, the present work may not include novel associations which were excluded from the analyzed reviews, as the main aim was to summarize secondary studies, such as reviews and meta-analyses. Finally, studies assessing the benefit of A/AFPDs on cancer risk are also limited by the heterogeneity in the timing of oncological evaluation and, therefore, disease progression, as well as in the histological subtypes and previous/concomitant treatments [ 72 – 75 ].

In conclusion, this umbrella review offers valuable insights on the estimated reduction of risk factors for cardiometabolic diseases and cancer, and the CVDs-associated mortality, offered by the adoption of plant-based diets through pleiotropic mechanisms. Through the improvement of glycolipid profile, reduction of body weight/BMI, blood pressure, and systemic inflammation, A/AFPDs significantly reduce the risk of ischemic heart disease, gastrointestinal and prostate cancer, as well as related mortality.

However, data should be taken with caution because of the important methodological limitation associated with the original studies. Moreover, potential risks associated with insufficient intake of vitamin and other elements due to unbalanced and/or extremely restricted dietary regimens, together with specific patient needs should be considered, while promoting research on new and more specific markers (i.e. biochemical, genetic, epigenetic markers; microbiota profile) recently associated with cardiometabolic and cancer risk, before suggesting A/AFPDs on large scale.

Supporting information

S1 table. r-amstar..


S2 Table. PRISMA 2020 checklist.


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Pillars of Customer Retention in the Services Sector: Understanding the Role of Relationship Marketing, Customer Satisfaction, and Customer Loyalty

  • Published: 20 May 2024

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what is the sample size in a research study

  • Mazhar Hussain 1 ,
  • Asad Javed   ORCID: orcid.org/0000-0003-4630-3595 1 ,
  • Samar Hayat Khan 2 &
  • Muhammad Yasir 1  

This research aims to examine the impact of relationship marketing on customer retention, as well as the moderating role of customer loyalty and the mediating role of customer satisfaction by means of relationship marketing theory. In today’s business climate, companies of all sizes and types prioritize customer retention and acquisition. Customer retention, satisfaction, and loyalty are essential for a company’s profitability and growth, whereas building strong, long-term relationships with customers is at the core of relationship marketing. Developing trust, commitment, communication, and conflict management skills are all essential components of fostering these relationships, which ultimately result in increased customer satisfaction and retention. The study population consisted of 5276 employees working at different courier services offices across the Khyber Pakhtunkhwa province, Pakistan. A sample size of 385 was calculated using the Krejcie and Morgan sampling formula, and data were collected through self-administered questionnaires adapted from previous studies. The data-driven results indicate a positive relationship between relationship marketing, customer satisfaction, and customer retention in the services sector of Pakistan. Additionally, customer satisfaction mediates this relationship, while customer loyalty moderates it. These results have significant implications for companies seeking to retain and satisfy their customers.

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Hussain, M., Javed, A., Khan, S.H. et al. Pillars of Customer Retention in the Services Sector: Understanding the Role of Relationship Marketing, Customer Satisfaction, and Customer Loyalty. J Knowl Econ (2024). https://doi.org/10.1007/s13132-024-02060-2

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The International Journal of Indian Psychȯlogy

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Relationship among Perceived Parenting Styles, Self-Concept, and Fear of Intimacy: A Correlational Study

what is the sample size in a research study

| Published: May 18, 2024

what is the sample size in a research study

The research is directed at exploring relationships among parenting styles, self-concept, and fear of intimacy in the future, among young and older adults. In this study, 186 young adults’ between the ages 18-35 years reported parenting approaches, self-concept, and intimacy fear were compared. Correlational design was used in the study to look into possible relationships, building on the model of the Perceived Parenting Style Scale (PPSS), Self-Concept Questionnaire (SCQ), and Fear of Intimacy Scale (FIS) which are important for psychological health. Good parenting practices, a stable sense of self, and an openness to closeness are all factors in the development of good relationships and emotional adjustment. A mathematically significant positive connection (p <.01) was found between these constructs. This implies that young adults who believe they had a caring and supportive upbringing may have a healthier opinion of themselves and experience less intimacy fear, which will ultimately promote improved psychological health.  Nevertheless, restrictions like sample size and correlational design are noted, emphasizing the necessity for additional studies with a bigger and more varied sample to confirm these findings.

Perceived Parenting Styles , Self-Concept , Fear of Intimacy , Psychological Health

what is the sample size in a research study

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© 2024, Kansal, S. & Tripathi, K.M.

Received: April 18, 2024; Revision Received: May 12, 2024; Accepted: May 18, 2024

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what is the sample size in a research study

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Prefrontal tDCS for improving mental health and cognitive deficits in patients with Multiple Sclerosis: a randomized, double-blind, parallel-group study

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Background: Multiple Sclerosis (MS) is an autoimmune disease associated with physical disability, psychological impairment, and cognitive dysfunctions. Consequently, the disease burden is substantial, and treatment choices are limited. In this randomized, double-blind study, we used repeated prefrontal electrical stimulation and assessed mental health-related variables (including quality of life, sleep, psychological distress) and cognitive dysfunctions (psychomotor speed, working memory, attention/vigilance) in 40 patients with MS. Methods: The patients were randomly assigned (block randomization method) to two groups of sham (n=20), or 1.5-mA (n=20) transcranial direct current stimulation (tDCS) targeting the left dorsolateral prefrontal cortex (F3) and right frontopolar cortex (Fp2) with anodal and cathodal stimulation respectively (electrode size: 25 cm2). The treatment included 10 sessions of 20 minutes stimulation delivered every other day. Outcome measures were quality of life, sleep quality, psychological distress, and performance on a neuropsychological test battery dedicated to cognitive dysfunctions in MS (psychomotor speed, working memory, and attention). All outcome measures were examined pre-intervention and post-intervention. Both patients and technicians delivering the stimulation were unaware of the study hypotheses and the type of stimulation being used. Results: The active protocol significantly improved quality of life and reduced sleep difficulties and psychological distress compared to the sham group. The active protocol, furthermore, improved psychomotor speed, attention and vigilance, and some aspects of working memory performance compared to the sham protocol. Improvement in mental health outcome measures was significantly associated with better cognitive performance. Conclusions: Modulation of prefrontal regions with tDCS ameliorates secondary clinical symptoms and results in beneficial cognitive effects in patients with MS. These results support applying prefrontal tDCS in larger trials for improving mental health and cognitive dysfunctions in MS.

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Michael Nitsche is a member of the Scientific Advisory Boards of Neuroelectrics and Precisis. All other authors declare no competing interests

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This study did not receive any funding

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

All patients gave their written consent to participate in the study. The protocol was conducted in accordance with the latest version of the Declaration of Helsinki and was approved by the Institutional Review Board and ethical committee at the Mohaghegh Ardabili University. The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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All data produced in the present study are available upon reasonable request to the authors after publication of the peer-reviewed version

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Sarcopenia and sarcopenic obesity among older adults in the nordic countries: a scoping review

  • Fereshteh Baygi 1   na1 ,
  • Sussi Friis Buhl 1   na1 ,
  • Trine Thilsing 1 ,
  • Jens Søndergaard 1 &
  • Jesper Bo Nielsen 1  

BMC Geriatrics volume  24 , Article number:  421 ( 2024 ) Cite this article

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Sarcopenia and sarcopenic obesity (SO) are age-related syndromes that may compromise physical and mental health among older adults. The Nordic countries differ from other regions on prevalence of disease, life-style behavior, and life expectancy, which may impact prevalence of sarcopenia and SO. Therefore, the aim of this study is to review the available evidence and gaps within this field in the Nordic countries.

PubMed, Embase, and Web of science (WOS) were searched up to February 2023. In addition, grey literature and reference lists of included studies were searched. Two independent researcher assessed papers and extracted data.

Thirty-three studies out of 6,363 searched studies were included in this scoping review. Overall prevalence of sarcopenia varied from 0.9 to 58.5%. A wide prevalence range was still present for community-dwelling older adults when definition criteria and setting were considered. The prevalence of SO ranged from 4 to 11%, according to the only study on this field. Based on the included studies, potential risk factors for sarcopenia include malnutrition, low physical activity, specific diseases (e.g., diabetes), inflammation, polypharmacy, and aging, whereas increased levels of physical activity and improved dietary intake may reduce the risk of sarcopenia. The few available interventions for sarcopenia were mainly focused on resistance training with/without nutritional supplements (e.g., protein, vitamin D).

The findings of our study revealed inadequate research on SO but an increasing trend in the number of studies on sarcopenia. However, most of the included studies had descriptive cross-sectional design, small sample size, and applied different diagnostic criteria. Therefore, larger well-designed cohort studies that adhere to uniform recent guidelines are required to capture a full picture of these two age-related medical conditions in Nordic countries, and plan for prevention/treatment accordingly.

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The number of older adults with age-related disorders is expected to increase worldwide [ 1 , 2 ]. Sarcopenia and sarcopenic obesity (SO) are both age-related syndromes that may compromise the physical and mental health of older adults and increase their need for health care services in old age [ 3 , 4 ], and this may challenge the sustainability of health care systems economically and by shortage of health care personnel [ 5 ].

Sarcopenia is characterized by low muscle mass in combination with low muscle strength [ 4 ]. SO is characterized by the co-existence of obesity (excessive adipose tissue) and sarcopenia [ 3 ]. Sarcopenia and SO are both associated with physical disability, risk of falls, morbidity, reduced quality of life and early mortality [ 4 , 6 , 7 , 8 , 9 ]. In SO the consequences of sarcopenia and obesity are combined and maximized [ 4 , 6 , 7 , 8 ].

Etiology of sarcopenia and SO is multifactorial and closely linked to multimorbidity [ 3 , 7 , 8 , 9 , 10 ]. Nevertheless, lifestyle and behavioral components particularly diet and physical activity, are important interrelated factors that potentially can be modified. Physical inactivity and sedentary behavior may accelerate age-related loss of muscle mass, reduce energy expenditure, and increase risk of obesity [ 3 , 11 ]. In addition, weight cycling (the fluctuations in weight following dieting and regain) and an unbalanced diet (particularly inadequate protein intake) may accelerate loss of muscle mass and increase severity of sarcopenia and SO in older adults [ 3 , 12 ]. International guideline for the treatment of sarcopenia emphasizes the importance of resistance training potentially in combination with nutritional supplementation to improve muscle mass and physical function [ 13 ]. Similar therapeutic approach is suggested for treatment of SO [ 14 ]. However, more research is needed to confirm optimal treatment of SO [ 14 ].

According to a recently published meta-analysis the global prevalence of sarcopenia ranged from 10 to 27% in populations of older adults ≥ 60 years [ 15 ]. Further the global prevalence of SO among older adults was 11% [ 8 ]. So, sarcopenia and SO are prevalent conditions, with multiple negative health outcomes and should be given special attention [ 16 ]. Despite the large burden on patients and health care systems, the awareness of the importance of skeletal muscle maintenance in obesity is low among clinicians and scientists [ 3 , 16 ].

A recent meta-analysis on publication trends revealed that despite an increase in global research on sarcopenia, the Nordic countries were only limitedly represented [ 6 ]. Nordic countries may differ from other regions on aspects associated with the prevalence and trajectory of sarcopenia and SO and challenge the representativeness of research findings from other parts of the world. These include a different prevalence pattern of noncommunicable diseases [ 17 ], different life-style behavior and life-style associated risk factors [ 15 , 18 ], and higher life expectancy [ 18 ].

The Nordic countries including Sweden, Finland, Iceland, Norway, Denmark, and three autonomous areas (Åland Islands, Greenland and Faroe Islands) share common elements of social and economic policies such as a comprehensive publicly financed health care system [ 18 , 19 ]. Additionally, these countries have a strong tradition of collaboration including a common vision of a socially sustainable region by promoting equal health and inclusive participation in society for older adults [ 20 ]. Therefore, more insight into the etiology, prevalence, and risk factors for sarcopenia and SO among older adults is a prerequisite for the development and implementation of effective strategies to prevent and treat these complex geriatric conditions in this geographic region. So, the aim of this study is to conduct a scoping review to systematically identify and map the available evidence while also addressing knowledge gaps and exploring the following research questions: (1) What are the prevalence of sarcopenia and SO in older adults living in the Nordic countries? (2) Which risk factors or contributing conditions are involved in the development of sarcopenia and SO in the Nordic Countries? (3) Which interventions to prevent or counteract negative health outcomes of sarcopenia and SO have been tested or implemented among older adults living in the Nordic countries?

Identification of relevant studies

The development and reporting of this review were done by following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines [ 21 ].

The literature search was developed to target three main areas: Sarcopenia, sarcopenic obesity, and aging (See Appendix 1 for full search strategy). All studies published before the end of February 2023 were included in this scoping review. The optimal sensitivity of search was obtained by simultaneous search of the following databases: PubMed, Embase, and Web of science (WOS). Additionally, a detailed search for grey literature was performed in relevant databases (e.g., Research Portal Denmark, Libris, Oria, Research.fi). Besides, reference lists of the included studies were reviewed to identify eligible studies. Duplicates and non-peer reviewed evidence (e.g., PhD thesis) were excluded but if the latter contained published articles of relevance, these were included. If more than one publication on similar outcomes (e.g., prevalence) were based on a single study, just one publication was included. Data were extracted from large studies with combined data from several countries only when findings were presented separately for the Nordic countries.

Inclusion and exclusion criteria

The inclusion criteria were as follow : Broad selection criteria were used to be comprehensive: (1) studies with any outcome (e.g., prevalence, risk factors, etc.) to address our research questions on sarcopenia and SO, (2) studies on subjects with age ≥ 60 years in any type of settings (e.g., community, nursing homes, general practice, hospital, outpatients, homecare, etc.), (3) studies using any definition of sarcopenia and SO without restriction for criteria and cutoff values, (4) all type of study designs (e.g., randomized control trials, cohort studies, cross-sectional, etc.), (5) studies should be conducted in the Nordic countries The exclusion criteria are as follow : (1) studies without relevant outcome to sarcopenia or SO, (2) studies without sufficient information to determine eligibility.

Study selection and data extraction

Two independent researchers screened literature and conducted data extraction. Any discrepancies between them were resolved through discussion.

First, duplicates were removed by using EndNote 20.6 software, then titles and abstracts were screened to narrow down the list of potentially eligible studies. Finally, the full text review was done to examine in detail the studies that were not excluded in first step. For more clarification, the reasons for the exclusion were recorded (Fig.  1 ).

figure 1

PRISMA diagram for searching resources

The following information was extracted: (1) study characteristics (e.g., first author’s name, country, year of publication), (2) characteristics of the target population (e.g., age, sex), (3) study design, setting, intervention duration and follow-up time (if applicable), measurements, tools, criteria, and results.

Study selection

A combined total of 6,358 studies were identified through the initial electronic database and grey literature searches. An additional five articles were identified through other sources (citation searching). After removing duplication, 3,464 articles remained. A total of 3107 articles were excluded based on screening titles and abstracts. Out of the remaining 357 studies, 324 were excluded after the full-text review. Finally, 33 studies met our inclusion criteria and were included in this current scoping review [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ] (Fig.  1 ).

Study characteristics

Table  1 summarized characteristics of the included studies.

The number of documents showed an increasing trend between 2020 and 2021. A peak in the number of publications was observed in 2021 (24.2% of all documents). All the studies were conducted across four (Denmark, Norway, Sweden, and Finland) out of the five Nordic countries and three autonomous areas. The highest contribution in this field was made by Sweden ( n  = 12).

Most studies were conducted in community-dwelling settings [ 22 , 23 , 24 , 28 , 30 , 31 , 35 , 36 , 38 , 39 , 40 , 42 , 45 , 46 , 47 , 48 , 49 , 54 ]. Seven studies included patients with acute diseases (hospital-setting) [ 26 , 27 , 33 , 37 , 50 , 51 , 52 ], while four studies included patients with chronic conditions (out-patient setting) [ 25 , 32 , 41 , 44 ], and one study including nursing-home residents [ 34 ]. In terms of study design, most of the studies were observation studies with a cross-sectional or longitudinal design ( 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 33 , 34 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ), while three studies [ 32 , 35 , 46 ] applied interventions. It appears, however, that one study [ 32 ] out of the above three interventions is sub-project conducted within the framework of larger intervention program. Sample size ranged from 49 in a cross-sectional case control study [ 52 ] to 3334 in a cohort study [ 30 ].

Five studies were among males only [ 22 , 24 , 36 , 45 , 53 ] and three studies included females only [ 38 , 47 , 54 ]. The rest of the studies had a mixed sample. Top subject area was sarcopenia (31 out of the 33 included studies), and on this subject, publications were categorized into the following research areas (with some studies addressing more areas): prevalence [ 22 , 23 , 24 , 25 , 26 , 27 , 29 , 30 , 33 , 35 , 36 , 37 , 40 , 42 , 44 , 45 , 47 , 49 , 50 , 51 , 52 , 53 , 54 ], risk factors [ 24 , 27 , 28 , 30 , 31 , 34 , 38 , 40 , 42 , 44 , 47 , 49 , 50 , 51 ], and effectiveness of interventions on sarcopenia or indicator of sarcopenia [ 32 , 35 , 46 ].

In most studies sarcopenia was defined according to the criteria set by the European Working Group on Sarcopenia in Older People in the updated version from 2019 (EWGSOP2) ( n  = 15) or the original version from 2010 (EWGSOP) ( n  = 14). However, in some studies multiple criteria such as EWGSOP, EWGSOP2, and National Institutes of Health Sarcopenia Project definition (FNIH) were applied [ 27 , 39 , 43 ], and in other studies alternative criteria were used [ 26 , 33 , 35 , 45 , 57 ].

Different assessment methods of muscle mass including Dual energy X-ray absorptiometry (DXA) [ 22 , 24 , 25 , 27 , 29 , 30 , 32 , 33 , 38 , 39 , 40 , 41 , 45 , 46 , 47 , 52 , 53 , 54 ], Bioelectrical Impedance Analysis (BIA) [ 28 , 31 , 34 , 44 , 48 , 49 ], Bioimpedance Spectroscopy (BIS) [ 35 , 42 , 43 ], Computed Tomography (CT) [ 33 ], and Computed Tomography Angiogram (CTA) [ 26 ] were used in the included studies.

SO were defined by the co-existence of sarcopenia with obesity. Studies on SO used the EWGSOP2 criteria [ 39 ], or the EWGSOP2 criteria for hand grip strength only (probable sarcopenia) [ 23 ] in combination with obesity estimated from BMI cut points [ 23 , 39 ], waist circumference [ 23 , 39 ], and fat mass percentage [ 39 ]. Lastly, one study used measures of body composition measures that reflect adiposity as estimates of SO [ 48 ].

Four studies reported the prevalence of “probable sarcopenia” [ 23 , 30 , 36 , 45 ], while two studies reported the prevalence of sarcopenia and comorbidities (e.g., osteopenia, pre-frailty, malnutrition) [ 33 , 40 ].

Narrative synthesis

Due to the heterogeneity of the studies in definition of sarcopenia, settings, and sample size, the overall reported prevalence was variable and ranged from 0.9% [ 54 ] to 58.5% [ 26 ]. However, according to the most commonly used criteria (EWGSOP2) the highest (46%) and lowest (1%) prevalence of sarcopenia was reported in Sweden among inpatients in geriatric care [ 27 ], and community-dwelling older adults [ 30 ], respectively.

Prevalence of sarcopenia according to population and definition criteria is illustrated in Table  2 . Higher prevalence rates of sarcopenia were found in females compared to males among community-dwelling older adults [ 49 ] and in older adults acutely admitted to hospital [ 51 ]. Further, acutely admitted female patients also presented with more severe sarcopenia compared to male patients [ 51 ].

Frequency of sarcopenia was higher (9.1–40.0%) in patients with diabetes (with and without complications of charcot osteoarthropathy), compared to age-matched healthy adults [ 52 ].

The prevalence of “probable sarcopenia” ranged between 20.4% (reduced muscle strength only) and 38.1% (fulfilling one of the following criteria: reduced muscle strength, reduced muscle mass, or low physical function) in Finnish community-dwelling adults [ 23 , 36 ], while longitudinal studies on Swedish community-dwelling old (70 years) and very old adults (≥ 85 years) the prevalence of “probable sarcopenia” (reduced muscle strength only) ranged from 1.8 to 73%, respectively [ 30 , 45 ]. Lastly, in a Swedish study among nursing home residents the prevalence of probable sarcopenia was 44% (evaluated by an impaired chair stand test) [ 34 ].

Prevalence of Osteosarcopenia (sarcopenia and osteoporosis) was 1.5% [ 36 ], and the prevalence of co-occurrence of all three following conditions: pre-frail, malnutrition, and sarcopenia was 7% [ 34 ].

We only identified two studies with prevalence of SO [ 39 ] and probable SO [ 23 ]. The prevalence of SO in a Swedish population was 4% and 11% in females and males, respectively, while the prevalence of probable SO among Finnish community-dwelling ranged between 5.8% and 12.6%, depending on the criteria to define the obesity (e.g., BMI, waist circumference, etc.) [ 23 ].

Several studies investigated aspects of etiology and risk factors for sarcopenia [ 24 , 27 , 28 , 30 , 31 , 34 , 36 , 38 , 40 , 42 , 43 , 44 , 47 , 49 , 50 , 51 ] and one study focused on SO [ 49 ]. Higher physical activity was associated with a decreased likelihood of sarcopenia [ 30 ]. In addition, adhering to world health organization (WHO) guidlines for physical activity and the Nordic nutritional recommendations for protein intake was positively associated with greater physical function and lower fat mass in older female community-dwellers [ 38 ]. In older adults who are physically active, eating a healthy diet (based on the frequency of intake of favorable food like fish, fruits, vegetables, and whole grains versus unfavorable foods like red/processed meats, desserts/sweets/sugar-sweetened beverages, and fried potatoes) was associated with lower risk of sarcopenia [ 28 ]. Further, among older adults who already meet the physical activity guidelines, additional engagement in muscle-strengthening activities was associated with a lower sarcopenia risk score and improved muscle mass and chair rise time [ 31 ].

Associations between sarcopenia, risk of sarcopenia and malnutrition or nutritional status was identified in geriatric patients [ 27 , 51 ], older patients with hip fracture [ 50 ], nursing home residents [ 34 ] and in community-dwelling older adults [ 49 ]. Moreover, the importance of nutritional intake was investigated in the following studies [ 24 , 36 , 47 ]. A study among community-dwelling men revealed an inverse association between total energy intake, protein intake (total, plant, and fish protein), intake of dietary fibers, fat (total and unsaturated), and vitamin D with sarcopenia status [ 36 ]. In a cohort of 71-year-old men a dietary pattern characterized by high consumption of fruit, vegetables, poultry, rice and pasta was associated with lower prevalence of sarcopenia after 16 years [ 24 ]. A longitudinal Finnish study on sarcopenia indices among postmenopausal older women, showed that lower adherence to the Mediterranean (focuses on high consumption of olive oil) or Baltic Sea (focuses on the dietary fat quality and low-fat milk intake) diets resulted in higher loss of lean mass over a 3-year period [ 47 ]. Further, a higher adherence to the Baltic Sea diet was associated with greater lean mass and better physical function, and higher adherence to the Mediterranean diet was associated with greater muscle quality [ 47 ].

In a study of patients with hip fracture age, polypharmacy, and low albumin levels was associated with sarcopenia [ 50 ]. Exocrine pancreatic insufficiency was an independent risk factor for sarcopenia [ 44 ]. This study also revealed that sarcopenia was associated with reduced quality of life, physical function, and increased risk of hospitalization [ 44 ]. In a longitudinal study of community-dwelling adults (+ 75 years) at risk of sarcopenia, high physical function, muscle strength, muscle mass and low BMI predicted better physical function and reduced need for care after four years [ 42 ]. Furthermore, in community-dwelling adults with sarcopenia, muscle mass, muscle strength and physical function are independent predictors of all-cause mortality. As a result, they have been proposed by researchers as targets for the prevention of sarcopenia-related over-mortality [ 43 ]. Lastly, community-dwelling older adults with sarcopenia had lower bone mineral density compared to those without sarcopenia and they were more likely to develop osteoporosis (Osteosarcopenia) [ 40 ].

Regarding SO risk factors, a longitudinal study among community-dwelling older adults in Finland found that SO (operationalized by measures of adiposity) were associated with poorer physical function after ten years [ 48 ].

Our literature search identified three randomized controlled trials investigating the effectiveness of interventions to prevent or counteract sarcopenia in older adults of Norway, Finland, and Sweden, respectively [ 32 , 35 , 46 ]. The Norwegian study [ 32 ] was a double-blinded randomized controlled trial (RCT). The study included those who were at risk of developing sarcopenia, including patients with chronic obstructive pulmonary disease (COPD) or individuals who showed diagnostic indications of sarcopenia. Participants received either vitamin D 3 or placebo supplementation for 28 weeks. Additionally, resistance training sessions were provided to all participants from weeks 14 to 27. Vitamin D supplementation did not significantly affect response to resistance training in older adults at risk of sarcopenia with or without COPD [ 32 ].

Furthermore, a RCT among pre-sarcopenic Swedish older adults investigated the effectiveness of three weekly sessions of instructor-led progressive resistance training in combination with a non-mandatory daily nutritional supplement (175 kcal, 19 g protein) compared to control group. The 10 weeks intervention resulted in significant between group improvements of physical function and a significant improvement in body composition in the intervention group [ 46 ].

Another intervention study revealed that a 12-month intervention with two daily nutritional supplements (each containing 20 g whey protein) did not attenuate the deterioration of physical function and muscle mass in sarcopenic older community-dwelling adults compared to isocaloric placebo supplements or no supplementation. All participants were given instructions on home-based exercises, importance of dietary protein and vitamin D supplementation [ 35 ].

Based on our broad literature search 33 studies were identified that concerned sarcopenia and SO and met the inclusion criteria. However, research on SO was very limited with only three studies identified. Narrative synthesis of the included studies revealed that the most reported classification tool for sarcopenia in Nordic countries was the EWGSOP2. Moreover, some studies estimated sarcopenia using EWGSOP. The overall prevalence of sarcopenia in Nordic countries according to EWGSOP2 ranged between 1% and 46% [ 25 , 28 ]. The prevalence of SO, however, was reported only in one study in Sweden (4–11%) [ 39 ]. Even though the previous systematic reviews and meta-analysis have reported the prevalence of sarcopenia and SO in different regions and settings (e.g., community-dwelling, nursing home, etc.) [ 8 , 15 , 55 , 56 ], this current scoping review is to the best of our knowledge the first study that provides an overview of research on sarcopenia and SO in the Nordic countries.

Based on our findings from 24 studies, there were large variability in prevalence of sarcopenia in studies conducted in the Nordic countries. We think that the wide variation in estimated prevalence of sarcopenia in our scoping review might be due to a different definition/diagnostic criterion (e.g., EWGSOP, EWGSOP2, FNIH), methodology to measure muscle mass (DXA, BIA, CT), and heterogeneity in characteristics of the study population (e.g., setting, age, medical conditions, co-occurrence of multiple risk factors). A previous study on prevalence of sarcopenia in Swedish older people showed significant differences between prevalence of sarcopenia based on EWGSOP2 and EWGSOP1 [ 29 ]. Therefore, researchers stressed that prevalence is more dependent on cut-offs than on the operational definition [ 29 , 57 ]. Further, we know that various international sarcopenia working groups have issued expert consensus and such diagnostic criteria are being updated [ 4 , 58 ]. Since the revision of criteria focuses primarily on the adjustment of cut-off values, the main reason for differences in prevalence even when using an updated version of one diagnosis criteria is modification in cut-off values. For instance, if the cut-off value for gait speed was increased by 0.2 m/s, the prevalence of sarcopenia may increase by 8.5% [ 57 ]. Meaning that even a small change in cut-off value can have a big impact on how sarcopenia is diagnosed. Besides when we take definition criteria into account (Table  2 ), the prevalence of sarcopenia is still variable in the population of community-dwelling adults for instance. We believe it is basically because studies have applied different assessment tools and tests to identify older adults with low muscle mass and muscle strength, although using the same definition criteria (Table  1 ). Previous studies have illustrated that choice of methodology to assess muscle strength (e.g., hand grip strength, chair rise) [ 59 ] and muscle mass (e.g., DXA, BIA, anthropometry) [ 60 , 61 , 62 ] in older adults may impact findings and this variability may explain some of the variability in our findings. So, adherence to the latest uniform diagnostic criteria for future studies is recommended to simplify the comparison of findings within the same country, across countries, and regions. Moreover, we suggest that medical community particularly GPs to come to an agreement on assessment methods for muscle mass and muscle strength and the use of one set of definition criteria for sarcopenia.

In previous meta-analyses [ 15 ], sub-group analyses based on region and classification tool, revealed that the prevalence of sarcopenia was higher in European studies using EWGSOP (12%) compared to rest of the studies using Asian Working Group for Sarcopenia (AWGS), FNIH, and EWGSOP (3%) [ 15 ]. In our scoping review, we also found a high prevalence of sarcopenia in Nordic countries. Longevity and life expectancy is higher in the Nordic countries compared to estimates for rest of the world [ 18 ], which means that in this region many people reach old age, and consequently they are more likely to be diagnosed with sarcopenia as an age-related disorder. Therefore, the authors of this current scoping review emphasis the importance of preventive strategies targeted major risk factors and effective interventions to limit the consequences of sarcopenia in the Nordic populations. Besides, we think that the health care system in the Nordic countries should be better equipped with the necessary healthcare resources for both a timely diagnosis and dealing with this major age-related issue in the years to come. However, due to the limitations regarding the timely diagnosis, we highly recommend a comprehensive approach including establishment of support services, implement educational programs, offer training for health care professionals, and engage the community.

Many countries have conducted research on SO [ 7 , 39 , 63 , 64 , 65 ]. Based on our findings, however, among the Nordic countries only Sweden and Finland have investigated the prevalence of probable SO and SO [ 23 , 29 ]. Besides, we only found one study investigating the association between body adiposity and physical function over time [ 54 ]. We did not find any literature on risk factors or interventions among older adults with SO in this region. Therefore, we call on medical and research community in Nordic countries to attach importance to screening of SO in elderly people to capture a full picture of this public health risk to aging society and allocate healthcare resources accordingly.

In terms of risk factors for sarcopenia, our study revealed that malnutrition, low levels of physical activity, specific diseases (e.g., diabetes, osteoporosis), inflammation, polypharmacy (multiple medicines), BMI, and ageing are potential risk factor for sarcopenia in populations of the Nordic region. However, evidence on risk factors derived mainly from cross-sectional associations [ 27 , 28 , 30 , 31 , 34 , 40 , 44 , 49 , 50 , 51 ], and only to a limited extend from longitudinal studies [ 24 , 38 , 43 , 47 ]. Therefore, the associations between risk factors and sarcopenia should be interpreted with caution due to the possibility of reverse causality and confounding affecting the results. Moreover, our findings on risk factors mainly came from community-dwelling older adults, and only to a limited extend hospital and nursing home settings. We think that risk factors may vary depending on population characteristics (e.g., age, sex, health condition) and setting (e.g., hospital, nursing home, community). Therefore, we encourage researchers of the Nordic countries to perform well-designed prospective cohort studies in different settings to enhance the possibility to establish causal inference as well as understanding degree and direction of changes over time.

A recently published meta-analyses revealed a higher risk of having polypharmacy in Europe among individuals with sarcopenia compared to people without this condition [ 66 ]. A nationwide register-based study in Swedish population also showed that the prevalence of polypharmacy has increased in Sweden over the last decade [ 67 ]. Sarcopenia itself is associated with morbidity (identified by specific disease or inflammatory markers) and different health-related outcomes (e.g., disability) [ 7 ]; therefore, future research should investigate whether polypharmacy is a major factor to sarcopenia development [ 66 ]. Although we lack information on polypharmacy in Nordic countries other than Sweden, we encourage researchers in this region to examine the above research gap in their future studies.

According to previous studies physiological changes in ageing include systemic low-grade inflammation which results in insulin resistance, affect protein metabolism and leads to increased muscle wasting [ 68 ]. Acute and chronic disease may increase the inflammatory response and accelerate age-related loss of muscle mass and increase risk of sarcopenia [ 68 , 69 ]. Hence, we think that special attention should be made by health care professionals particularly GPs to older adults with acute or chronic conditions to limit the risk of sarcopenia.

Literature from the Nordic countries also indicated that higher levels of physical activity and different dietary patterns (e.g., higher protein intake, fruit, vegetables, fibers) were associated with reduced risk of sarcopenia or improvement in indicators of sarcopenia. There was a large heterogeneity in the studied aspect which makes direct comparison of studies difficult. Nevertheless, according to findings from a recent systematic review of meta-analyses on sarcopenia the identified risk factors are in alignment with previously identified risk factors globally [ 70 ]. Other potential lifestyle-related risk factors suggested from the above meta-analysis included smoking and extreme sleep duration. However, we did not identify studies investigating these health behaviors in the Nordic populations. Therefore, high-quality cohort studies are needed to deeply understand such associations with the risk of sarcopenia.

In this current review, we only found three intervention studies in Nordic countries. However, two of them were sub-projects of big intervention programs, meaning that such studies were not designed explicitly for the prevention/treatment of sarcopenia. Therefore, explicit intervention studies on sarcopenia in this region is recommended.

We believe that on a global level, research on sarcopenia will carry on with nutrition, exercise, and understanding of molecular mechanisms. Furthermore, examining the link between sarcopenia and other medical conditions/diseases would be the next step [ 6 ]. In the Nordic countries, however, already performed studies have a basic and descriptive design, so that, well-designed research and advanced analyses are lacking. Hence, we recommend conducting large well-designed and adequately powered studies to (a) explore the scale of this age-related health issue on country and regional level, (b) investigate the patterns of physical activity and sedentary behavior to understand if this should be a target in older adults with SO and sarcopenia, (c) determine whether elderly populations are suffering from nutritional deficiency or are at risk of malnutrition. The latest can support further studies to assess the impact of combined physical activity and dietary intake, which are still lacking globally [ 6 ].

A previous systematic review on therapeutic strategies for SO revealed that exercise-based interventions (e.g., resistance training) reduced total adiposity and consequently improved body composition. However, evidence of other therapeutic strategies (e.g., nutritional supplementation) was limited due to scarcity of data and lack of unique definition for SO [ 69 ]. Therefore, authors suggested that more research should be done to clarify optimal treatment options for various age-groups and not only for older adults [ 14 ].

In our scoping review, the included studies, did not provide a status of either SO or the prevention/treatment methods in this region. We believe that SO is practically neglected in clinical practice and research as well, and this is mainly because it is difficult to separate it from general obesity. The consequence of lacking knowledge in this research area is that when older adults with SO are recommended weight loss- a frequently used strategy for management of general obesity- this may accelerate the loss of muscle mass and increase the severity of the sarcopenia [ 3 ]. Consequently, we think that this issue may have adverse effects both on patients (e.g., decreasing quality of their life) and on the health care system (e.g., increasing the health care demands) of this region. Therefore, we encourage researchers to perform cohort studies to understand the epidemiology and etiological basis of SO, which are poorly understood even on a global scale [ 8 ]. We think that the consensus definition on SO from the European Society for Clinical Nutrition and Metabolism (ESPEN) and European Association for the Study of Obesity (EASO) which was published in 2022 [ 3 ], can positively affect the ability to define studies on prevalence and prevention of SO. Besides, we recommend conducting further research to find the optimal treatment for SO and reduce its adverse consequences both at individual and society levels. Additionally, we think that the concepts of sarcopenia and SO might be somehow unfamiliar to health care personnel. Therefore, it is highly recommended that more information be provided to bring their attention to the significance of prevention, timely diagnosis, and treatment of these two aging disorders.

Strengths and limitations of the study

This is the first study providing an overview of available evidence on sarcopenia and SO among older adults in the Nordic countries. These countries have important similarities in welfare sectors and on a population level and we believe that our findings will be a significant benefit for researchers and health care providers to understand the knowledge gaps and plan for future studies in this geographical region. However, the current scoping review has limitations. This review was limited to studies among individuals more than 60 years old which may limit the overview of available research in this field, as well as understanding risk factors, confounders for prevention, and the potential for early detection of these two diseases in younger age population. The included cross-sectional studies in our review cannot provide information on causality of the associations.

Sarcopenia and SO are generally prevalent syndromes among older adults in Nordic countries, even though the prevalence of them varies according to the criteria for definition, population, and setting. Research among older adults with SO was very limited in this region. Besides, studies on risk factors were primarily cross-sectional and only few intervention studies were identified. Therefore, we encourage researchers performing well-designed studies (e.g., prospective cohorts) to understand the epidemiology and etiological basis of these two age-related disorders. For the next step, implementation of interventions targeting risk factors (e.g., combined physical activity and dietary intake) and evaluating of their impact on prevention or treatment of sarcopenia and SO is recommended. Furthermore, for the comprehensive advancement of muscle health in older adults, we recommend implementing interventions directed at health care personnel and encouraging more collaboration among clinicians, professional societies, researchers, and policy makers to ensure comprehensive and effective approach to health care initiatives.

Data availability

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


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FB conceived and designed the review, participated in literature review, data extraction, interpretation of the results and wrote the manuscript. SFB designed the review, participated in literature review, data extraction, and revised the manuscript. TT, JBN and JS contributed to the conception of the study and revised the manuscript critically. All the authors approved the final manuscript.

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Baygi, F., Buhl, S.F., Thilsing, T. et al. Sarcopenia and sarcopenic obesity among older adults in the nordic countries: a scoping review. BMC Geriatr 24 , 421 (2024). https://doi.org/10.1186/s12877-024-04970-x

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    sample size for the study being reported, power of a test becomes a viabl e option. The power of a The power of a statistical test is defined as the probability of rejecting a null hypothesis or ...

  19. Sample size calculation: Basic principles

    Addressing a sample size is a practical issue that has to be solved during planning and designing stage of the study. The aim of any clinical research is to detect the actual difference between two groups (power) and to provide an estimate of the difference ...

  20. Determining the Sample Size in Qualitative Research

    finds a variation of the sample size from 1 to 95 (averages being of 31 in the first ca se and 28 in the. second). The research region - one of t he cultural factors, plays a significant role in ...

  21. Unlocking the Impact of Sample Size on Study Power

    Effect size is a quantitative measure of the magnitude of the phenomenon under study. When planning your research, estimating the expected effect size helps determine the necessary sample size for ...

  22. Cardiovascular health and cancer risk associated with plant based diets

    Study quality was average. A key limitation is represented by the high heterogeneity of the study population in terms of sample size, demography, geographical origin, dietary patterns, and other lifestyle confounders. Conclusions Plant-based diets appear beneficial in reducing cardiometabolic risk factors, as well as CVDs, cancer risk and ...

  23. Embedded approaches to academic literacy development: a systematic

    Introduction. This systematic review contributes to the field of research into the development of academic literacy through learning discipline-specific knowledge, often referred to as an embedded approach (e.g. Wingate Citation 2018).Proponents of embedded approaches argue for teaching academic literacy within disciplinary contexts because it is tailored to specific assessments (i.e. it is ...

  24. Pillars of Customer Retention in the Services Sector ...

    The appropriate sample size for the research was calculated using Krejcie & Morgan's sampling formula, which determined a sample size of 385 respondents. A sample frame was designed using information collected from personal visits and information available on the website. ... Economic Computation & Economic Cybernetics Studies & Research, 55 ...

  25. Sample Size and its Importance in Research

    The sample size for a study needs to be estimated at the time the study is proposed; too large a sample is unnecessary and unethical, and too small a sample is unscientific and also unethical. The necessary sample size can be calculated, using statistical software, based on certain assumptions. If no assumptions can be made, then an arbitrary ...

  26. Relationship among Perceived Parenting Styles, Self-Concept, and Fear

    The research is directed at exploring relationships among parenting styles, self-concept, and fear of intimacy in the future, among young and older adults. ... restrictions like sample size and correlational design are noted, emphasizing the necessity for additional studies with a bigger and more varied sample to confirm these findings.

  27. Prefrontal tDCS for improving mental health and cognitive deficits in

    Background: Multiple Sclerosis (MS) is an autoimmune disease associated with physical disability, psychological impairment, and cognitive dysfunctions. Consequently, the disease burden is substantial, and treatment choices are limited. In this randomized, double-blind study, we used repeated prefrontal electrical stimulation and assessed mental health-related variables (including quality of ...

  28. JCM

    Background: Anterior cruciate ligament (ACL) injury is one of the most prevalent factors contributing to knee instability worldwide. This study aimed to evaluate modified metal fixation techniques for ACL reconstruction compared to factory-made implants, such as polyether ether ketone (PEEK) screws, bioabsorbable screws, and modified metal implants. Methods: A retrospective cohort analysis was ...

  29. How sample size influences research outcomes

    The use of sample size calculation directly influences research findings. Very small samples undermine the internal and external validity of a study. Very large samples tend to transform small differences into statistically significant differences - even when they are clinically insignificant. As a result, both researchers and clinicians are ...

  30. Sarcopenia and sarcopenic obesity among older adults in the nordic

    The findings of our study revealed inadequate research on SO but an increasing trend in the number of studies on sarcopenia. However, most of the included studies had descriptive cross-sectional design, small sample size, and applied different diagnostic criteria.