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Research Gap – Types, Examples and How to Identify

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Research Gap

Definition:

Research gap refers to an area or topic within a field of study that has not yet been extensively researched or is yet to be explored. It is a question, problem or issue that has not been addressed or resolved by previous research.

How to Identify Research Gap

Identifying a research gap is an essential step in conducting research that adds value and contributes to the existing body of knowledge. Research gap requires critical thinking, creativity, and a thorough understanding of the existing literature . It is an iterative process that may require revisiting and refining your research questions and ideas multiple times.

Here are some steps that can help you identify a research gap:

• Review existing literature: Conduct a thorough review of the existing literature in your research area. This will help you identify what has already been studied and what gaps still exist.
• Identify a research problem: Identify a specific research problem or question that you want to address.
• Analyze existing research: Analyze the existing research related to your research problem. This will help you identify areas that have not been studied, inconsistencies in the findings, or limitations of the previous research.
• Brainstorm potential research ideas : Based on your analysis, brainstorm potential research ideas that address the identified gaps.
• Consult with experts: Consult with experts in your research area to get their opinions on potential research ideas and to identify any additional gaps that you may have missed.
• Refine research questions: Refine your research questions and hypotheses based on the identified gaps and potential research ideas.
• Develop a research proposal: Develop a research proposal that outlines your research questions, objectives, and methods to address the identified research gap.

Types of Research Gap

There are different types of research gaps that can be identified, and each type is associated with a specific situation or problem. Here are the main types of research gaps and their explanations:

Theoretical Gap

This type of research gap refers to a lack of theoretical understanding or knowledge in a particular area. It can occur when there is a discrepancy between existing theories and empirical evidence or when there is no theory that can explain a particular phenomenon. Identifying theoretical gaps can lead to the development of new theories or the refinement of existing ones.

Empirical Gap

An empirical gap occurs when there is a lack of empirical evidence or data in a particular area. It can happen when there is a lack of research on a specific topic or when existing research is inadequate or inconclusive. Identifying empirical gaps can lead to the development of new research studies to collect data or the refinement of existing research methods to improve the quality of data collected.

Methodological Gap

This type of research gap refers to a lack of appropriate research methods or techniques to answer a research question. It can occur when existing methods are inadequate, outdated, or inappropriate for the research question. Identifying methodological gaps can lead to the development of new research methods or the modification of existing ones to better address the research question.

Practical Gap

A practical gap occurs when there is a lack of practical applications or implementation of research findings. It can occur when research findings are not implemented due to financial, political, or social constraints. Identifying practical gaps can lead to the development of strategies for the effective implementation of research findings in practice.

Knowledge Gap

This type of research gap occurs when there is a lack of knowledge or information on a particular topic. It can happen when a new area of research is emerging, or when research is conducted in a different context or population. Identifying knowledge gaps can lead to the development of new research studies or the extension of existing research to fill the gap.

Examples of Research Gap

Here are some examples of research gaps that researchers might identify:

• Theoretical Gap Example : In the field of psychology, there might be a theoretical gap related to the lack of understanding of the relationship between social media use and mental health. Although there is existing research on the topic, there might be a lack of consensus on the mechanisms that link social media use to mental health outcomes.
• Empirical Gap Example : In the field of environmental science, there might be an empirical gap related to the lack of data on the long-term effects of climate change on biodiversity in specific regions. Although there might be some studies on the topic, there might be a lack of data on the long-term effects of climate change on specific species or ecosystems.
• Methodological Gap Example : In the field of education, there might be a methodological gap related to the lack of appropriate research methods to assess the impact of online learning on student outcomes. Although there might be some studies on the topic, existing research methods might not be appropriate to assess the complex relationships between online learning and student outcomes.
• Practical Gap Example: In the field of healthcare, there might be a practical gap related to the lack of effective strategies to implement evidence-based practices in clinical settings. Although there might be existing research on the effectiveness of certain practices, they might not be implemented in practice due to various barriers, such as financial constraints or lack of resources.
• Knowledge Gap Example: In the field of anthropology, there might be a knowledge gap related to the lack of understanding of the cultural practices of indigenous communities in certain regions. Although there might be some research on the topic, there might be a lack of knowledge about specific cultural practices or beliefs that are unique to those communities.

Examples of Research Gap In Literature Review, Thesis, and Research Paper might be:

• Literature review : A literature review on the topic of machine learning and healthcare might identify a research gap in the lack of studies that investigate the use of machine learning for early detection of rare diseases.
• Thesis : A thesis on the topic of cybersecurity might identify a research gap in the lack of studies that investigate the effectiveness of artificial intelligence in detecting and preventing cyber attacks.
• Research paper : A research paper on the topic of natural language processing might identify a research gap in the lack of studies that investigate the use of natural language processing techniques for sentiment analysis in non-English languages.

How to Write Research Gap

By following these steps, you can effectively write about research gaps in your paper and clearly articulate the contribution that your study will make to the existing body of knowledge.

Here are some steps to follow when writing about research gaps in your paper:

• Identify the research question : Before writing about research gaps, you need to identify your research question or problem. This will help you to understand the scope of your research and identify areas where additional research is needed.
• Review the literature: Conduct a thorough review of the literature related to your research question. This will help you to identify the current state of knowledge in the field and the gaps that exist.
• Identify the research gap: Based on your review of the literature, identify the specific research gap that your study will address. This could be a theoretical, empirical, methodological, practical, or knowledge gap.
• Provide evidence: Provide evidence to support your claim that the research gap exists. This could include a summary of the existing literature, a discussion of the limitations of previous studies, or an analysis of the current state of knowledge in the field.
• Explain the importance: Explain why it is important to fill the research gap. This could include a discussion of the potential implications of filling the gap, the significance of the research for the field, or the potential benefits to society.
• State your research objectives: State your research objectives, which should be aligned with the research gap you have identified. This will help you to clearly articulate the purpose of your study and how it will address the research gap.

Importance of Research Gap

The importance of research gaps can be summarized as follows:

• Advancing knowledge: Identifying research gaps is crucial for advancing knowledge in a particular field. By identifying areas where additional research is needed, researchers can fill gaps in the existing body of knowledge and contribute to the development of new theories and practices.
• Guiding research: Research gaps can guide researchers in designing studies that fill those gaps. By identifying research gaps, researchers can develop research questions and objectives that are aligned with the needs of the field and contribute to the development of new knowledge.
• Enhancing research quality: By identifying research gaps, researchers can avoid duplicating previous research and instead focus on developing innovative research that fills gaps in the existing body of knowledge. This can lead to more impactful research and higher-quality research outputs.
• Informing policy and practice: Research gaps can inform policy and practice by highlighting areas where additional research is needed to inform decision-making. By filling research gaps, researchers can provide evidence-based recommendations that have the potential to improve policy and practice in a particular field.

Applications of Research Gap

Here are some potential applications of research gap:

• Informing research priorities: Research gaps can help guide research funding agencies and researchers to prioritize research areas that require more attention and resources.
• Identifying practical implications: Identifying gaps in knowledge can help identify practical applications of research that are still unexplored or underdeveloped.
• Stimulating innovation: Research gaps can encourage innovation and the development of new approaches or methodologies to address unexplored areas.
• Improving policy-making: Research gaps can inform policy-making decisions by highlighting areas where more research is needed to make informed policy decisions.
• Enhancing academic discourse: Research gaps can lead to new and constructive debates and discussions within academic communities, leading to more robust and comprehensive research.

Advantages of Research Gap

Here are some of the advantages of research gap:

• Identifies new research opportunities: Identifying research gaps can help researchers identify areas that require further exploration, which can lead to new research opportunities.
• Improves the quality of research: By identifying gaps in current research, researchers can focus their efforts on addressing unanswered questions, which can improve the overall quality of research.
• Enhances the relevance of research: Research that addresses existing gaps can have significant implications for the development of theories, policies, and practices, and can therefore increase the relevance and impact of research.
• Helps avoid duplication of effort: Identifying existing research can help researchers avoid duplicating efforts, saving time and resources.
• Helps to refine research questions: Research gaps can help researchers refine their research questions, making them more focused and relevant to the needs of the field.
• Promotes collaboration: By identifying areas of research that require further investigation, researchers can collaborate with others to conduct research that addresses these gaps, which can lead to more comprehensive and impactful research outcomes.

Disadvantages of Research Gap

While research gaps can be advantageous, there are also some potential disadvantages that should be considered:

• Difficulty in identifying gaps: Identifying gaps in existing research can be challenging, particularly in fields where there is a large volume of research or where research findings are scattered across different disciplines.
• Lack of funding: Addressing research gaps may require significant resources, and researchers may struggle to secure funding for their work if it is perceived as too risky or uncertain.
• Time-consuming: Conducting research to address gaps can be time-consuming, particularly if the research involves collecting new data or developing new methods.
• Risk of oversimplification: Addressing research gaps may require researchers to simplify complex problems, which can lead to oversimplification and a failure to capture the complexity of the issues.
• Bias : Identifying research gaps can be influenced by researchers’ personal biases or perspectives, which can lead to a skewed understanding of the field.
• Potential for disagreement: Identifying research gaps can be subjective, and different researchers may have different views on what constitutes a gap in the field, leading to disagreements and debate.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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How To Find A Research Gap, Quickly

A step-by-step guide for new researchers

By: Derek Jansen (MBA) | Reviewer: Eunice Rautenbach (DTech) | April 2023

If you’ve got a dissertation, thesis or research project coming up, one of the first (and most important) things you’ll need to do is find a suitable research gap . In this post, we’ll share a straightforward process to help you uncover high-quality, original research gaps in a very time-efficient manner.

Overview: Finding Research Gaps

• What exactly is a research gap?
• Research gap vs research topic
• How to find potential research gaps
• How to evaluate research gaps (and topics)
• Key takeaways

What is a research gap?

As a starting point, it’s useful to first define what we mean by research gap, to ensure we’re all on the same page. The term “research gap” gets thrown around quite loosely by students and academics alike, so let’s clear that up.

Simply put, a research gap is any space where there’s a lack of solid, agreed-upon research regarding a specific topic, issue or phenomenon. In other words, there’s a lack of established knowledge and, consequently, a need for further research.

Let’s look at a hypothetical example to illustrate a research gap.

Within the existing research regarding factors affect job satisfaction , there may be a wealth of established and agreed-upon empirical work within a US and UK context , but very little research within Eastern nations such as Japan or Korea . Given that these nations have distinctly different national cultures and workforce compositions compared to the West, it’s plausible that the factors that contribute toward job satisfaction may also be different. Therefore, a research gap emerges for studies that explore this matter.

This example is purely hypothetical (and there’s probably plenty of research covering this already), but it illustrates the core point that a research gap reflects a lack of firmly established knowledge regarding a specific matter . Given this lack, an opportunity exists for researchers (like you) to go on and fill the gap.

So, it’s the same as a research topic?

Not quite – but they are connected. A research gap refers to an area where there’s a lack of settled research , whereas a research topic outlines the focus of a specific study . Despite being different things, these two are related because research gaps are the birthplace of research topics. In other words, by identifying a clear research gap, you have a foundation from which you can build a research topic for your specific study. Your study is unlikely to resolve the entire research gap on it’s own, but it will contribute towards it .

If you’d like to learn more, we’ve got a comprehensive post that covers research gaps (including the different types of research gaps), as well as an explainer video below.

How to find a research gap

Now that we’ve defined what a research gap is, it’s time to get down to the process of finding potential research gaps that you can use as a basis for potential research topics. Importantly, it’s worth noting that this is just one way (of many) to find a research gap (and consequently a topic). We’re not proposing that it’s the only way or best way, but it’s certainly a relatively quick way to identify opportunities.

Step 1: Identify your broad area of interest

The very first step to finding a research gap is to decide on your general area of interest . For example, if you were undertaking a dissertation as part of an MBA degree, you may decide that you’re interested in corporate reputation, HR strategy, or leadership styles. As you can see, these are broad categories – there’s no need to get super specific just yet. Of course, if there is something very specific that you’re interested in, that’s great – but don’t feel pressured to narrow it down too much right now.

Equally important is to make sure that this area of interest is allowed by your university or whichever institution you’ll be proposing your research to. This might sound dead obvious, but you’ll be surprised how many times we’ve seen students run down a path with great excitement, only to later learn that their university wants a very specific area of focus in terms of topic (and their area of interest doesn’t qualify).

Step 2: Do an initial literature scan

Once you’ve pinned down your broad area (or areas) of interest, the next step is to head over to Google Scholar to undertake an initial literature scan . If you’re not familiar with this tool, Google Scholar is a great starting point for finding academic literature on pretty much any topic, as it uses Google’s powerful search capabilities to hunt down relevant academic literature. It’s certainly not the be-all and end-all of literature search tools, but it’s a useful starting point .

Within Google Scholar, you’ll want to do a few searches using keywords that are relevant to your area of interest. Sticking with our earlier example, we could use the key phrase “job satisfaction”, or we may want to get a little more specific – perhaps “job satisfaction for millennials” or “job satisfaction in Japan”.

It’s always a good idea to play around with as many keywords/phrases as you can think up.  Take an iterative approach here and see which keywords yield the most relevant results for you. Keep each search open in a new tab, as this will help keep things organised for the next steps.

Once you’ve searched for a few different keywords/phrases, you’ll need to do some refining for each of the searches you undertook. Specifically, you’ll need to filter the results down to the most recent papers . You can do this by selecting the time period in the top left corner (see the example below).

Filtering to the current year is typically a good choice (especially for fast-moving research areas), but in some cases, you may need to filter to the last two years . If you’re undertaking this task in January or February, for example, you’ll likely need to select a two-year period.

Need a helping hand?

Step 3: Review and shortlist articles that interest you

Once you’ve run a few searches using different keywords and phrases, you’ll need to scan through the results to see what looks most relevant and interesting to you. At this stage, you can just look at the titles and abstracts (the description provided by Google Scholar) – don’t worry about reading the actual article just yet.

Next, select 5 – 10 articles that interest you and open them up. Here, we’re making the assumption that your university has provided you with access to a decent range of academic databases. In some cases, Google Scholar will link you directly to a PDF of the article, but in most cases, you’ll need paid access. If you don’t have this (for example, if you’re still applying to a university), you can look at two options:

Open-access articles – these are free articles which you can access without any journal subscription. A quick Google search (the regular Google) will help you find open-access journals in your area of interest, but you can also have a look at DOAJ and Elsevier Open Access.

DeepDyve – this is a monthly subscription service that allows you to get access to a broad range of journals. At the time of shooting this video, their monthly subscription is around $50 and they do offer a free trial, which may be sufficient for your project.

Step 4: Skim-read your article shortlist

Now, it’s time to dig into your article shortlist and do some reading. But don’t worry, you don’t need to read the articles from start to finish – you just need to focus on a few key sections.

Specifically, you’ll need to pay attention to the following:

• The abstract (which you’ve probably already read a portion of in Google Scholar)
• The introduction – this will give you a bit more detail about the context and background of the study, as well as what the researchers were trying to achieve (their research aims)
• The discussion or conclusion – this will tell you what the researchers found

By skimming through these three sections for each journal article on your shortlist, you’ll gain a reasonable idea of what each study was about, without having to dig into the painful details. Generally, these sections are usually quite short, so it shouldn’t take you too long.

Step 5: Go “FRIN hunting”

This is where the magic happens. Within each of the articles on your shortlist, you’ll want to search for a few very specific phrases , namely:

• Future research
• Further research
• Research opportunities
• Research directions

All of these terms are commonly found in what we call the “FRIN” section . FRIN stands for “further research is needed”. The FRIN is where the researchers explain what other researchers could do to build on their study, or just on the research area in general. In other words, the FRIN section is where you can find fresh opportunities for novel research . Most empirical studies will either have a dedicated FRIN section or paragraph, or they’ll allude to the FRIN toward the very end of the article. You’ll need to do a little scanning, but it’s usually pretty easy to spot.

It’s worth mentioning that naturally, the FRIN doesn’t hand you a list of research gaps on a platter. It’s not a silver bullet for finding research gaps – but it’s the closest thing to it. Realistically, the FRIN section helps you shortcut the gap-hunting process  by highlighting novel research avenues that are worth exploring.

This probably sounds a little conceptual, so let’s have a look at a few examples:

The impact of overeducation on job outcomes: Evidence from Saudi Arabia (Alzubaidi, 2020)

If you scroll down to the bottom of this article, you’ll see there’s a dedicated section called “Limitations and directions for future research”. Here they talk about the limitations of the study and provide suggestions about how future researchers could improve upon their work and overcome the limitations.

Perceived organizational support and job satisfaction: a moderated mediation model of proactive personality and psychological empowerment (Maan et al, 2020)

In this article, within the limitations section, they provide a wonderfully systematic structure where they discuss each limitation, followed by a proposal as to how future studies can overcome the respective limitation. In doing so, they are providing very specific research opportunities for other researchers.

Medical professionals’ job satisfaction and telemedicine readiness during the COVID-19 pandemic: solutions to improve medical practice in Egypt (El-Mazahy et al, 2023)

In this article, they don’t have a dedicated section discussing the FRIN, but we can deduct it based on the limitations section. For example, they state that an evaluation of the knowledge about telemedicine and technology-related skills would have enabled studying their independent effect on the perception of telemedicine.

Follow this FRIN-seeking process for the articles you shortlisted and map out any potentially interesting research gaps . You may find that you need to look at a larger number of articles to find something interesting, or you might find that your area of interest shifts as you engage in the reading – this is perfectly natural. Take as much time as you need to develop a shortlist of potential research gaps that interest you.

Importantly, once you’ve developed a shortlist of potential research gaps, you need to return to Google Scholar to double-check that there aren’t fresh studies that have already addressed the gap. Remember, if you’re looking at papers from two years ago in a fast-moving field, someone else may have jumped on it . Nevertheless, there could still very well be a unique angle you could take – perhaps a contextual gap (e.g. a specific country, industry, etc.).

Ultimately, the need for originality will depend on your specific university’s requirements and the level of study. For example, if you’re doing an undergraduate research project, the originality requirements likely won’t be as gruelling as say a Masters or PhD project. So, make sure you have a clear understanding of what your university’s expectations are. A good way to do this is to look at past dissertations and theses for your specific programme. You can usually find these in the university library or by asking the faculty.

How to evaluate potential research gaps

Once you’ve developed a shortlist of potential research gaps (and resultant potential research topics) that interest you, you’ll need to systematically evaluate  them  to choose a winner. There are many factors to consider here, but some important ones include the following:

• Originality and value – is the topic sufficiently novel and will addressing it create value?
• Data access – will you be able to get access to the sample of interest?
• Costs – will there be additional costs involved for data collection and/or analysis?
• Timeframes – will you be able to collect and analyse the data within the timeframe required by your university?
• Supervisor support – is there a suitable supervisor available to support your project from start to finish?

To help you evaluate your options systematically, we’ve got a topic evaluation worksheet that allows you to score each potential topic against a comprehensive set of criteria. You can access the worksheet completely free of charge here .

Recap: Key Takeaways

We’ve covered quite a lot of ground in this post. Here are the key takeaways:

• A research gap is any space where there’s a lack of solid, agreed-upon research regarding a specific topic/issue/phenomenon.
• Unique research topics emerge from research gaps , so it’s essential to first identify high-quality research gaps before you attempt to define a topic.
• To find potential research gaps, start by seeking out recent journal articles on Google Scholar and pay particular attention to the FRIN section to identify novel opportunities.
• Once you have a shortlist of prospective research gaps and resultant topic ideas, evaluate them systematically using a comprehensive set of criteria.

If you’d like to get hands-on help finding a research gap and research topic, be sure to check out our private coaching service , where we hold your hand through the research journey, step by step.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

Very useful for me, but i am still confusing review of literature review, how to find out topic related previous research.

Powerful notes! Thanks a lot.

This is helpful. Thanks a lot.

Thank you very much for this. It is really a great opportunity for me to learn the research journey.

Very Useful

It nice job

You have sharpened my articulations of these components to the core. Thanks so much.

It’s educative and an inspiring way of impacting research knowledge…

Thanks to the writer

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Identifying Research Gaps to Pursue Innovative Research

This article is an excerpt from a lecture given by my Ph.D. guide, a researcher in public health. She advised us on how to identify research gaps to pursue innovative research in our fields.

What is a Research Gap?

Today we are talking about the research gap: what is it, how to identify it, and how to make use of it so that you can pursue innovative research. Now, how many of you have ever felt you had discovered a new and exciting research question , only to find that it had already been written about? I have experienced this more times than I can count. Graduate studies come with pressure to add new knowledge to the field. We can contribute to the progress and knowledge of humanity. To do this, we need to first learn to identify research gaps in the existing literature.

A research gap is, simply, a topic or area for which missing or insufficient information limits the ability to reach a conclusion for a question. It should not be confused with a research question, however. For example, if we ask the research question of what the healthiest diet for humans is, we would find many studies and possible answers to this question. On the other hand, if we were to ask the research question of what are the effects of antidepressants on pregnant women, we would not find much-existing data. This is a research gap. When we identify a research gap, we identify a direction for potentially new and exciting research.

How to Identify Research Gap?

Considering the volume of existing research, identifying research gaps can seem overwhelming or even impossible. I don’t have time to read every paper published on public health. Similarly, you guys don’t have time to read every paper. So how can you identify a research gap?

There are different techniques in various disciplines, but we can reduce most of them down to a few steps, which are:

• Identify your key motivating issue/question
• Identify key terms associated with this issue
• Review the literature, searching for these key terms and identifying relevant publications
• Review the literature cited by the key publications which you located in the above step
• Identify issues not addressed by  the literature relating to your critical  motivating issue

It is the last step which we all find the most challenging. It can be difficult to figure out what an article is  not  saying. I like to keep a list of notes of biased or inconsistent information. You could also track what authors write as “directions for future research,” which often can point us towards the existing gaps.

Different Types of Research Gaps

Identifying research gaps is an essential step in conducting research, as it helps researchers to refine their research questions and to focus their research efforts on areas where there is a need for more knowledge or understanding.

1. Knowledge gaps

These are gaps in knowledge or understanding of a subject, where more research is needed to fill the gaps. For example, there may be a lack of understanding of the mechanisms behind a particular disease or how a specific technology works.

2. Conceptual gaps

These are gaps in the conceptual framework or theoretical understanding of a subject. For example, there may be a need for more research to understand the relationship between two concepts or to refine a theoretical framework.

3. Methodological gaps

These are gaps in the methods used to study a particular subject. For example, there may be a need for more research to develop new research methods or to refine existing methods to address specific research questions.

4. Data gaps

These are gaps in the data available on a particular subject. For example, there may be a need for more research to collect data on a specific population or to develop new measures to collect data on a particular construct.

5. Practical gaps

These are gaps in the application of research findings to practical situations. For example, there may be a need for more research to understand how to implement evidence-based practices in real-world settings or to identify barriers to implementing such practices.

Examples of Research Gap

Limited understanding of the underlying mechanisms of a disease:.

Despite significant research on a particular disease, there may be a lack of understanding of the underlying mechanisms of the disease. For example, although much research has been done on Alzheimer’s disease, the exact mechanisms that lead to the disease are not yet fully understood.

Inconsistencies in the findings of previous research:

When previous research on a particular topic has inconsistent findings, there may be a need for further research to clarify or resolve these inconsistencies. For example, previous research on the effectiveness of a particular treatment for a medical condition may have produced inconsistent findings, indicating a need for further research to determine the true effectiveness of the treatment.

Limited research on emerging technologies:

As new technologies emerge, there may be limited research on their applications, benefits, and potential drawbacks. For example, with the increasing use of artificial intelligence in various industries, there is a need for further research on the ethical, legal, and social implications of AI.

How to Deal with Literature Gap?

Once you have identified the literature gaps, it is critical to prioritize. You may find many questions which remain to be answered in the literature. Often one question must be answered before the next can be addressed. In prioritizing the gaps, you have identified, you should consider your funding agency or stakeholders, the needs of the field, and the relevance of your questions to what is currently being studied. Also, consider your own resources and ability to conduct the research you’re considering. Once you have done this, you can narrow your search down to an appropriate question.

Tools to Help Your Search

There are thousands of new articles published every day, and staying up to date on the literature can be overwhelming. You should take advantage of the technology that is available. Some services include  PubCrawler ,  Feedly ,  Google Scholar , and PubMed updates. Stay up to date on social media forums where scholars share new discoveries, such as Twitter. Reference managers such as  Mendeley  can help you keep your references well-organized. I personally have had success using Google Scholar and PubMed to stay current on new developments and track which gaps remain in my personal areas of interest.

The most important thing I want to impress upon you today is that you will struggle to  choose a research topic  that is innovative and exciting if you don’t know the existing literature well. This is why identifying research gaps starts with an extensive and thorough  literature review . But give yourself some boundaries.  You don’t need to read every paper that has ever been written on a topic. You may find yourself thinking you’re on the right track and then suddenly coming across a paper that you had intended to write! It happens to everyone- it happens to me quite often. Don’t give up- keep reading and you’ll find what you’re looking for.

Class dismissed!

How do you identify research gaps? Share your thoughts in the comments section below.

Frequently Asked Questions

A research gap can be identified by looking for a topic or area with missing or insufficient information that limits the ability to reach a conclusion for a question.

Identifying a research gap is important as it provides a direction for potentially new research or helps bridge the gap in existing literature.

Gap in research is a topic or area with missing or insufficient information. A research gap limits the ability to reach a conclusion for a question.

Thank u for your suggestion.

Very useful tips specially for a beginner

Thank you. This is helpful. I find that I’m overwhelmed with literatures. As I read on a particular topic, and in a particular direction I find that other conflicting issues, topic a and ideas keep popping up, making me more confused.

I am very grateful for your advice. It’s just on point.

The clearest, exhaustive, and brief explanation I have ever read.

Thanks for sharing

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Thank you for such informative explanation.

Great job smart guy! Really outdid yourself!

Nice one! I thank you for this as it is just what I was looking for!😃🤟

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Write Like a Scientist

A Guide to Scientific Communication

Gap Statements

  A gap is something that remains to be done or learned in an area of research; it’s a gap in the knowledge of the scientists in the field of research of your study. Every research project must, in some way, address a gap–that is, attempt to fill in some piece of information missing in the scientific literature. Otherwise, it is not novel research and is therefore not contributing to the overall goals of science.

Identify the gap.

  A gap statement is found in the Introduction section of a journal article or poster or in the Goals and Importance section of a research proposal and succinctly identifies for your audience the gap that you will attempt to address in your project.

A gap might be a lack of understanding about how well a particular instrument works in a certain situation. It could be introducing a new method that needs to be tested. Or it could be that you are studying a whole new organism, system, or part of a process. Your project may also address multiple gaps, in which case you should be sure to identify each of them clearly!

In a class, you might not always be studying something brand “new.” But, in most cases, you should still try to come up with something unique about your project, however small. Talk to your professor about what they expect for your gap statement if nothing seems to work.

Here, the authors signal to us that this is a gap because they use the words “has not yet been clarified.” Other phrases that might help you identify (or form!) a gap statement are:

• …has/have not been… (studied/reported/elucidated)
• …is required/needed…
• …the key question is/remains…
• …it is important to address…

Fill the gap.

  Once you identify the gap in the literature, you must tell your audience how you attempt to at least somewhat address in your project this lack of knowledge or understanding . In a journal article or poster, this is often done in a new paragraph and should be accomplished in one summary statement, such as:

Therefore, the purpose of this study was to determine the effects of lead on the hepatobiliary system, especially on the liver and on the gallbladder (adapted from Sipos et al. 2003 ).

You’ll often find that the first sentence of the last paragraph in a paper’s introduction will start somewhat like this, indicating the gap fill.  

Remember–always keep your voice professional! Colloquial phrases such as “we looked into” or “we checked if” should be avoided when introducing your gap fill.

So let’s look at this idea in context by looking at some examples from a couple of types of papers. The gap statements are underlined; the fills are italicized.  

In the second and third examples, the gap may be a little less obvious–it doesn’t use any phrases to signal to you that there’s something missing, such as “has not been clarified” or “have not been reported.” But because of the way the paragraph is laid out–following the conventions of our move structures–we can see that the underlined section of text is indeed the missing information in the literature that the group sought to address in their project.

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• Writing Tips

How to Identify a Research Gap

5-minute read

• 10th January 2024

If you’ve been tasked with producing a thesis or dissertation, one of your first steps will be identifying a research gap. Although finding a research gap may sound daunting, don’t fret! In this post, we will define a research gap, discuss its importance, and offer a step-by-step guide that will provide you with the essential know-how to complete this critical step and move on to the rest of your research project.

What Is a Research Gap?

Simply put, a research gap is an area that hasn’t been explored in the existing literature. This could be an unexplored population, an untested method, or a condition that hasn’t been investigated yet. 

Why Is Identifying a Research Gap Important?

Identifying a research gap is a foundational step in the research process. It ensures that your research is significant and has the ability to advance knowledge within a specific area. It also helps you align your work with the current needs and challenges of your field. Identifying a research gap has many potential benefits.

1. Avoid Redundancy in Your Research

Understanding the existing literature helps researchers avoid duplication. This means you can steer clear of topics that have already been extensively studied. This ensures your work is novel and contributes something new to the field.

2. Guide the Research Design

Identifying a research gap helps shape your research design and questions. You can tailor your studies to specifically address the identified gap. This ensures that your work directly contributes to filling the void in knowledge.

3. Practical Applications

Research that addresses a gap is more likely to have practical applications and contributions. Whether in academia, industry, or policymaking, research that fills a gap in knowledge is often more applicable and can inform decision-making and practices in real-world contexts.

4. Field Advancements

Addressing a research gap can lead to advancements in the field . It may result in the development of new theories, methodologies, or technologies that push the boundaries of current understanding.

5. Strategic Research Planning

Identifying a research gap is crucial for strategic planning . It helps researchers and institutions prioritize areas that need attention so they can allocate resources effectively. This ensures that efforts are directed toward the most critical gaps in knowledge.

6. Academic and Professional Recognition

Researchers who successfully address significant research gaps often receive peer recognition within their academic and professional communities. This recognition can lead to opportunities for collaboration, funding, and career advancement.

How Do I Identify a Research Gap?

1. clearly define your research topic .

Begin by clearly defining your research topic. A well-scoped topic serves as the foundation for your studies. Make sure it’s not too broad or too narrow; striking the right balance will make it easier to identify gaps in existing literature.

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2. Conduct a Thorough Literature Review

A comprehensive literature review is a vital step in any research. Dive deep into the existing research related to your topic. Look for patterns, recurring themes, and consensus among scholars. Pay attention to areas where conflicting opinions or gaps in understanding emerge.

3. Evaluate Existing Studies

Critically evaluate the studies you encounter during your literature review. Assess the paradigms , methodologies, findings, and limitations of each. Note any discrepancies, unanswered questions, or areas where further investigation is warranted. These are potential indicators of research gaps.

4. Identify Unexplored Perspectives

Consider the perspectives presented in the existing literature. Are there alternative viewpoints or marginalized voices that haven’t been adequately explored? Identifying and incorporating diverse perspectives can often lead to uncharted territory and help you pinpoint a unique research gap.

Additional Tips

Stay up to date with emerging trends.

The field of research is dynamic, with new developments and emerging trends constantly shaping the landscape. Stay up to date with the latest publications, conferences, and discussions in your field and make sure to regularly check relevant academic search engines . Often, identifying a research gap involves being at the forefront of current debates and discussions.

Seek Guidance From Experts

Don’t hesitate to reach out to experts in your field for guidance. Attend conferences, workshops, or seminars where you can interact with seasoned researchers. Their insights and experience can provide valuable perspectives on potential research gaps that you may have overlooked. You can also seek advice from your academic advisor .

Use Research Tools and Analytics

Leverage tech tools to analyze patterns and trends in the existing literature. Tools like citation analysis, keyword mapping, and data visualization can help you identify gaps and areas with limited exploration.

Identifying a research gap is a skill that evolves with experience and dedication. By defining your research topic, meticulously navigating the existing literature, critically evaluating studies, and recognizing unexplored perspectives, you’ll be on your way to identifying a research gap that will serve as the foundation for your paper, thesis, or dissertation topic .

If you need any help with proofreading your research paper , we can help with our research paper editing services . You can even try a sample of our services for free . Good luck with all your research!

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Research Process

• Brainstorming
• Explore Google This link opens in a new window
• Explore Web Resources
• Explore Background Information
• Explore Books
• Explore Scholarly Articles
• Narrowing a Topic
• Primary and Secondary Resources
• Academic, Popular & Trade Publications
• Scholarly and Peer-Reviewed Journals
• Grey Literature
• Clinical Trials
• Evidence Based Treatment
• Scholarly Research
• Database Research Log
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• Keyword Searching
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• Field Codes
• Subject Terms and Database Thesauri
• Reading a Scientific Article
• Website Evaluation
• Article Keywords and Subject Terms
• Cited References
• Citing Articles
• Related Results
• Search Within Publication
• Database Alerts & RSS Feeds
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• Persistent URLs
• Literature Gap and Future Research
• Web of Knowledge
• Annual Reviews
• Systematic Reviews & Meta-Analyses
• Finding Seminal Works
• Exhausting the Literature
• Finding Dissertations
• Researching Theoretical Frameworks
• Research Methodology & Design
• Tests and Measurements
• Organizing Research & Citations This link opens in a new window
• Picking Where to Publish
• Bibliometrics
• Learn the Library This link opens in a new window

Research Articles

These examples below illustrate how researchers from different disciplines identified gaps in existing literature. For additional examples, try a NavigatorSearch using this search string: ("Literature review") AND (gap*)

• Addressing the Recent Developments and Potential Gaps in the Literature of Corporate Sustainability
• Applications of Psychological Science to Teaching and Learning: Gaps in the Literature
• Attitudes, Risk Factors, and Behaviours of Gambling Among Adolescents and Young People: A Literature Review and Gap Analysis
• Do Psychological Diversity Climate, HRM Practices, and Personality Traits (Big Five) Influence Multicultural Workforce Job Satisfaction and Performance? Current Scenario, Literature Gap, and Future Research Directions
• Entrepreneurship Education: A Systematic Literature Review and Identification of an Existing Gap in the Field
• Evidence and Gaps in the Literature on HIV/STI Prevention Interventions Targeting Migrants in Receiving Countries: A Scoping Review
• Homeless Indigenous Veterans and the Current Gaps in Knowledge: The State of the Literature
• A Literature Review and Gap Analysis of Emerging Technologies and New Trends in Gambling
• A Review of Higher Education Image and Reputation Literature: Knowledge Gaps and a Research Agenda
• Trends and Gaps in Empirical Research on Open Educational Resources (OER): A Systematic Mapping of the Literature from 2015 to 2019
• Where Should We Go From Here? Identified Gaps in the Literature in Psychosocial Interventions for Youth With Autism Spectrum Disorder and Comorbid Anxiety

What is a ‘gap in the literature’?

The gap, also considered the missing piece or pieces in the research literature, is the area that has not yet been explored or is under-explored. This could be a population or sample (size, type, location, etc.), research method, data collection and/or analysis, or other research variables or conditions.

It is important to keep in mind, however, that just because you identify a gap in the research, it doesn't necessarily mean that your research question is worthy of exploration. You will want to make sure that your research will have valuable practical and/or theoretical implications. In other words, answering the research question could either improve existing practice and/or inform professional decision-making (Applied Degree), or it could revise, build upon, or create theoretical frameworks informing research design and practice (Ph.D Degree). See the Dissertation Center  for additional information about dissertation criteria at NU.

For a additional information on gap statements, see the following:

• How to Find a Gap in the Literature
• Write Like a Scientist: Gap Statements

How do you identify the gaps?

Conducting an exhaustive literature review is your first step. As you search for journal articles, you will need to read critically across the breadth of the literature to identify these gaps. You goal should be to find a ‘space’ or opening for contributing new research. The first step is gathering a broad range of research articles on your topic. You may want to look for research that approaches the topic from a variety of methods – qualitative, quantitative, or mixed methods. 

See the videos below for further instruction on identifying a gap in the literature.

Identifying a Gap in the Literature - Dr. Laurie Bedford

How Do You Identify Gaps in Literature? - SAGE Research Methods

Literature Gap & Future Research - Library Workshop

This workshop presents effective search techniques for identifying a gap in the literature and recommendations for future research.

Where can you locate research gaps?

As you begin to gather the literature, you will want to critically read for what has, and has not, been learned from the research. Use the Discussion and Future Research sections of the articles to understand what the researchers have found and where they point out future or additional research areas. This is similar to identifying a gap in the literature, however, future research statements come from a single study rather than an exhaustive search. You will want to check the literature to see if those research questions have already been answered.

Roadrunner Search

Identifying the gap in the research relies on an exhaustive review of the literature. Remember, researchers may not explicitly state that a gap in the literature exists; you may need to thoroughly review and assess the research to make that determination yourself.

However, there are techniques that you can use when searching in NavigatorSearch to help identify gaps in the literature. You may use search terms such as "literature gap " or "future research" "along with your subject keywords to pinpoint articles that include these types of statements.

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• Last Updated: Aug 12, 2024 7:35 AM
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Educational resources and simple solutions for your research journey

How to Identify Gaps in Research: Tips to Speed Up the Process

If you have ever wondered how to identify research gaps, well, you’re not alone. All researchers looking to make a solid contribution to their field need to start by identifying a topic or issue that hasn’t been tackled before and coming up with possible solutions for it. This is where learning what is a research gap, knowing about some research gap examples, and knowing how to identify research gaps becomes important. Through this article, we will try answering these questions for you.

Table of Contents

What is a research gap ?  

Research gaps are areas requiring more studies or research. 1  They can be:   

• an unsolved question or problem within your field.   
• a case where inconclusive or contradictive results exist.   
• a new concept or idea that hasn’t been studied.   
• a new/updated research to replace the outdated existing research.   
• a specific demographic or location that has not been well studied.   

Why is it important to identify research gaps ?  

Identifying and prioritizing research gaps  is an essential part of any research for the following reasons. 2  This can help you:  

• ensure the rapid generation of subsequent research that is informed by input from previous research studies.    
• understand areas of uncertainty within the research problem.   
• establish the research problem and scope of the study.   
• determine the scope of funding opportunities.   

Identifying research gaps : A challenge for early researchers  

Coming up with original, innovative ideas in your chosen area of research can be tricky, especially if you are an early career researcher, for the following reasons: 3,4

• Enormous information available : The introduction, discussion, and future research sections in published research articles provide information about gaps in the research field. It is easy to get overwhelmed and feel confused about which one to address. Using digital tools can help you seek out popular topics or the most cited research papers.   
• Difficulty in organizing the data : One can quickly lose ideas if not appropriately noted. Mapping the question to the resource and maintaining a record can help narrow research gap s.  
• Fear of challenging the existing knowledge : Beginner researchers may not feel confident to question established norms in their field. A good plan of action would be discussing such ideas with your advisor and proceeding according to their feedback or suggestions.   
• Lack of direction and motivation : Early researchers have reported negative emotions regarding academic research, including feeling directionless or frustrated with the effort required in identifying research topics. Again a good advisor can help you stay focused. Mentors can help novice researchers avoid cases with a high risk of failure, from misunderstanding the literature, weak design, or too many unknowns. Talking with other fellow researchers can also help overcome some of the anxiety.

How to identify research gaps  in the literature  

More than 7 million papers get published annually. 5  Considering the volume of existing research, identifying research gaps  from existing literature may seem a daunting task. While there are no hard rules for identifying research gaps, the literature has provided some guidelines for identifying problems worth investigating.   

1. Observe : Personal interests and experiences can provide insight into possible research problems. For example, a researcher interested in teaching may start with a simple observation of students’ classroom behavior and observe the link with learning theories. Developing the habit of reading literature using smart apps like  R Discovery   can keep you updated with the latest trends and developments in the field.   

2. Search : Exploring existing literature will help to identify if the observed problem is documented. One approach is identifying the independent variables used to solve the researcher’s topic of interest (i.e., the dependent variable). Databases such as Emerald, ProQuest, EbscoHost, PubMed, and ScienceDirect can help potential researchers explore existing research gaps. The following steps can help with optimizing the search process once you decide on the key research question based on your interests.

-Identify key terms.

-Identify relevant articles based on the keywords.

-Review selected articles to identify gaps in the literature.  

3. Map : This involves mapping key issues or aspects across the literature. The map should be updated whenever a researcher comes across an article of interest.   

4. Synthesize : Synthesis involves integrating the insights of multiple but related studies. A research gap is identified by combining results and findings across several interrelated studies. 6

5. Consult:  Seeking expert feedback will help you understand if the  research gaps identified are adequate and feasible or if improvements are required.  

6. Prioritize : It is possible that you have identified multiple questions requiring answers. Prioritize the question that can be addressed first, considering their relevance, resource availability, and your research strengths.  

7. Enroll : Research Skills Development Programs, including workshops and discussion groups within or outside the research institution, can help develop research skills, such as framing the research problem. Networking and corroborating in such events with colleagues and experts might help you know more about current issues and problems in your research domain.   

While there is no well-defined process to identify gaps in knowledge, curiosity, judgment, and creativity can help you in identifying these research gaps . Regardless of whether the  research gaps identified are large or small, the study design must be sufficient to contribute toward advancing your field of research.    

References  

• Dissanayake, D. M. N. S. W. (2013). Research, research gap and the research problem.  
• Nyanchoka, L., Tudur-Smith, C., Porcher, R., & Hren, D. Key stakeholders’ perspectives and experiences with defining, identifying and displaying gaps in health research: a qualitative study.  BMJ open ,  10 (11), e039932 (2020).  
• Müller-Bloch, C., & Kranz, J. (2015). A framework for rigorously identifying research gaps in qualitative literature reviews.  
• Creswell, J. W., & Clark, V. L. P. (2017).  Designing and conducting mixed methods research . Sage publications.  
• Fire, M., & Guestrin, C. Over-optimization of academic publishing metrics: observing Goodhart’s Law in action.  GigaScience ,  8 (6), giz053 (2019).  
• Ellis, T. J., & Levy, Y. Framework of problem-based research: A guide for novice researchers on the development of a research-worthy problem.  Informing Science: the International Journal of an Emerging Transdiscipline Volume 11, 2008 ). 

Frequently Asked Questions (FAQs)

Question: How can research gaps be addressed?

Research gaps can be addressed by conducting further studies, experiments, or investigations that specifically target the areas where knowledge is lacking or incomplete. This involves conducting a thorough literature review to identify existing gaps, designing research methodologies to address these gaps, and collecting new data or analyzing existing data to fill the void. Collaboration among researchers, interdisciplinary approaches, and innovative research designs can also help bridge research gaps and contribute to the advancement of knowledge in a particular field.

Question: Can research gaps change over time?

Yes, research gaps can change over time. As new studies are conducted, technologies advance, and societal needs evolve, gaps in knowledge may be identified or existing gaps may become more pronounced. Research gaps are dynamic and subject to shifts as new discoveries are made, new questions arise, and priorities change. It is crucial for researchers to continuously assess and update their understanding of the field to identify emerging research gaps and adapt their research efforts accordingly.

Question: Are research gaps specific to a particular discipline or field?

Research gaps can exist within any discipline or field. Each discipline has its own unique body of knowledge and areas where understanding may be limited. Research gaps can arise from unanswered questions, unexplored phenomena, conflicting findings, practical challenges, or new frontiers of knowledge. They are not limited to a specific discipline or field, as gaps can exist in natural sciences, social sciences, humanities, engineering, or any other area of study.

Question: How can research gaps contribute to the research proposal?

Research gaps play a significant role in the development of research proposals. They help researchers identify a clear rationale and justification for their study. By addressing identified gaps in knowledge, researchers can demonstrate the significance and relevance of their proposed research. Research proposals often include a literature review section that highlights existing gaps and positions the proposed study as a contribution to the field. By explicitly addressing research gaps, researchers can strengthen the credibility and importance of their research proposal, as well as its potential impact on advancing knowledge and addressing critical questions or challenges.

R Discovery is a literature search and research reading platform that accelerates your research discovery journey by keeping you updated on the latest, most relevant scholarly content. With 250M+ research articles sourced from trusted aggregators like CrossRef, Unpaywall, PubMed, PubMed Central, Open Alex and top publishing houses like Springer Nature, JAMA, IOP, Taylor & Francis, NEJM, BMJ, Karger, SAGE, Emerald Publishing and more, R Discovery puts a world of research at your fingertips.  

Try R Discovery Prime FREE for 1 week or upgrade at just US$72 a year to access premium features that let you listen to research on the go, read in your language, collaborate with peers, auto sync with reference managers, and much more. Choose a simpler, smarter way to find and read research – Download the app and start your free 7-day trial today !  

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What Is A Research Gap? (With Tips + Examples)

A research gap is a specific area within a field of study that remains unexplored or under-explored. Identifying a research gap involves recognizing where existing research is lacking or where there are unanswered questions that could provide opportunities for further investigation. Understanding research gaps is crucial for advancing knowledge, as it helps scholars and researchers focus their efforts on areas that can contribute significantly to their field.

What Is A Research Gap?

It is actually a question or any issue that needs to be solved by any pre-existing work or research in your area of study. A research gap can also exist where some new idea still needs to be studied.

Tips on Identifying Research Gap

Research always plays an essential role in acquiring more knowledge and addressing the gaps in different fields. When you are identifying a research gap, you are taking a very important step in the whole research process. This aids the researchers in contributing meaningful insights and triggers the knowledge boundaries.

Understanding the Literature You Are Studying: In order to identify any research gap, it is essential to have an excellent advertising of the preexisting literature in your study field.

Here, you need to conduct a review of many books, scholarly articles, conferences, and other relevant sources. In this way, you can get a good foundation as well as insights into any present state of in-depth knowledge in your own study area.

Defining Your Own Research Question: After getting a good knowledge of the pre-existing literature, you need to define a concise and clear idea of the research question. This research question needs to be very specific, attainable, measurable, time-bound and relevant. An acronym for this entire thing is known as SMART. This also needs to address any significant issue that still needs to be fully solved or adequately answered.

Identifying Your Study Objectives: Here, you need to identify the major objectives of your research paper. All these objectives need to be aligned with the identified research gap. These objectives always guide the researcher and aid you in determining the direction and scope of your research study.

Analyze the Existing Studies: Here, you need to analyze very carefully all the existing studies that are related to your research question. Here, it would help if you looked at the most common recurring findings, themes, and patterns of the discussed literature. Here, you also need to pay a lot of attention to the conflicted areas with the results, unanswered questions, and contradictory theories. These areas show the research gaps that can be explored later.

Consider The Practical Relevance: You always need to evaluate the very practical relevance of the research question as well as its potential impact on society. Here, it would help if you always considered the importance of addressing your own research gap as you identified it.

Here, you also need to assess whether your findings can contribute to the original theoretical framework and offer all the practical solutions for leading to the policy recommendations. These practical ads are relevant to the research paper and trigger its impact.

Consulting With the Experts and Peers: You always need to engage you’re discussing with your mentors, peers, and experts in your own field of study. Here, you always need to seek their opinions and perspectives on the research question to identify potential research gaps.

These can provide valuable insights into assumption challenges, and this helps you refine your research work. Your peers and experts can give you a new idea and help you identify the errors in your thinking.

Conducting Your Pilot Study: You need to conduct it to test the viability and feasibility of the research question. This pilot study provides you with feedback and data on the research design, approach and methodology.

This also helps you identify the potential limitations or challenges that need to be solved before conducting the full research studies.

Reflecting and Refining: You need to vividly reflect on the research progress to refine your research preferences. You need to add the objectives. As you go deeper into your research process, additional research gaps may be uncovered to refine your own research needs.

If you follow this process, you can adapt your own approach to ensure the research gaps.

As per the example of the research gap, identifying your research gap allows your research to contribute to gaining more knowledge to address the pre-existing limitations.

This way, you will understand the existing literature to define a crystal clear research statement. You can identify the research gaps by analyzing the existing studies to consider their relevance. According to the research gap finder, if you consult with your peers, doing all the pilot studies reflects on your research process progress.

If you follow the guide mentioned above, you can always embark on meaningful research studies to trigger your knowledge in your subject area and make a prominent contribution to your field.

Also Read: Struggling with Research Paper Writing?

Different Types of Research Gaps

Identifying research gaps is essential for advancing knowledge in any field. Research gaps are areas where more information is available or existing research needs to be more consistent or conclusive. Here are different types of research gaps:

• Evidence Gap

This gap occurs when no empirical evidence supports certain theories, practices, or interventions. It can also refer to areas where existing studies need to sufficiently cover the topic or lack rigorous methodological approaches.

Example: A need for randomized controlled trials on the effectiveness of a new drug.

• Knowledge Gap

This gap refers to areas where there is a deficiency in understanding or awareness about a particular topic. It can be due to outdated information, incomplete research, or the absence of research on emerging issues.

Example: Limited knowledge about the long-term effects of exposure to new environmental pollutants.

• Theoretical Gap

Theoretical gaps arise when existing theories do not fully explain certain phenomena or when there is a lack of theoretical frameworks to guide research in a particular area.

Example: More theoretical models need to be developed to explain the psychological impacts of social media usage on teenagers.

• Methodological Gap

Methodological gaps exist when current research methods are inadequate for addressing certain research questions or when there is a need for new or improved methodologies.

Example: More robust qualitative methods are needed to study the experiences of marginalized communities.

• Population Gap

This type of gap occurs when certain populations are underrepresented in research. It can involve demographics like age, gender, ethnicity, socioeconomic status, or geographic location.

Example: Lack of research on the mental health of older adults living in rural areas.

Geographical Gap

Geographical gaps refer to areas or regions that are under-researched. These gaps highlight the need for studies in different geographic contexts to understand local issues better.

Example: Limited studies on the effects of climate change in the Arctic regions.

Strategies to Identify Research Gaps:

• Literature Reviews: Comprehensive reviews can help identify where current research is lacking or inconsistent.
• Systematic Reviews and Meta-Analyses: These methods provide a structured approach to synthesize existing research and identify gaps.
• Expert Consultations: Discussions with experts in the field can uncover areas that require further investigation.
• Research Databases: Utilizing databases and citation analysis tools to track research trends and identify under-researched areas.
• Interdisciplinary Approaches: Engaging with multiple disciplines can reveal gaps that are not apparent within a single field.

Understanding and addressing these gaps is crucial for advancing research and knowledge across various domains.

Read More: How To Get A+ Grade In Research Paper?

What is a Research Gap Example?

A Research Paper Example gives you a very clear idea of how to find your research gaps and examples in textual forms. A few examples are given below:

• Context Healthcare: Although there have been enough researchers on the management of diabetes, there has been a research gap in understanding the impact of digital health interventions in the rural areas of Europe.
• Content environmental science: In a wealth of research regarding the huge environmental pollution caused by the use of plastics, there are fewer findings of how the plastic material really accumulates in certain areas like lakes, rivers, etc. and why these materials are never biodegradable.
• Context Education: The empirical research surrounding the online mode has become tremendously popular over the past few years. However, there needs to be more solid studies regarding the impact of the online learning process on the students who need special education. In each of these examples, you can see that the writer begins by acknowledging the preexisting reach results and then explains thoroughly the present area where the research gap really exists.

Also Read: Why Research Is Essential For Students? 20 Common Reasons!

How to Find a Research Gap?

After getting a very clear idea of various types of research gaps, the vet’s next question comes to mind is how to find a research gap. There is a basic 2 step strategy to find the research gap.

In the beginning, you need to find a lot of literature reviews, meta-analyses, and systematic reviews covering your research area of interest. Moreover, it would help if you dug into the very recent journals for wrapping your head in your own knowledge area.

Here, you can also study the current theses and dissertations, especially those in the doctoral degree courses. A number of dissertation databases, such as Open Access, EBSCO, Pro-Quest, etc., are very useful in this regard. Here. You also need to ensure that you are always looking for the most recent sources.

After gathering a good collection of these resources, you need to focus on further research opportunities. In this section, you need to state explicitly where more studies are needed. It would help if you also looked at the present research study’s limitation areas and where the research gaps might exist.

Following this procedure will help you become oriented to the present research area. This can serve as a foundation for finding the potential research gaps. Then, you need to shortlist the main ideas and evaluate them as per the given topic. It would help if you also looked only for the recent articles here.

Also Read:  Expert Literature Review Writing Services

How to Deal with Literature Gap?

In any project, a literature review is always very important. It helps you in identifying your excusing knowledge, methods and theories in your own field. However, conducting a literature review has its own challenges.

• Defiling your research question: The very first step is to define your own research question very clearly and briefly. It will help you narrow your scope and focus on the crucial sources. It would help if you used less information here. Your research must always be very specific, answerable, and original. The research project always needs to have real objectives and a purpose.
• Searching and selecting the sources: Your next step is to search and select the sources. That is very much reliable and relevant to your research field. There are a number of databases, like keywords, search engines, etc., related to your study field. However, there are also a lot of limitations to these tools, like currency, coverage, and quality of the sources. Here, certain criteria have to be applied to filter the sources, such as relevance, authority, timeline, and accuracy of the information.
• Analyzing and synthesizing the literature: This is the third step, where you need to analyze and synthesize the literature you selected. Here, you need to summarize the sources and compare, contrast and critique them. In this section, you also need to look for the similarities and differences, the strengths and weaknesses, and the gaps and inconsistencies of the literature review paper. The writers can also identify the major trends, themes, and debates in the discussed field. These should also be related to your research question.
• Fill in the gaps after identifying them: This is the 4th step to filling the literature review research paper. This gap needs to be addressed or is under the researched area and is to be addressed by you with the help of your knowledge. These gaps can be filled by looking for the limitations, contradictions or controversies in the review. You can also do this by asking new questions or proposing new ideas. The gaps can also be filled by providing the newest evidence, arguments or even insights related to your field of study.
• Organizing and structuring the literature review: This is the 5th step of your review, where you need to organize and structure the whole paper in a compact and logical manner. Here, you always need to follow certain guidelines as given by your institute and use the best style and font. Proper headings, subheading citations, and traditions should also be used here. This will help your readers follow your arguments and understand what you want to say. A very clear introduction should also be written, along with a good conclusion and summary to highlight your writing.
• Refining and Revising: The literature review is the final step of writing your literature review. Here, you need to ensure that your review is quite accurate, concise and clear. You must check your literature review thoroughly to make it free from errors, gaps, or inconsistencies in language, content, or presentation. Here, you can also seek feedback from your peers, experts or supervisors in your own field. Their suggestions will help you in performing well. The whore literature review should be thoroughly proofread and edited before the final submission.

Last but not least, never copy from any source; it will be considered plagiarism, and your paper will be cancelled then and there. Thus, write only from your own creativity and not from the writing and articles of other writers.

Read More: Dissertation Literature Review For Masters & PhD

Final Words

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Last Updated: Jul 18, 2024 Views: 487508

What is a research gap.

A research gap is a question or a problem that has not been answered by any of the existing studies or research within your field. Sometimes, a research gap exists when there is a concept or new idea that hasn't been studied at all. Sometimes you'll find a research gap if all the existing research is outdated and in need of new/updated research (studies on Internet use in 2001, for example). Or, perhaps a specific population has not been well studied (perhaps there are plenty of studies on teenagers and video games, but not enough studies on toddlers and video games, for example). These are just a few examples, but any research gap you find is an area where more studies and more research need to be conducted. Please view this video clip from our Sage Research Methods database for more helpful information: How Do You Identify Gaps in Literature?

How do I find one?

It will take a lot of research and reading.  You'll need to be very familiar with all the studies that have already been done, and what those studies contributed to the overall body of knowledge about that topic. Make a list of any questions you have about your topic and then do some research to see if those questions have already been answered satisfactorily. If they haven't, perhaps you've discovered a gap!  Here are some strategies you can use to make the most of your time:

• One useful trick is to look at the “suggestions for future research” or conclusion section of existing studies on your topic. Many times, the authors will identify areas where they think a research gap exists, and what studies they think need to be done in the future.
• As you are researching, you will most likely come across citations for seminal works in your research field. These are the research studies that you see mentioned again and again in the literature.  In addition to finding those and reading them, you can use a database like Web of Science to follow the research trail and discover all the other articles that have cited these. See the FAQ: I found the perfect article for my paper. How do I find other articles and books that have cited it? on how to do this. One way to quickly track down these seminal works is to use a database like SAGE Navigator, a social sciences literature review tool. It is one of the products available via our SAGE Knowledge database.
• In the PsycINFO and PsycARTICLES databases, you can select literature review, systematic review, and meta analysis under the Methodology section in the advanced search to quickly locate these. See the FAQ: Where can I find a qualitative or quantitative study? for more information on how to find the Methodology section in these two databases.
• In CINAHL , you can select Systematic review under the Publication Type field in the advanced search. 
• In Web of Science , check the box beside Review under the Document Type heading in the “Refine Results” sidebar to the right of the list of search hits.
• If the database you are searching does not offer a way to filter your results by document type, publication type, or methodology in the advanced search, you can include these phrases (“literature reviews,” meta-analyses, or “systematic reviews”) in your search string.  For example, “video games” AND “literature reviews” could be a possible search that you could try.

Please give these suggestions a try and contact a librarian for additional assistance.

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Library Guide to Capstone Literature Reviews: Find a Research Gap

Find a research gap: tips to get started.

Finding a research gap is not an easy process and there is no one linear path. These tips and suggestions are just examples of possible ways to begin. 

In Ph.D. dissertations, students identify a gap in research. In other programs, students identify a gap in practice. The literature review for a gap in practice will show the context of the problem and the current state of the research. 

Research gap definition

A research gap exists when:

• a question or problem has not been answered by existing studies/research in the field 
• a concept or new idea has not been studied at all
• all the existing literature on a topic is outdated 
• a specific population/location/age group etc has not been studied 

A research gap should be:

• grounded in the literature
• amenable to scientific study
• Litmus Test for a Doctoral-Level Research Problem (Word) This tool helps students determine if they have identified a doctoral level research problem.

Identify a research gap

To find a gap you must become very familiar with a particular field of study. This will involve a lot of research and reading, because a gap is defined by what does (and does not) surround it.

• Search the research literature and dissertations (search all university dissertations, not just Walden!).
• Understand your topic! Review background information in books and encyclopedias . 
• Look for literature reviews, systematic reviews, and meta-analyses.
• Take notes on concepts, themes, and subject terms . 
• Look closely at each article's limitations, conclusions, and recommendations for future research. 
• Organize, analyze, and repeat! 

• Quick Answer: How do I find dissertations on a topic?

Start with broad searches

Use the Library Search (formerly Thoreau)  to do a broad search with just one concept at a time . Broad searches give you an idea of the academic conversation surrounding your topic.

• Try the terms you know (keywords) first.
• Look at the Subject Terms (controlled language) to brainstorm terms. 
• Subject terms help you understand what terms are most used, and what other terms to try.
• No matter what your topic is, not every researcher will be using the same terms. Keep an eye open for additional ways to describe your topic.
• Guide: Subject Terms & Index Searches: Index Overview

Keep a list of terms

• Create a list of terms
• Example list of terms

This list will be a record of what terms are: 

• related to or represent your topic
• synonyms or antonyms
• more or less commonly used
• keywords (natural language) or subject terms (controlled language)
• Synonyms & antonyms (database search skills)
• Turn keywords into subject terms

Term I started with:

culturally aware 

Subject terms I discovered:

cultural awareness (SU) 

cultural sensitivity (SU) 

cultural competence (SU) 

Search with different combinations of terms

• Combine search terms list
• Combine search terms table
• Video: Search by Themes

Since a research gap is defined by the absence of research on a topic, you will search for articles on everything that relates to your topic. 

• List out all the themes related to your gap.
• Search different combinations of the themes as you discover them 

For example, suppose your research gap is on the work-life balance of tenured and tenure-track women in engineering professions. In that case, you might try searching different combinations of concepts, such as: 

• women and STEM 
• STEM or science or technology or engineering or mathematics
• female engineering professors 
• tenure-track women in STEM
• work-life balance and women in STEM
• work-life balance and women professors
• work-life balance and tenure 

Topic adapted from one of the award winning Walden dissertations. 

• Walden University Award Winning Dissertations
• Gossage, Lily Giang-Tien, "Work-Life Balance of Tenured and Tenure-Track Women Engineering Professors" (2019). Walden Dissertations and Doctoral Studies. 6435.

Break your topic into themes and try combining the terms from different themes in different ways. For example: 

Theme 1 and Theme 4

Theme 2 and Theme 1

Theme 3 and Theme 4

Video: Search by Themes (YouTube)

(2 min 40 sec) Recorded April 2014 Transcript

Track where more research is needed

Most research articles will identify where more research is needed. To identify research trends, use the literature review matrix to track where further research is needed. 

• Download or create your own Literature Review Matrix (examples in links below).
• Do some general database searches on broad topics.
• Find an article that looks interesting.
• When you read the article, pay attention to the conclusions and limitations sections.
• Use the Literature Review Matrix to track where  'more research is needed' or 'further research needed'. NOTE:  you might need to add a column to the template.
• As you fill in the matrix you should see trends where more research is needed.

There is no consistent section in research articles where the authors identify where more research is needed. Pay attention to these sections: 

• limitations
• conclusions
• recommendations for future research 
• Literature Review Matrix Templates: learn how to keep a record of what you have read
• Literature Review Matrix (Excel) with color coding Sample template for organizing and synthesizing your research
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What is a Research Gap

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If you are a young researcher, or even still finishing your studies, you’ll probably notice that your academic environment revolves around certain research topics, probably linked to your department or to the interest of your mentor and direct colleagues. For example, if your department is currently doing research in nanotechnology applied to medicine, it is only natural that you feel compelled to follow this line of research. Hopefully, it’s something you feel familiar with and interested in – although you might take your own twists and turns along your career.

Many scientists end up continuing their academic legacy during their professional careers, writing about their own practical experiences in the field and adapting classic methodologies to a present context. However, each and every researcher dreams about being a pioneer in a subject one day, by discovering a topic that hasn’t been approached before by any other scientist. This is a research gap.

Research gaps are particularly useful for the advance of science, in general. Finding a research gap and having the means to develop a complete and sustained study on it can be very rewarding for the scientist (or team of scientists), not to mention how its new findings can positively impact our whole society.

How to Find a Gap in Research

How many times have you felt that you have finally formulated THAT new and exciting question, only to find out later that it had been addressed before? Probably more times than you can count.

There are some steps you can take to help identify research gaps, since it is impossible to go through all the information and research available nowadays:

• Select a topic or question that motivates you: Research can take a long time and surely a large amount of physical, intellectual and emotional effort, therefore choose a topic that can keep you motivated throughout the process.
• Find keywords and related terms to your selected topic: Besides synthesizing the topic to its essential core, this will help you in the next step.
• Use the identified keywords to search literature: From your findings in the above step, identify relevant publications and cited literature in those publications.
• Look for topics or issues that are missing or not addressed within (or related to) your main topic.
• Read systematic reviews: These documents plunge deeply into scholarly literature and identify trends and paradigm shifts in fields of study. Sometimes they reveal areas or topics that need more attention from researchers and scientists.

Keeping track of all the new literature being published every day is an impossible mission. Remember that there is technology to make your daily tasks easier, and reviewing literature can be one of them. Some online databases offer up-to-date publication lists with quite effective search features:

• Elsevier’s Scope
• Google Scholar

Of course, these tools may be more or less effective depending on knowledge fields. There might be even better ones for your specific topic of research; you can learn about them from more experienced colleagues or mentors.

Find out how FINER research framework can help you formulate your research question.

Literature Gap

The expression “literature gap” is used with the same intention as “research gap.” When there is a gap in the research itself, there will also naturally be a gap in the literature. Nevertheless, it is important to stress out the importance of language or text formulations that can help identify a research/literature gap or, on the other hand, making clear that a research gap is being addressed.

When looking for research gaps across publications you may have noticed sentences like:

…has/have not been… (studied/reported/elucidated) …is required/needed… …the key question is/remains… …it is important to address…

These expressions often indicate gaps; issues or topics related to the main question that still hasn’t been subject to a scientific study. Therefore, it is important to take notice of them: who knows if one of these sentences is hiding your way to fame.

Language Editing Services by Elsevier Author Services:

Systematic Review VS Meta-Analysis

Literature Review in Research Writing

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How to Find a Research Gap

Last Updated: February 16, 2024 Fact Checked

This article was reviewed by Gerald Posner and by wikiHow staff writer, Danielle Blinka, MA, MPA . Gerald Posner is an Author & Journalist based in Miami, Florida. With over 35 years of experience, he specializes in investigative journalism, nonfiction books, and editorials. He holds a law degree from UC College of the Law, San Francisco, and a BA in Political Science from the University of California-Berkeley. He’s the author of thirteen books, including several New York Times bestsellers, the winner of the Florida Book Award for General Nonfiction, and has been a finalist for the Pulitzer Prize in History. He was also shortlisted for the Best Business Book of 2020 by the Society for Advancing Business Editing and Writing. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 34,543 times.

Do you want to contribute original research and make an impact in your field? If so, it's important to look for research gaps, or areas of study that are either under-researched or currently unexplored. In this article, we'll explain in detail the best way to identify a research gap—by performing a comprehensive literature review—so you can dive deep into your research topic and analyze articles critically and effectively. For more tips and tricks on identifying potential research gaps and how to proceed when you find one, read on.

Researching Your Topic

• If you start with a narrow topic, you may struggle to find a gap in research, since you’ll be focused on fewer avenues of study.
• For instance, a broad topic for social sciences research might be "organizational development" or "human motivation." For urban planning, a broad topic might be "walkable cities" or "traffic management."

• While you can't include sources like Wikipedia and news websites on your literature review, it's okay to read them to get an overview of your topic and recent developments in your field.
• It’s okay to narrow your topic as you learn more about it. However, keep your options open until you’re sure you’ve found an area with gaps in research.
• Let's say you were researching human motivation. You might use search terms like "motivating workers," "goal setting," and "improving worker productivity."

• Your research needs to be very thorough to ensure that you’re actually finding a gap. If you only read a handful of articles, you may be missing other existing research that answers your proposed research question.

Tip: Look for both quantitative and qualitative research, if applicable to your field. This will give you a broader overview of the current research.

• Ask them questions like, “Which areas of research are hot right now?” “What kinds of changes are happening within the field?” “What possible avenues of research do you see?” or “Do you think this topic is a good fit for me?”

Analyzing the Literature

• If you decide an article is unhelpful, it’s okay to skip the second reading.

Tip: Conducting a literature review is often a very time-consuming task. However, it’s also an essential part of identifying a research gap. Additionally, you can use the notes you take during your literature review when it comes time to write your article, thesis, or dissertation.

• As an example, an author might identify their gap in research with a statement like: “This subject has not been previously studied,” or “This question remains unanswered.”

• If you keep your notes in a separate document, make sure you label them with the title of the article and the author’s name. This way you won’t accidentally get your notes mixed up.

• Save any questions that you can’t answer because they may be a starting point for writing a research question.

• For instance, you might make a research gap table in a spreadsheet. Create 3 columns and label them “Author,” “Year,” and "Summary." For each article, list the authors, year of publication, and a bullet point summary of the article contents.
• Similarly, you may make a Venn diagram to compare 1 or more articles. Look for overlapping themes and methods, as well as differences between the articles.

Using Current Research, Key Concepts, or Trends

• Keep in mind that other researchers may have addressed the gaps identified in a particular article since that article was written. However, this can give you a starting point for finding a potential gap.

• Don’t rely solely on these types of papers when conducting your research. However, they can make a great supplement.

• Some journals will even tell you how many articles are pertaining to that key concept. If you see a key concept that has fewer articles than the others, that might be a good avenue for further research because it’s been studied less.

• You can access Google trends here: https://trends.google.com/trends/?geo=US
• For instance, if you look up "organizational development" on Google trends, you'll see that people are looking for information on management development, mission statements, and software framework.

Expert Q&A

• Reading Wikipedia articles related to your topic of study may help you identify a gap in research, though you can’t use those articles as sources. Look for areas where more citations are needed, unanswered questions, or sections that are underdeveloped.

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• ↑ https://libanswers.snhu.edu/faq/264001
• ↑ https://resources.nu.edu/researchprocess/literaturegap
• ↑ https://guides.umd.umich.edu/c.php?g=529423&p=3621573
• ↑ https://www.ncbi.nlm.nih.gov/books/NBK62480/

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Research Gap

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A research gap is an area within a field that lacks sufficient information or understanding, highlighting opportunities for further investigation. Identified through literature review, it guides researchers to explore new questions and develop innovative theories. Addressing these gaps advances knowledge and solves real-world problems. In areas like Marketing Gap Analysis , identifying research gaps leads to more effective strategies and improved market performance.

What is a Research Gap?

A research gap is an area within a field of study that lacks sufficient information or understanding, presenting an opportunity for further investigation. It is identified through a thorough review of existing literature and highlights areas where more research is needed. Recognizing these gaps is essential for advancing knowledge, guiding researchers to explore new questions, develop innovative theories, and improve methodologies.

Examples of Research Gap

• Healthcare : Limited studies on the long-term effects of telemedicine on patient outcomes, especially in rural areas.
• Education : Insufficient research on the impact of virtual reality in improving student engagement and learning outcomes in primary education.
• Environmental Science : Lack of comprehensive data on the effects of microplastics on marine ecosystems.
• Marketing : Few studies exploring the influence of social media influencers on consumer behavior in emerging markets.
• Technology : Sparse research on the ethical implications of artificial intelligence in workplace decision-making processes.
• Psychology : Limited understanding of the mental health impacts of prolonged social media use among teenagers.
• Economics : Insufficient analysis of the effects of cryptocurrency adoption on traditional banking systems.
• Sociology : Lack of in-depth studies on the long-term effects of remote work on family dynamics and work-life balance.
• Public Health : Few studies examining the effectiveness of community-based interventions in reducing obesity rates among children.
• Renewable Energy : Limited research on the integration of renewable energy sources into existing power grids and their economic impacts.

Different Types of Research Gaps

Research gaps are areas where knowledge is lacking or where existing research could be expanded. Identifying and addressing these gaps is crucial for advancing knowledge in any field. Here are the different types of research gaps:

1. Evidence Gap

Definition : An evidence gap occurs when there is a lack of empirical data to support conclusions or theories. This gap signifies areas where more research is needed to provide solid evidence for or against a hypothesis.

Example : Limited studies on the long-term effects of a new medication.

2. Knowledge Gap

Definition : A knowledge gap refers to a lack of understanding or awareness about a specific topic. This gap often highlights areas where research has not yet been conducted or where findings are inconsistent.

Example : Insufficient knowledge about the impact of social media on mental health among teenagers.

3. Practical-Knowledge Gap

Definition : This gap arises when there is a disconnect between theoretical research and practical application. It points to areas where findings from research have not been implemented in real-world settings or where practical challenges are not addressed by existing research.

Example : Theoretical models for disaster management that are not tested in actual disaster scenarios.

4. Methodological Gap

Definition : A methodological gap is identified when current research methods are inadequate to address certain research questions. This gap indicates the need for new or improved research methods.

Example : The need for longitudinal studies to better understand the progression of chronic diseases.

5. Policy Gap

Definition : A policy gap occurs when research does not inform policy or when there is a lack of research supporting existing policies. This gap often highlights the need for research that can influence or evaluate policy decisions.

Example : Lack of research on the effectiveness of policies aimed at reducing carbon emissions.

6. Population Gap

Definition : This gap is present when certain populations or demographic groups are underrepresented in research. It calls attention to the need for more inclusive research that considers diverse populations.

Example : Underrepresentation of elderly populations in clinical trials for new medications.

7. Theory Gap

Definition : A theory gap is found when there is a lack of theoretical framework to explain certain phenomena. This gap suggests the need for developing or refining theories to better understand specific issues.

Example : Incomplete theoretical explanations for the rise of extremism in modern societies.

8. Contextual Gap

Definition : A contextual gap exists when research does not take into account the context in which a phenomenon occurs. This gap highlights the need for studies that consider environmental, cultural, or situational factors.

Example : Studies on education methods that do not consider cultural differences in learning styles.

9. Perspective Gap

Definition : This gap arises when certain perspectives or viewpoints are missing from the research. It emphasizes the need for more diverse viewpoints to provide a comprehensive understanding of a topic.

Example : Limited perspectives from minority groups in research on workplace diversity.

10. Data Gap

Definition : A data gap is identified when there is a lack of available data or when existing data is insufficient to support research conclusions. This gap indicates the need for more extensive data collection and analysis.

Example : Insufficient data on climate change impacts in specific geographic regions.

How to write Research Gap

Identifying and articulating a research gap is a crucial step in academic research. It highlights the need for your study and sets the stage for your research question and objectives. Here’s a step-by-step guide on how to write a research gap:

1. Literature Review

Conduct a thorough literature review to understand the current state of research in your field. Look for recent studies, key theories, and significant findings. Take note of any inconsistencies, unanswered questions, or areas that have not been explored.

2. Identify the Gap

After reviewing the literature, pinpoint the specific areas where research is lacking. This could be due to insufficient evidence, outdated studies, contradictory findings, or unaddressed issues.

3. Justify the Gap

Explain why this gap is important. Discuss the implications of not addressing this gap and how filling it could advance knowledge in your field or solve a practical problem.

4. Formulate Your Research Question

Based on the identified gap, formulate a clear and focused research question. This question should aim to address the gap and guide your study.

5. Contextualize the Gap

Place your research gap within the broader context of your field. Explain how your study will contribute to the existing body of knowledge and why it is timely and relevant.

6. Use Clear and Concise Language

When writing about the research gap, be clear and concise. Avoid jargon and ensure that your explanation is understandable to readers outside your immediate field.

How to Identify Research Gap?

Identifying a research gap is essential for developing a relevant and impactful research question. Here are the steps to effectively identify a research gap:

1. Conduct a Comprehensive Literature Review

Start by thoroughly reviewing existing literature in your area of interest. Use academic databases, journals, books, and conference papers to gather information. Focus on:

• Recent studies and their findings
• Key theories and models
• Methodologies used
• Areas of consensus and disagreement

2. Analyze the Literature Critically

While reviewing the literature, critically evaluate the studies. Look for:

• Inconsistencies : Contradictory findings or conclusions
• Outdated Information : Studies that need updating due to new data or advancements
• Methodological Flaws : Weaknesses or limitations in research methods
• Unanswered Questions : Questions that previous studies have raised but not answered

3. Identify Trends and Patterns

Identify trends and patterns in the existing research. Consider:

• Common themes and topics
• Frequently used methodologies
• Populations and settings studied
• Gaps in data and analysis

4. Look for Understudied Areas

Identify topics or subtopics that have not been extensively researched. Pay attention to:

• Emerging fields or new technologies
• Neglected populations or regions
• Interdisciplinary research opportunities

5. Consult Reviews and Meta-Analyses

Review articles and meta-analyses can provide a summary of the current state of research and highlight areas where further research is needed. They often suggest future research directions and gaps.

6. Analyze Research Agendas and Funding Opportunities

Research agendas and funding calls from academic institutions, government agencies, and private organizations can highlight priority areas and identify gaps that need addressing.

7. Discuss with Experts and Peers

Engage in discussions with experts, mentors, and peers in your field. They can provide insights into current research trends and gaps that you might have overlooked.

8. Examine Conference Proceedings

Conference proceedings often contain the latest research and can indicate emerging trends and gaps. Attend conferences and review the abstracts and presentations.

9. Evaluate the Practical Relevance

Consider the practical implications of existing research. Identify areas where research findings have not been applied or where practical challenges remain unaddressed.

10. Formulate Research Questions

Based on the identified gaps, develop specific research questions. These questions should address the gaps and guide your research towards filling them.

Research Gap Uses

1. advancing knowledge.

Filling a research gap helps in advancing the overall knowledge within a field. It allows researchers to build upon existing studies and contribute new insights, theories, or methods.

2. Innovative Solutions

Addressing a research gap can lead to the development of innovative solutions to existing problems. Researchers can explore new approaches, technologies, or applications that have not been previously considered.

3. Funding and Support

Identifying a significant research gap can attract funding and support from academic institutions, government bodies, and private organizations. Funders are often interested in supporting projects that promise new discoveries and advancements.

4. Publishing Opportunities

Research that addresses a gap is often seen as valuable and original, increasing the chances of publication in reputable academic journals. This can enhance the researcher’s profile and credibility within the academic community.

5. Educational Development

For educators and students, identifying research gaps can guide the development of curricula and educational programs. It ensures that teaching materials are up-to-date and relevant to current academic and industry trends.

FAQ’s

Why is identifying a research gap important.

Identifying a research gap helps focus efforts on unexplored areas, advancing knowledge and contributing to the field.

How can I identify a research gap?

Review current literature, analyze findings, and note areas lacking comprehensive studies or conflicting results.

What are the types of research gaps?

Types include evidence gaps, knowledge gaps, practical gaps, theoretical gaps, and methodological gaps.

What is an evidence gap?

An evidence gap exists when there is a lack of empirical data supporting a particular hypothesis or theory.

How does a theoretical gap differ from a practical gap?

A theoretical gap involves missing or underdeveloped concepts, while a practical gap involves real-world issues needing solutions.

What is a methodological gap?

A methodological gap arises when certain methods have not been applied to study a specific problem.

How can conflicting results indicate a research gap?

Conflicting results suggest inconsistencies in findings, pointing to areas needing further investigation.

What is the role of a literature review in identifying research gaps?

A literature review helps identify gaps by summarizing existing studies and highlighting areas needing further research.

Can technology advancements create research gaps?

Yes, new technologies can reveal gaps by enabling studies that were previously impossible or overlooked.

What is the impact of research gaps on funding opportunities?

Identifying significant gaps can attract funding by demonstrating the need for research in unexplored areas.

Text prompt

• Instructive
• Professional

10 Examples of Public speaking

20 Examples of Gas lighting

How to identify research gaps and include them in your thesis?

A thesis is an investigation that adheres to the principles of academic writing . It is critically evaluated on its reliability and significance for the industry (Chandra, 2017). The thesis research provides new insights into academia by reviewing existing research.

In this process, it is essential to identify the research gap. Research gaps are the centre of any research, determining the areas which lack crucial information.

Research gaps also help to frame:

The purpose of identifying research gaps in a thesis

A research gap is a problem that has not been addressed or answered in previous studies in the form of books, journal articles or reports. For instance, presently, there is a lack of research on the long-term effects of the Covid-19 vaccine. This can be a research gap in many studies such as social sciences, biotechnology, and medicine. Such problems need citation analysis and systematic review (Tsoulfas, 2021). To formulate an information-driven thesis, it is important to recognize the area or the topic that is unexplored or has insufficient information. Often research gaps in a thesis are confused with research questions and problem statements. However, there are fundamental differences in these concepts. The sole purpose of a research gap is to summarise problems with outdated or primitive studies. It is a part of the thesis introduction chapter and can range from 200 to 1000 words in length.

How to devote a section for research gaps in a thesis?

The first step in preparing the research gaps section is to outline the general state of knowledge and research in the field of study. This step helps in building the outline for the aspects that could be relevant to the research field.

The second step involves a thorough reading of earlier research and publication on the topic. For this, the researcher can refer to journal articles, library books, or reports. This step also involves consulting your supervisor.

Further, as per the reviewed articles, a viewpoint about the given topic must be framed by listing all relevant information.

Lastly, the need or significance of addressing the listed gaps should be presented.

Start the research gaps in a thesis with a summary of existing research findings. It does not need a detailed elaboration of the situation. For instance, statistics can be skipped. Similarly, you do not need to explain concepts or theories in this section. Next, state the limitations or lacuna in the area of research. This section needs more elaboration like who, what, when, where, why and how should be discussed. Each gap must be stated separately. For instance, consider these 3 gaps:

• there is a lack of research in your country’s context,
• there is a lack of empirical evidence and,
• there is a lack of consensus,

each should be explained separately. It should be structured in the form of citations wherever necessary. The writing pattern should move from generic to specific thus targeting the research problem for the thesis.

Points to avoid

• Too much description and analysis of the previously done studies must be avoided to keep the thesis research gap indicative and emblematic.
• Avoid giving too much statistical information.
• Avoid not reading enough. Identifying a research gap needs thorough reading, not skimping through facts.
• Avoid failing to accurately identify the need for further study and the lack of a persuasive framework for the identification of the research gap.
• Avoid not using enough citations for supporting the identified lacuna.
• Avoid not stating the significance of the identified gaps.

An example of research gaps in a thesis

Case topic: Impact of transformative heritage destinations on changing personal values of tourists

Travel behaviour today has shifted from global consumerism to a more meaningful and personalized experience. This has amplified the demand for heritage tourism, i.e. the movement of a person to places of cultural attraction away from their normal residential place to gain new experiences and information for satisfying cultural needs (G Richards, 2003; Rosenfeld, 2008). Tourists are also seeking transformative travel experiences which lead to positive changes in their values and attitudes. PineII & Gilmore (1999) have identified that heritage tourism is responding towards fulfilling the transformation needs of tourists. However, the lack of empirical evidence on the contribution of transformative heritage tourism in changing the personal values of tourists is restricting the formulation of strategies that can boost its growth.

Moreover, researchers have determined that authenticity, awareness, nostalgia, and satisfaction have a relationship with transformative effects and heritage tourism. Therefore, these factors may be interlinked. But despite this, not many academic studies have focused on addressing these tourist factors’ impact on the linkage between heritage tourism and transformative effect. This is another critical research gap.

• Chandra. (2017). How to Write a Thesis : A Working Guide . Retrieved from https://www.student.uwa.edu.au/__data/assets/pdf_file/0007/1919239/How-to-write-a-thesis-A-working-guide.pdf
• Oulu. (2012). GUIDELINES FOR WRITING A THESIS . Retrieved from https://www.oulu.fi/sites/default/files/content/Guidelines.pdf
• Pubrica. (2021). Framework for the Identirication Of the Research Gap. Retrieved September 30, 2021, from Pubrica website: http://pubrica.com/academy/uncategorized/a-framework-for-the-identification-of-the-research-gap/
• Tsoulfas, G. (2021). The Importance of Research. Journal of the American College of Surgeons , 232 (5), 680–681. https://doi.org/10.1016/j.jamcollsurg.2021.02.003
• Priya Chetty

I am a management graduate with specialisation in Marketing and Finance. I have over 12 years' experience in research and analysis. This includes fundamental and applied research in the domains of management and social sciences. I am well versed with academic research principles. Over the years i have developed a mastery in different types of data analysis on different applications like SPSS, Amos, and NVIVO. My expertise lies in inferring the findings and creating actionable strategies based on them. 

Over the past decade I have also built a profile as a researcher on Project Guru's Knowledge Tank division. I have penned over 200 articles that have earned me 400+ citations so far. My Google Scholar profile can be accessed here . 

I now consult university faculty through Faculty Development Programs (FDPs) on the latest developments in the field of research. I also guide individual researchers on how they can commercialise their inventions or research findings. Other developments im actively involved in at Project Guru include strengthening the "Publish" division as a bridge between industry and academia by bringing together experienced research persons, learners, and practitioners to collaboratively work on a common goal. 

I am a Senior Analyst at Project Guru, a research and analytics firm based in Gurugram since 2012. I hold a master’s degree in economics from Amity University (2019). Over 4 years, I have worked on worked on various research projects using a range of research tools like SPSS, STATA, VOSViewer, Python, EVIEWS, and NVIVO. My core strength lies in data analysis related to Economics, Accounting, and Financial Management fields.

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How to Write a Research Proposal: (with Examples & Templates)

Table of Contents

Before conducting a study, a research proposal should be created that outlines researchers’ plans and methodology and is submitted to the concerned evaluating organization or person. Creating a research proposal is an important step to ensure that researchers are on track and are moving forward as intended. A research proposal can be defined as a detailed plan or blueprint for the proposed research that you intend to undertake. It provides readers with a snapshot of your project by describing what you will investigate, why it is needed, and how you will conduct the research.  

Your research proposal should aim to explain to the readers why your research is relevant and original, that you understand the context and current scenario in the field, have the appropriate resources to conduct the research, and that the research is feasible given the usual constraints.  

This article will describe in detail the purpose and typical structure of a research proposal , along with examples and templates to help you ace this step in your research journey.  

What is a Research Proposal ?  

A research proposal¹ ,²  can be defined as a formal report that describes your proposed research, its objectives, methodology, implications, and other important details. Research proposals are the framework of your research and are used to obtain approvals or grants to conduct the study from various committees or organizations. Consequently, research proposals should convince readers of your study’s credibility, accuracy, achievability, practicality, and reproducibility.   

With research proposals , researchers usually aim to persuade the readers, funding agencies, educational institutions, and supervisors to approve the proposal. To achieve this, the report should be well structured with the objectives written in clear, understandable language devoid of jargon. A well-organized research proposal conveys to the readers or evaluators that the writer has thought out the research plan meticulously and has the resources to ensure timely completion.  

Purpose of Research Proposals  

A research proposal is a sales pitch and therefore should be detailed enough to convince your readers, who could be supervisors, ethics committees, universities, etc., that what you’re proposing has merit and is feasible . Research proposals can help students discuss their dissertation with their faculty or fulfill course requirements and also help researchers obtain funding. A well-structured proposal instills confidence among readers about your ability to conduct and complete the study as proposed.  

Research proposals can be written for several reasons:³  

• To describe the importance of research in the specific topic  
• Address any potential challenges you may encounter  
• Showcase knowledge in the field and your ability to conduct a study  
• Apply for a role at a research institute  
• Convince a research supervisor or university that your research can satisfy the requirements of a degree program  
• Highlight the importance of your research to organizations that may sponsor your project  
• Identify implications of your project and how it can benefit the audience  

What Goes in a Research Proposal?    

Research proposals should aim to answer the three basic questions—what, why, and how.  

The What question should be answered by describing the specific subject being researched. It should typically include the objectives, the cohort details, and the location or setting.  

The Why question should be answered by describing the existing scenario of the subject, listing unanswered questions, identifying gaps in the existing research, and describing how your study can address these gaps, along with the implications and significance.  

The How question should be answered by describing the proposed research methodology, data analysis tools expected to be used, and other details to describe your proposed methodology.   

Research Proposal Example  

Here is a research proposal sample template (with examples) from the University of Rochester Medical Center. 4 The sections in all research proposals are essentially the same although different terminology and other specific sections may be used depending on the subject.  

Structure of a Research Proposal  

If you want to know how to make a research proposal impactful, include the following components:¹  

1. Introduction  

This section provides a background of the study, including the research topic, what is already known about it and the gaps, and the significance of the proposed research.  

2. Literature review  

This section contains descriptions of all the previous relevant studies pertaining to the research topic. Every study cited should be described in a few sentences, starting with the general studies to the more specific ones. This section builds on the understanding gained by readers in the Introduction section and supports it by citing relevant prior literature, indicating to readers that you have thoroughly researched your subject.  

3. Objectives  

Once the background and gaps in the research topic have been established, authors must now state the aims of the research clearly. Hypotheses should be mentioned here. This section further helps readers understand what your study’s specific goals are.  

4. Research design and methodology  

Here, authors should clearly describe the methods they intend to use to achieve their proposed objectives. Important components of this section include the population and sample size, data collection and analysis methods and duration, statistical analysis software, measures to avoid bias (randomization, blinding), etc.  

5. Ethical considerations  

This refers to the protection of participants’ rights, such as the right to privacy, right to confidentiality, etc. Researchers need to obtain informed consent and institutional review approval by the required authorities and mention this clearly for transparency.  

6. Budget/funding  

Researchers should prepare their budget and include all expected expenditures. An additional allowance for contingencies such as delays should also be factored in.  

7. Appendices  

This section typically includes information that supports the research proposal and may include informed consent forms, questionnaires, participant information, measurement tools, etc.  

8. Citations  

Important Tips for Writing a Research Proposal  

Writing a research proposal begins much before the actual task of writing. Planning the research proposal structure and content is an important stage, which if done efficiently, can help you seamlessly transition into the writing stage. 3,5  

The Planning Stage  

• Manage your time efficiently. Plan to have the draft version ready at least two weeks before your deadline and the final version at least two to three days before the deadline.
• What is the primary objective of your research?  
• Will your research address any existing gap?  
• What is the impact of your proposed research?  
• Do people outside your field find your research applicable in other areas?  
• If your research is unsuccessful, would there still be other useful research outcomes?  

  The Writing Stage  

• Create an outline with main section headings that are typically used.  
• Focus only on writing and getting your points across without worrying about the format of the research proposal , grammar, punctuation, etc. These can be fixed during the subsequent passes. Add details to each section heading you created in the beginning.   
• Ensure your sentences are concise and use plain language. A research proposal usually contains about 2,000 to 4,000 words or four to seven pages.  
• Don’t use too many technical terms and abbreviations assuming that the readers would know them. Define the abbreviations and technical terms.  
• Ensure that the entire content is readable. Avoid using long paragraphs because they affect the continuity in reading. Break them into shorter paragraphs and introduce some white space for readability.  
• Focus on only the major research issues and cite sources accordingly. Don’t include generic information or their sources in the literature review.  
• Proofread your final document to ensure there are no grammatical errors so readers can enjoy a seamless, uninterrupted read.  
• Use academic, scholarly language because it brings formality into a document.  
• Ensure that your title is created using the keywords in the document and is neither too long and specific nor too short and general.  
• Cite all sources appropriately to avoid plagiarism.  
• Make sure that you follow guidelines, if provided. This includes rules as simple as using a specific font or a hyphen or en dash between numerical ranges.  
• Ensure that you’ve answered all questions requested by the evaluating authority.  

Key Takeaways   

Here’s a summary of the main points about research proposals discussed in the previous sections:  

• A research proposal is a document that outlines the details of a proposed study and is created by researchers to submit to evaluators who could be research institutions, universities, faculty, etc.  
• Research proposals are usually about 2,000-4,000 words long, but this depends on the evaluating authority’s guidelines.  
• A good research proposal ensures that you’ve done your background research and assessed the feasibility of the research.  
• Research proposals have the following main sections—introduction, literature review, objectives, methodology, ethical considerations, and budget.  

Frequently Asked Questions  

Q1. How is a research proposal evaluated?  

A1. In general, most evaluators, including universities, broadly use the following criteria to evaluate research proposals . 6  

• Significance —Does the research address any important subject or issue, which may or may not be specific to the evaluator or university?  
• Content and design —Is the proposed methodology appropriate to answer the research question? Are the objectives clear and well aligned with the proposed methodology?  
• Sample size and selection —Is the target population or cohort size clearly mentioned? Is the sampling process used to select participants randomized, appropriate, and free of bias?  
• Timing —Are the proposed data collection dates mentioned clearly? Is the project feasible given the specified resources and timeline?  
• Data management and dissemination —Who will have access to the data? What is the plan for data analysis?  

Q2. What is the difference between the Introduction and Literature Review sections in a research proposal ?  

A2. The Introduction or Background section in a research proposal sets the context of the study by describing the current scenario of the subject and identifying the gaps and need for the research. A Literature Review, on the other hand, provides references to all prior relevant literature to help corroborate the gaps identified and the research need.  

Q3. How long should a research proposal be?  

A3. Research proposal lengths vary with the evaluating authority like universities or committees and also the subject. Here’s a table that lists the typical research proposal lengths for a few universities.  

Q4. What are the common mistakes to avoid in a research proposal ?  

A4. Here are a few common mistakes that you must avoid while writing a research proposal . 7  

• No clear objectives: Objectives should be clear, specific, and measurable for the easy understanding among readers.  
• Incomplete or unconvincing background research: Background research usually includes a review of the current scenario of the particular industry and also a review of the previous literature on the subject. This helps readers understand your reasons for undertaking this research because you identified gaps in the existing research.  
• Overlooking project feasibility: The project scope and estimates should be realistic considering the resources and time available.   
• Neglecting the impact and significance of the study: In a research proposal , readers and evaluators look for the implications or significance of your research and how it contributes to the existing research. This information should always be included.  
• Unstructured format of a research proposal : A well-structured document gives confidence to evaluators that you have read the guidelines carefully and are well organized in your approach, consequently affirming that you will be able to undertake the research as mentioned in your proposal.  
• Ineffective writing style: The language used should be formal and grammatically correct. If required, editors could be consulted, including AI-based tools such as Paperpal , to refine the research proposal structure and language.  

Thus, a research proposal is an essential document that can help you promote your research and secure funds and grants for conducting your research. Consequently, it should be well written in clear language and include all essential details to convince the evaluators of your ability to conduct the research as proposed.  

This article has described all the important components of a research proposal and has also provided tips to improve your writing style. We hope all these tips will help you write a well-structured research proposal to ensure receipt of grants or any other purpose.  

References  

• Sudheesh K, Duggappa DR, Nethra SS. How to write a research proposal? Indian J Anaesth. 2016;60(9):631-634. Accessed July 15, 2024. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5037942/  
• Writing research proposals. Harvard College Office of Undergraduate Research and Fellowships. Harvard University. Accessed July 14, 2024. https://uraf.harvard.edu/apply-opportunities/app-components/essays/research-proposals  
• What is a research proposal? Plus how to write one. Indeed website. Accessed July 17, 2024. https://www.indeed.com/career-advice/career-development/research-proposal  
• Research proposal template. University of Rochester Medical Center. Accessed July 16, 2024. https://www.urmc.rochester.edu/MediaLibraries/URMCMedia/pediatrics/research/documents/Research-proposal-Template.pdf  
• Tips for successful proposal writing. Johns Hopkins University. Accessed July 17, 2024. https://research.jhu.edu/wp-content/uploads/2018/09/Tips-for-Successful-Proposal-Writing.pdf  
• Formal review of research proposals. Cornell University. Accessed July 18, 2024. https://irp.dpb.cornell.edu/surveys/survey-assessment-review-group/research-proposals  
• 7 Mistakes you must avoid in your research proposal. Aveksana (via LinkedIn). Accessed July 17, 2024. https://www.linkedin.com/pulse/7-mistakes-you-must-avoid-your-research-proposal-aveksana-cmtwf/  

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Get accurate academic translations, rewriting support, grammar checks, vocabulary suggestions, and generative AI assistance that delivers human precision at machine speed. Try for free or upgrade to Paperpal Prime starting at US$19 a month to access premium features, including consistency, plagiarism, and 30+ submission readiness checks to help you succeed.  

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• Published: 09 August 2024

Responsible development of clinical speech AI: Bridging the gap between clinical research and technology

• Visar Berisha   ORCID: orcid.org/0000-0001-8804-8874 1 &
• Julie M. Liss 2  

npj Digital Medicine volume  7 , Article number:  208 ( 2024 ) Cite this article

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• Neurological disorders

This perspective article explores the challenges and potential of using speech as a biomarker in clinical settings, particularly when constrained by the small clinical datasets typically available in such contexts. We contend that by integrating insights from speech science and clinical research, we can reduce sample complexity in clinical speech AI models with the potential to decrease timelines to translation. Most existing models are based on high-dimensional feature representations trained with limited sample sizes and often do not leverage insights from speech science and clinical research. This approach can lead to overfitting, where the models perform exceptionally well on training data but fail to generalize to new, unseen data. Additionally, without incorporating theoretical knowledge, these models may lack interpretability and robustness, making them challenging to troubleshoot or improve post-deployment. We propose a framework for organizing health conditions based on their impact on speech and promote the use of speech analytics in diverse clinical contexts beyond cross-sectional classification. For high-stakes clinical use cases, we advocate for a focus on explainable and individually-validated measures and stress the importance of rigorous validation frameworks and ethical considerations for responsible deployment. Bridging the gap between AI research and clinical speech research presents new opportunities for more efficient translation of speech-based AI tools and advancement of scientific discoveries in this interdisciplinary space, particularly if limited to small or retrospective datasets.

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Introduction.

Recently, there has been a surge in interest in leveraging the acoustic properties (how it sounds) and linguistic content (what is said) of human speech as biomarkers for various health conditions. The underlying premise is that disturbances in neurological, mental, or physical health, which affect the speech production mechanism, can be discerned through alterations in speech patterns. As a result, there is a growing emphasis on developing AI models that use speech for the diagnosis, prognosis, and monitoring of conditions such as mental health 1 , 2 , 3 , 4 , 5 , cognitive disorders 6 , 7 , 8 , 9 , 10 , and motor diseases 11 , 12 , 13 , 14 , 15 , among others.

The development of clinical speech AI has predominantly followed a supervised learning paradigm, building on the success of data-driven approaches for consumer speech applications 16 , 17 . For instance, analysis of published speech-based models for dementia reveals that most models rely on high-dimensional speech and language representations 18 , either explicitly extracted or obtained from acoustic foundation models 19 , 20 and language foundation models 21 , 22 , to predict diagnostic labels 9 , 23 , 24 , 25 ; a similar trend is observed for depression 5 , 26 . The foundational models, initially pre-trained on data from general populations, are subsequently fine-tuned using clinical data to improve predictive accuracy for specific conditions. While data-driven classification models based on deep learning have worked well for data-rich applications like automatic speech recognition (ASR), the challenges in high-stakes clinical speech technology are distinctly different due to a lack of data availability at scale. For example, in the ASR literature, speech corpora can amount to hundreds of thousands of hours of speech samples and corresponding transcripts upon which models can be robustly trained in supervised fashion 16 , 17 . In contrast, currently available clinical datasets are much smaller, with the largest samples in the meta-analysis 9 , 24 , 25 consisting of only tens to hundreds of minutes of speech or a few thousand words. This is because clinical data collection is inherently more challenging than in other speech-based applications. Clinical populations are more diverse and present with variable symptoms that must be simultaneously collected with the speech samples, ensuring proper sampling from relevant strata.

Compounding the data problem is the fact that the ground truth accuracy of diagnostic labels for different conditions where speech is impacted varies from 100% certainty to less than 50% certainty, particularly in the early stages of disease when mild symptoms are nonspecific and present similarly across many different diseases 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 . Retrospective data often used to train published models does not always report diagnostic label accuracy or the criteria used to arrive at a diagnosis. Collecting representative, longitudinal speech corpora with paired consensus diagnoses is time-intensive and further impedes the development of large-scale corpora, which are required for developing diagnostic models based on supervised learning. Unfortunately, supervised models built on smaller-scale corpora often exhibit over-optimistic performance in controlled environments 35 and fail to generalize in out-of-sample deployments 36 , 37 . This begs the question of how we can successfully harness the power of AI to advance clinical practice and population health in the context of data availability constraints.

Here we propose that the clinical data constraints provide an opportunity for co-design of new analytics pipelines with lower sample complexity in collaboration with the clinical speech science community. The clinical speech science community has long studied the correlational and causal links between various health conditions and speech characteristics 38 , 39 , 40 , 41 , 42 . This research has focused on the physiological, neurological, and psychological aspects of speech production and perception, primarily through acoustic analysis of the speech signal, and linguistic analysis of spoken language. They involve interpretable and conceptually meaningful attributes of speech, often measured perceptually 43 , via functional rating scales 15 , or self-reported questionnaires 44 . Contributions from speech scientists, neuroscientists, and clinical researchers have deepened our understanding of human speech production mechanisms and their neural underpinnings, and particularly how neurodegeneration manifests as characteristic patterns of speech decline across clinical conditions 43 , 45 .

A co-design of a new explainable analytics pipeline can intentionally integrate scientific insights from speech science and clinical research into existing supervised models. We hypothesize that this will reduce timelines to translation, therefore providing an opportunity to grow clinical data scale through in-clinic use. As data size grows, data-driven methods with greater analytic flexibility can be used to discover new relations between speech and different clinical conditions and to develop more nuanced analytical models that can be confidently deployed for high-stakes clinical applications.

Bridging the gap between speech AI and clinical speech research leads to new opportunities in both fields. There is a clear benefit to the development of more sensitive tools for the assessment of speech for the clinical speech community. Existing instruments for assessment of speech exhibit variable within-rater and between-rater variability 46 . Developing objective proxies for these clinically-relevant constructs has the potential for increased sensitivity and reduced variability. More sensitive objective measures can also catalyze scientific discovery, enabling the identification of yet-to-be-discovered speech patterns across different clinical conditions. Conversely, effectively connecting speech AI research with clinical research enables AI developers to prioritize challenges directly aligned with clinical needs and streamline model building by leveraging domain-specific knowledge to mitigate the need for large datasets. To date, model developers have often overlooked feasibility constraints imposed by the inherent complexity of the relationship between speech production and the condition of interest. For example, recent efforts in clinical speech AI have focused on the cross-sectional classification of depression from short speech samples 5 , 26 . Given the well-documented variability in speech production 47 , the limitations of existing instruments for detecting depression 40 , and the heterogeneity in the manifestation of depression symptoms 48 , it is unlikely that stand-alone speech-based models will yield high-accuracy diagnostic models. Other studies have proposed using speech to predict conditions like coronary artery disease 49 or diabetes 50 . However, to the best of our knowledge, there is no substantial literature supporting the hypothesis that speech changes are specific enough to these conditions to serve as stand-alone indicators. In working with small data sets, understanding the approximate limits of prediction is critical for resource allocation and avoiding unwarranted conclusions that could lead to premature model deployment.

This perspective article advocates for a stronger link between the speech AI community and clinical speech community for the development of scientifically-grounded explainable models in clinical speech analytics. We begin by presenting a new framework for organizing clinical conditions based on their impact on the speech production mechanism (see Fig. 1 ). We believe such a framework is important to facilitate a shared understanding of the impact of clinical conditions on speech and stimulate interdisciplinary thought and discussion. It is useful in categorizing health conditions by the complexity and uncertainty they present for speech-based clinical AI models and provides a mental model for considering the inherent limitations of speech-based classification across different conditions. It orients researchers to consider the challenges posed by limited clinical datasets during model development, and helps prevent frequent methodological errors. This has the potential to expedite progress and further foster collaboration between the speech AI community and the clinical speech community. We then explore various contexts of use for speech analytics beyond cross-sectional classification, highlighting their clinical value and the value they provide to the clinical speech research community (see Fig. 2 ). The discussion further examines how the selected context of use influences model development and validation, advocating for the use of lower-dimensional, individually-validated and explainable measures with potential to reduce sample size requirements (see Fig. 3 ). The paper concludes with a discussion on ethical, privacy, and security considerations, emphasizing the importance of rigorous validation frameworks and responsible deployment (see Fig. 4 ).

The clinically-relevant information in speech

The production of spoken language is a complex, multi-stage process that involves precise integration of language, memory, cognition, and sensorimotor functions. Here we use the term ‘speech production’ to refer broadly to the culmination of these spoken language processes. There are several extant speech production models, each developed to accomplish different goals (see, for example 51 , 52 , 53 , 54 , 55 ). Common to these models is that speech begins with a person conceptualizing an idea to be communicated, formulating the language that will convey that idea, specifying the sensorimotor patterns that will actualize the language, and then speaking 56 :

Conceptualization: the speaker forms an abstract idea that they want to verbalize (Abstract idea formulation) and the intention to share through speech (Intent to speak).

Formulation: the speaker selects the words that best convey their idea and sequences them in an order allowed by the language (Linguistic formulation). Then they plan the sequence of phonemes and the prosodic pattern of the speech to be produced (Morphological encoding). Next, they program a sequence of neuromuscular commands to move speech structures (Phonetic encoding).

Articulation: the speaker produces words via synergistic movement of the speech production system. Respiratory muscles produce a column of air that drives the vocal folds (Phonation) to produce sound. This sound is shaped by the Articulator movements to produce speech. Two feedback loops (Acoustic feedback and Proprioceptive feedback) refine the neuromuscular commands produced during the Phonetic encoding stage over time.

Figure 1 introduces a hierarchy, or ordering, of health conditions based on how direct their impact is on the speech production mechanism. This hierarchy, motivated by initial work on speech and stress 57 , roughly aligns with the three stages of speech production and has direct consequences for building robust clinical speech models based on supervised learning.

The production of spoken language is a complex, multi-stage process that involves precise integration of language, memory, cognition, and sensorimotor functions. The three stages are Conceptualization, Formulation, and Articulation. This figure introduces a hierarchy, or ordering, of health conditions based on how direct their impact is on the speech production mechanism.

This hierarchy compels researchers to ask and answer three critical questions prior to engaging in AI model development for a particular health condition. First, how directly and specifically does the health condition impact speech and/or language? In general, the further upstream the impact of a health condition on speech, the more indeterminate and nuanced the manifestations become, making it challenging to build supervised classification models on diagnostic labels. As we move from lower to higher-order health conditions, there are more mediating variables between the health condition and the observed speech changes, making the relationship between the two more variable and complex.

The second question the model compels researchers to ask and answer is what are the sensitivity and specificity of ground truth labels for the health condition? In general (but with notable exceptions), the objective accuracy of ground truth labels for the presence or absence of a health condition generally becomes less certain from lower to higher-order conditions, adding noise and uncertainty to any supervised classification models built upon the labels. High specificity of ground truth labels is critical for the development of models that distinguish between health conditions with overlapping speech and language symptoms. The answers to these two questions provide a critical context for predicting the utility of an eventual model prior to model building.

Finally, the hierarchy asks model developers to consider the relevant clinical speech symptoms to be considered in the model. In Table 1 , we provide a more complete definition of each level in the hierarchy, a list of example conditions associated with the hierarchy, and primary speech symptoms associated with the condition. The list is not exhaustive and does not consider second and third-order impacts on speech. For example, Huntington’s disease (HD) has a first-order impact on speech causing hyperkinetic dysarthria (e.g. see Table 1 ). But it also has a second- and third-order impact to the extent one experiences cognitive issues and personality changes with the disease. Nevertheless, the table serves as a starting point for developing theoretically-grounded models. Directly modeling the subset of primary speech symptoms known to be impacted by the condition of interest may help reduce sample size requirements and result in smaller models that are more likely to generalize.

Ordering of health conditions based on speech impact

Zeroth-order conditions have direct, tangible effects on the speech production mechanism (including the structures of respiration, phonation, articulation, and resonance) that manifest in the acoustic signal, impacting the Articulation stage in our model in Fig. 1 . This impact of the physical condition on the acoustic signal can be understood using physical models of the vocal tract and vocal folds 58 that allow for precise characterization of the relationship between the health condition and the acoustics. As an example, benign vocal fold masses increase the mass of the epithelial cover of the vocal folds, thereby altering the stiffness ratio between the epithelial cover and the muscular body. The impact on vocal fold vibration and the resulting acoustic signal are amenable to modeling. These types of conditions are physically verifiable upon laryngoscopy, providing consistent ground truth labeling of the condition; and the direct relationship between the condition, its impact on the physical apparatus, and the voice acoustics is direct and quantifiable (although, note that differential diagnosis of vocal fold mass subtype is more difficult, see refs. 59 , 60 ). Thus, zeroth-order health conditions directly impact the speech apparatus anatomy and often have verifiable ground-truth labels.

First-order conditions interfere with the transduction of neuromuscular commands into movement of the articulators (e.g. dysarthria secondary to motor disorder). As with zeroth-order conditions, first-order conditions also disturb the physical speech apparatus and the Articulation stage in our model, however the cause is indirect. Injury or damage to the cortical and subcortical neural circuits and nerves impacts sensorimotor control of the speech structures by causing weakness, improper muscle tone and/or mis-scaling and incoordination of speech movements 61 . The sensorimotor control of speech movements is mediated through five neural pathways and circuits, each associated with a set of cardinal and overlapping speech symptoms: Upper and lower motor neuron pathways; the direct and indirect basal ganglia circuits; and the cerebellar circuit . Damage to these areas causes distinct changes in speech:

The lower motor neurons (cranial and spinal nerves, originating in brainstem and spinal cord, respectively) directly innervate speech musculature. Damage to lower motor neurons results in flaccid paralysis and reduced or absent reflexes in the muscles innervated by the damaged nerves, and a flaccid dysarthria when cranial nerves are involved.

The upper motor neurons originate in the motor cortex and are responsible for initiating and inhibiting activation of the lower motor neurons. Damage to upper motor neurons supplying speech musculature results in spastic paralysis and hyperreflexia, and a spastic dysarthria.

The basal ganglia circuit is responsible for facilitating and scaling motor programs and for inhibiting involuntary movements. Damage to the direct basal ganglia circuit causes too little movement (hypokinesia, as in Parkinson’s disease), resulting in a hypokinetic dysarthria; while damage to the indirect basal ganglia circuit causes too much movement (hyperkinesia, as in Huntington’s disease), resulting in a hyperkinetic dysarthria.

The cerebellar circuit is responsible for fine-tuning movements during execution. Damage to the cerebellar circuits result in incoordination, resulting in an ataxic dysarthria.

Speech symptoms are characteristic when damage occurs to any of these (or multiple) neural pathways, although there is symptom overlap and symptoms evolve in presence and severity as the disease progresses 61 . The diagnostic accuracy and test-retest reliability (within and between raters) of dysarthria speech labels from the speech signal alone (i.e., without knowledge of the underlying health condition) is known to be modest, except for expert speech-language pathologists with large and varied neurology caseloads 62 . Diagnosis of the corresponding health conditions relies on a physician’s clinical assessment and consideration of other confirmatory information beyond speech. Diagnostic accuracy is impacted by the physician’s experience and expertise, whether the symptoms presenting in the condition are textbook or unusual, and whether genetic, imaging, or other laboratory tests provide supporting or confirmatory evidence is available. For example, unilateral vocal fold paralysis is a first-order health condition with direct impact on the speech apparatus (impaired vocal fold vibration) and high-ground truth accuracy and specificity (can be visualized by laryngoscopy). In contrast, Parkinson’s disease (PD) has a diffuse impact on the speech apparatus (affecting phonation, articulation, and prosody) which is hard to distinguish from healthy speech or other similar health conditions (e.g., progressive supranuclear palsy) in early disease. The reported ground-truth accuracy of the initial clinical diagnosis ranges from 58% to 80%, calling into question clinical labels in early stage PD 28 .

Second-order conditions move away from the speech production mechanism’s structure and function and into the cognitive (i.e., memory and language) and perceptual processing domains. These conditions impact the Formulation stage of speaking and manifest as problems finding and sequencing the words to convey one’s intended message and may include deficits in speech comprehension. Alzheimer’s disease (AD) is a second-order condition that deserves particular attention because of the burgeoning efforts in the literature to develop robust supervised classification models 63 . AD disrupts the Formulation stage of speaking with word-finding problems, and the tendency to use simpler and more general semantic and syntactic structures. Natural language processing (NLP) techniques have been used to characterize these patterns and acoustic analysis has identified speech slowing with greater pausing while speaking, presumably because of decreased efficiency of cognitive processing and early sensorimotor changes 9 , 24 , 25 .

While the clinical study of speech and language in AD has consistently found evidence of such pattern changes in individuals diagnosed with probable AD, progress toward developing generalizable speech-based supervised learning clinical models for mild cognitive impairment (MCI) and AD has been relatively slow despite optimistic performance results reported in the literature 35 , 63 . We posit that this can be explained by answers to the first two questions that model in Fig. 1 compels researchers to consider. First, there is a lack of specificity of early speech and language symptoms to MCI and AD, given that the output is mediated by several intermediate stages and the variability associated with speech production. Mild and nonspecific speech and language symptoms will always pose a challenge for the development of clinical early detection/diagnostic speech tools until sufficient training data can result in the identification of distinct signatures (if they exist). Furthermore, given the current difficulty in accurately diagnosing MCI and AD, models based on supervised learning may be unwittingly using mislabeled training data and testing samples in their models. At present, AD is a clinical diagnosis, often preceded by a period of another clinical diagnosis of MCI. MCI is extremely difficult to diagnose with certainty, owing to variability in symptoms and their presentation over time, the overlap of speech and language symptoms with other etiologies, and the diagnostic reliance on self-report 33 . With the current absence of a definitive ground truth label for MCI or early Alzheimer’s disease, and the lack of specificity in speech changes, supervised learning models trained on small, questionably labeled data likely will continue to struggle to generalize to new data.

Third-order conditions impact the Conceptualization stage of speech production and include mental health conditions affecting mood and thought. These conditions can manifest in significant deficits and differences in speech and language, and this has been well-characterized in the literature 4 . For example, acoustic analysis can reveal rapid, pressed speech associated with mania, as well as slowed speech without prosodic variation that might accompany depression. Natural language processing can reveal and quantify disjointed and incoherent thought in the context of psychiatric disorders 64 . Despite this, the impact of these mood and thought conditions on the speech apparatus and language centers in the brain may be indirect and nonspecific relative to low-order conditions. Mental health conditions frequently cause a mixture or fluctuation of positive symptoms (e.g., hallucinations, mania) and negative symptoms (e.g., despondence, depression), which can present chronically, acutely, or intermittently. The associated speech and language patterns can be attributed to any number of other reasons (fatigue, anxiety, etc.) With regard to ground-truth accuracy and specificity, studies have shown that around half of schizophrenia diagnoses are inaccurate 65 . This problem has resulted in a push to identify objective biomarkers to distinguish schizophrenia from anxiety and other mood disorders 66 , 67 . This complicates the development of models for health condition detection and diagnosis; however, machine-learning models may be developed to objectively measure speech and language symptoms associated with specific symptomatology. For example, distinguishing between negative versus positive disease symptoms may be achievable with careful construction of speech elicitation tasks and normative reference data, given the central role that language plays in the definition of these symptoms 68 , 69 .

Across all health conditions, extraneous and comorbid factors can exert meaningful influence on speech production. For example, anxiety, depression, and fatigue, perhaps even as a consequence of an underlying illness, are known to impact the speech signal. It would not be straightforward to distinguish their influence from those of primary interest, adding complexity and uncertainty for models based on supervised learning, regardless of the health condition’s order. However, the increased variability in both data and diagnostic accuracy for many higher-order conditions makes speech-based models trained using supervised learning on small datasets vulnerable to reduced sensitivity and specificity. This is not merely a matter of augmenting the dimensionality of speech features or enlarging the dataset; it reflects the intrinsic variability in how humans generate speech. Finally, the accuracy and specificity of ground truth labels for health conditions are critical to consider in assessing the feasibility of interpretable model development. Unlike the static link between speech and the health condition, as diagnostic technologies advance and criteria evolve, the accuracy of these labels is expected to improve over time, thereby potentially enabling more robust model development.

Defining an appropriate context of use

As mentioned before, most published clinical speech AI development studies are based on supervised learning where developers build AI models to distinguish between two classes or to predict disease severity. This approach generally presumes the same context of use for clinical speech analytics across different applications: namely, the cross-sectional detection of a specific condition or a prediction of clinical severity based on a speech sample. As we established in the foregoing discussion, this approach, when combined with limited training data, is less likely to generalize.

Nevertheless, there are a number of use cases, in which speech analytics and AI can provide more immediate value and expedite model translation. These are outlined in Fig. 2 , where we explore these applications in greater depth. Focusing on these use cases will reduce timelines to translation, providing an opportunity to grow clinical data scale through in-clinic collection. With increased data size and diversity, researchers will better characterize currently-unknown fundamental limits of prediction for speech-based classification models for higher-order conditions (e.g. how well can we classify between depressed and non-depressed speech); and can bring to bear more advanced data-driven methods to problems that provide clinical value.

A listing of different contexts of use for the development and validation of clinical tools based on speech AI.

Diagnostic assistance

Despite rapid advancements in biomedical diagnostics, the majority of neurodegenerative diseases are diagnosed by the presence of cardinal symptoms on clinical exams. As discussed previously and as shown in Table 1 , many health conditions include changes in speech as a core symptom. For example, diagnosis of psychiatric conditions involves analysis of speech and language attributes, such as coherence, fluency, and tangentiality 70 . Likewise, many neurodegenerative diseases lead to dysarthria, and a confirmatory speech deficit pattern can be used to support their diagnoses 61 . Tools for the assessment of these speech deficit patterns in the clinical setting typically depend on the clinical judgment or on scales reported by patients themselves. There is a large body of evidence indicating that these methods exhibit variable reliability, both between different raters and within the same rater over time 46 , 62 . Clinical speech analytics has the potential to enhance diagnostic accuracy by providing objective measures of clinical speech characteristics that contribute to diagnosis, such as hypernasality, impaired vocal quality, and articulation issues in dysarthria; or measures of coherence and tangentiality in psychosis. These objective measures can provide utility for manual diagnosis in clinic or can be used as input into multi-modal diagnostic systems based on machine learning.

Non-specific risk assessment tools

While differential diagnosis based on speech alone is likely not possible for many conditions, progressive and unremitting changes in certain aspects of speech within an individual can be a sign of an underlying illness or disorder 61 . Clinical speech analytics can be used to develop tools that track changes in speech along specific dimensions known to be vulnerable to degradation in different conditions. This could provide value as an early-warning indicator, particularly as the US health system moves toward home-based care and remote patient monitoring. Such a tool could be used as a non-specific risk assessment tool triggering additional tests when key speech changes reach some threshold or is supported by changes in other monitored modalities.

Longitudinal tracking post-diagnosis

In many conditions, important symptoms can be tracked via speech post-diagnosis. For example, tracking bulbar symptom severity in ALS, as a proxy for general disease progression, can provide insights on when AAC devices should be considered or to inform end-of-life planning 71 . In Parkinson’s disease, longitudinal tracking of speech symptoms would be beneficial for drug titration 72 , 73 . In dementia, longitudinal tracking of symptoms measurable via speech (e.g. memory, cognitive-linguistic function) can provide valuable information regarding appropriate care and when changes need to be made.

Speech as a clinically meaningful endpoint

Speech is our principal means of communication and social interaction. Conditions that impair speech can severely hinder a patient’s communicative abilities, thereby diminishing their overall quality of life. Current methods for assessing communication outcomes include perceptual evaluations, such as listening and rating, or self-reported questionnaires 61 , 69 . In contrast to the use case as a solitary diagnostic tool, employing clinical speech analytics to objectively assess communicative abilities is inherently viable across many conditions. This is due to the direct correlation between the construct (communicative ability) and the input (speech). For instance, in dysarthria, clinical speech analytics may be utilized to estimate intelligibility, the percentage of words understood by listeners, which significantly affects communicative participation 74 . In psychosis, speech analytics can facilitate the creation of objective tools for assessing social competencies; these competencies are closely tied to quality of life indicators 69 . Similarly, in dementia, a decline in social interaction can lead to isolation and depression, perhaps hastening cognitive decline 75 . A related emerging use case in Alzheimer’s disease is providing context for blood-based diagnostics. As new biomarkers with confirmatory evidence of pathophysiology emerge, there will likely be an increase in Alzheimer’s diagnoses without co-occurring clinical-behavioral features. The group of patients with AD diagnoses, but without symptoms, will require context around this diagnosis. Speech analytics will be important as measures of behavioral change that are related to quality of life.

Improving clinical trial design

The Food and Drug Administration (FDA) prioritizes patient-relevant measures as endpoints in clinical trials. They have also identified speech and communication metrics as particularly underdeveloped for orphan diseases 76 . Objective and clinically-meaningful measures based on speech analytics that are collected more frequently can result in an improved sensitivity for detecting intervention effects. Such measures have the potential to decrease the required sample sizes for drug trials, enable more efficient enrollment, or to ascertain efficacy with greater efficiency 77 .

Facilitating development of digital therapeutics

There has been significant recent interest in development of digital therapeutics for various neurological and mental health conditions. Several of these devices target improving the patients’ social skills or communication abilities 78 . In this evolving space, introducing concrete digital markers of social competence allows for more efficient evaluation of efficacy and precision approaches for customizing therapeutics for the patient.

Development and validation of robust models

The context of use profoundly influences the development of clinical speech AI models, shaping their design, validation, and implementation strategies. For example, for contexts of use involving home monitoring, robustness to background noise, variability in recording conditions and usability are essential. For longitudinal monitoring, developed tools must be sensitive to subtle changes in speech characteristics relevant to the progression of the condition being monitored. This necessitates longitudinal data collection for development and validation to ensure stability and sensitivity over time. Screening tools in diverse populations require a training dataset that captures demographic variability to avoid bias. Solutions based on noisy diagnostic labels may require uncertainty modeling through Bayesian machine learning or ensemble methods that quantify prediction confidence 79 . Concurrently, techniques like label smoothing 80 and robust loss functions 81 can enhance model resilience under label noise.

Each context of use presents a custom development path to address the unique challenges and a parallel validation strategy that spans hardware, analytical validation, and clinical validation - see Fig. 3 . The current approach focused on data-driven supervised learning on diagnostic labels limits the development and understanding of new models and makes model validation challenging. While there are many validation metrics for evaluating AI model performance, the prevalent metrics in published speech-based models primarily focus on estimating “model accuracy” (e.g. what percent of the time does the model correctly classify between Healthy and Dementia labels based on speech) using a number of methods (e.g. cross-validation, held-out test accuracy). However, accurately estimating the model accuracy of high-dimensional supervised learning models is challenging, and current methods are prone to overoptimism 35 . In addition, many supervised machine learning models are sensitive to input perturbations, which is a significant concern for speech features known for their day-to-day variability 82 . Consequently, model performance diminishes with any temporal variation in the data.

The development of clinical speech AI models begins with a context of use. The context of use informs downstream development and validation of resulting models. The Verification, Analytical Validation, and Clinical Validation (V3) framework has been proposed as a conceptual framework for the initial validation of biometric monitoring technologies.

A starting point for clinical model validation is the Verification/Analytical Validation/Clinical Validation (V3) framework, a framework for validating digital biometric monitoring technologies. The original version of the framework proposes a structured approach with three evaluation levels: Verification of hardware, Analytical Validation, and Clinical Validation 83 . This framework has roots in principles of Verification and Validation for software quality product management and deployment 84 . While these existing validation systems are designed to confirm that the end system accurately measures what it purports to measure, the V3 framework adds the additional step of confirming that the clinical tools are meaningful to a defined clinical population. To that end, Verification ascertains the sensor data’s fidelity within its intended environment. Analytical validation examines the accuracy of algorithms processing sensor data to yield behavioral or physiological metrics, and clinical validation evaluates clinical model outputs with clinic ground truths or established measures known to be meaningful to patients. This includes existing clinical scales like the PHQ-9 (depression) or the UPDRS (Parkinson’s disease). In Fig. 3 we provide a high-level overview of the end-to-end development and validation process for clinical speech AI. It is important to note that the V3 is a conceptual framework that must be specifically instantiated for the validation of different clinical speech applications. While it can help guide the development of a validation plan, it does not provide one out of the box. Furthermore, this level of validation is only a starting point as the FDA suggests constant model monitoring post-deployment to ensure continued generalization 85 .

Supervised learning approaches based on uninterpretable input features and clinical diagnostic labels make adoption of the complete V3 framework challenging. Analytical validation is especially challenging as it’s difficult to ensure that learned speech representations are measuring or detecting physiological behaviors of interest. For example, in Parkinson’s disease, both the speaking rate and the rate of opening and closing of vocal folds is impacted. Uninterpretable features have unknown relationships with these behavioral and physiological parameters. As an alternative, model developers can use representations that are analytically validated relative to these constructs. This would lead to more interpretable clinical models. Validation should be approached end-to-end during the development process, with different stages (and purposes of analysis) employing different validation methods. Small-scale pilot tests may focus on parts of this framework. However, for work with deployment as a goal, ensuring generalizability and clinical utility requires validating the hardware on which the speech was collected, ensuring that intermediate representations are valid indicators of behavioral and physiological measures (e.g speaking rate, articulatory precision, language coherence), and clinical models developed using these speech measures are associated with existing clinical ground truths or scales that are meaningful to patients 86 .

Interpretable, clinically-important measures based on speech are currently missing from the literature. Clinically-relevant feature discovery and model performance evaluation in speech analytics are challenged by the high-dimensionality of speech, complex patterns, and limited datasets. Table 1 highlights several speech constructs that have been studied relative to various conditions; however, most of these constructs do not have standardized operational definitions in the clinical speech analytics literature. Instead, model developers rely on high-dimensional representations that have been developed for other purposes. For example, adopted from the ASR literature, many clinical models use representations based on mel-frequency cepstral coefficients or mel-spectra 18 ; or representations learned by pre-trained foundation models 19 , 20 . However, these features are not interpretable, making analytical and clinical validation challenging.

Development of a clinically-tailored speech representation could significantly refine the development process, favoring smaller, individually validated, and clinically-grounded features that allow scientists to make contact with the existing literature and mitigate model overfitting and variability. This field would benefit from a concerted and synergistic effort in the speech AI community and the speech science community to operationalize and validate a measurement model for the intermediate constructs like those listed in Table 1 87 . For example, in our previous work, we made progress in this direction by developing measurement models for the assessment of hypernasality and consontant-vowel transitions and used it to evaluate cleft lip and palate and dysarthria 88 , 89 ; several measures of volition and coherence for schizophrenia 69 ; and measures of semantic relevance for dementia 10 . Individually-validated interpretable measures allow for easier alignment to different contexts of use, integration within larger multi-modal systems, and establish a more direct link to the existing clinical literature. Furthermore, they can be used as a way of explaining the operation of larger, more complex models via bottleneck constraints 90 or they can be combined with new methods in causal machine learning for development of explainable models 91 .

Finally, clinically-interpretable representations can also play a pivotal role in integrating the patient’s perspective into the design of algorithms. The idea is that by aligning closely with the lived experiences and symptoms important to patients, these representations ensure that algorithmic outcomes resonate with the quality of life impact of health conditions. The hypothesis is that this patient-centric approach could have the added benefit of reinforcing patient trust and engagement in digital health.

Ethical, privacy, and security considerations

The deployment and regulation of clinical speech models in healthcare present multiple challenges and risks. Prematurely launched models (without robust validation) risk delivering clinically inaccurate results and potentially causing patient harm, while biases in model training can lead to skewed performance across diverse populations. Moreover, the use of speech data for health analytics raises significant privacy and security concerns. We outline these considerations in Fig. 4 and expand on them below.

An overview of key risks and corresponding mitigation strategies for the development of clinical speech AI models.

Premature deployment of inaccurate models

A primary risk of prematurely-deployed models is that they will provide clinically inaccurate output. As discussed in previous work 35 , current strategies to validate AI models are insufficient and produce overoptimistic estimates of accuracy. Several studies have highlighted this as a more general problem in AI-based science 92 , 93 . However, reported accuracy metrics carry much weight when presented to the public and can lead to premature deployment. There is considerable risk that these models will fail if deployed and potentially harm patients 94 . For example, consider the Cigna StressWaves Test model, deployed after only internal evaluation and no public efficacy data. This model analyzes a user’s voices to predict their stress level and is publicly available on the Cigna Website. Independent testing of the model reveals that it has poor test-retest reliability (measured via intraclass correlation) and poor agreement with existing instruments for measuring stress 37 .

Biased models

An additional risk of clinical speech-based models stems from the homogeneity of the data often used to train these models. Biological and socio-cultural differences contribute significantly to the variation in both the speech signal and the clinical conditions (impacting aspects from risk factors to treatment efficacy). Careful consideration of these differences in model building necessitates robust experiment design and representative stratification of data. However, a recent study demonstrates that published clinical AI models are heavily biased demographically, with 71% of the training data coming from only three states: California, Massachusetts, and New York, with 34 of the states not represented at all 95 . Similarly, analysis of clinical speech datasets indicates a significant skew towards the English language, overlooking the linguistic diversity of global populations. To accurately capture health-related speech variations, it’s essential to broaden data collection efforts to include a more representative range of the world’s native languages as health-related changes in speech can be native language-specific 96 . It becomes challenging to determine how models trained on unrepresentative data would perform when deployed for demographic groups for which they were not trained.

Privacy and security considerations

Speech and language data is widely available and, as we continue to interact with our mobile devices, we generate an ever-growing personal footprint of our health status. Previous studies have shown that this data (speeches, social media posts, interviews) can be analyzed for health analytics 97 , 98 , 99 . There is a risk that similar data on an even larger scale and over longer periods of time can be accessed by technology companies to make claims about the health or emotional state of their users without their permission or by national or international adversaries to advance a potentially false narrative on the health of key figures. The risks to the privacy of this type of analysis, if used outside of academic research, is considerable, with national and international political ramifications. Internally, political adversaries can advance a potentially false narrative on the health of candidates. Internationally, geopolitical adversaries could explore this as an additional dimension of influence in elections.

There is no silver bullet to reduce these risks, however, there are several steps that can be taken as mitigation strategies. With the public availability of speech technology, building AI models has become commoditized; however, the bottleneck remains prospective validation. Thorough validation of the model based on well-accepted frames such as the V3 framework is crucial prior to deployment 83 . This validation must extend beyond initial data sets and include diverse demographic groups to mitigate biases. Moreover, developers should engage in continuous post-deployment monitoring to identify and rectify any deviations in model performance or emergent biases. Transparency in methodology and results, coupled with responsible communication to the public, can reduce the risks of misperceived model accuracy.

On the privacy front, there are emerging technical solutions to parts of this problem based on differential privacy and federated learning 100 , 101 , 102 ; however, a complete socio-technical solution will require stringent data protection regulations and ethical guidelines to safeguard personal health information. First, it is wise to reconsider IRB review protocols in light of new technologies and publicly available data; in industry, proactive collaboration with regulatory bodies (e.g. FDA) can help establish clear guidelines. This is clear for companies focused on clinical solutions, however, the regulation of AI-based devices for technology companies, particularly those focused on wellness, is less well-defined. Recent guidance from the Federal Trade Commission (FTC) advising companies to only make evidence-backed claims about AI-driven products is a step in the right direction 103 .

Data availability

There is no data associated with this manuscript as it is a perspectives article centered around a theoretical framework.

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Acknowledgements

This work is funded in part by the John and Tami Marick Family Foundation, NIH NIA grant 1R01AG082052-01, NIH NIDCD grants R01DC006859-11 and R21DC019475, and NIH NIDCR grant R21DE026252-01A.

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Berisha, V., Liss, J.M. Responsible development of clinical speech AI: Bridging the gap between clinical research and technology. npj Digit. Med. 7 , 208 (2024). https://doi.org/10.1038/s41746-024-01199-1

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Closing the gap in access to child mental health care: provider feedback from the Wisconsin Child Psychiatry Consultation Program

• Lora Daskalska   ORCID: orcid.org/0000-0003-3385-0565 1 ,
• Michelle Broaddus   ORCID: orcid.org/0000-0002-1712-4734 2 &
• Staci Young   ORCID: orcid.org/0000-0003-3562-2400 3  

BMC Primary Care volume  25 , Article number:  300 ( 2024 ) Cite this article

Metrics details

Mental illnesses are common among children and negatively impact wellbeing during childhood as well as later in life. However, many children with these conditions are not able to access needed mental health care. The Wisconsin Child Psychiatry Consultation Program (WI CPCP) was created to reduce gaps in access to care by providing primary care providers with referral resources, access to behavioral health consultations, and training on mental health topics.

The purpose of this study was 1) to assess the effectiveness of the WI CPCP in Milwaukee County, providing specific insights into provider’s ability to care for child mental health, and 2) identify challenges Milwaukee PCPs faced in providing mental health care to child patients and contextualize these challenges in a conceptual framework of access to health care.

A cross-sectional mixed-methods secondary data analysis was conducted using data collected from online baseline and nine-month follow-up surveys completed by providers participating in the program practicing in Milwaukee County from 2014 to 2022. Provider confidence and skill in providing mental health care was analyzed quantitatively using Two-sample Wilcoxon rank-sum (Mann–Whitney) tests (baseline vs. follow-up survey responses) and descriptive statistics (follow-up survey only). Provider challenges to providing mental health care were analyzed qualitatively using a thematic analysis research approach.

Results from quantitative analyses showed that provider confidence and skill in treating childhood anxiety and depression improved from baseline to follow-up. Results from qualitative analyses were categorized by factors within and beyond the scope of WI CPCP. Within the scope of WI CPCP, providers reported a lack of knowledge of referral options and a lack of training in mental health care as well as a lack of knowledge in assessing and treating mental disorders. Still, many barriers to mental healthcare access persist that are beyond the scope of WI CPCP, such as long wait times and a lack of insurance coverage.

Conclusions

This study supports the effectiveness of the program to improve access to care for children. However, there is a need for additional solutions such as better reimbursement for mental health professionals and expanded insurance coverage.

Peer Review reports

Unfortunately, mental illnesses are common among children [ 1 ]. For example, about one in three high school students reported experiencing poor mental health often during the COVID-19 pandemic [ 2 ]. Furthermore, childhood mental illnesses not only impact wellbeing during childhood, but are also associated with worse health, social, and economic outcomes in adulthood [ 3 ]. However, approximately one in five children with any need for mental health care do not receive it [ 4 ]. With the shortage in child mental health providers, multisectoral solutions are needed to creatively fill these gaps including fellowship stipends, expansion of telehealth, expansion of prescriptive authority, integrated behavioral health, and training primary care providers in mental health care [ 5 ].

Wisconsin’s Child Psychiatry Consultation Program (WI CPCP) was developed to address the mental health workforce shortage [ 6 ] by assisting primary care providers (PCPs) in addressing their patients’ mild to moderate mental health concerns [ 7 ]. Health care providers in the primary care setting often take on the burden of mental health care where mental health professionals are scarce [ 7 ]. Physicians, Nurse Practitioners, Physician Assistants, and Residents in primary care settings are eligible to receive WI CPCP services. Through this program, PCPs can receive consultation from child psychiatrists, child psychologists, and resource and clinical coordinators for their patients’ mental health needs [ 7 ]. PCPs also receive skills training in pediatric mental health through curriculum developed by child and adolescent psychiatrists and psychologists [ 7 ]. Finally, WI CPCP provides information about mental health resources to refer patients and their families [ 7 ]. CPCP resource consultants can share referral information with PCPs over the phone, though most often this is done through a personalized email. The program accesses multiple databases to identify resources, including an internal database with information regarding hours of operation, insurance taken, ages seen, languages spoken, etc. Through these efforts, WI CPCP increases availability, accessibility, and awareness of mental health care for providers and patients [ 8 ].

WI CPCP was created in 2014 with donations by the Kubly family in collaboration with the Medical College of Wisconsin [ 6 ]. This program was funded by the State of Wisconsin Act 127, passed in 2014, and funding was expanded in the 2017–2019 Biennial Budget, resulting in a total of 26 counties serviced in Wisconsin [ 9 ]. Since then, WI CPCP coverage has fully expanded into all counties of Wisconsin [ 10 ]. By June 2022, there were 1,794 providers participating in the program and 7,925 consults provided [ 11 ]. Between July 2021 and June 2022, the most common conditions for which consults were requested include depression (41% of consults), ADHD (37%), anxiety (37%), and disruptive behavior (20%) [ 11 ]. The most common medication-related questions for which consults were requested were related to initiation of a new medication (42% of consults), dosage of a new medication (32%), side effect profile (31%), and switching medications (22%) [ 11 ]. In 2017, almost half of PCPs felt confident in meeting the mental health needs of their patients and 79% felt that they could receive a consultation in a reasonable amount of time, 9–12 months after agreeing to participate in WI CPCP vs. 21% and 17% at the time of initiation of participation, respectively [ 9 ]. In fact, most providers received a response from the mental health specialist within 30 min [ 7 ].

The success of WI CPCP aligns with that of other child psychiatry consultation programs that provide similar resources [ 12 ]. For example, an evaluation of the Massachusetts Child Psychiatry Access Project reported significant improvements in adequate access to child psychiatry for patients and ability for primary care providers to meet needs of children with psychiatric problems and receive consults in a timely manner at follow-up when compared to baseline [ 13 ]. An evaluation the Washington State Partnership Access Line found that PCPs also reported high satisfaction including that the program helped increase their skill in mental health care and helped them better manage their patient’s care [ 14 ]. Overall, children in states with child psychiatry consultation programs available in all counties are more likely to receive mental health care than children in states without such programs [ 15 ].

The first aim of this study was to quantitatively investigate the association between WI CPCP participation and PCP confidence and skill in providing mental health care, and specifically provide care for children with anxiety and depression. We were particularly interested in understanding provider confidence and skill with treating anxiety and depression, as anxiety and depression are among the most common childhood mental illnesses [ 1 , 16 ]. Also, children with internalizing disorders are less likely to be identified and referred to mental health care than those with externalizing disorders [ 17 ]. The research question for this aim was “What is the association between WI CPCP participation and PCP confidence and skill in mental health care?” (Research Question 1). The second aim of this study was to use a qualitative, pragmatic approach to better understand PCP challenges to providing mental health care. We specifically examined these factors in Milwaukee County, which was of particular interest to the study team due to eleven designated Health Professional Shortage Areas (HPSAs) for mental health in the county [ 18 ]. Milwaukee County PCPs were eligible to participate in WI CPCP since the program began in 2014. The two research questions were “What are the biggest challenges for PCPs in finding mental health specialist resources for child patients?” (Research Question 2) and “What are the biggest challenges that PCPs face in providing mental health assessment and treatment for child patients?” (Research Question 3).

This study used data from the Wisconsin Child Psychiatry Consultation Program (WI CPCP) database collected between August 2014 and December 2022. PCPs participating in the program completed a baseline survey before starting the program, and nine months later they completed a follow-up survey (see the Supplementary File for survey items used for this study). Participants had the opportunity to enter a lottery for a $100 gift card once per quarter. Both surveys were anonymous, so responses could not be individually linked from baseline to follow-up. This study has been approved for human subjects research by the Medical College of Wisconsin Institutional Review Board.

A cross-sectional mixed-methods secondary data analysis was developed to address the two study aims. The first research question quantitatively examined Milwaukee PCP confidence in diagnosing childhood behavioral health disorders as well as confidence and skill in providing mental health care for child patients who have anxiety or depression. The following survey items were included in this analysis (Likert scale response options can be found in the Figs. 1 , 2 , 3 , 4 , 5 and 6 ): 1) I feel confident in accurately diagnosing childhood behavioral health disorders, 2) I feel confident in my ability to provide mental health management for my child patients with anxiety, 3) I feel confident in my ability to provide mental health management for my child patients with depression, 4) Please rate your clinical skill level with the management and treatment of anxiety disorders, 5) Please rate your clinical skill level with the management and treatment of depressive disorders, and 6) How helpful have you found WI CPCP-provided educational services on pharmacological management of depression and anxiety? Distributions of baseline and follow-up survey responses for items were compared at α < 0.05 using Two-sample Wilcoxon rank-sum (Mann–Whitney) tests for all items except item number six. Item number six is only available in the follow-up survey, thus descriptive statistics were used for analysis. Survey data was collected using REDCap software. Statistical analyses were conducted using Stata IC 15.1 software.

The second and third research questions qualitatively examined overall challenges Milwaukee PCPs faced in providing mental health care to child patients. Survey items used for this analysis included: What are the biggest challenges that Milwaukee PCPs have in finding mental health specialist resources for child patients? and What are the biggest challenges that Milwaukee PCPs face in providing mental health assessment and treatment for child patients? Survey data was exported from REDCap and imported to MAXQDA software for qualitative data analysis. The interpretive framework of this research is pragmatism, and the research approach is thematic analysis [ 19 , 20 ]. One coder coded each transcript using descriptive, structural, in-vivo, process, and holistic codes [ 21 ]. Then, this coder used thematic maps to organize codes into preliminary themes and test relationships between codes and themes [ 19 ]. Themes were refined and finalized through the writing process, discussion with another study team member, and by revising the original thematic maps [ 19 ]. Starting with codes related to the primary care setting, quotes were collated for each code and multiple illustrative or representative quotes categorized by code were added to a document to begin summarizing and reporting findings. After adding primary care-related factors and quotes to the word document the study team discussed possible groupings to best present the findings. The categorization of factors within and beyond the scope of WI CPCP was chosen. Next, additional codes beyond the primary care setting and those which grouped concepts patterned throughout the dataset, were added to the document grouping by the two overarching themes. Final revisions of the results included selecting illustrative quotes and creating detailed descriptions of these patterns in the dataset. Finally, themes were contextualized using the Levesque, Harris, and Russell (2013) conceptual framework of patient-centered access to health care [ 22 ]. This framework has been used to contextualize efforts to improve access to childhood mental health care [ 23 ].

The sample includes 342 PCPs at baseline and 114 at follow-up who were practicing in Milwaukee County at the time of each survey (Table 1 ). A majority of respondents had Medical Degrees (73% at baseline and 77% at follow-up) and were in practice for two years or less (41% and 35.1%), followed by sixteen years or more (21% and 25%). About nine months into the program, 38% of PCPs that completed the follow-up survey had not yet had an encounter with a psychiatric consultant for example, regarding medication, diagnosis, or referral information. About 30% had one to two encounters with a consultant, 19% had three or four encounters, and the remaining 13% had six to ten encounters.

Research Question 1: What is the association between WI CPCP participation and PCP confidence and skill in mental health care?

There were statistically significant differences in the distributions between the baseline and the follow-up group for each of the five baseline vs. follow-up items. For each of these items, the sum of the follow-up group ranks was higher than expected, while the sum of the baseline ranks was lower. All comparisons were statistically significant with p  < 0.05. The following abbreviations will be used to report the results: Baseline Rank Sum (RS b ), Baseline Expected (E b ), Follow-up Rank Sum (RS f ) and Follow-up Expected (E f ). Figure 1 shows histograms for provider confidence in accurately diagnosing childhood behavioral health disorders (RS b  = 62,890, E b  = 66,202; RS f  = 24,682, E f  = 21,369). Figure 2 shows histograms for provider confidence in ability to provide mental health management for children with anxiety (RS b  = 62,414, E b  = 66,202; RS f  = 25,158, E f  = 21,369). Figure 3 shows histograms for provider confidence in ability to provide mental health management for children with depression (RS b  = 62,145, E b  = 65,835; RS f  = 25,008, E f  = 21,318). Figure 4 shows histograms for clinical skill with the management and treatment of anxiety disorders (RS b  = 60,503, E b  = 64,222; RS f  = 24,575, E f  = 20,857). Figure 5 shows histograms for clinical skill with the management and treatment of depressive disorders (RS b  = 61,170, E b  = 65,312; RS f  = 25,150, E f  = 21,008). A majority of providers found WI CPCP educational services on pharmacological management of depression and anxiety to be helpful (25% responded Helpful and 40% responded Very helpful; Fig. 6  ).

Primary Care Provider’s confidence in accurately diagnosing childhood behavioral health disorders

Two-sample Wilcoxon Rank-sum (Mann-Whitney) Test (N baseline =316, N follow-up =102):

Baseline Rank Sum=62890, Baseline Expected=66202

Follow-up Rank Sum=24682, Follow-up Expected=21369

Primary Care Provider’s confidence in ability to provide mental health management for child patients with anxiety

Baseline Rank Sum=62414, Baseline Expected=66202

Follow-up Rank Sum=25158, Follow-up Expected=21369

Primary Care Provider’s confidence in ability to provide mental health management for child patients with depression

Two-sample Wilcoxon Rank-sum (Mann-Whitney) Test (N baseline =315, N follow-up =102):

Baseline Rank Sum=62145, Baseline Expected=65835

Follow-up Rank Sum=25008, Follow-up Expected=21318

Primary Care Provider’s clinical skill level with the management and treatment of anxiety disorders

Two-sample Wilcoxon Rank-sum (Mann-Whitney) Test (N baseline =311, N follow-up =101):

Baseline Rank Sum=60503, Baseline Expected=64222

Follow-up Rank Sum=24575, Follow-up Expected=20857

Primary Care Provider’s clinical skill level with the management and treatment of depressive disorders

Two-sample Wilcoxon Rank-sum (Mann-Whitney) Test (N baseline =314, N follow-up =101):

Baseline Rank Sum=61170, Baseline Expected=65312

Follow-up Rank Sum=25150, Follow-up Expected=21008

p <0.0001

Helpfulness of WI CPCP-provided educational services on pharmacological management of depression and anxiety for Primary Care Providers (Follow-up survey only)

N =73, Not applicable=27%, Neutral=8%, Helpful=25%, Very helpful=40%

Percentages add to over 100% due to rounding

Research question 2: What are the biggest challenges for PCPs in finding mental health specialist resources for child patients?

Sixty-six percent of survey participants responded to this item in the baseline ( N  = 227) and seventy-two percent responded in the follow-up ( N  = 82). Themes identified during analysis for Research Questions 2 and 3 were categorized using the Levesque, Harris, and Russell (2013) conceptual framework of patient-centered access to health care [ 22 ]. See Fig. 7 for our adapted conceptual framework.

Conceptual framework of primary care provider challenges to providing childhood mental health care

This framework is adapted from the Levesque, Harris, and Russell 2013 conceptual framework of access to health care [ 22 ].  Participants reported challenges related to a variety of individual and service-related access constructs (black boxes). Challenges within the scope of the Wisconsin Child Psychiatry Consultation Program have an asterisk (*) next to the box

Factor within the scope of WI CPCP

Knowledge of referral options.

Not knowing options to refer patients to mental health services was one challenge reported by participants. Providers shared not knowing what services exist in the community, what mental health professionals specialize in, what insurances are accepted, or which offer quality care, culturally appropriate services, and are skilled in working with children. Several providers mentioned wanting an organized, central source of information to help them with referrals. About nine months into WI CPCP, some still reported being unfamiliar with local resources.

“Also, I don't feel I have personal recommendations re: specific counselors and psychiatrists whom I know will be able to provide competent, compassionate care in particular situations (e.g. when dealing with issues of race, bullying, childhood adverse events/trauma informed care, concerns re: ADHD v ODD v other non-specific behavioral concerns, eating disorders, etc.). A centralized listing of providers specializing in each of these areas, both psychiatrists and therapists, would be very helpful.” Provider 171, Baseline Survey “I am still relatively unfamiliar with therapist/counselors and psychiatrists in the area. This becomes problematic when a patient would benefit from a specific kind of therapist (such as one who works well with LGBTQ populations) and I am unsure what direction to point them in. I think it is also hard to determine who will be covered by my patient's insurance and then to point them in the right direction only to make a ton of phone calls that makes an already stressful situation (supporting your child's mental health) even more stressful.” Provider 107, Follow-up Survey

Factors beyond the scope of WI CPCP

Access to onsite support (integrated care).

Having mental health care onsite can help PCPs connect patients to specialists. One provider noted that wait times to see mental health specialists have improved with therapists in primary care clinics. However, barriers to care still exist including a great need in the community for care and finding external resources for patients with “severe mental health issues or for complex med[ication] management.” Provider 338, Baseline Survey.

Referrals limited by healthcare system

Referrals to services outside of the PCP’s healthcare system posed challenges to connecting patients to mental health specialists, and some providers were not able to refer outside of their system at all. One provider shared frustration with their mental health referrals being directed to a triage system and feeling like they are not trusted to make direct referrals to psychiatrists. Another identified that their referrals to external organizations are rejected due to the patient not having a PCP within the organization or insurance not covering the patient.

Wait time to be seen by a specialist

Most providers reported the wait time to be seen by a specialist as a challenge. One shared that it is a challenge to “access providers in a reasonable amount of time (< 1 month)” and a couple reported wait times greater than six months. A long wait time may lead to not successfully connecting with a specialist, as one provider notes (Provider 56, Follow-up Survey), “Mental health is something that needs to be addressed when there is a crisis. If families have to wait 6–10 weeks for a first appointment they often don’t follow thru-as something else becomes more critical by then.” Long wait times are especially challenging for patients that have more complex or severe mental health concerns that are beyond the comfort level of PCPs.

Lack of mental health professionals

Many providers reported that there are not enough mental health specialists, especially psychiatrists, to meet the needs of patients in a timely manner. Desire to connect patients to psychiatrists was related to making a diagnosis, medication initiation or management, and seeing patients with complex or severe mental health needs. Desire to connect with counselors, therapists, or psychologists was related to a patient need for counseling or therapy, such as Cognitive Behavioral Therapy.

Insurance coverage for mental health specialist services was also a reported barrier to finding mental health specialist resources for patients. Some providers specifically identified challenges finding counselors and psychiatrists that accept Medicaid insurance or that will see uninsured patients. Insurance barriers were related to accessibility or transportation challenges as well as longer wait times.

“Our clinic has pretty much all [Medicaid] patients who have lived through a lot of trauma. We are so lucky to have a great therapist in our clinic, but the medication piece can be tricky-given insurance as well as transportation issues, and often the dx [i.e., diagnosis] is not straightforward given all they've lived through.” Provider 220, Baseline Survey “Insurance is the major limiting factor. The majority of my patients have Medicaid and families often have to call many (> 10-20) locations to get on a wait list. It often takes about 3 months once they are on a wait list. Our clinic does not have a Social Worker on site and so it is often me trying to look into other resources/follow-up. Another limiting factor is finding services for children younger than age 5. Counseling is a bit easier to get access for compared to psychiatry, however is still harder for Medicaid vs private insurance patients (often 1-2 months wait list).” Provider 35, Follow-up Survey “Access to psychiatry for my medicaid patients, particularly for any psychiatric provider that is located in an accessible location for patients living in the central city of Milwaukee is a tremendous problem. Due to this, the time to obtain appointment is often a 5-6 month wait if patient is not admitted or enrolled in [an intensive outpatient program].” Provider 73, Follow-up Survey

Additional factors

Additional barriers to finding mental health specialist resources for child patients included cost for families, a lack of options for services in the patient’s language (particularly Spanish), and frequent relocation of clinics or specialists.

Research question 3: What are the biggest challenges that PCPs face in providing mental health assessment and treatment for child patients?

Sixty-five percent of survey participants responded to this item in the baseline ( N  = 223) and fifty-five percent responded in the follow-up survey ( N  = 63).

Factors within the scope of WI CPCP

Need for support.

Some providers shared a need for support from mental health specialists for assessment and treatment management. This need speaks to the importance of WI CPCP consultation services and of having access to a support system that one can contact with questions about diagnosis or treatment, patient referrals, or to follow up on a patient’s care.

Training in mental health care

A lack of training in certain areas was shared in both baseline and follow-up surveys. In the follow-up survey, one provider reported not having enough training on assessment and treatment of anxiety and depression and another provider asked for additional information potentially through “an online study group with recommended articles.” Provider 28, Follow-up Survey.

Knowledge for assessment and treatment

Related to a lack of training, providers shared their lack of knowledge in making an accurate diagnosis using the appropriate scale, prescribing or managing medications, and laying out a plan that is safe and can be followed by the family were challenges to providing mental health assessment and treatment. This lack of knowledge played a role in their confidence and comfort with providing mental health care. One provider said they need more training and patient exposure to become more confident. Another shared not feeling as comfortable with disorders other than ADHD, anxiety, and depression.

“I feel comfortable making the diagnosis of depression and anxiety, but it is hard to find a counselor to provide CBT [i.e., cognitive behavioral therapy]. For children with behavioral issues apart from ADHD, I don't feel as comfortable counseling parents on interventions and don't know where to refer them.” Provider 49, Follow-up Survey

Many providers shared their lack of knowledge or discomfort with medication for mental health disorders. Specific areas that they lacked confidence include knowing what dosage to prescribe, the side effects are for children including suicidal risk with antidepressants, knowing when to switch to another medication, and treating children who need multiple medications.

Time available during appointments

The time that providers have during appointments to assess or treat mental health concerns was reported as a challenge in baseline and follow-up surveys. For example, fifteen-minute-long appointments are not long enough to evaluate mental health concerns and provide care thoroughly. However, as one provider notes (Provider 32, Baseline Survey) “reimbursement is poor for longer [appointments].” Another provider highlighted the connection between a lack of time to identify the correct diagnosis within a visit, the challenge of balancing time in a visit for mental health as well as other health concerns, and the frustration with trying to get help for mental health care from psychiatrists but facing the challenges of long wait times or lack of insurance coverage.

“There are often multiple possible diagnoses for the children (ex: developmental delay vs ADHD vs autism in children younger than 5 / ADHD vs ODD vs learning disorders in older children) and it is hard to sort out in the limited time that I have for a visit. Often times I meet the child for the first time at a well child check and am expected to address their mental health needs along with their other chronic and preventive issues within 30 minutes. It would be easier if I had more time for each patient but since I don't I have to rely more on psychiatry and the [child health clinic] to aid in diagnosis. This gets to be very frustrating when there are long wait lists for the [child health clinic] (> 6 months) and I can't find a psychiatrist due to insurance coverage.” Provider 35, Follow-up Survey

Family buy-in and follow-through

Providers shared that patient and family buy-in to address mental health conditions and follow-through with care was a challenge for both connecting patients to mental health services and for providing mental health assessment and treatment through primary care. Believing that the child’s mental health concerns are serious enough was an important factor in families scheduling and attending appointments as well as following treatment recommendations. To make referrals more successful, one provider suggested the clinic reaching out to the patient to schedule the appointment.

“Also, if it is up to patients/families to follow through on the referral, often this will not happen and we do not presently have the resources to usher patients through this referral process (as they are currently over-burdened w/ non-psychiatric referral coordination work). Knowing that once I place the referral, the pt/family will be contacted either by the clinic they were referred to or by a centralized referral hub for psychiatric services would be very helpful.” Provider 171, Baseline Survey

Mental health care and the scope of primary care

Some providers shared that treating or managing mental health conditions is outside the scope of a PCP. One provider reported feeling unsafe practicing outside their scope of practice by prescribing medications they normally would not and with which they are not as comfortable, especially for patients that do not often attend follow-up appointments. However, the provider does this to provide a bridge in care until the patient can be seen by a specialist, which can sometimes be a long time. Other reasons for “do[ing] things that are not really in the scope of practice of a general pediatrician” were the inability to find a specialist and family challenges affording specialty services.

A nurse practioner shared that they are not trained in psychiatry and are “very strict with knowing the scope of [their] licensure.” Another provider noted they are “not a therapist and cannot provide adequate behavioral intervention therapy.” Provider 56, Follow-up Survey.

Another provider shared their frustration with feeling forced to prescribe medication for mental illnesses because access to psychiatry for patients is so difficult. They identified the issue as systemic and called for improvements to be made in accessible and affordable psychiatric services instead of relying on primary care. This provider added that this added responsibility is leading to burnout among primary care providers.

“I do NOT want to be prescribing antidepressants or anxiety meds for my patients but I am forced to due to our flawed health care system. I understand the goal of this program, but I sure wish you would focus on improving access to psychiatrists instead. In the end this is just another thing dumped on general pediatricians and it's not right. ... I do not want to be doing this. It was not a part of my training, I am not interested in doing it, and I don't get paid for it well. This is one of the greatest frustrations with my job and it's causing burnout amongst all of us. Someone needs to step up and provide better access and affordable care from psychiatry” Provider 41, Follow-up Survey

This mixed methods study analyzed PCP responses to a baseline survey and a follow-up survey received 9-months after initiating participation in the Wisconsin Children’s Psychiatry Consultation Program. Quantitative analyses found that provider confidence and skill in treating childhood anxiety and depression, as well as diagnosis of behavioral health disorders improved from baseline to follow up. This may result from access to consultations with behavioral health professionals on childhood mental health concerns and CME-accredited educational opportunities on mental health topics. Our finding aligns with a growing body of literature supporting that participation in a Child Psychiatry Access Program (CPAP) is associated with improved confidence and skill in mental health management for pediatric PCPs [ 12 ].

This is the first study to our knowledge that assesses the effectiveness of a psychiatry consultation program on PCPs’ self-assessed ability to treat anxiety and depression. Data from the Wisconsin Health Information Organization (WHIO) All-Payer Insurance Claims Database indicate that participation in WI CPCP is associated with increases in the use of mental health-related diagnostic codes, suggesting increased comfort with mental health diagnoses [ 24 ]. These increases were particularly prominent for diagnostic codes related to the online educational opportunities offered by WI CPCP, specifically ADHD, depressive or mood disorders, and anxiety [ 24 ]. Future research can explore the effectiveness of such programs for treatment of ADHD and other specific conditions. This could help identify which educational content areas may need improvement, as well as opportunities for resource sharing between CPAPs.

PCP challenges to making mental health specialty referrals were categorized into two groups. The only challenge to referral within the scope of WI CPCP was knowledge of referral options. Although the WI CPCP offers a central referral resource list, it may need to better promote this service to increase awareness among PCPs. Beyond the scope of WI CPCP, providers reported the benefits of having onsite support and the challenges with limited referral options based on the provider’s healthcare system and long wait times to access care caused by a lack of specialists and a lack of insurance coverage for services. Integrated behavioral health is a health care model in which primary care and behavioral health providers work together with patients and families to provide patient-centered care [ 25 ]. It is associated with better outcomes in a variety of mental health problems (including depression and anxiety) for children and adolescents than regular primary care [ 26 ]. Furthermore, living in a state with health insurance parity is associated with more affordable childhood mental health care for families and fewer mental health visits as a young adult [ 27 , 28 ].

PCP challenges to providing mental health assessment and treatment for child patients were categorized into three groups. Multiple factors were within the scope of WI CPCP including the need for support from mental health specialists, training in providing mental health care, and a lack of knowledge in providing assessment and treatment. These three factors are part of the consultation and education core components of the program [ 7 ]. The time available during appointments and family buy-in and follow through were challenges beyond the scope of WI CPCP. Similarly, a CPAP study in New York found that having enough time during an appointment to talk about mental health concerns was an issue for both PCPs that had and those that had not yet participated in the program [ 29 ]. In the same study, family buy-in was reported as key to a provider’s ability to apply their mental health training [ 29 ], suggesting increasing buy-in as a potentially valuable training opportunity for PCPs.

Lastly, the final theme illustrates two key points. One is that although the American Academy of Pediatrics does identify necessary competencies in basic mental health care for PCPs to address conditions of mild to moderate severity [ 30 ], there remains an unease among some providers. Similarly, a Maryland CPAP study found participants were more comfortable with screening and referral than with direct intervention [ 31 ]. This suggests an opportunity for continued training for PCPs in mental health care to improve confidence and clarification on the boundaries of the scope of mental health treatment in primary care. The second key point is that access to mental health care for children is a systemic issue. Primary care and programs like WI CPCP alone cannot solve the problem. Lack of mental health specialists, long wait times, challenges with insurance coverage and the other factors mentioned in this study have forced PCPs to provide mental health care. Thus, WI CPCP is needed, as well as better reimbursement for mental health specialists to incentivize more people to choose the profession. Additional ways to improve recruitment and retention include lower caseloads (associated with lower levels of burnout and higher job satisfaction) [ 32 , 33 ], more loan repayment programs, and financial reimbursement to increase the number of professionals willing to supervise trainees and support their time committed to this work [ 34 ].

Limitations

Both baseline and follow-up surveys are anonymous, so responses cannot be individually linked from baseline to follow up. Thus, statistically these two samples were treated as independent, even though they are linked. Furthermore, one individual conducted the qualitative coding. Therefore, it was not possible to calculate intercoder agreement as a measure of reliability. Another limitation is that data included in this analysis was collected over an eight-year period and did not distinguish between responses before and after the start of the COVID-19 pandemic. Thus, some findings may be lacking in the situational context of access to mental health care at the time the providers completed the surveys. Still, this study generated findings similar to studies using more recent data confirming the validity of our findings and ability to apply this knowledge to current practice.

This study supports the need for continued support of the Wisconsin Child Psychiatry Consultation Program to improve access to mental health care for children in Milwaukee and other underserved areas of Wisconsin. The results also support increasing awareness among participating PCPs of the services available through the program, such as a central source for vetted referral resources. Increasing access to integrated behavioral health and improving insurance parity can help supplement the efforts of WI CPCP and lead to better outcomes for patients.

Future research should evaluate the impact of participation in WI CPCP on provider skill and confidence with managing other mental health conditions. This can help tailor future educational trainings to areas where PCPs need improvement. In addition, challenges to finding specialists and providing assessment and treatment should be analyzed for the rest of the state of Wisconsin to identify challenges specific to other contexts, such as rural areas. Lastly, the impact of the COVID-19 pandemic on PCP ability to provide mental health care should be explored by comparing survey responses before and after the start of the pandemic.

Availability of data and materials

The dataset generated and analyzed during the current study is not publicly available nor available upon request to protect the confidentiality of study participants.

Abbreviations

Wisconsin Child Psychiatry Consultation Program

Primary care provider

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Acknowledgements

We thank the primary care providers participating in the Wisconsin Child Psychiatry Consultation Program for their participation in these surveys. We also thank the team at the Wisconsin Department of Health Services and the Medical College of Wisconsin for managing the Wisconsin Child Psychiatry Consultation Program.

The Wisconsin Child Psychiatry Consultation Program is funded by the State of Wisconsin and a federal Health Resources & Services Administration grant (HRSA-18–122).

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Lora Daskalska

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All authors contributed to the conceptualization of the study (LD, MB, and SY). LD and MB developed the methodology. LD conducted data analysis and wrote the original draft. All authors read, reviewed, edited, and approved the final manuscript (LD, MB, and SY).

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The researcher’s credentials at the time of data collection and analysis were BS for LD and PhD for the remaining authors. This study is part of the doctoral dissertation of LD. LD’s training for this work includes coursework in qualitative analysis and survey research methods. LD also has experience conducting qualitative and quantitative analyses for other studies.

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Daskalska, L., Broaddus, M. & Young, S. Closing the gap in access to child mental health care: provider feedback from the Wisconsin Child Psychiatry Consultation Program. BMC Prim. Care 25 , 300 (2024). https://doi.org/10.1186/s12875-024-02538-7

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#ForYou? the impact of pro-ana TikTok content on body image dissatisfaction and internalisation of societal beauty standards

Roles Conceptualization, Formal analysis, Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing

Affiliation Faculty of Business, School of Psychology, Justice and Behavioural Science, Charles Sturt University, Wagga Wagga, New South Wales, Australia

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

* E-mail: [email protected]

• Madison R. Blackburn, 
• Rachel C. Hogg

• Published: August 7, 2024
• https://doi.org/10.1371/journal.pone.0307597
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Videos glamourising disordered eating practices and body image concerns readily circulate on TikTok. Minimal empirical research has investigated the impact of TikTok content on body image and eating behaviour. The present study aimed to fill this gap in current research by examining the influence of pro-anorexia TikTok content on young women’s body image and degree of internalisation of beauty standards, whilst also exploring the impact of daily time spent on TikTok and the development of disordered eating behaviours. An experimental and cross-sectional design was used to explore body image and internalisation of beauty standards in relation to pro-anorexia TikTok content. Time spent on TikTok was examined in relation to the risk of developing orthorexia nervosa. A sample of 273 female-identifying persons aged 18–28 years were exposed to either pro-anorexia or neutral TikTok content. Pre- and post-test measures of body image and internalisation of beauty standards were obtained. Participants were divided into four groups based on average daily time spent on TikTok. Women exposed to pro-anorexia content displayed the greatest decrease in body image satisfaction and an increase in internalisation of societal beauty standards. Women exposed to neutral content also reported a decrease in body image satisfaction. Participants categorised as high and extreme daily TikTok users reported greater average disordered eating behaviour on the EAT-26 than participants with low and moderate use, however this finding was not statistically significant in relation to orthorexic behaviours. This research has implications for the mental health of young female TikTok users, with exposure to pro-anorexia content having immediate consequences for internalisation and body image dissatisfaction, potentially increasing one’s risk of developing disordered eating beliefs and behaviours.

Citation: Blackburn MR, Hogg RC (2024) #ForYou? the impact of pro-ana TikTok content on body image dissatisfaction and internalisation of societal beauty standards. PLoS ONE 19(8): e0307597. https://doi.org/10.1371/journal.pone.0307597

Editor: Barbara Guidi, University of Pisa, ITALY

Received: November 2, 2023; Accepted: July 8, 2024; Published: August 7, 2024

Copyright: © 2024 Blackburn, Hogg. 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: The data for this study can be found on Figshare via the following link: https://doi.org/10.6084/m9.figshare.25756800.v1 .

Funding: We acknowledge the financial support provided by Charles Sturt University.

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

Introduction

Social media is a self-presentation device, a mode of entertainment, and a means of connecting with others [ 1 ], allowing for performance and the performance of identity [ 2 ], with social rewards built into its systems. Five to six years of the average human lifespan are now spent on social media sites [ 3 ] and visual platforms such as Instagram and TikTok increasingly dominate the cultural landscape of social media. Such visually oriented platforms are associated with higher levels of dysfunction in body image [ 4 ], while the COVID-19 pandemic has seen a rise in disordered eating behaviour [ 5 ]. Despite this, the field lacks a clear theoretical framework for understanding how social media usage heightens body image issues [ 6 ] and little research has specifically examined the impacts of TikTok based content. In this research, we sought to explore the impact of pro-anorexia TikTok content on body image satisfaction and internalisation of beauty standards for young women. The forthcoming sections of this literature review will highlight the features of social media content that may be particularly pernicious for young female users and will explore disordered eating and orthorexia in a social media context, concluding with a theoretical analysis of the relationship between social media and body image and internalisation of beauty standards, respectively.

Social media offers instant, quantifiable feedback coupled with idealised online imagery that may intersect with the value adolescents attribute to peer relationships and the sociocultural gender socialisation processes germane to this period of development, creating the “perfect storm” for young social media users, especially females [ 6 ]. In a study of 85 young, largely female eating disorder patients, a rise in awareness of online sites emphasizing thinness as beauty was evident from 2017 to 2020, with 60% of participants indicating that they knew of pro-ana websites and 22% of participants admitting to visiting them [ 7 ]. Research suggests that social media may also trigger those with extant eating disorders while simultaneously influencing healthy individuals to engage in disordered eating behaviour [ 8 ].

“Pro” eating disorder communities, hereafter referred to as “pro-ana” (pro-anorexia) communities, are a particular concern in a social media context. These communities encourage disordered eating, normalise disordered behaviours, and provide a means of connection for individuals who endorse anti-recovery from eating disorders [ 8 ]. Weight-loss tips, excessive exercise routines, and images of emaciated figures are routinely shared in these online communities [ 9 ], with extant research highlighting the association between viewing eating disorder content online and offline eating disorder behaviour [ 8 ]. Women who view pro-ana websites display increased eating disturbances, lowered body satisfaction, an increased drive for thinness, and higher levels of perfectionism when compared to women who have not viewed pro-ana content [ 10 , 11 ]. In research on adolescent girls, Stice [ 12 ] investigated the influence of exposure to media portraying the “thin-ideal” and found that perceived pressure to be thin was a predictor of increased body image dissatisfaction, which in turn led to increases in disordered eating behaviour. In similar research, Green [ 10 ] found that individuals with diagnosed eating disorders reported worsening symptoms after just 10-minutes of exposure to pro-ana content on the online platform, Tumblr.

Disordered eating #ForYou

The most downloaded social application (app) of 2021, TikTok is a social media platform that allows short-form video creation and sharing within a social media context [ 13 ]. Since its launch in 2017, TikTok has had over two billion downloads and has an estimated one billion users, the vast majority of which are children and teenagers [ 14 ]. Unlike other social media platforms where users have greater autonomy over the content generated on their homepage newsfeed, TikTok’s algorithm records data from single users and proposes videos designed to catch a user’s attention specifically, by creating a personalised “For You” page [ 15 ]. This feed will suggest videos from any creator on the platform, not just followed accounts. As such, if a user ‘interacts’ with a video, such as liking, sharing, commenting, or searching for related content, the algorithm will continue to produce similar videos on their “For You” page. The speed with which TikTok content can be created and consumed online may also be key to its impact. Any given social media user could watch more than a thousand videos on TikTok in an hour, creating a reinforcing effect that may have more impact than longer form content from a single creator [ 2 ].

Whilst the popularity of TikTok’s “For You” page has prompted global leaders in social media to build their own recommended content features, this feature remains most pronounced on TikTok. The “For You” page is the homepage of TikTok where users spend the majority of their time, compared to other social media platforms where homepages consist of a curation of content from followed accounts. Instagram’s explore page continues to emphasise established influencer culture and promote accounts of public figures or influencers with large followings. Contrastingly, TikTok’s unique algorithm makes content discoverability an even playing field, as any user’s content has the potential to reach a vast audience regardless of follower count or celebrity status. TikTok users therefore have less control over their homepage newsfeed compared to other social media platforms where users elect who they follow.

Unlike other social media platforms that implicitly showcase body ideals, TikTok contains explicit eating disorder content [ 16 ], while the “For You” page means that simply interacting with health and fitness videos can lead to unintended exposure to disordered eating content. Even seemingly benign “fitspiration” content may have psychological consequences for viewers. Beyond explicit pro-ana content, #GymTok and #FoodTok are two popular areas of content that provide a forum for users to create and consume content around their and others’ daily eating routines, weight loss transformations, and workout routines [ 2 ]. TikTok also frequently features content promoting clean eating, detox cleanses, and limited ingredient diets reflective of the current “food as medicine” movement of western culture [ 17 ], otherwise known as orthorexia. Despite efforts to ban such pro-ana related content, some videos easily circumvent controls [ 18 ], in part because many TikTok creators are non-public figures who are not liable to the backlash or cancellation that a public figure might receive for circulating socially irresponsible content.

Orthorexia: The rise of ‘healthy’ eating pathologies

Psychological analyses of eating disorders have historically focused on restrictive eating and the binge-purge cycle, however, more recently “positive” interests in nutrition have been examined. Orthorexia nervosa is characterised by a restrictive diet, ritualized patterns of eating, and rigid avoidance of foods deemed unhealthy or impure that consumes an individual’s focus [ 19 ]. Despite frequent observation of this distinct behavioural pattern by clinicians, orthorexia has received limited empirical attention and is not formally recognised as a psychiatric disorder [ 19 ]. Orthorexia and anorexia nervosa share traits of perfectionism, high trait anxiety, a high need to exert control, plus the potential for significant weight loss [ 19 ]. Termed ‘the disorder that cannot be diagnosed’ due to limited consensus around its features and the line between healthy and pathological eating practices, orthorexia mirrors the narrative of neoliberal self-improvement culture, wherein the body is treated as a site of performance and transformation.

Orthorexic restrictions and obsessions are routinely interpreted as signs of morality, health consciousness, and wellness [ 20 , 21 ]. Social media wellness influencers have played a significant role in normalising “clean [disordered] eating”. As one example, Turner and Lefevre [ 22 ] conducted an online survey of social media users following health food accounts and found that higher Instagram use was associated with a greater tendency towards orthorexia, with the prevalence of orthorexia among the study population at 49%, substantially higher than the general population (<1%). Similar health and food-related content on TikTok may provoke orthorexic tendencies among TikTok users, however, limited research has investigated orthorexic eating behaviour in the context of TikTok. The current study aims to bridge this gap in the literature around TikTok use and orthorexic tendencies. Disordered eating behaviour in the present study was measured by two separate but related constructs. ‘Restrictive’ disordered eating relates to dieting, oral control, and bulimic symptoms, whilst ‘healthy’ disordered eating constitutes orthorexic-like preoccupation with health food.

Theoretical analysis of body image and social media

An established risk factor in the development and maintenance of disordered eating behaviour is negative body image. Body image is a multidimensional construct that represents an individual’s perceptions and attitudes about their physical-self and encompasses an evaluative function through which individuals compare perceptions of their actual “self” to “ideal” images [ 23 ]. This comparison may produce feelings of dissatisfaction about one’s own body image if a significant discrepancy exists between the actual and ideal self-image [ 23 ]. Body image is not necessarily congruent with actual physique, with research demonstrating that women categorised as having a healthy body mass index (BMI) nonetheless report dissatisfaction with their weight and engage in restrictive dietary behaviours to reduce their weight [ 24 ]. In addition, body image dissatisfaction is considered normative in Western society, particularly among adolescent women [ 25 ]. This may be attributable to the constant flow of media that exposes women to unrealistic images of thinness idealized within society [ 26 ].

One theoretical framework for understanding social media’s relationship with body image is the Social Comparison Theory, proposed by Festinger [ 27 ] who suggests that people naturally evaluate themselves in comparison to others via upward or downward social comparisons. Research supports the notion that women who frequently engage in maladaptive upward appearance-related social comparisons are more likely to experience body image dissatisfaction and disordered eating [ 25 , 28 ], while visual exposure to thin bodies may detrimentally modulate one’s level of body image satisfaction [ 29 – 31 ]. In their study of undergraduate females, Engeln-Maddox [ 29 ] found that participants made upward social comparisons to images of thin models which were strongly associated with decreases in body image satisfaction and internalisation of thinness. Similarly, Tiggemann [ 32 ] found that adolescents who spent more time watching television featuring attractive actors and actresses reported an increased desire for thinness, theorised to be a result of increased social comparison to attractive media personalities.

The Transactional Model [ 33 ] extends Social Comparison Theory by emphasising the multifaceted and complex nature of social media influences on body image. This model acknowledges that individual differences may predispose a person to utilise social media for gratification, and highlights that as time spent on social media increases, so too does body image dissatisfaction [ 33 ]. In line with this, a recent review of literature by Frieiro Padín and colleagues [ 34 ] indicated that time spent on social media was strongly correlated with eating disorder psychopathologies, as well as heightened body image concerns, internalisation of the thin ideal, and lower levels of self-esteem. Time on social media also correlated with heightened body image concerns to a far greater extent than general internet usage [ 35 , 36 ].

Body image ideals are not static. The traditional ideal of rib-protruding bodies from the 90s, known colloquially as “heroin chic”, have recently shifted to a celebration of the “slim-thicc” figure, consisting of a cinched, flat waist with curvy hips, ample breasts, and large behinds [ 37 ]. The “slim-thicc” aesthetic allows women to be bigger than previous body ideals, yet this figure is arguably more unattainable than the thin-ideal as surgical intervention is commonly needed to achieve it, depending on genetics and body type. The idealisation of the “slim-thicc” figure is highlighted by the “Brazilian butt lift” (BBL), a potentially life-threatening procedure that is nonetheless the fastest growing category of plastic surgery, doubling in growth over the past five years, despite the life-threatening potential of the procedure [ 38 ]. Research suggests that the slim-thicc ideal is no less damaging nor threatening of body image than the thin-ideal. Indeed, in experimental research on body ideals, McComb and Mills [ 39 ] found that the greatest body dissatisfaction levels in female undergraduate students were observed among those exposed to imagery of the slim-thicc physique, relative to that exhibited by those exposed to the thin-ideal and fit-ideal physique, as well as the control condition.

Recent body ideals have also favoured muscular thin presentations, considered to represent health and fitness as evident in the “#fitspiration” Instagram hashtag that features over 65 million images [ 40 ]. Fitspiration has the potential to positively influence women’s health and wellbeing by promoting exercise engagement and healthy eating, yet various content analyses of fitspiration images highlight aspects of fitspiration that warrant concern [see 40 , 41 ]. Notably, fitspiration typically showcases only one body type and women whose bodies do not meet this standard may experience body dissatisfaction [ 40 ], while the gamification of exercise, such as receiving likes for every ten sit-ups, segues with the intensive self-control and competitiveness that often underpins eating disorders and eating disorder communities [ 1 ].

In recent experimental research, Pryde and Prichard [ 42 ] examined the effect of exposure to fitspiration TikTok content on the body dissatisfaction, appearance comparison, and mood of young Australian women. Viewing fitspiration TikTok videos led to increased negative mood and increased appearance comparison but did not impact body dissatisfaction. This finding contradicts previous research and may be due to fitspiration content showcasing body functionality rather than aesthetic, which may lead to positive outcomes for viewers. The fitspiration content used by Pryde and Prichard [ 42 ] did not contain the harmful themes regularly found in other forms of fitspiration content. Appearance comparison was significant in the relationship between TikTok content and body dissatisfaction and mood, suggesting that this may be a key mechanism through which fitspiration content leads to negative body image outcomes and supporting the notion that fitspiration promotes a focus on appearance rather than health.

Body image dissatisfaction among women is associated with co-morbid psychological disturbances and the development of disordered eating behaviours [ 43 , 44 ]. A large body of research indicates that higher levels of both general and appearance-related social comparison are associated with disordered eating in undergraduate populations [ 10 , 28 , 45 – 48 ]. As one example, Lindner et al. [ 46 ] investigated the impact of the female-to-male ratio of college campuses on female students’ engagement in social comparison and eating pathology. Their findings lend support to the Social Comparison Theory, indicating that the highest levels of eating pathology and social comparison were found among women attending colleges with predominantly female undergraduate populations. A strong relationship was also found between eating pathology and engagement in appearance-related social comparisons independent of actual weight. Lindner et al. [ 46 ] surmised that these results suggest social comparison and eating pathology behaviours are due to students’ perceptual distortions of their own bodies, potentially fostered by pressures exerted from peers to be thin.

Similarly, Corning et al. [ 45 ] investigated the social comparison behaviours of women with eating disorder symptoms and their asymptomatic peers. Results illustrated that a greater tendency to engage in everyday social comparison predicted the presence of eating disorder symptoms, while women with eating disorder symptoms made significantly more social comparisons of their own bodies. Such findings are supported by subsequent research, with Hamel et al. [ 28 ] finding that adolescents with a diagnosed eating disorder engaged in significantly more body-related social comparison than adolescents diagnosed with a depressive disorder or no diagnosis. Body-related social comparison was also significantly positively correlated with disordered eating behaviours. While extant research has focused upon social comparison as it has occurred through traditional media outlets, less research has investigated the facilitation of social comparison through social media platforms, particularly contemporary platforms such as TikTok.

Theoretical analysis of internalisation processes and social media

The extent to which one’s body image is impacted by images and messages conveyed by the media is determined by the degree to which these images and messages are internalised. Some may argue that social media platforms are distinct from what occurs in “real” life, creating fewer opportunities for internalisation to occur. Yet as Pierce [ 2 ] argues, platforms such as TikTok create their own realities, allowing users to explore their identities, form relationships, engage with culture and world events, and even develop new patterns of speech and writing. TikTok trends commonly infiltrate society, underscoring the impact of social media on life beyond the online world and thus a sociocultural analysis of TikTok is warranted. Sociocultural theories suggest that society portrays thinness as the ideal body shape for women, resulting in an internationalisation of the “thin is good” assumption for women. This in turn results in lowered body image satisfaction and other negative outcomes [ 43 ]. The significance of social influences, including the role of family, peers, and the media, is emphasised by sociocultural theory, with individuals more likely to internalise the thin ideal when they encounter pressuring messages that they are not thin enough from social influences [ 48 ]. Within this theoretical approach, an individual’s degree of thin ideal internalisation is theorised to depend on their acceptance of socially defined ideals of attractiveness and is reflected in their engagement in behaviours that adhere to these socially defined ideals [ 49 ].

Building on this, the tripartite influence model suggests that disordered eating behaviours arise due to pressure from social agents, specifically media, family, and peers. This pressure centres on conforming to appearance ideals and may lead to engagement in social comparison and the internalisation of thin ideals [ 48 ]. This is relevant in a digital context given social media provides endless opportunities for individuals to practice social comparison and for many users, social comparison on TikTok is peer-based as well as media-based. According to the tripartite model, social comparisons have been consistently associated with a higher degree of thin ideal internalisation, self-objectification, drive for thinness, and weight dissatisfaction [ 50 ]. Furthermore, and in contrast to traditional media where social agents are mainly models, celebrities, and movie stars, social agents on social media can include peers, friends, family, and individuals who have a relationship with the individual. Social media content generated by “everyday” people, rather than super models or movie stars, may result in comparisons that are more horizontal in nature. This is particularly evident on TikTok where content creators are rarely famous before creating a TikTok account, often remain micro-influencers after achieving some notoriety, and are usually around the same age as those viewing their content.

Pressure to be thin from alike peers may have a particularly pronounced impact on one’s degree of internalisation of the thinness ideal. Indeed, Stice et al. [ 51 ] found that after listening to young thin women complain about “feeling fat”, their adolescent participant sample reported increased body image dissatisfaction, suggesting that pressure from peers perpetuates the thinness ideal, leading to internalisation of the ideal and subsequent body dissatisfaction. Similarly, it was found that adolescent females were more likely to engage in weight loss behaviour if a high portion of peers with a similar BMI were also engaging in these behaviours, illustrating that appearance pressure exerted by alike peers may result in thin-ideal internalisation and engagement in weight loss behaviours to control body weight and shape [ 52 ]. Such findings raise questions around whether those most similar to us have the greatest impact upon thin-ideal internalisation, body image dissatisfaction, and disordered eating behaviours.

In further support for the tripartite influence model, research by Thompson et al. [ 48 ] indicates that the ideals promoted through social media trends are internalized despite being unattainable, resulting in body image dissatisfaction and disordered eating behaviour. Similarly, Mingoia et al. [ 53 ] found a positive association between the use of social networking sites and thin ideal internalisation in women, indicating that greater use of social networking sites was linked to significantly higher internalisation of the thin ideal. Interestingly, the use of appearance-related features (e.g., posting or viewing photographs or videos) was more strongly related with internalisation than the broad use of social networking sites (e.g., writing status’, messaging features) [ 53 ]. Correlational and experimental research alike has demonstrated that thin ideal internalisation is related to body image dissatisfaction and leads to expressions of disordered eating such as restrictive dieting and binge-purge symptoms [ 31 , 48 , 54 , 55 ]. Subsequent expressions of disordered eating may be seen as an attempt to control weight and body shape to conform to societal beauty standards of thinness [ 51 ].

This sociocultural perspective is exemplified by Grabe et al’s. [ 54 ] meta-analysis of research on the associations between media exposure to women’s body dissatisfaction, internalisation of the thin ideal, and eating behaviours and beliefs, illustrating that exposure to media images propagating the thin ideal is related to and indeed, may lead to body image concerns and increased endorsement of disordered eating behaviours in women. Similarly, Groesz et al. [ 55 ] conducted a meta-analysis to examine experimental manipulations of the thin beauty ideal. They found that body image was significantly more negative after viewing thin media images than after viewing images of average size models, plus size models, or inanimate objects. This effect size was stronger for participants who were more vulnerable to activation of the thinness schema. Groesz et al. [ 55 ] conclude that their results align with the sociocultural theory perspective that media promulgates a thin ideal that in turn provokes body dissatisfaction.

Current research

Existing research has established the relationship between body image dissatisfaction and disordered eating behaviours and social media platforms such as Instagram and Twitter. The unique implications of the TikTok ‘For You Page’, as well as the dominance of peer-created and explicit disordered eating content on TikTok suggests that the influence of this platform warrants specific consideration. This study adds to extant literature by utilising an experimental design to examine the influence of exposure to pro-ana TikTok content on body image and internalisation of societal beauty standards. A cross-sectional design was used to investigate the effect of daily TikTok and the development of disordered eating behaviours. Although body image disturbance and eating disorders are not limited to women, varying sociocultural factors have been implicated in the development of disordered eating behaviour in men and women [ 45 ], while issues facing trans people warrant specific consideration beyond the scope of this study, therefore the present sample contains only female-identifying participants.

Aims and hypotheses.

The current study aimed to investigate the impact of pro-ana TikTok content on young women’s body image satisfaction and internalisation of beauty standards, as well as exploring daily TikTok use and the development of disordered eating behaviour. First, in line with the cross-sectional component of the study, it was hypothesized that women who spend greater time on TikTok per day would report significantly more disordered eating behaviour than women who spend low amounts of time on TikTok per day. Second, it was hypothesized that women in the pro-ana TikTok group would report a significant decrease in body image satisfaction state following exposure to the pro-ana content compared to women in the control group. Third, it was hypothesized that women in the pro-ana Tik Tok group would report increased internalisation of societal beauty standards following exposure to pro-ana TikTok content compared to women in the control group.

Participants

Participants in the current study included 273 women aged between 18 to 28 years sourced from the general population of TikTok users. The predominant country of residence of the sample was Australia, with 15 participants indicating they currently reside outside of Australia. Of the remaining data relating to the two conditions of the study, 126 participants were randomly allocated into the experimental condition, and 147 participants were randomly allocated into the control condition. Snowball sampling was used to recruit participants through social media, online survey sharing platforms, and word-of-mouth, with first-year University students targeted for recruitment by offering class credit in return for participation. Participants could withdraw their consent at any time by exiting the study prior to completion of the survey.

The current study employed a questionnaire set that included a demographic questionnaire, and five scales measuring disordered eating behaviour, body satisfaction, and internalisation of societal beauty standards, as well as perfectionism, the latter of which was not examined in the present study.

Demographic questionnaire.

The demographic questionnaire required participants to answer a series of questions relating to their gender, age, relationship status, ethnicity, country of residence, TikTok usage, and exercise routine. A screening question redirected non-female-identifying persons from the study. Responses to the TikTok usage items were examined cross-sectionally with responses on the EAT-26 and ORTO15 used to examine the influence of daily TikTok use and the presentation of disordered eating behaviours.

Eating attitudes test.

The Eating Attitudes Test (EAT-26, [ 56 ]) is a short form of the original 40-item EAT scale [ 57 ] which measures symptoms and concerns characteristic of eating disorders. The 26-item short-form version of the EAT was utilised in the present study due to its established reliability and validity, and strong correlation with the EAT-40 [ 56 ].

Responses to the 26-items are self-reported using a 6-point Likert scale ranging from Always (3) to Never (0) [ 56 ]. The EAT-26 consists of three subscales including dieting, bulimia and food preoccupation, and oral control. Five behavioural questions are included in Part C of the EAT-26 to determine the presence and frequency of extreme weight-control behaviours including binge eating, self-induced vomiting, laxative usage, and excessive exercise [ 56 ]. Higher scores indicate greater disordered eating behaviour, and those with a total score of 20 or greater are, in clinical contexts, typically highlighted as requiring further assessment and advice of a mental health professional [ 56 ].

Internal consistency of the EAT-26 was established in initial psychometric studies which reported a Cronbach’s alpha of.85 [ 58 ]. For the current study, the Cronbach’s alpha = .91. Previous research has also demonstrated that the EAT-26 has strong test-retest reliability (e.g., 0.84) [ 59 ], as well as acceptable criterion-related validity for differentiating between eating disorder populations and non-disordered populations [ 56 ]. In the current study, the EAT-26 was used to measure disordered eating behaviour, and the cut-off score of 20 and above was adopted to categorise increased disordered eating behaviour. Given how this construct is measured, from this point forward the present study will refer to EAT-26 responses as ‘restrictive’ type disordered eating.

The ORTO-15 is a 15-item screening measure that assesses orthorexia nervosa risk through questions regarding the perceived effects of eating healthy food (e.g. “Do you think that consuming healthy food may improve your appearance?”), eating habits (e.g. “At present, are you alone when having meals?”), and the extent to which concerns about food influence daily life (e.g. “Does the thought of food worry you for more than three hours a day?”) [ 19 ]. Responses are self-reported using a 4-point Likert scale ranging from always , often , sometimes , or never . Individual items are coded and summed to derive a total score. Donini et al. [ 60 ] established a cut off total score of 40; scores below 40 indicate orthorexia behaviours, whilst scores 40 or above reflect normal eating behaviour. This cut off score was determined by Donini et al. [ 60 ] as their results revealed the ORTO-15 demonstrated good predictive capability at the threshold of 40 compared to other potential threshold values.

Although the ORTO-15 is the most widely accepted screening tool to assess orthorexia risk, it is still only partially validated [ 61 ], and inconsistencies of the measures’ reliability and validity exist in current literature. For example, Roncero et al. [ 62 ] estimated that the reliability of the ORTO-15 using Cronbach’s alpha was between 0.20 and 0.23, however, after removing certain items, the reliability coefficients were between 0.74 and 0.83. Contrastingly, Costa and colleagues’ [ 63 ] review of current literature surrounding orthorexia suggested adequate internal consistency (Cronbach’s alpha = 0.83 to 0.91) with all 15-items.

In the present study, a reliability analysis revealed unacceptable reliability for the ORTO-15 (α = .24). Principal components factor analysis identified two factors within the ORTO-15, one relating to dieting and the other to preoccupation with health food. Separate reliability analyses were performed on the items that comprised these two factors and the diet-related items did not have acceptable reliability (α = -.40), whilst the health food-related items bordered on acceptable reliability at α = .63. Consequently, only the health food-related items were retained in the current study following consideration of Pallant’s [ 64 ] assertion that Cronbach alpha values are sensitive to the number of items on a scale and it is therefore common to obtain low values on scales with less than ten items. Pallant [ 64 ] notes that in cases such as this, it is appropriate to report the inter-item correlation of the items, while Briggs and Cheek [ 65 ] advise an optimal range for the inter-item correlation between.2 to.4, with the health food-related items in the current study obtaining an inter-item correlation of.25. Throughout this study, the construct measured by these ORTO-15 items will be referred to as ‘healthy’ type disordered eating to reflect this obsessive health food preoccupation and differentiate between the two disordered eating dependent variables measured in the current study.

Body image states scale.

The Body Image States Scale (BISS) by Cash and colleagues [ 66 ] is a six-item measure of momentary evaluative and affective experiences of one’s own physical appearance. The BISS evaluates the following aspects of current body experience: dissatisfaction-satisfaction with overall physical appearance; dissatisfaction-satisfaction with one’s body size and shape; dissatisfaction-satisfaction with one’s weight; feelings of physical attractiveness-unattractiveness; current feelings about one’s looks relative to how one usually feels; and evaluation of one’s appearance relative to how the average person looks [ 66 ]. Participants responded to these items using a 9-point Likert-type scale which is presented in a negative-to-positive direction for half of the items, and a positive-to-negative direction for the other half [ 66 ]. Respondents were instructed to select the statement that best captured how they felt “ right now at this very moment ”. A total BISS score was calculated by reverse-scoring the three positive-to-negative items, summing the six-items, and finding the mean, with higher total BISS scores indicating more favourable body image states.

During the development and implementation of the BISS, Cash and colleagues [ 66 ] report acceptable internal consistency and moderate stability over time, an anticipated outcome due to the nature of the BISS as a state assessment tool. The BISS was also appropriately correlated with a range of trait measures of body image, highlighting its convergent validity [ 66 ]. Cash and colleagues [ 66 ] also report that the BISS is sensitive to reactions in positive and negative situational contexts and has good construct validity. An acceptable Cronbach’s alpha coefficient of.88 was obtained in the current study.

Sociocultural Attitudes Towards Appearance Questionnaire—4.

The Sociocultural Attitudes Towards Appearance Questionnaire– 4 (SATAQ-4) [ 67 ] is a 22-item self-report questionnaire that assesses the influence of interpersonal and sociocultural appearance ideals on one’s body image, eating disturbance, and self-esteem. Ratings are captured on a 5-point Likert scale which asks participants to specify their level of agreement with each statement by choosing from 1 ( definitely disagree ) through to 5 ( definitely agree ), with higher scores indicative of greater pressure to conform to, or greater internalisation of, interpersonal and sociocultural appearance ideals [ 67 ]. The five subscales of the SATAQ-4 measure: internalisation of thin/low body fat ideals, internalisation of muscular/athletic ideals, influence of pressures from family, influence of pressure from peers, and influence of pressures from the media [ 67 ]. For the purposes of the present study, the questions from the media pressure subscale were modified to enquire specifically about social media rather than traditional forms of media.

Across all samples in Schaefer et al’s. [ 67 ] study, the internal consistency of the five SATAQ-4 subscales is considered acceptable to excellent, with Cronbach’s alpha scores between 0.75 and 0.95. These subscales also displayed good convergent validity with other measures of body satisfaction, eating disorder risk, and self-esteem [ 67 ]. Pearson product-moment correlations between the SATAQ-4 subscales and convergent measures revealed medium to large positive associations with eating disorder symptomology, medium negative associations with body satisfaction, and small negative associations with self-esteem [ 67 ]. A Cronbach’s alpha of.87 was obtained in the present study, demonstrating acceptable internal consistency.

Ethical approval for the present study was granted by the Charles Sturt University Human Research Ethics Committee (Approval number H21155) prior to data collection. Participants were directed to the study via an online link to QuestionPro where they were provided an explanation of the study, their rights, and contact details of relevant support services if they were to become distressed. Participants gave informed consent by clicking on a link that read, “I consent to participate” at the beginning of the survey and then again through the submission of their completed survey. Any incomplete responses were not included in the dataset. Data collection commenced on the 30 th of July 2021 and ceased on the 1st of October 2021. In line with the cross-sectional and descriptive aspects of the research, participants were asked demographic questions about their gender, age, relationship status, ethnicity, country of residence, TikTok usage, and exercise habits. Participants then completed the experimental set in the following order: BISS (pre-test), SATAQ-4 (pre-test), EAT-26, ORTO-15, Experimental intervention (control or experimental TikTok video condition), SATAQ-4 (post-test), BISS (post-test), and debrief. All questionnaires presented to each participant were identical. Measures were not randomised to ensure that body image and internalisation were assessed at both pre- and post-test to evaluate the experimental manipulation.

Participants were randomly allocated to one of two conditions: experimental (pro-ana TikTok video) or control (“normal” TikTok video). Participants allocated to the experimental condition watched a compilation of TikTok videos containing explicit disordered eating messages such as young women restricting their food, displaying gallows humour about their disordered eating behaviour, starving themselves, and providing weight loss tips such as eating ice cubes and chewing gum to curve hunger. Participants in the experimental condition were also exposed to more implicit body image ideals typical of fitspiration-style content. This included thin women displaying their abdomens, cinched waists, dancing in two-piece swimwear, along with workout and juice cleanse videos promising fast weight loss. Participants in the control condition viewed a compilation of TikTok videos containing scenes relating to nature, cooking and recipes, animals, and comedy. After viewing the 7- to 8-minute TikTok video, all participants completed measures of internalisation and body satisfaction again to assess the influence of either the pro-ana TikTok video or the normal TikTok video. The debrief statement made explicit to participants the rationale of the study and explained the non-normative content of the videos shown to the experimental group. A small financial incentive was offered via a prize draw of five vouchers.

Statistical analysis

The data from QuestionPro was collated and analysed using IBM SPSS Statistics software, Version 28. All measures and manipulations in the study have been disclosed, alongside the method of determining the final sample size. No data collection was conducted following analysis of the data. Data for this study is available via the Figshare data repository and can be accessed at https://doi.org/10.6084/m9.figshare.25756800.v1 . This study was not preregistered. Sample size was determined before any data analysis. A priori power analyses were conducted using G*Power to determine the minimum sample sizes required to test the study hypotheses. Results indicated the required sample sizes to achieve 90% power for detecting medium effects, with a significance criterion of α = 0.05, were: N = 108 for the mixed between-within subjects ANOVAs and N = 232 for the one-way between groups ANOVAs. According to these recommendations, adequate statistical power was achieved. All univariate and multivariate assumptions were checked and found to be met. All scales and independent variables were normally distributed.

The analysis of the current study including data screening processes, descriptive statistics, and hypothesis testing will be presented in this section. Hypothesis testing began with two separate mixed between-within subjects analysis of variance models (ANOVAs) to examine the impact of the experimental manipulation on the independent variables of body image and internalisation of appearance ideals and pressure. Finally, the effect of time spent using TikTok daily on restrictive and ‘healthy’ disordered eating behaviour was explored cross-sectionally using two separate one-way between-subjects ANOVAs.

Data screening

Prior to statistical analysis, data were screened for entry errors and missing data. Of the 838 participants who initially consented to participate in the survey, 555 responses were insufficiently complete for data analysis. As participants were permitted to withdraw their consent by exiting the online survey, these results were excluded from all subsequent analyses. Of those that did not complete the study, the majority withdrew during the BISS (pre-test) and the ORTO-15, suggesting that these participants potentially experienced discomfort or distress when asked to reflect on their appearance and their eating behaviours. Of the completed responses, nine were excluded due to not meeting the study’s stated age eligibility and another case was excluded due to disclosure of a previous eating disorder diagnosis. The remaining data set comprised of 273 participants.

Descriptive statistics

Demographic characteristics..

In the current sample, 50% of participants reported being currently single and most participants (83%) were Caucasian, with 71% of participants indicating that they spent up to two hours per day using TikTok. Further demographic information is provided in Table 1 .

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https://doi.org/10.1371/journal.pone.0307597.t001

#ForYou: TikTok consumption demographics.

Participants in the current study reported that entertainment (75%), fashion (59%), beauty/skincare (54%), cooking/recipes (51%) and life hacks/advice (51%) content frequently occurred on their For You page. Largely in keeping with this, participants reported experiencing the most enjoyment from viewing entertainment (84%), life hacks/advice (57%), home renovation (56%), recipes/cooking (56%), and fashion (54%) content on their For You page.

In the current sample, 64% of participants reported being exposed to disordered eating content via their For You page. Only 15% of participants had not been exposed to any negative content themes. Further descriptive For You page content information is displayed below in Table 2 .

https://doi.org/10.1371/journal.pone.0307597.t002

Notably, 43% of the participant sample were frequently exposed to fitness and sports related content and the same percentage of the sample enjoyed seeing this content, suggesting that content broadly aligned with #fitspiration was consumed and appreciated by nearly half of participants. Concerningly, 40–60% of participants had been exposed to negative TikTok content via the For You Page, with content ranging from self-harm and suicidality to violence and illegal activity. No data was collected on the specifics of this content, however, and it is possible that some “negative” content may be framed from a proactive, preventative perspective, and this warrants further consideration.

Hypothesis testing: Cross-sectional analysis

Hypothesis 1: daily tiktok use and disordered eating behaviour..

To test the cross-sectional analysis of this study, two separate one-way between-groups ANOVAs were conducted to explore the impact of daily amount of TikTok use on ‘healthy’ disordered eating and restrictive disordered eating behaviour. This was necessary as time on TikTok was measured categorically. Participants were divided into four groups according to their average daily time spent using TikTok (Low use group: 1 hour or less; Moderate use group: 1–2 hours; High use group: 2–3 hours; Extreme use group: 3+ hours). Homogeneity of variance could be assumed for each ANOVA as indicated by non-significant Levene’s Test Statistics.

There was no statistically significant difference at the p < .05 level in ORTO15 scores for the four TikTok usage groups: F (3, 269) = .38, p = .78, indicating that ‘healthy’ disordered eating did not significantly differ across women who use TikTok for different periods of time per day. The effect size, calculated using eta squared, was.004, which is considered small in Cohen’s [ 68 ] terms. This small effect size is congruent with the non-significant finding.

The second ANOVA measuring differences among EAT-26 scores across the four TikTok usage groups also yielded a non-significant result: F (3, 269) = 1.21, p = .31. Eta squared was calculated as.01, representing a small effect size [ 68 ] consistent with this non-significant result. The means and standard deviations of the four TikTok usage groups across dependent variables of ‘healthy’ and restrictive disordered eating, as measured by the ORTO15 and the EAT-26 respectively, are displayed in Table 3 .

https://doi.org/10.1371/journal.pone.0307597.t003

Hypothesis testing: Experimental analyses

Hypothesis 2: body image satisfaction across groups from pre-test to post-test..

To evaluate the effect of the experimental intervention on body image, a 2 x 2 mixed between-within subjects ANOVA was conducted with condition (experimental vs control) as the between subjects factor and time (pre-manipulation vs post-manipulation) as the within subjects factor. All assumptions were upheld, including homogeneity of variance-covariance as indicated by Box’s M ( p >.001) and Levene’s ( p >.05) tests [ 64 ].

The interaction between condition and time was significant, Wilks’ Lambda = .98, F (1, 271) = 6.83, p = .009, partial eta squared = .03, demonstrating that the change in body image scores from pre-manipulation to post-manipulation was significantly different for the two groups. The body image satisfaction scores for women in both conditions decreased from pre-manipulation to post-manipulation. As anticipated, participants in the experimental condition reported a greater decrease in body image satisfaction than women in the control condition (see Table 4 ). This interaction effect is displayed in Fig 1 .

https://doi.org/10.1371/journal.pone.0307597.g001

https://doi.org/10.1371/journal.pone.0307597.t004

Although not consequential to the testing of the experimental manipulation, statistically significant main effects were also found for time, Wilks’ Lambda = .89, F (1, 271) = 32.99, p = < .001, partial eta squared = .109 and condition, F (1, 271) = 4.42, p = .036, partial eta squared = .016. The means and standard deviations of these main effects are displayed in Table 4 .

Hypothesis 3: Internalisation of societal beauty standards across groups from pre-test to post-test.

A second 2 x 2 mixed between-within subjects ANOVA was conducted to investigate the effect of the experimental manipulation on participants’ internalisation scores. All assumptions for the mixed model ANOVA were met with no violations.

A statistically significant interaction was found between group condition and time, Wilks’ Lambda = .97, F (1, 271) = 8.16, p = .005, partial eta squared = .029. This significant interaction highlights that the change in degree of internalisation at pre-manipulation and post-manipulation is not the same for the two conditions. Interestingly, the internalisation scores for women in the control group decreased from pre-manipulation to post-manipulation, whilst as anticipated, internalisation scores for women in the experimental group increased following exposure to the manipulation (see Table 5 ). This interaction is displayed in Fig 2 .

https://doi.org/10.1371/journal.pone.0307597.g002

https://doi.org/10.1371/journal.pone.0307597.t005

No statistically significant main effects were found for time, Wilks’ Lambda = .987, F (1, 271) = 3.59, p = .059, partial eta squared = .013 or condition, F (1, 271) = 2.65, p = .104, partial eta squared = .010. The means and standard deviations of internalisation scores for each condition at pre-manipulation and post-manipulation are displayed below in Table 5 .

The current study investigated the effect of TikTok content on women’s body image satisfaction and degree of internalisation of appearance ideals, and whether greater TikTok use contributed to increased disordered eating behaviour. In support of the hypotheses, exposure to pro-ana TikTok content significantly decreased participants’ body image satisfaction and increased participants’ degree of internalisation of appearance ideals. The hypothesis that greater daily TikTok use would contribute to increased disordered eating behaviour was not supported, as no statistically significant differences in restrictive disordered eating or ‘healthy’ disordered eating were found between the low, moderate, high, and extreme daily TikTok use groups.

Cross-sectional findings

Daily tiktok use and disordered eating behaviour..

Contrary to expectations, differences among groups on measures of restrictive disordered eating and ‘healthy’ disordered eating did not reach statistical significance. The proposed hypothesis that greater daily TikTok usage would be associated with disordered eating behaviour and attitudes was therefore unsupported. Despite lacking statistical support, participants categorised in the ‘high’ and ‘extreme’ daily TikTok use groups reported an average EAT-26 score of 18.16 and 19.09, respectively. Considering that an EAT-26 cut-off of ≥ 20 indicates potential clinical psychopathology, this mean score illustrates that exposure to TikTok content for two or more hours per day may contribute to a clinical degree of restrictive disordered eating.

The failure of the present study to detect any significant differences in disordered eating behaviours among participants with different TikTok daily usage does not align with the Transactional Model [ 33 ]. According to this model, risk factors such as low self-esteem and high thin ideal internalisation may predispose an individual to seek gratification via social media, resulting in body dissatisfaction and negative affect. The Transactional Model therefore proposes that a positive correlation exists between time spent on social media and body image dissatisfaction. Our findings also do not align with the conclusions Frieiro Padín et al. [ 34 ] drew from their review of the literature, in which a strong connection was identified between time on social media and heightened body image concerns and internalisation of the thin ideal, as well as eating disorder psychopathologies, though a distinction in outcome measures must be noted.

Based on the aforementioned sociocultural theory and previous research [see 28 , 43 , 48 ], it was assumed that increased body dissatisfaction as a result of increased time spent on social media (as stipulated by the Transactional Model), would lead to greater disordered eating behaviour. However, this was not supported statistically in the data. As postulated by Culbert et al. [ 69 ], disordered eating behaviour may instead only be a risk of media exposure if individuals are prone to endorse thin-ideals. Individuals in the present study that reported ‘high’ and ‘extreme’ daily TikTok use may have felt satisfied with their bodies and experienced lower thin-ideal internalisation. This could have potentially buffered the negative effect of greater TikTok content exposure and accounted for the lack of significant differences in disordered eating behaviour between groups. The quantity of TikTok consumption remains a pertinent question for disordered eating behaviour. As per the present study’s brief experimental manipulation, findings suggest that high frequency of daily TikTok use does not necessarily contribute to greater disordered eating behaviour than short exposures to this content.

Content presented to the pro-ana TikTok group included a mix of explicit and implicit pro- eating disorder messages as well as fitspiration content. Fitspiration content presented in the current study included workout videos to achieve a “smaller waist” and “toned abs” where female creators with slim, toned physiques sporting activewear took viewers through a series of exercises, advising viewers that they would “see results in a week”. In the present study, diet-related fitspiration content presented included the concoction of juices to “get rid of belly fat” and advice on the best “diet for a small waist” which requires avoidance of all meat, dairy, junk food, soda, and above all, to make “no excuses”. Fitspiration style content in the current study totalled one-minute, compared to disordered eating themes which totalled six minutes. The integration of these various types of content, although reflective of the For You function in TikTok, impeded our ability to determine the singular impact of fitspiration or disordered eating content, respectively, on body image and internalisation of societal beauty standards, but did reflect social media as it is consumed beyond experimental research settings.

Experimental findings

Tiktok and body image states..

The hypothesis that women exposed to pro-ana TikTok content would experience a significant decrease in body image compared to women who viewed the control TikTok content was supported. The present study found a significant interaction effect of body image between group condition (control vs experimental) and time period (pre-manipulation vs post-manipulation), as well as significant main effects. It is important to note that the statistic of interest in evaluating the success of the experimental manipulation is the interaction effect, thus main effects must be interpreted secondarily and with caution [ 64 ]. Women in the experimental group reported significantly lower body image satisfaction after exposure to the pro-ana TikTok content and compared to women who viewed the control content. This finding corroborates Festinger’s [ 27 ] Social Comparison Theory that posits people naturally evaluate themselves in comparison to others. Exposure to the pro-ana TikTok content, consisting of various thin bodies and messaging around weight loss, may have provided the opportunity for women to engage in maladaptive upward social comparisons, resulting in reduced body image satisfaction. The present study upholds previous findings of Engeln-Maddox, Tiggemann, McComb and Mills, and Gibson [ 29 , 32 , 39 , 70 ] who suggest that visual exposure to thin bodies may adversely affect one’s level of body image satisfaction and extends this research by replicating this finding in the context of a contemporary media platform, TikTok, and by utilising an experimental design.

Contradicting the present study and previous research, Pryde and Prichard [ 42 ] found no significant increase in young women’s body dissatisfaction following exposure to fitspiration TikTok content. A potential explanation for this finding is that the performance of physical movements captured in fitspiration videos may shift the focus of viewers from aesthetics to functionality, highlighting physical competencies and capabilities which has been shown to improve body image satisfaction in young women [ 71 ]. Pryde and Prichard’s [ 42 ] fitspiration content did not include typically occurring harmful themes as the present study did, potentially reducing the negative implications for body image satisfaction of exposure to such content in real world contexts.

Interestingly, women in the control group also reported a statistically significant decrease in body image satisfaction after viewing the neutral TikTok content, a finding that underscores the possible complexity of social media’s influence on body image, as identified in research by Huülsing [ 72 ]. This is an unexpected finding, as the TikTok content displayed to the control group was selected specifically to be unrelated to appearance ideals and pressures. One possible reason for this result is the repetition of administration of the BISS within a short time period. Completing the BISS twice may have caused participants to focus more attention on their body appearance than usual, resulting in more critical appraisals regardless of the experimental stimuli to which they were exposed. This notion aligns with previous research that found focusing on the appearance of body was associated with lower body image satisfaction, whereas focus on the function of the body was associated with more positive body image states [ 71 ].

One potential explanation for this finding is that the control group stimuli was contaminated and produced an unintentional effect on body image scores. Two-minutes of footage within the seven-minute control group TikTok compilation presented the human body including legs, arms, and hands. Although this body-related content was neutral in nature, it may be that even ‘harmless’ representations of the human body are sufficient to elicit a social comparison response in participants or in some capacity, reinforce the #fitspiration motifs commonly depicted on TikTok [ 1 ], therefore impacting body image scores at post-manipulation. This possible explanation has implications for TikTok use and women’s body image, as it suggests that viewing even benign content of human bodies for less than 10-minutes can have an immediate detrimental impact on body image states, even when this content is unrelated to body dissatisfaction, thinness, or weight loss. Furthermore, although a statistically significant body image decrease was detected in the control group, this finding must be interpreted with caution due to the significant interaction effect obtained.

TikTok and internalisation of societal beauty standards.

In accordance with the hypothesis, women in the experimental group reported a significant increase in their degree of internalisation of appearance ideals following exposure to pro-ana TikTok content. Women in the experimental group also reported significantly greater internalisation of appearance ideals than women in the control group. Conversely to the experimental group, internalisation scores of the control group decreased after viewing the neutral TikTok content. These findings are in line with the sociocultural theory, as women reported increased internalisation of societal beauty standards following exposure to media content explicitly and implicitly portraying the thinness ideal. The present study supports Mingoia et al’s. [ 53 ] meta-analysis, which yielded a positive association between social networking site use and the extent of internalisation of the thin ideal and furthers this notion by replicating the finding with TikTok specifically and utilising an experimental design.

In the current study, participants were subject to a single brief exposure of pro-ana TikTok content, whereas most of the sample indicated that their TikTok use was up to two hours per day. This suggests that the degree of internalisation of appearance ideals in participants lives outside of the experiment are likely to be much greater. Mingoia et al. [ 53 ] also found that the use of appearance-related features on social networking sites, such as posting and viewing photos and videos, demonstrated a stronger relationship with the internalisation of the thin ideal than the use of social networking features that were not appearance-related, such as messaging and writing status updates. As TikTok is a video sharing app and most of its content generally features full-body-length camera shots rather than a face or head shot, this finding suggests that TikTok users could potentially internalise body-related societal standards to a greater extent than users of other social media apps that typically feature head shots.

The finding that women internalised societal beauty standards to a greater degree after being exposed to pro-ana TikTok content corroborates the sociocultural theory’s emphasis of the significance of social influences in internalisation. TikTok users may be exposed to all three social influences (i.e., media, peers, and family) simultaneously on a single platform which may encourage internalisation of appearance-ideals in a more profound manner than any of these three influences in isolation. One point of difference between TikTok and other social media apps is that much content on the app is generated by “ordinary” individuals, rather than supermodels or celebrities. This enables blatantly insidious and diet-related content to circulate the app with less policing and scrutiny compared to content produced by an influencer or celebrity who may be more likely to be criticised or cancelled for socially irresponsible messaging and also provides the opportunity for more horizontal social comparisons and peer-to-peer style interactions rather than upward social comparisons.

Indeed, in their study of American teens, Mueller et al. [ 52 ] identified that girls were especially likely to engage in weight loss behaviour if a high proportion of girls with a similar BMI were also engaging in weight loss behaviours. This indicates that internalisation was strongest when appearance-ideals were promoted by alike peers. Due to the fact that much pro-ana TikTok content is created by young women, Mueller et al’s. [ 52 ] finding has problematic implications for the young female users of TikTok, in that harmful diet-related messages could be internalised to a greater extent on TikTok than on other platforms and potentially lead to body image disturbances, disordered eating behaviour, and other negative outcomes among young women.

General discussion

The findings of the current study are important but must also be understood within the broader context of participant’s daily lives beyond their participation in this study. Everyday female-identifying individuals are exposed to a multitude of different sources of information from which body image related stimuli can be drawn. The present study’s experiment was not conducted in a controlled environment due to its online nature, therefore researchers did not have the ability to assess and control for other pieces of body image-related information that participants might have consumed prior to participation that may have been salient for their body image. Further research is required to identify how sustained a change in body image states as measured by the BISS may be over time.

The findings of this study provide some insights into how social media influences disordered eating behaviour and mental health; a theoretical gap in the literature that Choukas-Bradley et al. [ 6 ] highlight as holding back research in this domain. In particular, the findings of the current study indicate that short periods of exposure to disordered TikTok content have an effect, while the high-range EAT-26 scores observed for those who engaged with TikTok for two or more hours a day also raise questions about the duration of exposure. Nonetheless, our findings demonstrate that short exposure periods are sufficient to have a negative effect on body image and internalisation of the thin ideal.

One point that may be readily overlooked in developing a theoretical framework around social media’s influence is that the narrative arc of TikTok videos is such that users are exposed to many short stories in quick succession, which may have a different effect to longer form content from a single content creator. As Pierce [ 2 ] notes, the speed of exposure to overlapping, but separate narratives depicted in successive videos, is an important feature of TikTok content and may contribute to the influence of such platforms on disordered eating and body attitudes. Each piece of content serves as a standalone narrative but may also overlap and interact with the viewer’s experience of the next video they watch to build a cumulative, normalised narrative of disordered body- and eating-practices.

In the current study, participants who engaged with TikTok for two-three hours a day were classified as high users, and those who used TikTok for three or more hours were classed as extreme. These rates of usage may, however, be quite normative, with Santarossa and Woodruff [ 73 ] citing three-four hours a day on social media as normative for their sample of young adults, though notably participants in the current study were only questioned about their TikTok usage, not their general use of social media.

While we examined the effect of pro-ana content in this study, that some changes were observed in the control group as well as the experimental group indicates that the social media environment, characterised as it is by idealisation, instant feedback, and readily available social comparison [ 6 ], may play a general role in diminishing positive body image attitudes and healthy aspirations. This is supported by Tiggemann and Slater’s [ 35 , 36 ] research in which social media usage was found to correlate positively with higher levels of body image concerns, in contrast to time spent on the internet more generally, and this may be particularly true for visually oriented platforms that sensitize viewers to their own appearance and that of others. As noted previously, of the visually-oriented social media platforms, predominantly TikTok and Instagram, videos are commonly framed on TikTok so that the subject’s whole body is visible, particularly in dance videos and in #GymTok content, where on Instagram, cameo style head-shot videos appear more likely to feature, which further suggests that TikTok may provide more body-related stimuli than other platforms, even when the intention of the content does not relate to body-image or #fitspiration.

Importantly, the algorithm on TikTok functions in such a way that those who actively seek out body positivity content may also be exposed to nefarious body-related content such as body checking, a competitive, self-surveillance type of content where users are encouraged to test out their weight by attempting to drink from a glass of water while their arm encircles another’s waist. As McGuigan [ 74 ] reports, watching just one body checking video may result in hundreds more filtering through a user’s For You page, with those actively attempting to seek out positive body image content likely to be inadvertently exposed to disordered content due to the configuration of the algorithm. This function of the For You page is demonstrated in the current study, with 64% of participants reporting having seen disordered eating content on their For You page, higher than any other kind of harmful content, including suicide and bullying. The current study did not assess participants’ consumption of #FoodTok, #GymTok, and #Fitspiration. Engagement with these dimensions of TikTok and the type of content that participants seek out via the search function warrant consideration in future research.

The TikTok algorithm underscores Logrieco et al’s. [ 18 ] findings that even anti-anorexia content can be problematic, especially given complexities in determining and controlling what is performatively problematic, including videos discussing recovery and positive body attitudes that may somewhat paradoxically further body policing and competition among users and consumers of social media content. Furthermore, as Logrieco et al. [ 18 ] highlight, TikTok is replete in both pro-ana and much more implicit body-related content that may be harmful to viewers, not to mention those creating the content, whose experiences also warrant consideration.

Theoretical and practical implications

The present study bridged an important gap in the literature by utilising both experimental and cross-sectional designs to examine the influence of pro-ana TikTok content on users’ body image satisfaction, internalisation of body ideals, and disordered eating behaviours. While the negative impact of social media on body image and eating behaviours has been established in relation to platforms such as Instagram and Twitter, TikTok’s rapid emergence and unique algorithm warrant independent analysis.

The present findings have important theoretical implications for the understanding of sociocultural influences of orthorexia nervosa development. Notably, this study is one of the first to highlight the association between orthorexia nervosa and the tripartite model of disordered eating using an experimental design. The results illustrate that the internalisation of sociocultural appearance ideals predicts the development of ‘healthy’ disordered eating, as suggested by the tripartite theory. Western culture ideals do seem to influence the expression of orthorexic tendencies, thus caution should be exercised by women when interacting with appearance-related TikTok content.

Unlike explicit pro-ana content, which is open to condemnation, the moral and health-related discourses underpinning much body-related content in which thinness and health are espoused as goodness, reflects a new trend in diet culture masquerading as wellness culture [ 20 , 21 ]. Questions are raised around the ethics of social media algorithms when the technologically fostered link between recovery-focused content and disordered-content on TikTok is laid bare, particularly considering that extant research has found individuals with experience of eating disorders often seek out support, safety, and connection online [ 49 ] and in doing so on a platform like TikTok, may be exposed to more disordered eating content than the average user. Given visual social media platforms are associated with higher levels of dysfunction in relation to body image [ 4 ], the policy and ethics of such platforms warrant scrutiny from a variety of stakeholders in management, marketing, technology regulation, with psychology playing an important role in the marketing of these platforms. As traditional journalistic platforms have been subjected to scrutiny and reform, so too must a climate of accountability be established within the social media nexus.

The widespread growth of social media may warrant greater concern than traditional forms of mass media, not only because of the full-time accessibility and diverse range of platforms, but also due to the prevalence of peer-to-peer interactions. According to the social comparison theory, comparison of oneself to others has traditionally considered more removed, higher status influences (e.g., celebrities, actors/actresses, supermodels) as a greater source of pressure than those in the individuals’ natural environment (e.g., family and peers). Re-examination of this theoretical perspective is warranted considering the contemporary challenges of social media and the perpetuation of body image messages from alike peers. Furthermore, a diverse range of “content” may trigger disordered body- and eating-related attitudes, including #fitspiration and #GymTok, which poses challenges for social media platforms in regulating content. The inclusion of orthorexia in the milieu highlights the disordered nature of seemingly benign health practices and social media content.

That TikTok content containing explicit and implicit pro-ana themes may readily remain on the app uncensored exemplifies the importance of protective strategies to build resilience at the individual level. One such protective strategy is shifting focus from body appearance to functionality. Alleva and colleagues [ 71 ] investigated the Expand Your Horizon programme, designed to improve body image by training women to focus on body functionality. They report that women who engaged with the Expand Your Horizon programme experienced greater satisfaction with body image and functionality, body appreciation, and reduced self-objectification compared to women who did not engage with the program. Health professionals involved in the care of women with eating disorders and other mental health issues should also be educated to ensure they are knowledgeable about the social media content their clients may be exposed to, equipping them with skills to engage in conversations about the potential detrimental impacts of viewing pro-ana and other harmful TikTok content [ 53 ].

The administration of such programs in schools, universities, community groups, and clinical settings could prove effectual in the prevention of disordered eating and body image disturbance development and may reduce symptom severity of a pre-established disorder. Such programs must be developed with great care, however, given the propensity for even anti-anorexia content to have a negative effect on those consuming it [ 18 ]. The development of self-compassion may also build resilience in women, with research confirming that self-compassion can be effectively taught [ 75 ]. Subsequently, programs have been developed such as Compassion Focused Therapy (CFT) in which clients are trained to develop more compassionate self-talk during negative thought processes and to foster more constructive thought patterns [ 76 ]. The value of CFT has been established in the literature with both clinical and non-clinical samples and has promising outcomes particularly for those high in self-criticism [ 77 ].

Young women should be provided with media literacy tools that can assist in advancing critical evaluations of the online world. Digital manipulation of advertising and celebrity images is well known to many people, however, this awareness may be lacking regarding social media images, as they are generally disseminated within one’s peer network rather than outside of it [ 33 ]. Media literacy interventions may educate women about how social media perpetuates appearance-ideals that are often unrealistic and unattainable [ 53 ]. As an example, Posavac et al. [ 78 ] revealed that a single media literacy intervention resulted in a reduction in women’s social comparison to body ideals portrayed in the media.

Such interventions might be extended to female-identifying TikTok users to educate them on the manipulation of videos to produce idealised portrayals of the self. Media literacy should be commenced from an early age by teaching children, adolescents, and adults to understand the influence of implicit messages conveyed through social media and to create media content that is responsible and psychologically safe for others [ 79 ]. Increased understanding of messages portrayed by social media content may prevent thin-ideal endorsement and internet misuse. Notably, however, the most effective approach would be to address the problem at its source and increase the regulation of social media companies, rather than upskill users in how to respond to harmful online environments, which creates further labour for the individual while allowing organisations to continue to produce harmful but easily monetizable content.

Limitations and future directions

To meet the requirements to run multivariate analyses, the continuous data of body image and internalisation scores were dichotomised using a median split to create ‘low’ and ‘high’ groups for each variable. Although dichotomisation was necessary to perform appropriate analyses and power analyses deemed the sample size as adequate following performance of the median split, dichotomising these variables may have contributed to a loss of statistical power to detect true effects.

Limitations are implicated in the use of the ORTO-15 in the present study. The ORTO-15 does not account for different lifestyle factors that may alter a participants’ response, such as dietary restrictions, food intolerances, or medical dietary guidelines. The discrepancies in literature surrounding the psychometric properties of the ORTO-15 may be attributable to the lack of established diagnostic criteria of orthorexia nervosa, cultural differences in expressions of eating disorders, and difficulty comparing research results in determining orthorexia nervosa diagnoses due to inconsistencies in testing questions and cut-off values [ 61 ]. Due to unacceptable reliability in the present study, a factor analysis was performed which identified a factor relating to health food preoccupation. This identified factor was used as the ORTO-15 measure and data from these 5-items were used in analyses and referred to throughout the present study as ‘healthy’ disordered eating. Using the 5-items related to ‘healthy’ disordered eating rather than the complete 15-item scale may not have accurately assessed participants’ degree of orthorexic tendencies. Despite these limitations, the ORTO-15 is the only accepted measure of orthorexic tendencies available [ 63 ]. Additionally, more limitations would likely have been encountered by using the full 15-item measure lacking reliability, compared to utilising the 5-item factor with acceptable reliability.

Future studies of TikTok and disordered eating behaviour should incorporate a measure of social comparison to verify whether social comparison is the vehicle through which women experience decreased body image satisfaction after viewing TikTok content. Future research should also examine the influence of TikTok content creation on body image, internalisation of thinness, and disordered eating behaviour and explore the association between what individuals consume on TikTok and the social media content that they produce. This research should be conducted using more diverse samples of women, including transgender women, to determine whether the findings of the present study are relevant for this population given the unique challenges regarding body image and societal beauty standards that they may experience.

Longitudinal studies are also warranted to examine the effect of exposure to pro-ana TikTok content over time, and to assess the effects of pro-ana TikTok content on body image satisfaction and eating disorder symptomology over time. Further research on orthorexia nervosa is needed to establish a more reliable measure of orthorexic tendencies and this would enable future investigation of the impact of pro-ana TikTok content on the development of orthorexia nervosa, as well as individual differences as predisposing factors in the development of orthorexic tendencies. Finally, future research should examine the efficacy of media literacy and self-compassion intervention programs as a protective factor specifically in the TikTok context, where disordered eating messages are more explicit in nature than traditional media and other social media platforms.

The findings of the current study support the notion that pro-ana TikTok content decreases body image satisfaction and increases internalisation of societal beauty standards in young women. This research is timely given reliance on social media for social interaction, particularly for young adults. Our findings indicate that female-identifying TikTok users may experience psychological harm even when explicit pro-ana content is not sought out and even when their TikTok use is time-limited in nature. The findings of this study suggest cultural and organisation change is needed. There is a need for more stringent controls and regulations from TikTok in relation to pro-ana content as well as more subtle forms of disordered eating- and body-related content. Prohibiting or restricting access to pro-ana content on TikTok may reduce the development of disordered eating and the longevity and severity of established eating disorder symptomatology among young women in the TikTok community. There are current steps being taken to delete dangerous content, including blocking searches such as “#anorexia”, however, there are various ways users circumvent these controls and further regulation is required. Unless effective controls are implemented within the platform to prevent the circulation of pro-ana content, female-identifying TikTok users may continue to experience immediate detrimental consequences for body image satisfaction, thin-ideal internalisation, and may experience an increased risk of developing disordered eating behaviours.

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UPDATED Aug. 14, 2024, at 10:11 AM

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