can descriptive research be qualitative

41.1 What Is Descriptive Research?

The type of question asked by the researcher will ultimately determine the type of approach necessary to complete an accurate assessment of the topic at hand. Descriptive studies, primarily concerned with finding out "what is," might be applied to investigate the following questions: Do teachers hold favorable attitudes toward using computers in schools? What kinds of activities that involve technology occur in sixth-grade classrooms and how frequently do they occur? What have been the reactions of school administrators to technological innovations in teaching the social sciences? How have high school computing courses changed over the last 10 years? How do the new multimediated textbooks compare to the print-based textbooks? How are decisions being made about using Channel One in schools, and for those schools that choose to use it, how is Channel One being implemented? What is the best way to provide access to computer equipment in schools? How should instructional designers improve software design to make the software more appealing to students? To what degree are special-education teachers well versed concerning assistive technology? Is there a relationship between experience with multimedia computers and problem-solving skills? How successful is a certain satellite-delivered Spanish course in terms of motivational value and academic achievement? Do teachers actually implement technology in the way they perceive? How many people use the AECT gopher server, and what do they use if for?

Descriptive research can be either quantitative or qualitative. It can involve collections of quantitative information that can be tabulated along a continuum in numerical form, such as scores on a test or the number of times a person chooses to use a-certain feature of a multimedia program, or it can describe categories of information such as gender or patterns of interaction when using technology in a group situation. Descriptive research involves gathering data that describe events and then organizes, tabulates, depicts, and describes the data collection (Glass & Hopkins, 1984). It often uses visual aids such as graphs and charts to aid the reader in understanding the data distribution. Because the human mind cannot extract the full import of a large mass of raw data, descriptive statistics are very important in reducing the data to manageable form. When in-depth, narrative descriptions of small numbers of cases are involved, the research uses description as a tool to organize data into patterns that emerge during analysis. Those patterns aid the mind in comprehending a qualitative study and its implications.

Most quantitative research falls into two areas: studies that describe events and studies aimed at discovering inferences or causal relationships. Descriptive studies are aimed at finding out "what is," so observational and survey methods are frequently used to collect descriptive data (Borg & Gall, 1989). Studies of this type might describe the current state of multimedia usage in schools or patterns of activity resulting from group work at the computer. An example of this is Cochenour, Hakes, and Neal's (1994) study of trends in compressed video applications with education and the private sector.

Descriptive studies report summary data such as measures of central tendency including the mean, median, mode, deviance from the mean, variation, percentage, and correlation between variables. Survey research commonly includes that type of measurement, but often goes beyond the descriptive statistics in order to draw inferences. See, for example, Signer's (1991) survey of computer-assisted instruction and at-risk students, or Nolan, McKinnon, and Soler's (1992) research on achieving equitable access to school computers. Thick, rich descriptions of phenomena can also emerge from qualitative studies, case studies, observational studies, interviews, and portfolio assessments. Robinson's (1994) case study of a televised news program in classrooms and Lee's (1994) case study about identifying values concerning school restructuring are excellent examples of case studies.

Descriptive research is unique in the number of variables employed. Like other types of research, descriptive research can include multiple variables for analysis, yet unlike other methods, it requires only one variable (Borg & Gall, 1989). For example, a descriptive study might employ methods of analyzing correlations between multiple variables by using tests such as Pearson's Product Moment correlation, regression, or multiple regression analysis. Good examples of this are the Knupfer and Hayes (1994) study about the effects of the Channel One broadcast on knowledge of current events, Manaev's (1991) study about mass media effectiveness, McKenna's (1993) study of the relationship between attributes of a radio program and it's appeal to listeners, Orey and Nelson's (1994) examination of learner interactions with hypermedia environments, and Shapiro's (1991) study of memory and decision processes.

On the other hand, descriptive research might simply report the percentage summary on a single variable. Examples of this are the tally of reference citations in selected instructional design and technology journals by Anglin and Towers (1992); Barry's (1994) investigation of the controversy surrounding advertising and Channel One; Lu, Morlan, Lerchlorlarn, Lee, and Dike's (1993) investigation of the international utilization of media in education (1993); and Pettersson, Metallinos, Muffoletto, Shaw, and Takakuwa's (1993) analysis of the use of verbo-visual information in teaching geography in various countries.

Descriptive statistics utilize data collection and analysis techniques that yield reports concerning the measures of central tendency, variation, and correlation. The combination of its characteristic summary and correlational statistics, along with its focus on specific types of research questions, methods, and outcomes is what distinguishes descriptive research from other research types.

Three main purposes of research are to describe, explain, and validate findings. Description emerges following creative exploration, and serves to organize the findings in order to fit them with explanations, and then test or validate those explanations (Krathwohl, 1993). Many research studies call for the description of natural or man-made phenomena such as their form, structure, activity, change over time, relation to other phenomena, and so on. The description often illuminates knowledge that we might not otherwise notice or even encounter. Several important scientific discoveries as well as anthropological information about events outside of our common experiences have resulted from making such descriptions. For example, astronomers use their telescopes to develop descriptions of different parts of the universe, anthropologists describe life events of socially atypical situations or cultures uniquely different from our own, and educational researchers describe activities within classrooms concerning the implementation of technology. This process sometimes results in the discovery of stars and stellar events, new knowledge about value systems or practices of other cultures, or even the reality of classroom life as new technologies are implemented within schools.

Educational researchers might use observational, survey, and interview techniques to collect data about group dynamics during computer-based activities. These data could then be used to recommend specific strategies for implementing computers or improving teaching strategies. Two excellent studies concerning the role of collaborative groups were conducted by Webb (1982), and Rysavy and Sales (1991). Noreen Webb's landmark study used descriptive research techniques to investigate collaborative groups as they worked within classrooms. Rysavy and Sales also apply a descriptive approach to study the role of group collaboration for working at computers. The Rysavy and Sales approach did not observe students in classrooms, but reported certain common findings that emerged through a literature search.

Descriptive studies have an important role in educational research. They have greatly increased our knowledge about what happens in schools. Some of the important books in education have reported studies of this type: Life in Classrooms, by Philip Jackson; The Good High School, by Sara Lawrence Lightfoot; Teachers and Machines: The Classroom Use of Technology Since 1920, by Larry Cuban; A Place Called School, by John Goodlad; Visual Literacy: A Spectrum of Learning, by D. M. Moore and Dwyer; Computers in Education: Social, Political, and Historical Perspectives, by Muffoletto and Knupfer; and Contemporary Issues in American Distance Education, by M. G. Moore.

Henry J. Becker's (1986) series of survey reports concerning the implementation of computers into schools across the United States as well as Nancy Nelson Knupfer's (1988) reports about teacher's opinions and patterns of computer usage also fit partially within the realm of descriptive research. Both studies describe categories of data and use statistical analysis to examine correlations between specific variables. Both also go beyond the bounds of descriptive research and conduct further statistical procedures appropriate to their research questions, thus enabling them to make further recommendations about implementing computing technology in ways to support grassroots change and equitable practices within the schools. Finally, Knupfer's study extended the analysis and conclusions in order to yield suggestions for instructional designers involved with educational computing.

41.1.1 The Nature of Descriptive Research

The descriptive function of research is heavily dependent on instrumentation for measurement and observation (Borg & Gall, 1989). Researchers may work for many years to perfect such instrumentation so that the resulting measurement will be accurate, reliable, and generalizable. Instruments such as the electron microscope, standardized tests for various purposes, the United States census, Michael Simonson's questionnaires about computer usage, and scores of thoroughly validated questionnaires are examples of some instruments that yield valuable descriptive data. Once the instruments are developed, they can be used to describe phenomena of interest to the researchers.

The intent of some descriptive research is to produce statistical information about aspects of education that interests policy makers and educators. The National Center for Education Statistics specializes in this kind of research. Many of its findings are published in an annual volume

called Digest of Educational Statistics. The center also administers the National Assessment of Educational Progress (NAEP), which collects descriptive information about how well the nation's youth are doing in various subject areas. A typical NAEP publication is The Reading Report Card, which provides descriptive information about the reading achievement of junior high and high school students during the past 2 decades.

On a larger scale, the International Association for the Evaluation of Education Achievement (IEA) has done major descriptive studies comparing the academic achievement levels of students in many different nations, including the United States (Borg & Gall, 1989). Within the United States, huge amounts of information are being gathered continuously by the Office of Technology Assessment, which influences policy concerning technology in education. As a way of offering guidance about the potential of technologies for distance education, that office has published a book called Linking for Learning: A New Course for Education, which offers descriptions of distance education and its potential.

There has been an ongoing debate among researchers about the value of quantitative (see 40.1.2) versus qualitative research, and certain remarks have targeted descriptive research as being less pure than traditional experimental, quantitative designs. Rumors abound that young researchers must conduct quantitative research in order to get published in Educational Technology Research and Development and other prestigious journals in the field. One camp argues the benefits of a scientific approach to educational research, thus preferring the experimental, quantitative approach, while the other camp posits the need to recognize the unique human side of educational research questions and thus prefers to use qualitative research methodology. Because descriptive research spans both quantitative and qualitative methodologies, it brings the ability to describe events in greater or less depth as needed, to focus on various elements of different research techniques, and to engage quantitative statistics to organize information in meaningful ways. The citations within this chapter provide ample evidence that descriptive research can indeed be published in prestigious journals.

Descriptive studies can yield rich data that lead to important recommendations. For example, Galloway (1992) bases recommendations for teaching with computer analogies on descriptive data, and Wehrs (1992) draws reasonable conclusions about using expert systems to support academic advising. On the other hand, descriptive research can be misused by those who do not understand its purpose and limitations. For example, one cannot try to draw conclusions that show cause and effect, because that is beyond the bounds of the statistics employed.

Borg and Gall (1989) classify the outcomes of educational research into the four categories of description, prediction, improvement, and explanation. They say that descriptive research describes natural or man-made educational phenomena that is of interest to policy makers and educators. Predictions of educational phenomenon seek to determine whether certain students are at risk and if teachers should use different techniques to instruct them. Research about improvement asks whether a certain technique does something to help students learn better and whether certain interventions can improve student learning by applying causal-comparative, correlational, and experimental methods. The final category of explanation posits that research is able to explain a set of phenomena that leads to our ability to describe, predict, and control the phenomena with a high level of certainty and accuracy. This usually takes the form of theories.

The methods of collecting data for descriptive research can be employed singly or in various combinations, depending on the research questions at hand. Descriptive research often calls upon quasi-experimental research design (Campbell & Stanley, 1963). Some of the common data collection methods applied to questions within the realm of descriptive research include surveys, interviews, observations, and portfolios.

Exploring Phenomena: A Brief Guide to Conducting Descriptive Qualitative Research

This article summarizes descriptive qualitative research, a method used to explore and understand the characteristics and qualities of a phenomenon. The article explains key features of the method, such as the importance of detailed descriptions, open-ended questions, and context and meaning.

Table of Contents

Key features of the descriptive qualitative research.

Descriptive qualitative research is a method of research that is focused on understanding a phenomenon by examining its characteristics and qualities. We use this type of research when we want to explore a topic that has not been studied in depth before, or when we want to gain a better understanding of a previously studied topic but using a different perspective and gain valuable insights in the process.

Descriptive qualitative research is a type of qualitative research that explores the characteristics of a phenomenon, rather than explaining the underlying causes or mechanisms.

Goal of descriptive qualitative research

The goal of descriptive qualitative research is to provide a rich and detailed account of the phenomenon under study. Doing so allows us to develop further research questions. The activity will also help inform policy or practice.

Applicability of descriptive qualitative research

Researchers in various fields can use descriptive qualitative research, including social sciences, education, psychology, health sciences, and business.

In social sciences, for example, descriptive qualitative research can be used to explore social, cultural, or political issues, and to understand the perspectives and experiences of marginalized or underrepresented groups.

Data Collection Methods Used in Descriptive Qualitative Research

The data collection methods used in descriptive qualitative research can vary. Typically, the method involves an observation or interaction with the phenomenon being studied.

Strengths of the Descriptive Qualitative Method

Flexible research method, few and easily obtained resources.

Descriptive qualitative research can be conducted using relatively few resources, easily accessible, and can often be completed more quickly than other types of research. These resources include the following:

Captures the complexity and richness of a phenomenon

Another strength of descriptive qualitative research is its ability to capture the complexity and richness of a phenomenon.

Limitations of Descriptive Qualitative Research

Can be time consuming, potential for researcher bias.

Because descriptive qualitative research often involves the interpretation of data, researchers may inadvertently introduce their own biases into the analysis. One researcher’s perspective may vary from another researcher’s viewpoint in studying the same phenomenon.

The researcher’s bias can be minimized through careful data collection and analysis techniques, but it is important for researchers to be aware of their own biases and to mitigate their impact on the research.

Does not provide the same level of generalizability as quantitative research methods

Because we often focus descriptive qualitative research on a specific phenomenon or context, it may not be possible to generalize the findings to other contexts or populations.

Steps in Conducting Descriptive Qualitative Research

Step 1. identify the research question or topic of interest.

The first step is to identify the research question or topic of interest. Knowledge of the research agenda of an organization or institution where the researcher belongs will be most helpful.

Step 2. Determine the data collection method or methods to use

The data collection methods should be chosen based on their ability to provide rich and detailed information about the phenomenon under study.

Step 3. Analyze the data collected

Step 4. disseminate the findings.

Finally, the results of the descriptive qualitative research should be communicated to others. This may involve writing a report, presenting the findings at a conference, or publishing the research in a peer-reviewed journal . Other researchers can build on the findings.

Usefulness of the Qualitative Descriptive Research

While there are some limitations to descriptive qualitative research, it can still be an important method for understanding specific phenomena and contexts.

As with any research method, it is important for researchers to approach descriptive qualitative research with a critical eye and to be aware of the potential biases and limitations of the method.

Creswell, J. W. (2013). Qualitative inquiry and research design: Choosing among five approaches. Sage publications.

Guest, G., MacQueen, K. M., & Namey, E. E. (2012). Applied thematic analysis. Sage publications.

Patton, M. Q. (2002). Qualitative research and evaluation methods. Sage publications.

Van der Riet, P., & Durrheim, K. (2012). Qualitative data analysis and interpretation. Doing research in the real world. Sage publications.

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Qualitative research: three ethnographic research techniques, theoretical framework in research: 5 important points, 10 research methods: types and applications, about the author, patrick regoniel.

Dr. Regoniel, a hobbyist writer, served as consultant to various environmental research and development projects covering issues and concerns on climate change, coral reef resources and management, economic valuation of environmental and natural resources, mining, and waste management and pollution. He has extensive experience on applied statistics, systems modelling and analysis, an avid practitioner of LaTeX, and a multidisciplinary web developer. He leverages pioneering AI-powered content creation tools to produce unique and comprehensive articles in this website.

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Aug 19, 2024 • 17 minutes read

Qualitative research examples: How to unlock, rich, descriptive insights

Qualitative research uncovers in-depth user insights, but what does it look like? Here are seven methods and examples to help you get the data you need.

Armin Tanovic

Behind every what, there’s a why . Qualitative research is how you uncover that why. It enables you to connect with users and understand their thoughts, feelings, wants, needs, and pain points.

There’s many methods for conducting qualitative research, and many objectives it can help you pursue—you might want to explore ways to improve NPS scores, combat reduced customer retention, or understand (and recreate) the success behind a well-received product. The common thread? All these metrics impact your business, and qualitative research can help investigate and improve that impact.

In this article, we’ll take you through seven methods and examples of qualitative research, including when and how to use them.

Qualitative UX research made easy

Conduct qualitative research with Maze, analyze data instantly, and get rich, descriptive insights that drive decision-making.

7 Qualitative research methods: An overview

There are various qualitative UX research methods that can help you get in-depth, descriptive insights. Some are suited to specific phases of the design and development process, while others are more task-oriented.

Here’s our overview of the most common qualitative research methods. Keep reading for their use cases, and detailed examples of how to conduct them.

1. User interviews

A user interview is a one-on-one conversation between a UX researcher, designer or Product Manager and a target user to understand their thoughts, perspectives, and feelings on a product or service. User interviews are a great way to get non-numerical data on individual experiences with your product, to gain a deeper understanding of user perspectives.

Interviews can be structured, semi-structured, or unstructured . Structured interviews follow a strict interview script and can help you get answers to your planned questions, while semi and unstructured interviews are less rigid in their approach and typically lead to more spontaneous, user-centered insights.

When to use user interviews

Interviews are ideal when you want to gain an in-depth understanding of your users’ perspectives on your product or service, and why they feel a certain way.

Interviews can be used at any stage in the product design and development process, being particularly helpful during:

• The discovery phase: To better understand user needs, problems, and the context in which they use your product—revealing the best potential solutions
• The design phase: To get contextual feedback on mockups, wireframes, and prototypes, helping you pinpoint issues and the reasons behind them
• Post-launch: To assess if your product continues to meet users’ shifting expectations and understand why or why not

How to conduct user interviews: The basics

• Draft questions based on your research objectives
• Recruit relevant research participants and schedule interviews
• Conduct the interview and transcribe responses
• Analyze the interview responses to extract insights
• Use your findings to inform design, product, and business decisions

💡 A specialized user interview tool makes interviewing easier. With Maze Interview Studies , you can recruit, host, and analyze interviews all on one platform.

User interviews: A qualitative research example

Let’s say you’ve designed a recruitment platform, called Tech2Talent , that connects employers with tech talent. Before starting the design process, you want to clearly understand the pain points employers experience with existing recruitment tools'.

You draft a list of ten questions for a semi-structured interview for 15 different one-on-one interviews. As it’s semi-structured, you don’t expect to ask all the questions—the script serves as more of a guide.

One key question in your script is: “Have tech recruitment platforms helped you find the talent you need in the past?”

Most respondents answer with a resounding and passionate ‘no’ with one of them expanding:

“For our company, it’s been pretty hit or miss honestly. They let just about anyone make a profile and call themselves tech talent. It’s so hard sifting through serious candidates. I can’t see any of their achievements until I invest time setting up an interview.”

You begin to notice a pattern in your responses: recruitment tools often lack easily accessible details on talent profiles.

You’ve gained contextual feedback on why other recruitment platforms fail to solve user needs.

2. Focus groups

A focus group is a research method that involves gathering a small group of people—around five to ten users—to discuss a specific topic, such as their’ experience with your new product feature. Unlike user interviews, focus groups aim to capture the collective opinion of a wider market segment and encourage discussion among the group.

When to use focus groups

You should use focus groups when you need a deeper understanding of your users’ collective opinions. The dynamic discussion among participants can spark in-depth insights that might not emerge from regular interviews.

Focus groups can be used before, during, and after a product launch. They’re ideal:

• Throughout the problem discovery phase: To understand your user segment’s pain points and expectations, and generate product ideas
• Post-launch: To evaluate and understand the collective opinion of your product’s user experience
• When conducting market research: To grasp usage patterns, consumer perceptions, and market opportunities for your product

How to conduct focus group studies: The basics

• Draft prompts to spark conversation, or a series of questions based on your UX research objectives
• Find a group of five to ten users who are representative of your target audience (or a specific user segment) and schedule your focus group session
• Conduct the focus group by talking and listening to users, then transcribe responses
• Analyze focus group responses and extract insights
• Use your findings to inform design decisions

The number of participants can make it difficult to take notes or do manual transcriptions. We recommend using a transcription or a specialized UX research tool , such as Maze, that can automatically create ready-to-share reports and highlight key user insights.

Focus groups: A qualitative research example

You’re a UX researcher at FitMe , a fitness app that creates customized daily workouts for gym-goers. Unlike many other apps, FitMe takes into account the previous day’s workout and aims to create one that allows users to effectively rest different muscles.

However, FitMe has an issue. Users are generating workouts but not completing them. They’re accessing the app, taking the necessary steps to get a workout for the day, but quitting at the last hurdle.

Time to talk to users.

You organize a focus group to get to the root of the drop-off issue. You invite five existing users, all of whom have dropped off at the exact point you’re investigating, and ask them questions to uncover why.

A dialog develops:

Participant 1: “Sometimes I’ll get a workout that I just don’t want to do. Sure, it’s a good workout—but I just don’t want to physically do it. I just do my own thing when that happens.”

Participant 2: “Same here, some of them are so boring. I go to the gym because I love it. It’s an escape.”

Participant 3: “Right?! I get that the app generates the best one for me on that specific day, but I wish I could get a couple of options.”

Participant 4: “I’m the same, there are some exercises I just refuse to do. I’m not coming to the gym to do things I dislike.”

Conducting the focus groups and reviewing the transcripts, you realize that users want options. A workout that works for one gym-goer doesn’t necessarily work for the next.

A possible solution? Adding the option to generate a new workout (that still considers previous workouts)and the ability to blacklist certain exercises, like burpees.

3. Ethnographic research

Ethnographic research is a research method that involves observing and interacting with users in a real-life environment. By studying users in their natural habitat, you can understand how your product fits into their daily lives.

Ethnographic research can be active or passive. Active ethnographic research entails engaging with users in their natural environment and then following up with methods like interviews. Passive ethnographic research involves letting the user interact with the product while you note your observations.

When to use ethnographic research

Ethnographic research is best suited when you want rich insights into the context and environment in which users interact with your product. Keep in mind that you can conduct ethnographic research throughout the entire product design and development process —from problem discovery to post-launch. However, it’s mostly done early in the process:

• Early concept development: To gain an understanding of your user's day-to-day environment. Observe how they complete tasks and the pain points they encounter. The unique demands of their everyday lives will inform how to design your product.
• Initial design phase: Even if you have a firm grasp of the user’s environment, you still need to put your solution to the test. Conducting ethnographic research with your users interacting with your prototype puts theory into practice.

How to conduct ethnographic research:

• Recruit users who are reflective of your audience
• Meet with them in their natural environment, and tell them to behave as they usually would
• Take down field notes as they interact with your product
• Engage with your users, ask questions, or host an in-depth interview if you’re doing an active ethnographic study
• Collect all your data and analyze it for insights

While ethnographic studies provide a comprehensive view of what potential users actually do, they are resource-intensive and logistically difficult. A common alternative is diary studies. Like ethnographic research, diary studies examine how users interact with your product in their day-to-day, but the data is self-reported by participants.

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Ethnographic research: A qualitative research example

You're a UX researcher for a project management platform called ProFlow , and you’re conducting an ethnographic study of the project creation process with key users, including a startup’s COO.

The first thing you notice is that the COO is rushing while navigating the platform. You also take note of the 46 tabs and Zoom calls opened on their monitor. Their attention is divided, and they let out an exasperated sigh as they repeatedly hit “refresh” on your website’s onboarding interface.

You conclude the session with an interview and ask, “How easy or difficult did you find using ProFlow to coordinate a project?”

The COO answers: “Look, the whole reason we turn to project platforms is because we need to be quick on our feet. I’m doing a million things so I need the process to be fast and simple. The actual project management is good, but creating projects and setting up tables is way too complicated.”

You realize that ProFlow ’s project creation process takes way too much time for professionals working in fast-paced, dynamic environments. To solve the issue, propose a quick-create option that enables them to move ahead with the basics instead of requiring in-depth project details.

4. Qualitative observation

Qualitative observation is a similar method to ethnographic research, though not as deep. It involves observing your users in a natural or controlled environment and taking notes as they interact with a product. However, be sure not to interrupt them, as this compromises the integrity of the study and turns it into active ethnographic research.

When to qualitative observation

Qualitative observation is best when you want to record how users interact with your product without anyone interfering. Much like ethnographic research, observation is best done during:

• Early concept development: To help you understand your users' daily lives, how they complete tasks, and the problems they deal with. The observations you collect in these instances will help you define a concept for your product.
• Initial design phase: Observing how users deal with your prototype helps you test if they can easily interact with it in their daily environments

How to conduct qualitative observation:

• Recruit users who regularly use your product
• Meet with users in either their natural environment, such as their office, or within a controlled environment, such as a lab
• Observe them and take down field notes based on what you notice

Qualitative observation: An qualitative research example

You’re conducting UX research for Stackbuilder , an app that connects businesses with tools ideal for their needs and budgets. To determine if your app is easy to use for industry professionals, you decide to conduct an observation study.

Sitting in with the participant, you notice they breeze past the onboarding process, quickly creating an account for their company. Yet, after specifying their company’s budget, they suddenly slow down. They open links to each tool’s individual page, confusingly switching from one tab to another. They let out a sigh as they read through each website.

Conducting your observation study, you realize that users find it difficult to extract information from each tool’s website. Based on your field notes, you suggest including a bullet-point summary of each tool directly on your platform.

5. Case study research

Case studies are a UX research method that provides comprehensive and contextual insights into a real-world case over a long period of time. They typically include a range of other qualitative research methods, like interviews, observations, and ethnographic research. A case study allows you to form an in-depth analysis of how people use your product, helping you uncover nuanced differences between your users.

When to use case studies

Case studies are best when your product involves complex interactions that need to be tracked over a longer period or through in-depth analysis. You can also use case studies when your product is innovative, and there’s little existing data on how users interact with it.

As for specific phases in the product design and development process:

• Initial design phase: Case studies can help you rigorously test for product issues and the reasons behind them, giving you in-depth feedback on everything between user motivations, friction points, and usability issues
• Post-launch phase: Continuing with case studies after launch can give you ongoing feedback on how users interact with the product in their day-to-day lives. These insights ensure you can meet shifting user expectations with product updates and future iterations

How to conduct case studies:

• Outline an objective for your case study such as examining specific user tasks or the overall user journey
• Select qualitative research methods such as interviews, ethnographic studies, or observations
• Collect and analyze your data for comprehensive insights
• Include your findings in a report with proposed solutions

Case study research: A qualitative research example

Your team has recently launched Pulse , a platform that analyzes social media posts to identify rising digital marketing trends. Pulse has been on the market for a year, and you want to better understand how it helps small businesses create successful campaigns.

To conduct your case study, you begin with a series of interviews to understand user expectations, ethnographic research sessions, and focus groups. After sorting responses and observations into common themes you notice a main recurring pattern. Users have trouble interpreting the data from their dashboards, making it difficult to identify which trends to follow.

With your synthesized insights, you create a report with detailed narratives of individual user experiences, common themes and issues, and recommendations for addressing user friction points.

Some of your proposed solutions include creating intuitive graphs and summaries for each trend study. This makes it easier for users to understand trends and implement strategic changes in their campaigns.

6. Secondary research

Secondary research is a research method that involves collecting and analyzing documents, records, and reviews that provide you with contextual data on your topic. You’re not connecting with participants directly, but rather accessing pre-existing available data. For example, you can pull out insights from your UX research repository to reexamine how they apply to your new UX research objective.

Strictly speaking, it can be both qualitative and quantitative—but today we focus on its qualitative application.

When to use secondary research

Record keeping is particularly useful when you need supplemental insights to complement, validate, or compare current research findings. It helps you analyze shifting trends amongst your users across a specific period. Some other scenarios where you need record keeping include:

• Initial discovery or exploration phase: Secondary research can help you quickly gather background information and data to understand the broader context of a market
• Design and development phase: See what solutions are working in other contexts for an idea of how to build yours

Secondary research is especially valuable when your team faces budget constraints, tight deadlines, or limited resources. Through review mining and collecting older findings, you can uncover useful insights that drive decision-making throughout the product design and development process.

How to conduct secondary research:

• Outline your UX research objective
• Identify potential data sources for information on your product, market, or target audience. Some of these sources can include: a. Review websites like Capterra and G2 b. Social media channels c. Customer service logs and disputes d. Website reviews e. Reports and insights from previous research studies f. Industry trends g. Information on competitors
• Analyze your data by identifying recurring patterns and themes for insights

Secondary research: A qualitative research example

SafeSurf is a cybersecurity platform that offers threat detection, security audits, and real-time reports. After conducting multiple rounds of testing, you need a quick and easy way to identify remaining usability issues. Instead of conducting another resource-intensive method, you opt for social listening and data mining for your secondary research.

Browsing through your company’s X, you identify a recurring theme: many users without a background in tech find SafeSurf ’s reports too technical and difficult to read. Users struggle with understanding what to do if their networks are breached.

After checking your other social media channels and review sites, the issue pops up again.

With your gathered insights, your team settles on introducing a simplified version of reports, including clear summaries, takeaways, and step-by-step protocols for ensuring security.

By conducting secondary research, you’ve uncovered a major usability issue—all without spending large amounts of time and resources to connect with your users.

7. Open-ended surveys

Open-ended surveys are a type of unmoderated UX research method that involves asking users to answer a list of qualitative research questions designed to uncover their attitudes, expectations, and needs regarding your service or product. Open-ended surveys allow users to give in-depth, nuanced, and contextual responses.

When to use open-ended surveys

User surveys are an effective qualitative research method for reaching a large number of users. You can use them at any stage of the design and product development process, but they’re particularly useful:

• When you’re conducting generative research : Open-ended surveys allow you to reach a wide range of users, making them especially useful during initial research phases when you need broad insights into user experiences
• When you need to understand customer satisfaction: Open-ended customer satisfaction surveys help you uncover why your users might be dissatisfied with your product, helping you find the root cause of their negative experiences
• In combination with close-ended surveys: Get a combination of numerical, statistical insights and rich descriptive feedback. You’ll know what a specific percentage of your users think and why they think it.

How to conduct open-ended surveys:

• Design your survey and draft out a list of survey questions
• Distribute your surveys to respondents
• Analyze survey participant responses for key themes and patterns
• Use your findings to inform your design process

Open-ended surveys: A qualitative research example

You're a UX researcher for RouteReader , a comprehensive logistics platform that allows users to conduct shipment tracking and route planning. Recently, you’ve launched a new predictive analytics feature that allows users to quickly identify and prepare for supply chain disruptions.

To better understand if users find the new feature helpful, you create an open-ended, in-app survey.

The questions you ask your users:

• “What has been your experience with our new predictive analytics feature?"
• “Do you find it easy or difficult to rework your routes based on our predictive suggestions?”
• “Does the predictive analytics feature make planning routes easier? Why or why not?”

Most of the responses are positive. Users report using the predictive analytics feature to make last-minute adjustments to their route plans, and some even rely on it regularly. However, a few users find the feature hard to notice, making it difficult to adjust their routes on time.

To ensure users have supply chain insights on time, you integrate the new feature into each interface so users can easily spot important information and adjust their routes accordingly.

💡 Surveys are a lot easier with a quality survey tool. Maze’s Feedback Surveys solution has all you need to ensure your surveys get the insights you need—including AI-powered follow-up and automated reports.

Qualitative research vs. quantitative research: What’s the difference?

Alongside qualitative research approaches, UX teams also use quantitative research methods. Despite the similar names, the two are very different.

Here are some of the key differences between qualitative research and quantitative research .

Before selecting either qualitative or quantitative methods, first identify what you want to achieve with your UX research project. As a general rule of thumb, think qualitative data collection for in-depth understanding and quantitative studies for measurement and validation.

Conduct qualitative research with Maze

You’ll often find that knowing the what is pointless without understanding the accompanying why . Qualitative research helps you uncover your why.

So, what about how —how do you identify your 'what' and your 'why'?

The answer is with a user research tool like Maze.

Maze is the leading user research platform that lets you organize, conduct, and analyze both qualitative and quantitative research studies—all from one place. Its wide variety of UX research methods and advanced AI capabilities help you get the insights you need to build the right products and experiences faster.

Frequently asked questions about qualitative research examples

What is qualitative research?

Qualitative research is a research method that aims to provide contextual, descriptive, and non-numerical insights on a specific issue. Qualitative research methods like interviews, case studies, and ethnographic studies allow you to uncover the reasoning behind your user’s attitudes and opinions.

Can a study be both qualitative and quantitative?

Absolutely! You can use mixed methods in your research design, which combines qualitative and quantitative approaches to gain both descriptive and statistical insights.

For example, user surveys can have both close-ended and open-ended questions, providing comprehensive data like percentages of user views and descriptive reasoning behind their answers.

Is qualitative or quantitative research better?

The choice between qualitative and quantitative research depends upon your research goals and objectives.

Qualitative research methods are better suited when you want to understand the complexities of your user’s problems and uncover the underlying motives beneath their thoughts, feelings, and behaviors. Quantitative research excels in giving you numerical data, helping you gain a statistical view of your user's attitudes, identifying trends, and making predictions.

What are some approaches to qualitative research?

There are many approaches to qualitative studies. An approach is the underlying theory behind a method, and a method is a way of implementing the approach. Here are some approaches to qualitative research:

• Grounded theory: Researchers study a topic and develop theories inductively
• Phenomenological research: Researchers study a phenomenon through the lived experiences of those involved
• Ethnography: Researchers immerse themselves in organizations to understand how they operate

• Open access
• Published: 14 August 2024

Qualitative studies involving users of clinical neurotechnology: a scoping review

• Georg Starke 1 , 2 ,
• Tugba Basaran Akmazoglu 3 ,
• Annalisa Colucci 4 ,
• Mareike Vermehren 4 ,
• Amanda van Beinum 5 ,
• Maria Buthut 4 ,
• Surjo R. Soekadar 4 ,
• Christoph Bublitz 7 ,
• Jennifer A. Chandler 6 &
• Marcello Ienca 1 , 2  

BMC Medical Ethics volume  25 , Article number:  89 ( 2024 ) Cite this article

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Metrics details

The rise of a new generation of intelligent neuroprostheses, brain-computer interfaces (BCI) and adaptive closed-loop brain stimulation devices hastens the clinical deployment of neurotechnologies to treat neurological and neuropsychiatric disorders. However, it remains unclear how these nascent technologies may impact the subjective experience of their users. To inform this debate, it is crucial to have a solid understanding how more established current technologies already affect their users. In recent years, researchers have used qualitative research methods to explore the subjective experience of individuals who become users of clinical neurotechnology. Yet, a synthesis of these more recent findings focusing on qualitative methods is still lacking.

To address this gap in the literature, we systematically searched five databases for original research articles that investigated subjective experiences of persons using or receiving neuroprosthetics, BCIs or neuromodulation with qualitative interviews and raised normative questions.

36 research articles were included and analysed using qualitative content analysis. Our findings synthesise the current scientific literature and reveal a pronounced focus on usability and other technical aspects of user experience. In parallel, they highlight a relative neglect of considerations regarding agency, self-perception, personal identity and subjective experience.

Conclusions

Our synthesis of the existing qualitative literature on clinical neurotechnology highlights the need to expand the current methodological focus as to investigate also non-technical aspects of user experience. Given the critical role considerations of agency, self-perception and personal identity play in assessing the ethical and legal significance of these technologies, our findings reveal a critical gap in the existing literature. This review provides a comprehensive synthesis of the current qualitative research landscape on neurotechnology and the limitations thereof. These findings can inform researchers on how to study the subjective experience of neurotechnology users more holistically and build patient-centred neurotechnology.

Peer Review reports

Introduction

Due to a rapid expansion in public-private investment, market size and availability of Artificial Intelligence (AI) tools for functional optimization, the clinical advancement of novel neurotechnologies is accelerating its pace [ 1 ]. Bidirectional intelligent Brain-Computer interfaces (BCI) that aim at merging both read-out and write-in devices are in active development and are expanding in functional capabilities and commercial availability. [ 2 , 3 ]. Such BCIs that can decode and modulate neural activity through direct stimulation of brain tissue, promise additional avenues in the treatment of neurological diseases by adapting to the particularities of individual users’ brain. Potential applications are Parkinson’s disease [ 4 ] or epilepsy [ 5 ] as well as psychiatric disorders, such as major depressive disorder [ 6 ] or obsessive compulsive disorder [ 7 ]. Driven by these advances and in conjunction with progress in deep learning and generative AI software as well as higher-bandwidth hardware, clinical neurotechnology is likely to take an increasingly central role in the prevention, diagnosis and treatment of neuropsychiatric disorders.

In line with these scientific trends, the last decade has seen a consequent fast rise in the ethical attention devoted to neurotechnological systems that establish a direct connection with the human central nervous system [ 8 ], including neurostimulation devices. Yet, at times, neuroethical concerns may have outpaced real-life possibilities, particularly with view to the impact of neurotechnology on personality, identity, autonomy, authenticity, agency or self (PIAAAS) [ 9 ]. This points to the need for basing ethical assessments and personal decisions about deploying devices on solid empirical grounds. In particular, it is crucial to gain a comprehensive understanding of the lived experience of using neurotechnologies from the epistemically privileged first-person perspective of users – “what it is like” to use neurotechnologies. Its examination by empirical studies have added a vital contribution to the literature [ 10 ].

Yet, few reviews have attempted to synthesize the growing body of empirical studies on user experience with clinical neurotechnology. Burwell et al. [ 11 ] reviewed literature from biomedical ethics on BCIs up to 2016, identifying key ethical, legal and societal challenges, yet noting a lack of concrete ethical recommendations for implementation. Worries about a lack of attention to ethics in BCI studies have been further corroborated by two reviews by Specker Sullivan and Illes, reviewing BCI research published up until 2015. They critically assessed the rationales of BCI research studies [ 12 ] and found a remarkable absence of ethical language in published BCI research [ 13 ]. Taking a different focus, Kögel et al. [ 14 ] have provided a scoping review summarizing empirical studies investigating ethics of BCIs until 2017, with a strong focus on quantitative methods in the reviewed papers. Most recently, this list of reviews has been complemented by van Velthoven et al. [ 15 ], who review empirical and conceptual ethical literature on the use of visual neuroprostheses.

To the best of our knowledge, a specific review of qualitative research on the ethics of emerging neurotechnologies such as neuroprosthetics, BCIs and neuromodulation systems is outstanding. We believe that qualitative research involving actual or prospective neurotechnology users is particularly significant as it allows researchers to tap into the richness of first-person experiences as compared to standardized questionnaires without the option of free report. In the following, we synthesize published research on the subjective experience of using clinical neurotechnologies to enrich the ethical debate and provide guidance to developers and regulators.

On January 13, 2022 we conducted a search of relevant scientific literature across 5 databases, namely Pubmed (89 results), Scopus (178 results), Web of Science (79 results), PsycInfo (134 results) and IEEE Xplore (4 results). The search was performed for title, abstract and keywords, using a search string to identify articles employing qualitative methods that engaged with users of neurotechnology, and covered normative issues: [“qualitative” OR “interview” OR “focus group” OR “ethnography” OR “grounded theory” OR “discourse analysis” OR “interpretative phenomenological analysis” OR “thematic analysis”] AND [“user” OR “patient” OR “people” OR “person” OR “participant” OR “subject”] AND [“Brain-Computer” OR “BCI” OR “Brain-Machine” OR “neurostimulation” OR “neuromodulation” OR “TMS” OR “transcranial” OR “neuroprosthetic*” OR “neuroprosthesis” OR “DBS”] AND [“ethic*” OR “bioethic*” OR “normative” OR “value” OR “evaluation”].

Across databases, search syntax was adapted to reflect the respective logic of each library. Our search yielded a total of 484 articles. Of these, 133 duplicates were removed. 52 further results were marked as ineligible by automation tools, due to either not being written in English or not representing original research in a peer-reviewed journal. The remaining 299 were screened manually, with screening tasks being shared equally among the authors GS, TBA, AC, MV, CB, JC, and MI. Articles were included if they were written in English, published in a peer-reviewed journal, and reported original research of empirical qualitative findings among human users of a neurotechnological system that establishes a direct connection with the human central nervous system (including neurostimulation devices). Other types of articles such as perspectives, letters to the editor, or review articles were not included. Potential methods included individual interviews, focus groups, stakeholder consultations but excluded studies that did not use any direct verbal input from the users. Each abstract was screened individually by two reviewers. Unclear cases were resolved by discussion among reviewers. This process resulted in the exclusion of 247 articles, leaving 52 publications for inclusion into the final synthesis.

Full texts of these 52 articles were retrieved and assessed for eligibility. Again, this task was shared equally across the 7 authors who made independent recommendations whether an article was included for further analysis, and disagreement was resolved by discussion. 20 articles were excluded at this stage, due to not meeting the inclusion criteria. This resulted in a body of 32 articles plus 4 additional papers identified through citation chaining, as customary in scoping reviews.

In the data analysis phase, we compiled a descriptive summary of the findings and conducted a thematic analysis. When compiling the descriptive summary, we followed the recommendations by Arksey and O’Malley [ 16 ] and included comprehensive information beyond authors, year, and title of the study, extracting also study location, methodology, study population, type of neurotechnology, and more. For the thematic analysis, the full text was read and coded by the authors through annotations in pdf files, with papers evenly distributed among the group. Coding was based on a previously agreed coding structure of four thematic families, covering (1) subjective experience with BCIs, (2) aspects concerning usability and technology, (3) ethical questions, (4) impact on social relations, and a fifth miscellaneous category for future resolution. In accordance with the suggestions by Braun and Clarke [ 17 ], codes that were not clearly covered by the coding tree were grouped into a category “miscellaneous”, and after discussion used to develop new themes or subsumed under the existing thematic families. The results were compiled and unified by the first author and imported into the Atlas.ti software (version 22.2), with adaptations to the coding tree being discussed between first and last author.

In line with the framework suggested by Pham, Rajić [ 18 ], we adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) in conducting and presenting our results [ 19 ]. A flow diagram representing the entire process is depicted in Fig.  1 .

PRISMA flow diagram: search and screening strategy. Based on Page et al [ 19 ]

Descriptive findings

Our study included 36 papers reporting original qualitative research among users of BCIs, neuroprosthetics and neuromodulation. We found a pronounced increase in the number of publications employing qualitative methods in the investigation of such neurotechnology users over time, with the earliest study dating back to 2012. However, contrary to what one may expect as reflection of the growing number of neurotechnology users, we did not find an increase in the average sample size of participants enrolled in qualitative studies nor a correlation between year of publication and number of participants (see Fig.  2 ).

Average number of participants and number of publications over time

The included studies were exclusively conducted in Western countries, with 11 studies from the US, 9 from Australia and the remaining 16 distributed across Europe (UK: 6, Germany: 4, Sweden, Netherlands and Switzerland 2 each). The majority of studies investigated the effects of invasive neurotechnology in the form of Deep Brain Stimulators (DBS) (26/36), especially in patients with Parkinson’s Disease (PD) (19/36). Many papers also investigated users’ experiences with non-invasive EEG-based BCIs (7/36), whereas all other technologies such as TMS, ECT, FES, intracortical microelectrode arrays, or spinal cord stimulation were only covered by one or two papers each. Footnote 1 Due to the large focus on PD patients, other potential fields for clinical neurotechnological applications were much less present in the analysed research, with only 4 papers each investigating the effects of DBS on patients with major depressive disorder (4/36) or obsessive-compulsive disorder (OCD) (4/36). Across all technologies and patient groups, studies most frequently relied on semi-structured interviews with individual participants (28/36), with much fewer studies using focus groups (3/36) or other qualitative methods.

We found that a large number of papers (14/36) incorporated longitudinal aspects in their study design. With view to non-invasive BCIs, this comprised involving users in the development and testing of BCIs for acquired brain injury [ 20 , 21 ], assessing subjective reports across sessions for experimental BCI training [ 22 ], or having a 2-month follow-up interview for users of a BCI for pain management after spinal cord injury [ 23 ]. Studies of invasive devices often included interviews pre- and post-implantation, with a potential third follow-up. In studies with two interviews, the first interview after implantation took place a few weeks after implantation [ 24 , 25 ], after 3 months [ 26 ], after 9 months [ 27 , 28 ] or after a year [ 29 ]. In studies with 3 interviews, post-implantation interviews were either conducted after surgery and again after 3 months in a study on spinal cord stimulation [ 30 ] or, in the case of DBS for PD, after 3 and 6 months [ 31 , 32 ] or after 3–6 and 9–12 months respectively [ 33 ]. Table  1 provides a full overview over the included studies.

Thematic findings

Our findings from the thematic analysis can be grouped into four overlapping thematic families, namely (1) ethical challenges of neurotechnology use, (2) subjective experience with clinical neurotechnologies, (3) impact on social relations, and (4) usability and technological aspects. The raw data of our findings are accessible in the supplementary file.

Ethical concerns

With respect to users’ experiences of neurotechnology that touch on classical ethical topics, we found that autonomy played a central role in slightly more than half of all papers (20/36), yet in four different ways. Many papers noted the positive impact neurotechnology has on users’ autonomy. Users often perceive the technology as enabler of greater control over their own life, allowing them “to become who they wanted to be” [ 2 ], providing them with agency and greater independence, restoring their ability to help others, or allowing them to be more spontaneous in their everyday life [ 2 , 10 , 28 , 31 , 32 , 34 , 35 , 36 , 37 ]. Some studies reported how neurotechnology may impact users’ autonomy negatively, especially by making them more dependent on technological and medical support [ 25 , 28 , 35 , 38 , 39 ]. When balancing these positive and negative impacts, some users seem to prefer such dependency and to leave control over the devices to healthcare professionals, to ensure its safe and appropriate working [ 2 , 32 , 39 , 40 ]. Also related to autonomy were concerns about consent, especially with a view to the level of information patients received before the implantation of an invasive device, which was deemed inadequate by some patients [ 2 , 24 , 31 , 34 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 ]. Several papers called to include patients during the technology design process [ 2 , 31 , 39 ]. In addition, questions of responsibility and accountability in case of malfunctioning were repeatedly named as key concern [ 10 , 25 , 37 , 38 , 45 , 47 ].

Concerns about beneficence and about harming patients also featured prominently in most of the analysed papers (24/36), yet with substantive differences on a more granular level. While symptom improvement and restorative changes were widely reported [ 2 , 10 , 23 , 26 , 29 , 31 , 33 , 34 , 35 , 38 , 39 , 40 , 43 , 44 , 46 ], some users reported experiencing physical or psychological side effects, such as postoperative complications, new worries – for instance about magnetic fields or about changing batteries –, stigma, or becoming more aware of their past suffering [ 23 , 25 , 26 , 28 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 42 , 46 , 48 , 49 ]. Less frequently we found concerns about patient-doctor-relationships [ 2 , 24 , 32 , 40 , 42 , 43 ], which seem to mediate the acceptance of clinical neurotechnologies but are also themselves impacted by technology use. For instance, while some research points to the importance of patients’ trust in healthcare professionals for the acceptance of neurotechnology [ 24 ], a personal narrative described a breakdown of patient-physician relationship following a distressful DBS implantation for treating PD [ 42 ].

Impact on subjective experiences

Since the subjective lived experiences of neurotechnology users commonly constituted the central element of the reviewed qualitative papers, we found a rich field of reports in the vast majority of paper (31/36), describing experiences that were perceived as positive, negative or neutral. Neurotechnology-induced behavioural changes [ 28 , 36 , 37 , 40 , 42 , 46 , 47 , 49 ], as well as changes in feelings [ 27 , 41 , 42 ], (self-) perception [ 10 , 23 , 34 , 36 , 40 , 41 , 42 , 44 , 48 , 50 ], personality [ 27 , 29 , 34 , 35 , 36 , 37 , 42 , 43 , 44 , 47 , 49 ], preferences [ 49 , 50 ] or thinking [ 10 , 41 ] were also reported, particularly in users receiving continuous, non-adaptive deep brain stimulation (DBS).

Behavioural changes often concerned desired outcomes such as fewer obsessive thoughts and compulsive behaviours after successful OCD treatment [ 49 ], acting with less impediment due to seizure predictions [ 36 ], or acting more boldly with more energy and increased confidence due to symptom improvement in PD [ 37 , 47 ]. Nevertheless, it was necessary for patients and for their environment to adapt and get used to new patterns of behaviour. Some patients also reported undesirable behavioural changes after subthalamic DBS implantation, “bordering on mania” [ 42 ], such as being excessively talkative [ 46 ] or shopping compulsions that were later described by the patient as “ridiculous” [ 28 ].

These outwardly observable changes were often related to psychological changes that users reported. Some DBS users experienced mood changes, ranging from elevated to depressed [ 27 , 41 , 42 , 44 ], while others reported changed preferences. Sometimes this affected what users valued as important in life [ 50 ], sometimes it related to very particular preferences, such as taste in music, with one patient attributing a transition from The Rolling Stones and The Beatles to Johnny Cash to their DBS implantation [ 49 ]. In patients treated for OCD or motor disorders, two studies also found positive impact on users’ thinking, whether by freeing them from obsessive thoughts [ 41 ] or improving their concentration skill [ 10 ]. In line with the large neuroethical debate on the subject, changes at times amounted to what neurotechnology users described as personality changes. Such changes included negative impacts such as being more irritable, anxious or less patient [ 34 , 35 ] or overly increased libido [ 49 ], neutral changes, such as (re-)taking an interest in politics or movies [ 49 ], and positive changes linked to improvement of psychiatric symptoms, such as being more easy-going and daring, being more expressive and assertive, or simply being more confident [ 35 , 49 ].

In line with the diversity of these changes, patients reported a vast spectrum of different attitudes towards and relations with the neurotechnology. Some users embraced the BCI explicitly as part of themselves [ 14 , 37 , 39 , 49 ] and described how “DBS becomes a part of who you are rather than changing you” [ 37 ]. Others felt estranged using the BCI [ 28 , 36 , 37 , 42 , 49 ] and even expressed desires to remove the alien device in forceful terms: “I hate it! I wish I could pull it out!” [ 37 ]. Aside from changes brought about by the device, the patients’ state before using neurotechnology and especially their relation to their illness seemed to play a crucial role [ 28 , 51 ]. An overview over the different thematic findings is provided in Fig.  3 .

Impact of clinical neurotechnology on subjective experience. The colours represent the valence of the impact, with orange dots representing negative, green dots representing positive, and blue dots representing ambivalent changes

The overwhelming majority of studies (23/36) reported improvements of the treated symptoms [ 2 , 26 , 28 , 31 , 33 , 34 , 35 , 37 , 40 , 41 , 42 , 43 , 46 , 47 , 48 , 49 , 50 , 52 ], making patients’ lives easier [ 48 , 49 ] or – as some put it – even saving their lives [ 34 , 45 , 48 ]. Patients felt that the neurotechnology allowed them an increase in activity [ 33 , 34 , 40 ] and a return to previous forms of behaviour [ 33 , 40 , 48 , 49 ], strengthening their sense of freedom and independence [ 2 , 10 , 22 , 33 , 34 , 35 , 36 , 40 , 43 , 49 , 50 , 53 ]. Emotionally, users reported feeling more daring [ 29 , 35 ], self-confident [ 28 , 35 , 36 , 37 , 44 ] or more stable [ 34 , 50 ] as well as feelings of hope or joy [ 10 , 22 , 35 , 50 ]. For better or worse, such changes were sometimes perceived as providing a “new start” [ 34 , 48 ] or even a “new identity” [ 34 , 41 , 42 , 49 ], while others perceived their changes as a reversion to their “former” [ 28 , 29 , 47 , 49 , 50 ] or their “real” self [ 36 , 42 , 49 ].

Among the negative subjective impacts of clinical neurotechnology mentioned in the literature (16/36), users commonly reported issues of estrangement, caused by self-perceived changes to behaviour, feelings, personality traits, or patients’ relation to their disease or disorder [ 28 , 36 , 37 , 42 , 49 ]. The negative impact differed largely depending on the type of neurotechnology used as well as on the disorders and symptoms treated with the technology. While ALS patients as users of non-invasive BCIs for spelling interfaces reported increased anxiety in interaction with the devices [ 53 ], PD patients with invasive DBS reported presurgical fears of pain and of the invasive procedure as well as fear of outward manipulation within their brain through the DBS implantation [ 40 , 43 , 54 ]. Frequently, it was not entirely clear whether adverse developments such as further cognitive decline were attributable to the implanted device or to the persisting disease and its natural trajectory [ 31 , 33 , 34 , 40 , 43 , 48 , 50 ]. However, occasionally very severe psychiatric consequences of treatment were reported, notably by one PD patient who experienced mania and depressive symptoms through DBS treatment, resulting in a suicide attempt [ 42 ]. For DBS patients with OCD, negative impacts seem more related to difficulties of adapting to the new situation [ 35 , 49 ], for instance to their suddenly increased libido as a side-effect of DBS use that may be perceived as “too much” [ 49 ], or to a perceived lack of preparation for their new (OCD-free) identity [ 41 ]. In two studies on patients with OCD, the sudden improvement of symptoms also led to moments of existential crisis, given that the symptoms had shaped a great part of their previous daily activities [ 41 , 49 ].

Impact on social relations

Using a neurotechnology not only impacts users but can also affect social relations with others (23/36), particularly primary caregivers. While some neurotechnologies such as non-invasive BCIs for communication may create additional workload for caregivers if the BCI needs to be set up, neurotechnologies can also reduce their burden by rendering patients more independent [ 10 , 34 , 40 , 53 ]. Beyond workload, neurotechnologies were also reported to enrich social relations by facilitating communication [ 10 , 34 , 53 ], though in some cases, they led to potential tension between informal caregivers and patients, e.g. due to personality changes [ 28 , 35 , 37 , 40 , 42 , 47 , 49 , 55 ] or if the device was blamed for a patient’s behaviour or suggested as a solution to interpersonal problems [ 2 ]. Whether positive or negative, family and social support were reportedly playing a vital role in the treatment [ 2 , 28 , 40 , 50 ].

Similarly important was support by clinicians [ 39 , 40 ] and the wish for support groups with fellow neurotechnology users [ 27 , 30 , 40 , 41 ]. Inclusion in research activities was also reported as a positive effect of (experimental) BCIs [ 10 , 38 ]. More importantly though, in a large number of studies, neurotechnology users reported positive effects on their social relations [ 2 , 29 , 35 , 43 , 46 , 48 , 50 ], with some users reporting an increased wish to help others [ 35 , 50 ]. A negative social consequence in public was perceived stigma [ 25 , 35 , 48 ], even though some patients chose to actively show their device in public, “to spread information and knowledge about this treatment” [ 39 ].

Usability concerns

Concerns with technical questions and usability issues comprising efficiency, effectiveness and satisfaction [ 52 ] were also raised by almost half of the research papers (17/36), yet differed greatly between neurotechnologies, owing to large differences in hardware (e.g., between EEG caps and implanted electrodes) and handling (e.g., between passive neurostimulation or training-intensive active BCIs). Across all applications, invasive as much as non-invasive, the most frequent concerns (8/36 each) related to hardware issues [ 2 , 22 , 23 , 38 , 39 , 46 , 52 , 53 ] as well as to the required fine-tuning of devices to find optimal settings, associated with time-burden for their users [ 20 , 23 , 27 , 32 , 39 , 46 , 50 , 56 ]. Similarly, the training of patients required for the successful use of non-invasive, active BCIs was reported as being perceived as cumbersome or complicated, providing a potential obstacle to their implementation in everyday contexts [ 38 , 52 ]. Several studies reported that the use of such active BCIs required considerable concentration, leading to fatigue after prolonged use [ 10 , 38 , 53 ]. Mediating factors to address such obstacles were the availability of technical support [ 33 , 53 ], general attitudes towards technology [ 53 ], ease of integrating the technology into everyday life [ 10 , 38 , 53 ] and realistic expectations regarding the neurotechnology’s effects [ 30 , 38 , 40 , 46 ].

The identified publications highlight that qualitative research through interviews and focus groups offers a useful way to gain access to the subjective experience of users of a diverse range of neurotechnologies. Such investigation of users’ privileged knowledge about novel devices in turn is crucial to improve future neurotechnological developments and align them with ethical considerations already at an early stage [ 57 ]. Here, we discuss our findings by comparing different clinical neurotechnologies, identify gaps in the literature and point to the limitations of our scoping review.

One finding of our scoping review is that qualitative research on neurotechnologies has so far primarily focused on users of DBS treated for PD. In part, this may reflect that DBS is an established, effective treatment for controlling motor symptoms in PD, improving patients’ quality of life, resulting in its wide-spread adoption in many different healthcare systems worldwide [ 58 , 59 , 60 , 61 ]. Still, it would be highly beneficial to extend qualitative research to different patient groups and other clinical neurotechnologies that directly target mental states or processes, where more pronounced effects of subjective experiences may be expected.

A potential obstacle to involving more neurotechnology users beyond PD patients treated with DBS is that, for many other technologies, users are still likely to receive their treatment as part of an experimental trial. Qualitative research with such patients may face the additional practical barrier of convincing the other researchers to facilitate access to their patients. Better communication across disciplines and research fields may facilitate such access, providing much-needed insights into user experiences of experimental neurotechnologies.

Some of the articles reviewed here already offer such perspectives, e.g. the ones investigating DBS used for major depressive disorder or OCD. Such research may also help to further clarify which differences in subjective outcome are owed to technology and which are owed to differences in the treated disorders. As different patient groups are likely to have different needs and views, further research is needed to explore those needs and views and develop implementation strategies designed to address them in a patient-tailored manner. Furthermore, different neurotechnologies (and applications thereof) are likely to impact the mind of their users in a different way. Therefore, future research should investigate whether the type and modality of stimulation exert differential impacts on the subjective experience of the end users.

Our findings reveal differential effects among patients using DBS for the treatment of PD and patients using DBS for the treatment of OCD, respectively. For example, some reported effects of invasive neurotechnology such as the induction of more assertive behaviour may be a reason for concern in PD [ 28 ], while being considered a successful treatment outcome in OCD [ 35 , 49 ]. More comparative research among DBS users treated for OCD or other neuropsychiatric disorders, such as depression, are needed [ 62 ] and may help to better understand which experiences are directly attributable to the stimulation of specific brain areas such as the subthalamic nucleus for PD and the nucleus accumbens for OCD, and which result from other factors, e.g., related to undergoing surgery or to different treatment settings in neurological and psychiatric care [ 63 , 64 ].

Research on such differences may also imply practical consequences. For instance, one may wonder whether different preparation stages and possibly different degrees of information for obtaining consent may be called for between invasive clinical neurotechnologies used in psychiatry and neurology—or whether, on the contrary, similarities in the use of neurotechnologies ultimately point towards ending the distinction between mental and neurological illnesses [ 63 ]. In either case, our findings highlight that psychological impacts of clinical neurotechnologies are complex and multi-faceted phenomena—mediated by many factors—calling for more qualitative research to better grasp the lived experiences of those using novel neurotechnologies.

Our scoping review identified several gaps in the literature related to research methodology, investigated topics and investigated neurotechnologies. First, while a large number of studies embrace a longitudinal approach to investigating users’ experiences, none of the included studies looked at impacts beyond a timeframe of one year. However, as is known from DBS studies in major depressive disorder, it is important to investigate and evaluate long-term effects of neurotechnologies such as DBS [ 6 ]. Future qualitative research should therefore address this gap. Connected to this are, second, research questions that have not yet been investigated in full, such as long-term impacts of clinical neurotechnologies on memory or belief continuity. Third, empirical findings on closed-loop neurotechnologies that integrate artificial intelligence are so far nascent [ 2 , 36 ]. As there are important conceptual and ethical questions that arise specifically from the integration of human and artificial intelligence, e.g. questions of control and responsibility, further qualitative research should be conducted on users of such devices.

Finally, our findings reveal a complex and multifaceted landscape of ethical considerations. While considerations regarding personal autonomy appear largely prevalent among users, the perceived or expected impacts of neurotechnology use on personal autonomy differ significantly. Some studies suggest that neurotechnology use may enhance personal autonomy by allowing users to be more autonomous and independent in their daily lives and even restore part of the autonomous control that was disrupted by their disorders. Other studies suggest that some neurotechnologies, especially neural implants relying on autonomous components, may diminish autonomy as they may override some users’ intentions. Sometimes this ambivalent effect is observed within the same study. This is consistent with previous theoretical reflections on this topic [ 65 ] and urges scientists to develop fine-grained and patient-centred models for assessing the impact of neurotechnology on personal autonomy. These models should distinguish on-target and off-target effects and elucidate which subcomponents of personal autonomy (e.g., volition, behavioural control, authenticity etc.) are impacted by the use of neurotechnology.

Our scoping review has several limitations. Owing to the nature of a scoping review and to our inclusion criteria, there may be relevant literature that we missed to identify and analyse. For instance, since we only included English publications, we may have missed relevant research published in other languages, which may explain why we only found qualitative studies conducted in Western countries. Furthermore, our narrow search strategy excluded other relevant research, for instance qualitative studies conducted with potential users of clinical neurotechnology or with caregivers. Yet, a scoping reviews can provide a useful tool to map existing literature [ 16 , 18 ], and given recent advances in technology and accompanying qualitative research, an update of earlier reviews such as the one by Kögel et al. [ 14 ], provides an important addition to the existing literature. By looking at qualitative studies only we further import general limitations of qualitative studies, such as a lack of generalizability and a dependency on the skills and experience of the involved researchers. More standardized instruments to complement the investigation of subjective experiences of neurotechnology users therefore seem highly desirable. Recent quantitative approaches such as online surveys assessing the subjective preferences of DBS users concerning the timing of implantation [ 66 ] or studies combining qualitative data with quantitative assessments [ 67 ] point in this direction. Additionally, experimental approaches to the monitoring and evaluation of the effects of neurotechnology on the user’s experience are currently absent. Therefore, future research should complement qualitative and quantitative user evaluations based on social science methods (e.g., interviews, focus groups and questionnaires) with experimental models.

The findings of our review emphasize the diversity of individual experiences with neurotechnology across individuals and different technologies. They underscore the need to conduct qualitative research among diverse groups at different time-points to better assess the impact of such technologies on their users, which are important to inform requirements of efficacy and safety for clinical neurotechnologies. In addition, qualitative research offers one way to implement user-centred ethical considerations into product development through user-centred design and to accompany the development of novel neurotechnologies with ethical considerations as they mature and become clinical standard.

Data availability

The availability of the full data supporting the findings of this study is subject to restrictions due to the copyright of the included papers. The quotes analysed during this study are included in this published article and its supplementary information files. Further data are available from the authors upon request.

As many publications included patients with different diagnoses or investigated the effects of different neurotechnologies, the numbers indicated here do not add up.

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Acknowledgements

GS would like to thank the attendees of the ERA-NET NEURON mid-term seminar (Madrid, January 2023) for kind and constructive feedback on an earlier draft.

This work was supported by the ERA-NET NEURON project HYBRIDMIND (SNSF 32NE30_199436; BMBF, 01GP2121A and -B), and in part by the European Research Council (ERC) under the project NGBMI (759370), the Federal Ministry of Research and Education (BMBF) under the projects SSMART (01DR21025A), NEO (13GW0483C), QHMI (03ZU1110DD), QSHIFT (01UX2211) and NeuroQ (13N16486), as well as the Einstein Foundation Berlin (A-2019-558).

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Starke, G., Akmazoglu, T.B., Colucci, A. et al. Qualitative studies involving users of clinical neurotechnology: a scoping review. BMC Med Ethics 25 , 89 (2024). https://doi.org/10.1186/s12910-024-01087-z

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How to use and assess qualitative research methods

Loraine busetto.

1 Department of Neurology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany

Wolfgang Wick

2 Clinical Cooperation Unit Neuro-Oncology, German Cancer Research Center, Heidelberg, Germany

Christoph Gumbinger

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This paper aims to provide an overview of the use and assessment of qualitative research methods in the health sciences. Qualitative research can be defined as the study of the nature of phenomena and is especially appropriate for answering questions of why something is (not) observed, assessing complex multi-component interventions, and focussing on intervention improvement. The most common methods of data collection are document study, (non-) participant observations, semi-structured interviews and focus groups. For data analysis, field-notes and audio-recordings are transcribed into protocols and transcripts, and coded using qualitative data management software. Criteria such as checklists, reflexivity, sampling strategies, piloting, co-coding, member-checking and stakeholder involvement can be used to enhance and assess the quality of the research conducted. Using qualitative in addition to quantitative designs will equip us with better tools to address a greater range of research problems, and to fill in blind spots in current neurological research and practice.

The aim of this paper is to provide an overview of qualitative research methods, including hands-on information on how they can be used, reported and assessed. This article is intended for beginning qualitative researchers in the health sciences as well as experienced quantitative researchers who wish to broaden their understanding of qualitative research.

What is qualitative research?

Qualitative research is defined as “the study of the nature of phenomena”, including “their quality, different manifestations, the context in which they appear or the perspectives from which they can be perceived” , but excluding “their range, frequency and place in an objectively determined chain of cause and effect” [ 1 ]. This formal definition can be complemented with a more pragmatic rule of thumb: qualitative research generally includes data in form of words rather than numbers [ 2 ].

Why conduct qualitative research?

Because some research questions cannot be answered using (only) quantitative methods. For example, one Australian study addressed the issue of why patients from Aboriginal communities often present late or not at all to specialist services offered by tertiary care hospitals. Using qualitative interviews with patients and staff, it found one of the most significant access barriers to be transportation problems, including some towns and communities simply not having a bus service to the hospital [ 3 ]. A quantitative study could have measured the number of patients over time or even looked at possible explanatory factors – but only those previously known or suspected to be of relevance. To discover reasons for observed patterns, especially the invisible or surprising ones, qualitative designs are needed.

While qualitative research is common in other fields, it is still relatively underrepresented in health services research. The latter field is more traditionally rooted in the evidence-based-medicine paradigm, as seen in " research that involves testing the effectiveness of various strategies to achieve changes in clinical practice, preferably applying randomised controlled trial study designs (...) " [ 4 ]. This focus on quantitative research and specifically randomised controlled trials (RCT) is visible in the idea of a hierarchy of research evidence which assumes that some research designs are objectively better than others, and that choosing a "lesser" design is only acceptable when the better ones are not practically or ethically feasible [ 5 , 6 ]. Others, however, argue that an objective hierarchy does not exist, and that, instead, the research design and methods should be chosen to fit the specific research question at hand – "questions before methods" [ 2 , 7 – 9 ]. This means that even when an RCT is possible, some research problems require a different design that is better suited to addressing them. Arguing in JAMA, Berwick uses the example of rapid response teams in hospitals, which he describes as " a complex, multicomponent intervention – essentially a process of social change" susceptible to a range of different context factors including leadership or organisation history. According to him, "[in] such complex terrain, the RCT is an impoverished way to learn. Critics who use it as a truth standard in this context are incorrect" [ 8 ] . Instead of limiting oneself to RCTs, Berwick recommends embracing a wider range of methods , including qualitative ones, which for "these specific applications, (...) are not compromises in learning how to improve; they are superior" [ 8 ].

Research problems that can be approached particularly well using qualitative methods include assessing complex multi-component interventions or systems (of change), addressing questions beyond “what works”, towards “what works for whom when, how and why”, and focussing on intervention improvement rather than accreditation [ 7 , 9 – 12 ]. Using qualitative methods can also help shed light on the “softer” side of medical treatment. For example, while quantitative trials can measure the costs and benefits of neuro-oncological treatment in terms of survival rates or adverse effects, qualitative research can help provide a better understanding of patient or caregiver stress, visibility of illness or out-of-pocket expenses.

How to conduct qualitative research?

Given that qualitative research is characterised by flexibility, openness and responsivity to context, the steps of data collection and analysis are not as separate and consecutive as they tend to be in quantitative research [ 13 , 14 ]. As Fossey puts it : “sampling, data collection, analysis and interpretation are related to each other in a cyclical (iterative) manner, rather than following one after another in a stepwise approach” [ 15 ]. The researcher can make educated decisions with regard to the choice of method, how they are implemented, and to which and how many units they are applied [ 13 ]. As shown in Fig.  1 , this can involve several back-and-forth steps between data collection and analysis where new insights and experiences can lead to adaption and expansion of the original plan. Some insights may also necessitate a revision of the research question and/or the research design as a whole. The process ends when saturation is achieved, i.e. when no relevant new information can be found (see also below: sampling and saturation). For reasons of transparency, it is essential for all decisions as well as the underlying reasoning to be well-documented.

Iterative research process

While it is not always explicitly addressed, qualitative methods reflect a different underlying research paradigm than quantitative research (e.g. constructivism or interpretivism as opposed to positivism). The choice of methods can be based on the respective underlying substantive theory or theoretical framework used by the researcher [ 2 ].

Data collection

The methods of qualitative data collection most commonly used in health research are document study, observations, semi-structured interviews and focus groups [ 1 , 14 , 16 , 17 ].

Document study

Document study (also called document analysis) refers to the review by the researcher of written materials [ 14 ]. These can include personal and non-personal documents such as archives, annual reports, guidelines, policy documents, diaries or letters.

Observations

Observations are particularly useful to gain insights into a certain setting and actual behaviour – as opposed to reported behaviour or opinions [ 13 ]. Qualitative observations can be either participant or non-participant in nature. In participant observations, the observer is part of the observed setting, for example a nurse working in an intensive care unit [ 18 ]. In non-participant observations, the observer is “on the outside looking in”, i.e. present in but not part of the situation, trying not to influence the setting by their presence. Observations can be planned (e.g. for 3 h during the day or night shift) or ad hoc (e.g. as soon as a stroke patient arrives at the emergency room). During the observation, the observer takes notes on everything or certain pre-determined parts of what is happening around them, for example focusing on physician-patient interactions or communication between different professional groups. Written notes can be taken during or after the observations, depending on feasibility (which is usually lower during participant observations) and acceptability (e.g. when the observer is perceived to be judging the observed). Afterwards, these field notes are transcribed into observation protocols. If more than one observer was involved, field notes are taken independently, but notes can be consolidated into one protocol after discussions. Advantages of conducting observations include minimising the distance between the researcher and the researched, the potential discovery of topics that the researcher did not realise were relevant and gaining deeper insights into the real-world dimensions of the research problem at hand [ 18 ].

Semi-structured interviews

Hijmans & Kuyper describe qualitative interviews as “an exchange with an informal character, a conversation with a goal” [ 19 ]. Interviews are used to gain insights into a person’s subjective experiences, opinions and motivations – as opposed to facts or behaviours [ 13 ]. Interviews can be distinguished by the degree to which they are structured (i.e. a questionnaire), open (e.g. free conversation or autobiographical interviews) or semi-structured [ 2 , 13 ]. Semi-structured interviews are characterized by open-ended questions and the use of an interview guide (or topic guide/list) in which the broad areas of interest, sometimes including sub-questions, are defined [ 19 ]. The pre-defined topics in the interview guide can be derived from the literature, previous research or a preliminary method of data collection, e.g. document study or observations. The topic list is usually adapted and improved at the start of the data collection process as the interviewer learns more about the field [ 20 ]. Across interviews the focus on the different (blocks of) questions may differ and some questions may be skipped altogether (e.g. if the interviewee is not able or willing to answer the questions or for concerns about the total length of the interview) [ 20 ]. Qualitative interviews are usually not conducted in written format as it impedes on the interactive component of the method [ 20 ]. In comparison to written surveys, qualitative interviews have the advantage of being interactive and allowing for unexpected topics to emerge and to be taken up by the researcher. This can also help overcome a provider or researcher-centred bias often found in written surveys, which by nature, can only measure what is already known or expected to be of relevance to the researcher. Interviews can be audio- or video-taped; but sometimes it is only feasible or acceptable for the interviewer to take written notes [ 14 , 16 , 20 ].

Focus groups

Focus groups are group interviews to explore participants’ expertise and experiences, including explorations of how and why people behave in certain ways [ 1 ]. Focus groups usually consist of 6–8 people and are led by an experienced moderator following a topic guide or “script” [ 21 ]. They can involve an observer who takes note of the non-verbal aspects of the situation, possibly using an observation guide [ 21 ]. Depending on researchers’ and participants’ preferences, the discussions can be audio- or video-taped and transcribed afterwards [ 21 ]. Focus groups are useful for bringing together homogeneous (to a lesser extent heterogeneous) groups of participants with relevant expertise and experience on a given topic on which they can share detailed information [ 21 ]. Focus groups are a relatively easy, fast and inexpensive method to gain access to information on interactions in a given group, i.e. “the sharing and comparing” among participants [ 21 ]. Disadvantages include less control over the process and a lesser extent to which each individual may participate. Moreover, focus group moderators need experience, as do those tasked with the analysis of the resulting data. Focus groups can be less appropriate for discussing sensitive topics that participants might be reluctant to disclose in a group setting [ 13 ]. Moreover, attention must be paid to the emergence of “groupthink” as well as possible power dynamics within the group, e.g. when patients are awed or intimidated by health professionals.

Choosing the “right” method

As explained above, the school of thought underlying qualitative research assumes no objective hierarchy of evidence and methods. This means that each choice of single or combined methods has to be based on the research question that needs to be answered and a critical assessment with regard to whether or to what extent the chosen method can accomplish this – i.e. the “fit” between question and method [ 14 ]. It is necessary for these decisions to be documented when they are being made, and to be critically discussed when reporting methods and results.

Let us assume that our research aim is to examine the (clinical) processes around acute endovascular treatment (EVT), from the patient’s arrival at the emergency room to recanalization, with the aim to identify possible causes for delay and/or other causes for sub-optimal treatment outcome. As a first step, we could conduct a document study of the relevant standard operating procedures (SOPs) for this phase of care – are they up-to-date and in line with current guidelines? Do they contain any mistakes, irregularities or uncertainties that could cause delays or other problems? Regardless of the answers to these questions, the results have to be interpreted based on what they are: a written outline of what care processes in this hospital should look like. If we want to know what they actually look like in practice, we can conduct observations of the processes described in the SOPs. These results can (and should) be analysed in themselves, but also in comparison to the results of the document analysis, especially as regards relevant discrepancies. Do the SOPs outline specific tests for which no equipment can be observed or tasks to be performed by specialized nurses who are not present during the observation? It might also be possible that the written SOP is outdated, but the actual care provided is in line with current best practice. In order to find out why these discrepancies exist, it can be useful to conduct interviews. Are the physicians simply not aware of the SOPs (because their existence is limited to the hospital’s intranet) or do they actively disagree with them or does the infrastructure make it impossible to provide the care as described? Another rationale for adding interviews is that some situations (or all of their possible variations for different patient groups or the day, night or weekend shift) cannot practically or ethically be observed. In this case, it is possible to ask those involved to report on their actions – being aware that this is not the same as the actual observation. A senior physician’s or hospital manager’s description of certain situations might differ from a nurse’s or junior physician’s one, maybe because they intentionally misrepresent facts or maybe because different aspects of the process are visible or important to them. In some cases, it can also be relevant to consider to whom the interviewee is disclosing this information – someone they trust, someone they are otherwise not connected to, or someone they suspect or are aware of being in a potentially “dangerous” power relationship to them. Lastly, a focus group could be conducted with representatives of the relevant professional groups to explore how and why exactly they provide care around EVT. The discussion might reveal discrepancies (between SOPs and actual care or between different physicians) and motivations to the researchers as well as to the focus group members that they might not have been aware of themselves. For the focus group to deliver relevant information, attention has to be paid to its composition and conduct, for example, to make sure that all participants feel safe to disclose sensitive or potentially problematic information or that the discussion is not dominated by (senior) physicians only. The resulting combination of data collection methods is shown in Fig.  2 .

Possible combination of data collection methods

Attributions for icons: “Book” by Serhii Smirnov, “Interview” by Adrien Coquet, FR, “Magnifying Glass” by anggun, ID, “Business communication” by Vectors Market; all from the Noun Project

The combination of multiple data source as described for this example can be referred to as “triangulation”, in which multiple measurements are carried out from different angles to achieve a more comprehensive understanding of the phenomenon under study [ 22 , 23 ].

Data analysis

To analyse the data collected through observations, interviews and focus groups these need to be transcribed into protocols and transcripts (see Fig.  3 ). Interviews and focus groups can be transcribed verbatim , with or without annotations for behaviour (e.g. laughing, crying, pausing) and with or without phonetic transcription of dialects and filler words, depending on what is expected or known to be relevant for the analysis. In the next step, the protocols and transcripts are coded , that is, marked (or tagged, labelled) with one or more short descriptors of the content of a sentence or paragraph [ 2 , 15 , 23 ]. Jansen describes coding as “connecting the raw data with “theoretical” terms” [ 20 ]. In a more practical sense, coding makes raw data sortable. This makes it possible to extract and examine all segments describing, say, a tele-neurology consultation from multiple data sources (e.g. SOPs, emergency room observations, staff and patient interview). In a process of synthesis and abstraction, the codes are then grouped, summarised and/or categorised [ 15 , 20 ]. The end product of the coding or analysis process is a descriptive theory of the behavioural pattern under investigation [ 20 ]. The coding process is performed using qualitative data management software, the most common ones being InVivo, MaxQDA and Atlas.ti. It should be noted that these are data management tools which support the analysis performed by the researcher(s) [ 14 ].

From data collection to data analysis

Attributions for icons: see Fig. ​ Fig.2, 2 , also “Speech to text” by Trevor Dsouza, “Field Notes” by Mike O’Brien, US, “Voice Record” by ProSymbols, US, “Inspection” by Made, AU, and “Cloud” by Graphic Tigers; all from the Noun Project

How to report qualitative research?

Protocols of qualitative research can be published separately and in advance of the study results. However, the aim is not the same as in RCT protocols, i.e. to pre-define and set in stone the research questions and primary or secondary endpoints. Rather, it is a way to describe the research methods in detail, which might not be possible in the results paper given journals’ word limits. Qualitative research papers are usually longer than their quantitative counterparts to allow for deep understanding and so-called “thick description”. In the methods section, the focus is on transparency of the methods used, including why, how and by whom they were implemented in the specific study setting, so as to enable a discussion of whether and how this may have influenced data collection, analysis and interpretation. The results section usually starts with a paragraph outlining the main findings, followed by more detailed descriptions of, for example, the commonalities, discrepancies or exceptions per category [ 20 ]. Here it is important to support main findings by relevant quotations, which may add information, context, emphasis or real-life examples [ 20 , 23 ]. It is subject to debate in the field whether it is relevant to state the exact number or percentage of respondents supporting a certain statement (e.g. “Five interviewees expressed negative feelings towards XYZ”) [ 21 ].

How to combine qualitative with quantitative research?

Qualitative methods can be combined with other methods in multi- or mixed methods designs, which “[employ] two or more different methods [ …] within the same study or research program rather than confining the research to one single method” [ 24 ]. Reasons for combining methods can be diverse, including triangulation for corroboration of findings, complementarity for illustration and clarification of results, expansion to extend the breadth and range of the study, explanation of (unexpected) results generated with one method with the help of another, or offsetting the weakness of one method with the strength of another [ 1 , 17 , 24 – 26 ]. The resulting designs can be classified according to when, why and how the different quantitative and/or qualitative data strands are combined. The three most common types of mixed method designs are the convergent parallel design , the explanatory sequential design and the exploratory sequential design. The designs with examples are shown in Fig.  4 .

Three common mixed methods designs

In the convergent parallel design, a qualitative study is conducted in parallel to and independently of a quantitative study, and the results of both studies are compared and combined at the stage of interpretation of results. Using the above example of EVT provision, this could entail setting up a quantitative EVT registry to measure process times and patient outcomes in parallel to conducting the qualitative research outlined above, and then comparing results. Amongst other things, this would make it possible to assess whether interview respondents’ subjective impressions of patients receiving good care match modified Rankin Scores at follow-up, or whether observed delays in care provision are exceptions or the rule when compared to door-to-needle times as documented in the registry. In the explanatory sequential design, a quantitative study is carried out first, followed by a qualitative study to help explain the results from the quantitative study. This would be an appropriate design if the registry alone had revealed relevant delays in door-to-needle times and the qualitative study would be used to understand where and why these occurred, and how they could be improved. In the exploratory design, the qualitative study is carried out first and its results help informing and building the quantitative study in the next step [ 26 ]. If the qualitative study around EVT provision had shown a high level of dissatisfaction among the staff members involved, a quantitative questionnaire investigating staff satisfaction could be set up in the next step, informed by the qualitative study on which topics dissatisfaction had been expressed. Amongst other things, the questionnaire design would make it possible to widen the reach of the research to more respondents from different (types of) hospitals, regions, countries or settings, and to conduct sub-group analyses for different professional groups.

How to assess qualitative research?

A variety of assessment criteria and lists have been developed for qualitative research, ranging in their focus and comprehensiveness [ 14 , 17 , 27 ]. However, none of these has been elevated to the “gold standard” in the field. In the following, we therefore focus on a set of commonly used assessment criteria that, from a practical standpoint, a researcher can look for when assessing a qualitative research report or paper.

Assessors should check the authors’ use of and adherence to the relevant reporting checklists (e.g. Standards for Reporting Qualitative Research (SRQR)) to make sure all items that are relevant for this type of research are addressed [ 23 , 28 ]. Discussions of quantitative measures in addition to or instead of these qualitative measures can be a sign of lower quality of the research (paper). Providing and adhering to a checklist for qualitative research contributes to an important quality criterion for qualitative research, namely transparency [ 15 , 17 , 23 ].

Reflexivity

While methodological transparency and complete reporting is relevant for all types of research, some additional criteria must be taken into account for qualitative research. This includes what is called reflexivity, i.e. sensitivity to the relationship between the researcher and the researched, including how contact was established and maintained, or the background and experience of the researcher(s) involved in data collection and analysis. Depending on the research question and population to be researched this can be limited to professional experience, but it may also include gender, age or ethnicity [ 17 , 27 ]. These details are relevant because in qualitative research, as opposed to quantitative research, the researcher as a person cannot be isolated from the research process [ 23 ]. It may influence the conversation when an interviewed patient speaks to an interviewer who is a physician, or when an interviewee is asked to discuss a gynaecological procedure with a male interviewer, and therefore the reader must be made aware of these details [ 19 ].

Sampling and saturation

The aim of qualitative sampling is for all variants of the objects of observation that are deemed relevant for the study to be present in the sample “ to see the issue and its meanings from as many angles as possible” [ 1 , 16 , 19 , 20 , 27 ] , and to ensure “information-richness [ 15 ]. An iterative sampling approach is advised, in which data collection (e.g. five interviews) is followed by data analysis, followed by more data collection to find variants that are lacking in the current sample. This process continues until no new (relevant) information can be found and further sampling becomes redundant – which is called saturation [ 1 , 15 ] . In other words: qualitative data collection finds its end point not a priori , but when the research team determines that saturation has been reached [ 29 , 30 ].

This is also the reason why most qualitative studies use deliberate instead of random sampling strategies. This is generally referred to as “ purposive sampling” , in which researchers pre-define which types of participants or cases they need to include so as to cover all variations that are expected to be of relevance, based on the literature, previous experience or theory (i.e. theoretical sampling) [ 14 , 20 ]. Other types of purposive sampling include (but are not limited to) maximum variation sampling, critical case sampling or extreme or deviant case sampling [ 2 ]. In the above EVT example, a purposive sample could include all relevant professional groups and/or all relevant stakeholders (patients, relatives) and/or all relevant times of observation (day, night and weekend shift).

Assessors of qualitative research should check whether the considerations underlying the sampling strategy were sound and whether or how researchers tried to adapt and improve their strategies in stepwise or cyclical approaches between data collection and analysis to achieve saturation [ 14 ].

Good qualitative research is iterative in nature, i.e. it goes back and forth between data collection and analysis, revising and improving the approach where necessary. One example of this are pilot interviews, where different aspects of the interview (especially the interview guide, but also, for example, the site of the interview or whether the interview can be audio-recorded) are tested with a small number of respondents, evaluated and revised [ 19 ]. In doing so, the interviewer learns which wording or types of questions work best, or which is the best length of an interview with patients who have trouble concentrating for an extended time. Of course, the same reasoning applies to observations or focus groups which can also be piloted.

Ideally, coding should be performed by at least two researchers, especially at the beginning of the coding process when a common approach must be defined, including the establishment of a useful coding list (or tree), and when a common meaning of individual codes must be established [ 23 ]. An initial sub-set or all transcripts can be coded independently by the coders and then compared and consolidated after regular discussions in the research team. This is to make sure that codes are applied consistently to the research data.

Member checking

Member checking, also called respondent validation , refers to the practice of checking back with study respondents to see if the research is in line with their views [ 14 , 27 ]. This can happen after data collection or analysis or when first results are available [ 23 ]. For example, interviewees can be provided with (summaries of) their transcripts and asked whether they believe this to be a complete representation of their views or whether they would like to clarify or elaborate on their responses [ 17 ]. Respondents’ feedback on these issues then becomes part of the data collection and analysis [ 27 ].

Stakeholder involvement

In those niches where qualitative approaches have been able to evolve and grow, a new trend has seen the inclusion of patients and their representatives not only as study participants (i.e. “members”, see above) but as consultants to and active participants in the broader research process [ 31 – 33 ]. The underlying assumption is that patients and other stakeholders hold unique perspectives and experiences that add value beyond their own single story, making the research more relevant and beneficial to researchers, study participants and (future) patients alike [ 34 , 35 ]. Using the example of patients on or nearing dialysis, a recent scoping review found that 80% of clinical research did not address the top 10 research priorities identified by patients and caregivers [ 32 , 36 ]. In this sense, the involvement of the relevant stakeholders, especially patients and relatives, is increasingly being seen as a quality indicator in and of itself.

How not to assess qualitative research

The above overview does not include certain items that are routine in assessments of quantitative research. What follows is a non-exhaustive, non-representative, experience-based list of the quantitative criteria often applied to the assessment of qualitative research, as well as an explanation of the limited usefulness of these endeavours.

Protocol adherence

Given the openness and flexibility of qualitative research, it should not be assessed by how well it adheres to pre-determined and fixed strategies – in other words: its rigidity. Instead, the assessor should look for signs of adaptation and refinement based on lessons learned from earlier steps in the research process.

Sample size

For the reasons explained above, qualitative research does not require specific sample sizes, nor does it require that the sample size be determined a priori [ 1 , 14 , 27 , 37 – 39 ]. Sample size can only be a useful quality indicator when related to the research purpose, the chosen methodology and the composition of the sample, i.e. who was included and why.

Randomisation

While some authors argue that randomisation can be used in qualitative research, this is not commonly the case, as neither its feasibility nor its necessity or usefulness has been convincingly established for qualitative research [ 13 , 27 ]. Relevant disadvantages include the negative impact of a too large sample size as well as the possibility (or probability) of selecting “ quiet, uncooperative or inarticulate individuals ” [ 17 ]. Qualitative studies do not use control groups, either.

Interrater reliability, variability and other “objectivity checks”

The concept of “interrater reliability” is sometimes used in qualitative research to assess to which extent the coding approach overlaps between the two co-coders. However, it is not clear what this measure tells us about the quality of the analysis [ 23 ]. This means that these scores can be included in qualitative research reports, preferably with some additional information on what the score means for the analysis, but it is not a requirement. Relatedly, it is not relevant for the quality or “objectivity” of qualitative research to separate those who recruited the study participants and collected and analysed the data. Experiences even show that it might be better to have the same person or team perform all of these tasks [ 20 ]. First, when researchers introduce themselves during recruitment this can enhance trust when the interview takes place days or weeks later with the same researcher. Second, when the audio-recording is transcribed for analysis, the researcher conducting the interviews will usually remember the interviewee and the specific interview situation during data analysis. This might be helpful in providing additional context information for interpretation of data, e.g. on whether something might have been meant as a joke [ 18 ].

Not being quantitative research

Being qualitative research instead of quantitative research should not be used as an assessment criterion if it is used irrespectively of the research problem at hand. Similarly, qualitative research should not be required to be combined with quantitative research per se – unless mixed methods research is judged as inherently better than single-method research. In this case, the same criterion should be applied for quantitative studies without a qualitative component.

The main take-away points of this paper are summarised in Table ​ Table1. 1 . We aimed to show that, if conducted well, qualitative research can answer specific research questions that cannot to be adequately answered using (only) quantitative designs. Seeing qualitative and quantitative methods as equal will help us become more aware and critical of the “fit” between the research problem and our chosen methods: I can conduct an RCT to determine the reasons for transportation delays of acute stroke patients – but should I? It also provides us with a greater range of tools to tackle a greater range of research problems more appropriately and successfully, filling in the blind spots on one half of the methodological spectrum to better address the whole complexity of neurological research and practice.

Take-away-points

Acknowledgements

Abbreviations.

Authors’ contributions

LB drafted the manuscript; WW and CG revised the manuscript; all authors approved the final versions.

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• Qualitative Assessments
• Mpox Rapid Risk Assessment
• 2023-2024 Respiratory Disease Season Outlook
• Respiratory Disease Season Outlook

Related Topics:

• Center for Forecasting & Outbreak Analytics
• About Behind the Model
• Modeling and Forecasting
• Insight Net

What to know

In response to the recent spread of clade I mpox in Sub-Saharan Africa, CDC is working to update our July 8, 2024, risk assessment.

As of July 8, 2024

CDC assessed the overall risk to the United States posed by the clade I mpox outbreak in the Democratic Republic of Congo (DRC) for two populations.

• The risk to the general population is assessed as VERY LOW. A
• The risk to gay, bisexual, and other men who have sex with men (MSM) who have more than one sexual partner and people who have sex with MSM, regardless of gender, is assessed as LOW to MODERATE.

The purpose of this assessment is to provide time-sensitive health information about the ongoing clade I outbreak of mpox in the DRC for public health agencies.

We have moderate confidence in this qualitative assessment. The assessment relies on subject-matter experts evaluating a range of evidence related to risk, including limited epidemiologic data from the DRC outbreak, historical data on clade I mpox epidemiology and clinical severity, and extrapolations based on the ongoing clade IIb mpox outbreak in the United States. We will review available evidence every two weeks, or if the situation changes significantly, and update this assessment as warranted.

This assessment is an update to the previously published risk assessment on May 10, 2024. This update includes new evidence, but there are no changes to the overall risk assessment. We do not anticipate producing additional updates to this risk assessment. We continue to monitor the outbreak of clade I mpox in DRC, and will update this risk assessment if the risk to the United States changes.

Risk assessment for general population in the United States

Overall risk.

We assess the overall risk to the general U.S. population as very low , with moderate confidence. Overall risk is assessed by considering the likelihood and impact of infection across the population (see Methods section). This assessment may change as new evidence becomes available.

We assess the likelihood of infection for the general population as extremely low . Factors that informed our assessment of likelihood included the following:

There are no known cases of clade I mpox in the U.S. or any other country outside of endemic areas , although the virus could potentially spread from DRC to the United States or other countries through infected travelers from the DRC.

While the DRC outbreak has resulted in more than 7,000 confirmed or suspected cases so far in 2024, we assess that similar levels of transmission are unlikely in the United States.

• Experts do not believe that a similar scenario of transmission is likely in the United States for several reasons, including smaller average household sizes, increased access to improved sanitation and healthcare, and the lack of zoonotic reservoirs of disease.
• CDC modeling suggests close-contact transmission within and between households is unlikely to result in a large number of mpox clade I cases in the United States.
• The DRC outbreak represents multiple, ongoing provincial-level outbreaks in 25 of 26 provinces. Data suggest that cases have stemmed from many separate zoonotic introductions and subsequent chains of household transmission, with the exception of one outbreak in Kamituga, described below.

Although there is evidence of sexual transmission in some localities in the DRC, widespread sexual transmission in the U.S. general population is unlikely.

• A recent outbreak in the Kamituga mining region in DRC has been associated with sexual transmission, with 29% of confirmed cases identifying as sex workers. One recent cohort study of 371 Kamituga hospital patients with suspected mpox found that 88% of patients reported recent transactional sex.
• There is no current confirmed evidence of widespread heterosexual transmission in other DRC provinces, and it is unclear what proportion of cases elsewhere may be linked to sexual transmission. However, the high number of cases in children ≤ 15 years old is similar to historical trends in DRC and suggests that factors outside of sexual transmission have important roles. In general, epidemiologic data are limited for mpox patients, and we do not know the true number of cases nor how each case acquired infection.
• Modeling work from the United Kingdom related to the 2022 outbreak indicated that lower partnership formation rates among heterosexuals prevented sustained transmission within these networks.
• CDC modeling has shown similar results for heterosexual networks in the U.S. 1
• The likelihood of infection among gay, bisexual, and other men who have sex with men (MSM) is assessed separately in the risk assessment for MSM in the United States, reported below.

As of June 2, 2024 , there are 109 reported cases (confirmed, probable, or suspected) in the Republic of Congo, which borders DRC to the west. As of November 2023, WHO indicated it is unknown whether there are links between the Republic of Congo outbreak and DRC’s outbreak. Clade I is endemic in Republic of Congo; these cases may have resulted from zoonotic introductions or spread across the border in a relatively localized area and as such, do not yet suggest wider regional spread.

We assess the impact of infection for the general population as low to moderate . Factors that informed our assessment of impact included the following:

• Acquired immunity from previous infection with the mpox virus is extremely low for the general population (see separate analysis for MSM, who were disproportionately affected by mpox during the 2022 outbreak).
• Vaccine-induced immunity from mpox vaccination during the 2022 outbreak is also very low across the general population. During the 2022 outbreak, vaccination was targeted to those at highest risk of infection, including MSM and their partners.
• Prior smallpox vaccination , which can offer some protection against mpox, has declined substantially in the United States since the 1970s, when population-wide smallpox vaccination ceased.
• DRC health authorities reported the case fatality rate (CFR) from suspected clade I mpox in 2023 was 4.6% and rose to 6.7% throughout early 2024; however, this may be an overestimation because of challenges in completeness of case reporting. In a study of 216 mpox patients from one DRC hospital from 2007-2011, investigators found a CFR of 1.4% among 216 patients who received limited supportive care and no mpox therapeutics.
• In the United States, the CFR would likely be lower, given better access to high-quality supportive medical care and therapeutics.

We have moderate confidence in this assessment.

We note uncertainties around epidemiologic and genomic data in DRC, including transmission dynamics in children and non-MSM sexual networks.

Risk assessment for MSM in the United States

Overall risk for this population.

We assess the overall risk for gay, bisexual, and other men who have sex with men (MSM) in the United States, who have more than one sexual partner, and their sexual partners, regardless of gender as low to moderate, with moderate confidence. Overall risk is assessed by considering the likelihood and impact of infection across the population (see Methods section). This assessment may change as new evidence becomes available.

We assess the likelihood of infection for MSM as low to moderate . Factors that informed our assessment of likelihood included the following:

• There are no known cases of clade I mpox in the United States or any other country outside of endemic areas, although the virus could potentially spread from DRC to the United States or from other countries through infected travelers who expose others at their destinations.
• Health authorities have documented sexually transmitted clade I mpox in DRC in MSM in six cases during 2023, indicating the virus could spread among these sexual networks in the United States if cases were imported.
• During the ongoing 2022 global mpox outbreak, most U.S. cases were among MSM and their sexual partners, suggesting this population could be at increased risk for clade I mpox infection if the clade I mpox virus were to spread to the United States. Furthermore, only a minority of MSM who CDC has estimated would benefit from vaccination have been fully vaccinated, though rates vary widely by jurisdiction.
• A 2023 modeling study estimated that depending on population mpox immunity levels, jurisdictions face varying degrees of risk for sustained mpox recurrence, indicating that many jurisdictions may have a high likelihood of sustained transmission.
• More recent CDC modeling found that as population-level immunity increased, the chances of a prolonged or large outbreak decreased. The study also found that if clade I mpox were to be introduced to MSM sexual networks in the United States, counties with greater than 50% population-level immunity would have smaller outbreaks on average (fewer than 100 infections).

We assess the impact of infection for this population as low to moderate . Factors that informed our assessment of impact included the following:

• Like the general population, we expect that the impacts of clade I mpox among MSM are likely lower in the United States, compared to DRC, because of the availability of high-quality supportive care and access to medical countermeasures.
• Population immunity among MSM and their sexual partners is likely to additionally reduce the severity of infection.

Given uncertainties around the level of prior immunity and the extent to which behavior adaptations initiated during the 2022 outbreak have continued or could recur, we have moderate confidence in this assessment.

Factors that could change our assessment

Geographic and population spread.

• Detection of clade I mpox cases in the United States, particularly if there is domestic person-to-person transmission in jurisdictions with low estimated population immunity.
• Clade I mpox spreads outside sub-Saharan Africa, including among people attending mass gatherings or among other highly mobile populations.
• The outbreak in DRC intensifies or spreads to countries where mpox is not endemic, in the region or globally.

Transmission dynamics

• Evidence of person-to-person spread among children in DRC, outside of already recognized high-risk activities , such as exposure to animals or close contact with mpox patients.
• Evidence of widespread, prolonged chains of sexual transmission in DRC.

Natural history and medical countermeasures

• Additional data to suggest an increased or decreased illness severity of clade I mpox infection.
• Increased mpox vaccination coverage among high-risk groups in United States.

Descriptive Epidemiology

There is an ongoing outbreak of mpox in DRC caused by the clade I mpox virus, which is distinct from the clade IIb mpox virus that caused the 2022 global outbreak. As of June 2, 2024, DRC reported 7,281 confirmed or suspected cases of mpox in 2024. In previous outbreaks, clade I has caused more severe disease and has been more transmissible than clade II within close-contact settings, typically in a household. Although clade I mpox is endemic in DRC, in 2023, health authorities reported a higher number of suspect cases and deaths across a wider geographic area that in some provinces affected atypical demographic groups. Approximately 70% of suspected mpox cases in DRC in 2024 were in children under age 15, similar to historical observations. However, adults were disproportionately affected in South Kivu province, where sexual transmission was predominant.

CDC has not detected any cases of clade I mpox in the United States, despite testing a high proportion of presumed mpox specimens—those positive for non-variola orthopoxvirus (NVO)—with tests that can identify mpox by clade. In addition, several commercial and other non-CDC laboratories perform clade II testing in addition to NVO tests, and all specimens tested to date have been clade II. If these laboratories see anything unusual—for example, an NVO positive but clade II negative result—they would alert CDC immediately to ensure additional genotyping is conducted to determine if they are clade I. CDC continues to work, including with other U.S. government agencies, on multiple approaches to expand clade-specific testing domestically. Several public health laboratories (PHLs) have begun or are working to begin clade-specific testing.

Transmission

The DRC outbreak of clade I mpox has likely resulted from transmission through several modes and in different settings. Most cases in 2023 were in children, and in past outbreaks, children have been more likely to acquire infection through contact with infected animals. Transmission caused by close contact within households has occurred in past clade I mpox outbreaks . Household transmission chains have typically been small, although occasionally have involved up to six generations of transmission. Transmission risk is highest among unvaccinated contacts; children and young adults are less likely to have vaccine-induced orthopoxvirus immunity since smallpox vaccination programs ended in 1980 in DRC. Sexual transmission has also been recently reported in Kamituga, South Kivu province in Eastern DRC, primarily among female sex workers , and in a small outbreak affecting MSM and women in Kenge, Kwango Province in March 2023.

Importation risk

In 2023, outbreaks were reported in urban areas of DRC, including Kinshasa and less-populated regions of DRC, such as Équateur and South Kivu Provinces, where cross-border movement elevates the risk of spread of the disease outside of the country. As of June 2, 2024, there are 109 clade I mpox cases (confirmed, probable, and suspected) in the neighboring Republic of Congo, where clade I mpox is endemic, likely representing localized transmission in a region with frequent cross-border movement. There are no direct commercial air passenger flights arriving from DRC and its neighboring countries into the United States.

CDC subject-matter experts specializing in risk assessment methods, infectious disease modeling, global health, and mpox and other orthopoxviruses collaborated to develop this rapid assessment. Experts initially convened in February 2024 to discuss the need for an assessment examining the risks posed by the DRC outbreak to the United States, key evidence related to the DRC outbreak, and specific populations to include in the assessment. To conduct this assessment, experts considered evidence including epidemiologic data from DRC, data from the ongoing mpox outbreak in the United States caused by clade IIb, and historical data on clade I mpox outbreaks in DRC. After the initial assessment was finalized in February, experts subsequently re-reviewed evidence and updated this assessment in early March, mid-March, and mid-April.

Overall risk was estimated by combining the likelihood of infection and the impact of the disease. For example, low likelihood of infection combined with high impact of disease would result in moderate risk. The likelihood of infection refers to the probability that members of the general U.S. population or MSM acquire mpox throughout 2024, which in turn depends on the likelihood of exposure, infectiousness of the disease, and susceptibility of the population. The impact of infection considers several factors affecting the consequences of infection, including the severity of disease, level of population immunity, availability of treatments, and necessary public health response resources. A degree of confidence was assigned to each level of the assessment, taking into account evidence quality, extent, and corroboration of information.

For more details on our methods, please see our rapid risk assessment methods webpage .

Previous Updates

See the previous updates of the Mpox Rapid Risk Assessment.

• We note that the general population includes gay, bisexual, and other men who have sex with men (MSM), but we assess this group in a separate assessment because the clade I mpox outbreak in DRC may pose a higher risk to this population.
• Pollock E, Nakazawa Y, Asher J, Gift T, Spicknall I. Potential mpox transmission among college-attending 18-25-year-olds with opposite-sex contacts in the United States. (2023, July 24-27). The International Society for Sexually Transmitted Diseases Research.
• Center for Forecasting and Outbreak Analytics | CFA | CDC

CFA: Qualitative Assessments

The Center for Forecasting and Outbreak Analytics qualitative assessments - including risk assessments, seasonal outlooks, and more.