chapter 3 research methodology and research method

Chapter 3 The Research Process

In Chapter 1, we saw that scientific research is the process of acquiring scientific knowledge using the scientific method. But how is such research conducted? This chapter delves into the process of scientific research, and the assumptions and outcomes of the research process.

Paradigms of Social Research

Our design and conduct of research is shaped by our mental models or frames of references that we use to organize our reasoning and observations. These mental models or frames (belief systems) are called paradigms. The word “paradigm” was popularized by

Thomas Kuhn (1962) in his book The Structure of Scientific Revolutions, where he examined the history of the natural sciences to identify patterns of activities that shape the progress of science. Similar ideas are applicable to social sciences as well, where a social reality can be viewed by different people in different ways, which may constrain their thinking and reasoning about the observed phenomenon. For instance, conservatives and liberals tend to have very different perceptions of the role of government in people’s lives, and hence, have different opinions on how to solve social problems. Conservatives may believe that lowering taxes is the best way to stimulate a stagnant economy because it increases people’s disposable income and spending, which in turn expands business output and employment. In contrast, liberals may believe that governments should invest more directly in job creation programs such as public works and infrastructure projects, which will increase employment and people’s ability to consume and drive the economy. Likewise, Western societies place greater emphasis on individual rights, such as one’s right to privacy, right of free speech, and right to bear arms. In contrast, Asian societies tend to balance the rights of individuals against the rights of families, organizations, and the government, and therefore tend to be more communal and less individualistic in their policies. Such differences in perspective often lead Westerners to criticize Asian governments for being autocratic, while Asians criticize Western societies for being greedy, having high crime rates, and creating a “cult of the individual.” Our personal paradigms are like “colored glasses” that govern how we view the world and how we structure our thoughts about what we see in the world.

Paradigms are often hard to recognize, because they are implicit, assumed, and taken for granted. However, recognizing these paradigms is key to making sense of and reconciling differences in people’ perceptions of the same social phenomenon. For instance, why do liberals believe that the best way to improve secondary education is to hire more teachers, but conservatives believe that privatizing education (using such means as school vouchers) are more effective in achieving the same goal? Because conservatives place more faith in competitive markets (i.e., in free competition between schools competing for education dollars), while liberals believe more in labor (i.e., in having more teachers and schools). Likewise, in social science research, if one were to understand why a certain technology was successfully implemented in one organization but failed miserably in another, a researcher looking at the world through a “rational lens” will look for rational explanations of the problem such as inadequate technology or poor fit between technology and the task context where it is being utilized, while another research looking at the same problem through a “social lens” may seek out social deficiencies such as inadequate user training or lack of management support, while those seeing it through a “political lens” will look for instances of organizational politics that may subvert the technology implementation process. Hence, subconscious paradigms often constrain the concepts that researchers attempt to measure, their observations, and their subsequent interpretations of a phenomenon. However, given the complex nature of social phenomenon, it is possible that all of the above paradigms are partially correct, and that a fuller understanding of the problem may require an understanding and application of multiple paradigms.

Two popular paradigms today among social science researchers are positivism and post-positivism. Positivism , based on the works of French philosopher Auguste Comte (1798-1857), was the dominant scientific paradigm until the mid-20 th century. It holds that science or knowledge creation should be restricted to what can be observed and measured. Positivism tends to rely exclusively on theories that can be directly tested. Though positivism was originally an attempt to separate scientific inquiry from religion (where the precepts could not be objectively observed), positivism led to empiricism or a blind faith in observed data and a rejection of any attempt to extend or reason beyond observable facts. Since human thoughts and emotions could not be directly measured, there were not considered to be legitimate topics for scientific research. Frustrations with the strictly empirical nature of positivist philosophy led to the development of post-positivism (or postmodernism) during the mid-late 20 th century. Post-positivism argues that one can make reasonable inferences about a phenomenon by combining empirical observations with logical reasoning. Post-positivists view science as not certain but probabilistic (i.e., based on many contingencies), and often seek to explore these contingencies to understand social reality better. The post -positivist camp has further fragmented into subjectivists , who view the world as a subjective construction of our subjective minds rather than as an objective reality, and critical realists , who believe that there is an external reality that is independent of a person’s thinking but we can never know such reality with any degree of certainty.

Burrell and Morgan (1979), in their seminal book Sociological Paradigms and Organizational Analysis, suggested that the way social science researchers view and study social phenomena is shaped by two fundamental sets of philosophical assumptions: ontology and epistemology. Ontology refers to our assumptions about how we see the world, e.g., does the world consist mostly of social order or constant change. Epistemology refers to our assumptions about the best way to study the world, e.g., should we use an objective or subjective approach to study social reality. Using these two sets of assumptions, we can categorize social science research as belonging to one of four categories (see Figure 3.1).

If researchers view the world as consisting mostly of social order (ontology) and hence seek to study patterns of ordered events or behaviors, and believe that the best way to study such a world is using objective approach (epistemology) that is independent of the person conducting the observation or interpretation, such as by using standardized data collection tools like surveys, then they are adopting a paradigm of functionalism . However, if they believe that the best way to study social order is though the subjective interpretation of participants involved, such as by interviewing different participants and reconciling differences among their responses using their own subjective perspectives, then they are employing an interpretivism paradigm. If researchers believe that the world consists of radical change and seek to understand or enact change using an objectivist approach, then they are employing a radical structuralism paradigm. If they wish to understand social change using the subjective perspectives of the participants involved, then they are following a radical humanism paradigm.

Figure 3.1. Four paradigms of social science research (Source: Burrell and Morgan, 1979)

Figure 3.2. Functionalistic research process

The first phase of research is exploration . This phase includes exploring and selecting research questions for further investigation, examining the published literature in the area of inquiry to understand the current state of knowledge in that area, and identifying theories that may help answer the research questions of interest.

The first step in the exploration phase is identifying one or more research questions dealing with a specific behavior, event, or phenomena of interest. Research questions are specific questions about a behavior, event, or phenomena of interest that you wish to seek answers for in your research. Examples include what factors motivate consumers to purchase goods and services online without knowing the vendors of these goods or services, how can we make high school students more creative, and why do some people commit terrorist acts. Research questions can delve into issues of what, why, how, when, and so forth. More interesting research questions are those that appeal to a broader population (e.g., “how can firms innovate” is a more interesting research question than “how can Chinese firms innovate in the service-sector”), address real and complex problems (in contrast to hypothetical or “toy” problems), and where the answers are not obvious. Narrowly focused research questions (often with a binary yes/no answer) tend to be less useful and less interesting and less suited to capturing the subtle nuances of social phenomena. Uninteresting research questions generally lead to uninteresting and unpublishable research findings.

The next step is to conduct a literature review of the domain of interest. The purpose of a literature review is three-fold: (1) to survey the current state of knowledge in the area of inquiry, (2) to identify key authors, articles, theories, and findings in that area, and (3) to identify gaps in knowledge in that research area. Literature review is commonly done today using computerized keyword searches in online databases. Keywords can be combined using “and” and “or” operations to narrow down or expand the search results. Once a shortlist of relevant articles is generated from the keyword search, the researcher must then manually browse through each article, or at least its abstract section, to determine the suitability of that article for a detailed review. Literature reviews should be reasonably complete, and not restricted to a few journals, a few years, or a specific methodology. Reviewed articles may be summarized in the form of tables, and can be further structured using organizing frameworks such as a concept matrix. A well-conducted literature review should indicate whether the initial research questions have already been addressed in the literature (which would obviate the need to study them again), whether there are newer or more interesting research questions available, and whether the original research questions should be modified or changed in light of findings of the literature review. The review can also provide some intuitions or potential answers to the questions of interest and/or help identify theories that have previously been used to address similar questions.

Since functionalist (deductive) research involves theory-testing, the third step is to identify one or more theories can help address the desired research questions. While the literature review may uncover a wide range of concepts or constructs potentially related to the phenomenon of interest, a theory will help identify which of these constructs is logically relevant to the target phenomenon and how. Forgoing theories may result in measuring a wide range of less relevant, marginally relevant, or irrelevant constructs, while also minimizing the chances of obtaining results that are meaningful and not by pure chance. In functionalist research, theories can be used as the logical basis for postulating hypotheses for empirical testing. Obviously, not all theories are well-suited for studying all social phenomena. Theories must be carefully selected based on their fit with the target problem and the extent to which their assumptions are consistent with that of the target problem. We will examine theories and the process of theorizing in detail in the next chapter.

The next phase in the research process is research design . This process is concerned with creating a blueprint of the activities to take in order to satisfactorily answer the research questions identified in the exploration phase. This includes selecting a research method, operationalizing constructs of interest, and devising an appropriate sampling strategy.

Operationalization is the process of designing precise measures for abstract theoretical constructs. This is a major problem in social science research, given that many of the constructs, such as prejudice, alienation, and liberalism are hard to define, let alone measure accurately. Operationalization starts with specifying an “operational definition” (or “conceptualization”) of the constructs of interest. Next, the researcher can search the literature to see if there are existing prevalidated measures matching their operational definition that can be used directly or modified to measure their constructs of interest. If such measures are not available or if existing measures are poor or reflect a different conceptualization than that intended by the researcher, new instruments may have to be designed for measuring those constructs. This means specifying exactly how exactly the desired construct will be measured (e.g., how many items, what items, and so forth). This can easily be a long and laborious process, with multiple rounds of pretests and modifications before the newly designed instrument can be accepted as “scientifically valid.” We will discuss operationalization of constructs in a future chapter on measurement.

Simultaneously with operationalization, the researcher must also decide what research method they wish to employ for collecting data to address their research questions of interest. Such methods may include quantitative methods such as experiments or survey research or qualitative methods such as case research or action research, or possibly a combination of both. If an experiment is desired, then what is the experimental design? If survey, do you plan a mail survey, telephone survey, web survey, or a combination? For complex, uncertain, and multi-faceted social phenomena, multi-method approaches may be more suitable, which may help leverage the unique strengths of each research method and generate insights that may not be obtained using a single method.

Researchers must also carefully choose the target population from which they wish to collect data, and a sampling strategy to select a sample from that population. For instance, should they survey individuals or firms or workgroups within firms? What types of individuals or firms they wish to target? Sampling strategy is closely related to the unit of analysis in a research problem. While selecting a sample, reasonable care should be taken to avoid a biased sample (e.g., sample based on convenience) that may generate biased observations. Sampling is covered in depth in a later chapter.

At this stage, it is often a good idea to write a research proposal detailing all of the decisions made in the preceding stages of the research process and the rationale behind each decision. This multi-part proposal should address what research questions you wish to study and why, the prior state of knowledge in this area, theories you wish to employ along with hypotheses to be tested, how to measure constructs, what research method to be employed and why, and desired sampling strategy. Funding agencies typically require such a proposal in order to select the best proposals for funding. Even if funding is not sought for a research project, a proposal may serve as a useful vehicle for seeking feedback from other researchers and identifying potential problems with the research project (e.g., whether some important constructs were missing from the study) before starting data collection. This initial feedback is invaluable because it is often too late to correct critical problems after data is collected in a research study.

Having decided who to study (subjects), what to measure (concepts), and how to collect data (research method), the researcher is now ready to proceed to the research execution phase. This includes pilot testing the measurement instruments, data collection, and data analysis.

Pilot testing is an often overlooked but extremely important part of the research process. It helps detect potential problems in your research design and/or instrumentation (e.g., whether the questions asked is intelligible to the targeted sample), and to ensure that the measurement instruments used in the study are reliable and valid measures of the constructs of interest. The pilot sample is usually a small subset of the target population. After a successful pilot testing, the researcher may then proceed with data collection using the sampled population. The data collected may be quantitative or qualitative, depending on the research method employed.

Following data collection, the data is analyzed and interpreted for the purpose of drawing conclusions regarding the research questions of interest. Depending on the type of data collected (quantitative or qualitative), data analysis may be quantitative (e.g., employ statistical techniques such as regression or structural equation modeling) or qualitative (e.g., coding or content analysis).

The final phase of research involves preparing the final research report documenting the entire research process and its findings in the form of a research paper, dissertation, or monograph. This report should outline in detail all the choices made during the research process (e.g., theory used, constructs selected, measures used, research methods, sampling, etc.) and why, as well as the outcomes of each phase of the research process. The research process must be described in sufficient detail so as to allow other researchers to replicate your study, test the findings, or assess whether the inferences derived are scientifically acceptable. Of course, having a ready research proposal will greatly simplify and quicken the process of writing the finished report. Note that research is of no value unless the research process and outcomes are documented for future generations; such documentation is essential for the incremental progress of science.

Common Mistakes in Research

The research process is fraught with problems and pitfalls, and novice researchers often find, after investing substantial amounts of time and effort into a research project, that their research questions were not sufficiently answered, or that the findings were not interesting enough, or that the research was not of “acceptable” scientific quality. Such problems typically result in research papers being rejected by journals. Some of the more frequent mistakes are described below.

Insufficiently motivated research questions. Often times, we choose our “pet” problems that are interesting to us but not to the scientific community at large, i.e., it does not generate new knowledge or insight about the phenomenon being investigated. Because the research process involves a significant investment of time and effort on the researcher’s part, the researcher must be certain (and be able to convince others) that the research questions they seek to answer in fact deal with real problems (and not hypothetical problems) that affect a substantial portion of a population and has not been adequately addressed in prior research.

Pursuing research fads. Another common mistake is pursuing “popular” topics with limited shelf life. A typical example is studying technologies or practices that are popular today. Because research takes several years to complete and publish, it is possible that popular interest in these fads may die down by the time the research is completed and submitted for publication. A better strategy may be to study “timeless” topics that have always persisted through the years.

Unresearchable problems. Some research problems may not be answered adequately based on observed evidence alone, or using currently accepted methods and procedures. Such problems are best avoided. However, some unresearchable, ambiguously defined problems may be modified or fine tuned into well-defined and useful researchable problems.

Favored research methods. Many researchers have a tendency to recast a research problem so that it is amenable to their favorite research method (e.g., survey research). This is an unfortunate trend. Research methods should be chosen to best fit a research problem, and not the other way around.

Blind data mining. Some researchers have the tendency to collect data first (using instruments that are already available), and then figure out what to do with it. Note that data collection is only one step in a long and elaborate process of planning, designing, and executing research. In fact, a series of other activities are needed in a research process prior to data collection. If researchers jump into data collection without such elaborate planning, the data collected will likely be irrelevant, imperfect, or useless, and their data collection efforts may be entirely wasted. An abundance of data cannot make up for deficits in research planning and design, and particularly, for the lack of interesting research questions.

• Social Science Research: Principles, Methods, and Practices. Authored by : Anol Bhattacherjee. Provided by : University of South Florida. Located at : http://scholarcommons.usf.edu/oa_textbooks/3/ . License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

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Chapter 3 - Research Methodology and Research Method

2012, Research Methodology and Research Method

This chapter looks at the various research methodologies and research methods that are commonly used by researchers in the field of information systems. The research methodology and research method used in this research is acknowledged and discussed. The chapter starts off by providing a comprehensive introduction to research. Then the research methodologies and research methods particularly used in information systems are discussed. A significant effort has been made to clarify and provide distinctions between research methodology and research method. During the course of this research, when investigating the literature on research methodology and research methods, it was found that many researchers were using the two interchangeably. Therefore the two sections on research methodology and research methods have been treated separately. A section that compares and differentiates between the two is presented first, followed by the section on research methodology. Then the different types of research methodology are described and the two main types of research methodologies namely qualitative research methodology and qualitative research methodology is discussed. The research methodology that has been utilised for this research is discussed and the reason why the particular research method was chosen with proper justification is explained. Then research methods in general are discussed and the types of research methods suitable for information systems research are explained. The differences between the qualitative and quantitative research methods are elaborated upon. Since secondary data sources have been used in this research, a section is included to discuss the differences between the two and to explain the advantages of using secondary data sources for research. Then the research method, that is, the actual data collection and data analysis method is described and justification is provided on why the particular research method was chosen. Case study research method is combined with grounded theory research method for document analysis of archival data that was accessed via the Internet. Descriptive methods have been used to investigate the opportunities and issues of cloud computing with mobile phones for developing countries.

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International organizations and research methods: an introduction

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Timon Forster, International organizations and research methods: an introduction, International Affairs , Volume 100, Issue 3, May 2024, Pages 1303–1304, https://doi.org/10.1093/ia/iiae084

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Research methods are predominantly viewed as techniques that enable academics to collect new data, test hypotheses, advance scholarly debates and produce knowledge. International organizations and research methods challenges readers to think about methods as performative tools as well. Accordingly, the application of any method generates a distinct representation of the social world in which international institutions operate. From this vantage point, the choice of method itself is a function of the researchers’ academic training and background. The editors, Fanny Badache, Leah R. Kimber and Lucile Maertens, therefore call for a ‘deliberate and reflexive stance in the research process’ (p. 4). By treating methods as technical and performative tools, this edited volume showcases an extensive range of approaches that illuminate both the well-trodden paths and the uncharted territory of international organizations.

Badache, Kimber and Maertens, alongside their 59 contributors, have written a gentle methodological introduction to the study of international organizations. The book consists of five parts dedicated to different steps of the research process: observing, interviewing, documenting, measuring and combining. Each chapter describes a particular method (or set of methods) and its relevance, offering a brief how-to guide and discussing common challenges. Moreover, the book features 26 boxes that focus on more specific methodological tools or tricks, and it includes five interludes to take stock of the pertinent debates in the field. Thus, the book invites readers to approach international organizations in new ways. For instance, one could perceive the headquarters of the International Monetary Fund and the World Bank on 19th Street in Washington DC as artefacts (Box o); an exhibition celebrating the United Nations’ 80th anniversary suddenly lends itself to branding analysis (Box n) or to composing collages (chapter 29); and when a former high-level official publishes a memoir, an opportunity to conduct prosopography emerges (chapter 26).

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Augmenting Group Contributions Online: How do Visual Chart Structures Applied to Social Data Affect Group Perceptions and Contributions

Humans are social beings and throughout our evolution we have survived and thrived thanks to our ability to cooperate [7]. Overcoming our current societal challenges from sustainability and energy conservation [8] to democracy, public health, and community building [9] will all require our continued cooperation. Yet, many of these present us with a dilemma where our short-term personal goals are at odds with the collective long-term benefits. For example, many of us listen NPR radio but never make a donation to help cover its operational costs. The success of cooperation during such dilemmatic situations often depends on communication, reward and punishment structures, social norms and cues [10], [11], [12], [13]. But how to encourage cooperation online where social cues are not readily available?

Accelerated by the COVID-19 pandemic and the prevalence of digital technologies, cooperation among individuals increasingly happens online where data-based feedback supports our decisions. Problematically, people online are often not only remote and asynchronous, but often also anonymous, which has resulted in de-individuation and antinormative behavior [14]. Social data, information that users share about themselves via digital technologies, may offer opportunities for social feedback design that affords perceptions of social cohesion and may support successful cooperation online.

This dissertation seeks to answer the normative question of how to design for cooperation in social data feedback charts in dilemmatic situations online. I conducted mixed methods design research by combining theory-driven design with a series of controlled experiments on Amazon Mechanical Turk to understand the perceptual and behavioral effects of visually unifying social data feedback charts. To achieve this, I mapped the design space for home energy feedback ( Chapter 2 ) to guide my iterative and user-centered theorizing about how visual unity in social feedback charts might prime viewers with unified group perceptions ( Chapter 3 ). I then validated my theorizing with controlled perceptual ( Chapter 4 ) and decision experiments ( Chapter 5 ).

The triangulated results offer evidence for visually unifying cues in feedback charts affecting social data interpretation ( Chapter 4 ) and cooperation online ( Chapter 5 ). Two visual properties: data point proximity and enclosure -, trigger variable levels of perceivable social unity that play a partial role in participants’ decision to cooperate in a non-monetary social dilemma situation online. I discuss the implications for future research and design ( Chapter 6 ).

Several sources have funded the data collection in support of my Ph.D. work: Dr. Victor Yingjie Chen (2019), the Bilsland Dissertation Fellowship by the Dean of the Graduate School at Purdue University (2018), the National Science Foundation’s (NSF) Smart and Connected Communities (S&CC) grant (#1737591) (2017), The Dean of Purdue Polytechnic’s Research Award of Excellence (2016), and Whirlpool Corporation (2015).

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• → The 6-Step Guide to Market Research P...

The 6-Step Guide to Market Research Processes

Looking for a step-by-step guide to market research processes? Learn more about the marketing research process and methods to gather data—and make the most of it.

Latest posts on Tips

Typeform    |    05.2024

Typeform    |    04.2024

Say what you will about McDonald’s, but one of the things most respected about their brand is the international menu concept.

From maple and bacon poutine in Canada and gazpacho in Spain to India’s McPaneer Royale, McDonald’s knows how to give the people what they want.

And how do they inject local appeal in a global brand? By gaining a deep understanding of the consumers in every target market they plan to enter.

If you’re thinking about doing consumer insights research, you should be familiar with market research processes. Let’s start with the basics. What is market research, and how is it different from marketing research?

What is market research?

People often confuse market research and marketing research. Aren’t they just different words for the same thing?

ESOMAR, the global research and data association, and the American Marketing Association would disagree. Here’s the gist:

Market research emphasizes the process of collecting consumer data , while marketing research refers to the product of that information and/or a function within an organization.

Essentially, you might be looking for a marketing researcher to conduct market research. Market research will help you answer questions about your customers, your competitors, or current and potential markets.

The 6-step marketing research process

Market research can seem like a mystery.

However, market research processes are quite systematic—well, in theory. In practice, the steps involve exploration, creativity, and abstraction.

Here are a few steps you can follow to make it a bit easier.

1. Identify the problem

Researchers are curious people. That’s why every research project starts with a question.

What is the part of your business you want to know more about? Identifying the problem is the most important step in market research processes. It’s going to determine every step you take in the future—of market research, anyway.

Not sure where to start? Here are a few tips:

Look for marketing challenges or opportunities. Maybe your brand awareness could use a boost. You've noticed declining customer loyalty, or you’re considering opportunities in emerging markets.

Frame it as a question. Why is customer loyalty decreasing? How can we enter the market for luxury hotels? What does our customer’s typical path to purchase look like?

Determine what type of problem you have. In market research, a problem can be ambiguous, clearly defined, or somewhere in the middle. Do you know the variables and factors influencing what you want to measure? This is important as it'll influence your overall research design, which is up next.

2. Design the research

There are three types of research designs. The design you choose will be informed by how well-defined your problem is.

If you don’t know much about the problem, you need:

Exploratory research: If you don’t know the major variables or factors at play, your research is ambiguous. Exploratory research can help you develop a hypothesis or ask a more precise question. If you have a vague idea about what’s important to solve the problem, you need:

Descriptive research: Descriptive research does what it says on the box— it describes a certain phenomenon or the characteristics of a population. It can build on exploratory research but doesn’t give insight into the how, when, or why. Descriptive research is useful for parsing out market segments and measuring performance. Consequently, you need a pretty good idea of what you’re measuring and how it'll be measured. If you want to know how cause and effect are linked, you need:

Causal research: Market researchers conduct causal research when they want to understand the relationships between two or more variables. Simply put, causal research helps you understand cause and effect.

3. Choose your sample and market research methods

Data is the essence of market research. At the end of these market research processes, data is analyzed, interpreted, and turned into information and actionable insights.

Data can be qualitative or quantitative . Qualitative data can take many forms, from descriptions to audio and video. Quantitative data is typically presented in values and figures.

When choosing your sample, you must select the population you want to study. A population is a group with some shared characteristics that you’re interested in gathering data from. It can be broad (Canadians) or narrow (independent gym owners in Chicago).

No matter how small or large your population, you’ll unlikely be able to work with everyone.

The key to choosing a good sample is that it is representative. That means the people you select to participate (the sample) should reflect the larger group you’re studying.

4. Get the data

There are two forms of data you can collect: primary and secondary data.

Primary data is gathered specifically for your project. Secondary data has already been collected, either internally or externally through government agencies, consulting or market research firms, websites, social networks, and so on.

Depending on your research design, you may want to check internally for secondary data. For example, let’s say you’re trying to understand the annual purchase cycle for your business. You'd gather sales and reports and company records—that's secondary data.

But of course, secondary data still needs to be prepared for analysis

There are two ways to collect primary data: directly or indirectly. Direct data collection is just that—you are speaking to your participants directly. That can be through surveys, interviews, focus groups, and so on. Indirect data collection typically means observation. Think in-store observation, shelf experiments, or website heatmaps.

5. Analyze the data

Data analysis is a process of looking for patterns in data and trying to understand why those patterns exist. Data can be analyzed quantitatively or qualitatively.

Quantitative data analysis is a process more complicated than can be described here. Unless you’re a math whiz, you’ll probably just use a data analysis software like SPSS or StatCrunch.

Qualitative data analysis typically involves coding—but not the computer programming kind, don’t you worry. This type of coding can be done by hand or using software such as NVivo. It involves looking for themes, concepts, and words that are repeated throughout the data.

6. Interpret and present the insights

Interpretation involves answering the question: What does the data tell me about what I wanted to know?

That’s where themes and patterns come in. You can describe trends and present them using figures or descriptions drawn from your participants.

Part of interpretation is using what you know about customers, businesses, or markets to provide recommendations for how to move forward. These data-driven suggestions should offer a solution to the initial problem. The results of the research can also bring to light a problem you weren’t even aware you had.

Overview of market research methods

Market researchers are able to draw on a large toolbox of market research methods. Typically, they fall into the qualitative or quantitative category because of the type of data they produce.

Focus groups

Best for: Exploratory research

Type of method: Qualitative

A market research technique that involves a group discussion about certain topics led by a moderator to uncover the thoughts and opinions of participants.

In-depth interviews

Best for: Descriptive research

An interview that's conducted with an individual aimed at getting deeper insights about attitudes, motivations, or experiences.

Ethnography

Best for: Descriptive research 

Also known as participant observation, it involves spending time with participants in their natural environment (as opposed to a lab setting). 

Observational

Carefully watch people to understand what they’re doing. It allows you to learn about consumer or employee behavior but not the motivation behind it.

Discourse analysis

Best for: Exploratory or descriptive research

This is a fancy way of saying “analyzing what people say.” Social listening is a form of discourse analysis. Examining customer reviews, help transcripts, social media comments, and more are all forms of discourse analysis.

Type of method: Quantitative

Surveys are the crux of market research. They involve collecting facts, figures, and opinions using a questionnaire. Surveys can also yield qualitative data if participants write out answers. Surveys may seem simple, but there are a lot of factors that can turn good intentions into bad data—be sure to read our tips on the right question types to ask . 

Structured observation

Observation research can also be quantitative if you are observing participants without direct involvement and assigning values to certain behaviors.

A/B testing

Also called split testing, this is a way to compare responses to a variation of a single variable to see which performs better. For example, presenting users with two versions of an ad to see which gets more clicks.

Best for: Causal research

Marketing experimentation typically involves manipulating a variable to see how it influences behavior. It can be conducted in a lab or in the field. 

Examples of market research

Time to put this into practice. Let’s look at market research examples of various types of research designs. 

Exploratory market research

Mobile phone company HTC wanted to understand how they could improve the user experience of their phones. This problem required exploratory research because there wasn’t a specific feature they wanted to test. They simply wanted to learn more from their customers.

With market research, they observed how participants interacted with their phones. They looked for challenges people had with everyday usage. After analyzing these pain points, they added new functions to their next model that made the phones easier to use.

Descriptive market research

Company ABC wants to understand how large the market for vegan cheese is in Canada. They have a somewhat defined research problem: What is the potential market share for vegan cheese?

In order to provide an answer, market researchers will have to describe various characteristics: who the customers are, why they buy vegan cheese, competitor market penetration, and potential opportunities.

This requires mixed-method research. The researchers might collect secondary data on the number of vegans in Canada or how much vegan cheese is sold in the country and through which companies. They may also conduct focus groups to understand what motivates people to buy vegan cheese.

Once complete, they'll be able to present statistics on vegans in Canada and estimate Company ABC’s potential market share.

Causal market research

Causal research requires keeping variables and conditions the same, save for the one you are testing. German marketing and sensory research company iSi is a company that runs both field and lab experiments.

They worked with a chocolate bar company to design an experiment that tested 12 different chocolate bar recipes.

The consumers sequentially tested the recipes and provided ratings (quantitative data) and descriptions (qualitative data) of each one. The result was that consumers were most satiated by “a firm, tough texture and a higher amount of caramel and peanuts.”

Discovering market research processes

One thing to remember is that market research is an iterative process. You can keep using what you learn to conduct better studies and evaluate the changing market conditions and the whims of consumers. 

Ready to tackle the market research process? Build a market research survey with Typeform—choose from one of our customizable templates to gather beautifully designed data.

About the author

We're Typeform - a team on a mission to transform data collection by bringing you refreshingly different forms.

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Mini review article, imaging analysis for muscle stem cells and regeneration.

• 1 Stem Cell Institute, University of Minnesota Medical School, Minneapolis, MN, United States
• 2 Greg Marzolf Jr. Muscular Dystrophy Center, University of Minnesota Medical School, Minneapolis, MN, United States
• 3 Department of Neurology, University of Minnesota Medical School, Minneapolis, MN, United States

Composed of a diverse variety of cells, the skeletal muscle is one of the body’s tissues with the remarkable ability to regenerate after injury. One of the key players in the regeneration process is the muscle satellite cell (MuSC), a stem cell population for skeletal muscle, as it is the source of new myofibers. Maintaining MuSC quiescence during homeostasis involves complex interactions between MuSCs and other cells in their corresponding niche in adult skeletal muscle. After the injury, MuSCs are activated to enter the cell cycle for cell proliferation and differentiate into myotubes, followed by mature myofibers to regenerate muscle. Despite decades of research, the exact mechanisms underlying MuSC maintenance and activation remain elusive. Traditional methods of analyzing MuSCs, including cell cultures, animal models, and gene expression analyses, provide some insight into MuSC biology but lack the ability to replicate the 3-dimensional (3-D) in vivo muscle environment and capture dynamic processes comprehensively. Recent advancements in imaging technology, including confocal, intra-vital, and multi-photon microscopies, provide promising avenues for dynamic MuSC morphology and behavior to be observed and characterized. This chapter aims to review 3-D and live-imaging methods that have contributed to uncovering insights into MuSC behavior, morphology changes, interactions within the muscle niche, and internal signaling pathways during the quiescence to activation (Q-A) transition. Integrating advanced imaging modalities and computational tools provides a new avenue for studying complex biological processes in skeletal muscle regeneration and muscle degenerative diseases such as sarcopenia and Duchenne muscular dystrophy (DMD).

Introduction

As the most abundant tissue in the human body, the skeletal muscle is a highly ordered tissue that performs the following vital functions for the body, including breathing, posture maintenance, and body locomotion. The skeletal muscle tissue itself contains a variety of the following different cell types: multinucleated muscle fibers (myofibers), muscle stem cells (also known as muscle satellite cells or MuSCs), endothelial cells (ECs), pericytes (PCs), side population (SP) cells, mesenchymal progenitors/fibro-adipogenic progenitors (FAPs), motor neurons, Schwann cells, muscle spindle cells for peripheral nerves, immune cells, and undefined fibroblastic cells ( Asakura et al., 2001 ; Asakura et al., 2002 ; Verma et al., 2018 ; Wosczyna et al., 2018 ; Cutler et al., 2022 ). The multinucleated myofibers are long-lived and elongated cells with little turnover without disease or injury ( Kann et al., 2021 ; Krauss and Kann, 2023 ). However, following injury, the skeletal muscle has the unique ability to regenerate.

Skeletal muscle regeneration is a complex process orchestrated by the dynamic interplay between the diverse variety of cells in the skeletal tissue. The leading player in the regeneration process is the resident MuSCs, a stem cell population for skeletal myocytes—the source of new myofibers in normal conditions ( Asakura et al., 2001 ). During homeostasis, MuSCs maintain a quiescent state in which the cells have reversibly exited the cell cycle but retain their ability to re-enter the division process in response to specific environmental cues. MuSC quiescence is a state that must be actively maintained by a combination of cell-autonomous factors and external signals provided by the MuSC niche ( Kann et al., 2021 ; Ma and Mourkioti, 2023 ). Upon injury, MuSCs undergo an activation process in which they activate, proliferate, and differentiate to form new muscle fibers. In addition, MuSCs undergo a self-renewal process to replenish the resident stem cell pool, allowing the muscle to undergo the regeneration process for multiple injuries ( Kann et al., 2021 ; Krauss and Kann, 2023 ). Although several decades of research have gone into understanding the signaling factors and cellular interactions involved in the MuSC quiescence-to-activation transition (Q-A transition), the overall mechanism of this process still requires further exploration. Understanding the mechanisms of the Q-A transition is vital for understanding/modeling the skeletal muscle regeneration process in general.

Duchenne muscular dystrophy (DMD) is a progressive neuromuscular disease caused by dystrophin deficiency that inevitably leads to death. Although well characterized concerning muscle lesions in DMD, it is still not fully understood why MuSC-induced muscle regeneration fades with progression, ultimately resulting in skeletal muscle atrophy. Furthermore, the possible vascular changes that may affect the mechanism of skeletal muscle regeneration are poorly understood ( Latroche et al., 2015a ).

Historically, in vivo MuSC behavior has been inferred through histological analysis of skeletal muscle tissue cross-sections, cell cultures of single myofibers, and ex vivo studies utilizing isolated MuSCs ( Asakura et al., 2001 ; Asakura et al., 2002 ). Although these approaches have provided great insight into MuSC behavior, they cannot replicate the true in vivo environment in which the MuSCs receive several signaling cues from their niche. In addition, these methods only provide a snapshot view, which does not represent the entirety of MuSC dynamics ( Ratnayake and Currie, 2017 ; Ma et al., 2022 ).

With the recent advancements in both in vivo models and microscopy technologies, scientists have overcome some of the above issues in previous research methods. The combination of advanced imaging modalities (such as confocal, multi-photon, and spinning disk microscopes), improved stability of fluorescent proteins, and expanded computational power for image analysis has given researchers a whole new set of tools to study complex biological processes in detail, including, MuSC self-renewal, proliferation and activation during muscle regeneration and DMD progression ( Figure 1 ; Verma et al., 2016 ; Ratnayake and Currie, 2017 ; Verma et al., 2018 ; Kann et al., 2022 ; Ma et al., 2022 ). This chapter will review several imaging methods for MuSC behavior in the skeletal muscle. With these imaging methods, we intend to review several advances in understanding the mechanisms behind the MuSC Q-A transition, including changes in MuSC morphology, interactions with other cells in the MuSC niche, and internal signaling pathways.

Figure 1 . MuSCs reside in close proximity to capillary endothelial cells for MuSC self-renewal (A) Snapshots of MuSCs (red) and capillaries (green) from a 3-D image of mouse skeletal muscle. (B) Images of MuSCs are colored based on their distance from the nearest capillary. Capillaries (green) from a 3-D image of mouse skeletal muscle. Capillaries (green). Orange cells indicate activated MuSCs away from the capillaries (red arrows), and dark blue cells indicate quiescent MuSCs close to the capillaries (white arrows). (C) When activated (A)-MuSCs express VEGFA during regeneration, vascular endothelial cells are recruited in the vicinity of the MuSCs, forming a vascular niche. Subsequently, Notch signaling in MuSCs is activated by Dll4 derived from adjacent vascular endothelial cells, inducing self-renewal of Q-MuSCs. Q-MuSCs have longer, multiple projections (yellow allows), which regress after activation.

Previous work and barriers to studying the Q-A transition and MuSC morphology

Historically, MuSCs have been described as small, fusiform cells with a high nuclear-to-cytoplasmic ratio ( Kann et al., 2021 ; Krauss and Kann, 2023 ) ( Table 1 ). With improvements to imaging and muscle preparation techniques, studies have revealed that quiescent MuSCs are a heterogeneous population with cellular projections. In a study conducted by Schmalbruch in 1978, rat hind-limb skeletal muscles were freeze-fractured, and an electron microscope was used to characterize MuSC shape and its relationship to its underlying muscle fiber. After fracture examination, this study noted that most MuSCs had projections that indented the surface of the underlying muscle fiber. In addition, several cross-sections showcased how the underlying muscle fibers had projections that embraced the MuSCs ( Schmalbruch, 1978 ). An example of such an interaction is the co-localization of MuSCs and vascular ECs. It has been suggested that MuSCs are preferentially located near blood vessels ( Christov et al., 2007 ). However, this interaction may be underestimated in a typical two-dimensional (2-D) analysis. This scenario underscores the importance of analyzing cell-cell interactions in muscle tissue in their native three-dimensional (3-D) state. These observations lead to the hypotheses that 1) there is a cellular cross-talk between MuSCs and the surrounding niche cells, such as myofibers and ECs, and 2) the MuSC projections play a role in their renowned migratory movements.

Table 1 . Methods for in vivo and ex vivo MuSC characterization (A) Freeze Fracture Electron Microscopy is a technique of physically breaking apart (fracturing) a frozen biological sample followed by electron microscopic observation. This method analyzes cells, typically looking at the cell membrane and lipid-containing structure. (B) Myofiber/MuSC Culture is an ex vivo study in which MuSCs or myofibers are isolated and cultured. Behavior is then characterized via histological analysis. (C) Confocal or light-sheet fluorescence microscopy is a technique that allows for the 3-D optical resolution of tissues. Used in conjunction with fluorescence, it allows for the labeling of cells via cell-specific proteins. (D) Intravital Microscopy is a live imaging technique that allows visualization of cells in vivo .

A limitation of the above study is that it is a static analysis. While it provided valuable insights into MuSC behavior, such static analyses from cross-sections only provide a snapshot of MuSC behavior. As a result, MuSC dynamic behavior (both in quiescence and during regeneration) is left to be inferred ( Christov et al., 2007 ; Ratnayake and Currie, 2017 ). This limitation highlights the need for more advanced techniques that can capture the dynamic nature of MuSCs, especially during the Q-A transition after injury.

In general, studying MuSC quiescence has proven difficult as the isolation of MuSCs leads to rapid activation. Such sensitivity to niche perturbations prevents early examination of cellular events in the Q-A transition. Today, the identification of quiescence-promoting factors has mainly come from loss-of-function experiments in which the loss of such factors led to an activated MuSC phenotype. The mechanisms by which these factors maintain MuSC quiescence and how their dynamics change during the Q-A transition have remained unclear until recently with the advent of imaging advancements ( Kann et al., 2021 ; Krauss and Kann, 2023 ). These recent advancements in imaging techniques have opened up new possibilities for studying MuSCs, instilling optimism in the field and stimulating further research into the field.

A closer look into MuSC projections

Fluorescent intravital imaging of muscs.

One prominent imaging advancement is the visualization of live tissues of living organisms utilizing intravital microscopy. Such microscopy allows scientists to visualize MuSC dynamics in muscle in vivo in model organisms like mice and zebrafish. In a review of various MuSC live imaging studies, Siegel et al. (2011) demonstrated that time-lapse imaging studies of mouse myofiber-associated MuSCs have provided new data on the dynamic migration on the myofibers, and timing and direction of MuSC division following single myofiber cultures, revealing persistent differences in the behavior of daughter cells in planar and vertical divisions. Webster et al. (2016a) established a stepwise protocol to measure the behavior of fluorescently labeled MuSCs during homeostasis quantitatively and after mouse muscle injury. Using 3-D time-lapse intravital imaging to directly visualize the regenerating mouse tibialis anterior (TA) muscle in living mice, they showed that extracellular matrix remnants of injured skeletal muscle fibers dominate the behavior of MuSCs during regeneration as ghost fibers. After the injury, MuSC migration and cell division were primarily oriented bidirectionally along the longitudinal axis of the ghost fiber, allowing MuSCs to spread throughout the ghost fiber. Thus, ghost fibers are autonomous structural units required for proportional regeneration after tissue injury ( Webster et al., 2016b ). Evano et al. (2023) demonstrated in vivo intravital imaging for MuSC-mediated muscle regeneration using intact flexor digitorum brevis (FDB) muscle in mice. After several hours of in vivo imaging of regenerating muscle to monitor the initial response to muscle injury, cell migration, cell division, and cell fusion of MuSCs using two-photon microscopy or standard confocal microscopy. Konagaya et al. (2020) utilized an intravital imaging system for measuring migration rate, extracellular signal-regulated kinase (ERK) activity, and cell cycle of MuSCs within mouse TA muscle. Jacobsen et al. injected adult mice with fluorescent dextran and performed intravital imaging to assess in vivo microvascular perfusion of the gluteus maximus (GM) muscle. They concluded that angiogenesis precedes myogenesis in regeneration after muscle injury ( Jacobsen et al., 2023 ). Collins et al. (2024) utilized whole-mount imaging in 3-D with fluorescently labeled regenerating muscle and demonstrated that muscle fibers form via two distinct phases: Cell fusion of MuSCs first establishes muscle fibers, then MuSC-muscle fiber fusion enlarges the fibers. They also found the essential roles of residual muscle fiber basement membrane that promotes myogenic fusion and orients regenerating muscle fibers.

Ratnayake and Currie highlight how this live-imaging technique has provided insights into MuSC morphology during the zebrafish regeneration. The directional migration behavior of MuSCs after injury was observed using live-microscopic imaging and fluorescently labeled zebrafish larvae. Utilizing a double transgenic zebrafish line in which differentiated myofibers were labeled via mCherry and MuSCs were labeled with GFP (Myf5 expressing cells), laser ablation was used to create a minor focal injury by ablating a small number of myofibers. Then, the zebrafish larvae were allowed to resolve this injury while being time-lapsed imaged for 48 h. The images from this experiment revealed that the skeletal muscle regeneration process is marked by stages with distinct MuSC morphologies/behaviors. MuSCs migrating to the wound site displayed a polarized bipolar phenotype with extended cytoplasmic projections. At the wound site, the initially bipolar MuSCs change to a more rounded shape to associate with the dying myofibers to generate a progenitor pool. The subsequent progenitors disassociate from the dying myofiber and assume a bipolar phenotype. The progenitors were noted to interact with each other via their cytoplasmic extensions. Finally, the uninjured myofibers guide these progenitor cells to regions of injury in which the progenitors differentiate into myofibers ( Ratnayake and Currie, 2017 ).

Investigating the role of filopodia in myoblast fusion using time-lapse confocal imaging

Focusing on the last stages of the myofiber regeneration process, an imaging study conducted by Hammers et al. (2021) elucidates the role of cellular projections in myoblast fusion. Myoblast fusion is generally described as merging two opposing myoblast lipid bilayers. This process involves several widely expressed protein classes: cytoskeleton elements, phagocytosis receptors, and calcium-sensing membrane repair proteins. In addition, thin, actin-filled projections have been observed to be involved in the fusion process using confocal time-lapse imaging ( Hammers et al., 2021 ).

To determine if these projections are filopodia, the class x myosin (Myo10) expression pattern was monitored via immunofluorescence in cultures of differentiating MuSCs. Myo10 presence is a hallmark of filopodia as it is a molecular motor associated with the initiation/elongation of filopodia and transport within filopodia. Immunofluorescent confocal images of the differentiated myoblast cultures showcased that the projections were Myo10-positive and, therefore, were filopodia. In subsequent experiments, Myo10-knockout MuSCs did not form filopodia and continued with cell fusion, suggesting that Myo10 (and, therefore, filopodia) is essential for myoblast fusion ( Hammers et al., 2021 ). In exploring filopodia’s role in the fusion process, further fluorescence imaging experiments showcased how muscle fusion proteins Myomaker and Myomixer localize on myoblast filopodia ( Hammers et al., 2021 ). The results of this study illustrate the great potential of confocal imaging with the level of detail this imaging advancement provides in studying protein expression in the muscle regeneration process.

Investigation of intracellular signaling and MuSC projections

Interestingly, fluorescent-labeled observation of MuSCs revealed that during quiescence, MuSCs have projections of various lengths and numbers in mice ( Figure 1 ; Verma et al., 2018 ; Kann et al., 2022 ; Ma et al., 2022 ). The morphology of the projections is consistent with the dynamic and motile structure of MuSCs, which form actin-based filopodia at their distal ends. Filopodia are essential for signaling ligand sensing, cell-cell interactions, and cell migration. These MuSC projections are very long, often branched, and highly heterogeneous, and loss of these projections is observed in activated MuSCs, which require active motility ( Kann et al., 2022 ; Ma et al., 2022 ). As demonstrated in the previous studies above, MuSCs change their shapes and structure during different stages of the regeneration process. Such changes in cell shapes and structure have been reported to be driven by the Rho family of GTPases (Rho, Rac, and Cdc42). These membrane-bound GTPases mediate cytoskeletal rearrangements, downstream signaling, and transcriptional changes in response to other extracellular signaling cues ( Burridge and Wennerberg, 2004 ). Previous literature denotes how an inverse relationship exists between Rac and Rho, therefore functioning as a molecular switch coordinating changes in cell morphology ( Guilluy et al., 2011 ).

To reveal the molecular mechanisms in the Q-A transition of MuSCs with the projections via the Rho/Rac switch, tissue clearing with modified ex vivo single myofiber experiments was conducted in a study by Kann et al. (2022) . The results of this study showcased how the long cytoplasmic projections seen in quiescent MuSCs are associated with high levels of Rac/Cdc42 activity. Therefore, Rac activity functions to promote projection outgrowth. In early activation (physiologically in response to injury), Rac activity in MuSCs is downregulated, and Rho activity increases. With increased Rho activity, MuSC projections were shown to retract, marking the beginning of the Q-A transition ( Kann et al., 2022 ). The results of this study illustrate the great potential of tissue-clearing and confocal imaging with the level of detail this imaging advancement provides in studying intracellular signaling in quiescent MuSCs.

Ma et al. (2022) demonstrated that the transition between these different MuSC states is regulated by Piezo1, a sensing protein that promotes regeneration. While pharmacological activation of Piezo1 was shown to activate MuSCs during muscle regeneration, deletion of Piezo1 in MuSCs shifts MuSCs to less activated cells, mimicking the disease phenotype seen in DMD muscle. They also found that reactivation of Piezo1 ameliorates the morphological and regenerative defects of MuSCs in DMD muscle. These results advance our understanding of how MuSCs respond to muscle injury and demonstrate that the sensing protein Piezo1 is essential for MuSC activation in muscle regeneration.

Investigation of MuSC quiescence maintenance via cellular cross-talk with surrounding vasculature niche

One of the critical components of the skeletal muscle niche is the vascular network that stretches within skeletal muscle and envelopes MuSCs and muscle fibers. ECs, which constitute the vascular network, have been reported to exist in close proximity to MuSCs, and the number of MuSCs significantly correlates with the density of capillaries. Myofibers promote angiogenesis via the expression of vascular endothelial growth factor (VEGF) ( Latroche et al., 2015b ). However, it has yet to be verified whether ECs in the vicinity of MuSCs are involved in MuSC activation, self-renewal, and maintenance of quiescence.

Using our proprietary skeletal muscle-specific tissue-clearing technology, mice in which MuSCs and ECs are simultaneously fluorescently labeled in skeletal muscle, and confocal microscopy, we have successfully visualized the spatial relationship between the complex network of capillaries and MuSCs in 3-D ( Verma et al., 2016 ; Karthikeyan et al., 2023a ; Karthikeyan et al., 2023b ). This analysis revealed that in uninjured skeletal muscle, 40%–80% of MuSCs reside in the vicinity of ECs, indicating that MuSCs residing in ECs have more of a stem cell character than those living farther away. Furthermore, transcriptome analysis showed that quiescent and activated MuSCs express high levels of VEGFA, a subtype of VEGF, and VEGFA gene deletion experiments in MuSCs indicated that MuSC-derived VEGFA is essential for recruiting ECs expressing the VEGFA receptor ( Figure 1 ; Verma et al., 2018 ).

To further explore how adjacent ECs regulate MuSC self-renewal and quiescence, transcriptome analysis identified Delta-Like-4 (Dll4), one of the ligands for Notch, as being strongly expressed in ECs. Previous studies have shown that activation of the Notch pathway is essential for MuSC self-renewal and quiescence maintenance, and our experiments with co-cultures of MuSC and ECs revealed that Notch signaling in MuSC is activated by Dll4, a Notch ligand, derived from ECs, potentially via direct cell-cell contact. These results indicate that MuSCs recruit ECs via VEGFA to form vascular niches and that Notch signaling-mediated cross-talk between ECs and MuSCs is essential for MuSC replenishment and maintenance ( Figure 1 ; Verma et al., 2018 ).

Our recent studies have shown that ECs secrete the Notch ligand Dll4 as an extracellular domain and activate Notch2 expressed in muscle fibers. Dll4 with this extracellular domain induces disuse muscle atrophy and diabetes-induced muscle atrophy via Notch2. These results demonstrate that direct cellular contact is not essential for the activation of Notch signaling and prove a new mechanism by which Notch ligands secreted from surrounding cells can activate Notch signaling. Furthermore, this activation of Notch2 by ECs-derived Dll4 is essential for the progression of muscle atrophy seen in these pathologies, suggesting that this secreted Dll4 may be a potential therapeutic target for disuse muscle and diabetes-induced muscle atrophy ( Fujimaki et al., 2022 ). Eliazer demonstrated that Dll4 is expressed in myofibers, and myofiber-derived Dll4 is essential for maintaining MuSCs ( Eliazer et al., 2020 ).

Macrophages as a transient muscle stem cell niche

Macrophages (MPs) have long been known to be essential actors of skeletal muscle regeneration, and the lack of an MP subset severely impairs muscle regeneration ( Manole et al., 2021 ; Scala et al., 2021 ; Henrot et al., 2023 ). In the early phase of skeletal muscle regeneration, the innate immune response plays essential roles in the activation of neutrophils, mast cells, and the complement system, resulting in the recruitment of monocytes at the damaged muscle region. The monocytes can differentiate into two different MP phenotypes by different acting stimuli. The migrated monocytes give rise to matured MPs with first a pro-inflammatory phenotype (M1-MPs) within 24 h after muscle damage, followed by an anti-inflammatory phenotype (M2-MPs) within 2–4 days. Both MPs co-operatory regulate MuSCs to promote muscle regeneration: Initially, M1-MPs promote clearance of necrotic debris and suppress MuSC differentiation, followed by the polarization shift from M1 to M2- MPs that promote muscle regeneration by attenuating inflammation and stimulating MuSC proliferation, migration, and differentiation. M2-MPs also play a role in the angiogenesis-myogenesis coupling by promoting capillaries and myotube formation ( Latroche et al., 2017 ).

Recent live imaging studies using zebrafish have unveiled a novel regulatory mechanism of MuSCs by MPs ( Ratnayake et al., 2021 ). These studies have identified a specific subset of MPs termed “dwelling MPs” within the injury site. These dwelling MPs provide a transient niche for MuSC proliferation through the secretion of specific molecules, such as the cytokine nicotinamide phosphoribosyltransferase (NAMPT), which acts through the C-C motif chemokine receptor type 5 (Ccr5) expressed on MuSCs. The live imaging analysis has demonstrated that these specific MPs can directly interact with MuSCs, supplying proliferation-inducing cues for muscle regeneration. These findings open up new avenues for research and deepen our understanding of the complex interactions in muscle regeneration.

In summary, analyzing skeletal muscles that maintain three-dimensional structures will be essential to better understanding MuSCs and the niche cells surrounding them, a dynamic understanding of aging-induced muscle weakness and skeletal muscle diseases, such as DMD, and the development of therapeutic strategies.

Author contributions

SK: Writing–original draft, Writing–review and editing. AA: Conceptualization, Funding acquisition, Resources, Supervision, Validation, Writing–original draft, Writing–review and editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Regenerative Medicine Minnesota (RMM) grants (RMM 092319 and RMM 091621) and NIH R21 grants (1R21AR078400 and 1R21AR079033).

Acknowledgments

We would like to thank the University Imaging Center in University of Minnesota.

Conflict of interest

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

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Publisher’s note

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

Asakura, A., Komaki, M., and Rudnicki, M. (2001). Muscle satellite cells are multipotential stem cells that exhibit myogenic, osteogenic, and adipogenic differentiation. Differentiation 68, 245–253. doi:10.1046/j.1432-0436.2001.680412.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Asakura, A., Seale, P., Girgis-Gabardo, A., and Rudnicki, M. A. (2002). Myogenic specification of side population cells in skeletal muscle. J. Cell Biol. 159, 123–134. doi:10.1083/jcb.200202092

Burridge, K., and Wennerberg, K. (2004). Rho and rac take center stage. Cell 116, 167–179. doi:10.1016/S0092-8674(04)00003-0

Christov, C., Chrétien, F., Abou-Khalil, R., Bassez, G., Vallet, G., Authier, F. J., et al. (2007). Muscle satellite cells and endothelial cells: close neighbors and privileged partners. Mol. Biol. Cell. 18, 1397–1409. doi:10.1091/mbc.e06-08-0693

Collins, B. C., Shapiro, J. B., Scheib, M. M., Musci, R. V., Verma, M., and Kardon, G. (2024). Three-dimensional imaging studies in mice identify cellular dynamics of skeletal muscle regeneration. Dev. Cell 29 (24), S1534–S5807. doi:10.1016/j.devcel.2024.03.017

CrossRef Full Text | Google Scholar

Cutler, A. A., Pawlikowski, B., Wheeler, J. R., Dalla Betta, N., Elston, T., O’Rourke, R., et al. (2022). The regenerating skeletal muscle niche drives satellite cell return to quiescence. iScience 25, 104444. doi:10.1016/j.isci.2022.104444

Eliazer, S., Sun, X., and Brack, A. S. (2020). Heterogeneous levels of delta-like 4 within a multinucleated niche cell maintains muscle stem cell diversity. eLife 11, e68180. doi:10.7554/eLife.68180

Evano, B., Sarde, L., and Tajbakhsh, S. (2023). Temporal static and dynamic imaging of skeletal muscle in vivo . Exp. Cell. Res. 424, 113484. doi:10.1016/j.yexcr.2023.113484

Fujimaki, S., Matsumoto, T., Muramatsu, M., Nagahisa, H., Horii, N., Seko, D., et al. (2022). The endothelial Dll4-muscular Notch2 axis regulates skeletal muscle mass. Nat. Metab. 4, 180–189. doi:10.1038/s42255-022-00533-9

Guilluy, C., Garcia-Mata, R., and Burridge, K. (2011). Rho protein crosstalk: another social network? Trends Cell Biol. 21, 718–726. doi:10.1016/j.tcb.2011.08.002

Hammers, D. W., Hart, C. C., Matheny, M. K., Heimsath, E. G., Lee, Y. il, Hammer, J. A., et al. (2021). Filopodia powered by class x myosin promote fusion of mammalian myoblasts. eLife 10, e72419. doi:10.7554/eLife.72419

Henrot, P., Blervaque, L., Dupin, I., Zysman, M., Esteves, P., Gouzi, F., et al. (2023). Cellular interplay in skeletal muscle regeneration and wasting: insights from animal models. J. Cachexia Sarcopenia Muscle 14, 745–757. doi:10.1002/jcsm.13103

Jacobsen, N. L., Morton, A. B., and Segal, S. S. (2023). Angiogenesis precedes myogenesis during regeneration following biopsy injury of skeletal muscle. Skelet. Muscle. 13, 3. doi:10.1186/s13395-023-00313-3

Kann, A. P., Hung, M., and Krauss, R. S. (2021). Cell–cell contact and signaling in the muscle stem cell niche. Curr. Opin. Cell Biol. 73, 78–83. doi:10.1016/j.ceb.2021.06.003

Kann, A. P., Hung, M., Wang, W., Nguyen, J., Gilbert, P. M., Wu, Z., et al. (2022). An injury-responsive Rac-to-Rho GTPase switch drives activation of muscle stem cells through rapid cytoskeletal remodeling. Cell Stem Cell 29, 933–947.e6. doi:10.1016/j.stem.2022.04.016

Karthikeyan, S., Asakura, Y., Verma, M., and Asakura, A. (2023a). Tissue clearing and confocal microscopic imaging for skeletal muscle. Methods Mol. Biol. 2640, 453–462. doi:10.1007/978-1-0716-3036-5_31

Karthikeyan, S., Kim, K., Asakura, Y., Verma, M., and Asakura, A. (2023b). Three-dimensional imaging analysis for skeletal muscle. Methods Mol. Biol. 2640, 463–477. doi:10.1007/978-1-0716-3036-5_32

Konagaya, Y., Takakura, K., Sogabe, M., Bisaria, A., Liu, C., Meyer, T., et al. (2020). Intravital imaging reveals cell cycle-dependent myogenic cell migration during muscle regeneration. Cell Cycle 19, 3167–3181. doi:10.1080/15384101.2020.1838779

Krauss, R. S., and Kann, A. P. (2023). Muscle stem cells get a new look: dynamic cellular projections as sensors of the stem cell niche. BioEssays 45 (5), 2200249. doi:10.1002/bies.202200249

Latroche, C., Gitiaux, C., Chrétien, F., Desguerre, I., Mounier, R., and Chazaud, B. (2015b). Skeletal muscle microvasculature: a highly dynamic lifeline. Physiol. (Bethesda) 30 (6), 417–427. doi:10.1152/physiol.00026.2015

Latroche, C., Matot, B., Martins-Bach, A., Briand, D., Chazaud, B., Wary, C., et al. (2015a). Structural and functional alterations of skeletal muscle microvasculature in dystrophin-deficient mdx mice. Am. J. Pathol. 185, 2482–2494. doi:10.1016/j.ajpath.2015.05.009

Latroche, C., Weiss-Gayet, M., Muller, L., Gitiaux, C., Leblanc, P., Liot, S., et al. (2017). Coupling between myogenesis and angiogenesis during skeletal muscle regeneration is stimulated by restorative macrophages. Stem Cell Rep. 9, 2018–2033. doi:10.1016/j.stemcr.2017.10.027

Ma, N., Chen, D., Lee, J.-H., Kuri, P., Hernandez, E. B., Kocan, J., et al. (2022). Piezo1 regulates the regenerative capacity of skeletal muscles via orchestration of stem cell morphological states. Sci. Adv. 8, eabn0485. doi:10.1126/sciadv.abn0485

Ma, N., and Mourkioti, F. (2023). Ex vivo two-photon imaging of whole-mount skeletal muscles to visualize stem cell behavior. Star. Protoc. 5, 102772. doi:10.1016/j.xpro.2023.102772

Manole, E., Niculite, C., Lambrescu, I. M., Gaina, G., Ioghen, O., Ceafalan, L. C., et al. (2021). Macrophages and stem cells-two to tango for tissue repair? Biomolecules 11, 697. doi:10.3390/biom11050697

Ratnayake, D., and Currie, P. D. (2017). Stem cell dynamics in muscle regeneration: insights from live imaging in different animal models. BioEssays 39 (6), 1700011. doi:10.1002/bies.201700011

Ratnayake, D., Nguyen, P. D., Rossello, F. J., Wimmer, V. C., Tan, J. L., Galvis, L. A., et al. (2021). Macrophages provide a transient muscle stem cell niche via NAMPT secretion. Nature 591, 281–287. doi:10.1038/s41586-021-03199-7

Scala, P., Rehak, L., Giudice, V., Ciaglia, E., Puca, A. A., Selleri, C., et al. (2021). Stem cell and macrophage roles in skeletal muscle regenerative medicine. Int. J. Mol. Sci. 22, 10867. doi:10.3390/ijms221910867

Schmalbruch, H. (1978). Satellite cells of rat muscles as studied by freeze-fracturing. Anat. Rec. 191, 371–376. doi:10.1002/ar.1091910309

Siegel, A. L., Kuhlmann, P. K., and Cornelison, D. D. (2011). Muscle satellite cell proliferation and association: new insights from myofiber time-lapse imaging. Skelet. Muscle. 1, 7. doi:10.1186/2044-5040-1-7

Verma, M., Asakura, Y., Murakonda, B. S. R., Pengo, T., Latroche, C., Chazaud, B., et al. (2018). Muscle satellite cell cross-talk with a vascular niche maintains quiescence via VEGF and Notch signaling. Cell Stem Cell 23, 530–543. doi:10.1016/j.stem.2018.09.007

Verma, M., Murkonda, B. S., Asakura, Y., and Asakura, A. (2016). Skeletal muscle tissue clearing for LacZ and fluorescent reporters, and immunofluorescence staining. Methods Mol. Biol. 1460, 129–140. doi:10.1007/978-1-4939-3810-0_10

Webster, M. T., Harvey, T., and Fan, C. M. (2016a). Quantitative 3D time lapse imaging of muscle progenitors in skeletal muscle of live mice. Bio. Protoc. 6, e2066. doi:10.21769/BioProtoc.2066

Webster, M. T., Manor, U., Lippincott-Schwartz, J., and Fan, C. M. (2016b). Intravital imaging reveals ghost fibers as architectural units guiding myogenic progenitors during regeneration. Cell Stem Cell 18, 243–252. doi:10.1016/j.stem.2015.11.005

Wosczyna, M. N., and Rando, T. A. (2018). A muscle stem cell support group: coordinated cellular responses in muscle regeneration. Dev. Cell. 46, 135–143. doi:10.1016/j.devcel.2018.06.018

Nomenclature

Keywords: myogenesis, muscle stem cell, muscle regeneration, Duchenne muscular dystrophy, satellite cell, endothelial cell, skeletal muscle, niche

Citation: Karthikeyan S and Asakura A (2024) Imaging analysis for muscle stem cells and regeneration. Front. Cell Dev. Biol. 12:1411401. doi: 10.3389/fcell.2024.1411401

Received: 02 April 2024; Accepted: 15 April 2024; Published: 07 May 2024.

Reviewed by:

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

*Correspondence: Atsushi Asakura, [email protected]

This article is part of the Research Topic

Muscle Stem Cells for Duchenne Muscular Dystrophy

This paper is in the following e-collection/theme issue:

Published on 8.5.2024 in Vol 26 (2024)

Application of Patient-Reported Outcome Measurements in Adult Tumor Clinical Trials in China: Cross-Sectional Study

Authors of this article:

Original Paper

• Yan Jia 1, 2 *   ; 
• Qi Li 1, 2 *   ; 
• Xiaowen Zhang 1 , MS   ; 
• Yi Yan 3   ; 
• Shiyan Yan 4 , PhD   ; 
• Shunping Li 5 , PhD   ; 
• Wei Li 6 , PhD   ; 
• Xiaowen Wu 7 , PhD   ; 
• Hongguo Rong 1, 8 * , PhD   ; 
• Jianping Liu 1, 8 , PhD  

1 Center for Evidence-Based Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China

2 Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China

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

4 College of Acupuncture and Massage, Beijing University of Chinese Medicine, Beijing, China

5 Centre for Health Management and Policy Research, Shandong University, Shandong, China

6 International Research Center for Medicinal Administration, Peking University, Beijing, China

7 Peking University Cancer Hospital & Institute, Peking University, Beijng, China

8 Institute for Excellence in Evidence-Based Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China

*these authors contributed equally

Corresponding Author:

Hongguo Rong, PhD

Center for Evidence-Based Chinese Medicine

Beijing University of Chinese Medicine

No. 11 Beisanhuan East Road, Chaoyang District

Beijing, 100029

Phone: 86 (10)64286757

Email: [email protected]

Background: International health policies and researchers have emphasized the value of evaluating patient-reported outcomes (PROs) in clinical studies. However, the characteristics of PROs in adult tumor clinical trials in China remain insufficiently elucidated.

Objective: This study aims to assess the application and characteristics of PRO instruments as primary or secondary outcomes in adult randomized clinical trials related to tumors in China.

Methods: This cross-sectional study identified tumor-focused randomized clinical trials conducted in China between January 1, 2010, and June 30, 2022. The ClinicalTrials.gov database and the Chinese Clinical Trial Registry were selected as the databases. Trials were classified into four groups based on the use of PRO instruments: (1) trials listing PRO instruments as primary outcomes, (2) trials listing PRO instruments as secondary outcomes, (3) trials listing PRO instruments as coprimary outcomes, and (4) trials without any mention of PRO instruments. Pertinent data, including study phase, settings, geographic regions, centers, participant demographics (age and sex), funding sources, intervention types, target diseases, and the names of PRO instruments, were extracted from these trials. The target diseases involved in the trials were grouped according to the American Joint Committee on Cancer Staging Manual, 8th Edition .

Results: Among the 6445 trials examined, 2390 (37.08%) incorporated PRO instruments as part of their outcomes. Within this subset, 26.82% (641/2390) listed PRO instruments as primary outcomes, 52.72% (1260/2390) as secondary outcomes, and 20.46% (489/2390) as coprimary outcomes. Among the 2,155,306 participants included in these trials, PRO instruments were used to collect data from 613,648 (28.47%) patients as primary or secondary outcomes and from 74,287 (3.45%) patients as coprimary outcomes. The most common conditions explicitly using specified PRO instruments included thorax tumors (217/1280, 16.95%), breast tumors (176/1280, 13.75%), and lower gastrointestinal tract tumors (173/1280, 13.52%). Frequently used PRO instruments included the European Organisation for Research and Treatment of Cancer Quality of Life Core Questionnaire–30, the visual analog scale, the numeric rating scale, the Traditional Chinese Medicine Symptom Scale, and the Pittsburgh Sleep Quality Index.

Conclusions: Over recent years, the incorporation of PROs has demonstrated an upward trajectory in adult randomized clinical trials on tumors in China. Nonetheless, the infrequent measurement of the patient’s voice remains noteworthy. Disease-specific PRO instruments should be more effectively incorporated into various tumor disease categories in clinical trials, and there is room for improvement in the inclusion of PRO instruments as clinical trial end points.

Introduction

Patient-reported outcome (PRO) instruments are defined as any report regarding a patient’s health status obtained directly from the patient, excluding interpretation of the patient’s responses by clinicians or other individuals [ 1 ]. PRO data consist of information obtained directly from patients concerning their health status, symptoms, treatment adherence, physical and social functioning, health-related quality of life, and satisfaction with health care [ 2 - 4 ]. Serving as noninvasive, comprehensive, and patient-centered metrics, PROs play a pivotal role in enhancing patient engagement, facilitating informed clinical decisions, and improving patient-clinician communication [ 5 - 9 ]. High-quality PRO measures examined in rigorous trials can evaluate treatment effectiveness, assess patient adherence to treatment, guide drug research, and inform health care policies [ 2 , 5 ]. In addition, some PRO instruments could supplement safety data and contribute to the assessment of tolerability (eg, Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events [PRO-CTCAE]) [ 2 , 5 ].

In particular, PROs are valuable end points in trials of disabling, chronic, and incurable conditions because they systematically capture the patients’ perspectives in a scientifically rigorous way [ 3 , 10 , 11 ]. Recognizing their importance, clinical trials focused on tumors are increasingly incorporating PRO instruments as primary or secondary outcomes [ 12 - 15 ]. The European Commission has indicated the priority of preventing cancer and ensuring a high quality of life for patients with cancer within the framework of Europe’s Beating Cancer Plan [ 16 ]. The incorporation of PROs in clinical trials offers distinct advantages, including improvements in health-related quality of life, patient-clinician communication, and economic benefits from reduced health care use [ 17 - 20 ]. To uphold best practices in tumor clinical trials that use PROs, several methodological recommendations have emerged in recent years, such as SPIRIT-PRO (Standard Protocol Items: Recommendations for Interventional Trials–Patient-Reported Outcome), CONSORT-PRO (Consolidated Standards of Reporting Trials–Patient-Reported Outcome), SISAQOL (Setting International Standards in Analysing Patient-Reported Outcomes and Quality of Life Endpoints), and other relevant guidelines [ 2 - 4 , 21 ]. However, PRO measures often receive lower priority in the design of oncology-related clinical trials when compared to survival, imaging, and biomarker-related outcomes [ 22 ].

In China, PROs are increasingly being used in clinical trials, but there are challenges as well. A cross-sectional survey of interventional clinical trials conducted in China revealed that only 29.7% of the included trials listed PRO instruments as primary or secondary outcomes [ 23 ]. Moreover, there is a notable absence of comprehensive assessments evaluating the application of PRO instruments in tumor clinical trials in China. Unlike previous cross-sectional studies that encompassed all types of clinical trials, our study primarily examined adult tumor clinical trials in China that have listed PRO instruments as primary or secondary outcomes, referencing the methodologies and reporting patterns of a previous study [ 23 ]. We extracted the registration information of adult randomized clinical trials conducted in China to systematically analyze the application of PRO instruments in tumor clinical trials, aiming to evaluate the application of PRO instruments in adult tumor clinical trials in China and provide potential directions for further investigation.

Study Design

This cross-sectional study was designed to describe the characteristics of adult tumor clinical trials conducted in China between January 1, 2010, and June 30, 2022, that listed PRO instruments as primary or secondary outcomes. All clinical trials should be registered, and data of clinical trials were collected from 2 clinical trial registries, namely ClinicalTrials.gov and the Chinese Clinical Trial Registry, with public disclosure. We conducted data retrieval and export in July 2022. The clinical trials covered 34 provincial-level administrative regions in accordance with the 2019 version of China’s administrative divisions. We further sought to describe the PRO instruments frequently used in trials encompassing diverse target tumor conditions.

Data Collection Strategy

This study focused on interventional randomized clinical trials conducted in China involving participants aged ≥18 years ( Figure 1 ). Duplicate trials with 2 registration identification numbers were treated as a single trial (ClinicalTrials.gov records were retained). The evaluation of tumor clinical trials included three types of information: (1) basic information (registration number, registration date, scientific name, recruiting country, and other information), (2) key information (outcome, target disease, and age and sex of participants), and (3) characteristic information (main sponsor’s location, study settings, number of setting centers, study stage, funding source, and intervention type).

Data Classification

PRO instruments were defined by the US Food and Drug Administration in 2009 [ 1 ] as any report about a patient’s health status obtained directly from the patient, excluding interpretation of the patient’s response by clinicians or other individuals. Trials using PRO instruments as primary or secondary outcomes were considered PRO trials. On the basis of a previous study of PRO labeling of new US Food and Drug Administration–approved drugs (2016-2020) [ 24 ], eligible trials were classified into four groups: (1) trials that listed PRO instruments as primary outcomes, (2) trials that listed PRO instruments as secondary outcomes, (3) trials that listed PRO instruments as coprimary outcomes, and (4) trials without any mention of PRO instruments.

Statistical Analysis

Data related to the characteristics of the included trials (clinical phase, study setting, participant age and sex, region of the primary sponsor, setting center, number of PROs, funding source, and type of intervention) were extracted independently by 2 authors with a predesigned data extraction table. Owing to the varied categories and wide variation of target diseases, we classified similar target diseases based on classifications from the American Joint Committee on Cancer Staging Manual, 8th Edition ( Multimedia Appendix 1 ). On the basis of this categorization of diseases, we consolidated the PRO instruments used in each trial to identify those used most frequently. We conducted quantitative analysis only on items that listed the names of PRO instruments for a more detailed understanding of the commonly used evaluation tools. All data analyses were performed using Stata (version 14.0; StataCorp LLC).

Ethical Considerations

According to the Common Rule (45 CFR part 46) of the US Department of Health and Human Services (Office for Human Research Protections), this study is exempt from institutional review board approval and the requirement for informed patient consent because it did not involve clinical data or human participants. This study followed the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) reporting guidelines designed for observational studies in epidemiology.

Trial Characteristics

Table 1 presents a comprehensive overview of the included trials. The study included 7251 tumor-focused randomized controlled trials conducted in China between January 1, 2010, and June 30, 2022. Of these 7251 trials, 3276 (45.18%) were sourced from ClinicalTrials.gov, and 3975 (54.82%) were identified from the Chinese Clinical Trial Registry. Of these 7251 trials, after excluding 806 (11.12%) trials (n=5, 0.6% duplicates; n=465, 57.7% non-Chinese trials; n=321, 39.8% trials involving children; and n=15, 1.9% trials with incomplete reports), 6445 (88.88%) eligible trials were identified for analysis.

a The early phase trials included a clinical pretest as well as phase 0 and phase 1 trials.

b Diagnostic new technique clinical study, inspection technology, and trials involving multiple phases.

c Rehabilitation center, nursing home, campus, centers for disease control, home, and research institute.

d The trials were conducted in China, but their sponsor was based overseas.

e N/A: not applicable.

f Combination trials were funded partly by industry and partly by nonindustry institutions, such as universities, hospitals, and so on.

Of the 2,155,306 participants recruited in all included trials, 139,297 (6.46%) were involved in trials with PRO instruments as primary outcomes, 400,064 (18.56%) in trials with PRO instruments as secondary outcomes, and 74,287 (3.45%) in trials with PRO instruments as coprimary outcomes. Among the 6445 trials included, 2390 (37.08%) used PRO instruments as either primary or secondary outcomes, while 4055 (62.92%) did not use any PRO instrument.

The majority of the studies (6098/6445, 94.62%) did not impose any age restrictions on participants (children were excluded). In trials involving PROs, the proportion of older participants (aged >65 y; 42/2390, 1.78%) was slightly higher than in those without PROs (100/6445, 1.55%). Among all trials that incorporated PRO measurements, 17.15% (410/2390) included only female participants, while 4.48% (107/2390) included only male participants. Furthermore, in trials involving only female participants, the vast majority (974/1000, 97.4%) studied breast and female reproductive organ tumors. In trials exclusively involving male participants, more than half (135/267, 50.5%) centered around male genital organ tumors.

Regarding trial phases, of the 6445 clinical trials, early phase trials were the most prevalent (n=1317, 20.43%), followed by phase 3 trials (n=1004, 15.58%), phase 2 trials (n=873, 13.56%), and phase 4 trials (n=779, 12.09%). Of the 2390 PRO-related trials, early phase trials were again the most common (n=575, 24.06%), followed by phase 3 trials (n=284, 11.88%), phase 4 trials (n=269, 11.26%), and phase 2 trials (n=218, 9.12%).

Most of the trials (6034/6445, 93.62%) were conducted in hospitals, with hardly any (3/6445, 0.05%) conducted in community settings. More than half of the primary sponsors were located in eastern China (3745/6445, 58.11%), followed by northern (797/6445, 12.37%) and southern (682/6445, 10.58%) China, while 18.85% (1215/6445) of the primary sponsors were situated in other regions of China, such as the southwestern, central, northwestern, and northeastern regions. Similar patterns were observed for studies involving PROs. The majority of the major sponsors (1916/2390, 80.17%) originated from the eastern, northern, and southern regions of China, while 19.79% (473/2390) hailed from the southwestern, central, northeastern, and northwestern regions. There were differences in the proportions of PRO trials were noted among different provinces; the distribution of PRO instruments across Chinese provinces can be found in Multimedia Appendix 2 .

Moreover, 87.29% (5626/6445) of the trials were single-center trials, and only 11.11% (716/6445) were multicenter trials. Similar phenomena were observed for PRO-related studies, but multicenter trials accounted for a slightly higher percentage (312/2390, 13.05%). Of the 2390 PRO trials, 2144 (89.71%) used 1 to 3 PRO instruments, followed by 4 to 6 (n=218, 9.12%) and 7 to 9 (n=25, 1.05%) PRO instruments. The majority of the trials were nonindustry-funded trials (5443/6445, 84.45%), while 11.67% (752/6445) were industry-funded trials.

Table 2 shows the frequency of intervention types used across different trial classifications. The data indicated that more than a third of the included trials used drugs as the intervention (2496/6445, 38.73%), followed by combination therapies (1350/6445, 20.95%) and surgery (1044/6445, 16.2%). Among clinical trials involving drug interventions, nearly four-tenths (989/2496, 39.62%) used PRO instruments as their outcomes. Trials using drugs as the intervention exhibited a higher incidence of using PRO instruments as their primary or coprimary outcomes (468/989, 47.32%) compared to trials using other intervention types.

a PRO: patient-reported outcome.

b Other interventions included acupuncture, physical exercise, and psychosocial treatment.

Conditions and Participants

The annual counts of tumor clinical trials are listed in Figure 2 . During the study period—from January 1, 2010, to June 30, 2022—the number of tumor clinical trial registrations exhibited a consistent upward trajectory, paralleled by a commensurate increase in the number of clinical trials related to PROs.

Figures 3 and 4 depict the distribution of trial counts and corresponding participant numbers across different tumor types, respectively, wherein PROs served as outcomes. Among the 2390 tumor-related trials that used PRO instruments as primary or secondary outcomes, the top 5 tumors were thorax (448/2390, 18.74%), upper gastrointestinal tract (306/2390, 12.8%), lower gastrointestinal tract (300/2390, 12.55%), breast (289/2390, 12.09%), and head and neck (177/2390, 7.41%) tumors. Trials regarding female reproductive organ (168/2390, 7.03%) and hepatobiliary system (146/2390, 6.11%) tumors were also frequently observed. Male genital organ tumors (56/2390, 2.34%), central nervous system tumors (51/2390, 2.13%), endocrine system tumors (47/2390, 1.97%), and urinary tract tumors (33/2390, 1.38%) all accounted for proportions ranging from 1% to 5%, and hematologic malignant tumors (22/2390, 0.92%), neuroendocrine tumors (14/2390, 0.59%), bone tumors (8/2390, 0.33%), skin tumors (4/2390, 0.17%), ophthalmic tumors (2/2390, 0.08%), and soft tissue sarcoma (1/2390, 0.04%) constituted <1% of the trials.

Among the 613,648 participants enrolled in these PRO trials, 134,940 (22%) were diagnosed with lower gastrointestinal tract tumors, 131,470 (21.42%) with upper gastrointestinal tract tumors, and 79,068 (12.88%) with thorax tumors. Furthermore, there were a number of patients with breast tumors (63,238/613,648, 10.31%), female reproductive organ tumors (440,975/613,648, 6.68%), head and neck tumors (35,642/613,648, 5.81%), or hepatobiliary system tumors (22,044/613,648, 3.59%), each involving >10,000 patients. By contrast, conditions with <10,000 participants encompassed central nervous system tumors (8897/613,648, 1.45%), endocrine system tumors (8472/613,648, 1.38%), male genital organ tumors (8357/613,648, 1.36%), urinary tract tumors (6784/613,648, 1.11%), neuroendocrine tumors (3539/613,648, 0.58%), hematologic malignant tumors (2629/613,648, 0.43%), bone tumors (825/613,648, 0.13%), skin tumors (311/613,648, 0.05%), ophthalmic tumors (274/613,648, 0.04%), and soft tissue sarcoma (266/613,648, 0.04%).

PRO Instruments Used in Clinical Trials

Table 3 presents the number of explicitly specified PROs where trials precisely listed the names of the PRO instruments and the number of implicitly specified PROs where trials referenced patients’ subjective feelings without specifying the instruments used, separately for the 3 trial types. Specifically, the trial that specified the PRO instruments used was classified into “explicitly specified PROs,” and the trial that did not specify the instruments used was classified into “implicitly specified PROs.” It was evident that in primary and coprimary outcome trial sets, a greater number of trials explicitly listed the PRO instruments compared to those that did not specify the instruments used. Among the 3 trial types, the coprimary outcome category exhibited the highest proportion of explicitly specified PROs (339/489, 69.3%).

Tables 4 - 6 display the frequency of use of PRO scales for different diseases under the 3 categories. In trials using PRO instruments as coprimary outcomes, the visual analog scale (VAS) and the numeric rating scale (NRS) were the most commonly used scales for various tumors. For trials using PRO instruments as primary outcomes, the VAS was the most commonly used scale for various diseases. For trials using PRO instruments as secondary outcomes, the most commonly used scale for each disease was the European Organisation for Research and Treatment of Cancer Quality of Life Core Questionnaire-30 (EORTC QLQ-C30).

a VAS: visual analog scale.

b NRS: numeric rating scale.

c EORTC QLQ-LC43: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Lung Cancer 43.

d SF-36: 36-item Short Form Health Survey.

e PSQI: Pittsburgh Sleep Quality Index.

f IPSS: International Prostate Symptom Score.

g LARS: Low Anterior Resection Syndrome.

h EORTC QLQ-C30: European Organisation for Research and Treatment of Cancer Quality of Life Core Questionnaire-30.

i EORTC QLQ-STO22: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Stomach 22.

j UW-QOL: University of Washington Quality of Life Questionnaire.

k QoR-40: Quality of Recovery-40.

l IDS: Involvement-Detachment Scale.

m IIEF-15: International Index of Erectile Function-15.

n QoR-15: Quality of Recovery-15.

o TCMSS: Traditional Chinese Medicine Symptom Scale.

p N/A: not applicable.

a EORTC QLQ-C30: European Organisation for Research and Treatment of Cancer Quality of Life Core Questionnaire-30.

b FACT-L: Functional Assessment of Cancer Therapy–Lung.

c EORTC QLQ-LC13: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Lung Cancer 13.

d FACT-B: Functional Assessment of Cancer Therapy–Breast.

e EORTC QLQ-BR23: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Breast Cancer 23.

f VAS: visual analog scale.

g EORTC QLQ-OES18: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Oesophageal Cancer 18.

h EORTC QLQ-H&N35: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Head and Neck Cancer 35.

i NRS: numeric rating scale.

j EORTC QLQ-CX24: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Cervical Cancer 24.

k EORTC QLQ-HCC18: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Hepatocellular Carcinoma 18.

l FACT-P: Functional Assessment of Cancer Therapy–Prostate.

m BPI-SF: Brief Pain Inventory–Short Form.

n FACT-G: Functional Assessment of Cancer Therapy–General.

o QoR-40: Quality of Recovery-40.

p SF-36: 36-item Short Form Health Survey.

q QoR-15: Quality of Recovery-15.

r WHOQOL-BREF: World Health Organization Quality of Life Brief Version.

s EORTC QLQ-PAN26: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Pancreatic Cancer 26.

t FACIT: Functional Assessment of Chronic Illness Therapy.

u HF-QOL: Hand-Foot Skin Reaction and Quality of Life.

v N/A: not applicable.

w EORTC QLQ-OPT30: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Ophthalmic Cancer 30.

c EORTC QLQ-C30: European Organisation for Research and Treatment of Cancer Quality of Life Core Questionnaire-30.

d QoR-15: Quality of Recovery-15.

e TNSS: Total Nasal Symptom Score.

f BCS: Bruggemann Comfort Scale.

g PSQI: Pittsburgh Sleep Quality Index.

h ICIQ-SF: International Consultation on Incontinence Questionnaire–Short Form.

i FACT-P: Functional Assessment of Cancer Therapy–Prostate.

j HADS: Hospital Anxiety and Depression Scale.

k EORTC IADL-BN32: European Organisation for Research and Treatment of Cancer Instrumental Activities of Daily Living in Patients With Brain Tumors-32.

l N/A: not applicable.

m SAS: Self-Rating Anxiety Scale.

n SDS: Self-Rating Depression Scale.

To analyze the overall application of scales in explicitly specified PROs by condition, we examined the specific PRO instruments used in trials that explicitly mentioned the PRO instruments as primary or secondary outcomes ( Table 7 ). Of the 1280 trials, 321 (25.08%) used the EORTC QLQ-C30 ( Multimedia Appendix 3 ), which was the most commonly used PRO scale. Of note, the EORTC QLQ-C30 was the most commonly used scale in trials concerning lower gastrointestinal tract, upper gastrointestinal tract, head and neck, female reproductive organ, hepatobiliary system, bone, neuroendocrine, skin, and ophthalmic tumors as well as hematologic malignancies. In addition, the VAS was used in 24.77% (317/1280) of the trials ( Multimedia Appendix 3 ), predominating in trials involving thorax, breast, male genital organ, endocrine system, central nervous system, and urinary tract tumors. The NRS was also frequently used (169/1280, 13.2%) in cancer trials. More targeted scales have been used for different tumor diseases; for example, the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ)–Head and Neck Cancer 35 (36/101, 35.6%) was more common in head and neck tumor trials, the EORTC QLQ–Oesophageal Cancer 18 (15/140, 10.7%) and the EORTC QLQ–Stomach 22 (14/140, 10%) were frequently observed in upper gastrointestinal cancer trials, the EORTC QLQ–Colorectal Cancer 29 (14/173, 8.1%) scale was prevalent in lower gastrointestinal cancer trials, the EORTC QLQ–Hepatocellular Carcinoma 18 (8/67, 12%) was frequently found in hepatobiliary system tumor trials, the Functional Assessment of Cancer Therapy (FACT)–Lung (21/217, 9.7%) and the EORTC QLQ–Lung Cancer 13 (19/217, 8.8%) commonly featured in thorax tumor trials, the FACT–Breast (29/176, 16.5%) and the EORTC QLQ–Breast Cancer 23 (16/176, 9.1%) were frequently seen in breast cancer trials, the EORTC QLQ–Ovarian Cancer 28 (6/85, 7%) was a typical scale used in female reproductive organ tumor trials, the FACT–Prostate (7/31, 23%) was often used in male genital organ tumor trials, and the FACT–Anemia (1/9, 11%) and the FACT–Lymphoma (1/9, 11%) were common choices in hematologic malignant tumor trials.

b EORTC QLQ-C30: European Organisation for Research and Treatment of Cancer Quality of Life Core Questionnaire-30.

c NRS: numeric rating scale.

d FACT-L: Functional Assessment of Cancer Therapy–Lung.

e EORTC QLQ-LC13: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Lung Cancer 13.

f FACT-B: Functional Assessment of Cancer Therapy–Breast.

g EORTC QLQ-BR23: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Breast Cancer 23.

h EORTC QLQ-CR29: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Colorectal Cancer 29.

i EORTC QLQ-OES18: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Oesophageal Cancer 18.

j EORTC QLQ-STO22: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Stomach 22.

k EORTC QLQ-H&N35: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Head and Neck Cancer 35.

l PG-SGA: Patient-Generated Subjective Global Assessment.

m SDS: Self-Rating Depression Scale.

n EORTC QLQ-OV28: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Ovarian Cancer 28.

o EORTC QLQ-HCC18: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Hepatocellular Carcinoma 18.

p TCMSS: Traditional Chinese Medicine Symptom Scale.

q FACT-P: Functional Assessment of Cancer Therapy–Prostate.

r BPI: Brief Pain Inventory.

s IPSS: International Prostate Symptom Score.

t QoR-15: Quality of Recovery-15.

u QoR-40: Quality of Recovery-40.

v PCSQ: Preparedness for Colorectal Cancer Surgery Questionnaire.

w WHOQOL-BREF: World Health Organization Quality of Life Brief Version.

x FACT-An: Functional Assessment of Cancer Therapy–Anemia.

y FACT-Lym: Functional Assessment of Cancer Therapy–Lymphoma.

z SF-36: 36-item Short Form Health Survey.

aa EORTC QLQ-PAN26: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Pancreatic Cancer 26.

ab N/A: not applicable.

ac HF-QoL: Hand-Foot Skin Reaction and Quality of Life.

ad EORTC QLQ-OPT30: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Ophthalmic Cancer 30.

ae PSQI: Pittsburgh Sleep Quality Index.

af BFI: Brief Fatigue Inventory.

Principal Findings

This cross-sectional study depicted the general characteristics of adult tumor clinical trials incorporating PROs in China and analyzed the application of PRO instruments in randomized clinical trials of tumors to provide potential directions for future research and serve as a reference for tumor clinical practice. The findings revealed that a significant proportion, specifically 62.92% (4055/6445) of the included trials, missed the opportunity to capture patients’ subjective evaluations. Of the trials with PRO instruments as end points, 26.82% (641/2390) used PRO instruments as primary outcomes, 52.72% (1260/2390) as secondary outcomes, and 20.46% (489/2390) as coprimary outcomes. The majority of PRO trials (2144/2390, 89.71%) used 1 to 3 PRO instruments. Given that PROs can authentically represent patients’ subjective experiences and evaluations, they should receive heightened emphasis in the context of tumor clinical trials. However, in light of the small proportion of tumor-related randomized clinical trials assessing PROs, policy makers and standard-setting bodies are recommended to further promote the collection of PROs in such trials in China.

This study delved into the yearly distribution of tumor clinical trials, indicating a notable surge in the use of PRO instruments as end points between January 1, 2010, and June 30, 2022. Among the trials incorporating PROs, early phase trials constituted the largest proportion (575/2390, 24.06%), followed by phase 3 (284/2390, 11.88%) and phase 4 (269/2390, 11.26%) trials. A retrospective cross-sectional study suggested a potential correlation between the use of PROs in late-stage trials and improved drug outcomes, such as overall survival [ 25 ]. However, the omission of PROs in late-stage trial results may reduce the value of patient participation in these trials. Previous work has shown that the concern regarding funding for PRO research seems significant, and additional funding was needed—and considered important—to pay for the use of PRO instruments to collect relevant data [ 26 ]. This may also be the reason why, among the included studies, there were few PRO tumor trials funded by industry. Relevant policies could provide more financial support for PRO tumor trials. In addition, our study indicated that the application of PRO instruments was more prevalent in trials involving drug interventions. PRO instruments can serve as valuable tools for assessing patient experiences during treatment, which is an essential aspect of drug discovery [ 27 ], and their absence can result in the exclusion of critical information, such as opportunities for patient-centered support programs and insights into benefit-risk profiles [ 27 ].

In accordance with prior research [ 23 ], our study also identified regional differences in the use of PROs. Tumor trials were more prevalent in the eastern, northern, and southern regions of China—especially in Shanghai, Beijing, Guangdong, and Jiangsu—and the adoption of PRO measurements followed a similar pattern. Conversely, in other regions of China, especially in the northwestern and northeastern regions—such as Qinghai, Tibet and Heilongjiang—both the overall number of tumor clinical trials and those incorporating PRO instruments as end points were conspicuously lower. These results indicated the relationship between the volume of tumor clinical trials and the adoption of PRO tools. In addition, other factors such as economic conditions and medical resources also played an important role in this phenomenon [ 28 ]. Relevant policies can continue to encourage medical resources to be tilted toward rural and less developed areas. Remarkably, the study suggested that in resource-constrained remote regions, simplified applications of PRO instruments may be considered in tumor clinical trials. Moreover, our investigation revealed a lower prevalence of industry-funded trials in tumor clinical trials in China. This discrepancy may be attributed to previous findings that tumor trials were characterized by increased risk and costliness [ 29 ].

This study further found that thorax tumors, breast tumors, and lower gastrointestinal tract tumors were the most common conditions in trials with explicit PRO instruments. This might be related to variances in tumor incidence and different clinical concerns [ 30 ]. In the primary and coprimary outcome trial sets, a higher proportion of trials explicitly listed the PRO instruments as end points compared to those not specifying PROs, underscoring the normative inclination to formalize the acquisition and application of PRO instruments. Adherence to guidelines and standardization of PRO application is essential to maximize the application of PRO trial data, enhance their impact, and minimize research waste [ 31 ]. In particular, studies have shown that the standardized PROs were conducive to making trials or clinical treatments more scientifically rigorous and ethically sound [ 32 - 35 ]. Therefore, the need to standardize the application of PRO instruments remains important, with an increased emphasis on explicitly specifying PRO instruments in clinical trials.

This study analyzed the frequency of the use of PRO instruments in different classifications of trials by medical condition and found that the VAS and the NRS were the most commonly used in trials where PROs were designated as coprimary outcomes. Meanwhile, in all trials that used PRO instruments as outcomes, the VAS and the NRS were consistently prevalent. This prevalence can be attributed to the precision, simplicity, and sensitivity of VAS scores, as well as the ease of use and standardized format of the NRS for assessing subjective indicators [ 36 - 38 ]. In addition, almost 90% of patients with cancer would experience pain during the course of their illness [ 39 ]. The pain is both prevalent and burdensome for patients, but there is a lack of objective evaluation indices available for this purpose [ 40 , 41 ]. Consequently, the VAS emerged as the preferred choice for pain assessment in clinical research. Similarly, the NRS, with its user-friendly nature and standardized format, has been the preferred tool for pain assessment [ 36 - 38 ]. PROs continue to represent the gold standard for evaluating patients’ core pain outcomes [ 42 - 44 ]. In this study, among the trials that used PRO instruments as secondary outcomes, the EORTC QLQ-C30 was the most commonly used (223/606, 36.8%), which might be attributed to the significance of addressing quality-of-life concerns for patients with tumors. This study also scrutinized the prevalent PRO instruments used in various medical conditions and found that the quality-of-life scale was frequently used in clinical trials involving tumors. The high frequency of the EORTC QLQ-C30 and FACT scale groups underscored the widespread application of these instruments in assessing patients’ quality of life in cancer clinical trials in China. Specific modules in the EORTC QLQ scale system, such as the EORTC QLQ–Breast Cancer 23, the EORTC QLQ–Lung Cancer 13, and the EORTC QLQ–Colorectal Cancer 29, have been widely used in various cancer diseases [ 45 , 46 ]. Similarly, specific modules in the FACT scales, such as FACT–Lung (lung cancer), FACT–Breast (breast cancer), and FACT–Prostate (prostate cancer), have exhibited a high rate of use in cancer clinical trials in China. The extensive use of various PRO scales indicates a growing awareness and acceptance of PRO instruments, which, in turn, encourages the development of more effective and reliable PRO instruments. PRO instruments can be divided into universal and disease-specific PRO instruments. Considering the heterogeneity of symptom types in patients with tumors, symptom assessment should be performed for specific diseases [ 47 ]. However, in different tumor trials, the explicitly specified PRO instruments were primarily quality-of-life scales, the VAS, and the NRS, suggesting a need for the application of disease-specific PRO scales for different tumor types in clinical trials. It is suggested that according to the heterogeneity of diseases, experts from different fields should be brought together to develop or improve the disease-specific scale through participatory and consensus approaches under the guidance of relevant guidelines [ 33 , 47 , 48 ]. Acceptance of the scale by a wide range of stakeholders would be beneficial to improve the quality and specificity of the scale [ 48 ]. Training of clinicians and researchers on disease-specific scales is recommended. In addition, regarding the implementation of PRO measurement, it can be attempted as part of routine clinical care delivery for corresponding diseases, as well as continuous quality improvement as a clinical care priority [ 48 ].

This study undertook an in-depth analysis of the fundamental aspects of tumor clinical trials encompassing PROs in China, involving categorizing tumors and assessing the application of specific PRO tools for each tumor type. The findings underscore the critical importance of integrating PRO measures into tumor clinical trials in China and the need to standardize the use of PRO instruments within these trials. In recent years, the Chinese government has attached great importance to the application of PRO instruments in clinical trials. To encourage the patient-centered concept of new drug development and make reasonable use of PRO instruments, the National Medical Products Administration formulated the Guiding Principles for the Application of Patient Reported Outcomes in Drug Clinical Research and Development in 2022. To further promote these guiding principles, the relevant departments can educate researchers about the importance of regulating the application of PRO instruments, provide an interpretation of these principles to researchers, and advise them to follow the guidelines. We encourage researchers to communicate relevant information to regulators in a timely manner to conduct higher-quality clinical trials, such as the background of the study, the type of study, and the scale used. Policy makers should further formulate and implement pertinent policies, and PRO application platforms need to be developed and promoted to accelerate rational use of PROs in tumor clinical trials. It is recommended to define or form an institution or department to coordinate and standardize the use of PROs in clinical trials [ 49 ]. The institution or department can provide researchers with some support, such as methodological guidance for PRO applications, interpretation of relevant guidelines, and guidance on internet technologies. Efforts should also be made to encourage communication and collaboration among policy makers, researchers, and medical institutions to promote the high-quality application of PROs in clinical trials. Furthermore, it is crucial to train clinicians in how to use PRO instruments in clinical practice. Ideally, this training can be part of standard medical education programs in the future. The most successful and effective way of training involved real patient cases and problem-based learning using audio and video clips, which could enable clinicians to know how to use PRO instruments and refer to the PRO data [ 50 ]. Researchers are encouraged to follow relevant guidelines and principles and actively engage in conducting high-quality tumor clinical trials to improve well-established PRO protocols and enrich the array of available PRO instruments, thereby advancing personalized population health. In addition, it is suggested to encourage and provide relevant support to patients who have difficulties in completing the PRO reports [ 51 ].

Limitations

It is important to acknowledge several limitations to this study. First, we excluded trials lacking detailed end point information, which may have introduced bias into the results. Second, the inclusion of trials that have not yet commenced participant recruitment, although necessary for our investigation, may have inflated the reported outcomes. Finally, the exclusion of trials involving children due to their limited expressive ability and the potential influence of parental reporting on outcomes may have introduced bias in the findings.

Conclusions

In China, the incorporation of PROs has demonstrated an upward trajectory in adult randomized clinical trials of tumors in recent years. Nonetheless, the infrequent measurement of the patient’s voice remains noteworthy. This study highlights the need for a more comprehensive evaluation of patients’ experiences in adult tumor clinical trials in China. The incorporation of patients’ subjective feelings in the context of tumor diseases is necessary. Disease-specific PRO instruments should be widely used in different categories of tumor disease. Pertinent policies should be formulated and implemented, and PRO application platforms need to be developed and promoted as well. In addition, researchers should actively engage in conducting high-quality tumor clinical trials. There is room for improvement in the standardization of PROs in China.

Acknowledgments

This work was supported by the high-level traditional Chinese Medicine Key Subjects Construction Project of the National Administration of Traditional Chinese Medicine—Evidence-Based Traditional Chinese Medicine (zyyzdxk-2023249).

Data Availability

The data sets generated and analyzed during this study are available from the corresponding author on reasonable request.

Authors' Contributions

HR and JL conceived of the presented idea. YJ and QL coordinated the data collection and analysis. XW, YY, and YJ performed the data extraction. YJ and QL wrote the first draft of the paper; and SY, SL, WL, and XW provided inputs for subsequent drafts. JL and HR provided comments related to the presentation of the findings and critically reviewed the manuscript. All authors read and approved the final manuscript.

Conflicts of Interest

None declared.

Classification of specific diseases.

The number of trials with patient-reported outcomes in each province of China.

Patient-reported outcome tests used most frequently.

• Patrick DL, Burke LB, Powers JH, Scott JA, Rock EP, Dawisha S, et al. Patient-reported outcomes to support medical product labeling claims: FDA perspective. Value Health. 2007;10 Suppl 2:S125-S137. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Calvert M, Kyte D, Mercieca-Bebber R, Slade A, Chan AW, King MT, the SPIRIT-PRO Group, et al. Guidelines for inclusion of patient-reported outcomes in clinical trial protocols: the SPIRIT-PRO extension. JAMA. Feb 06, 2018;319(5):483-494. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Calvert M, Blazeby J, Altman DG, Revicki DA, Moher D, Brundage MD. Reporting of patient-reported outcomes in randomized trials: the CONSORT PRO extension. JAMA. Feb 27, 2013;309(8):814-822. [ CrossRef ] [ Medline ]
• U.S. Department of Health and Human Services FDA Center for Drug Evaluation and Research, U.S. Department of Health and Human Services FDA Center for Biologics Evaluation and Research, U.S. Department of Health and Human Services FDA Center for Devices and Radiological Health. Guidance for industry: patient-reported outcome measures: use in medical product development to support labeling claims: draft guidance. Health Qual Life Outcomes. Oct 11, 2006;4:79. [ FREE Full text ] [ CrossRef ] [ Medline ]
• The Lancet Neurology. Patient-reported outcomes in the spotlight. Lancet Neurol. Nov 2019;18(11):981. [ CrossRef ]
• Marshall S, Haywood K, Fitzpatrick R. Impact of patient-reported outcome measures on routine practice: a structured review. J Eval Clin Pract. Oct 2006;12(5):559-568. [ CrossRef ] [ Medline ]
• Greenhalgh J, Gooding K, Gibbons E, Dalkin S, Wright J, Valderas J, et al. How do patient reported outcome measures (PROMs) support clinician-patient communication and patient care? A realist synthesis. J Patient Rep Outcomes. Dec 2018;2:42. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Chan G, Bezuidenhout L, Walker L, Rowan R. The Impact on Life questionnaire: validation for elective surgery prioritisation in New Zealand prioritisation criteria in orthopaedic surgery. N Z Med J. Apr 01, 2016;129(1432):26-32. [ Medline ]
• Black N. Patient reported outcome measures could help transform healthcare. BMJ. Jan 28, 2013;346:f167. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Tong A, Oberbauer R, Bellini MI, Budde K, Caskey FJ, Dobbels F, et al. Patient-reported outcomes as endpoints in clinical trials of kidney transplantation interventions. Transpl Int. May 20, 2022;35:10134. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Qian Y, Walters SJ, Jacques R, Flight L. Comprehensive review of statistical methods for analysing patient-reported outcomes (PROs) used as primary outcomes in randomised controlled trials (RCTs) published by the UK's Health Technology Assessment (HTA) journal (1997-2020). BMJ Open. Sep 06, 2021;11(9):e051673. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Dai W, Feng W, Zhang Y, Wang XS, Liu Y, Pompili C, et al. Patient-reported outcome-based symptom management versus usual care after lung cancer surgery: a multicenter randomized controlled trial. J Clin Oncol. Mar 20, 2022;40(9):988-996. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Kouzy R, Abi Jaoude J, Lin D, Nguyen ND, El Alam MB, Ludmir EB, et al. Patient-reported outcome measures in pancreatic cancer receiving radiotherapy. Cancers (Basel). Sep 02, 2020;12(9):2487. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Singhal U, Skolarus TA, Gore JL, Parry MG, Chen RC, Nossiter J, et al. Implementation of patient-reported outcome measures into health care for men with localized prostate cancer. Nat Rev Urol. May 2022;19(5):263-279. [ CrossRef ] [ Medline ]
• Stover AM, Basch EM. Using patient-reported outcome measures as quality indicators in routine cancer care. Cancer. Feb 01, 2016;122(3):355-357. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Albreht T. Europe's beating cancer plan-a new step towards more comprehensive and equitable cancer control in Europe. Eur J Public Health. Jul 13, 2021;31(3):456-457. [ CrossRef ] [ Medline ]
• Lizán L, Pérez-Carbonell L, Comellas M. Additional value of patient-reported symptom monitoring in cancer care: a systematic review of the literature. Cancers (Basel). Sep 15, 2021;13(18):4615. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Graupner C, Kimman ML, Mul S, Slok AH, Claessens D, Kleijnen J, et al. Patient outcomes, patient experiences and process indicators associated with the routine use of patient-reported outcome measures (PROMs) in cancer care: a systematic review. Support Care Cancer. Feb 2021;29(2):573-593. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Engstrom T, Tanner S, Lee WR, Forbes C, Walker R, Bradford N, et al. Patient reported outcome measure domains and tools used among adolescents and young adults with cancer: a scoping review. Crit Rev Oncol Hematol. Jan 2023;181:103867. [ CrossRef ] [ Medline ]
• Yang LY, Manhas DS, Howard AF, Olson RA. Patient-reported outcome use in oncology: a systematic review of the impact on patient-clinician communication. Support Care Cancer. Jan 2018;26(1):41-60. [ CrossRef ] [ Medline ]
• Coens C, Pe M, Dueck AC, Sloan J, Basch E, Calvert M, et al. International standards for the analysis of quality-of-life and patient-reported outcome endpoints in cancer randomised controlled trials: recommendations of the SISAQOL Consortium. Lancet Oncol. Feb 2020;21(2):e83-e96. [ CrossRef ]
• Basch E, Geoghegan C, Coons SJ, Gnanasakthy A, Slagle AF, Papadopoulos EJ, et al. Patient-reported outcomes in cancer drug development and US regulatory review: perspectives from industry, the food and drug administration, and the patient. JAMA Oncol. Jun 01, 2015;1(3):375-379. [ CrossRef ] [ Medline ]
• Zhou H, Yao M, Gu X, Liu M, Zeng R, Li Q, et al. Application of patient-reported outcome measurements in clinical trials in China. JAMA Netw Open. May 02, 2022;5(5):e2211644. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Gnanasakthy A, Norcross L, DeMuro Romano C, Carson RT. A review of patient-reported outcome labeling of FDA-approved new drugs (2016-2020): counts, categories, and comprehensibility. Value Health. Apr 2022;25(4):647-655. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Haslam A, Herrera-Perez D, Gill J, Prasad V. Patient experience captured by quality-of-life measurement in oncology clinical trials. JAMA Netw Open. Mar 02, 2020;3(3):e200363. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Watkins Bruner D, Bryan CJ, Aaronson N, Blackmore CC, Brundage M, Cella D, et al. Issues and challenges with integrating patient-reported outcomes in clinical trials supported by the National Cancer Institute–sponsored clinical trials networks. J Clin Oncol. Nov 10, 2007;25(32):5051-5057. [ CrossRef ]
• Basch E, Dueck AC. Patient-reported outcome measurement in drug discovery: a tool to improve accuracy and completeness of efficacy and safety data. Expert Opin Drug Discov. Aug 2016;11(8):753-758. [ CrossRef ] [ Medline ]
• Liniker E, Harrison M, Weaver JM, Agrawal N, Chhabra A, Kingshott V, et al. Treatment costs associated with interventional cancer clinical trials conducted at a single UK institution over 2 years (2009-2010). Br J Cancer. Oct 15, 2013;109(8):2051-2057. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Wong CH, Siah KW, Lo AW. Estimation of clinical trial success rates and related parameters. Biostatistics. Apr 01, 2019;20(2):273-286. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Cao W, Chen HD, Yu YW, Li N, Chen WQ. Changing profiles of cancer burden worldwide and in China: a secondary analysis of the global cancer statistics 2020. Chin Med J (Engl). Mar 17, 2021;134(7):783-791. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Rivera SC, Kyte DG, Aiyegbusi OL, Slade AL, McMullan C, Calvert MJ. The impact of patient-reported outcome (PRO) data from clinical trials: a systematic review and critical analysis. Health Qual Life Outcomes. Oct 16, 2019;17(1):156. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Di Maio M, Gallo C, Leighl NB, Piccirillo MC, Daniele G, Nuzzo F, et al. Symptomatic toxicities experienced during anticancer treatment: agreement between patient and physician reporting in three randomized trials. J Clin Oncol. Mar 10, 2015;33(8):910-915. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Howell D, Molloy S, Wilkinson K, Green E, Orchard K, Wang K, et al. Patient-reported outcomes in routine cancer clinical practice: a scoping review of use, impact on health outcomes, and implementation factors. Ann Oncol. Sep 2015;26(9):1846-1858. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Crites JS, Chuang C, Dimmock A, Hwang W, Johannes B, Paranjape A, et al. PROs in the balance: ethical implications of collecting patient reported outcome measures in the electronic health record. Am J Bioeth. Mar 16, 2016;16(4):67-68. [ CrossRef ] [ Medline ]
• Kyte D, Draper H, Calvert M. Patient-reported outcome alerts: ethical and logistical considerations in clinical trials. JAMA. Sep 25, 2013;310(12):1229-1230. [ CrossRef ] [ Medline ]
• Jensen MP. The validity and reliability of pain measures in adults with cancer. J Pain. Feb 2003;4(1):2-21. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Caraceni A, Brunelli C, Martini C, Zecca E, De Conno F. Cancer pain assessment in clinical trials. A review of the literature (1999-2002). J Pain Symptom Manage. May 2005;29(5):507-519. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Hjermstad MJ, Fayers PM, Haugen DF, Caraceni A, Hanks GW, Loge JH, et al. Studies comparing Numerical Rating Scales, Verbal Rating Scales, and Visual Analogue Scales for assessment of pain intensity in adults: a systematic literature review. J Pain Symptom Manage. Jun 2011;41(6):1073-1093. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Hjermstad MJ, Fainsinger R, Kaasa S, European Palliative Care Research Collaborative (EPCRC). Assessment and classification of cancer pain. Curr Opin Support Palliat Care. Mar 2009;3(1):24-30. [ CrossRef ] [ Medline ]
• Santoni A, Santoni M, Arcuri E. Chronic cancer pain: opioids within tumor microenvironment affect neuroinflammation, tumor and pain evolution. Cancers (Basel). Apr 30, 2022;14(9):2253. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Morishita S, Hirabayashi R, Tsubaki A, Aoki O, Fu JB, Onishi H, et al. Relationship between balance function and QOL in cancer survivors and healthy subjects. Medicine (Baltimore). Nov 19, 2021;100(46):e27822. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Comer SD, Zacny JP, Dworkin RH, Turk DC, Bigelow GE, Foltin RW, et al. Core outcome measures for opioid abuse liability laboratory assessment studies in humans: IMMPACT recommendations. Pain. Dec 2012;153(12):2315-2324. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Dworkin RH, Turk DC, McDermott MP, Peirce-Sandner S, Burke LB, Cowan P, et al. Interpreting the clinical importance of group differences in chronic pain clinical trials: IMMPACT recommendations. Pain. Dec 2009;146(3):238-244. [ CrossRef ] [ Medline ]
• Younger J, McCue R, Mackey S. Pain outcomes: a brief review of instruments and techniques. Curr Pain Headache Rep. Feb 23, 2009;13(1):39-43. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Wan C, Meng Q, Yang Z, Tu X, Feng C, Tang X, et al. Validation of the simplified Chinese version of EORTC QLQ-C30 from the measurements of five types of inpatients with cancer. Ann Oncol. Dec 2008;19(12):2053-2060. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Aaronson NK, Ahmedzai S, Bergman B, Bullinger M, Cull A, Duez NJ, et al. The European Organization for Research and Treatment of Cancer QLQ-C30: a quality-of-life instrument for use in international clinical trials in oncology. J Natl Cancer Inst. Mar 03, 1993;85(5):365-376. [ CrossRef ] [ Medline ]
• Taylor F, Reasner DS, Carson RT, Deal LS, Foley C, Iovin R, et al. Development of a symptom-based patient-reported outcome instrument for functional dyspepsia: a preliminary conceptual model and an evaluation of the adequacy of existing instruments. Patient. Oct 28, 2016;9(5):409-418. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Howell D, Fitch M, Bakker D, Green E, Sussman J, Mayo S, et al. Core domains for a person-focused outcome measurement system in cancer (PROMS-Cancer Core) for routine care: a scoping review and Canadian Delphi Consensus. Value Health. 2013;16(1):76-87. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Eton DT, Beebe TJ, Hagen PT, Halyard MY, Montori VM, Naessens JM, et al. Harmonizing and consolidating the measurement of patient-reported information at health care institutions: a position statement of the Mayo Clinic. Patient Relat Outcome Meas. Feb 10, 2014;5:7-15. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Santana MJ, Haverman L, Absolom K, Takeuchi E, Feeny D, Grootenhuis M, et al. Training clinicians in how to use patient-reported outcome measures in routine clinical practice. Qual Life Res. Jul 2015;24(7):1707-1718. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Riedl D, Lehmann J, Rothmund M, Dejaco D, Grote V, Fischer MJ, et al. Usability of electronic patient-reported outcome measures for older patients with cancer: secondary analysis of data from an observational single center study. J Med Internet Res. Sep 21, 2023;25:e49476. [ FREE Full text ] [ CrossRef ] [ Medline ]

Abbreviations

Edited by A Mavragani; submitted 14.01.23; peer-reviewed by Y Chu, L Guo; comments to author 24.10.23; revised version received 29.10.23; accepted 09.02.24; published 08.05.24.

©Yan Jia, Qi Li, Xiaowen Zhang, Yi Yan, Shiyan Yan, Shunping Li, Wei Li, Xiaowen Wu, Hongguo Rong, Jianping Liu. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 08.05.2024.

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