findings sample in research

• Introduction for Types of Dissertations
• Overview of the Dissertation
• Self-Assessment Exercise
• What is a Dissertation Committee
• Different Types of Dissertations
• Introduction for Overview of the Dissertation Process
• Responsibilities: the Chair, the Team and You
• Sorting Exercise
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• Managing Your Time
• Create Your Own Timeline
• Working with a Writing Partner
• Key Deadlines
• Self Assessment Exercise
• Additional Resources
• Purpose and Goals
• Read and Evaluate Chapter 1 Exemplars
• Draft an Introduction of the Study
• Outline the Background of the Problem
• Draft your Statement of the Problem
• Draft your Purpose of the Study
• Draft your Significance of the Study
• List the Possible Limitations and Delimitations
• Explicate the Definition of Terms
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• Recommended Resources and Readings
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• Preparing for Your Qualifying Exam (aka Proposal Defense)
• What is Needed for Your Proposal Defense?
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• Pre-observation – Issues to consider
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• Recommended Resources and Readings (Qualitative)
• Quantitative Data Collection
• Recommended Resources and Readings (Quantitative)
• Qualitative: Before you Start
• Qualitative: During Analysis
• Qualitative: After Analysis
• Qualitative: Recommended Resources and Readings
• Quantitative: Deciding on the Right Analysis
• Quantitative: Data Management and Cleaning
• Quantitative: Keep Track of your Analysis
• The Purpose of Chapter 4
• The Elements of Chapter 4
• Presenting Results (Quantitative)
• Presenting Findings (Qualitative)
• Chapter 4 Considerations
• The Purpose of Chapter 5
• Preparing Your Abstract for the Graduate School
• Draft the Introduction for Chapter 5
• Draft the Summary of Findings
• Draft Implications for Practice
• Draft your Recommendations for Research
• Draft your Conclusions
• What is Needed
• What Happens During the Final Defense?
• What Happens After the Final Defense?

Presenting Findings (Qualitative)  Topic 1:  Chapter 4

• Your findings should provide sufficient evidence from your data to support the  conclusions you have made. Evidence takes the form of quotations from interviews  and excerpts from observations and documents. 
• Ethically you have to make sure you have confidence in your findings and account  for counter-evidence (evidence that contradicts your primary finding) and not report  something that does not have sufficient evidence to back it up. 
• Your findings should be related back to your conceptual framework. 
• Your findings should be in response to the problem presented (as defined by the  research questions) and should be the “solution” or “answer” to those questions. 
• You should focus on data that enables you to answer your research questions, not  simply on offering raw data. 
• Qualitative research presents “best examples” of raw data to demonstrate an  analytic point, not simply to display data. 
• Numbers (descriptive statistics) help your reader understand how prevalent or  typical a finding is. Numbers are helpful and should not be avoided simply because  this is a qualitative dissertation. 

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Presenting and Evaluating Qualitative Research

The purpose of this paper is to help authors to think about ways to present qualitative research papers in the American Journal of Pharmaceutical Education . It also discusses methods for reviewers to assess the rigour, quality, and usefulness of qualitative research. Examples of different ways to present data from interviews, observations, and focus groups are included. The paper concludes with guidance for publishing qualitative research and a checklist for authors and reviewers.

INTRODUCTION

Policy and practice decisions, including those in education, increasingly are informed by findings from qualitative as well as quantitative research. Qualitative research is useful to policymakers because it often describes the settings in which policies will be implemented. Qualitative research is also useful to both pharmacy practitioners and pharmacy academics who are involved in researching educational issues in both universities and practice and in developing teaching and learning.

Qualitative research involves the collection, analysis, and interpretation of data that are not easily reduced to numbers. These data relate to the social world and the concepts and behaviors of people within it. Qualitative research can be found in all social sciences and in the applied fields that derive from them, for example, research in health services, nursing, and pharmacy. 1 It looks at X in terms of how X varies in different circumstances rather than how big is X or how many Xs are there? 2 Textbooks often subdivide research into qualitative and quantitative approaches, furthering the common assumption that there are fundamental differences between the 2 approaches. With pharmacy educators who have been trained in the natural and clinical sciences, there is often a tendency to embrace quantitative research, perhaps due to familiarity. A growing consensus is emerging that sees both qualitative and quantitative approaches as useful to answering research questions and understanding the world. Increasingly mixed methods research is being carried out where the researcher explicitly combines the quantitative and qualitative aspects of the study. 3 , 4

Like healthcare, education involves complex human interactions that can rarely be studied or explained in simple terms. Complex educational situations demand complex understanding; thus, the scope of educational research can be extended by the use of qualitative methods. Qualitative research can sometimes provide a better understanding of the nature of educational problems and thus add to insights into teaching and learning in a number of contexts. For example, at the University of Nottingham, we conducted in-depth interviews with pharmacists to determine their perceptions of continuing professional development and who had influenced their learning. We also have used a case study approach using observation of practice and in-depth interviews to explore physiotherapists' views of influences on their leaning in practice. We have conducted in-depth interviews with a variety of stakeholders in Malawi, Africa, to explore the issues surrounding pharmacy academic capacity building. A colleague has interviewed and conducted focus groups with students to explore cultural issues as part of a joint Nottingham-Malaysia pharmacy degree program. Another colleague has interviewed pharmacists and patients regarding their expectations before and after clinic appointments and then observed pharmacist-patient communication in clinics and assessed it using the Calgary Cambridge model in order to develop recommendations for communication skills training. 5 We have also performed documentary analysis on curriculum data to compare pharmacist and nurse supplementary prescribing courses in the United Kingdom.

It is important to choose the most appropriate methods for what is being investigated. Qualitative research is not appropriate to answer every research question and researchers need to think carefully about their objectives. Do they wish to study a particular phenomenon in depth (eg, students' perceptions of studying in a different culture)? Or are they more interested in making standardized comparisons and accounting for variance (eg, examining differences in examination grades after changing the way the content of a module is taught). Clearly a quantitative approach would be more appropriate in the last example. As with any research project, a clear research objective has to be identified to know which methods should be applied.

Types of qualitative data include:

• Audio recordings and transcripts from in-depth or semi-structured interviews
• Structured interview questionnaires containing substantial open comments including a substantial number of responses to open comment items.
• Audio recordings and transcripts from focus group sessions.
• Field notes (notes taken by the researcher while in the field [setting] being studied)
• Video recordings (eg, lecture delivery, class assignments, laboratory performance)
• Case study notes
• Documents (reports, meeting minutes, e-mails)
• Diaries, video diaries
• Observation notes
• Press clippings
• Photographs

RIGOUR IN QUALITATIVE RESEARCH

Qualitative research is often criticized as biased, small scale, anecdotal, and/or lacking rigor; however, when it is carried out properly it is unbiased, in depth, valid, reliable, credible and rigorous. In qualitative research, there needs to be a way of assessing the “extent to which claims are supported by convincing evidence.” 1 Although the terms reliability and validity traditionally have been associated with quantitative research, increasingly they are being seen as important concepts in qualitative research as well. Examining the data for reliability and validity assesses both the objectivity and credibility of the research. Validity relates to the honesty and genuineness of the research data, while reliability relates to the reproducibility and stability of the data.

The validity of research findings refers to the extent to which the findings are an accurate representation of the phenomena they are intended to represent. The reliability of a study refers to the reproducibility of the findings. Validity can be substantiated by a number of techniques including triangulation use of contradictory evidence, respondent validation, and constant comparison. Triangulation is using 2 or more methods to study the same phenomenon. Contradictory evidence, often known as deviant cases, must be sought out, examined, and accounted for in the analysis to ensure that researcher bias does not interfere with or alter their perception of the data and any insights offered. Respondent validation, which is allowing participants to read through the data and analyses and provide feedback on the researchers' interpretations of their responses, provides researchers with a method of checking for inconsistencies, challenges the researchers' assumptions, and provides them with an opportunity to re-analyze their data. The use of constant comparison means that one piece of data (for example, an interview) is compared with previous data and not considered on its own, enabling researchers to treat the data as a whole rather than fragmenting it. Constant comparison also enables the researcher to identify emerging/unanticipated themes within the research project.

STRENGTHS AND LIMITATIONS OF QUALITATIVE RESEARCH

Qualitative researchers have been criticized for overusing interviews and focus groups at the expense of other methods such as ethnography, observation, documentary analysis, case studies, and conversational analysis. Qualitative research has numerous strengths when properly conducted.

Strengths of Qualitative Research

• Issues can be examined in detail and in depth.
• Interviews are not restricted to specific questions and can be guided/redirected by the researcher in real time.
• The research framework and direction can be quickly revised as new information emerges.
• The data based on human experience that is obtained is powerful and sometimes more compelling than quantitative data.
• Subtleties and complexities about the research subjects and/or topic are discovered that are often missed by more positivistic enquiries.
• Data usually are collected from a few cases or individuals so findings cannot be generalized to a larger population. Findings can however be transferable to another setting.

Limitations of Qualitative Research

• Research quality is heavily dependent on the individual skills of the researcher and more easily influenced by the researcher's personal biases and idiosyncrasies.
• Rigor is more difficult to maintain, assess, and demonstrate.
• The volume of data makes analysis and interpretation time consuming.
• It is sometimes not as well understood and accepted as quantitative research within the scientific community
• The researcher's presence during data gathering, which is often unavoidable in qualitative research, can affect the subjects' responses.
• Issues of anonymity and confidentiality can present problems when presenting findings
• Findings can be more difficult and time consuming to characterize in a visual way.

PRESENTATION OF QUALITATIVE RESEARCH FINDINGS

The following extracts are examples of how qualitative data might be presented:

Data From an Interview.

The following is an example of how to present and discuss a quote from an interview.

The researcher should select quotes that are poignant and/or most representative of the research findings. Including large portions of an interview in a research paper is not necessary and often tedious for the reader. The setting and speakers should be established in the text at the end of the quote.

The student describes how he had used deep learning in a dispensing module. He was able to draw on learning from a previous module, “I found that while using the e learning programme I was able to apply the knowledge and skills that I had gained in last year's diseases and goals of treatment module.” (interviewee 22, male)

This is an excerpt from an article on curriculum reform that used interviews 5 :

The first question was, “Without the accreditation mandate, how much of this curriculum reform would have been attempted?” According to respondents, accreditation played a significant role in prompting the broad-based curricular change, and their comments revealed a nuanced view. Most indicated that the change would likely have occurred even without the mandate from the accreditation process: “It reflects where the profession wants to be … training a professional who wants to take on more responsibility.” However, they also commented that “if it were not mandated, it could have been a very difficult road.” Or it “would have happened, but much later.” The change would more likely have been incremental, “evolutionary,” or far more limited in its scope. “Accreditation tipped the balance” was the way one person phrased it. “Nobody got serious until the accrediting body said it would no longer accredit programs that did not change.”

Data From Observations

The following example is some data taken from observation of pharmacist patient consultations using the Calgary Cambridge guide. 6 , 7 The data are first presented and a discussion follows:

Pharmacist: We will soon be starting a stop smoking clinic. Patient: Is the interview over now? Pharmacist: No this is part of it. (Laughs) You can't tell me to bog off (sic) yet. (pause) We will be starting a stop smoking service here, Patient: Yes. Pharmacist: with one-to-one and we will be able to help you or try to help you. If you want it. In this example, the pharmacist has picked up from the patient's reaction to the stop smoking clinic that she is not receptive to advice about giving up smoking at this time; in fact she would rather end the consultation. The pharmacist draws on his prior relationship with the patient and makes use of a joke to lighten the tone. He feels his message is important enough to persevere but he presents the information in a succinct and non-pressurised way. His final comment of “If you want it” is important as this makes it clear that he is not putting any pressure on the patient to take up this offer. This extract shows that some patient cues were picked up, and appropriately dealt with, but this was not the case in all examples.

Data From Focus Groups

This excerpt from a study involving 11 focus groups illustrates how findings are presented using representative quotes from focus group participants. 8

Those pharmacists who were initially familiar with CPD endorsed the model for their peers, and suggested it had made a meaningful difference in the way they viewed their own practice. In virtually all focus groups sessions, pharmacists familiar with and supportive of the CPD paradigm had worked in collaborative practice environments such as hospital pharmacy practice. For these pharmacists, the major advantage of CPD was the linking of workplace learning with continuous education. One pharmacist stated, “It's amazing how much I have to learn every day, when I work as a pharmacist. With [the learning portfolio] it helps to show how much learning we all do, every day. It's kind of satisfying to look it over and see how much you accomplish.” Within many of the learning portfolio-sharing sessions, debates emerged regarding the true value of traditional continuing education and its outcome in changing an individual's practice. While participants appreciated the opportunity for social and professional networking inherent in some forms of traditional CE, most eventually conceded that the academic value of most CE programming was limited by the lack of a systematic process for following-up and implementing new learning in the workplace. “Well it's nice to go to these [continuing education] events, but really, I don't know how useful they are. You go, you sit, you listen, but then, well I at least forget.”

The following is an extract from a focus group (conducted by the author) with first-year pharmacy students about community placements. It illustrates how focus groups provide a chance for participants to discuss issues on which they might disagree.

Interviewer: So you are saying that you would prefer health related placements? Student 1: Not exactly so long as I could be developing my communication skill. Student 2: Yes but I still think the more health related the placement is the more I'll gain from it. Student 3: I disagree because other people related skills are useful and you may learn those from taking part in a community project like building a garden. Interviewer: So would you prefer a mixture of health and non health related community placements?

GUIDANCE FOR PUBLISHING QUALITATIVE RESEARCH

Qualitative research is becoming increasingly accepted and published in pharmacy and medical journals. Some journals and publishers have guidelines for presenting qualitative research, for example, the British Medical Journal 9 and Biomedcentral . 10 Medical Education published a useful series of articles on qualitative research. 11 Some of the important issues that should be considered by authors, reviewers and editors when publishing qualitative research are discussed below.

Introduction.

A good introduction provides a brief overview of the manuscript, including the research question and a statement justifying the research question and the reasons for using qualitative research methods. This section also should provide background information, including relevant literature from pharmacy, medicine, and other health professions, as well as literature from the field of education that addresses similar issues. Any specific educational or research terminology used in the manuscript should be defined in the introduction.

The methods section should clearly state and justify why the particular method, for example, face to face semistructured interviews, was chosen. The method should be outlined and illustrated with examples such as the interview questions, focusing exercises, observation criteria, etc. The criteria for selecting the study participants should then be explained and justified. The way in which the participants were recruited and by whom also must be stated. A brief explanation/description should be included of those who were invited to participate but chose not to. It is important to consider “fair dealing,” ie, whether the research design explicitly incorporates a wide range of different perspectives so that the viewpoint of 1 group is never presented as if it represents the sole truth about any situation. The process by which ethical and or research/institutional governance approval was obtained should be described and cited.

The study sample and the research setting should be described. Sampling differs between qualitative and quantitative studies. In quantitative survey studies, it is important to select probability samples so that statistics can be used to provide generalizations to the population from which the sample was drawn. Qualitative research necessitates having a small sample because of the detailed and intensive work required for the study. So sample sizes are not calculated using mathematical rules and probability statistics are not applied. Instead qualitative researchers should describe their sample in terms of characteristics and relevance to the wider population. Purposive sampling is common in qualitative research. Particular individuals are chosen with characteristics relevant to the study who are thought will be most informative. Purposive sampling also may be used to produce maximum variation within a sample. Participants being chosen based for example, on year of study, gender, place of work, etc. Representative samples also may be used, for example, 20 students from each of 6 schools of pharmacy. Convenience samples involve the researcher choosing those who are either most accessible or most willing to take part. This may be fine for exploratory studies; however, this form of sampling may be biased and unrepresentative of the population in question. Theoretical sampling uses insights gained from previous research to inform sample selection for a new study. The method for gaining informed consent from the participants should be described, as well as how anonymity and confidentiality of subjects were guaranteed. The method of recording, eg, audio or video recording, should be noted, along with procedures used for transcribing the data.

Data Analysis.

A description of how the data were analyzed also should be included. Was computer-aided qualitative data analysis software such as NVivo (QSR International, Cambridge, MA) used? Arrival at “data saturation” or the end of data collection should then be described and justified. A good rule when considering how much information to include is that readers should have been given enough information to be able to carry out similar research themselves.

One of the strengths of qualitative research is the recognition that data must always be understood in relation to the context of their production. 1 The analytical approach taken should be described in detail and theoretically justified in light of the research question. If the analysis was repeated by more than 1 researcher to ensure reliability or trustworthiness, this should be stated and methods of resolving any disagreements clearly described. Some researchers ask participants to check the data. If this was done, it should be fully discussed in the paper.

An adequate account of how the findings were produced should be included A description of how the themes and concepts were derived from the data also should be included. Was an inductive or deductive process used? The analysis should not be limited to just those issues that the researcher thinks are important, anticipated themes, but also consider issues that participants raised, ie, emergent themes. Qualitative researchers must be open regarding the data analysis and provide evidence of their thinking, for example, were alternative explanations for the data considered and dismissed, and if so, why were they dismissed? It also is important to present outlying or negative/deviant cases that did not fit with the central interpretation.

The interpretation should usually be grounded in interviewees or respondents' contributions and may be semi-quantified, if this is possible or appropriate, for example, “Half of the respondents said …” “The majority said …” “Three said…” Readers should be presented with data that enable them to “see what the researcher is talking about.” 1 Sufficient data should be presented to allow the reader to clearly see the relationship between the data and the interpretation of the data. Qualitative data conventionally are presented by using illustrative quotes. Quotes are “raw data” and should be compiled and analyzed, not just listed. There should be an explanation of how the quotes were chosen and how they are labeled. For example, have pseudonyms been given to each respondent or are the respondents identified using codes, and if so, how? It is important for the reader to be able to see that a range of participants have contributed to the data and that not all the quotes are drawn from 1 or 2 individuals. There is a tendency for authors to overuse quotes and for papers to be dominated by a series of long quotes with little analysis or discussion. This should be avoided.

Participants do not always state the truth and may say what they think the interviewer wishes to hear. A good qualitative researcher should not only examine what people say but also consider how they structured their responses and how they talked about the subject being discussed, for example, the person's emotions, tone, nonverbal communication, etc. If the research was triangulated with other qualitative or quantitative data, this should be discussed.

Discussion.

The findings should be presented in the context of any similar previous research and or theories. A discussion of the existing literature and how this present research contributes to the area should be included. A consideration must also be made about how transferrable the research would be to other settings. Any particular strengths and limitations of the research also should be discussed. It is common practice to include some discussion within the results section of qualitative research and follow with a concluding discussion.

The author also should reflect on their own influence on the data, including a consideration of how the researcher(s) may have introduced bias to the results. The researcher should critically examine their own influence on the design and development of the research, as well as on data collection and interpretation of the data, eg, were they an experienced teacher who researched teaching methods? If so, they should discuss how this might have influenced their interpretation of the results.

Conclusion.

The conclusion should summarize the main findings from the study and emphasize what the study adds to knowledge in the area being studied. Mays and Pope suggest the researcher ask the following 3 questions to determine whether the conclusions of a qualitative study are valid 12 : How well does this analysis explain why people behave in the way they do? How comprehensible would this explanation be to a thoughtful participant in the setting? How well does the explanation cohere with what we already know?

CHECKLIST FOR QUALITATIVE PAPERS

This paper establishes criteria for judging the quality of qualitative research. It provides guidance for authors and reviewers to prepare and review qualitative research papers for the American Journal of Pharmaceutical Education . A checklist is provided in Appendix 1 to assist both authors and reviewers of qualitative data.

ACKNOWLEDGEMENTS

Thank you to the 3 reviewers whose ideas helped me to shape this paper.

Appendix 1. Checklist for authors and reviewers of qualitative research.

Introduction

• □ Research question is clearly stated.
• □ Research question is justified and related to the existing knowledge base (empirical research, theory, policy).
• □ Any specific research or educational terminology used later in manuscript is defined.
• □ The process by which ethical and or research/institutional governance approval was obtained is described and cited.
• □ Reason for choosing particular research method is stated.
• □ Criteria for selecting study participants are explained and justified.
• □ Recruitment methods are explicitly stated.
• □ Details of who chose not to participate and why are given.
• □ Study sample and research setting used are described.
• □ Method for gaining informed consent from the participants is described.
• □ Maintenance/Preservation of subject anonymity and confidentiality is described.
• □ Method of recording data (eg, audio or video recording) and procedures for transcribing data are described.
• □ Methods are outlined and examples given (eg, interview guide).
• □ Decision to stop data collection is described and justified.
• □ Data analysis and verification are described, including by whom they were performed.
• □ Methods for identifying/extrapolating themes and concepts from the data are discussed.
• □ Sufficient data are presented to allow a reader to assess whether or not the interpretation is supported by the data.
• □ Outlying or negative/deviant cases that do not fit with the central interpretation are presented.
• □ Transferability of research findings to other settings is discussed.
• □ Findings are presented in the context of any similar previous research and social theories.
• □ Discussion often is incorporated into the results in qualitative papers.
• □ A discussion of the existing literature and how this present research contributes to the area is included.
• □ Any particular strengths and limitations of the research are discussed.
• □ Reflection of the influence of the researcher(s) on the data, including a consideration of how the researcher(s) may have introduced bias to the results is included.

Conclusions

• □ The conclusion states the main finings of the study and emphasizes what the study adds to knowledge in the subject area.

Plasma Processing of Materials: Scientific Opportunities and Technological Challenges (1991)

Chapter: 1 summary, findings, conclusions, and recommendations, 1 summary, findings, conclusions, and recommendations.

This study focuses on the plasma processing of materials, a technology that impacts and is of vital importance to several of the largest manufacturing industries in the world. Foremost among these industries is the electronics industry, in which plasma-based processes are indispensable for the manufacture of very large-scale integrated (VLSI) microelectronic circuits (or chips). Plasma processing of materials is also a critical technology in the aerospace, automotive, steel, biomedical, and toxic waste management industries. Because plasma processing is an integral part of the infrastructure of so many American industries, it is important for both the economy and the national security that America maintain a strong leadership role in this technology.

A plasma is a partially or fully ionized gas containing electrons, ions, and neutral atoms or molecules. In Chapter 2 , the panel categorizes different kinds of plasmas and focuses on properties of man-made low-energy, highly collisional plasmas that are particularly useful in materials processing applications. The outstanding properties of most plasmas applied to processing of materials are associated with nonequilibrium conditions. These properties present a challenge to the plasma scientist and an opportunity to the technologist. The opportunities for materials processing stem from the ability of a plasma to provide a highly excited medium that has no chemical or physical counterpart in a natural, equilibrium environment. Plasmas alter the normal pathways through which chemical systems evolve from one stable state to another, thus providing the potential to produce materials with properties that are not attainable by any other means.

Applications of plasma-based systems used to process materials are diverse because of the broad range of plasma conditions, geometries, and excitation methods that may be used. The scientific underpinnings of plasma applications are multidisciplinary and include elements of electrodynamics, atomic science, surface science, computer science, and industrial process control. Because of the diversity of applications and the multidisciplinary nature of the science, scientific understanding lags technology. This report highlights this critical issue.

A summary of the many industrial applications of plasma-based systems for processing materials is included in Chapter 2 . Electronics and aerospace are the two major industries that are served by plasma processing technologies, although the automotive industry is likely to become a significant user of plasma-processed materials like those now in widespread use in the aerospace industry. The critical role of plasma processing technology in industry is illustrated in Chapter 2 .

For the electronics industry more than for any other considered by the panel, the impact of—and the critical and urgent need for—plasma-based materials processing is overwhelming. Thus Chapter 3 further elucidates plasma processing of electronic materials and, in particular, the use of plasmas in fabricating microelectronic components. The plasma equipment industry is an integral part of the electronics industry and has experienced dramatic growth in recent years because of the increasing use of plasma processes to meet the demands of fabricating devices with continually shrinking dimensions. In this country, the plasma equipment industry

is composed of many small companies loosely connected to integrated circuit manufacturers. In Japan, on the other hand, equipment vendors and device manufacturers are tightly linked and are often parts of the same company.

Plasma processes used today in fabricating microelectronic devices have been developed largely by time-consuming, costly, empirical exploration. The chemical and physical complexity of plasma-surface interactions has so far eluded the accurate numerical simulation that would enable process design. Similarly, plasma reactors have also been developed by trial and error. This is due, in part, to the fact that reactor design is intimately intertwined with the materials process for which it will be used. Nonetheless, fundamental studies of surface processes and plasma phenomena—both experimental and numerical—have contributed to process development by providing key insights that enable limitation of the broad process-variable operating space. The state of the science that underpins plasma processing technology in the United States is outlined in Chapter 4 . Although an impressive arsenal of both experimental and numerical tools has been developed, significant gaps in understanding and lack of instrumentation limit progress.

The broad interdisciplinary nature of plasma processing is highlighted in the discussion of education issues outlined in Chapter 5 , which addresses the challenges and opportunities associated with providing a science education in the area of plasma processing. For example, graduate programs specifically focused on plasma processing are rare because of insufficient funding of university research programs in this field. By contrast, both Japan and France have national initiatives that support education and research in plasma processing.

FINDINGS, CONCLUSIONS, AND RECOMMENDATIONS

Finding and Conclusion : In recent years, the number of applications requiring plasmas in the processing of materials has increased dramatically. Plasma processing is now indispensable to the fabrication of electronic components and is widely used in the aerospace industry and other industries. However, the United States is seeing a serious decline in plasma reactor development that is critical to plasma processing steps in the manufacture of VLSI microelectronic circuits. In the interest of the U.S. economy and national defense, renewed support for low-energy plasma science is imperative.

Finding and Conclusion : The demand for technology development is outstripping scientific understanding of many low-energy plasma processes. The central scientific problem underlying plasma processing concerns the interaction of low-energy collisional plasmas with solid surfaces. Understanding this problem requires knowledge and expertise drawn from plasma physics, atomic physics, condensed matter physics, chemistry, chemical engineering, electrical engineering, materials science, computer science, and computer engineering. In the absence of a coordinated approach, the diversity of the applications and of the science tends to diffuse the focus of both.

Finding : Technically, U.S. laboratories have made many excellent contributions to plasma processing research—making fundamental discoveries, developing numerical algorithms, and inventing new diagnostic techniques. However, poor coordination and inefficient transfer of insights gained from this research have inhibited its use in the design of new plasma reactors and processes.

Finding : The Panel on Plasma Processing of Materials finds that plasma processing of materials is a critical technology that is necessary to implement key recommendations contained in the National Research Council report Materials Science and Engineering for the 1990s (National Academy Press, Washington, D.C., 1989) and to enhance the health of technologies as identified in Report of the National Critical Technologies Panel (U.S. Government Printing Office, Washington, D.C., 1991). Specifically, plasma processing is an essential element in the synthesis and processing arsenal for manufacturing electronic, photonic, ceramic, composite, high-performance metal, and alloy materials.

Accordingly, the panel recommends:

Plasma processing should be identified as a component program of the Federal Initiative on advanced materials synthesis and processing that currently is being developed by the Office of Science and Technology Policy.

Through such a Plasma Processing Program, federal funds should be allocated specifically to stimulate focused research in plasma processing, both basic and applied, consistent with the long-term economic and defense goals of the nation.

The Plasma Processing Program should not only provide focus on common goals and promote coordination of the research performed by the national laboratories, universities, and industrial laboratories, but also integrate plasma equipment suppliers into the program.

Finding and Conclusion : Currently, computer-based modeling and plasma simulation are inadequate for developing plasma reactors. As a result, the detailed descriptions required to guide the transfer of processes from one reactor to another or to scale processes from a small to a large reactor are not available. Until we understand how geometry, electromagnetic design, and plasma-surface interactions affect material properties, the choice of plasma reactor for a given process will not be obvious, and costly trial-and-error methods will continue to be used. Yet there is no fundamental obstacle to improved modeling and simulation nor to the eventual creation of computer-aided design (CAD) tools for designing plasma reactors. The key missing ingredients are the following:

A reliable and extensive plasma data base against which the accuracy of simulations of plasmas can be compared . Plasma measurement technologies are sophisticated, but at present experiments are performed on a large variety of different reactors under widely varying conditions. A coordinated effort to diagnose simple, reference reactors is necessary to generate the necessary data base for evaluation of simulation results and to test new and old experimental methodology.

A reliable and extensive input data base for calculating plasma generation, transport, and surface interaction . The dearth of basic data needed for simulation of plasma generation, transport, and surface reaction processes results directly from insufficient generation of data, insufficient data compilation, insufficient distribution of data, and insufficient funding of these activities. The critical basic data needed for simulations and experiments have not been prioritized. For plasma-surface interactions, in particular, lack of data has precluded the formation of mechanistic models on which simulation tools are based. Further experimental studies are needed to elucidate these mechanisms.

Efficient numerical algorithms and supercomputers for simulating magnetized plasmas in three dimensions . The advent of unprecedented supercomputer capability in the next 5 to 10 years will have a major impact in this area, provided that current simulation methods are expanded to account for multidimensional effects in magnetized plasmas.

The Plasma Processing Program should include a thrust toward development of computer-aided design tools for developing and designing new plasma reactors.

The Plasma Processing Program should emphasize a coordinated approach toward generating the diagnostic and basic data needed for improved plasma and plasma-surface simulation capability.

A program to extend current algorithms for plasma reactor simulation should be included among the activities funded under the umbrella of the federal High

Performance Computing and Communications program 1 developed by the Office of Science and Technology Policy and started in FY92.

Finding and Conclusion : In the coming decade, custom-designed and custom-manufactured chips, i.e ., application-specific integrated circuits (ASICs), will gain an increasing fraction of the world market in microelectronic components. This market, in turn, will belong to the flexible manufacturer who uses a common set of processes and equipment to fabricate many different circuit designs. Such flexibility in processing will result only from real understanding of processes and reactors. On the other hand, plasma processes in use today have been developed using a combination of intuition, empiricism, and statistical optimization. Although it is unlikely that detailed, quantitative, first-principles-based simulation tools will be available for process design in the near future, design aids such as expert systems, which can be used to guide engineers in selecting initial conditions from which the final process is derived, could be developed if gaps in our fundamental understanding of plasma chemistry were filled.

Finding and Conclusion : Three areas are recognized by the panel as needing concerted, coordinated experimental and theoretical research: surface processes, plasma generation and transport, and plasma-surface interactions. For surface processes, studies using well-controlled reactive beams impinging on well-characterized surfaces are essential for enhancing our understanding and developing mechanistic models. For plasma generation and transport, chemical kinetic data and diagnostic data are needed to augment the basic plasma reactor CAD tool. For studying plasma-surface interactions, there is an urgent need for in situ analytical tools that provide information on surface composition, electronic structure, and material properties.

Finding and Conclusion : Breakthroughs in understanding the science will be paced by development of tools for the characterization of the systems. To meet the coming demands for flexible device manufacturing, plasma processes will have to be actively and precisely controlled. But today no diagnostic techniques exist that can be used unambiguously to determine material properties related to device yield. Moreover, the parametric models needed to relate diagnostic data to process variables are also lacking.

According, the panel recommends:

The Plasma Processing Program should be dedicated in part to the development of plasma process expert systems.

A coordinated program should be supported to generate basic data and simulation of surface processes, plasma generation and transport, and plasma-surface interactions.

A program should be supported that focuses on development of new instrumentation for real-time, in situ monitoring for control and analysis.

Finding : Research resources in low-energy plasma science in the United States are eroding at an alarming rate. U.S. scientists trained in this area in the 1950s and early 1960s are retiring or are moving to other areas of science for which support is more forthcoming. When compared to those in Japan and France, the U.S. educational infrastructure in plasma processing lacks focus, coordination, and funding. As a result, the United States will not be prepared to maintain its leading market position in plasma processing, let alone capture more market share as the plasma process industry grows into the 21st century.

Finding : Graduate programs are not offering adequate educational opportunities in the science of weakly ionized, highly collisional plasmas. An informal survey by the panel indicated that only a few U.S. universities offer formal course work in this science and that there are

insufficient texts on collisional plasmas and plasma processing. These deficiencies are a direct result of low-level funding for graduate research in plasma processing and low-energy plasmas.

Finding and Conclusion : The most serious need in undergraduate education is adequate, modern teaching laboratories. Due to the largely empirical nature of many aspects of plasma processing, proper training in the traditional scientific method, as provided in laboratory classes, is a necessary component of undergraduate education. The Instrumentation and Laboratory Improvement Program sponsored by the National Science Foundation has been partly successful in fulfilling these needs, but it is not sufficient.

Finding and Conclusion : Research experiences for undergraduates made available through industrial cooperative programs or internships are essential for high-quality technical education. But teachers and professors themselves must first be educated in low-energy plasma science and plasma processing before they can be expected to educate students. Industrial-university links can also help to impart a much needed, longer-term view to industrial research efforts.

As part of the Plasma Processing Program, government and industry together should support cooperative programs specific to plasma processing with universities and national laboratories.

A program should be established to provide industrial internships for teachers and professors in the area of plasma processing.

Plasma processing of materials is a critical technology to several of the largest manufacturing industries in the world—electronics, aerospace, automotive, steel, biomedical, and toxic waste management. This book describes the relationship between plasma processes and the many industrial applications, examines in detail plasma processing in the electronics industry, highlights the scientific foundation underlying this technology, and discusses education issues in this multidisciplinary field.

The committee recommends a coordinated, focused, and well-funded research program in this area that involves the university, federal laboratory, and industrial sectors of the community. It also points out that because plasma processing is an integral part of the infrastructure of so many American industries, it is important for both the economy and the national security that America maintain a strong leadership role in this technology.

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Data Analysis in Research

Ai generator.

Data analysis in research involves systematically applying statistical and logical techniques to describe, illustrate, condense, and evaluate data. It is a crucial step that enables researchers to identify patterns, relationships, and trends within the data, transforming raw information into valuable insights. Through methods such as descriptive statistics, inferential statistics, and qualitative analysis, researchers can interpret their findings, draw conclusions, and support decision-making processes. An effective data analysis plan and robust methodology ensure the accuracy and reliability of research outcomes, ultimately contributing to the advancement of knowledge across various fields.

What is Data Analysis in Research?

Data analysis in research involves using statistical and logical techniques to describe, summarize, and compare collected data. This includes inspecting, cleaning, transforming, and modeling data to find useful information and support decision-making. Quantitative data provides measurable insights, and a solid research design ensures accuracy and reliability. This process helps validate hypotheses, identify patterns, and make informed conclusions, making it a crucial step in the scientific method.

Examples of Data analysis in Research

• Survey Analysis : Researchers collect survey responses from a sample population to gauge opinions, behaviors, or characteristics. Using descriptive statistics, they summarize the data through means, medians, and modes, and then inferential statistics to generalize findings to a larger population.
• Experimental Analysis : In scientific experiments, researchers manipulate one or more variables to observe the effect on a dependent variable. Data is analyzed using methods such as ANOVA or regression analysis to determine if changes in the independent variable(s) significantly affect the dependent variable.
• Content Analysis : Qualitative research often involves analyzing textual data, such as interview transcripts or open-ended survey responses. Researchers code the data to identify recurring themes, patterns, and categories, providing a deeper understanding of the subject matter.
• Correlation Studies : Researchers explore the relationship between two or more variables using correlation coefficients. For example, a study might examine the correlation between hours of study and academic performance to identify if there is a significant positive relationship.
• Longitudinal Analysis : This type of analysis involves collecting data from the same subjects over a period of time. Researchers analyze this data to observe changes and developments, such as studying the long-term effects of a specific educational intervention on student achievement.
• Meta-Analysis : By combining data from multiple studies, researchers perform a meta-analysis to increase the overall sample size and enhance the reliability of findings. This method helps in synthesizing research results to draw broader conclusions about a particular topic or intervention.

Data analysis in Qualitative Research

Data analysis in qualitative research involves systematically examining non-numeric data, such as interviews, observations, and textual materials, to identify patterns, themes, and meanings. Here are some key steps and methods used in qualitative data analysis:

• Coding : Researchers categorize the data by assigning labels or codes to specific segments of the text. These codes represent themes or concepts relevant to the research question.
• Thematic Analysis : This method involves identifying and analyzing patterns or themes within the data. Researchers review coded data to find recurring topics and construct a coherent narrative around these themes.
• Content Analysis : A systematic approach to categorize verbal or behavioral data to classify, summarize, and tabulate the data. This method often involves counting the frequency of specific words or phrases.
• Narrative Analysis : Researchers focus on the stories and experiences shared by participants, analyzing the structure, content, and context of the narratives to understand how individuals make sense of their experiences.
• Grounded Theory : This method involves generating a theory based on the data collected. Researchers collect and analyze data simultaneously, continually refining and adjusting their theoretical framework as new data emerges.
• Discourse Analysis : Examining language use and communication patterns within the data, researchers analyze how language constructs social realities and power relationships.
• Case Study Analysis : An in-depth analysis of a single case or multiple cases, exploring the complexities and unique aspects of each case to gain a deeper understanding of the phenomenon under study.

Data analysis in Quantitative Research

Data analysis in quantitative research involves the systematic application of statistical techniques to numerical data to identify patterns, relationships, and trends. Here are some common methods used in quantitative data analysis:

• Descriptive Statistics : This includes measures such as mean, median, mode, standard deviation, and range, which summarize and describe the main features of a data set.
• Inferential Statistics : Techniques like t-tests, chi-square tests, and ANOVA (Analysis of Variance) are used to make inferences or generalizations about a population based on a sample.
• Regression Analysis : This method examines the relationship between dependent and independent variables. Simple linear regression analyzes the relationship between two variables, while multiple regression examines the relationship between one dependent variable and several independent variables.
• Correlation Analysis : Researchers use correlation coefficients to measure the strength and direction of the relationship between two variables.
• Factor Analysis : This technique is used to identify underlying relationships between variables by grouping them into factors based on their correlations.
• Cluster Analysis : A method used to group a set of objects or cases into clusters, where objects in the same cluster are more similar to each other than to those in other clusters.
• Hypothesis Testing : This involves testing an assumption or hypothesis about a population parameter. Common tests include z-tests, t-tests, and chi-square tests, which help determine if there is enough evidence to reject the null hypothesis.
• Time Series Analysis : This method analyzes data points collected or recorded at specific time intervals to identify trends, cycles, and seasonal variations.
• Multivariate Analysis : Techniques like MANOVA (Multivariate Analysis of Variance) and PCA (Principal Component Analysis) are used to analyze data that involves multiple variables to understand their effect and relationships.
• Structural Equation Modeling (SEM) : A multivariate statistical analysis technique that is used to analyze structural relationships. This method is a combination of factor analysis and multiple regression analysis and is used to analyze the structural relationship between measured variables and latent constructs.

Data analysis in Research Methodology

Data analysis in research methodology involves the process of systematically applying statistical and logical techniques to describe, condense, recap, and evaluate data. Here are the key components and methods involved:

• Data Preparation : This step includes collecting, cleaning, and organizing raw data. Researchers ensure data quality by handling missing values, removing duplicates, and correcting errors.
• Descriptive Analysis : Researchers use descriptive statistics to summarize the basic features of the data. This includes measures such as mean, median, mode, standard deviation, and graphical representations like histograms and pie charts.
• Inferential Analysis : This involves using statistical tests to make inferences about the population from which the sample was drawn. Common techniques include t-tests, chi-square tests, ANOVA, and regression analysis.
• Qualitative Data Analysis : For non-numeric data, researchers employ methods like coding, thematic analysis, content analysis, narrative analysis, and discourse analysis to identify patterns and themes.
• Quantitative Data Analysis : For numeric data, researchers apply statistical methods such as correlation, regression, factor analysis, cluster analysis, and time series analysis to identify relationships and trends.
• Hypothesis Testing : Researchers test hypotheses using statistical methods to determine whether there is enough evidence to reject the null hypothesis. This involves calculating p-values and confidence intervals.
• Data Interpretation : This step involves interpreting the results of the data analysis. Researchers draw conclusions based on the statistical findings and relate them back to the research questions and objectives.
• Validation and Reliability : Ensuring the validity and reliability of the analysis is crucial. Researchers check for consistency in the results and use methods like cross-validation and reliability testing to confirm their findings.
• Visualization : Effective data visualization techniques, such as charts, graphs, and plots, are used to present the data in a clear and understandable manner, aiding in the interpretation and communication of results.
• Reporting : The final step involves reporting the results in a structured format, often including an introduction, methodology, results, discussion, and conclusion. This report should clearly convey the findings and their implications for the research question.

Types of Data analysis in Research

• Purpose : To summarize and describe the main features of a dataset.
• Methods : Mean, median, mode, standard deviation, frequency distributions, and graphical representations like histograms and pie charts.
• Example : Calculating the average test scores of students in a class.
• Purpose : To make inferences or generalizations about a population based on a sample.
• Methods : T-tests, chi-square tests, ANOVA (Analysis of Variance), regression analysis, and confidence intervals.
• Example : Testing whether a new teaching method significantly affects student performance compared to a traditional method.
• Purpose : To analyze data sets to find patterns, anomalies, and test hypotheses.
• Methods : Visualization techniques like box plots, scatter plots, and heat maps; summary statistics.
• Example : Visualizing the relationship between hours of study and exam scores using a scatter plot.
• Purpose : To make predictions about future outcomes based on historical data.
• Methods : Regression analysis, machine learning algorithms (e.g., decision trees, neural networks), and time series analysis.
• Example : Predicting student graduation rates based on their academic performance and demographic data.
• Purpose : To provide recommendations for decision-making based on data analysis.
• Methods : Optimization algorithms, simulation, and decision analysis.
• Example : Suggesting the best course of action for improving student retention rates based on various predictive factors.
• Purpose : To identify and understand cause-and-effect relationships.
• Methods : Controlled experiments, regression analysis, path analysis, and structural equation modeling (SEM).
• Example : Determining the impact of a specific intervention, like a new curriculum, on student learning outcomes.
• Purpose : To understand the specific mechanisms through which variables affect one another.
• Methods : Detailed modeling and simulation, often used in scientific research to understand biological or physical processes.
• Example : Studying how a specific drug interacts with biological pathways to affect patient health.

How to write Data analysis in Research

Data analysis is crucial for interpreting collected data and drawing meaningful conclusions. Follow these steps to write an effective data analysis section in your research.

1. Prepare Your Data

Ensure your data is clean and organized:

• Remove duplicates and irrelevant data.
• Check for errors and correct them.
• Categorize data if necessary.

2. Choose the Right Analysis Method

Select a method that fits your data type and research question:

• Quantitative Data : Use statistical analysis such as t-tests, ANOVA, regression analysis.
• Qualitative Data : Use thematic analysis, content analysis, or narrative analysis.

3. Describe Your Analytical Techniques

Clearly explain the methods you used:

• Software and Tools : Mention any software (e.g., SPSS, NVivo) used.
• Statistical Tests : Detail the statistical tests applied, such as chi-square tests or correlation analysis.
• Qualitative Techniques : Describe coding and theme identification processes.

4. Present Your Findings

Organize your findings logically:

• Use Tables and Figures : Display data in tables, graphs, and charts for clarity.
• Summarize Key Results : Highlight the most significant findings.
• Include Relevant Statistics : Report p-values, confidence intervals, means, and standard deviations.

5. Interpret the Results

Explain what your findings mean in the context of your research:

• Compare with Hypotheses : State whether the results support your hypotheses.
• Relate to Literature : Compare your results with previous studies.
• Discuss Implications : Explain the significance of your findings.

6. Discuss Limitations

Acknowledge any limitations in your data or analysis:

• Sample Size : Note if the sample size was small.
• Biases : Mention any potential biases in data collection.
• External Factors : Discuss any factors that might have influenced the results.

7. Conclude with a Summary

Wrap up your data analysis section:

• Restate Key Findings : Briefly summarize the main results.
• Future Research : Suggest areas for further investigation.

Importance of Data analysis in Research

Data analysis is a fundamental component of the research process. Here are five key points highlighting its importance:

• Enhances Accuracy and Reliability Data analysis ensures that research findings are accurate and reliable. By using statistical techniques, researchers can minimize errors and biases, ensuring that the results are dependable.
• Facilitates Informed Decision-Making Through data analysis, researchers can make informed decisions based on empirical evidence. This is crucial in fields like healthcare, business, and social sciences, where decisions impact policies, strategies, and outcomes.
• Identifies Trends and Patterns Analyzing data helps researchers uncover trends and patterns that might not be immediately visible. These insights can lead to new hypotheses and areas of study, advancing knowledge in the field.
• Supports Hypothesis Testing Data analysis is vital for testing hypotheses. Researchers can use statistical methods to determine whether their hypotheses are supported or refuted, which is essential for validating theories and advancing scientific understanding.
• Provides a Basis for Predictions By analyzing current and historical data, researchers can develop models that predict future outcomes. This predictive capability is valuable in numerous fields, including economics, climate science, and public health.

FAQ’s

What is the difference between qualitative and quantitative data analysis.

Qualitative analysis focuses on non-numerical data to understand concepts, while quantitative analysis deals with numerical data to identify patterns and relationships.

What is descriptive statistics?

Descriptive statistics summarize and describe the features of a data set, including measures like mean, median, mode, and standard deviation.

What is inferential statistics?

Inferential statistics use sample data to make generalizations about a larger population, often through hypothesis testing and confidence intervals.

What is regression analysis?

Regression analysis examines the relationship between dependent and independent variables, helping to predict outcomes and understand variable impacts.

What is the role of software in data analysis?

Software like SPSS, R, and Excel facilitate data analysis by providing tools for statistical calculations, visualization, and data management.

What are data visualization techniques?

Data visualization techniques include charts, graphs, and maps, which help in presenting data insights clearly and effectively.

What is data cleaning?

Data cleaning involves removing errors, inconsistencies, and missing values from a data set to ensure accuracy and reliability in analysis.

What is the significance of sample size in data analysis?

Sample size affects the accuracy and generalizability of results; larger samples generally provide more reliable insights.

How does correlation differ from causation?

Correlation indicates a relationship between variables, while causation implies one variable directly affects the other.

What are the ethical considerations in data analysis?

Ethical considerations include ensuring data privacy, obtaining informed consent, and avoiding data manipulation or misrepresentation.

Text prompt

• Instructive
• Professional

10 Examples of Public speaking

20 Examples of Gas lighting

Structuring a qualitative findings section

Reporting the findings from a qualitative study in a way that is interesting, meaningful, and trustworthy can be a struggle. Those new to qualitative research often find themselves trying to quantify everything to make it seem more “rigorous,” or asking themselves, “Do I really need this much data to support my findings?” Length requirements and word limits imposed by academic journals can also make the process difficult because qualitative data takes up a lot of room! In this post, I’m going to outline a few ways to structure qualitative findings, and a few tips and tricks to develop a strong findings section.

There are A LOT of different ways to structure a qualitative findings section. I’m going to focus on the following:

Tables (but not ONLY tables)

Themes/Findings as Headings

Research Questions as Headings

Anchoring Quotations

Anchoring Excerpts from Field Notes

Before I get into each of those, however, here is a bit of general guidance. First, make sure that you are providing adequate direct evidence for your findings. Second, be sure to integrate that direct evidence into the narrative. In other words, if for example, you were using quotes from a participant to support one of your themes, you should present and explain the theme (akin to a thesis statement), introduce the supporting quote, present it, explain the quote, and connect it to your finding. Below is an example of what I mean from one of my articles on implementation challenges in personalized learning ( Bingham, Pane, Steiner, & Hamilton, 2018 ). The finding supported by this paragraph was: “Inadequate Teacher Preparation, Development, and Support”

To mitigate the difficulties of enacting personalized learning in their classrooms, teachers wanted a model from which they could extrapolate practices that might serve them well in their own classrooms. As one teacher explained, “the ideas and the implementation is what’s lacking I think. I don’t feel like I know what I’m doing. I need to see things modeled and I need to know what it is. I need to be able to touch it. Show me a model, model for me.” Unfortunately, teachers had little to draw on for effective practices. Professional development was not as helpful as teachers had hoped, outside training on using the digital content or learning platforms fell short, and few examples or best practices existed for teachers to use in their own classrooms. As a result, teachers had to work harder to address gaps in their own knowledge. 

Finally, you should not leave quotations to speak for themselves and you should not have quotations as standalone paragraphs or sentences, with no introduction or explanation. Don’t make the reader do the analytic work for you.

Now, on to some specific ways to structure your findings section.

Tables can be used to give an overview of what you’re about to present in your findings, including the themes, some supporting evidence, and the meaning/explanation of the theme. Tables can be a useful way to give readers a quick reference for what your findings are. However, tables should not be used as your ONLY means of presenting those findings.

If you are choosing to use a table to present qualitative findings, you must also describe the findings in context, and provide supporting evidence in a narrative format (as in the paragraph outlined in the previous section).

2). Themes/Findings as Headings

Another option is to present your themes/findings as general or specific headings in your findings section. Here are some examples of findings as general headings:

Importance of Data Utilization and Analysis in the Classroom  The Role of Student Discipline and Accountability Differences in the Experiences of Teachers 

As you can see these headings do not describe precisely what the finding is, but they give the general idea/subject of the finding. You can have sub-headings within these findings that are more specific if you would like.

Another way to do this would be to be a bit more specific. For example:

School Infrastructure and Available Technology Do Not yet Fully Align with Teachers’ Needs 

Structural support for high levels of technology use is not fully developed 

Using multiple sources of digital content led to alignment issues 

Measures of School and Student Success are Misaligned

Traditional methods of measuring student progress conflict with personalized learning

Difficulties communicating new measures of student success to colleges and universities.

As you can see, here the findings are shown as headings, but are structured as specific sentences, with sub-themes included as well.

3). Research Questions as Headings

You can also present your findings using your research questions as the headings in the findings section. This is a useful strategy that ensures you’re answering your research questions and also allows the reader to quickly ascertain where the answers to your research questions are. Often, you will also need to present themes within each research question to keep yourself organized and to adequately flesh out your findings. The example below presents a research question from my study of blended learning at a charter high school (Bingham, 2016) , and an excerpt from my findings that answered that research question. I have also included the associated theme.

Research Question 1: What challenges, if any, do teachers face in implementing a blended model in a school’s first year? Theme: TROUBLESHOOTING AND TASK-MANAGING: TECHNOLOGY USE IN THE CLASSROOM In the original vision for instruction at Blended Academy, technology was to be an integral part of students’ learning, meant to allow students to find their own answers to their questions, to explore their personal interests, and to provide multiple opportunities for learning. The use of iPods in the classroom was partially intended to serve the social-emotional component of the model, allowing students to enjoy music and to “tune out” from other classroom activities when working on Digital X. Further, the iPods would allow stu- dents to listen to podcasts or teacher-created content at any time, in any location. However, prior to the school’s opening, little attention was paid to the management of these devices, and their potential for misuse. As a result, teachers spent much of their time managing students’ technology use, troubleshooting, and developing classroom procedures to ensure that technology use was relevant to learning. For example, in Ms. L’s classroom, she attempted to ensure learning was happening by instituting “Technology-Free” periods in the classroom. When students had to be working on their laptops in order to complete lessons or quizzes, the majority of her time was spent walking from student to student, watching for off-task behavior, and calling out students for how long they were “logged in” to the digital curriculum. In one typical interaction, Ms. L admonished one student, saying “It says you only logged in for one minute . . . when are you going to finish your English if you only logged in one minute today?” The difficulties around ensuring students were using technology productively resulted in teachers “hovering” over students, making it difficult to provide targeted instructional help. Teachers often responded to off-task behavior/ technology use by confiscating computers and devices or restricting their use, in order to ensure that students were working. However, because the majority of tasks were meant to be delivered online or through technological devices, this was not a productive or effective solution.

4). Vignettes

Vignettes can be a strategy to spark interest in your study, add narrative context, and provide a descriptive overview of your study/site/participants. They can also be used as a strategy to introduce themes. You can place them at the beginning of a paper, or at the start of the findings section, or in your discussion of each theme. They wouldn’t typically be the only representation of your findings that you present, but you can use them to hook the reader and provide a story that exemplifies findings, themes, contexts, participants, etc. Below is an example from one of my recent studies.

The Role of Pilot Teachers in Schoolwide Technology Integration Blended High School is a lot like many other charter schools. Students wear uniforms, and as you walk through the halls, there is almost always a teacher issuing a demerit to a student who is not wearing the right shoes, or who hasn’t tucked in their shirt. In this school, however, teachers use technology in almost every facet of their instruction, operating in a school model that blends face-to-face and online learning in the classroom in order to personalize students’ learning experiences. It has, however, been a long road to this level of technology use. BHS’s first year of operation was, arguably, disastrous. Teachers were overwhelmed and students didn’t progress as expected. In one staff meeting toward the end of the schools’ first year, teachers and administrators expressed frustration with each other and with the school model, with several teachers arguing that technology was hurting, not helping. The atmosphere was tense, with one teacher finally shrugging anxiously and saying “Maybe need to ask ourselves, ‘Is this the best model to use with some of our kids?’” Ultimately, by the end of the first year, technology was not a regular classroom practice. In BHS’s second year, the administration again pushed for full technology integration, but they wanted to start slow. In a fall semester staff meeting, the principal and the assistant principal ran what the principal referred to as a “technology therapy session,” where teachers could share their struggles with using technology to engage in PL. During the session, one of the new teachers mentions that she is having a difficult time letting go – changing her focus from lecturing to computer-based work. Another teacher worries about finding good online resources. Most of the teachers, new and veteran, are alarmed by the time it is taking for them design lessons that integrate technology. Some admit only engaging in technology use in a shallow way – uploading worksheets to Google Docs, recording Powerpoints, etc.  A few months after the discussion in which teachers aired their fears and struggles, the principal leads the teachers in analyzing student data from that week and spends a bit of time highlighting the work of a few teachers whose students are doing particularly well and who have been able to use technology in everyday classroom practice. Those teachers are part of a small group of “pilot teachers,” each of whom have been experimenting with various technology-based practices, including testing new learning management systems, designing their own online modules with personalized student objectives, providing students with technology-facilitated immediate feedback, and using up-to-the-minute data to develop technology-guided small-group instruction.  Over the course of the next several months, administrators encouraged teachers to continue to be transparent about their concerns and share those concerns in regular staff meetings. Administrators conferred with the pilot teachers and administrators and teachers together set incremental goals based on the pilot teachers’ recommendations. In weekly staff meetings, the pilot teachers shared their progress, including concerns and challenges. They collaborated with the other teachers to find solutions and worked with the administration to get what they needed to enact those solutions. For example, after a push from the pilot teachers, administration increased funding for technology purchases and introduced shifts in the school schedule to allow for planning in order to help teachers manage the demands of a high-tech classroom. Because the pilot teachers emphasized how much time meaningful technology integration took, and knew what worked and what didn’t, they were able to train other teachers in high-tech practices and to make the case to administration for needed changes.  By BHS’s third year, teachers schoolwide were able to fully integrate technology in their classrooms. All teachers were using the same learning management system, which had been initially chosen and tested by a pilot teacher. In every classroom, teachers were also engaging online modules, technology-facilitated breakout groups, and real time technology-based data analysis – all of which were practices the pilot teachers had tested and shared in the second year. The consistent collaboration between administration and pilot teachers and pilot teachers and other teachers helped calibrate classroom changes to manage the conflict between existing practices and new high-tech practices. By focusing on student learning data, creating the room for experimentation, collaborating consistently, and distributing the leadership for technology integration, teachers and administrators felt comfortable with the increasing reliance on tech-heavy practices.

I developed this vignette as a composite from my field notes and interviews and used it to set the stage for the rest of the findings section.

4). Anchoring Quotes

Using exemplar quotes from your participants is another way to structure your findings. In the following, which also comes from Bingham et al. (2018) , the finding itself is used as the heading, and the anchoring quotes come directly after the heading, prior to the rest of the narrative discussion of the finding. These quotations help provide some initial evidence and set the stage for what’s to come.

School Infrastructure and Available Technology Do Not Yet Fully Align With Teachers’ Needs  “I know that computer problems are an issue almost daily.” (Middle school personalized learning teacher)  “If the data was exactly what we needed, it would be easier. I think a lot of times we’re not using it enough because the way we’re using the data is not as effective as it should be.” (High school personalized learning teacher) 

You can note the source next to or after the quote. This can be done with your chosen pseudonyms, or with a general description, as I've done above.

5). Anchoring Excerpts from Field Notes

Similarly, excerpts from field notes can be used to start your discussion of a finding. Again, the finding itself is used as the heading, and the excerpt from field notes supporting that finding comes directly after the heading, prior to the rest of the narrative discussion of the finding. The example below comes from a study in which I explored how a personalized learning model evolved over the course of three years (Bingham, 2017) . I used excerpts from my field notes to open the discussion of each year.

Year 1: Navigating the disconnect between vision and practice  Walking into the large classroom space shared by Ms. Z and Ms. H, it is not immediately evident that these are high-tech PL classrooms. At first, there are no laptops out in either class. Both Ms. Z’s and Ms. H’s students are completing warm-up activities that are projected on each teacher’s white board. After a few minutes, Ms. Z’s students get up and get laptops. Ms. Z walks around to students and asks them what lesson from the digital curriculum they will be working on today. As Ms. Z speaks to a table of students, other students in the room listen to their iPods, sometimes singing loudly. Some students are on YouTube, watching music videos; others are messaging friends on GChat or Facebook. As Ms. Z makes her way around, students toggle back to the screen devoted to the digital curriculum. Sometimes, Ms. Z notices that students are off-task and she redirects them. Other times, she is too busy unlocking an online quiz for a student, or confiscating a student’s iPod. 

This excerpt from my field notes provided an overview of what teacher practice looked like in the first year of the school, so that I could then discuss several themes that were representative of how practice evolved over that first year.

The key takeaway here is that there are many ways to structure your findings section. You have to choose the method that best supports your study, and best represents your data and participants. No matter what you choose, the findings section itself should be constructed to answer your research questions, while also providing context and thick description, and, of course, telling a story.

Writing a discussion section

Some tips for academic writing.

Research Trends in STEM Clubs: A Content Analysis

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To identify the research trends in studies related to STEM Clubs, 56 publications that met the inclusion and extraction criteria were identified from the online databases ERIC and WoS in this study. These studies were analysed by using the descriptive content analysis research method based on the Paper Classification Form (PCF), which includes publishing years, keywords, research methods, sample levels and sizes, data collection tools, data analysis methods, durations, purposes, and findings. The findings showed that, the keywords in the studies were used under six different categories: disciplines, technological concepts, academic community, learning experiences, core elements of education, and psychosocial factors (variables). Case studies were frequently employed, with middle school students serving as the main participants in sample groups ranging from 11–15, 16–20, and 201–250. Surveys, questionnaires, and observations were the primary methods of data collection, and descriptive analysis was commonly used for data analysis. STEM Clubs had sessions ranging from 2 to 16 weeks, with each session commonly lasting 60 to 120 min. The study purposes mainly focused on four themes: the impact of participation on various aspects such as attitudes towards STEM disciplines, career paths, STEM major selection, and academic achievement; the development and implementation of a sample STEM Club program, including challenges and limitations; the examination of students' experiences, perceptions, and factors influencing their involvement and choice of STEM majors; the identification of some aspects such as attitudinal effects and non-academic skills; and the comparison of STEM experiences between in-school and out-of-school settings. The study results mainly focused on three themes: the increase in various aspects such as academic achievement, STEM major choice, engagement in STEM clubs, identity, interest in STEM, collaboration-communication skills; the design of STEM Clubs, including sample implementations, design principles, challenges, and factors affecting their success and sustainability; and the identification of factors influencing participation, motivation, and barriers. Overall, this study provides a comprehensive understanding of STEM Clubs, leading the way for more targeted and informed future research endeavours.

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Introduction

Worldwide, STEM education, which integrates the disciplines of science, technology, engineering, and math, is gaining popularity in K-12 settings due to its capacity to enhance 21st-century skills such as adaptability, problem-solving, and creative thinking (National Research Council [NRC], 2015 ). In STEM lessons, students are frequently guided by the engineering design process, which involves identifying problems or technical challenges and creating and developing solutions. Furthermore, higher achievement in STEM education has been linked to increased enrolment in post-secondary STEM fields, offering students greater opportunities to pursue careers in these domains (Merrill & Daugherty, 2010 ). However, STEM activities require dedicated time and the restructuring of integrated curricula, necessitating careful organization of lessons. Recognizing the complexity of developing 21st-century STEM proficiency, schools are not expected to tackle this challenge alone. In addition to regular STEM classes, there exists a diverse range of extended education programs, activities, and out-of-school learning environments (Baran et al., 2016 ; Kalkan & Eroglu, 2017 ; Schweingruber et al., 2014 ). In this paper, out-of-school learning environments, informal learning environments, extended education, and afterschool programs were used synonymously. It is worth noting that the literature lacks a universally accepted definition for out-of-school learning environments, leading to the use of various interchangeable terms (Donnelly et al., 2019 ). Some of these terms include informal learning environments, extended education, afterschool programs, all-day school, extracurricular activities, out-of-school time learning, extended schools, expanded learning, and leisure-time activities. These terms refer to optional programs and clubs offered by schools that exist outside of the standard academic curriculum (Baran et al., 2016 ; Cooper, 2011 ; Kalkan & Eroglu, 2017 ; Schweingruber et al., 2014 ).

Out-of-school learning, in contrast to traditional in-school learning, offers greater flexibility in terms of time and space, as it is not bound by the constraints of the school schedule, national or state standards, and standardized tests (Cooper, 2011 ). Out-of-school learning experiences typically involve collaborative engagement, the use of tools, and immersion in authentic environments, while school environments often emphasize individual performance, independent thinking, symbolic representations, and the acquisition of generalized skills and knowledge (Resnick, 1987 ). They encompass everyday activities such as family discussions, pursuing hobbies, and engaging in daily conversations, as well as designed environments like museums, science centres, and afterschool programs (Civil, 2007 ; Hein, 2009 ). On the other hand, extended education refers to intentionally structured learning and development programs and activities that are not part of regular classes. These programs are typically offered before and after school, as well as at locations outside the school (Bae, 2018 ). As a result, out-of-school learning environments encompass a wide range of experiences, including social, cultural, and technical excursions around the school, field studies at museums, zoos, nature centres, aquariums, and planetariums, project-based learning, sports activities, nature training, and club activities (Civil, 2007 ; Donnelly et al., 2019 ; Hein, 2009 ). At this point, STEM clubs are a specialized type of extracurricular activity that engage students in hands-on projects, experiments, and learning experiences related to scientific, technological, engineering, and mathematical disciplines. STEM Clubs, described as flexible learning environments unconstrained by time or location, offer an effective approach to conducting STEM studies outside of school (Blanchard et al., 2017 ; Cooper, 2011 ; Dabney et al., 2012 ).

Out-of-school learning environments, extended education or afterschool programs, hold tremendous potential for enhancing student learning and providing them with a diverse and enriching educational experience (Robelen, 2011 ). Extensive research supports the notion that these alternative educational programs not only contribute to students' academic growth but also foster their social, emotional, and intellectual development (NRC, 2015 ). Studies have consistently shown that after-school programs play a vital role in boosting students' achievement levels (Casing & Casing, 2024 ; Pastchal-Temple, 2012 ; Shernoff & Vandell, 2007 ), and contributing to positive emotional development, including improved self-esteem, positive attitudes, and enhanced social behaviour (Afterschool Alliance, 2015 ; Durlak & Weissberg, 2007 ; Lauer et al., 2006 ; Little et al., 2008 ). Moreover, engaging in various activities within these programs allows students to develop meaningful connections, expand their social networks, enhance leadership skills (Lipscomb et al., 2017 ), and cultivate cooperation, effective communication, and innovative problem-solving abilities (Mahoney et al., 2007 ).

Implementing STEM activities in out-of-school learning environments not only supports students in making career choices and fostering meaningful learning and interest in science, but also facilitates deep learning experiences (Bybee, 2001 ; Dabney et al., 2012 ; Sahin et al., 2018 ). Furthermore, STEM Clubs enhance students' emotional skills, such as a sense of belonging and peer-to-peer communication, while also fostering 21st-century skills, facilitating the acquisition of current content, and promoting career awareness and interest in STEM professions (Blanchard et al., 2017 ). In summary, engaging in STEM activities through social club activities not only addresses time constraints but also complements formal education and contributes to students' overall development. Hence, STEM Clubs, which are part of extended education, can be defined as dynamic and flexible learning environments that provide an effective approach to conducting STEM studies beyond traditional classroom settings. These clubs offer flexibility in terms of time and location, with intentionally structured programs and activities that take place outside of regular classes. They provide students with unique opportunities to explore and deepen their understanding of STEM subjects through collaborative engagement, hands-on use of tools, and immersive experiences in authentic environments (Bae, 2018 ; Blanchard, et al., 2017 ; Bybee, 2001 ; Cooper, 2011 ; Dabney et al., 2012 ). STEM Clubs have gained immense popularity worldwide, providing students with invaluable opportunities to explore and cultivate their interests and knowledge in these crucial fields (Adams et al., 2014 ; Bell et al., 2009 ). According to America After 3PM, nearly 75% of afterschool program participants, around 5,740,836 children, have access to STEM learning opportunities (Afterschool Alliance, 2015 ).

STEM Clubs as after-school programs come in various forms and provide diverse tutoring and instructional opportunities. For instance, the Boys and Girls Club of America (BGCA) operates in numerous cities across the United States, annually serving 4.73 million students (Boys and Girls Club of America, 2019 ). This program offers students the chance to engage in activities like sports, art, dance, field trips, and addresses the underrepresentation of African Americans in STEM. Another example is the Science Club for Girls (SCFG), established by concerned parents in Cambridge to address gender inequity in math, science, and technology courses and careers. SCFG brings together girls from grades K–7 through free after-school or weekend clubs, science explorations during vacations, and community science fairs, with approximately 800 to 1,000 students participating each year. The primary goal of these clubs is to increase STEM literacy and self-confidence among K–12 girls from underrepresented groups in these fields. More examples can be found in the literature, such as the St. Jude STEM Club (SJSC), where students conducted a 10-week paediatric cancer research project using accurate data (Ayers et al., 2020 ), and After School Matters, based in Chicago, offers project-based learning that enhances students' soft skills and culminates in producing a final project based on their activities (Hirsch, 2011 ).

The Purpose of The Study

The literature on STEM Clubs indicates a diverse range of such clubs located worldwide, catering to different student groups, operating on varying schedules, implementing diverse activities, and employing various strategies, methodologies, experiments, and assessments (Ayers et al., 2020 ; Blanchard et al., 2017 ; Boys and Girls Club of America, 2019 ; Hirsch, 2011 ; Sahin et al., 2018 ). However, it was previously unknown which specific sample groups were most commonly studied, which analytical methods were used frequently, and which results were primarily reported, even though the overall topic of STEM Clubs has gained significant attention. Therefore, organizing and categorizing this expansive body of literature is necessary to gain deeper insights into the current state of knowledge and practices in STEM Clubs. By systematically reviewing and synthesizing the diverse range of studies on this topic, we can develop a clearer understanding of the focus areas, methodologies, and key findings that have emerged from the existing research (Fraenkel et al., 2012 ). At this point, using a content analysis method is appropriate for this purpose because this method is particularly useful for examining trends and patterns in documents (Stemler, 2000 ). Similarly, some previous research on STEM education has conducted content analyses to examine existing studies and construct holistic patterns to understand trends (Bozkurt et al., 2019 ; Chomphuphra et al., 2019 ; Irwanto et al., 2022 ; Li et al., 2020 ; Lin et al., 2019 ; Martín-Páez et al., 2019 ; Noris et al., 2023 ). However, there is a lack of content analysis specifically focused on studies of STEM Clubs in the literature and showing the trends in this topic. Analysing research trends in STEM Clubs can help build upon existing knowledge, identify gaps, explore emerging topics, and highlight successful methodologies and strategies (Fraenkel et al., 2012 ; Noris et al., 2023 ; Stemler, 2000 ). This information can be valuable for researchers, educators, and policymakers to stay up-to-date and make informed decisions regarding curriculum design (Bozkurt et al., 2019 ; Chomphuphra et al., 2019 ; Irwanto et al., 2022 ; Li et al., 2020 ; Lin et al., 2019 ; Martín-Páez et al., 2019 ; Noris et al., 2023 ), the development of effective STEM Club programs, resource allocation, and policy formulation (Blanchard et al., 2017 ; Cooper, 2011 ; Dabney et al., 2012 ). Therefore, the identification of research trends in STEM Clubs was the aim of this study.

To identify research trends, studies commonly analysed documents by considering the dimensions of articles such as keywords, publishing years, research designs, purposes, sample levels, sample sizes, data collection tools, data analysis methods, and findings (Bozkurt et al., 2019 ; Chomphuphra et al., 2019 ; Irwanto et al., 2022 ; Li et al., 2020 ; Sozbilir et al., 2012 ). Using these dimensions as a framework is a useful and common approach in content analysis because this framework allows researchers to systematically examine the key aspects of existing studies and uncover patterns, relationships, and trends within the research data (Sozbilir et al., 2012 ). Hence, since the aim of this study is to identify and analyse research trends in STEM Clubs, it focused on publishing years, keywords, research designs, purposes, sample levels, sample sizes, data collection tools, data analysis methods, and findings of the studies on STEM Clubs.

As a conclusion, the main problem of this study is “What are the characteristics of the studies on STEM Clubs?”. The following sub-questions are addressed in this study:

What is the distribution of studies on STEM Clubs by year?

What are the frequently used keywords in studies on STEM Clubs?

What are the commonly employed research designs in studies on STEM Clubs?

What are the typical purposes explored in studies on STEM Clubs?

What are the commonly observed sample levels in studies on STEM Clubs?

What are the commonly observed sample sizes in studies on STEM Clubs?

What are the commonly utilized data collection tools in studies on STEM Clubs?

What are the commonly utilized data analysis methods in studies on STEM Clubs?

What are the typical durations reported in studies on STEM Clubs?

What are the commonly reported findings in studies on STEM Clubs?

In this study, the descriptive content analysis research method was employed, which allows for a systematic and objective examination of the content within articles, and description of the general trends and research results in a particular subject matter (Lin et al., 2014 ; Suri & Clarke, 2009 ; Sozbilir et al., 2012 ; Stemler, 2000 ). Given the aim of examining research trends in STEM Clubs, the utilization of this method was appropriate, as it provides a structured approach to identify patterns and trends (Gay et al., 2012 ). To implement the content analysis method, this study followed the three main phases proposed by Elo and Kyngäs ( 2008 ): preparation, organizing, and reporting. In the preparation phase, the unit of analysis, such as a word or theme, is selected as the starting point. So, in this study, the topic of STEM Clubs was carefully selected. During the organizing process, the researcher strives to make sense of the data and to learn "what is going on" and obtain a sense of the whole. So, in this study, during the analysis process, the content analysis framework (sample levels, sample sizes, data collection tools, research designs, etc.) was used to question the collected studies. Finally, in the reporting phase, the analyses are presented in a meaningful and coherent manner. So, the analyses were presented meaningfully with visual representations such as tables, graphs, etc. By adopting the content analysis research method and following the suggested phases, this study aimed to gain insights into research trends in STEM Clubs, identify recurring themes, and provide a comprehensive analysis of the collected data.

Search and Selection Process

The online databases ERIC and Web of Science were searched using keywords derived from a database thesaurus. These databases were chosen because of their widespread recognition and respect in the fields of education and academic research, and they offer a substantial amount of high-quality, peer-reviewed literature. The search process involved several steps. Firstly, titles, abstracts, and keywords were searched using Boolean operators for the keywords "STEM Clubs," "STEAM Clubs," "science-technology-engineering-mathematics clubs," "after school STEM program" and "extracurricular STEM activities" in the databases (criterion-1). Secondly, studies were collected beginning from November to the end of December 2023. So, the studies published until the end of December 2023 were included in the search, without a specific starting date restriction (criterion-2). Thirdly, the search was limited to scientific journal articles, book chapters, proceedings, and theses, excluding publications such as practices, letters to editors, corrections, and (guest) editorials (criterion-3). Fourthly, studies published in languages other than English were excluded, focusing exclusively on English language publications (criterion-4). Fifthly, duplicate articles found in both databases were identified and removed. Next, the author read the contents of all the studies, including those without full articles, with a particular focus on the abstract sections. After that, studies related to after school program and extracurricular activities that did not specifically involve the terms STEM or clubs were excluded, even though “extracurricular STEM activities” and “after school STEM program” were used in the search process, and there were studies related to after school program or extracurricular activities but not STEM (criterion-5). Additionally, studies conducted in formal and informal settings within STEM clubs were included, while studies conducted in settings such as museums or trips were excluded (criterion-6). Because STEM Clubs are a subset of informal STEM education settings, which also include museums and field trips, the main focus of this study is to show the trends specifically related to STEM Clubs. Moreover, studies focusing solely on technology without incorporating other STEM components were also excluded (criterion-7). Finally, 56 publications that met the inclusion and extraction criteria were identified. These publications comprised two dissertations, seven proceedings, and 47 articles from 36 different journals. By applying these criteria, the search process aimed to ensure the inclusion of relevant studies while excluding those that did not meet the specified criteria as shown in Fig.  1 .

Flowchart of article process selection

Data Analysing Process

Two different approaches were followed in the content analysis process of this study. In the first part, deductive content analysis was used, and a priori coding was conducted as the categories were established prior to the analysis. The categorization matrix was created based on the Paper Classification Form (PCF) developed by Sozbilir et al. ( 2012 ). The coding scheme devised consisted of eight classification groups for the sections of publication years, keywords, research designs, sample levels, sample sizes, data collection tools, data analysis methods, and durations, with sub-categories for each section. For example, under the research designs section, the sub-categories included qualitative and quantitative methods, case study, design-case study, comparative-case study, ethnographic study, phenomenological study, survey study, experimental study, mixed and longitudinal study, and literature review study. These sub-categories were identified prior to the analysis. Coding was then applied to the data using spreadsheets in the Excel program, based on the categorization matrix. Frequencies for the codes and categories created were calculated and presented in the findings section with tables. Line charts were used for the publication years section, while word clouds, which visually represent word frequency, were used for the keywords section. Word clouds display the most frequently used words in different sizes and colours based on their frequencies (DePaolo & Wilkinson, 2014 ). So, in this part, the analysis was certain since the studies mostly provided related information in their contents.

In the second part, open coding and the creation of categories and abstraction phases were followed for the purposes and findings sections. Firstly, the stated purposes and findings of the studies were written as text. The written text was then carefully reviewed, and any necessary terms were written down in the margins to describe all aspects of the content. Following this open coding, the lists of categories were grouped under higher order headings, taking into consideration their similarities or dissimilarities. Each category was named using content-characteristic words. The abstraction process was repeated to the extent that was reasonable and possible. In this coding process, two individuals independently reviewed ten studies, considering the coding scheme for the first part and conducting open coding for the second part. They then compared their notes and resolved any differences that emerged during their initial checklists. Inter-rater reliability was calculated as 0.84 using Cohen's kappa analysis. Once coding reliability was ensured, the remaining articles were independently coded by the author. After completing the coding process, consensus was reached through discussions regarding any disagreements among the researchers regarding the codes, as well as the codes and categories constructed for the purpose and findings sections. At this point, there were mostly agreements in the coding process since the studies had already clearly stated their key characteristics, such as research design, sample size, sample level, and data collection tools. Additionally, when coding the studies' stated purposes and results, the researchers closely referred to the original sentences in the studies, which led to a high level of consistency in the coded content between the two raters.

Studies related to the STEM Clubs were initially conducted in 2009 (Fig.  2 ). The noticeable increase in the number of studies conducted each year is remarkable. It can be seen that the majority of the 47 articles that were examined (56 articles) were published after 2015, despite a decrease in the year 2018. Additionally, it was observed that the articles were most frequently published (8) in the years 2019 and 2022, least frequently (1) in the years 2009, 2010, and 2014, and there were no publications in 2012.

Number of articles by years

Word clouds were utilized to present the most frequently used keywords in the articles, as shown in Fig.  3 . However, due to the lack of reported keywords in the ERIC database, only 30 articles were included for these analyses. The keywords that exist in these studies were represented in a word cloud in Fig.  3 . The most frequently appearing keywords, such as "STEM," "education" and "learning" were identified. Additionally, by using a content analysis method, these keywords were categorized into six different groups: disciplines, technological concepts, academic community, learning experiences, core elements of education, and psychosocial factors (variables) in Table  1 .

Word cloud of the keywords used in articles

The purposes of the identified studies identified were classified into six main themes: “effects of participation in STEM Clubs on” (25), “evolution of a sample program for STEM Clubs and its implementation” (25), “examination of” (11), “identification of” (3), “comparison of in-school and out-school STEM experiences” (2) and “others” (6). Table 2 presents the distribution of the articles’ purposes based on the classification regarding these themes. Therefore, it can be seen that purposes of “effects of participation in STEM Clubs on,” and “evolution of a sample program for STEM Clubs and its implementation” were given the highest and equal consideration, while the purposes related to "identification of" (3) and "comparison of in-school and out-of-school STEM experiences" (2) were given the least consideration among them.

Within the theme of "effects of participation in STEM Clubs on" there are 11 categories. The aims of the studies in this section are to examine the effect of participation in STEM Clubs on various aspects such as attitudes towards STEM disciplines or career paths, STEM major choice/career aspiration, achievement in math, science, STEM disciplines, or content knowledge, perception of scientists, strategies used, value of clubs, STEM career paths, enjoyment of physics, use of complex and scientific language, interest in STEM, creativity, critical thinking about STEM texts, images of mathematics, or climate-change beliefs/literacy. It is evident that the majority of research in this section focuses on the effects of participation in STEM Clubs on STEM major choice/career aspiration (5), achievement (4), perception of something (4), and interest in STEM (3).

Within the theme of "evolution of a sample program for STEM Clubs and its implementation" there are three categories: development of program/curriculum/activity (14), identification of program's challenges and limitations (3), and implementation of program/activity (8). The studies in this section aim to develop a sample program for STEM Clubs and describe its implementation. It can be seen that the most preferred purpose among them is the development of program/curriculum/activity (14), while the least preferred purpose is the identification of program's challenges and limitations (3). In addition, studies that focus on the development of the program, curriculum, or activity were classified under the "general" category (10). Sub-categories were created for studies specifically expressing the development of the program with a focus on a particular area, such as the maker movement or Arduino-assisted robotics and coding. Similarly, studies that explicitly mentioned the development of the program based on presented ideas and experiences formed another sub-category. Furthermore, the category related to the implementation of program/activity was divided into eight sub-categories, each indicating the specific centre of implementation, such as problem-based learning-centred and representation of blacks-centred.

The theme of "examination of" refers to studies that aim to examine certain aspects, such as the experiences and perceptions of students (7) and the factors influencing specific subjects (4). Studies focusing on examining the experiences and perceptions of students were labelled as "general" (4), while studies exploring their experiences and perceptions regarding specific content, such as influences and challenges to participation in STEM clubs (2) and assessment (1), were labelled accordingly. Additionally, studies that focused on examining factors affecting the choice of STEM majors (2), participation in STEM clubs (1), and motivation to develop interest in STEM (1) were categorized in line with their respective focuses. As shown in Table  2 , it is evident that studies focusing on examining the experiences and perceptions of students (7) were more frequently conducted compared to studies focusing on examining the factors affecting specific subjects (4).

The theme of "identification of" refers to studies that aim to identify certain aspects, such as the types of attitudinal effects (1), types of changes in affect toward engineering (1), and non-academic skills (1). Additionally, the theme of "comparison of in-school and out-of-school STEM experiences" (2) refers to studies that aim to compare STEM experiences within school and outside of school. Lastly, studies that did not fit into the aforementioned categories were included in the "others" theme (6) as no clear connection could be identified among them.

Research Designs

The research designs employed in the examined articles were identified as follows: qualitative methods (36), including case study (20), design-case study (6), comparative-case study (4), ethnographic study (2), phenomenological study (2), and survey study (2); quantitative methods (7), including survey study (4) and experimental study (3); mixed methods and longitudinal studies (10); and literature review (3), as illustrated in Table  3 . It can be observed that among these methods, case study was the most commonly utilized. Furthermore, it is evident that quantitative methods (7) and literature reviews (3) were employed less frequently compared to qualitative (36) and mixed methods (10). Additionally, survey studies were utilized in both quantitative and qualitative studies.

Sample Levels

The frequencies and percentages of sample levels in the examined articles are presented in Table  4 . The studies involved participants at different educational levels, including elementary school (8), middle school (23), high school (14), pre-service teachers or undergraduate students (6), teachers (4), parents (3), and others (1). It is apparent that middle school students (23) were the most commonly utilized sample among them, while high school students (14) were more frequently chosen compared to elementary school students (8). It should be noted that while grade levels were specified for both elementary and middle school students, separate grade levels were not identified for high school students in these studies. Additionally, studies that involved mixed groups were labelled as 3-5th and 6-8th grades. However, when the mixed groups included participants from different educational levels such as elementary, middle, or high school, teachers, parents, etc., they were counted as separate levels. Furthermore, the studies conducted with participants such as pre-service teachers, undergraduates, teachers, and parents were less frequently employed compared to K-12 students.

Sample Sizes

The frequencies of sample sizes in the examined articles are presented in Table  5 . It was observed that in 15 studies, the number of sample sizes was not provided. The intervals for the sample size were not equally separated; instead, they were arranged with intervals of 5, 10, 50, and 100. This choice was made to allow for a more detailed analysis of smaller samples, as smaller intervals can provide a more granular examination of data instead of cumulative amounts. The analysis reveals that the studies primarily prioritized sample groups with 11–15 (f:8) participants, followed by groups of 16–20 (f:4) and 201–250 (f:4). Additionally, it is evident that sample sizes of 6–10, 21–25, 41–50, 50–100, and more than 2000 (f:1) were the least commonly studied.

Data Collection Tools

The frequencies and percentages of data collection tools in the examined articles are presented in Table  6 . The analysis reveals that the studies primarily employed survey or questionnaires (31.6%) and observations (30.5%) as data collection methods, followed by interviews (15.8%), documents (13.7%), tests (4.2%), and field notes (4.2%). Regarding survey/questionnaires, Likert-type scales (f:23) were more commonly employed compared to open-ended questions (f:7). Tests were predominantly used as achievement tests (f:2) and assessments (f:2), representing the least preferred data collection tools. Furthermore, the table illustrates that multiple data collection tools were frequently employed, as the total number of tools (95) is nearly twice the number of studies (56).

Data Analysing Methods

The frequencies and percentages of data analysing methods in the examined articles are presented in Table  7 . The table reveals that the studies predominantly employed descriptive analysis (f:33, 41.25%), followed by inferential statistics (f:16, 20%), descriptive statistics (f:15, 18.75%), content analysis (f:14, 17.5%), and the constant-comparative method (f:2, 2.5%). It is notable that qualitative methods (f:49, 61.25%) were preferred more frequently than quantitative methods (f:31, 38.75%) in the examined studies related to STEM Clubs. Within the qualitative methods, descriptive analysis (f:33) was utilized nearly twice as often as content analysis (f:14), while within the quantitative methods, descriptive statistics (f:15) and inferential statistics (f:16), including t-tests, ANOVA, regression, and other methods, were used with comparable frequency.

The durations of STEM Clubs in the examined studies are presented in Table  8 . Based on the analysis, there are more studies (f:37) that do not state the duration of STEM Clubs than studies (f:19) that do provide information on the durations. Additionally, among the studies that do state the durations, there is no common period of time for STEM Clubs, as they were implemented for varying numbers of weeks and sessions, with session durations ranging from several minutes. Therefore, it can be observed that STEM Clubs were conducted over the course of 3 semesters (academic year and summer), 5 months, 2 to 16 weeks, with session durations ranging from 60 to 120 min. Furthermore, the durations of "3 semesters," "10 weeks with 90-min sessions per week," and "unknown weeks with 60-min sessions per week" were used more than once in the studies.

The content analysis of the findings of the identified examined articles are presented by their frequencies in Table  9 . Although the studies cover a diverse range of topics, the analysis indicates that the results can be broadly classified into three themes, namely, the "development of or increase in certain aspects" (f:68), "design of STEM Clubs" (f:17), and "identification of various aspects" (f:16). Based on the analysis, the findings in the studies are associated with the development of certain aspects such as skills or the increase in specific outcomes like academic achievement. Furthermore, the studies explore the design of STEM Clubs through the description of specific cases, such as sample implementations and challenges. Additionally, the studies focus on the identification of various aspects, such as factors and perceptions.

It is evident from the findings that the studies predominantly yield results related to the development of or increase in certain aspects (f:68). Within this theme, the most commonly observed result is the development of STEM or academic achievement or STEM competency (f:11). This is followed by an increase in STEM major choice or career aspiration (f:9), an increase in engagement or participation in STEM clubs (f:5), the development of identity including STEM, science, engineering, under-representative groups (f:5), the development of interest in STEM (f:4), an increase in enjoyment (f:4), and the development of collaboration, leadership, or communication skills (f:4). Furthermore, it can be observed that there are some results, such as the development of critical thinking, perseverance and the teachers’ profession, that were yielded less frequently (f:1). The results of 16 studies were found with a frequency of 1.

Within the design of STEM Clubs, the sample implementation or design model for different purposes such as the usage of robotic program or students with disabilities (f:7), design principles or ideas for STEM clubs, activities or curriculum (f:4), challenges or factors effecting STEM Clubs success and sustainability (f:3) were presented as a result. Additionally, the comparison was made between in-school and out-of-school learning environments (f:3), highlighting the contradictions of STEM clubs and science classes, as well as the differences in STEM activities and continues-discontinues learning experiences in mathematics. Within the identification of various aspects, the most commonly gathered result was the identification of factors affecting participation or motivation to STEM clubs (f:5). This was followed by the identification of barriers to participation (f:2). The identification of other aspects, such as parents' roles and perspectives on STEM, was comparatively less frequent.

Considering the wide variety of STEM Clubs found in different regions around the world, this study aimed to investigate the current state of research on STEM Clubs. It is not surprising to observe an increase in the number of studies conducted on STEM Clubs over the years. This can be attributed to the overall growth in research on STEM education (Zhan et al., 2022 ), as STEM education often includes activities and after-school programs as integral components (Blanchard et al., 2017 ). Identifying relevant keywords and incorporating them into a search strategy is crucial for conducting a comprehensive and rigorous systematic review (Corrin et al., 2022 ). To gain a broader understanding of keyword usage in the context of STEM Clubs, a word cloud analysis was performed (McNaught & Lam, 2010 ). Additionally, based on the content analysis method, six different categories for keywords were immerged: disciplines, technological concepts, academic community, learning experiences, core elements of education, and psychosocial factors (variables). The analysis revealed that the keyword "STEM" was used most frequently in the studies examined. This may be because authors want their studies to be easily found and widely searchable by others, so they use "STEM" as a general term for their studies (Corrin et al., 2022 ). Similarly, the frequent use of keywords like "education" and "learning" from the "core elements of education" category could be attributed to authors' desire to use broad, searchable terms to make their studies more discoverable (Corrin et al., 2022 ). Additionally, it was observed that from the STEM components, only "science" and "engineering" were used as keywords, while "mathematics" and "technology" were not present. This finding aligns with claims in the literature that mathematics is often underemphasized in STEM integration (Fitzallen, 2015 ; Maass et al., 2019 ; Stohlmann, 2018 ). Although the specific term "technology" did not appear in the word cloud, technology-related keywords such as "arduino," "robots," "coding," and "innovative" were present. Furthermore, the analysis revealed that authors preferred to use keywords related to their sample populations, such as "middle (school students)," "elementary (students)," "high school students," or "teachers." Additionally, keywords describing learning experiences, such as "extracurricular," "informal," "afterschool," "out-of-school," "social," "clubs," and "practice" were commonly used. This preference may stem from the fact that STEM clubs are often part of informal learning environments, out-of-school programs, or afterschool activities, and these concepts are closely related to each other (Baran et al., 2016 ; Cooper, 2011 ; Kalkan & Eroglu, 2017 ; Schweingruber et al., 2014 ). Moreover, the analysis showed that keywords related to psychosocial factors (variables), such as "disabilities," "skills," "interest," "attainment," "enactment," "expectancy-value," "self-efficacy," "engagement," "motivation," "career," "gender," "cognitive," and "identity" were also prevalent. This suggests that the articles investigated the effects of STEM club practices on these psychosocial variables. To sum up, by using these keywords, researchers can gain valuable insights and effectively search for relevant articles related to STEM clubs, enabling them to locate appropriate resources for their research (Corrin et al., 2022 ).

The popularity of case studies as a research design, based on the analysis, can be attributed to the fact that studies on STEM Clubs were conducted in diverse learning environments, highlighting sample implementation designs (Adams et al., 2014 ; Bell et al., 2009 ; Robelen, 2011 ). At this point, case studies offer the opportunity to present practical applications and real-world examples (Hamilton & Corbett-Whittier, 2012 ), which is highly valuable in the context of STEM Clubs. Additionally, the observation that quantitative methods were not as commonly utilized as qualitative methods in studies related to STEM Clubs contrasts with the predominant reliance on quantitative methods in STEM education research (Aslam et al., 2022 ; Irwanto et al., 2022 ; Lin et al., 2019 ). This suggests a lack of quantitative studies specifically focused on STEM Clubs, indicating a need for more research in this area employing quantitative approaches. Therefore, it is important to prioritize and conduct additional quantitative studies to further enhance our understanding of STEM Clubs and their impact. In studies on STEM Club, there is a higher frequency of research involving K-12 students, particularly middle school students, parallel to some studies on literature (Aslam et al., 2022 ), compared to other groups such as pre-service teachers, undergraduate students, teachers, and parents. This can be attributed to the fact that STEM Clubs are designed for K-12 students, and middle school is a crucial period for introducing them to STEM concepts and careers. Middle school students are developmentally ready for hands-on and inquiry-based learning, commonly used in STEM education. Additionally, time constraints, especially for high school students preparing for university, may limit their involvement in extensive STEM activities. Furthermore, STEM Clubs were primarily employed with sample groups ranging from 11–15, 16–20, and 201–250 participants. The preference for 11–20 participants, rather than less than 10, may be attributed to the collaborative nature of STEM activities, which often require a larger team for effective teamwork and group dynamics (Magaji et al., 2022 ). Utilizing small groups as samples can result in the case study research design being the most frequently employed approach due to its compatibility with smaller sample sizes. On the other hand, the inclusion of larger groups (201–250) is suitable for survey studies, as this number can represent the total student population attending STEM Clubs throughout a semester with multiple sessions (Boys & Girls Club of America, 2019 ).

According to studies on STEM Clubs, surveys or questionnaires and observations were predominantly used as data collection methods. This preference can be attributed to the fact that surveys or questionnaires allow researchers to gather data on diverse aspects, including students' attitudes, perceptions, and experiences related to STEM Clubs, facilitating generalization and comparison (McLafferty, 2016 ). Furthermore, observations were frequently employed because they can offer a deeper understanding of the lived experiences and actual practices within STEM Clubs (Baker, 2006 ). Along with data collection tools, descriptive analysis was predominantly utilized in studies on STEM Clubs, with quantitative methods including descriptive statistics and inferential statistics being used to a similar extent. The preference for descriptive analysis may arise from its effectiveness in describing activities, experiences, and practices within STEM Clubs. Given the predominance of case study research in the analysed studies, it is not surprising to observe a high frequency of descriptive statistics in the findings. On the other hand, the extensive use of quantitative analysing methods can be attributed to the need for statistical analysis of surveys and questionnaires (Young, 2015 ). Consequently, future studies on STEM Clubs could benefit from considering the use of tests and field notes as additional data collection tools, along with surveys, observations and interviews. Additionally, the development of tests specifically designed to assess aspects related to STEM could provide valuable insights (Capraro & Corlu, 2013 ; Grangeat et al., 2021 ). Moreover, increasing the utilization of content analysis and constant comparative analysis methods could further enhance the depth and richness of data analysis in STEM Club research (White & Marsh, 2006 ). In the studies on STEM Clubs, the duration and scheduling of the clubs varied considerably. While there was no common period of time for STEM Clubs, they were implemented for different numbers of weeks and sessions, with session durations ranging from several minutes to 60 to 120 min. However, it was observed that STEM Clubs were predominantly conducted over the course of three semesters, including the academic year and summer, or for durations of 2 to 16 weeks. This scheduling pattern can be attributed to the fact that STEM Clubs were often implemented as after-school programs, and they were designed to align with the academic semesters and summer school periods to effectively reach students. Additionally, the number of weeks in these studies may have been arranged according to the duration of academic semesters, although some studies were conducted for less than a semester (Gutierrez, 2016 ). The most common use of multiple sessions with a time range of 60 to 120 min can be attributed to the nature of the activities involved in STEM Clubs. These activities often require more time than regular class hours, and splitting them into separate sessions allows students to effectively concentrate on their work and engage in more in-depth learning experiences (Vennix et al., 2017 ).

The purposes of the studies on STEM Clubs were mostly related to effects of participation in STEM Clubs on various aspects such as attitudes towards STEM disciplines or career paths, STEM major choice/career aspiration, achievement etc., evolution of a sample program for STEM Clubs and its implementation including the development of program/activity, identification of program's challenges and limitations, and implementation of it, followed by the examination of certain aspects such as the experiences and perceptions of students and the factors influencing specific subjects, identification of such as the types of attitudinal effects and non-academic skills, and comparison of in-school and out-school STEM experiences. Therefore, the results of the studies parallel to the purposes were mostly related to development of or increase in certain aspects such as STEM or academic achievement or STEM competency STEM major choice or career aspiration engagement or participation in STEM Clubs, identity, interest in STEM, enjoyment, collaboration, communication skills, critical thinking, the design of STEM Clubs including the sample implementation or design model for different purposes such as the usage of robotic program or students with disabilities, design principles or ideas for STEM clubs or activities, challenges or factors effecting STEM Clubs success and sustainability, and the comparison between in-school and out-of-school learning environments. Also, they are related to the identification of various aspects such as factors affecting participation or motivation to STEM clubs, barriers to participation. At this point, it is evident that these identified categories align with the findings of studies in the literature. These studies claim that after-school programs, such as STEM Clubs, have positive impacts on students' achievement levels (NRC, 2015 ; Kazu & Kurtoglu Yalcin, 2021 ; Shernoff & Vandell, 2007 ), communication, and innovative problem-solving abilities (Mahoney et al., 2007 ), leadership skills (Lipscomb et al., 2017 ), career decision-making (Bybee, 2001 ; Dabney et al., 2012 ; Sahin et al., 2018 ; Tai et al., 2006 ), creativity (Wan et al., 2023 ), 21st-century skills (Hirsch, 2011 ; Zeng et al., 2018 ), interest in STEM professions (Blanchard et al., 2017 ; Chittum et al., 2017 ; Wang et al., 2011 ), and knowledge in STEM fields (Adams et al., 2014 ; Bell et al., 2009 ). Furthermore, it can be inferred that the studies on STEM Clubs paid significant attention to the design descriptions of programs or activities (Nation et al., 2019 ). This may be because there is a need for studies that focus on designing program models for different cases (Calabrese Barton & Tan, 2018 ; Estrada et al., 2016 ). These studies can serve as examples and provide guidance for the development of STEM clubs in various settings. By creating sample models, researchers can contribute to the improvement and expansion of STEM clubs across different environments (Cakir & Guven, 2019 ; Estrada et al., 2016 ).

In conclusion, as the studies on the trends in STEM education (Bozkurt et al., 2019 ; Chomphuphra et al., 2019 ; Irwanto et al., 2022 ; Li et al., 2020 ; Lin et al., 2019 ; Martín-Páez et al., 2019 ; Noris et al., 2023 ), the analysis of prevailing research trends specifically in STEM Clubs, which are implemented in diverse environments with varying methods and purposes, can provide a comprehensive understanding of these clubs as a whole.

It can also serve as a valuable resource for guiding future investigations in this field. By identifying common approaches and identifying gaps in methods and results, a holistic perspective on STEM Clubs can be achieved, leading to a more informed and targeted direction for future research endeavours.

Recommendations

Future research on STEM Clubs should consider the trends identified in the study and address methodological gaps. For instance, there is a lack of research in this area that employs quantitative approaches. Therefore, it is important for future studies to incorporate quantitative methods to enhance the understanding of STEM Clubs and their impact. This includes exploring underrepresented populations, investigating the long-term impacts of STEM Clubs, and examining the effectiveness of specific pedagogical approaches or interventions within these clubs. Researchers should conduct an analysis to identify common approaches used in STEM Clubs across different settings. This analysis can help uncover effective strategies, best practices, and successful models that can be replicated or adapted in various contexts. By undertaking these efforts, researchers can contribute to a more comprehensive understanding of STEM Clubs, leading to advancements in the field of STEM education.

Limitations

It is important to consider the limitations of the study when interpreting its findings. The study's findings are based on the literature selected from two databases, which may introduce biases and limitations. Additionally, the study's findings are constrained by the timeframe of the literature review, and new studies may have emerged since the cut-off date, potentially impacting the representation and generalizability of the research trends identified. Another limitation lies in the construction of categories during the coding process. The coding scheme used may not have fully captured or represented all relevant terms or concepts. Some relevant terms may have been inadequately represented or identified using different words or phrases, potentially introducing limitations to the analysis. While efforts were made to ensure validity and reliability, there is still a possibility of unintended biases or inconsistencies in the categorization process.

Data Availability

The datasets (documents, excel analysis) utilized in this article are available upon request from the corresponding author.

Adams, J. D., Gupta, P., & Cotumaccio, A. (2014). A museum program enhances girls’ STEM interest, motivation and persistence. Afterschool Matters, 12 , 14–20.

Google Scholar  

Afterschool Alliance (2015).  Full STEM ahead: Afterschool programs step up as key partners in STEM education . Retrieved November 2023 from http://www.afterschoolalliance.org/AA3PM/

Aslam, S., Saleem, A., Kennedy, T. J., Kumar, T., Parveen, K., Akram, H., & Zhang, B. (2022). Identifying the research and trends in STEM education in Pakistan: A systematic literature review. SAGE Open, 12 (3), 21582440221118544.

Article   Google Scholar  

Ayers, K. A., Wade-Jaimes, K., Wang, L., Pennella, R. A., & Pounds, S. B. (2020). The St. Jude STEM clubs: An after-school STEM club for upper elementary school students in Memphis, TN. Journal of STEM Outreach, 3 (1), 1–26. https://doi.org/10.15695/jstem/v3i1.13

Bae, S. H. (2018). Concepts, models, and research of extended education. International Journal for Research on Extended Education, 6 (2), 153–165.

Baker, L. (2006). Observation: A complex research method. Library Trends, 55 (1), 171–189.

Baran, E., Bilici, S. C., Mesutoglu, C., & Ocak, C. (2016). Moving STEM beyond schools: Students’ perceptions about an out-of-school STEM education program. International Journal of Education in Mathematics, Science and Technology, 4 (1), 9–19. https://doi.org/10.18404/ijemst.71338

Bell, P., Lewenstein, B., Shouse, A. W., & Feder, M. A. (2009). Learning science in informal environments: People, places and pursuits . National Research Council of the National Academies.

Blanchard, M. R., Hoyle, K. S., & Gutierrez, K. S. (2017). How to start a STEM club. Science Scope, 41 (3), 88–94.

Boys and Girls Club of America (2019). Annual report . Retrieved November 2023 from https://www.bgca.org/about-us/annual-report

Bozkurt, A., Ucar, H., Durak, G., & Idin, S. (2019). The current state of the art in STEM research: A systematic review study. Cypriot Journal of Educational Science,  14 (3), 374–383. https://doi.org/10.18844/cjes.v14i3.3447

Bybee, R. W. (2001). Achieving scientific literacy: Strategies for ensuring that free choice science education complements national formal science education efforts. In J. H. Falk (Ed.), Free choice education: How we learn science outside of school (pp. 44–63). Teachers College Press.

Cakir, N. K., & Guven, G. (2019). Arduino-assisted robotic and coding applications in science teaching: Pulsimeter activity in compliance with the 5E learning model. Science Activities, 56 (2), 42–51.

Calabrese Barton, A., & Tan, E. (2018). A longitudinal study of equity-oriented STEM-rich making among youth from historically marginalized communities. American Educational Research Journal, 55 (4), 761–800.

Capraro, R. M., & Corlu, M. S. (2013). Changing views on assessment for STEM project-based learning. In R. M. Capraro, M. M. Capraro, & J. R. Morgan (Eds.),  STEM project-based learning (pp. 109–118). Brill.

Chapter   Google Scholar  

Casing, P. I., & Casing, L. M. R. (2024). Fostering students’ mathematics achievement through after-school program in the 21st century. Online Submission, 12 (3), 118–122.

Chittum, J. R., Jones, B. D., Akalin, S., & Schram, A. B. (2017). The effects of an afterschool STEM program on students’ motivation and engagement. International Journal of STEM Education, 4 , 1–16.

Chomphuphra, P., Chaipidech, P., & Yuenyong, C. (2019). Trends and research issues of STEM education: A review of academic publications from 2007 to 2017. Journal of Physics: Conference Series, 1340 (1), 012069.

Civil, M. (2007). Building on community knowledge: An avenue to equity in mathematics education. In N. S. Nasir & P. Cobb (Eds.), Improving access to mathematics: Diversity and equity in the classroom (pp. 105–117). Teachers College.

Cooper, S. (2011). An exploration of the potential for mathematical experiences in informal learning environments. Visitor Studies, 14 (1), 48–65. https://doi.org/10.1080/10645578.2011.557628

Corrin, L., Thompson, K., Hwang, G. J., & Lodge, J. M. (2022). The importance of choosing the right keywords for educational technology publications. Australasian Journal of Educational Technology, 38 (2), 1–8.

Dabney, K. P., Tai, R. H., Almarode, J. T., Miller-Friedmann, J. L., Sonnert, G., Sadler, P. M., & Hazari, Z. (2012). Out-of-school time science activities and their association with a career interest in STEM. International Journal of Science Education, Part B, 2 (1), 63–79. https://doi.org/10.1080/21548455.2011.629455

DePaolo, C. A., & Wilkinson, K. (2014). Get your head into the clouds: Using word clouds for analyzing qualitative assessment data. TechTrends, 58 , 38–44. https://doi.org/10.1007/s11528-014-0750-9

Donnelly, M., ažetić, P., Sandoval-Hernandez, A., Kumar, K., & Whewall, S. (2019). An unequal playing field-extra-curricular activities, soft skills and social mobility . Social Mobility Commission.

Durlak, J. A., & Weissberg, R. P. (2007). The impact of after-school programs that promote personal and social skills. Collaborative for Academic, Social, and Emotional Learning (CASEL). Retrieved from www.casel.org

Elo, S., & Kyngäs, H. (2008). The qualitative content analysis process. Journal of Advanced Nursing, 62 (1), 107–115.

Estrada, M., Burnett, M., Campbell, A. G., Campbell, P. B., Denetclaw, W. F., Gutiérrez, C. G., Hurtado, S., John, G. H., Matsui, J., McGee, R., Okpodu, C. M, Robinson, T. J., Summers, M. F., Werner-Washburne, M., & Zavala, M. (2016). Improving underrepresented minority student persistence in STEM. CBE—Life Sciences Education , 15 (3), es5.

Fitzallen, N. (2015). STEM Education: What does mathematics have to offer? In M. Marshman, V. Geiger, & A. Bennison (Eds.), Mathematics education in the margins. Proceedings of The 38th Annual Conference of the Mathematics Education Research Group of Australasia (pp. 237–244). MERGA.

Fraenkel, J., Wallen, N., & Hyun, H. (2012). How to design and evaluate research in education (10th ed.). McGraw-Hill Education.

Gay, L. R., Mills, G. E., & Airasian, P. W. (2012). Educational research: competencies for analysis and applications (10th ed.). Pearson.

Grangeat, M., Harrison, C., & Dolin, J. (2021). Exploring assessment in STEM inquiry learning classrooms. International Journal of Science Education, 43 (3), 345–361.

Gutierrez, K. S. (2016). Investigating the climate change beliefs, knowledge, behaviors, and cultural worldviews of rural middle school students and their families during an out-of-school intervention: A mixed-methods study (Publication No. 11320) [Doctoral dissertation, North Carolina State University]. NC State University Libraries.

Hamilton, L., & Corbett-Whittier, C. (2012). Using case study in education research . Sage.

Hein, G. (2009). Learning science in informal environments: People, places, and pursuits. Museums & Social Issues, 4 (1), 113–124.

Hirsch, B. (2011). Learning and development in after-school programs. Phi Delta Kappan, 92 (5), 66–69. https://doi.org/10.1177/2F003172171109200516

Irwanto, I., Saputro, A. D., Widiyanti, W., Ramadhan, M. F., & Lukman, I. R. (2022). Research trends in STEM education from 2011 to 2020: A systematic review of publications in selected journals. International Journal of Interactive Mobile Technologies (iJIM), 16 (5), 19–32.

Kalkan, C., & Eroglu, S. (2017). Designing sample activities based on STEM materials for gifted/talented students in support education rooms. Journal of Gifted Education and Creativity , 4 (2), 36–46. Retrieved November 2023 from  https://dergipark.org.tr/tr/pub/jgedc/issue/38702/449432

Kazu, I. Y., & Kurtoglu Yalcin, C. (2021). The effect of STEM education on academic performance: A meta-analysis study. Turkish Online Journal of Educational Technology-TOJET, 20 (4), 101–116.

Lauer, P. A., Akiba, M., Wilkerson, S. B., Apthorp, H. S., Snow, D., & Martin-Glenn, M. L. (2006). Out-of-school-time programs: A meta-analysis of effects for at-risk students. Review of Educa- Tional Research, 76 (2), 275–313.

Li, Y., Wang, K., Xiao, Y., & Froyd, J. E. (2020). Research and trends in STEM education: A systematic review of journal publications. International Journal of STEM Education, 7 (1), 1–16.

Lin, T. C., Lin, T. J., & Tsai, C. C. (2014). Research trends in science education from 2008 to 2012: A systematic content analysis of publications in selected journals. International Journal of Science Education, 36 (8), 1346–1372.

Lin, T. J., Lin, T. C., Potvin, P., & Tsai, C. C. (2019). Research trends in science education from 2013 to 2017: A systematic content analysis of publications in selected journals. International Journal of Science Education, 41 (3), 367–387.

Lipscomb, S., Haimson, J., Liu, A. Y., Burghardt, J., Johnson, D. R., & Thurlow, M. L. (2017). Preparing for life after high school: The characteristics and experiences of youth in special education. Findings from the National Longitudinal Transition Study 2012. Volume 2: Comparisons across disability groups: Full report (Report No. NCEE 2017–4018). U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance.

Little, P., Wimer, C., & Weiss, H. B. (2008). After school programs in the 21st century: Their poten- tial and what it takes to achieve it. Issues and Opportunities in out-of-School Time Evaluation, 10 , 1–12.

Maass, K., Geiger, V., Ariza, M. R., & Goos, M. (2019). The role of mathematics in interdisciplinary STEM education. ZDM, 51 , 869–884. https://doi.org/10.1007/s11858-019-01100-5

Magaji, A., Ade-Ojo, G., & Bijlhout, D. (2022). The impact of after school science club on the learning progress and attainment of students. International Journal of Instruction, 15 (3), 171–190.

Mahoney, J. L., Parente, M. E., & Lord, H. (2007). After-school program engagement: Links to child competence and program quality and content. The Elementary School Journal, 107 (4), 385–404.

Martín-Páez, T., Aguilera, D., Perales-Palacios, F. J., & Vílchez-González, J. M. (2019). What are we talking about when we talk about STEM education? A Review of Literature. Science Education, 103 (4), 799–822.

McLafferty, S. (2016). Conducting questionnaire surveys. Key Methods in Geography, 3 , 129–142.

McNaught, C., & Lam, P. (2010). Using Wordle as a supplementary research tool. Qualitative Report, 15 (3), 630–643.

Merrill, C., & Daugherty, J. (2010). STEM education and leadership: A mathematics and science partnership approach. Journal of Technology Education, 21 (2), 21–34.

Nation, J. M., Harlow, D., Arya, D. J., & Longtin, M. (2019). Being and becoming scientists: Design-based STEM programming for girls. Afterschool Matters, 29 , 36–44.

National Research Council, Division of Behavioral, Board on Science Education, & Committee on Successful Out-of-School STEM Learning (2015). Identifying and supporting productive STEM programs in out-of-school settings . National Academies Press.

Noris, M., Saputro, S., & Ulimaz, A. (2023). STEM research trends from 2013 to 2022: A systematic literature review. International Journal of Technology in Education (IJTE), 6 (2), 224–237. https://doi.org/10.46328/ijte.390

Pastchal-Temple, A. S. (2012). The effect of regular participation in an after-school program on student achievement, attendance, and behavior (Publication No. 4368) [Doctoral dissertation, Mississippi State University]. Mississippi State University Libraries.

Resnick, L. B. (1987). Education and learning to think . National Academy Press.

Robelen, E. (2011). New STEM schools target underrepresented groups. Education Week, 31 (1), 18–19.

Sahin, A., Ekmekci, A., & Waxman, H. C. (2018). Collective effects of individual, behavioral, and contextual factors on high school students’ future STEM career plans. International Journal of Science and Mathematics Education, 16 , 69–89.

Schweingruber, H., Pearson, G., & Honey, M. (Eds.). (2014). STEM integration in K-12 education: Status, prospects, and an agenda for research . National Academies Press.

Shernoff, D. J., & Vandell, D. L. (2007). Engagement in after school program activities: Quality of experience from the perspective of participants. Journal of Youth Adolescence, 36 , 891–903.

Stemler, S. (2000). An overview of content analysis. Practical Assessment, Research & Evaluation, 7 (17), 1–6. https://doi.org/10.7275/z6fm-2e34

Stohlmann, M. (2018). A vision for future work to focus on the “m” in integrated STEM. School Science and Mathematics, 118 (7), 310–319. https://doi.org/10.1111/ssm.12301

Sozbilir, M., Kutu, H., & Yasar, M. D. (2012). Science education research in Turkey: A content analysis of selected features of papers published. In J. Dillon & D. Jorde (Eds.), The world of science education: Handbook of research in Europe (pp. 1–35). Sense publishers.

Suri, H., & Clarke, D. (2009). Advancements in research systhesis methods: From a methodologically inclusive perspective. Review of Educational Research, 79 (1), 395–430.

Tai, R. H., Qi Liu, C., Maltese, A. V., & Fan, X. (2006). Planning early for careers in science. Science, 312 (5777), 1143–1144.

Vennix, J., Den Brok, P., & Taconis, R. (2017). Perceptions of STEM-based outreach learning activities in secondary education. Learning Environments Research, 20 , 21–46.

Wan, Z. H., So, W. M. W., & Zhan, Y. (2023). Investigating the effects of design-based STEM learning on primary students’ STEM creativity and epistemic beliefs. International Journal of Science and Mathematics Education, 21 (Suppl. 1), 87–108.

Wang, H. H., Moore, T. J., Roehrig, G. H., & Park, M. S. (2011). STEM integration: Teacher perceptions and practice. Journal of Pre-College Engineering Education Research, 1 (2), 1–13.

White, M. D., & Marsh, E. E. (2006). Content analysis: A flexible methodology. Library Trends, 55 (1), 22–45.

Young, T. J. (2015). Questionnaires and surveys. In Z. Hua (Ed.), Research methods in intercultural communication: A practical guide (pp. 163–180). John Wiley & Sons. https://doi.org/10.1002/9781119166283.ch11

Zeng, Z., Yao, J., Gu, H., & Przybylski, R. (2018). A meta-analysis on the effects of STEM education on students’ abilities. Science Insights Education Frontiers, 1 (1), 3–16.

Zhan, Z., Shen, W., Xu, Z., Niu, S., & You, G. (2022). A bibliometric analysis of the global landscape on STEM education (2004–2021): Towards global distribution, subject integration, and research trends. Asia Pacific Journal of Innovation and Entrepreneurship, 16 (2), 171–203.

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The Dietary Approaches to Stop Hypertension (DASH) diet, which has a lot of emphasis on the consumption of fruits, vegetables, and whole grains, and on the other hand, the consumption of red meat and sodium is limited, due to its anti-inflammatory properties, which can be related to reducing the risk of asthma.

The aim of this study was to determine the relationship between the DASH diet and asthma symptoms among children and adolescents.

This cross-sectional study was conducted among7667 children (3414 boys and 4253 girls) aged 6–7 and 13–14 years living in central Iran. Dietary food consumption was assessed using a multiple-choice questionnaire. Logistic regression was used to obtain odds ratios for the association between the DASH-like diet with current asthma and asthma symptoms.

Our findings revealed that higher adherence to a DASH-like diet resulted in lower odds of asthma confirmed by a doctor among the whole population (OR = 0.53; 95%CI: 0.36–0.76) and also in females (OR = 0.47; 95%CI: 0.29–0.78). Moreover, the higher adherence to the DASH-like diet was inversely associated with the chance of wheezing in the past 12 months in all subjects (OR = 0.67; 95%CI: 0.51–0.86) and in boys (OR = 0.57; 95%CI: 0.38–0.85).

The findings of the present study showed that following the DASH diet can be associated with the improvement of asthma symptoms in children and adolescents. However, more research is needed to improve dietary recommendations for asthma prevention.

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Introduction

Asthma is a disease that causes many respiratory problems that affect modern societies and is known as inflammation or stenosis of the respiratory system [ 1 ]. It affects nearly 300 million people worldwide and 1,000 people per day pass away due to the illness. The prevalence of asthma has increased in the past 30 years, especially in industrialized countries where a large proportion of patients live [ 2 ]. According to statistics, one in 12 adults or 10 children worldwide suffer from asthma [ 3 ]. Asthma not only poses serious health risks such as increased mortality and disability, but also imposes significant financial and economic burdens on individuals and society [ 4 ]. According to statistics, the annual cost of asthma treatment in the U.S. is $81.9 billion, which means an average of $3,728 per person [ 5 ]. Based on the ISAAC questionnaire, more than 10% of Iranian children have asthma [ 6 ].

Common symptoms of asthma include wheezing, coughing, shortness of breath and fatigue [ 7 ]. Asthma is a multifactorial disease, and various factors such as genetics, drugs, environmental factors, and dietary intake play an essential role in its development or severity. Recognizing these factors can play a critical role in preventing, diagnosing, and treating this disease [ 8 ]. Diet is a key factor in influencing the occurrence or alleviation of asthma symptoms. Therefore, there has been a growing body of evidence on the link between dietary intake and asthma in recent years [ 9 , 10 ]. For example, a healthy diet that emphasizes the consumption of fruits, vegetables, and antioxidants showed beneficial effects in reducing allergic rhinitis and asthma-like symptoms in children, while consuming foods such as margarine and processed meat worsened these symptoms [ 11 , 12 ]. Epidemiological studies have reported a protective relationship between dietary antioxidants such as carotenoids, vitamin E, vitamin C, selenium and antioxidant-rich fruits with asthma [ 13 ]. Moreover, studies have indicated that eating fish at least once a week can help reduce the occurrence of asthma symptoms and lower the risk in children [ 14 ]. On the other hand, some interventional studies have shown disappointing results for the use of these antioxidants in asthma treatment [ 15 , 16 , 17 ].

The Dietary Approaches to Stop Hypertension (DASH) is an eating plan which emphasizes on receivinghighamountsoffruits, vegetables, low-fatdairyproducts, wholegrains, legumes, and nuts, and low amounts of consumption of red meat and processed meats, sodium consumption, and sweetened beverages [ 18 ]. Therefore, this dietary pattern is an antioxidant source diet (vitamins E, A, c and zinc), which might play a preventive role in asthma and respiratory problems by reducing the amount of malondialdehyde (MDA) and increasing glutathione (GSH) and generally reducing oxidative stress [ 19 , 20 , 21 ].

Few studies have assessed the relationship betweentheDash diet and asthma particularly in the Middle East.In such a way that, the results of these studies show the effectiveness of the Dash diet on asthma control [ 22 ]. Given the importance of asthma in adolescents and its impact on life, this study aimed to examine the association between the DASH diet and asthma symptoms among a large sample of Iranian adolescents.

Subjects and methods

Participants.

This study was part of the Global Asthma Network (GAN) which was conducted in 2020 in one of the central cities of Iran. The GAN study is a multicenter cross-sectional study that suggests a minimum of 3,000 samples to accurately estimate the prevalence of asthma [ 23 , 24 ].

Eighty-four schools from two educational districts of Yazd were randomly selected by using a cluster sampling design. The schools included both private and state elementary and high schools. We excluded non-Iranian people from the study. Due to the coronavirus pandemic and closures of schools, parents of 6–7 years old and subjects aged 13–14 answered an online questionnaire. Out of 7214 adolescents and 3026 children, 5141 and 2526 questionnaires were completed, respectively, and after reviewing the questionnaires, demographic data that were unacceptable were re-examined by telephone and necessary modifications were made if needed.

The Ethics Committee of Shahid Sadoughi University of Medical Sciences approved the study (IR.SSU.SPH.REC.1400.134). After that, the Yazd Education Department authorized the study in the relevant schools. A consent form was included at the beginning of the online questionnaires, and parents provided their consent for their children’s participation in the study.

Asthma and its symptoms confirmation

The GAN questionnaire assesses the risk factors and symptoms of allergic diseases and this questionnaire has been extracted from ISACC questionnaire [ 25 ]. At first, the questionnaire was translated into Persian and then the reliability of the translated version was confirmed by a study conducted on 100 selected subjects by using Cronbach’s alpha. In this study, the alpha coefficient for asthma symptoms was estimated to be 0.862, indicating the reliability of this questionnaire. Finally, the questionnaire was translated into English and sent to GAN’s managers for approval.

In this study, participants were asked questions about asthma symptoms, asthma confirmed by a doctor, use of asthma medications, and frequent consumption of DASH diet ingredients over the past year. Based on the guidelines of this study, current asthma was defined as a history of confirmed asthma by a doctor and having had wheezing and/or use of asthma medication in the past 12 months.

Assessment of dietary intakes

The frequency of dietary intake during the past year was evaluated by multiple choice questions in the GAN questionnaire [ 26 ]. Students were asked about the frequency of consumption of food groups such as fruits, vegetables, legumes, nuts, dairy, grains, meat, processed meats, sweets, and soft drinks, which are the main components of the DASH diet through food consumption frequency questionnaire.

Assessment of the DASH-style diet score

The Dash diet constructed based on seven food components including high intake of fruits, vegetables, dairy, nuts and legumes, and grains and low intakes of Sweetened beverages, and red and processed meats [ 27 ]. In such a way that people who have the lowest consumption of components such as fruits, vegetables, dairy, nuts and legumes were placed in the first tertile and received a score of 1, and the people who had the highest consumption rate were placed in the third group and received a score of three. Those who were between the two groups in terms of consumption received a score of 2. We used an inverse method for grains, sweetened beverages, and red and processed meats; such that those in the third group of these food items were received a score of 1. In this study, because most of the grains consumed in Iran are refined grains [ 28 ], high consumption of this component we considered this group as a harmful dietary component.

Assessment of other variables

In this study, the ethnicity of participants (Fars/Turk/Kurds/Arab/Baluch/Lur) height, weight and their use of computer and watching TV (2–4 h/5–8 h/9–14 h per day) were obtained using online self-reported questionnaire GAN. In addition, body mass index (BMI) was calculated using the following formula: weight (kg) divided by height squared (m 2 ).

Statistical methods

The Kolmogorov–Smirnov test was used to assess the normality. Individuals were categorized based on tertile of DASH scores. We used one-way ANOVA and chi-square tests to compare continuous and categorical variables, respectively, across tertile of DASH score. Multivariable logistic regression models were used to assess the association between adherence to the DASH-like diet and risk of asthma confirmed by a doctor, current asthma, usage of asthma medication, and wheezing in the last 12 months. The analyses were adjusted for age (continuous) and sex (girls, boys) first and additionally for watching TV & computer use (categorical) in model 2. We further controlled for BMI (continuous) in model 3. Pvalues < 0.05 were considered statistically significant. The analysiswas performed by STATA version 14 (State Corp., College Station, TX).

General characteristics of the subjects across tertiles of DASH-like diet intake are presented in Table  1 . The gender was significantly different between the tertiles (P value =0.02). Higher DASH-like diet scores were associated with older age (P value <0.01). The frequency of ethnicity, physical activity, ever had wheezing and wheezing in the past 12 months was different among tertiles of DASH-like diet intake (P value <0.01).

Table  2 shows the frequency of food consumption of participants based on the tertile of DASH-Like diet score. The frequency consumption of fruits, vegetables, legumes, nuts, dairy, grains, processed meats, sweets, and beverages, and meat was significantly different between tertiles of DASH-Like diet score (P value < 0.01).

The association between tertiles of DASH-like diet score and asthma confirmed by a doctor for the total population, and subgroup analyses by age and sex, is provided in Table  3 . A significant negative relationship was observed between risk of asthma and DASH-like diet score in the crude model, among the whole population [Odds ratio (OR):0.56, 95% confidence interval (CI): 0.39 to 0.80, P trend <0.01]. This relationship remained significant after adjustment for further confounders (OR:0.53, 95%CI: 0.36 to 0.76, P trend <0.001). Girls with highest adherence to DASH-like diet had a lowest odds for asthma confirmed by a doctor compared to those with lowest DASH-like diet score, in the crude model (OR: 0.52, 95%CI: 0.32 to 0.86, P trend = 0.01). This association was strengthened after controlling for further confounders (P trend < 0.01). There was no association between DASH-like diet score and asthma confirmed by a doctor among boys, in crude model, but after adjustment for further confounder, boys in higher tertile of DASH-like diet score had 41% decrease of asthma confirmed by a doctor (P trend =0.05). An inverse significant trend was found between DASH-like diet score and asthma confirmed by a doctor among 6–7 years old. In addition, children with highest adherence to DASH-like diet had a lowest odds of having asthma confirmed by a doctor compared to those with lowest DASH-like diet score among 13–14 years old in crude and full adjusted model (OR: 0.56, 95%CI: 0.37 to 0.85, P trend < 0.01).

There were no significant association between DASH-like diet intake and the likelihood of current asthma and asthma medication among whole population, girls, and boys (P trend > 0.05) (Tables  4 and 5 ). Table  6 provides information about the relationship between the score of the DASH-like diet and wheezing in the past 12 months. For the total population, in the crude model, individuals with the highest tertile of DASH-like diet score had a 33% lower wheezing chance compared to the lowest tertile (OR: 0.67, 95%CI: 0.52 to 0.86, P trend <0.01). Also, after adjustment for more confounders this relation remained significant (OR: 0.67, 95%CI: 0.51 to 0.86, P trend <0.01). In addition, we found that boys in the top tertile of DASH-like diet had lower odds of wheezing in the past 12 months, compared with those in the bottom tertile (OR: 0.57, 95%CI: 0.38 to 0.85, P trend <0.01). For girls, we did not find significant association in crude and full models. However, girls in top tertile of DASH-like diet had 28% lower risk of wheezing in the past 12 months, compared with those in the bottom tertile, when adjusted for age and energy.

As far as we know, no previous study has examined the link between DASH diet and asthma symptoms among adolescents. Due to the lack of data on sodium intake, we were unable to incorporate it into the score calculation. Therefore, we have provided a DASH-like diet score without considering sodium intake. Our findings revealed that higher adherence of DASH-like diet score resulted in lower odds of asthma confirmed by a doctor among whole population and subgroup analysis by sex. Moreover, the higher adherence to the DASH-like diet was inversely associated with the chance of wheezing in all adolescents, girls and boys.

The DASH diet is very similar to the Mediterranean diet in terms of its components. Luis Garcia-Marcos et al., in their cross-sectional study on schoolchildren, showed the protective effects of each Mediterranean score unit with current severe asthma in girls (adjusted OR 0.90, 95% CI 0.82 to 0.98) [ 29 ]. They also showed the protective effects of seafood and cereals for severe asthma, while fast food was a risk factor [ 29 ]. Another cross-sectional study on children revealed that greater adherence to the Mediterranean diet was negatively associated with ever diagnosed asthma [ 30 ]. A similar relationship was found in another cross-sectional study, as well [ 31 ]. Contrary to our results, in a population-based case-control study conducted by Bakolis et al. in 2010, no relationship was observed between a prudent diet (whole meal bread, fish, and vegetables) and asthma [ 32 ]. In addition, a study on a large population of adult French women (Varraso et al., 2009) did not observe any relationships between a prudent pattern, a Western pattern, and nuts and wine pattern with the incidence of asthma, ever asthma, or current asthma. They just found a lower frequency of asthma with nuts and wine consumption in the highest tertile (OR: 0.65; 95% CI: 0.31 to 0.96), and a higher frequency of asthma with the Western dietary pattern (OR: 1.79; 95% CI: 1.11 to 3.73) [ 33 ]. The present study differs from previous studies in several ways, such as the difference in sample size and a more comprehensive examination of different genders and a wider age range. Unlike in Iran, where wine consumption is prohibited due to religious reasons, and the diet is mostly composed of carbohydrate-rich, economical foods, the Western diet is more prevalent in the societies studied in previous research.

The current study showed that a DASH-like diet is inversely associated with asthma and wheezing. Consistent with our findings, previous studies have shown that a DASH diet has beneficial effects on asthma and asthma symptoms [ 34 , 35 ]. Some studies have evaluated the effects of various food groups in the DASH diet on the risk of asthma and its symptoms. According to a meta-analysis by Rezazadeh et al., there is an inverse relationship between the intake of fruits and vegetables and asthma symptoms [ 36 ]. Moreover, A prospective cohort study by Papadopoulou et al. revealed an association between fruit and vegetable consumption and lower risk of asthma symptoms [ 37 ]. Other studies confirmed the results of this study; In a cross-sectional study, an inverse relationship was observed between fruit consumption ≥ 3 times/week and asthma wheeze and severe asthma symptoms among children aged 6–7 years and adolescents [ 38 ].

Previous studies have shown that the DASH diet, which consists of low-fat dairy products, legumes, vegetables, and B-carotene, can lower the risk of asthma by reducing inflammation and pro-inflammatory markers such as interleukin-6 (IL-6) and tumor necrosis factor alpha (TNF-α) levels [ 39 , 40 , 41 ]. As TNF is a biomarker for severe asthma, which leads to the remodeling of smooth muscle, engagement of immune cells, and induction of chronic inflammation in the airways, anti-TNF agents are considered therapeutics for patients with severe asthma [ 42 ]. Moreover, IL-6 is involved in airway remodeling during asthma and maintains chronic inflammation in the respiratory tract according to research on human bronchial tissue samples. IL-6 increases Th2-associated cytokine production, initiates Th17-cell differentiation, inhibits Th1-cell expansion, and suppresses Treg cells. An increase in T-helper cells (Th2 or Th1/Th17) expands the infiltration of granulocytes into the airways, which leads to the release of pro-inflammatory cytokines and subsequent inflammation [ 42 ]. Therefore, dietary patterns with anti-inflammatory characteristics, such as the DASH diet, may play a role in preventing asthma.

The high content of antioxidants, including vitamin C, vitamin E, and β-carotene, in the DASH diet might also play an important role in reducing asthma risk [ 43 , 44 , 45 ]. The lack of balance between reactive oxygen species (ROS) and antioxidants leads to oxidative stress, which can exacerbate asthma by increasing inflammation [ 46 ]. Vitamin C supports the hydration of airway surfaces and decreases free radical levels [ 47 ]. Evidence has demonstrated antioxidant and anti-inflammatory effects of vitamin E on airway inflammation or injury [ 48 , 49 ]. In addition, vitamin E interrupts lipid peroxidation and prevents oxidant-induced membrane damage [ 50 ]. β-Carotene can reduce the highly reactive free radical superoxide anion and reacts with peroxyl free radicals [ 51 ].

The DASH diet has a high fiber content, which can play a role in reducing the risk of asthma and wheezing. A cohort study by Andrianasolo et al. on French adults showed a protective effect of fiber on asthma [ 52 ]. In a study by Saeed et al., a high-fiber diet was associated with a lower prevalence of current asthma in adults [ 53 ]. A potential mechanism for explaining the anti-inflammatory effect of fiber is the increased production of circulating short-chain fatty acids (SCFAs) formed after fiber fermentation by the gut microbiota [ 54 , 55 ]. SCFAs can reduce the pulmonary response to inflammatory stimuli through activation of free fatty acid receptors [ 56 , 57 ]. The results of the present study also confirm this issue, in that greater adherence to the DASH diet, which is a rich source of anti-inflammatory compounds, antioxidants, and high fiber content, was associated with a reduction in the risk of asthma confirmed by a doctor and the presence of wheezing.

This study had several strengths and limitations. To the best of our knowledge, this is the first study to evaluate the association between a DASH diet and asthma among adolescents. In addition, a large sample size, adjustment for multiple potential confounders, and conducting stratified analyses are the strengths of this study. Our study has several potential limitations that should be considered before interpreting its results. Firstly, the cross-sectional nature of this study does not imply a cause-and-effect association. Secondly, the data of our study were collected from self-reported questionnaires, which are prone to biases. Thirdly, estimation of dietary intake using the FFQ can lead to misclassification and misreporting.

In conclusion, the findings of the current study showed that following the DASH diet can be associated with the improvement of asthma symptoms in children and adolescents. However, more research is needed to improve dietary recommendations for asthma prevention.

Data availability

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

To T, Stanojevic S, Moores G, Gershon AS, Bateman ED, Cruz AA, et al. Global asthma prevalence in adults: findings from the cross-sectional world health survey. BMC Public Health. 2012;12(1):1–8.

Article   Google Scholar  

Varmaghani M, Farzadfar F, Sharifi F, Rashidian A, Moin M, Moradi-Lakeh M et al. Prevalence of asthma, COPD, and chronic bronchitis in Iran: a systematic review and meta-analysis. Iran J Allergy Asthma Immunol. 2016:93–104.

Lambrecht BN, Hammad H. The immunology of asthma. Nat Immunol. 2015;16(1):45–56.

Article   CAS   PubMed   Google Scholar  

Nunes C, Pereira AM, Morais-Almeida M. Asthma costs and social impact. Asthma Res Pract. 2017;3(1):1–11.

Article   PubMed   PubMed Central   Google Scholar  

Nurmagambetov T, Kuwahara R, Garbe P. The economic burden of asthma in the United States, 2008–2013. Annals Am Thorac Soc. 2018;15(3):348–56.

Ghaffari J, Aarabi M. The prevalence of pediatric asthma in the Islamic Republic of Iran: a systematic review and meta-analysis. J Pediatr Rev. 2013;1(1):2–11.

Google Scholar  

He Z, Feng J, Xia J, Wu Q, Yang H, Ma Q. Frequency of signs and symptoms in persons with asthma. Respiratory Care. 2020;65(2):252–64.

Alidadi R, Alekasir A, Bijanzadeh M. An Outlook on the role of genetic and environmental factors in Asthma. J Mazandaran Univ Med Scie. 2017;27(151):198–212.

McKeever TM, Britton J. Diet and Asthma. Am J Respir Crit Care Med. 2004;170(7):725–9.

Article   PubMed   Google Scholar  

Patel BD, Welch AA, Bingham SA, Luben RN, Day NE, Khaw K-T, et al. Dietary antioxidants and asthma in adults. Thorax. 2006;61(5):388–93.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Chatzi L, Apostolaki G, Bibakis I, Skypala I, Bibaki-Liakou V, Tzanakis N, et al. Protective effect of fruits, vegetables and the Mediterranean diet on asthma and allergies among children in Crete. Thorax. 2007;62(8):677–83.

Andrianasolo RM, Hercberg S, Touvier M, Druesne-Pecollo N, Adjibade M, Kesse-Guyot E, et al. Association between processed meat intake and asthma symptoms in the French NutriNet-Santé cohort. Eur J Nutr. 2020;59(4):1553–62.

Devereux G, Seaton A. Diet as a risk factor for atopy and asthma. J Allergy Clin Immunol. 2005;115(6):1109–17.

Papamichael M, Shrestha S, Itsiopoulos C, Erbas B. The role of fish intake on asthma in children: a meta-analysis of observational studies. Pediatr Allergy Immunol. 2018;29(4):350–60.

Allam MF, Lucena RA. Selenium supplementation for asthma. Cochrane Database Syst Reviews. 2004(2).

Fogarty A, Lewis S, Scrivener S, Antoniak M, Pacey S, Pringle M, et al. Oral magnesium and vitamin C supplements in asthma: a parallel group randomized placebo-controlled trial. Clin Experimental Allergy. 2003;33(10):1355–9.

Article   CAS   Google Scholar  

Pearson P, Lewis S, Britton J, Fogarty A. Vitamin E supplements in asthma: a parallel group randomised placebo controlled trial. Thorax. 2004;59(8):652–6.

Tseng E, Appel LJ, Yeh H-C, Pilla SJ, Miller ER, Juraschek SP, et al. Effects of the dietary approaches to stop hypertension diet and sodium reduction on blood pressure in persons with diabetes. Hypertension. 2021;77(2):265–74.

Mendes FC, Paciência I, Cavaleiro Rufo J, Farraia M, Silva D, Padrão P, et al. Higher diversity of vegetable consumption is associated with less airway inflammation and prevalence of asthma in school-aged children. Pediatr Allergy Immunol. 2021;32(5):925–36.

Papassotiriou I, Shariful Islam SM. Adherence to Mediterranean diet is associated with lung function in older adults: data from the Health and Retirement Study. J Am Coll Nutr. 2021;40(2):119–24.

Pirouzeh R, Heidarzadeh-Esfahani N, Morvaridzadeh M, Izadi A, Yosaee S, Potter E et al. Effect of DASH diet on oxidative stress parameters: A systematic review and meta-analysis of randomized clinical trials. Diabetes & Metabolic Syndrome: Clinical Research & Reviews. 2020;14(6):2131-8.

Ma J, Strub P, Lv N, Xiao L, Camargo CA, Buist AS, et al. Pilot randomised trial of a healthy eating behavioural intervention in uncontrolled asthma. Eur Respir J. 2016;47(1):122–32.

Asher Me, Keil U, Anderson H, Beasley R, Crane J, Martinez F, et al. International Study of Asthma and allergies in Childhood (ISAAC): rationale and methods. Eur Respir J. 1995;8(3):483–91.

Ellwood P, Ellwood E, Rutter C, Perez-Fernandez V, Morales E, García-Marcos L, et al. Global asthma network phase I surveillance: geographical coverage and response rates. J Clin Med. 2020;9(11):3688.

Han Y-Y, Forno E, Brehm JM, Acosta-Pérez E, Alvarez M, Colón-Semidey A, et al. Diet, interleukin-17, and childhood asthma in Puerto ricans. Ann Allergy Asthma Immunol. 2015;115(4):288–93. e1.

Behniafard N, Nafei Z, Mirzaei M, Karimi M, Vakili M. Prevalence and severity of adolescent asthma in Yazd, Iran: based on the 2020 Global Asthma Network (GAN) Survey adolescents Asthma Prevalence in Central Iran. Iran J Allergy Asthma Immunol. 2021;20(1):24.

PubMed   Google Scholar  

Fung TT, Chiuve SE, McCullough ML, Rexrode KM, Logroscino G, Hu FB. Adherence to a DASH-style diet and risk of coronary heart disease and stroke in women. Arch Intern Med. 2008;168(7):713–20.

Esmaillzadeh A, Mirmiran P, Azizi F. Whole-grain intake and the prevalence of hypertriglyceridemic waist phenotype in tehranian adults. Am J Clin Nutr. 2005;81(1):55–63.

Garcia-Marcos L, Canflanca IM, Garrido JB, Varela AL-S, Garcia-Hernandez G, Grima FG, et al. Relationship of asthma and rhinoconjunctivitis with obesity, exercise and Mediterranean diet in Spanish schoolchildren. Thorax. 2007;62(6):503–8.

Arvaniti F, Priftis KN, Papadimitriou A, Papadopoulos M, Roma E, Kapsokefalou M, et al. Adherence to the Mediterranean type of diet is associated with lower prevalence of asthma symptoms, among 10–12 years old children: the PANACEA study. Pediatr Allergy Immunol. 2011;22(3):283–9.

De Batlle J, Garcia-Aymerich J, Barraza-Villarreal A, Antó JM, Romieu I. Mediterranean diet is associated with reduced asthma and rhinitis in Mexican children. Allergy. 2008;63(10):1310–6.

Bakolis I, Hooper R, Thompson R, Shaheen S. Dietary patterns and adult asthma: population-based case–control study. Allergy. 2010;65(5):606–15.

Varraso R, Kauffmann F, Leynaert B, Le Moual N, Boutron-Ruault MC, Clavel-Chapelon F, et al. Dietary patterns and asthma in the E3N study. Eur Respir J. 2009;33(1):33–41.

Guilleminault L, Williams EJ, Scott HA, Berthon BS, Jensen M, Wood LG. Diet and Asthma: is it time to adapt our message? Nutrients. 2017;9(11):1227.

Ma J, Strub P, Lavori PW, Buist AS, Camargo CA Jr, Nadeau KC, et al. DASH for asthma: a pilot study of the DASH diet in not-well-controlled adult asthma. Contemp Clin Trials. 2013;35(2):55–67.

Seyedrezazadeh E, Pour Moghaddam M, Ansarin K, Reza Vafa M, Sharma S, Kolahdooz F. Fruit and vegetable intake and risk of wheezing and asthma: a systematic review and meta-analysis. Nutr Rev. 2014;72(7):411–28.

Papadopoulou A, Panagiotakos DB, Hatziagorou E, Antonogeorgos G, Matziou V, Tsanakas J, et al. Antioxidant foods consumption and childhood asthma and other allergic diseases: the Greek cohorts of the ISAAC II survey. Allergol Immunopathol. 2015;43(4):353–60.

Ellwood P, Asher MI, García-Marcos L, Williams H, Keil U, Robertson C, et al. Do fast foods cause asthma, rhinoconjunctivitis and eczema? Global findings from the International Study of Asthma and allergies in Childhood (ISAAC) phase three. Thorax. 2013;68(4):351–60.

Esmaillzadeh A, Azadbakht L. Dairy consumption and circulating levels of inflammatory markers among Iranian women. Public Health Nutr. 2010;13(9):1395–402.

Holt EM, Steffen LM, Moran A, Basu S, Steinberger J, Ross JA, et al. Fruit and vegetable consumption and its relation to markers of inflammation and oxidative stress in adolescents. J Am Diet Assoc. 2009;109(3):414–21.

Black P, Sharpe S. Dietary fat and asthma: is there a connection? Eur Respir J. 1997;10(1):6–12.

Namakanova OA, Gorshkova EA, Zvartsev RV, Nedospasov SA, Drutskaya MS, Gubernatorova EO. Therapeutic potential of combining IL-6 and TNF blockade in a mouse model of allergic asthma. Int J Mol Sci. 2022;23(7):3521.

Allen S, Britton J, Leonardi-Bee J. Association between antioxidant vitamins and asthma outcome measures: systematic review and meta-analysis. Thorax. 2009;64(7):610–9.

Cook-Mills JM, Averill SH, Lajiness JD. Asthma, allergy and vitamin E: current and future perspectives. Free Radic Biol Med. 2022;179:388–402.

Neuman I, Nahum H, Ben-Amotz A. Prevention of exercise-induced asthma by a natural isomer mixture of β-carotene. Ann Allergy Asthma Immunol. 1999;82(6):549–53.

Moreno-Macias H, Romieu I. Effects of antioxidant supplements and nutrients on patients with asthma and allergies. J Allergy Clin Immunol. 2014;133(5):1237–44.

Padayatty SJ, Katz A, Wang Y, Eck P, Kwon O, Lee J-H, et al. Vitamin C as an antioxidant: evaluation of its role in disease prevention. J Am Coll Nutr. 2003;22(1):18–35.

Li J, Li L, Chen H, Chang Q, Liu X, Wu Y, et al. Application of vitamin E to antagonize SWCNTs-induced exacerbation of allergic asthma. Sci Rep. 2014;4(1):1–10.

Wagner JG, Birmingham NP, Jackson-Humbles D, Jiang Q, Harkema JR, Peden DB. Supplementation with γ-tocopherol attenuates endotoxin-induced airway neutrophil and mucous cell responses in rats. Free Radic Biol Med. 2014;68:101–9.

Romieu I, Trenga C. Diet and obstructive lung diseases. Epidemiol Rev. 2001;23(2):268–87.

Sies H. Oxidative stress: oxidants and antioxidants. Experimental Physiology: Translation Integr. 1997;82(2):291–5.

Andrianasolo RM, Hercberg S, Kesse-Guyot E, Druesne-Pecollo N, Touvier M, Galan P, et al. Association between dietary fibre intake and asthma (symptoms and control): results from the French national e-cohort NutriNet-Santé. Br J Nutr. 2019;122(9):1040–51.

Saeed MA, Gribben KC, Alam M, Lyden ER, Hanson CK, LeVan TD. Association of dietary fiber on asthma, respiratory symptoms, and inflammation in the adult national health and nutrition examination survey population. Annals Am Thorac Soc. 2020;17(9):1062–8.

Meijer K, de Vos P, Priebe MG. Butyrate and other short-chain fatty acids as modulators of immunity: what relevance for health? Current opinion in Clinical Nutrition &. Metabolic Care. 2010;13(6):715–21.

CAS   Google Scholar  

Young RP, Hopkins RJ, Marsland B. The gut–liver–lung axis. Modulation of the innate immune response and its possible role in chronic obstructive pulmonary disease. Am J Respir Cell Mol Biol. 2016;54(2):161–9.

Wood LG. Diet, obesity, and asthma. Annals Am Thorac Soc. 2017;14(Supplement 5):S332–8.

Marsland BJ. Regulation of inflammatory responses by the commensal microbiota. Thorax. 2012;67(1):93–4.

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Acknowledgements

We would like to express our special thanks to the participation of the study subjects, without whom the study would not have been possible.

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Research Center for Food Hygiene and Safety, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran

Vahid Arabi, Bahareh Sasanfar & Amin Salehi-Abargouei

Yazd Cardiovascular Research Center, Non-communicable Diseases Research Institute, Shahid Sadoughi University of Medical Sciences, Yazd, Iran

Amin Salehi-Abargouei

Department of Nutrition, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran

Children Growth Disorder Research Center, Shahid Sadoughi University of Medical Sciences, Yazd, Iran

Zahra Nafei & Nasrin Behniafard

Department of Allergy and Clinical Immunology, Shahid Sadoughi University of Medical Sciences, Yazd, Iran

Nasrin Behniafard

Cancer Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran

Bahareh Sasanfar & Fatemeh Toorang

Departments of Medical and Surgical Sciences, University of Bologna, Bologna, Italy

Fatemeh Toorang

Shahid Sadoughi Hospital, Ebne Sina Boulevard, Yazd, Iran

Zahra Nafei

Student Research Committee, Shahid Sadoughi University of Medical Sciences, Yazd, Iran

Bahareh Sasanfar

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ZN, NB, and ASA participated in the study design. VA, BS and FT analysis and drafted the initial version. ASA helped in data analysis. VA implemented comments and suggestions from the co-authors. All authors reviewed the final version of the manuscript. ZN and ASA supervised the study.

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Arabi, V., Sasanfar, B., Toorang, F. et al. Association between DASH diet and asthma symptoms among a large sample of adolescents: a cross-sectional study. BMC Nutr 10 , 92 (2024). https://doi.org/10.1186/s40795-024-00884-4

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Association between waist circumference and lung function in American middle-aged and older adults: findings from NHANES 2007–2012

• Zichen Xu 1 ,
• Lingdan Zhuang 1 ,
• Luqing Jiang 1 ,
• Jianjun Huang 1 ,
• Daoqin Liu 2 &
• Qiwen Wu 1  

Journal of Health, Population and Nutrition volume  43 , Article number:  98 ( 2024 ) Cite this article

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There is a major epidemic of obesity, and many obese patients suffer from respiratory symptoms and disease. However, limited research explores the associations between abdominal obesity and lung function indices, yielding mixed results. This study aims to analyze the association between waist circumference (WC), an easily measurable marker of abdominal obesity, and lung function parameters in middle-aged and older adults using the National Health and Nutrition Examination Survey (NHANES).

This study utilized data obtained from the National Health and Nutrition Examination Survey (NHANES) spanning 2007 to 2012, with a total sample size of 6089 individuals. A weighted multiple regression analysis was conducted to assess the relationship between WC and three pulmonary function parameters. Additionally, a weighted generalized additive model and smooth curve fitting were applied to capture any potential nonlinear relationship within this association.

After considering all confounding variables, it was observed that for each unit increase in WC, in males, Forced Vital Capacity (FVC) increased by 23.687 ml, Forced Expiratory Volume in one second (FEV1) increased by 12.029 ml, and the FEV1/FVC ratio decreased by 0.140%. In females, an increase in waist circumference by one unit resulted in an FVC increase of 6.583 ml and an FEV1 increase of 4.453 ml. In the overall population, each unit increase in waist circumference led to a FVC increase of 12.014 ml, an FEV1 increase of 6.557 ml, and a decrease in the FEV1/FVC ratio by 0.076%. By constructing a smooth curve, we identified a positive correlation between waist circumference and FVC and FEV1. Conversely, there was a negative correlation between waist circumference and the FEV1/FVC ratio.

Conclusions

Our findings indicate that in the fully adjusted model, waist circumference, independent of BMI, positively correlates with FVC and FEV1 while exhibiting a negative correlation with FEV1/FVC among middle-aged and older adults in the United States. These results underscore the importance of considering abdominal obesity as a potential factor influencing lung function in American middle-aged and older adults.

Introduction

Obesity has emerged as a significant global public health challenge. Obesity has markedly increased in over 70 countries since 1980 and continues to rise in most others [ 1 , 2 ]. In 2015, the global population of individuals classified as obese surpassed one-third [ 3 ], and this number is projected to reach a staggering 1.12 billion by 2030 [ 4 ]. Obesity constitutes a substantial risk factor for numerous ailments, including metabolic disorders, cardiovascular and cerebrovascular diseases, dyslipidemia, asthma, chronic obstructive pulmonary disease (COPD), and cancer [ 5 , 6 , 7 , 8 ]. Obesity is commonly categorized into two types: abdominal obesity, assessed by waist circumference, and general obesity, determined by body mass index (BMI) [ 9 ]. However, BMI has inherent limitations, as it relies on weight and height measurements [ 10 , 11 ]. Consequently, BMI may not be a perfect indicator of obesity, particularly among men with higher muscle mass [ 12 ]. Furthermore, BMI fails to accurately assess the relationship between obesity and associated diseases due to its inability to account for variations in body fat distribution [ 13 , 14 , 15 ]. The commonly utilized pulmonary function parameters in the respiratory system include FVC, FEV1, and the FEV1/FVC ratio. The normal reference range for FVC is approximately 3000 ml to 5000 ml, while the normal reference range for FEV1 typically falls between 2000 ml and 4000ml [ 16 ]. However, these values are more influenced by factors such as age, gender, height, and weight [ 16 ]. A strong association between obesity, particularly abdominal obesity, and lung function has been established in the literature [ 17 , 18 ].

In addition, obesity can be divided into android obesity (fat distribution in the chest, abdomen and internal organs) and gynoid obesity (fat distribution in the subcutaneous tissue of the limbs and buttocks) according to the characteristics of fat distribution [ 19 ]. This difference in fat distribution leads to android obesity having a more direct effect on lung mechanics than female obesity, because the increase in chest fat and the increase in abdominal volume can affect diaphragm contraction and reduce lung volume [ 20 ]. Not only that, android obesity will also secrete more pro-inflammatory adipokines because of its special fat distribution, aggravating the activation of immune cells and metabolic disorders [ 20 ].

However, existing research has focused mainly on children and adolescents, with mixed results. A study of Chinese people aged 20–80 years showed that WC was positively correlated with FEV1 and FVC [ 21 ] whereas another study of Chinese elderly people reported that an increase in WC was associated with a decrease in FEV1 and FVC [ 22 ]. Marga et al. [ 23 ] reported no significant association between WC and FVC or FEV1 in 8-year-olds. In contrast, Feng et al. [ 24 ] found that WC in Chinese children was negatively correlated with lung function. Zhang et al. [ 10 ] discovered that abdominal obesity was associated with impaired lung function among adults with asthma. Since the decline in lung function is closely related to changes in body size, we hypothesize that WC, independent of BMI, may be associated with impairment of lung function.

Therefore, our study aimed to use the National Health and Nutrition Survey (NHANES) database to investigate the correlation between WC and lung function in middle-aged and older adults. By using WC as a measure, we aim to elucidate the potential association between abdominal obesity and lung function in this particular population.

Study sample

The data for our study were sourced from the National Health and Nutrition Examination Survey (NHANES), a comprehensive survey conducted by the Centers for Disease Control and Prevention (CDC). Our study drew on data from NHANES spanning 2007 to 2012. The dataset comprises demographic, examination, laboratory, and questionnaire information. After an initial screening of the NHANES database, we identified that lung function data were available only for the period mentioned. Consequently, we included all participants ( n  = 30,442) from the NHANES conducted between 2007 and 2012. We excluded individuals (1) aged < 40 years old ( n  = 18,679) (2); missing lung function test results data (FEV1 or FVC) or having low data quality (C, D, F) ( n  = 4619) (3); missing WC data ( n  = 159) (4); missing data about covariates at least one of following ( n  = 896): BMI, the ratio of family income to poverty (PIR), total cholesterol, total bilirubin, total protein, aspartate aminotransferase (AST), or alanine aminotransferase (ALT). Ultimately, our study incorporated a substantial and nationally representative sample of middle-aged and older adults from the United States. A flowchart illustrating the screening process is presented in Fig.  1 for clarity. This study was approved by the ethics review board of the National Center for Health Statistics (NCHS) and obtained written informed consent from all participants.

Flowchart for selecting analyzed participants FEV1, forced expiratory volume in one second; FVC, forced vital capacity; NHANES, National Health and Nutrition Examination Survey

Lung function assessment

Lung function tests are performed by trained professional researchers and are tested in a standing position, unless the participant was physically limited. Lung function assessments were conducted using the Ohio 822/827 dry-roll volume spirometer, following the recommended guidelines from the American Thoracic Society (ATS) and the European Respiratory Society (ERS). The spirometry variables utilized in this study included FEV1, FVC, and the FEV1/FVC ratio. To ensure the reliability and accuracy of the spirometry measurements, the ATS/ERS criteria for acceptability and reproducibility were applied, resulting in spirometry quality grades ranging from A to F. Grades A and B indicated measurements that fulfilled or exceeded the ATS criteria. In contrast, grade C could still be considered for analysis. Grades D to F, conversely, were deemed less likely to be useful.

It is important to note that our study only included data with spirometry quality grades A and B for FEV1 and FVC. This rigorous selection criterion was employed to guarantee the accuracy and reliability of the measurement data while excluding data with lower quality grades (C, D, and F).

Waist circumference measurement

WC measurements were conducted by trained health technicians in the Mobile Examination Center as part of the NHANES survey. The measurement procedure involved determining the waist circumference at the uppermost lateral border of the right ilium, with precision recorded to the nearest 0.1 cm.

Other covariates

The criteria for selecting covariates in this study were: (1) demographic data; (2) variables affecting WC and lung function parameters in the published literature [ 25 , 26 ]; (3) according to the recommendation of the STROBE statement, covariates with regression coefficients on the outcome variables with a P value < 0.10 or covariates that resulted in more than a 10% change in the regression coefficients of the risk factors after introduction of the covariates in the base model; (4) other variables accumulated on the basis of clinical experience.The demographic data consisted of age (in years), gender, race/ethnicity (including Mexican American, other Hispanic, non-Hispanic white, non-Hispanic black, and others), poverty-to-income ratio, educational level (categorized as less than high school, high school, and more than high school), and marital status (married, single, living with a partner). Furthermore, examination data and personal life history variables were included in our analysis. These variables encompassed BMI (in kg/m²), alcohol consumption (defined as having consumed at least 12 alcoholic drinks/1 year), smoking history (defined as having smoked at least 100 cigarettes in life), histories of diabetes, hypertension, and respiratory diseases. Last, laboratory data variables were incorporated, comprising measurements of total protein levels (in g/L), total cholesterol levels (in mmol/L), total bilirubin levels (in µmol/L), aspartate aminotransferase (AST) levels (in U/L), and alanine aminotransferase (ALT) levels (in U/L). For more detailed information regarding these variables, including specific measurement methods and ranges, ( https://www.cdc.gov/nchs/nhanes/ ) provides comprehensive access to publicly available data.

Statistical analysis

Statistical analyses were conducted following the guidelines provided by the Centers for Disease Control and Prevention (CDC) [CDC guideline criteria: https://wwwn.cdc.gov/nchs/nhanes/tutorials/default.aspx ]. Continuous variables were reported as the means ± standard deviations (SD). Categorical variables are presented as percentages. Initially, weighted χ^2 tests were employed for categorical variables, while weighted linear regression models were used for continuous variables. Subsequently, we constructed four weighted linear regression models (Model 1, Model 2, Model 3, and Model 4), adjusting various variables to examine the association between WC and lung function parameters. A stratified analysis was also performed based on the fully adjusted model to explore potential stratified associations between WC and lung function. Additionally, a generalized additive model (GAM) with a penalty spline method was utilized to construct a smoothed curve-fitting fully adjusted model, treating WC as a continuous variable. We also calculated the variance inflation factor (VIF) for the variables, with VIF values of 5.8 and 6.6 for BMI and WC (supplementary Table 1 ), respectively. As a rule of thumb, the threshold for VIF values with multicollinearity between variables is 10 [ 27 ].

All statistical analyses were performed using Empower Stats software and R version 4.2.0. A p value of less than 0.05 was considered statistically significant in our study.

Baseline characteristics of the participants

Table  1 shows the weighted distribution of baseline characteristics, including demographic, examination, laboratory, and questionnaire data, for the participants selected from the NHANES survey conducted between 2007 and 2012. A total of 6,089 participants aged 40–79 years were included in our study. The average age of the selected participants was 56.49 years (± 10.65), and non-Hispanic whites constituted the majority of the study population. The distribution of all included variables across the quartiles demonstrated statistically significant differences (p values < 0.05).

The associations between waist circumference and lung function parameters

Weighted multiple regression analysis was conducted to examine the association between WC and lung function parameters, as presented in Table  2 . In males, both Model 1 and Model 2, representing unadjusted and age, race adjusted associations, revealed a negative correlation between WC and FVC as well as FEV1, while a positive correlation was observed with FEV1/FVC. In Model 3, which additionally adjusted for BMI based on Model 2, WC exhibited a positive correlation with FVC and FEV1, and a negative correlation with FEV1/FVC. Finally, in the fully adjusted Model 4, WC showed a positive correlation with FVC (β = 23.687, 95% CI: 18.523, 28.852) and FEV1 (β = 12.029, 95% CI: 7.789, 16.270), but a negative correlation with FEV1/FVC (β = -0.140, 95% CI: -0.192, -0.088).Similar results were observed in females and the total population. In fully adjusted analyses for females, WC exhibited a positive correlation with FVC (β = 6.583, 95% CI: 3.629, 9.538) and FEV1 (β = 4.453, 95% CI: 1.988, 6.918), and a negative correlation with FEV1/FVC (β = -0.034, 95% CI: -0.072, 0.004), although without statistical significance. In the fully adjusted analysis for the total population, WC showed a positive correlation with FVC (β = 12.014, 95% CI: 9.251, 14.777) and FEV1 (β = 6.557, 95% CI: 4.284, 8.831), and a negative correlation with FEV1/FVC (β = -0.076, 95% CI: -0.107, -0.046). Due to partial collinearity between WC and BMI, we assessed individual associations between WC, BMI, and pulmonary function to elucidate the potential mediating role of BMI in the relationship between WC and pulmonary function (Supplementary Figs.  1 – 4 ).

Stratified associations between waist circumference and lung function parameters

To assess the stability of the multivariate regression analysis results, we conducted stratified analyses to examine the associations between WC and lung function parameters in different subgroups. The results are presented in Table  3 .

In the subgroup analyses, WC demonstrated a positive relationship with FVC in most subgroups, except for the subgroup of other races, less than high school, living with a partner, BMI > 30, and borderline diabetes history. Similarly, WC showed a positive relationship with FEV1 in most subgroups, except for the subgroup of age > 60, other race, less than high school, high school, living with a partner, BMI25-30, BMI > 30 (negative correlation with statistical significance), at least 12 alcohol drinks/1 year, with diabetes history, borderline diabetes history, and respiratory diseases history. On the other hand, WC exhibited a negative relationship with FEV1/FVC in most subgroups, except for the non-Hispanic Black, other race, more than high school, living with a partner, all BMI subgroups, no smoking, borderline diabetes history and hypertension history subgroups. Furthermore, gender and BMI have a significant interaction with FVC (p for interaction < 0.0001); BMI and diabetes history have a significant interaction with FEV1 (p for interaction < 0.0001).

Using GAM to explore the possible relationship between waist circumference and lung function parameters

To ensure the reliability of the regression analysis results, we used a generalized additive model (GAM) to investigate whether there is a linear or nonlinear correlation between WC and lung function parameters. In our study, based on Model 4 (adjusted for all covariates), we constructed a smooth-fitting curve to observe potential correlations. Figure  2 shows the results obtained from the GAM analysis. We observed a nonlinear relationship between WC and lung function parameters. After adjusting for all covariates, we found that WC, FVC and FEV1 were positively correlated and nonlinear. Conversely, we observe a nonlinear negative correlation between WC and FEV1/FVC ratios. With the increase of WC, the FEV1/FVC ratio tends to decrease.

Based on the fully adjusted model, the relationship between waist circumference and lung function

To our knowledge, there has been limited investigation into the relationship between WC and lung function parameters in middle-aged and older adults in the United States, accounting for the influence of BMI. We investigated the correlation between WC and lung function parameters in 6089 middle-aged and older adults who participated in the NHANES survey in the United States between 2007 and 2012. Four weighted multiple linear regression models were used to determine the relationship between WC and three lung function parameters. Based on NHANES data from 2007 to 2012, we found that WC was negatively associated with FVC and FEV1 and positively associated with FEV1/FVC in the unadjusted model and after adjusting for age and race. After adjusting for BMI, the correlation between WC and FVC and FEV1 became positive, and the correlation with FEV1/FVC became negative. Finally, the correlation between WC and lung function parameters in the fully adjusted model was the same as above (Male: FVC, β = 23.687; FEV1, β = 12.029; FEV1/FVC, β = -0.140; Female: FVC, β = 6.583; FEV1, β = 4.453; FEV1/FVC, β = -0.034; Total population: FVC, β = 12.014; FEV1, β = 6.557; FEV1/FVC, β=-0.076). To verify the accuracy and stability of this association, we performed a stratified analysis. Then, we build a smooth curve model to further assess the reliability of the results.

Our study results indicate an association between increased WC and decreased FEV1/FVC ratio, aligning with the majority of previously published findings. A study by Zhang et al. [ 28 ]. in American adults found that abdominal obesity was associated with an increased risk of airflow obstruction defined by FEV1/FVC. A cohort study in the Netherlands by Marga et al. [ 23 ]. found that large WC in girls only, independent of BMI, was associated with lower FEV1/FVC. Feng et al. [ 24 ]. found that waist-to-chest ratio (WCR) was negatively correlated with FVC, FEV1, FVC/FEV1 in Chinese adolescents and children, after adjusting for gender height and BMI. Chen et al [ 29 ]. found that an increase in WC in children aged 6–17 years is associated with an increase in FVC and FEV1, while it is associated with a decrease in the FEV1/FVC ratio. With respect to FVC and FEV1, Zeng et al. [ 21 ]. discovered that in the Chinese population aged 20–80 years, WC and obesity defined by WC are positively correlated with FVC and FEV1. A cohort study by Pan et al. [ 22 ]. reported that abdominal obesity and its indicators (WC, WHtR, WHR and body fat) were associated with decreased FVC and FEV1 in the older Chinese population. Zhang et al. [ 10 ] reported that in adult asthma patients in the United States, the abdominal obesity group was associated with lower FVC and FEV1 compared to the normal group. Our data reveals that in the model without adjusting for BMI, WC is negatively correlated with FVC and FEV1, while after adjusting for BMI, it exhibits a positive correlation. These divergent conclusions about FVC and FEV1 may be attributed to differences in study designs, study population, and the confounding factors included, particularly BMI.

Central obesity is a specific type of obesity characterized by the accumulation of fat in the chest, abdomen, and internal organs [ 30 ]. Obesity reduces respiratory compliance, alters breathing patterns, affecting lung function [ 19 , 31 ]. The fatty deposition also causes narrowing, closure, and hyperresponsiveness of the airways, resulting in uneven ventilation [ 32 , 33 ]. Excess body fat alters respiratory physiology and impairs lung function [ 34 ]. Abdominal fat accumulation can affect the contraction of the diaphragm and impair lung function. The effect of intra-abdominal pressure on the diaphragm is one of the important reasons for the impairment of lung function [ 14 , 35 ]. Thus, abdominal obesity leads to decreased lung compliance, increased airway resistance, and limited daily exercise [ 19 , 36 ]. People with abdominal obesity may also change their breathing pattern to rapid and shallow breathing. This style of breathing increases the risk of airflow limitation, hypoxia, respiratory overload, and respiratory complications [ 37 ]. In addition, inflammation and oxidative stress have been identified as key factors in impaired lung function due to abdominal obesity [ 38 , 39 ]. Systemic adipose tissue inflammation may be responsible for impaired lung function due to abdominal obesity [ 40 ]. Abdominal obesity is considered to be an inflammatory state [ 18 ], and many inflammatory factors come from visceral adipose tissue, such as IL-6, TNF-α, C-reactive protein (CRP), leptin, etc., which may lead to obesity-related airway inflammation [ 41 ]. In addition, CRP is also thought to cause impairment of lung function [ 42 ]. An in vitro study found that CRP is present in human respiratory secretions [ 43 ] and may play a local role in lung tissue, decreasing airway diameter and lung function [ 18 , 44 ]. Besides, studies have demonstrated that the relationship between lung function and abdominal obesity is also affected by CRP gene polymorphisms. The researchers found that the CRP rs1205 CC genotype was associated with impaired lung function [ 45 ], suggesting that the CRP gene plays a partial role in lung function inheritance.

Study strengths and limitations

Compared with previously published articles, our study has the following advantages. First, our sample includes 6089 nationally representative middle-aged and older adults, and the sample size is relatively large. Second, we have taken into account BMI, an important confounding factor, so that WC as an indicator of abdominal fat deposits can be understood in the context of body type so that we can understand its full impact on respiratory function. Also, we performed a stratified analysis that considered the possible impact of BMI and other confounding factors on the results, which helped verify the reliability of the results and identify possible susceptible populations. Finally, based on completely adjusting the model, we performed smooth curve fitting and explored the relationship between WC and lung function parameters.

However, it should be noted that our study design is a cross-sectional study and cannot prove a causal relationship between abdominal obesity and altered lung function, so more prospective cohort studies are needed to validate the conclusions. Second, we chose WC as a marker of abdominal obesity, while other markers, such as waist-to-height ratio or waist-hip ratio, were not included in the study due to lack of data or small sample sizes. Future studies are needed to confirm our results with other methods of measuring abdominal obesity. Third, while we adjusted for many confounders, other potential confounding factors were not considered, similar to other cross-sectional studies. Finally, our survey is based on the NHANES database, which applies to the US population and, therefore, is geographically limited in versatility. More comprehensive studies are needed to determine the relationship between WC and lung function parameters.

Data availability

No datasets were generated or analysed during the current study.

2015 GBD, Collaborators O, Afshin A, Forouzanfar MH, Reitsma MB, Sur P, Estep K et al. Health Effects of Overweight and Obesity in 195 Countries over 25 Years. N Engl J Med. 2017;377:13–27.

Chan M. Obesity and diabetes: the slow-motion disaster. Milbank Q. 2017;95:11–4.

Article   PubMed   PubMed Central   Google Scholar  

Chooi YC, Ding C, Magkos F. The epidemiology of obesity. Metabolism. 2019;92:6–10.

Article   CAS   PubMed   Google Scholar  

Kelly T, Yang W, Chen C-S, Reynolds K, He J. Global burden of obesity in 2005 and projections to 2030. Int J Obes. 2005. 2008;32:1431–7.

McClean KM, Kee F, Young IS, Elborn JS. Obesity and the lung: 1. Epidemiology. Thorax. 2008;63:649–54.

Avgerinos KI, Spyrou N, Mantzoros CS, Dalamaga M. Obesity and cancer risk: emerging biological mechanisms and perspectives. Metabolism. 2019;92:121–35.

Melo LC, Silva MAM da, Calles AC do N. Obesity and lung function: a systematic review. Einstein Sao Paulo Braz. 2014;12:120–5.

Matrone A, Ferrari F, Santini F, Elisei R. Obesity as a risk factor for thyroid cancer. Curr Opin Endocrinol Diabetes Obes. 2020;27:358–63.

Ross R, Neeland IJ, Yamashita S, Shai I, Seidell J, Magni P, et al. Waist circumference as a vital sign in clinical practice: a Consensus Statement from the IAS and ICCR Working Group on visceral obesity. Nat Rev Endocrinol. 2020;16:177–89.

Zhang H, Hu Z, Wang S, Xu J, Li S, Song X. Association of general and abdominal obesity with lung function, FeNO, and blood eosinophils in adult asthmatics: findings from NHANES 2007–2012. Front Physiol. 2023;14:1019123.

Bracht JR, Vieira-Potter VJ, De Souza Santos R, Öz OK, Palmer BF, Clegg DJ. The role of estrogens in the adipose tissue milieu. Ann N Y Acad Sci. 2020;1461:127–43.

von Hafe P, Pina F, Pérez A, Tavares M, Barros H. Visceral fat accumulation as a risk factor for prostate cancer. Obes Res. 2004;12:1930–5.

Article   Google Scholar  

Piché M-E, Poirier P, Lemieux I, Després J-P. Overview of epidemiology and contribution of obesity and body Fat distribution to Cardiovascular Disease: an update. Prog Cardiovasc Dis. 2018;61:103–13.

Article   PubMed   Google Scholar  

Després J-P, Lemieux I. Abdominal obesity and metabolic syndrome. Nature. 2006;444:881–7.

Wehrmeister FC, Menezes AMB, Muniz LC, Martínez-Mesa J, Domingues MR, Horta BL. Waist circumference and pulmonary function: a systematic review and meta-analysis. Syst Rev. 2012;1:55.

Quanjer PH, Stanojevic S, Cole TJ, Baur X, Hall GL, Culver BH, et al. Multi-ethnic reference values for spirometry for the 3–95-yr age range: the global lung function 2012 equations. Eur Respir J. 2012;40:1324–43.

Leone N, Courbon D, Thomas F, Bean K, Jégo B, Leynaert B, et al. Lung function impairment and metabolic syndrome: the critical role of abdominal obesity. Am J Respir Crit Care Med. 2009;179:509–16.

He H, Wang B, Zhou M, Cao L, Qiu W, Mu G, et al. Systemic inflammation mediates the associations between abdominal obesity indices and lung function decline in a Chinese General Population. Diabetes Metab Syndr Obes Targets Ther. 2020;13:141–50.

Article   CAS   Google Scholar  

Dixon AE, Peters U. The effect of obesity on lung function. Expert Rev Respir Med. 2018;12:755–67.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Palma G, Sorice GP, Genchi VA, Giordano F, Caccioppoli C, D’Oria R, et al. Adipose tissue inflammation and pulmonary dysfunction in obesity. Int J Mol Sci. 2022;23:7349.

Zeng X, Liu D, An Z, Li H, Song J, Wu W. Obesity parameters in relation to lung function levels in a large Chinese rural adult population. Epidemiol Health. 2021;43:e2021047.

Pan J, Xu L, Lam TH, Jiang CQ, Zhang WS, Jin YL, et al. Association of adiposity with pulmonary function in older Chinese: Guangzhou Biobank Cohort Study. Respir Med. 2017;132:102–8.

Bekkers MBM, Wijga AH, de Jongste JC, Kerkhof M, Postma D, Gehring U, et al. Waist circumference, BMI, and lung function in 8-year-old children: the PIAMA birth cohort study. Pediatr Pulmonol. 2013;48:674–82.

Feng K, Chen L, Han S-M, Zhu G-J. Ratio of waist circumference to chest circumference is inversely associated with lung function in Chinese children and adolescents. Respirol Carlton Vic. 2012;17:1114–8.

Wen J, Wei C, Giri M, Zhuang R, Shuliang G. Association between serum uric acid/serum creatinine ratios and lung function in the general American population: National Health and Nutrition Examination Survey (NHANES), 2007–2012. BMJ Open Respir Res. 2023;10:e001513.

Chen J, Zhu L, Yao X, Zhu Z. The association between abdominal obesity and femoral neck bone mineral density in older adults. J Orthop Surg. 2023;18:171.

Everitt BS, Howell DC. Encyclopedia of statistics in behavioral science. Hoboken, N.J: Wiley; 2005.

Book   Google Scholar  

Zhang X, Chen H, Gu K, Jiang X. Association of Body Mass Index and Abdominal obesity with the risk of airflow obstruction: National Health and Nutrition Examination Survey (NHANES) 2007–2012. COPD J Chronic Obstr Pulm Dis. 2022;19:99–108.

Chen Y, Rennie D, Cormier Y, Dosman JA. Waist circumference associated with pulmonary function in children. Pediatr Pulmonol. 2009;44:216–21.

Molani Gol R, Rafraf M. Association between abdominal obesity and pulmonary function in apparently healthy adults: a systematic review. Obes Res Clin Pract. 2021;15:415–24.

Littleton SW. Impact of obesity on respiratory function. Respirol Carlton Vic. 2012;17:43–9.

Chapman DG, Berend N, King GG, Salome CM. Increased airway closure is a determinant of airway hyperresponsiveness. Eur Respir J. 2008;32:1563–9.

Littleton SW, Tulaimat A. The effects of obesity on lung volumes and oxygenation. Respir Med. 2017;124:15–20.

Agudelo CW, Samaha G, Garcia-Arcos I. Alveolar lipids in pulmonary disease. A review. Lipids Health Dis. 2020;19:122.

Grassi L, Kacmarek R, Berra L. Ventilatory mechanics in the patient with obesity. Anesthesiology. 2020;132:1246–56.

Chen Y, Rennie D, Cormier YF, Dosman J. Waist circumference is associated with pulmonary function in normal-weight, overweight, and obese subjects. Am J Clin Nutr. 2007;85:35–9.

Kwon H, Kim D, Kim JS. Body Fat distribution and the risk of Incident Metabolic Syndrome: a longitudinal cohort study. Sci Rep. 2017;7:10955.

Mu G, Zhou Y, Ma J, Guo Y, Xiao L, Zhou M, et al. Combined effect of central obesity and urinary PAH metabolites on lung function: a cross-sectional study in urban adults. Respir Med. 2019;152:67–73.

Arteaga-Solis E, Zee T, Emala CW, Vinson C, Wess J, Karsenty G. Inhibition of leptin regulation of parasympathetic signaling as a cause of extreme body weight-associated asthma. Cell Metab. 2013;17:35–48.

Saltiel AR, Olefsky JM. Inflammatory mechanisms linking obesity and metabolic disease. J Clin Invest. 2017;127:1–4.

Mancuso P. Obesity and lung inflammation. J Appl Physiol Bethesda Md 1985. 2010;108:722–8.

Google Scholar  

Ren Z, Zhao A, Wang Y, Meng L, Szeto IM-Y, Li T, et al. Association between Dietary Inflammatory Index, C-Reactive protein and metabolic syndrome: a cross-sectional study. Nutrients. 2018;10:831.

Gould JM, Weiser JN. Expression of C-reactive protein in the human respiratory tract. Infect Immun. 2001;69:1747–54.

Määttä AM, Kotaniemi-Syrjänen A, Malmström K, Malmberg LP, Sundvall J, Pelkonen AS, et al. Vitamin D, high-sensitivity C-reactive protein, and airway hyperresponsiveness in infants with recurrent respiratory symptoms. Ann Allergy Asthma Immunol off Publ Am Coll Allergy Asthma Immunol. 2017;119:227–31.

Sunyer J, Pistelli R, Plana E, Andreani M, Baldari F, Kolz M, et al. Systemic inflammation, genetic susceptibility and lung function. Eur Respir J. 2008;32:92–7.

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This study was supported by the Natural Science Foundation of Education Department of Anhui Province (No. 2022AH051221), Anhui Province Key Laboratory of Biological Macromolecules Research of Wannan Medical College (No. LAB202204) and Anhui Province Key Clinical Specialist Construction Programs.

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ZXC and QWW designed the study and wrote the manuscript. LDZ, LL, LQJ, DQL, and JJH performed the statistical analysis and prepared Figs. 1 and 2. All authors reviewed and approved the final manuscript.

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Xu, Z., Zhuang, L., Li, L. et al. Association between waist circumference and lung function in American middle-aged and older adults: findings from NHANES 2007–2012. J Health Popul Nutr 43 , 98 (2024). https://doi.org/10.1186/s41043-024-00592-6

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• Published: 19 June 2024

Detection of microplastics in the human penis

• Jason Codrington   ORCID: orcid.org/0009-0003-1490-4211 1 ,
• Alexandra Aponte Varnum 1 ,
• Lars Hildebrandt 2 ,
• Daniel Pröfrock 2 ,
• Joginder Bidhan 1 ,
• Kajal Khodamoradi   ORCID: orcid.org/0000-0003-2951-4382 1 ,
• Anke-Lisa Höhme 3 ,
• Martin Held   ORCID: orcid.org/0000-0003-1869-463X 3 ,
• Aymara Evans 1 ,
• David Velasquez   ORCID: orcid.org/0009-0003-0475-4918 1 ,
• Christina C. Yarborough 1 ,
• Bahareh Ghane-Motlagh 4 ,
• Ashutosh Agarwal 1 , 5 ,
• Justin Achua   ORCID: orcid.org/0000-0002-4159-439X 6 ,
• Edoardo Pozzi   ORCID: orcid.org/0000-0002-0228-7039 1 , 7 , 8 ,
• Francesco Mesquita 1 ,
• Francis Petrella 1 ,
• David Miller 1 &
• Ranjith Ramasamy 1  

International Journal of Impotence Research ( 2024 ) Cite this article

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• Medical research
• Sexual dysfunction

The proliferation of microplastics (MPs) represents a burgeoning environmental and health crisis. Measuring less than 5 mm in diameter, MPs have infiltrated atmospheric, freshwater, and terrestrial ecosystems, penetrating commonplace consumables like seafood, sea salt, and bottled beverages. Their size and surface area render them susceptible to chemical interactions with physiological fluids and tissues, raising bioaccumulation and toxicity concerns. Human exposure to MPs occurs through ingestion, inhalation, and dermal contact. To date, there is no direct evidence identifying MPs in penile tissue. The objective of this study was to assess for potential aggregation of MPs in penile tissue. Tissue samples were extracted from six individuals who underwent surgery for a multi-component inflatable penile prosthesis (IPP). Samples were obtained from the corpora using Adson forceps before corporotomy dilation and device implantation and placed into cleaned glassware. A control sample was collected and stored in a McKesson specimen plastic container. The tissue fractions were analyzed using the Agilent 8700 Laser Direct Infrared (LDIR) Chemical Imaging System (Agilent Technologies. Moreover, the morphology of the particles was investigated by a Zeiss Merlin Scanning Electron Microscope (SEM), complementing the detection range of LDIR to below 20 µm. MPs via LDIR were identified in 80% of the samples, ranging in size from 20–500 µm. Smaller particles down to 2 µm were detected via SEM. Seven types of MPs were found in the penile tissue, with polyethylene terephthalate (47.8%) and polypropylene (34.7%) being the most prevalent. The detection of MPs in penile tissue raises inquiries on the ramifications of environmental pollutants on sexual health. Our research adds a key dimension to the discussion on man-made pollutants, focusing on MPs in the male reproductive system.

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Schwabl P, Köppel S, Königshofer P, Bucsics T, Trauner M, Reiberger T, et al. Detection of various microplastics in human stool: a prospective case series. Ann Intern Med. 2019;171:453–7.

Article   PubMed   Google Scholar  

Zhu L, Zhu J, Zuo R, Xu Q, Qian Y, An L. Identification of microplastics in human placenta using laser direct infrared spectroscopy. Sci Total Environ. 2023;856:159060

Article   CAS   PubMed   Google Scholar  

Ragusa A, Svelato A, Santacroce C, Catalano P, Notarstefano V, Carnevali O, et al. Plasticenta: first evidence of microplastics in human placenta. Environ Int. 2021;146:106274.

Amato-Lourenço LF, Carvalho-Oliveira R, Júnior GR, Dos Santos Galvão L, Ando RA, Mauad T. Presence of airborne microplastics in human lung tissue. J Hazard Mater. 2021;416:126124.

Jenner LC, Rotchell JM, Bennett RT, Cowen M, Tentzeris V, Sadofsky LR. Detection of microplastics in human lung tissue using μFTIR spectroscopy. Sci Total Environ. 2022;831:154907.

Yang Y, Xie E, Du Z, Peng Z, Han Z, Li L, et al. Detection of various microplastics in patients undergoing cardiac surgery. Environ Sci Technol. 2023;57:10911–8.

Wang C, Zhao J, Xing B. Environmental source, fate, and toxicity of microplastics. J Hazard Mater. 2021;407:124357.

da Silva Brito WA, Mutter F, Wende K, Cecchini AL, Schmidt A, Bekeschus S. Consequences of nano and microplastic exposure in rodent models: the known and unknown. Part Fibre Toxicol. 2022;19:28.

Article   PubMed   PubMed Central   Google Scholar  

Wright SL, Kelly FJ. Plastic and human health: a micro issue? Environ Sci Technol. 2017;51:6634–47.

Ragusa A, Notarstefano V, Svelato A, Belloni A, Gioacchini G, Blondeel C, et al. Raman microspectroscopy detection and characterisation of microplastics in human breastmilk. Polymers. 2022;14:2700.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Cox KD, Covernton GA, Davies HL, Dower JF, Juanes F, Dudas SE. Human consumption of microplastics. Environ Sci Technol. 2019;53:7068–74.

Barceló D, Picó Y, Alfarhan AH. Microplastics: detection in human samples, cell line studies, and health impacts. Environ Toxicol Pharmacol. 2023;101:104204.

Gautam R, Jo J, Acharya M, Maharjan A, Lee D, KC PB. et al. Evaluation of potential toxicity of polyethylene microplastics on human derived cell lines. Sci Total Environ. 2022;838:156089

Sorci G, Loiseau C. Should we worry about the accumulation of microplastics in human organs? EBioMedicine. 2022;82:104191.

Wang W, Ge J, Yu X. Bioavailability and toxicity of microplastics to fish species: A review. Ecotoxicol Environ Saf. 2020;189:109913.

Yong CQY, Valiyaveettil S, Tang BL. Toxicity of microplastics and nanoplastics in mammalian systems. Int J Environ Res Public Health. 2020;17:1509.

D’Angelo S, Meccariello R. Microplastics: a threat for male fertility. Int J Environ Res Public Health. 2021;18:2392.

Hou B, Wang F, Liu T, Wang Z. Reproductive toxicity of polystyrene microplastics: In vivo experimental study on testicular toxicity in mice. J Hazard Mater. 2021;405:124028.

Jaeger VK, Walker UA. Erectile dysfunction in systemic sclerosis. Curr Rheumatol Rep. 2016;18:49.

Jung J, Jo HW, Kwon H, Jeong NY. Clinical neuroanatomy and neurotransmitter-mediated regulation of penile erection. Int Neurourol J. 2014;18:58–62.

Sopko NA, Hannan JL, Bivalacqua TJ. Understanding and targeting the Rho kinase pathway in erectile dysfunction. Nat Rev Urol. 2014;11:622–8.

Sorkhi S, Sanchez CC, Cho MC, Cho SY, Chung H, Park MG, et al. Transpelvic magnetic stimulation enhances penile microvascular perfusion in a rat model: a novel interventional strategy to prevent penile fibrosis after cavernosal nerve injury. World J Mens Health. 2022;40:501–8.

Hildebrandt L, Zimmermann T, Primpke S, Fischer D, Gerdts G, Pröfrock D. Comparison and uncertainty evaluation of two centrifugal separators for microplastic sampling. J Hazard Mater. 2021;414:125482.

Morgado V, Palma C, Bettencourt da Silva RJN. Bottom-up evaluation of the uncertainty of the quantification of microplastics contamination in sediment samples. Environ Sci Technol. 2022;56:11080–90.

Hildebrandt L, El Gareb F, Zimmermann T, Klein O, Kerstan A, Emeis KC, et al. Spatial distribution of microplastics in the tropical Indian Ocean based on laser direct infrared imaging and microwave-assisted matrix digestion. Environ Pollut Barking Essex 1987. 2022;307:119547.

CAS   Google Scholar  

Hansen J, Hildebrandt L, Zimmermann T, El Gareb F, Fischer EK, Pröfrock D. Quantification and characterization of microplastics in surface water samples from the Northeast Atlantic Ocean using laser direct infrared imaging. Mar Pollut Bull. 2023;190:114880.

Rani M, Ducoli S, Depero LE, Prica M, Tubić A, Ademovic Z, et al. A complete guide to extraction methods of microplastics from complex environmental matrices. Molecules. 2023;28:5710.

Enders K, Lenz R, Beer S, Stedmon CA. Extraction of microplastic from biota: recommended acidic digestion destroys common plastic polymers. ICES J Mar Sci. 2017;74:326–31.

Article   Google Scholar  

Lopes C, Fernández-González V, Muniategui-Lorenzo S, Caetano M, Raimundo J. Improved methodology for microplastic extraction from gastrointestinal tracts of fat fish species. Mar Pollut Bull. 2022;181:113911.

Barboza LGA, Dick Vethaak A, Lavorante BRBO, Lundebye AK, Guilhermino L. Marine microplastic debris: an emerging issue for food security, food safety and human health. Mar Pollut Bull. 2018;133:336–48.

Wang S, Lu W, Cao Q, Tu C, Zhong C, Qiu L, et al. Microplastics in the lung tissues associated with blood test index. Toxics. 2023;11:759.

Ribeiro VV, Nobre CR, Moreno BB, Semensatto D, Sanz-Lazaro C, Moreira LB, et al. Oysters and mussels as equivalent sentinels of microplastics and natural particles in coastal environments. Sci Total Environ. 2023;874:162468.

Ourgaud M, Phuong NN, Papillon L, Panagiotopoulos C, Galgani F, Schmidt N, et al. Identification and quantification of microplastics in the marine environment using the laser direct infrared (LDIR) technique. Environ Sci Technol. 2022;56:9999–10009.

Zhao Q, Zhu L, Weng J, Jin Z, Cao Y, Jiang H, et al. Detection and characterization of microplastics in the human testis and semen. Sci Total Environ. 2023;877:162713.

Wu P, Lin S, Cao G, Wu J, Jin H, Wang C, et al. Absorption, distribution, metabolism, excretion and toxicity of microplastics in the human body and health implications. J Hazard Mater. 2022;437:129361.

Urbanek AK, Rymowicz W, Mirończuk AM. Degradation of plastics and plastic-degrading bacteria in cold marine habitats. Appl Microbiol Biotechnol. 2018;102:7669–78.

Jin Y, Qiu J, Zhang L, Zhu M. [Biodegradation of polyethylene terephthalate: a review]. Sheng Wu Gong Cheng Xue Bao Chin J Biotechnol. 2023;39:4445–62.

Çaykara T, Sande MG, Azoia N, Rodrigues LR, Silva CJ. Exploring the potential of polyethylene terephthalate in the design of antibacterial surfaces. Med Microbiol Immunol. 2020;209:363–72.

Sharifinia M, Bahmanbeigloo ZA, Keshavarzifard M, Khanjani MH, Lyons BP. Microplastic pollution as a grand challenge in marine research: A closer look at their adverse impacts on the immune and reproductive systems. Ecotoxicol Environ Saf. 2020;204:111109.

Potential toxicity of polystyrene microplastic particles. Scientific Reports. Available from: https://www.nature.com/articles/s41598-020-64464-9

Zhang C, Chen J, Ma S, Sun Z, Wang Z. Microplastics may be a significant cause of male infertility. Am J Mens Health. 2022;16:15579883221096549.

Compa M, Capó X, Alomar C, Deudero S, Sureda A. A meta-analysis of potential biomarkers associated with microplastic ingestion in marine fish. Environ Toxicol Pharmacol. 2024;107:104414.

Hildebrandt L, Nack FL, Zimmermann T, Pröfrock D. Microplastics as a Trojan horse for trace metals. J Hazard Mater Lett. 2021;2:100035.

Article   CAS   Google Scholar  

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Desai Sethi Urology Institute, Miller School of Medicine, University of Miami, Miami, FL, USA

Jason Codrington, Alexandra Aponte Varnum, Joginder Bidhan, Kajal Khodamoradi, Aymara Evans, David Velasquez, Christina C. Yarborough, Ashutosh Agarwal, Edoardo Pozzi, Francesco Mesquita, Francis Petrella, David Miller & Ranjith Ramasamy

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Dr. J.T. MacDonald Foundation BioNIUM, Miller School of Medicine, University of Miami, Miami, FL, USA

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Department of Biomedical Engineering, University of Miami, Miami, FL, USA

Ashutosh Agarwal

University of Colorado, Anschutz Medical Campus, Aurora, CO, USA

Justin Achua

Vita-Salute San Raffaele University, Milan, Italy

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Contributions

Jason Codrington—conceptualization, methodology, investigation, project administration, data curation, visualization, writing—original draft, editing. Alexandra Aponte Varnum—investigation, writing—original draft, editing, data curation, visualization. Lars Hildebrandt—investigation, writing—original draft, validation, resources. Daniel Pröfrock—investigation, editing, validation, resources. Joginder Bidhan—resources, writing—original draft. Kajal Khodamoradi—project administration, resources. Anke-Lisa Höhme—investigation, visualization. Martin Held—writing—original draft, editing. Aymara Evans—writing—original draft. David Velasquez—writing—original draft. Christina C. Yarborough—writing—original draft. Bahareh Ghane-Motlagh—investigation. Ashutosh Agarwal—investigation. Justin Achua—writing—original draft. Edoardo Pozzi—editing. Francesco Mesquita—editing. Francis Petrella—writing—review. David Miller—writing—review. Ranjith Ramasamy—conceptualization, methodology, project administration, resources, supervision, editing, funding acquisition

Corresponding author

Correspondence to Ranjith Ramasamy .

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Dr. Edoardo Pozzi is currently an Associate Editor for the International Journal of Impotence Research.

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The study was approved by the Institutional Review Board of the University of Miami (Study # 20150740) and conducted following the Declaration of Helsinki. All patients provided written and informed consent to participate in the study.

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Codrington, J., Varnum, A.A., Hildebrandt, L. et al. Detection of microplastics in the human penis. Int J Impot Res (2024). https://doi.org/10.1038/s41443-024-00930-6

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