different data collection methods in qualitative research

Chapter 10. Introduction to Data Collection Techniques

Introduction.

Now that we have discussed various aspects of qualitative research, we can begin to collect data. This chapter serves as a bridge between the first half and second half of this textbook (and perhaps your course) by introducing techniques of data collection. You’ve already been introduced to some of this because qualitative research is often characterized by the form of data collection; for example, an ethnographic study is one that employs primarily observational data collection for the purpose of documenting and presenting a particular culture or ethnos. Thus, some of this chapter will operate as a review of material already covered, but we will be approaching it from the data-collection side rather than the tradition-of-inquiry side we explored in chapters 2 and 4.

Revisiting Approaches

There are four primary techniques of data collection used in qualitative research: interviews, focus groups, observations, and document review. [1] There are other available techniques, such as visual analysis (e.g., photo elicitation) and biography (e.g., autoethnography) that are sometimes used independently or supplementarily to one of the main forms. Not to confuse you unduly, but these various data collection techniques are employed differently by different qualitative research traditions so that sometimes the technique and the tradition become inextricably entwined. This is largely the case with observations and ethnography. The ethnographic tradition is fundamentally based on observational techniques. At the same time, traditions other than ethnography also employ observational techniques, so it is worthwhile thinking of “tradition” and “technique” separately (see figure 10.1).

Figure 10.1. Data Collection Techniques

Each of these data collection techniques will be the subject of its own chapter in the second half of this textbook. This chapter serves as an orienting overview and as the bridge between the conceptual/design portion of qualitative research and the actual practice of conducting qualitative research.

Overview of the Four Primary Approaches

Interviews are at the heart of qualitative research. Returning to epistemological foundations, it is during the interview that the researcher truly opens herself to hearing what others have to say, encouraging her interview subjects to reflect deeply on the meanings and values they hold. Interviews are used in almost every qualitative tradition but are particularly salient in phenomenological studies, studies seeking to understand the meaning of people’s lived experiences.

Focus groups can be seen as a type of interview, one in which a group of persons (ideally between five and twelve) is asked a series of questions focused on a particular topic or subject. They are sometimes used as the primary form of data collection, especially outside academic research. For example, businesses often employ focus groups to determine if a particular product is likely to sell. Among qualitative researchers, it is often used in conjunction with any other primary data collection technique as a form of “triangulation,” or a way of increasing the reliability of the study by getting at the object of study from multiple directions. [2] Some traditions, such as feminist approaches, also see the focus group as an important “consciousness-raising” tool.

If interviews are at the heart of qualitative research, observations are its lifeblood. Researchers who are more interested in the practices and behaviors of people than what they think or who are trying to understand the parameters of an organizational culture rely on observations as their primary form of data collection. The notes they make “in the field” (either during observations or afterward) form the “data” that will be analyzed. Ethnographers, those seeking to describe a particular ethnos, or culture, believe that observations are more reliable guides to that culture than what people have to say about it. Observations are thus the primary form of data collection for ethnographers, albeit often supplemented with in-depth interviews.

Some would say that these three—interviews, focus groups, and observations—are really the foundational techniques of data collection. They are far and away the three techniques most frequently used separately, in conjunction with one another, and even sometimes in mixed methods qualitative/quantitative studies. Document review, either as a form of content analysis or separately, however, is an important addition to the qualitative researcher’s toolkit and should not be overlooked (figure 10.1). Although it is rare for a qualitative researcher to make document review their primary or sole form of data collection, including documents in the research design can help expand the reach and the reliability of a study. Document review can take many forms, from historical and archival research, in which the researcher pieces together a narrative of the past by finding and analyzing a variety of “documents” and records (including photographs and physical artifacts), to analyses of contemporary media content, as in the case of compiling and coding blog posts or other online commentaries, and content analysis that identifies and describes communicative aspects of media or documents.

In addition to these four major techniques, there are a host of emerging and incidental data collection techniques, from photo elicitation or photo voice, in which respondents are asked to comment upon a photograph or image (particularly useful as a supplement to interviews when the respondents are hesitant or unable to answer direct questions), to autoethnographies, in which the researcher uses his own position and life to increase our understanding about a phenomenon and its historical and social context.

Taken together, these techniques provide a wide range of practices and tools with which to discover the world. They are particularly suited to addressing the questions that qualitative researchers ask—questions about how things happen and why people act the way they do, given particular social contexts and shared meanings about the world (chapter 4).

Triangulation and Mixed Methods

Because the researcher plays such a large and nonneutral role in qualitative research, one that requires constant reflectivity and awareness (chapter 6), there is a constant need to reassure her audience that the results she finds are reliable. Quantitative researchers can point to any number of measures of statistical significance to reassure their audiences, but qualitative researchers do not have math to hide behind. And she will also want to reassure herself that what she is hearing in her interviews or observing in the field is a true reflection of what is going on (or as “true” as possible, given the problem that the world is as large and varied as the elephant; see chapter 3). For those reasons, it is common for researchers to employ more than one data collection technique or to include multiple and comparative populations, settings, and samples in the research design (chapter 2). A single set of interviews or initial comparison of focus groups might be conceived as a “pilot study” from which to launch the actual study. Undergraduate students working on a research project might be advised to think about their projects in this way as well. You are simply not going to have enough time or resources as an undergraduate to construct and complete a successful qualitative research project, but you may be able to tackle a pilot study. Graduate students also need to think about the amount of time and resources they have for completing a full study. Masters-level students, or students who have one year or less in which to complete a program, should probably consider their study as an initial exploratory pilot. PhD candidates might have the time and resources to devote to the type of triangulated, multifaceted research design called for by the research question.

We call the use of multiple qualitative methods of data collection and the inclusion of multiple and comparative populations and settings “triangulation.” Using different data collection methods allows us to check the consistency of our findings. For example, a study of the vaccine hesitant might include a set of interviews with vaccine-hesitant people and a focus group of the same and a content analysis of online comments about a vaccine mandate. By employing all three methods, we can be more confident of our interpretations from the interviews alone (especially if we are hearing the same thing throughout; if we are not, then this is a good sign that we need to push a little further to find out what is really going on). [3] Methodological triangulation is an important tool for increasing the reliability of our findings and the overall success of our research.

Methodological triangulation should not be confused with mixed methods techniques, which refer instead to the combining of qualitative and quantitative research methods. Mixed methods studies can increase reliability, but that is not their primary purpose. Mixed methods address multiple research questions, both the “how many” and “why” kind, or the causal and explanatory kind. Mixed methods will be discussed in more detail in chapter 15.

Let us return to the three examples of qualitative research described in chapter 1: Cory Abramson’s study of aging ( The End Game) , Jennifer Pierce’s study of lawyers and discrimination ( Racing for Innocence ), and my own study of liberal arts college students ( Amplified Advantage ). Each of these studies uses triangulation.

Abramson’s book is primarily based on three years of observations in four distinct neighborhoods. He chose the neighborhoods in such a way to maximize his ability to make comparisons: two were primarily middle class and two were primarily poor; further, within each set, one was predominantly White, while the other was either racially diverse or primarily African American. In each neighborhood, he was present in senior centers, doctors’ offices, public transportation, and other public spots where the elderly congregated. [4] The observations are the core of the book, and they are richly written and described in very moving passages. But it wasn’t enough for him to watch the seniors. He also engaged with them in casual conversation. That, too, is part of fieldwork. He sometimes even helped them make it to the doctor’s office or get around town. Going beyond these interactions, he also interviewed sixty seniors, an equal amount from each of the four neighborhoods. It was in the interviews that he could ask more detailed questions about their lives, what they thought about aging, what it meant to them to be considered old, and what their hopes and frustrations were. He could see that those living in the poor neighborhoods had a more difficult time accessing care and resources than those living in the more affluent neighborhoods, but he couldn’t know how the seniors understood these difficulties without interviewing them. Both forms of data collection supported each other and helped make the study richer and more insightful. Interviews alone would have failed to demonstrate the very real differences he observed (and that some seniors would not even have known about). This is the value of methodological triangulation.

Pierce’s book relies on two separate forms of data collection—interviews with lawyers at a firm that has experienced a history of racial discrimination and content analyses of news stories and popular films that screened during the same years of the alleged racial discrimination. I’ve used this book when teaching methods and have often found students struggle with understanding why these two forms of data collection were used. I think this is because we don’t teach students to appreciate or recognize “popular films” as a legitimate form of data. But what Pierce does is interesting and insightful in the best tradition of qualitative research. Here is a description of the content analyses from a review of her book:

In the chapter on the news media, Professor Pierce uses content analysis to argue that the media not only helped shape the meaning of affirmative action, but also helped create white males as a class of victims. The overall narrative that emerged from these media accounts was one of white male innocence and victimization. She also maintains that this narrative was used to support “neoconservative and neoliberal political agendas” (p. 21). The focus of these articles tended to be that affirmative action hurt white working-class and middle-class men particularly during the recession in the 1980s (despite statistical evidence that people of color were hurt far more than white males by the recession). In these stories fairness and innocence were seen in purely individual terms. Although there were stories that supported affirmative action and developed a broader understanding of fairness, the total number of stories slanted against affirmative action from 1990 to 1999. During that time period negative stories always outnumbered those supporting the policy, usually by a ratio of 3:1 or 3:2. Headlines, the presentation of polling data, and an emphasis in stories on racial division, Pierce argues, reinforced the story of white male victimization. Interestingly, the news media did very few stories on gender and affirmative action. The chapter on the film industry from 1989 to 1999 reinforces Pierce’s argument and adds another layer to her interpretation of affirmative action during this time period. She sampled almost 60 Hollywood films with receipts ranging from four million to 184 million dollars. In this chapter she argues that the dominant theme of these films was racial progress and the redemption of white Americans from past racism. These movies usually portrayed white, elite, and male experiences. People of color were background figures who supported the protagonist and “anointed” him as a savior (p. 45). Over the course of the film the protagonists move from “innocence to consciousness” concerning racism. The antagonists in these films most often were racist working-class white men. A Time to Kill , Mississippi Burning , Amistad , Ghosts of Mississippi , The Long Walk Home , To Kill a Mockingbird , and Dances with Wolves receive particular analysis in this chapter, and her examination of them leads Pierce to conclude that they infused a myth of racial progress into America’s cultural memory. White experiences of race are the focus and contemporary forms of racism are underplayed or omitted. Further, these films stereotype both working-class and elite white males, and underscore the neoliberal emphasis on individualism. ( Hrezo 2012 )

With that context in place, Pierce then turned to interviews with attorneys. She finds that White male attorneys often misremembered facts about the period in which the law firm was accused of racial discrimination and that they often portrayed their firms as having made substantial racial progress. This was in contrast to many of the lawyers of color and female lawyers who remembered the history differently and who saw continuing examples of racial (and gender) discrimination at the law firm. In most of the interviews, people talked about individuals, not structure (and these are attorneys, who really should know better!). By including both content analyses and interviews in her study, Pierce is better able to situate the attorney narratives and explain the larger context for the shared meanings of individual innocence and racial progress. Had this been a study only of films during this period, we would not know how actual people who lived during this period understood the decisions they made; had we had only the interviews, we would have missed the historical context and seen a lot of these interviewees as, well, not very nice people at all. Together, we have a study that is original, inventive, and insightful.

My own study of how class background affects the experiences and outcomes of students at small liberal arts colleges relies on mixed methods and triangulation. At the core of the book is an original survey of college students across the US. From analyses of this survey, I can present findings on “how many” questions and descriptive statistics comparing students of different social class backgrounds. For example, I know and can demonstrate that working-class college students are less likely to go to graduate school after college than upper-class college students are. I can even give you some estimates of the class gap. But what I can’t tell you from the survey is exactly why this is so or how it came to be so . For that, I employ interviews, focus groups, document reviews, and observations. Basically, I threw the kitchen sink at the “problem” of class reproduction and higher education (i.e., Does college reduce class inequalities or make them worse?). A review of historical documents provides a picture of the place of the small liberal arts college in the broader social and historical context. Who had access to these colleges and for what purpose have always been in contest, with some groups attempting to exclude others from opportunities for advancement. What it means to choose a small liberal arts college in the early twenty-first century is thus different for those whose parents are college professors, for those whose parents have a great deal of money, and for those who are the first in their family to attend college. I was able to get at these different understandings through interviews and focus groups and to further delineate the culture of these colleges by careful observation (and my own participation in them, as both former student and current professor). Putting together individual meanings, student dispositions, organizational culture, and historical context allowed me to present a story of how exactly colleges can both help advance first-generation, low-income, working-class college students and simultaneously amplify the preexisting advantages of their peers. Mixed methods addressed multiple research questions, while triangulation allowed for this deeper, more complex story to emerge.

In the next few chapters, we will explore each of the primary data collection techniques in much more detail. As we do so, think about how these techniques may be productively joined for more reliable and deeper studies of the social world.

Advanced Reading: Triangulation

Denzin ( 1978 ) identified four basic types of triangulation: data, investigator, theory, and methodological. Properly speaking, if we use the Denzin typology, the use of multiple methods of data collection and analysis to strengthen one’s study is really a form of methodological triangulation. It may be helpful to understand how this differs from the other types.

Data triangulation occurs when the researcher uses a variety of sources in a single study. Perhaps they are interviewing multiple samples of college students. Obviously, this overlaps with sample selection (see chapter 5). It is helpful for the researcher to understand that these multiple data sources add strength and reliability to the study. After all, it is not just “these students here” but also “those students over there” that are experiencing this phenomenon in a particular way.

Investigator triangulation occurs when different researchers or evaluators are part of the research team. Intercoding reliability is a form of investigator triangulation (or at least a way of leveraging the power of multiple researchers to raise the reliability of the study).

Theory triangulation is the use of multiple perspectives to interpret a single set of data, as in the case of competing theoretical paradigms (e.g., a human capital approach vs. a Bourdieusian multiple capital approach).

Methodological triangulation , as explained in this chapter, is the use of multiple methods to study a single phenomenon, issue, or problem.

Further Readings

Carter, Nancy, Denise Bryant-Lukosius, Alba DiCenso, Jennifer Blythe, Alan J. Neville. 2014. “The Use of Triangulation in Qualitative Research.” Oncology Nursing Forum 41(5):545–547. Discusses the four types of triangulation identified by Denzin with an example of the use of focus groups and in-depth individuals.

Mathison, Sandra. 1988. “Why Triangulate?” Educational Researcher 17(2):13–17. Presents three particular ways of assessing validity through the use of triangulated data collection: convergence, inconsistency, and contradiction.

Tracy, Sarah J. 2010. “Qualitative Quality: Eight ‘Big-Tent’ Criteria for Excellent Qualitative Research.” Qualitative Inquiry 16(10):837–851. Focuses on triangulation as a criterion for conducting valid qualitative research.

• Marshall and Rossman ( 2016 ) state this slightly differently. They list four primary methods for gathering information: (1) participating in the setting, (2) observing directly, (3) interviewing in depth, and (4) analyzing documents and material culture (141). An astute reader will note that I have collapsed participation into observation and that I have distinguished focus groups from interviews. I suspect that this distinction marks me as more of an interview-based researcher, while Marshall and Rossman prioritize ethnographic approaches. The main point of this footnote is to show you, the reader, that there is no single agreed-upon number of approaches to collecting qualitative data. ↵
• See “ Advanced Reading: Triangulation ” at end of this chapter. ↵
• We can also think about triangulating the sources, as when we include comparison groups in our sample (e.g., if we include those receiving vaccines, we might find out a bit more about where the real differences lie between them and the vaccine hesitant); triangulating the analysts (building a research team so that your interpretations can be checked against those of others on the team); and even triangulating the theoretical perspective (as when we “try on,” say, different conceptualizations of social capital in our analyses). ↵

Introduction to Qualitative Research Methods Copyright © 2023 by Allison Hurst is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

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The SAGE Handbook of Qualitative Data Collection

• Edited by: Uwe Flick
• Publisher: SAGE Publications Ltd
• Publication year: 2018
• Online pub date: December 13, 2018
• Discipline: Anthropology
• Methods: Qualitative data collection , Mixed methods
• DOI: https:// doi. org/10.4135/9781526416070
• Keywords: data collection , interviews , mixed methods , qualitative data collection , qualitative research , social research , triangulation Show all Show less
• Print ISBN: 9781473952133
• Online ISBN: 9781526416070
• Buy the book icon link

Subject index

How we understand and define qualitative data is changing, with implications not only for the techniques of data analysis, but also how data are collected. New devices, technologies and online spaces open up new ways for researchers to approach and collect images, moving images, text and talk. The SAGE Handbook of Qualitative Data Collection systematically explores the approaches, techniques, debates and new frontiers for creating, collecting and producing qualitative data. Bringing together contributions from internationally leading scholars in the field, the handbook offers a state-of-the-art look at key themes across six thematic parts: Part I Charting the Routes Part II Concepts, Contexts, Basics Part III Types of Data and How to Collect Them Part IV Digital and Internet Data Part V Triangulation and Mixed Methods Part VI Collecting Data in Specific Populations

Front Matter

• International Advisory Editorial Board
• List of Figures
• List of Tables
• Notes on the Editor and Contributors
• Acknowledgements
• Chapter 1 | Doing Qualitative Data Collection – Charting the Routes
• Chapter 2 | Collecting Qualitative Data: A Realist Approach
• Chapter 3 | Ethics of Qualitative Data Collection
• Chapter 4 | Deduction, Induction, and Abduction
• Chapter 5 | Upside Down – Reinventing Research Design
• Chapter 6 | Sampling and Generalization
• Chapter 7 | Accessing the Research Field
• Chapter 8 | Recording and Transcribing Social Interaction
• Chapter 9 | Collecting Data in Other Languages – Strategies for Cross-Language Research in Multilingual Societies
• Chapter 10 | From Scholastic to Emic Comparison: Generating Comparability and Handling Difference in Ethnographic Research
• Chapter 11 | Data Collection in Secondary Analysis
• Chapter 12 | The Virtues of Naturalistic Data
• Chapter 13 | Performance, Hermeneutics, Interpretation
• Chapter 14 | Quality of Data Collection
• Chapter 15 | Qualitative Interviews
• Chapter 16 | Focus Groups
• Chapter 17 | Narrative Data
• Chapter 18 | Data Collection in Conversation Analysis
• Chapter 19 | Collecting Data for Analyzing Discourses
• Chapter 20 | Observations
• Chapter 21 | Doing Ethnography: Ways and Reasons
• Chapter 22 | Go-Alongs
• Chapter 23 | Videography
• Chapter 24 | Collecting Documents as Data
• Chapter 25 | Collecting Images as Data
• Chapter 26 | Collecting Media Data: TV and Film Studies
• Chapter 27 | Sounds as Data
• Chapter 28 | The Concept of ‘Data’ in Digital Research
• Chapter 29 | Moving Through Digital Flows: An Epistemological and Practical Approach
• Chapter 30 | Ethics in Digital Research
• Chapter 31 | Collecting Data for Analyzing Blogs
• Chapter 32 | Collecting Qualitative Data from Facebook: Approaches and Methods
• Chapter 33 | Troubling the Concept of Data in Qualitative Digital Research
• Chapter 34 | Triangulation in Data Collection
• Chapter 35 | Toward an Understanding of a Qualitatively Driven Mixed Methods Data Collection and Analysis: Moving Toward a Theoretically Centered Mixed Methods Praxis
• Chapter 36 | Data-Related Issues in Qualitatively Driven Mixed-Method Designs: Sampling, Pacing, and Reflexivity
• Chapter 37 | Combining Digital and Physical Data
• Chapter 38 | Using Photographs in Interviews: When We Lack the Words to Say What Practice Means
• Chapter 39 | Collecting Qualitative Data with Children
• Chapter 40 | Collecting Qualitative Data with Older People
• Chapter 41 | Generating Qualitative Data with Experts and Elites
• Chapter 42 | Collecting Qualitative Data with Hard-to-Reach Groups

Back Matter

• Author Index

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• Open access
• Published: 27 May 2020

How to use and assess qualitative research methods

• Loraine Busetto   ORCID: orcid.org/0000-0002-9228-7875 1 ,
• Wolfgang Wick 1 , 2 &
• Christoph Gumbinger 1  

Neurological Research and Practice volume  2 , Article number:  14 ( 2020 ) Cite this article

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

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

What is qualitative research?

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

Why conduct qualitative research?

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

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

Research problems that can be approached particularly well using qualitative methods include assessing complex multi-component interventions or systems (of change), addressing questions beyond “what works”, towards “what works for whom when, how and why”, and focussing on intervention improvement rather than accreditation [ 7 , 9 , 10 , 11 , 12 ]. Using qualitative methods can also help shed light on the “softer” side of medical treatment. For example, while quantitative trials can measure the costs and benefits of neuro-oncological treatment in terms of survival rates or adverse effects, qualitative research can help provide a better understanding of patient or caregiver stress, visibility of illness or out-of-pocket expenses.

How to conduct qualitative research?

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

Iterative research process

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

Data collection

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

Document study

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

Observations

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

Semi-structured interviews

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

Focus groups

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

Choosing the “right” method

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

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

Possible combination of data collection methods

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

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

Data analysis

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

From data collection to data analysis

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

How to report qualitative research?

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

How to combine qualitative with quantitative research?

Qualitative methods can be combined with other methods in multi- or mixed methods designs, which “[employ] two or more different methods [ …] within the same study or research program rather than confining the research to one single method” [ 24 ]. Reasons for combining methods can be diverse, including triangulation for corroboration of findings, complementarity for illustration and clarification of results, expansion to extend the breadth and range of the study, explanation of (unexpected) results generated with one method with the help of another, or offsetting the weakness of one method with the strength of another [ 1 , 17 , 24 , 25 , 26 ]. The resulting designs can be classified according to when, why and how the different quantitative and/or qualitative data strands are combined. The three most common types of mixed method designs are the convergent parallel design , the explanatory sequential design and the exploratory sequential design. The designs with examples are shown in Fig.  4 .

Three common mixed methods designs

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

How to assess qualitative research?

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

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

Reflexivity

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

Sampling and saturation

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

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

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

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

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

Member checking

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

Stakeholder involvement

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

How not to assess qualitative research

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

Protocol adherence

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

Sample size

For the reasons explained above, qualitative research does not require specific sample sizes, nor does it require that the sample size be determined a priori [ 1 , 14 , 27 , 37 , 38 , 39 ]. Sample size can only be a useful quality indicator when related to the research purpose, the chosen methodology and the composition of the sample, i.e. who was included and why.

Randomisation

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

Interrater reliability, variability and other “objectivity checks”

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

Not being quantitative research

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

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

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Abbreviations

Endovascular treatment

Randomised Controlled Trial

Standard Operating Procedure

Standards for Reporting Qualitative Research

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Busetto, L., Wick, W. & Gumbinger, C. How to use and assess qualitative research methods. Neurol. Res. Pract. 2 , 14 (2020). https://doi.org/10.1186/s42466-020-00059-z

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• Published: 05 October 2018

Interviews and focus groups in qualitative research: an update for the digital age

• P. Gill 1 &
• J. Baillie 2  

British Dental Journal volume  225 ,  pages 668–672 ( 2018 ) Cite this article

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Highlights that qualitative research is used increasingly in dentistry. Interviews and focus groups remain the most common qualitative methods of data collection.

Suggests the advent of digital technologies has transformed how qualitative research can now be undertaken.

Suggests interviews and focus groups can offer significant, meaningful insight into participants' experiences, beliefs and perspectives, which can help to inform developments in dental practice.

Qualitative research is used increasingly in dentistry, due to its potential to provide meaningful, in-depth insights into participants' experiences, perspectives, beliefs and behaviours. These insights can subsequently help to inform developments in dental practice and further related research. The most common methods of data collection used in qualitative research are interviews and focus groups. While these are primarily conducted face-to-face, the ongoing evolution of digital technologies, such as video chat and online forums, has further transformed these methods of data collection. This paper therefore discusses interviews and focus groups in detail, outlines how they can be used in practice, how digital technologies can further inform the data collection process, and what these methods can offer dentistry.

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Introduction

Traditionally, research in dentistry has primarily been quantitative in nature. 1 However, in recent years, there has been a growing interest in qualitative research within the profession, due to its potential to further inform developments in practice, policy, education and training. Consequently, in 2008, the British Dental Journal (BDJ) published a four paper qualitative research series, 2 , 3 , 4 , 5 to help increase awareness and understanding of this particular methodological approach.

Since the papers were originally published, two scoping reviews have demonstrated the ongoing proliferation in the use of qualitative research within the field of oral healthcare. 1 , 6 To date, the original four paper series continue to be well cited and two of the main papers remain widely accessed among the BDJ readership. 2 , 3 The potential value of well-conducted qualitative research to evidence-based practice is now also widely recognised by service providers, policy makers, funding bodies and those who commission, support and use healthcare research.

Besides increasing standalone use, qualitative methods are now also routinely incorporated into larger mixed method study designs, such as clinical trials, as they can offer additional, meaningful insights into complex problems that simply could not be provided by quantitative methods alone. Qualitative methods can also be used to further facilitate in-depth understanding of important aspects of clinical trial processes, such as recruitment. For example, Ellis et al . investigated why edentulous older patients, dissatisfied with conventional dentures, decline implant treatment, despite its established efficacy, and frequently refuse to participate in related randomised clinical trials, even when financial constraints are removed. 7 Through the use of focus groups in Canada and the UK, the authors found that fears of pain and potential complications, along with perceived embarrassment, exacerbated by age, are common reasons why older patients typically refuse dental implants. 7

The last decade has also seen further developments in qualitative research, due to the ongoing evolution of digital technologies. These developments have transformed how researchers can access and share information, communicate and collaborate, recruit and engage participants, collect and analyse data and disseminate and translate research findings. 8 Where appropriate, such technologies are therefore capable of extending and enhancing how qualitative research is undertaken. 9 For example, it is now possible to collect qualitative data via instant messaging, email or online/video chat, using appropriate online platforms.

These innovative approaches to research are therefore cost-effective, convenient, reduce geographical constraints and are often useful for accessing 'hard to reach' participants (for example, those who are immobile or socially isolated). 8 , 9 However, digital technologies are still relatively new and constantly evolving and therefore present a variety of pragmatic and methodological challenges. Furthermore, given their very nature, their use in many qualitative studies and/or with certain participant groups may be inappropriate and should therefore always be carefully considered. While it is beyond the scope of this paper to provide a detailed explication regarding the use of digital technologies in qualitative research, insight is provided into how such technologies can be used to facilitate the data collection process in interviews and focus groups.

In light of such developments, it is perhaps therefore timely to update the main paper 3 of the original BDJ series. As with the previous publications, this paper has been purposely written in an accessible style, to enhance readability, particularly for those who are new to qualitative research. While the focus remains on the most common qualitative methods of data collection – interviews and focus groups – appropriate revisions have been made to provide a novel perspective, and should therefore be helpful to those who would like to know more about qualitative research. This paper specifically focuses on undertaking qualitative research with adult participants only.

Overview of qualitative research

Qualitative research is an approach that focuses on people and their experiences, behaviours and opinions. 10 , 11 The qualitative researcher seeks to answer questions of 'how' and 'why', providing detailed insight and understanding, 11 which quantitative methods cannot reach. 12 Within qualitative research, there are distinct methodologies influencing how the researcher approaches the research question, data collection and data analysis. 13 For example, phenomenological studies focus on the lived experience of individuals, explored through their description of the phenomenon. Ethnographic studies explore the culture of a group and typically involve the use of multiple methods to uncover the issues. 14

While methodology is the 'thinking tool', the methods are the 'doing tools'; 13 the ways in which data are collected and analysed. There are multiple qualitative data collection methods, including interviews, focus groups, observations, documentary analysis, participant diaries, photography and videography. Two of the most commonly used qualitative methods are interviews and focus groups, which are explored in this article. The data generated through these methods can be analysed in one of many ways, according to the methodological approach chosen. A common approach is thematic data analysis, involving the identification of themes and subthemes across the data set. Further information on approaches to qualitative data analysis has been discussed elsewhere. 1

Qualitative research is an evolving and adaptable approach, used by different disciplines for different purposes. Traditionally, qualitative data, specifically interviews, focus groups and observations, have been collected face-to-face with participants. In more recent years, digital technologies have contributed to the ongoing evolution of qualitative research. Digital technologies offer researchers different ways of recruiting participants and collecting data, and offer participants opportunities to be involved in research that is not necessarily face-to-face.

Research interviews are a fundamental qualitative research method 15 and are utilised across methodological approaches. Interviews enable the researcher to learn in depth about the perspectives, experiences, beliefs and motivations of the participant. 3 , 16 Examples include, exploring patients' perspectives of fear/anxiety triggers in dental treatment, 17 patients' experiences of oral health and diabetes, 18 and dental students' motivations for their choice of career. 19

Interviews may be structured, semi-structured or unstructured, 3 according to the purpose of the study, with less structured interviews facilitating a more in depth and flexible interviewing approach. 20 Structured interviews are similar to verbal questionnaires and are used if the researcher requires clarification on a topic; however they produce less in-depth data about a participant's experience. 3 Unstructured interviews may be used when little is known about a topic and involves the researcher asking an opening question; 3 the participant then leads the discussion. 20 Semi-structured interviews are commonly used in healthcare research, enabling the researcher to ask predetermined questions, 20 while ensuring the participant discusses issues they feel are important.

Interviews can be undertaken face-to-face or using digital methods when the researcher and participant are in different locations. Audio-recording the interview, with the consent of the participant, is essential for all interviews regardless of the medium as it enables accurate transcription; the process of turning the audio file into a word-for-word transcript. This transcript is the data, which the researcher then analyses according to the chosen approach.

Types of interview

Qualitative studies often utilise one-to-one, face-to-face interviews with research participants. This involves arranging a mutually convenient time and place to meet the participant, signing a consent form and audio-recording the interview. However, digital technologies have expanded the potential for interviews in research, enabling individuals to participate in qualitative research regardless of location.

Telephone interviews can be a useful alternative to face-to-face interviews and are commonly used in qualitative research. They enable participants from different geographical areas to participate and may be less onerous for participants than meeting a researcher in person. 15 A qualitative study explored patients' perspectives of dental implants and utilised telephone interviews due to the quality of the data that could be yielded. 21 The researcher needs to consider how they will audio record the interview, which can be facilitated by purchasing a recorder that connects directly to the telephone. One potential disadvantage of telephone interviews is the inability of the interviewer and researcher to see each other. This is resolved using software for audio and video calls online – such as Skype – to conduct interviews with participants in qualitative studies. Advantages of this approach include being able to see the participant if video calls are used, enabling observation of non-verbal communication, and the software can be free to use. However, participants are required to have a device and internet connection, as well as being computer literate, potentially limiting who can participate in the study. One qualitative study explored the role of dental hygienists in reducing oral health disparities in Canada. 22 The researcher conducted interviews using Skype, which enabled dental hygienists from across Canada to be interviewed within the research budget, accommodating the participants' schedules. 22

A less commonly used approach to qualitative interviews is the use of social virtual worlds. A qualitative study accessed a social virtual world – Second Life – to explore the health literacy skills of individuals who use social virtual worlds to access health information. 23 The researcher created an avatar and interview room, and undertook interviews with participants using voice and text methods. 23 This approach to recruitment and data collection enables individuals from diverse geographical locations to participate, while remaining anonymous if they wish. Furthermore, for interviews conducted using text methods, transcription of the interview is not required as the researcher can save the written conversation with the participant, with the participant's consent. However, the researcher and participant need to be familiar with how the social virtual world works to engage in an interview this way.

Conducting an interview

Ensuring informed consent before any interview is a fundamental aspect of the research process. Participants in research must be afforded autonomy and respect; consent should be informed and voluntary. 24 Individuals should have the opportunity to read an information sheet about the study, ask questions, understand how their data will be stored and used, and know that they are free to withdraw at any point without reprisal. The qualitative researcher should take written consent before undertaking the interview. In a face-to-face interview, this is straightforward: the researcher and participant both sign copies of the consent form, keeping one each. However, this approach is less straightforward when the researcher and participant do not meet in person. A recent protocol paper outlined an approach for taking consent for telephone interviews, which involved: audio recording the participant agreeing to each point on the consent form; the researcher signing the consent form and keeping a copy; and posting a copy to the participant. 25 This process could be replicated in other interview studies using digital methods.

There are advantages and disadvantages of using face-to-face and digital methods for research interviews. Ultimately, for both approaches, the quality of the interview is determined by the researcher. 16 Appropriate training and preparation are thus required. Healthcare professionals can use their interpersonal communication skills when undertaking a research interview, particularly questioning, listening and conversing. 3 However, the purpose of an interview is to gain information about the study topic, 26 rather than offering help and advice. 3 The researcher therefore needs to listen attentively to participants, enabling them to describe their experience without interruption. 3 The use of active listening skills also help to facilitate the interview. 14 Spradley outlined elements and strategies for research interviews, 27 which are a useful guide for qualitative researchers:

Greeting and explaining the project/interview

Asking descriptive (broad), structural (explore response to descriptive) and contrast (difference between) questions

Asymmetry between the researcher and participant talking

Expressing interest and cultural ignorance

Repeating, restating and incorporating the participant's words when asking questions

Creating hypothetical situations

Asking friendly questions

Knowing when to leave.

For semi-structured interviews, a topic guide (also called an interview schedule) is used to guide the content of the interview – an example of a topic guide is outlined in Box 1 . The topic guide, usually based on the research questions, existing literature and, for healthcare professionals, their clinical experience, is developed by the research team. The topic guide should include open ended questions that elicit in-depth information, and offer participants the opportunity to talk about issues important to them. This is vital in qualitative research where the researcher is interested in exploring the experiences and perspectives of participants. It can be useful for qualitative researchers to pilot the topic guide with the first participants, 10 to ensure the questions are relevant and understandable, and amending the questions if required.

Regardless of the medium of interview, the researcher must consider the setting of the interview. For face-to-face interviews, this could be in the participant's home, in an office or another mutually convenient location. A quiet location is preferable to promote confidentiality, enable the researcher and participant to concentrate on the conversation, and to facilitate accurate audio-recording of the interview. For interviews using digital methods the same principles apply: a quiet, private space where the researcher and participant feel comfortable and confident to participate in an interview.

Box 1: Example of a topic guide

Study focus: Parents' experiences of brushing their child's (aged 0–5) teeth

1. Can you tell me about your experience of cleaning your child's teeth?

How old was your child when you started cleaning their teeth?

Why did you start cleaning their teeth at that point?

How often do you brush their teeth?

What do you use to brush their teeth and why?

2. Could you explain how you find cleaning your child's teeth?

Do you find anything difficult?

What makes cleaning their teeth easier for you?

3. How has your experience of cleaning your child's teeth changed over time?

Has it become easier or harder?

Have you changed how often and how you clean their teeth? If so, why?

4. Could you describe how your child finds having their teeth cleaned?

What do they enjoy about having their teeth cleaned?

Is there anything they find upsetting about having their teeth cleaned?

5. Where do you look for information/advice about cleaning your child's teeth?

What did your health visitor tell you about cleaning your child's teeth? (If anything)

What has the dentist told you about caring for your child's teeth? (If visited)

Have any family members given you advice about how to clean your child's teeth? If so, what did they tell you? Did you follow their advice?

6. Is there anything else you would like to discuss about this?

Focus groups

A focus group is a moderated group discussion on a pre-defined topic, for research purposes. 28 , 29 While not aligned to a particular qualitative methodology (for example, grounded theory or phenomenology) as such, focus groups are used increasingly in healthcare research, as they are useful for exploring collective perspectives, attitudes, behaviours and experiences. Consequently, they can yield rich, in-depth data and illuminate agreement and inconsistencies 28 within and, where appropriate, between groups. Examples include public perceptions of dental implants and subsequent impact on help-seeking and decision making, 30 and general dental practitioners' views on patient safety in dentistry. 31

Focus groups can be used alone or in conjunction with other methods, such as interviews or observations, and can therefore help to confirm, extend or enrich understanding and provide alternative insights. 28 The social interaction between participants often results in lively discussion and can therefore facilitate the collection of rich, meaningful data. However, they are complex to organise and manage, due to the number of participants, and may also be inappropriate for exploring particularly sensitive issues that many participants may feel uncomfortable about discussing in a group environment.

Focus groups are primarily undertaken face-to-face but can now also be undertaken online, using appropriate technologies such as email, bulletin boards, online research communities, chat rooms, discussion forums, social media and video conferencing. 32 Using such technologies, data collection can also be synchronous (for example, online discussions in 'real time') or, unlike traditional face-to-face focus groups, asynchronous (for example, online/email discussions in 'non-real time'). While many of the fundamental principles of focus group research are the same, regardless of how they are conducted, a number of subtle nuances are associated with the online medium. 32 Some of which are discussed further in the following sections.

Focus group considerations

Some key considerations associated with face-to-face focus groups are: how many participants are required; should participants within each group know each other (or not) and how many focus groups are needed within a single study? These issues are much debated and there is no definitive answer. However, the number of focus groups required will largely depend on the topic area, the depth and breadth of data needed, the desired level of participation required 29 and the necessity (or not) for data saturation.

The optimum group size is around six to eight participants (excluding researchers) but can work effectively with between three and 14 participants. 3 If the group is too small, it may limit discussion, but if it is too large, it may become disorganised and difficult to manage. It is, however, prudent to over-recruit for a focus group by approximately two to three participants, to allow for potential non-attenders. For many researchers, particularly novice researchers, group size may also be informed by pragmatic considerations, such as the type of study, resources available and moderator experience. 28 Similar size and mix considerations exist for online focus groups. Typically, synchronous online focus groups will have around three to eight participants but, as the discussion does not happen simultaneously, asynchronous groups may have as many as 10–30 participants. 33

The topic area and potential group interaction should guide group composition considerations. Pre-existing groups, where participants know each other (for example, work colleagues) may be easier to recruit, have shared experiences and may enjoy a familiarity, which facilitates discussion and/or the ability to challenge each other courteously. 3 However, if there is a potential power imbalance within the group or if existing group norms and hierarchies may adversely affect the ability of participants to speak freely, then 'stranger groups' (that is, where participants do not already know each other) may be more appropriate. 34 , 35

Focus group management

Face-to-face focus groups should normally be conducted by two researchers; a moderator and an observer. 28 The moderator facilitates group discussion, while the observer typically monitors group dynamics, behaviours, non-verbal cues, seating arrangements and speaking order, which is essential for transcription and analysis. The same principles of informed consent, as discussed in the interview section, also apply to focus groups, regardless of medium. However, the consent process for online discussions will probably be managed somewhat differently. For example, while an appropriate participant information leaflet (and consent form) would still be required, the process is likely to be managed electronically (for example, via email) and would need to specifically address issues relating to technology (for example, anonymity and use, storage and access to online data). 32

The venue in which a face to face focus group is conducted should be of a suitable size, private, quiet, free from distractions and in a collectively convenient location. It should also be conducted at a time appropriate for participants, 28 as this is likely to promote attendance. As with interviews, the same ethical considerations apply (as discussed earlier). However, online focus groups may present additional ethical challenges associated with issues such as informed consent, appropriate access and secure data storage. Further guidance can be found elsewhere. 8 , 32

Before the focus group commences, the researchers should establish rapport with participants, as this will help to put them at ease and result in a more meaningful discussion. Consequently, researchers should introduce themselves, provide further clarity about the study and how the process will work in practice and outline the 'ground rules'. Ground rules are designed to assist, not hinder, group discussion and typically include: 3 , 28 , 29

Discussions within the group are confidential to the group

Only one person can speak at a time

All participants should have sufficient opportunity to contribute

There should be no unnecessary interruptions while someone is speaking

Everyone can be expected to be listened to and their views respected

Challenging contrary opinions is appropriate, but ridiculing is not.

Moderating a focus group requires considered management and good interpersonal skills to help guide the discussion and, where appropriate, keep it sufficiently focused. Avoid, therefore, participating, leading, expressing personal opinions or correcting participants' knowledge 3 , 28 as this may bias the process. A relaxed, interested demeanour will also help participants to feel comfortable and promote candid discourse. Moderators should also prevent the discussion being dominated by any one person, ensure differences of opinions are discussed fairly and, if required, encourage reticent participants to contribute. 3 Asking open questions, reflecting on significant issues, inviting further debate, probing responses accordingly, and seeking further clarification, as and where appropriate, will help to obtain sufficient depth and insight into the topic area.

Moderating online focus groups requires comparable skills, particularly if the discussion is synchronous, as the discussion may be dominated by those who can type proficiently. 36 It is therefore important that sufficient time and respect is accorded to those who may not be able to type as quickly. Asynchronous discussions are usually less problematic in this respect, as interactions are less instant. However, moderating an asynchronous discussion presents additional challenges, particularly if participants are geographically dispersed, as they may be online at different times. Consequently, the moderator will not always be present and the discussion may therefore need to occur over several days, which can be difficult to manage and facilitate and invariably requires considerable flexibility. 32 It is also worth recognising that establishing rapport with participants via online medium is often more challenging than via face-to-face and may therefore require additional time, skills, effort and consideration.

As with research interviews, focus groups should be guided by an appropriate interview schedule, as discussed earlier in the paper. For example, the schedule will usually be informed by the review of the literature and study aims, and will merely provide a topic guide to help inform subsequent discussions. To provide a verbatim account of the discussion, focus groups must be recorded, using an audio-recorder with a good quality multi-directional microphone. While videotaping is possible, some participants may find it obtrusive, 3 which may adversely affect group dynamics. The use (or not) of a video recorder, should therefore be carefully considered.

At the end of the focus group, a few minutes should be spent rounding up and reflecting on the discussion. 28 Depending on the topic area, it is possible that some participants may have revealed deeply personal issues and may therefore require further help and support, such as a constructive debrief or possibly even referral on to a relevant third party. It is also possible that some participants may feel that the discussion did not adequately reflect their views and, consequently, may no longer wish to be associated with the study. 28 Such occurrences are likely to be uncommon, but should they arise, it is important to further discuss any concerns and, if appropriate, offer them the opportunity to withdraw (including any data relating to them) from the study. Immediately after the discussion, researchers should compile notes regarding thoughts and ideas about the focus group, which can assist with data analysis and, if appropriate, any further data collection.

Qualitative research is increasingly being utilised within dental research to explore the experiences, perspectives, motivations and beliefs of participants. The contributions of qualitative research to evidence-based practice are increasingly being recognised, both as standalone research and as part of larger mixed-method studies, including clinical trials. Interviews and focus groups remain commonly used data collection methods in qualitative research, and with the advent of digital technologies, their utilisation continues to evolve. However, digital methods of qualitative data collection present additional methodological, ethical and practical considerations, but also potentially offer considerable flexibility to participants and researchers. Consequently, regardless of format, qualitative methods have significant potential to inform important areas of dental practice, policy and further related research.

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Gill, P., Baillie, J. Interviews and focus groups in qualitative research: an update for the digital age. Br Dent J 225 , 668–672 (2018). https://doi.org/10.1038/sj.bdj.2018.815

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Home » Data Collection – Methods Types and Examples

Data Collection – Methods Types and Examples

Table of Contents

Data Collection

Definition:

Data collection is the process of gathering and collecting information from various sources to analyze and make informed decisions based on the data collected. This can involve various methods, such as surveys, interviews, experiments, and observation.

In order for data collection to be effective, it is important to have a clear understanding of what data is needed and what the purpose of the data collection is. This can involve identifying the population or sample being studied, determining the variables to be measured, and selecting appropriate methods for collecting and recording data.

Types of Data Collection

Types of Data Collection are as follows:

Primary Data Collection

Primary data collection is the process of gathering original and firsthand information directly from the source or target population. This type of data collection involves collecting data that has not been previously gathered, recorded, or published. Primary data can be collected through various methods such as surveys, interviews, observations, experiments, and focus groups. The data collected is usually specific to the research question or objective and can provide valuable insights that cannot be obtained from secondary data sources. Primary data collection is often used in market research, social research, and scientific research.

Secondary Data Collection

Secondary data collection is the process of gathering information from existing sources that have already been collected and analyzed by someone else, rather than conducting new research to collect primary data. Secondary data can be collected from various sources, such as published reports, books, journals, newspapers, websites, government publications, and other documents.

Qualitative Data Collection

Qualitative data collection is used to gather non-numerical data such as opinions, experiences, perceptions, and feelings, through techniques such as interviews, focus groups, observations, and document analysis. It seeks to understand the deeper meaning and context of a phenomenon or situation and is often used in social sciences, psychology, and humanities. Qualitative data collection methods allow for a more in-depth and holistic exploration of research questions and can provide rich and nuanced insights into human behavior and experiences.

Quantitative Data Collection

Quantitative data collection is a used to gather numerical data that can be analyzed using statistical methods. This data is typically collected through surveys, experiments, and other structured data collection methods. Quantitative data collection seeks to quantify and measure variables, such as behaviors, attitudes, and opinions, in a systematic and objective way. This data is often used to test hypotheses, identify patterns, and establish correlations between variables. Quantitative data collection methods allow for precise measurement and generalization of findings to a larger population. It is commonly used in fields such as economics, psychology, and natural sciences.

Data Collection Methods

Data Collection Methods are as follows:

Surveys involve asking questions to a sample of individuals or organizations to collect data. Surveys can be conducted in person, over the phone, or online.

Interviews involve a one-on-one conversation between the interviewer and the respondent. Interviews can be structured or unstructured and can be conducted in person or over the phone.

Focus Groups

Focus groups are group discussions that are moderated by a facilitator. Focus groups are used to collect qualitative data on a specific topic.

Observation

Observation involves watching and recording the behavior of people, objects, or events in their natural setting. Observation can be done overtly or covertly, depending on the research question.

Experiments

Experiments involve manipulating one or more variables and observing the effect on another variable. Experiments are commonly used in scientific research.

Case Studies

Case studies involve in-depth analysis of a single individual, organization, or event. Case studies are used to gain detailed information about a specific phenomenon.

Secondary Data Analysis

Secondary data analysis involves using existing data that was collected for another purpose. Secondary data can come from various sources, such as government agencies, academic institutions, or private companies.

How to Collect Data

The following are some steps to consider when collecting data:

• Define the objective : Before you start collecting data, you need to define the objective of the study. This will help you determine what data you need to collect and how to collect it.
• Identify the data sources : Identify the sources of data that will help you achieve your objective. These sources can be primary sources, such as surveys, interviews, and observations, or secondary sources, such as books, articles, and databases.
• Determine the data collection method : Once you have identified the data sources, you need to determine the data collection method. This could be through online surveys, phone interviews, or face-to-face meetings.
• Develop a data collection plan : Develop a plan that outlines the steps you will take to collect the data. This plan should include the timeline, the tools and equipment needed, and the personnel involved.
• Test the data collection process: Before you start collecting data, test the data collection process to ensure that it is effective and efficient.
• Collect the data: Collect the data according to the plan you developed in step 4. Make sure you record the data accurately and consistently.
• Analyze the data: Once you have collected the data, analyze it to draw conclusions and make recommendations.
• Report the findings: Report the findings of your data analysis to the relevant stakeholders. This could be in the form of a report, a presentation, or a publication.
• Monitor and evaluate the data collection process: After the data collection process is complete, monitor and evaluate the process to identify areas for improvement in future data collection efforts.
• Ensure data quality: Ensure that the collected data is of high quality and free from errors. This can be achieved by validating the data for accuracy, completeness, and consistency.
• Maintain data security: Ensure that the collected data is secure and protected from unauthorized access or disclosure. This can be achieved by implementing data security protocols and using secure storage and transmission methods.
• Follow ethical considerations: Follow ethical considerations when collecting data, such as obtaining informed consent from participants, protecting their privacy and confidentiality, and ensuring that the research does not cause harm to participants.
• Use appropriate data analysis methods : Use appropriate data analysis methods based on the type of data collected and the research objectives. This could include statistical analysis, qualitative analysis, or a combination of both.
• Record and store data properly: Record and store the collected data properly, in a structured and organized format. This will make it easier to retrieve and use the data in future research or analysis.
• Collaborate with other stakeholders : Collaborate with other stakeholders, such as colleagues, experts, or community members, to ensure that the data collected is relevant and useful for the intended purpose.

Applications of Data Collection

Data collection methods are widely used in different fields, including social sciences, healthcare, business, education, and more. Here are some examples of how data collection methods are used in different fields:

• Social sciences : Social scientists often use surveys, questionnaires, and interviews to collect data from individuals or groups. They may also use observation to collect data on social behaviors and interactions. This data is often used to study topics such as human behavior, attitudes, and beliefs.
• Healthcare : Data collection methods are used in healthcare to monitor patient health and track treatment outcomes. Electronic health records and medical charts are commonly used to collect data on patients’ medical history, diagnoses, and treatments. Researchers may also use clinical trials and surveys to collect data on the effectiveness of different treatments.
• Business : Businesses use data collection methods to gather information on consumer behavior, market trends, and competitor activity. They may collect data through customer surveys, sales reports, and market research studies. This data is used to inform business decisions, develop marketing strategies, and improve products and services.
• Education : In education, data collection methods are used to assess student performance and measure the effectiveness of teaching methods. Standardized tests, quizzes, and exams are commonly used to collect data on student learning outcomes. Teachers may also use classroom observation and student feedback to gather data on teaching effectiveness.
• Agriculture : Farmers use data collection methods to monitor crop growth and health. Sensors and remote sensing technology can be used to collect data on soil moisture, temperature, and nutrient levels. This data is used to optimize crop yields and minimize waste.
• Environmental sciences : Environmental scientists use data collection methods to monitor air and water quality, track climate patterns, and measure the impact of human activity on the environment. They may use sensors, satellite imagery, and laboratory analysis to collect data on environmental factors.
• Transportation : Transportation companies use data collection methods to track vehicle performance, optimize routes, and improve safety. GPS systems, on-board sensors, and other tracking technologies are used to collect data on vehicle speed, fuel consumption, and driver behavior.

Examples of Data Collection

Examples of Data Collection are as follows:

• Traffic Monitoring: Cities collect real-time data on traffic patterns and congestion through sensors on roads and cameras at intersections. This information can be used to optimize traffic flow and improve safety.
• Social Media Monitoring : Companies can collect real-time data on social media platforms such as Twitter and Facebook to monitor their brand reputation, track customer sentiment, and respond to customer inquiries and complaints in real-time.
• Weather Monitoring: Weather agencies collect real-time data on temperature, humidity, air pressure, and precipitation through weather stations and satellites. This information is used to provide accurate weather forecasts and warnings.
• Stock Market Monitoring : Financial institutions collect real-time data on stock prices, trading volumes, and other market indicators to make informed investment decisions and respond to market fluctuations in real-time.
• Health Monitoring : Medical devices such as wearable fitness trackers and smartwatches can collect real-time data on a person’s heart rate, blood pressure, and other vital signs. This information can be used to monitor health conditions and detect early warning signs of health issues.

Purpose of Data Collection

The purpose of data collection can vary depending on the context and goals of the study, but generally, it serves to:

• Provide information: Data collection provides information about a particular phenomenon or behavior that can be used to better understand it.
• Measure progress : Data collection can be used to measure the effectiveness of interventions or programs designed to address a particular issue or problem.
• Support decision-making : Data collection provides decision-makers with evidence-based information that can be used to inform policies, strategies, and actions.
• Identify trends : Data collection can help identify trends and patterns over time that may indicate changes in behaviors or outcomes.
• Monitor and evaluate : Data collection can be used to monitor and evaluate the implementation and impact of policies, programs, and initiatives.

When to use Data Collection

Data collection is used when there is a need to gather information or data on a specific topic or phenomenon. It is typically used in research, evaluation, and monitoring and is important for making informed decisions and improving outcomes.

Data collection is particularly useful in the following scenarios:

• Research : When conducting research, data collection is used to gather information on variables of interest to answer research questions and test hypotheses.
• Evaluation : Data collection is used in program evaluation to assess the effectiveness of programs or interventions, and to identify areas for improvement.
• Monitoring : Data collection is used in monitoring to track progress towards achieving goals or targets, and to identify any areas that require attention.
• Decision-making: Data collection is used to provide decision-makers with information that can be used to inform policies, strategies, and actions.
• Quality improvement : Data collection is used in quality improvement efforts to identify areas where improvements can be made and to measure progress towards achieving goals.

Characteristics of Data Collection

Data collection can be characterized by several important characteristics that help to ensure the quality and accuracy of the data gathered. These characteristics include:

• Validity : Validity refers to the accuracy and relevance of the data collected in relation to the research question or objective.
• Reliability : Reliability refers to the consistency and stability of the data collection process, ensuring that the results obtained are consistent over time and across different contexts.
• Objectivity : Objectivity refers to the impartiality of the data collection process, ensuring that the data collected is not influenced by the biases or personal opinions of the data collector.
• Precision : Precision refers to the degree of accuracy and detail in the data collected, ensuring that the data is specific and accurate enough to answer the research question or objective.
• Timeliness : Timeliness refers to the efficiency and speed with which the data is collected, ensuring that the data is collected in a timely manner to meet the needs of the research or evaluation.
• Ethical considerations : Ethical considerations refer to the ethical principles that must be followed when collecting data, such as ensuring confidentiality and obtaining informed consent from participants.

Advantages of Data Collection

There are several advantages of data collection that make it an important process in research, evaluation, and monitoring. These advantages include:

• Better decision-making : Data collection provides decision-makers with evidence-based information that can be used to inform policies, strategies, and actions, leading to better decision-making.
• Improved understanding: Data collection helps to improve our understanding of a particular phenomenon or behavior by providing empirical evidence that can be analyzed and interpreted.
• Evaluation of interventions: Data collection is essential in evaluating the effectiveness of interventions or programs designed to address a particular issue or problem.
• Identifying trends and patterns: Data collection can help identify trends and patterns over time that may indicate changes in behaviors or outcomes.
• Increased accountability: Data collection increases accountability by providing evidence that can be used to monitor and evaluate the implementation and impact of policies, programs, and initiatives.
• Validation of theories: Data collection can be used to test hypotheses and validate theories, leading to a better understanding of the phenomenon being studied.
• Improved quality: Data collection is used in quality improvement efforts to identify areas where improvements can be made and to measure progress towards achieving goals.

Limitations of Data Collection

While data collection has several advantages, it also has some limitations that must be considered. These limitations include:

• Bias : Data collection can be influenced by the biases and personal opinions of the data collector, which can lead to inaccurate or misleading results.
• Sampling bias : Data collection may not be representative of the entire population, resulting in sampling bias and inaccurate results.
• Cost : Data collection can be expensive and time-consuming, particularly for large-scale studies.
• Limited scope: Data collection is limited to the variables being measured, which may not capture the entire picture or context of the phenomenon being studied.
• Ethical considerations : Data collection must follow ethical principles to protect the rights and confidentiality of the participants, which can limit the type of data that can be collected.
• Data quality issues: Data collection may result in data quality issues such as missing or incomplete data, measurement errors, and inconsistencies.
• Limited generalizability : Data collection may not be generalizable to other contexts or populations, limiting the generalizability of the findings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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

Published on 28.3.2024 in Vol 26 (2024)

Augmenting K-Means Clustering With Qualitative Data to Discover the Engagement Patterns of Older Adults With Multimorbidity When Using Digital Health Technologies: Proof-of-Concept Trial

Authors of this article:

Original Paper

• Yiyang Sheng 1 , MSc   ; 
• Raymond Bond 2 , PhD   ; 
• Rajesh Jaiswal 3 , PhD   ; 
• John Dinsmore 4 , PhD   ; 
• Julie Doyle 1 , PhD  

1 NetwellCASALA, Dundalk Institution of Technology, Dundalk, Ireland

2 School of Computing, Ulster University, Jordanstown, United Kingdom

3 School of Enterprise Computing and Digital Transformation, Technological University Dublin, Dublin, Ireland

4 Trinity Centre for Practice and Healthcare Innovation, School of Nursing and Midwifery, Trinity College Dublin, Dublin, Ireland

Corresponding Author:

Yiyang Sheng, MSc

NetwellCASALA

Dundalk Institution of Technology

Dublin Road, PJ Carrolls Building, Dundalk Institute of Technology

Co.Louth, Ireland

Dundalk, A91 K584

Phone: 353 894308214

Email: [email protected]

Background: Multiple chronic conditions (multimorbidity) are becoming more prevalent among aging populations. Digital health technologies have the potential to assist in the self-management of multimorbidity, improving the awareness and monitoring of health and well-being, supporting a better understanding of the disease, and encouraging behavior change.

Objective: The aim of this study was to analyze how 60 older adults (mean age 74, SD 6.4; range 65-92 years) with multimorbidity engaged with digital symptom and well-being monitoring when using a digital health platform over a period of approximately 12 months.

Methods: Principal component analysis and clustering analysis were used to group participants based on their levels of engagement, and the data analysis focused on characteristics (eg, age, sex, and chronic health conditions), engagement outcomes, and symptom outcomes of the different clusters that were discovered.

Results: Three clusters were identified: the typical user group, the least engaged user group, and the highly engaged user group. Our findings show that age, sex, and the types of chronic health conditions do not influence engagement. The 3 primary factors influencing engagement were whether the same device was used to submit different health and well-being parameters, the number of manual operations required to take a reading, and the daily routine of the participants. The findings also indicate that higher levels of engagement may improve the participants’ outcomes (eg, reduce symptom exacerbation and increase physical activity).

Conclusions: The findings indicate potential factors that influence older adult engagement with digital health technologies for home-based multimorbidity self-management. The least engaged user groups showed decreased health and well-being outcomes related to multimorbidity self-management. Addressing the factors highlighted in this study in the design and implementation of home-based digital health technologies may improve symptom management and physical activity outcomes for older adults self-managing multimorbidity.

Introduction

According to the United Nations, the number of people aged ≥65 years is growing faster than all other age groups [ 1 ]. The worldwide population of people aged ≥65 years will increase from approximately 550 million in 2000 to 973 million in 2030 [ 2 ]. Furthermore, by 2050, approximately 16% of the world’s population will be aged >65 years, whereas 426 million people will be aged >80 years [ 1 ]. Living longer is a great benefit to today’s society. However, this comes with several challenges. Aging can be associated with many health problems, including multimorbidity (ie, the presence of ≥2 chronic conditions) [ 3 ]. The prevalence rate of multimorbidity among older adults is estimated to be between 55% and 98%, and the factors associated with multimorbidity are older age, female sex, and low socioeconomic status [ 4 ]. In the United States, almost 75% of older adults have multimorbidity [ 5 ], and it was estimated that 50 million people in the European Union were living with multimorbidity in 2015 [ 6 ]. Likewise, the prevalence rate of multimorbidity is 69.3% among older adults in China [ 5 ].

Home-based self-management for chronic health conditions involves actions and behaviors that protect and promote good health care practices comprising the management of physical, emotional, and social care [ 7 ]. Engaging in self-management can help older adults understand and manage their health conditions, prevent illness, and promote wellness [ 7 , 8 ]. However, self-management for older adults with multimorbidity is a long-term, complex, and challenging mission [ 9 , 10 ]. There are numerous self-care tasks to engage in, which can be very complicated, especially for people with multiple chronic health conditions. Furthermore, the severity of the disease can negatively impact a person’s ability to engage in self-management [ 10 ].

Digital home-based health technologies have the potential to support better engagement with self-management interventions, such as the monitoring of symptom and well-being parameters as well as medication adherence [ 10 , 11 ]. Such technologies can help older adults understand their disease or diseases, respond to changes, and communicate with health care providers [ 12 - 14 ]. Furthermore, digital health technologies can be tailored to individual motivations and personal needs [ 13 ], which can improve sustained use [ 15 ] and result in people feeling supported [ 16 ]. Digital self-management can also create better opportunities for adoption and adherence in the long term compared with paper booklet self-management [ 16 ]. Moreover, digital health technologies, such as small wearable monitoring devices, can increase the frequency of symptom monitoring for patients with minimal stress compared with symptom monitoring with manual notifications [ 17 ].

A large body of research implements data mining and machine learning algorithms using data acquired from home-based health care data sets. Data mining techniques, such as data visualization, clustering, classification, and prediction, to name a few, can help researchers understand users, behaviors, and health care phenomena by identifying novel, interesting patterns. These techniques can also be used to build predictive models [ 18 - 21 ]. In addition, data mining techniques can help in designing health care management systems and tracking the state of a person’s chronic disease, resulting in appropriate interventions and a reduction in hospital admissions [ 18 , 22 ]. Vast amounts of data can be generated when users interact with digital health technologies, which provides an opportunity to understand chronic illnesses as well as elucidate how users engage with digital health technologies in the real world. Armstrong et al [ 23 ] used the k-means algorithm to identify previously unknown patterns of clinical characteristics in home care rehabilitation services. The authors used k-means cluster analysis to analyze data from 150,253 clients and discovered new insights into the clients’ characteristics and their needs, which led to more appropriate rehabilitation services for home care clients. Madigan and Curet [ 22 ] used classification and regression trees to investigate a home-based health care data set that comprised 580 patients who had 3 specific conditions: chronic obstructive pulmonary disease (COPD), heart failure (HF), and hip replacement. They found that data mining methods identified the dependencies and interactions that influence the results, thereby improving the accuracy of risk adjustment methods and establishing practical benchmarks [ 22 ]. Other research [ 24 ] has developed a flow diagram of a proposed platform by using machine learning methods to analyze multiple health care data sets, including medical images as well as diagnostic and voice records. The authors believe that the system could help people in resource-limited areas, which have lower ratios of physicians and hospitals, to diagnose diseases such as breast cancer, heart disease (HD), diabetes, and liver disease at a lower cost and in less time than local hospitals. In the study, the accuracy of disease detection was >95% [ 24 ].

There are many different approaches to clustering analysis of health care data sets, such as k-means, density-based spatial clustering of applications with noise, agglomerative hierarchical clustering, self-organizing maps, partitioning around medoids algorithm, hybrid hierarchical clustering, and so on [ 25 - 28 ]. K-means clustering is 1 of the most commonly used clustering or unsupervised machine learning algorithms [ 19 , 29 ], and it is relatively easy to implement and relatively fast [ 30 - 32 ]. In addition, k-means has been used in research studies related to chronic health conditions such as diabetes [ 33 ], COPD [ 34 , 35 ], and HF [ 36 ]; for example, a cloud-based framework with k-means clustering technique has been used for the diagnosis of diabetes and was found to be more efficient and suitable for handling extensive data sets in cloud computing platforms than hierarchical clustering [ 32 ]. Violán et al [ 37 ] analyzed data from 408,994 patients aged 45 to 64 years with multimorbidity using k-means clustering to ascertain multimorbidity patterns. The authors stratified the k-means clustering analysis by sex, and 6 multimorbidity patterns were found for each sex. They also suggest that clusters identified by multimorbidity patterns obtained using nonhierarchical clustering analysis (eg, k-means and k-medoids) are more consistent with clinical practice [ 37 ].

The majority of data mining studies on chronic health conditions focus on the diseases themselves and their symptoms; there is less exploration of the patterns of engagement of persons with multimorbidity with digital health technologies. However, data mining and machine learning are excellent ways to understand users’ engagement patterns with digital health technologies. A study by McCauley et al [ 38 ] compared clustering analysis of the user interaction event log data from a reminiscence mobile app that was designed for people living with dementia. In addition to performing quantitative user interaction log analysis, the authors also gathered data on the qualitative experience of users. The study showed the benefits of using data mining to analyze the user log data with complementary qualitative data analysis [ 38 ]. This is a research challenge where both quantitative and qualitative methods can be combined to fully understand users; for example, the quantitative analysis of the user event data can tell us about use patterns, the preferred times of day to use the app, the feature use, and so on, but qualitative data (eg, user interviews) are necessary to understand why these use patterns exist.

The aim of this study was to analyze how older adults with multimorbidity engage with digital symptom and health monitoring over a period of approximately 12 months using a digital health platform. In this study, user log data of engagement with digital health technology and user interview qualitative data were examined to explore the patterns of engagement. K-means clustering was used to analyze the user log data. The study had four research questions: (1) How do clusters differ in terms of participant characteristics such as age, sex, and health conditions? (2) How do clusters differ in terms of patterns of engagement, such as the number of days a week participants take readings (eg, weight and blood pressure [BP])? (3) How do engagement rates with the different devices correlate with each other (determined by analyzing the weekly submissions of every parameter and the interviews of participants)? and (4) How do engagement rates affect participants’ health condition symptoms, such as BP, blood glucose (BG) level, weight, peripheral oxygen saturation (SpO 2 ) level, and physical activity (PA)?

The study was a proof-of-concept trial with an action research design and mixed methods approach. Action research is a period of investigation that “describes, interprets, and explains social situations while executing a change intervention aimed at improvement and involvement” [ 39 ]. An action research approach supports the generation of solutions to practical problems while using methods to understand the contexts of care as well as the needs and experiences of participants.

Recruitment and Sample

Although 120 participants consented to take part across Ireland and Belgium, this paper reports on data from 60 Irish older adults with multiple chronic health conditions (≥2 of the following: COPD, HF, HD, and diabetes). Participants were recruited through purposive sampling and from multiple sources, including through health care organizations (general practitioner clinics and specialist clinics), relevant older adult networks, chronic disease support groups, social media, and local newspaper advertising. Recruitment strategies included the use of study flyers and advertisements as well as giving talks and platform demonstrations.

Sources of Data

The data set was collected during the Integrated Technology Systems for Proactive Patient Centred Care (ProACT) project proof-of-concept trial. As the trial was a proof-of-concept of a novel digital health platform, the main goal was to understand how the platform worked or did not work, rather than whether it worked. Thus, to determine sample size, a pragmatic approach was taken in line with two important factors: (1) Is the sample size large enough to provide a reliable analysis of the ecosystem? and (2) Is the sample size small enough to be financially feasible? The literature suggests that overall sample size in proof-of-concept digital health trials is low. A review of 1030 studies on technical interventions for management of chronic disease that focused on HF (436 studies), stroke (422 studies), and COPD (172 studies) suggested that robust sample sizes were 17 for COPD, 19 for HF, and 21 for stroke [ 40 ]. Full details on the study protocol can be found in the study by Dinsmore et al [ 41 ].

Participants used a suite of sensor devices (ie, BP monitors, weight scales, glucometers, pulse oximeters, and activity watches) and a tablet app to monitor their health conditions and well-being. All participants received a smartwatch to measure PA levels and sleep, a BP monitor to measure BP and pulse rate, and a weight scale. A BG meter was provided to participants with diabetes, and a pulse oximeter was provided to those with COPD to measure SpO 2 levels. In addition, all participants received an iPad with a custom-designed app, the ProACT CareApp, that allowed users to view their data, provide self-report (SR) data on symptoms that could not be easily captured through a sensor (eg, breathlessness and edema) and well-being (eg, mood and satisfaction with social life), receive targeted education based on their current health status, set PA goals, and share their data with others. The ProACT platform was designed and developed following an extensive user-centered design process. This involved interviews, focus groups, co-design sessions (hands-on design activities with participants), and usability testing before the platform’s deployment in the trial. A total of 58 people with multimorbidity and 106 care network participants, including informal carers, formal carers, and health care professionals, took part in this process. Findings from the user-centered design process have been published elsewhere [ 42 , 43 ]. More detailed information about the full ProACT platform and the CareApp used by participants can be found in the study by Doyle et al [ 44 ].

The study took place between April 1, 2018, and June 30, 2019. Participants in the trial typically participated for 12 months, although some stayed on for 14 months and others for 9 months (in the case of those who entered the trial later). One of the trial objectives was to understand real-world engagement. Therefore, participants were asked to take readings with the devices and provide SR data in the ProACT CareApp whenever they wished (not necessarily daily). As part of the trial, participants were assisted by technical help desk staff who responded to questions about the technology, and home visits were conducted as needed to resolve issues. In addition, a clinical triage service monitored the participants’ readings and contacted them in instances of abnormal parameter values (eg, high BP and low SpO 2 levels) [ 45 ]. Participants also received a monthly check-in telephone call from 1 of the triage nurses.

Table 1 outlines the types of health and well-being metrics that were collected, as well as the collection method and the number of participants who collected that type of data. The health and well-being metrics were determined from the interviews and focus groups held with health care professionals during the design of the ProACT platform to determine the most important symptom and well-being parameters to monitor across the health conditions of interest [ 42 ]. Off-the-shelf digital devices manufactured by 2 providers, Withings and iHealth, were used during the trial. Data from these providers were extracted into a custom platform called Context-Aware Broker and Inference Engine–Subject Information Management System (CABIE-SIMS), which includes a data aggregator for storing health and well-being data. All devices require the user to interact with them in some way. However, some devices needed more interaction than others (eg, taking a BG reading involved several steps, but PA and sleep only required participants to open the activity watch app to sync the relevant data). The activity watch was supposed to synchronize automatically without user interaction. However, inconsistencies with syncing meant that users were advised to open the Withings app to sync their data. The CABIE-SIMS platform would display the readings in near real time, apart from PA data, which were collected at regular intervals throughout the day, whereas sleep data were gathered every morning. Table 1 lists the types of data that were collected and the number of participants who collected them. In addition, semistructured interviews were conducted with all participants at 4 time points throughout the trial to understand their experience of using the ProACT platform. Although a full qualitative thematic analysis was outside the scope of this study and was reported on elsewhere [ 44 ], interview transcripts for participants of interest to the analysis presented in this paper were reviewed as part of this study to provide an enhanced understanding of the results.

a SpO 2 : peripheral oxygen saturation.

b HF: heart failure.

c ProACT: Integrated Technology Systems for Proactive Patient Centred Care.

d CABIE-SIMS: Context-Aware Broker and Inference Engine–Subject Information Management System.

e COPD: chronic obstructive pulmonary disease.

Data Analysis Methods

The original data set in the CABIE-SIMS platform was formatted using the JSON format. As a first step, a JSON-to-CSV file converter was used to make the data set more accessible for data analysis. The main focus was on dealing with duplicate data and missing data during the data cleaning phase. Data duplication might occur when a user uploads their SpO 2 reading 3 times in 2 minutes as a result of mispressing the button. In such cases, only 1 record was added to the cleaned data file. As for missing data, the data set file comprised “N/A” (not available) values for all missing data.

The cleaned data set was preprocessed using Microsoft Excel, the R programming language (R Foundation for Statistical Computing), and RStudio (Posit Software, PBC). The preprocessed data set included participants’ details (ID, sex, age, and chronic health conditions) and the number of days of weekly submissions of every parameter (BP, pulse rate, SpO 2 level, BG level, weight, PA, SR data, and sleep). All analyses (including correlation analysis, principal component analysis [PCA], k-means clustering, 2-tailed t test, and 1-way ANOVA) were implemented in the R programming language and RStudio.

After performing Shapiro-Wilk normality tests on the data submitted each week, we found that the data were not normally distributed. Therefore, Spearman correlation was used to check the correlation among the parameters. Correlation analysis and PCA were used to determine which portions of the data would be included in the k-means clustering. Correlation analysis determined which characteristics or parameters should be selected, and PCA determined the number of dimensions that should be selected as features for clustering. In the clustering process, the weekly submission of each parameter was considered as an independent variable for the discovery of participant clusters, and the outcome of the clustering was a categorical taxonomy that was used to label the 3 discovered clusters. Similarly, the Shapiro-Wilk test was conducted to check the normality of the variables in each group. It was found that most of the variables in each group were normally distributed, and only the weight data submission records of cluster 3, the PA data submission records of cluster 2, the SR data submission records of cluster 3, and the sleep data submission records of cluster 1 were not normally distributed. Therefore, the 2-tailed t test and 1-way ANOVA were used to compare different groups of variables. The 2-tailed t test was used to compare 2 groups of variables, whereas 1-way ANOVA was used to compare ≥2 groups of variables. P values >.05 indicated that there were no statistically significant differences among the groups of variables [ 46 ].

As for the qualitative data from the interviews, we performed keyword searches after a review of the entire interview; for example, when the data analysis was related to BP and weight monitoring, a search with the keywords “blood pressure,” “weight,” or “scale” was performed to identify relevant information. In addition, when the aim was to understand the impact of digital health care technology, we focused on specific questions in the second interview, such as “Has it had any impact on the management of your health?”

Ethical Considerations

Ethics approval was received from 3 ethics committees: the Health Service Executive North East Area Research Ethics Committee, the School of Health and Science Research Ethics Committee at Dundalk Institute of Technology, and the Faculty of Health Sciences Research Ethics Committee at Trinity College Dublin. All procedures were in line with the European Union’s General Data Protection Regulation for research projects, with the platform and trial methods and procedures undergoing data protection impact assessments. Written informed consent was obtained on an individual basis from participants in accordance with legal and ethics guidelines after a careful explanation of the study and the provision of patient information and informed consent forms in plain language. All participants were informed of their right to withdraw from the study at any time without having to provide a reason. Participants were not compensated for their time. Data stored within the CABIE-SIMS platform were identifiable because they were shared (with the participant’s consent) with the clinical triage teams and health care professionals. This was clearly outlined in the participant information leaflet and consent form. However, the data set that was extracted for the purpose of the analysis presented in this paper was pseudonymized.

Participants

A total of 60 older adults were enrolled in the study. The average age of participants was 74 (SD 6.4; range 65-92) years; 60% (36) were male individuals, and 40% (24/60) were female individuals. The most common combination of health conditions was diabetes and HD (30/60, 50%), which was followed by COPD and HD (16/60, 27%); HF and HD (7/60, 12%); diabetes and COPD (3/60, 5%); diabetes and HF (1/60, 2%); COPD and HF (1/60, 2%); HF, HD, and COPD (1/60, 2%); and COPD, HD, and diabetes (1/60, 2%). Of the 60 participants, 11 (18%) had HF, 55 (92%) had HD, 22 (37%) had COPD, and 31 (52%) had diabetes. Over the course of the trial, of the 60 participants, 8 (13%) withdrew, and 3 (5%) died. However, this study included data from all participants in the beginning, as long as the participant had at least 1 piece of data. Hence, of the 60 participants, we included 56 (93%) in our analysis, whereas 4 (7%) were excluded because no data were recorded.

Correlation of Submission Parameters

To help determine which distinct use characteristics or parameters (such as the weekly frequency of BP data submissions) should be selected as features for clustering, the correlations among the parameters were calculated. Figure 1 shows the correlation matrix for all parameter weekly submissions (days). In this study, a moderate correlation (correlation coefficient between 0.3 to 0.7 and −0.7 to −0.3) [ 47 , 48 ] was chosen as the standard for selecting parameters. First, every participant received a BP monitor to measure BP, and pulse rate was collected as part of the BP measurement. Moreover, the correlation coefficient between BP and pulse rate was 0.93, a strong correlation. In this case, BP was selected for clustering rather than pulse rate. As for the other parameters, the correlations between BP and weight (0.51), PA (0.55), SR data (0.41), and sleep (0.55) were moderate, whereas the correlations between BP and SpO 2 level (0.05) and BG (0.24) were weak. In addition, the correlations between SpO 2 level and weight (−0.25), PA (0.16), SR data (0.29), and sleep (−0.24) were weak. Therefore, SpO 2 level was not selected for clustering. Likewise, the correlations between BG and weight (0.19), PA (0.2), SR data (−0.06), and sleep (0.25) were weak. Therefore, BG was not selected for clustering. Thus, BP, weight, PA, SR data, and sleep were selected for clustering.

PCA and Clustering

The fundamental question for k-means clustering is this: how many clusters (k) should be discovered? To determine the optimum number of clusters, we further investigated the data through visualization offered by PCA. As can be seen from Figure 2 , the first 2 principal components (PCs) explain 73.6% of the variation, which is an acceptably large percentage. However, after a check of individual contributions, we found that there were 3 participants—P038, P016, and P015—who contributed substantially to PC1 and PC2. After a check of the original data set, we found that P038 submitted symptom parameters only on 1 day, and P016 submitted symptom parameters only on 2 days. Conversely, P015 submitted parameters almost every day during the trial. Therefore, P038 and P016 were omitted from clustering.

After removing the outliers (P038 and P016), we found that the first 2 PCs explain 70.5% of the variation ( Figure 3 ), which is an acceptably large percentage.

The clusters were projected into 2 dimensions as shown in Figure 4 . Each subpart in Figure 4 shows a different number of clusters (k). When k=2, the data are obviously separated into 2 big clusters. Similarly, when k=3, the clusters are still separated very well into 3 clusters. When k=4, the clusters are well separated, but compared with the subpart with 3 clusters, 2 clusters are similar, whereas cluster 1, which only has 3 participants, is a relatively small cluster. When k=5, there is some overlap between cluster 1 and cluster 2. Likewise, Figure 5 shows the optimal number of clusters using the elbow method. In view of this, we determined that 3 clusters of participants separate the data set best. The 3 clusters can be labeled as the least engaged user group (cluster 1), the highly engaged user group (cluster 2), and the typical user group (cluster 3).

In the remainder of this section, we report on the examination of the clusters with respect to participant characteristics and the weekly submissions (days) of different parameters in a visual manner to reveal potential correlations and insights. Finally, we report on the examination of the correlations among all parameters by PCA.

Participant Characteristics

As seen in Figure 6 , the distribution of age within the 3 clusters is similar, with the P value of the 1-way ANOVA being .93, because all participants in this trial were older adults. However, the median age in the cluster 3 box plot is slightly higher than the median ages in the box plots of the other 2 clusters, and the average age of cluster 2 participants (74.1 years) is lower than that of cluster 1 (74.6 years) and cluster 3 (74.8 years; Table 2 ) participants. As Table 2 shows, 6 (26%) of the 23 female participants are in cluster 1 compared with 7 (23%) of the 31 male participants. However, the male participants in cluster 2 (10/31, 32%) and cluster 3 (14/31, 45%) represent higher proportions of total male participants compared with female participants in cluster 2 (7/23, 30%) and cluster 3 (10/23, 43%). Figure 7 shows the proportion of the 4 chronic health conditions within the 3 clusters. Cluster 1 has the largest proportion of participants with COPD and the smallest proportion of participants with diabetes. Moreover, cluster 3 has the smallest proportion of participants with HF (3/24, 13%; Table 2 ).

a COPD: chronic obstructive pulmonary disease.

Participant Engagement Outcomes

Cluster 2 has the longest average enrollment time at 352 days compared with cluster 3 at 335 days and cluster 1 at 330 days. As seen in Figure 8 , the overall distribution of the BP data weekly submissions is different, with the P value of the 1-way ANOVA being 8.4 × 10 −9 . The frequency of BP data weekly submissions (days) of cluster 2 exceeds the frequencies of cluster 1 and cluster 3, which means that participants in cluster 2 have a higher frequency of BP data submissions than those in the other 2 clusters. The median and maximum of cluster 3 are higher than those of cluster 1, but the minimum of cluster 3 is lower than that of cluster 1. Likewise, as seen in Table 3 , the mean and SD of cluster 1 (mean 2.5, SD 1.4) are smaller than those of cluster 3 (mean 2.9, SD 2.9).

As Figure 9 shows, the overall distribution of the weekly submissions of weight data is different, with the P value of the 1-way ANOVA being 1.4 × 10 −13 , because the participants in cluster 2 submitted weight parameters more frequently than those in cluster 1 and cluster 3. In addition, similar to the BP data submissions, the median of cluster 3 is higher than that of cluster 1. As seen in Figure 9 , there are 3 outliers in cluster 2. The top outlier is P015, who submitted a weight reading almost every day. During the trial, this participant mentioned many times in the interviews that his goal was to lose weight and that he used the scale to check his progress:

I’ve set out to reduce my weight. The doctor has been saying to me you know there’s where you are and you should be over here. So, I’ve been using the weighing thing just to clock, to track reduction of weight. [P015]

The other 2 outliers are P051 and P053, both of whom mentioned taking their weight measurements as part of their daily routine:

Once I get up in the morning the first thing is I weigh myself. That is, the day starts off with the weight, right. [P053]

Although their frequency of weekly weight data submissions is lower than that of all other participants in cluster 2, it is still higher than that of most of the participants in the other 2 clusters.

In Table 3 , it can be observed that the average frequency of weekly submissions of PA and sleep data for every cluster is higher than the frequencies of other variables, and the SDs are relatively low. This is likely because participants only needed to open the Withings app once a day to ensure the syncing of data. However, the overall distributions of PA and sleep data submissions are different in Figure 10 and Figure 11 , with the P values of the 1-way ANOVA being 1.1 × 10 −9 and 3.7 × 10 −10 , respectively. Moreover, as Figure 10 and Figure 11 show, there are still some outliers who have a low frequency of submissions, and the box plot of cluster 1 is lower than the box plots of cluster 2 and cluster 3 in both figures. The reasons for the low frequency of submissions can mostly be explained by (1) technical issues, including internet connection issues, devices not syncing, and devices needing to be paired again; (2) participants forgetting to put the watch back on after taking it off; and (3) participants stopping using the devices (eg, some participants do not like wearing the watch while sleeping or when they go on holiday):

I was without my watch there for the last month or 3 or 4 weeks [owing to technical issues], and I missed it very badly because everything I look at the watch to tell the time, I was looking at my steps. [P042]

I don’t wear it, I told them I wouldn’t wear the watch at night, I don’t like it. [P030]

Unlike in the case of other variables, the submission of SR data through the ProACT CareApp required participants to reflect on each question and their status before selecting the appropriate answer. Participants had different questions to answer based on their health conditions; for example, participants with HF and COPD were asked to answer symptom-related questions, whereas those with diabetes were not. All participants were presented with general well-being and mood questions. Therefore, for some participants, self-reporting could possibly take more time than using the health monitoring devices. As shown in Table 3 , the frequency of average weekly submissions of SR data within the 3 clusters is relatively small and the SDs are large, which means that the frequency of SR data submissions is lower than that of other variables. Furthermore, there were approximately 5 questions asked daily about general well-being, and some participants would skip the questions if they thought the question was unnecessary or not relevant:

Researcher: And do you answer your daily questions? P027: Yeah, once a week.

Researcher: Once a week, okay. P027: But they’re the same.

As Figure 12 shows, the distribution of SR data submissions is different, with the P value of the 1-way ANOVA being .001. In Figure 12 , the median of cluster 2 is higher than the medians of the other 2 clusters, and compared with other variables, but unlike other parameters, cluster 2 also has some participants who had very low SR data submission rates (close to 0). SR data is the only parameter where cluster 1 has a higher median than cluster 3.

a Lowest submission rate across the clusters.

b Highest submission rate across the clusters.

The Correlation Among the Weekly Submissions of Different Parameters

As seen in Figure 13 , the arrows of BP and weight point to the same side of the plot, which shows a strong correlation. Likewise, PA and sleep also have a strong correlation. As noted previously, the strong correlation between PA and sleep is because the same device collected these 2 measurements, and participants only needed to sync the data once a day. By contrast, BP and weight were collected by 2 different devices but are strongly correlated. During interviews, many participants mentioned that their daily routine with the ProACT platform involved taking both BP and weight readings:

Usually in the morning when I get out of the bed, first, I go into the bathroom, wash my hands and come back, then weigh myself, do my blood pressure, do my bloods. [P008]

I now have a routine that I let the system read my watch first thing, then I do my blood pressure thing and then I do the weight. [P015]

As I said, it’s keeping me in line with my, when I dip my finger, my weight, my blood pressure. [P040]

I use it in the morning and at night for putting in the details of blood pressure in the morning and then the blood glucose at night. Yes, there’s nothing else, is there? Oh, every morning the [weight] scales. [P058]

By contrast, as shown in Figure 13 , SR data have a weak correlation with other parameters, for reasons noted earlier.

Parameter Variation Over Time

Analysis was conducted to determine any differences among the clusters in terms of symptom and well-being parameter changes over the course of the trial. Table 4 provides a description of each cluster in this regard. As Figure 14 shows, the box plot of cluster 2 is comparatively short in every time period of the trial, and the medians of cluster 2 and cluster 3 are more stable than the median of cluster 1. In addition, the median of cluster 1 is increasing over time, whereas the medians of cluster 2 and cluster 3 are decreasing and within the normal systolic BP of older adults [ 49 ] ( Figure 14 ). As can be seen in Table 5 , cluster 2 has a P value of .51 for systolic BP and a P value of .52 for diastolic BP, which are higher than the P values of cluster 1 ( P =.19 and P =.16, respectively) and cluster 3 ( P =.27 and P =.35, respectively). Therefore, participants in cluster 2, as highly engaged users, have more stable B P values than those in the other 2 clusters. By contrast, participants in cluster 1, as the least engaged users, have the most unstable B P values.

As seen in Figure 15 , the median of cluster 2 is relatively higher than the medians of the other 2 clusters. The median of cluster 3 is increasing over time. In the second and third time periods of the trial, the box plot of cluster 1 is comparatively short. Normal SpO 2 levels are between 95% and 100%, but older adults may have SpO 2 levels closer to 95% [ 50 ]. In addition, for patients with COPD, SpO 2 levels range between 88% and 92% [ 51 ]. In this case, there is not much difference in terms of SpO 2 levels, and most of the SpO 2 levels are between 90% and 95% in this study. However, the SpO 2 levels of cluster 1 and cluster 2 were maintained at a relatively high level during the trial. As for cluster 3, the SpO 2 levels were comparatively low but relatively the same as those in the other 2 clusters in the later period of the trial. Therefore, the SpO 2 levels of cluster 3 ( P =.25) are relatively unstable compared with those of cluster 1 ( P =.66) and cluster 2 ( P =.59). As such, there is little correlation between SpO 2 levels and engagement with digital health monitoring.

In relation to BG, Figure 16 shows that the box plot of cluster 2 is relatively lower than the box plots of the other 2 clusters in the second and third time periods. Moreover, the medians of cluster 2 and cluster 3 are lower than those of cluster 1 in the second and third time periods. The BG levels in cluster 2 and cluster 3 decreased at later periods of the trial compared with the beginning of the trial, but those in cluster 1 increased. Cluster 3 ( P =.25), as the typical user group, had more significant change than cluster 1 ( P =.50) and cluster 2 ( P =.41). Overall, participants with a higher engagement rate had better BG control.

In relation to weight, Figure 17 shows that the box plot of cluster 2 is lower than the box plots of the other 2 clusters and comparatively short. As Table 5 shows, the P value of cluster 2 weight data is .72, which is higher than the P values of cluster 1 (.47) and cluster 3 (.61). Therefore, participants in cluster 2 had a relatively stable weight during the trial. In addition, as seen in Figure 17 , the median weight of cluster 1 participants is decreasing, whereas that of cluster 3 participants is increasing. It is well known that there are many factors that can influence body weight, such as PA, diet, environmental factors, and so on. [ 52 ]. In this case, engagement with digital health and well-being monitoring may help control weight but the impact is not significant.

As Table 5 shows, the P value of cluster 2 PA (.049) is lower than .05, which means that there are significant differences among the 3 time slots in cluster 2. However, the median of cluster 2 PA, as seen in Figure 18 , is still higher than the medians of the other 2 clusters. In cluster 2, approximately 50% of daily PA (steps) consists of >2500 steps. Overall, participants with a higher engagement rate also had a higher level of PA.

a BP: blood pressure.

b BG: blood glucose.

c SR: self-report.

d PA: physical activity.

b SpO 2 : peripheral oxygen saturation.

c BG: blood glucose.

Principal Findings

Digital health technologies hold great promise to help older adults with multimorbidity to improve health management and health outcomes. However, such benefits can only be realized if users engage with the technology. The aim of this study was to explore the engagement patterns of older adults with multimorbidity with digital self-management by using data mining to analyze users’ weekly submission data. Three clusters were identified: cluster 1 (the least engaged user group), cluster 2 (the highly engaged user group), and cluster 3 (the typical user group). The subsequent analysis focused on how the clusters differ in terms of participant characteristics, patterns of engagement, and stabilization of health condition symptoms and well-being parameters over time, as well as how engagement rates with the different devices correlate with each other.

The key findings from the study are as follows:

• There is no significant difference in participants’ characteristics among the clusters in general. The highly engaged group had the lowest average age ( Table 4 ), and there was no significant difference with regard to sex and health conditions among these clusters. The least engaged user group had fewer male participants and participants with diabetes.
• There are 3 main factors influencing the correlations among the submission rates of different parameters. The first concerns whether the same device was used to submit the parameters, the second concerns the number of manual operations required to submit the parameter, and the third concerns the daily routine of the participants.
• Increased engagement with devices may improve the participants’ health and well-being outcomes (eg, symptoms and PA levels). However, the difference between the highly engaged user group and the typical user group was relatively minimal compared with the difference between the highly engaged user group and the least engaged user group.

Each of these findings is discussed in further detail in the following subsections.

Although the findings presented in this paper focus on engagement based on the ProACT trial participants’ use data, the interviews that were carried out as part of the trial identified additional potential factors of engagement. As reported in the study by Doyle et al [ 44 ], participants spoke about how they used the data to support their self-management (eg, taking action based on their data) and experienced various benefits, including increased knowledge of their health conditions and well-being, symptom optimization, reductions in weight, increased PA, and increased confidence to participate in certain activities as a result of health improvements. The peace of mind and encouragement provided by the clinical triage service as well as the technical support available were also identified during the interviews as potential factors positively impacting engagement [ 44 ]. In addition, the platform was found to be usable, and it imposed minimal burden on participants ( Table 1 ). These findings supplement the quantitative findings presented in this paper.

Age, Sex, Health Condition Types, and Engagement

In this study, the difference in engagement with health care technologies between the sex was not significant. Of the 23 female participants, 6 (26%) were part of the least engaged user group compared with 7 (23%) of the 31 male participants. Moreover, there were lower proportions of female participants in the highly engaged user group (7/23, 30%) and typical user group (10/23, 43%) compared with male participants (10/31, 32% and 14/31, 45%, respectively). Other research has found that engagement with mobile health technology for BP monitoring was independent of sex [ 53 ]. However, there are also some studies that show that female participants are more likely to engage with digital mental health care interventions [ 54 , 55 ]. Therefore, sex cannot be considered as a separate criterion when comparing engagement with health care technologies, and it was not found to have significant impact on engagement in this study. Regarding age, many studies have shown that younger people are more likely to use health care technologies than older adults [ 56 , 57 ]. Although all participants in our study are older adults, the highly engaged user group is the youngest group. However, there was no significant difference in age among the clusters, with some of the oldest users being part of cluster 3, the typical user cluster. Similarly, the health conditions of a participant did not significantly impact their level of engagement. Other research [ 53 ] found that participants who were highly engaged with health monitoring had higher rates of hypertension, chronic kidney disease, and hypercholesterolemia than those with lower engagement levels. Our findings indicate that the highly engaged user group had a higher proportion of participants with diabetes, and the least engaged user group had a higher proportion of participants with COPD. Further research is needed to understand why there might be differences in engagement depending on health conditions. In our study, participants with COPD also self-reported on certain symptoms, such as breathlessness, chest tightness, and sputum amount and color. Although engagement with specific questions was not explored, participants in cluster 1, the least engaged user group, self-reported more frequently than those in cluster 3, the typical user group. Our findings also indicate that participants monitoring BG level and BP experienced better symptom stabilization over time than those monitoring SpO 2 level. It has been noted that the expected benefits of technology (eg, increased safety and usefulness) and need for technology (eg, subjective health status and perception of need) are 2 important factors that can influence the acceptance and use of technology by older adults [ 58 ]. It is also well understood that engaging in monitoring BG level can help people with diabetes to better self-manage and make decisions about diet, exercise, and medication [ 59 ].

Factors Influencing Engagement

Many research studies use P values to show the level of similarity or difference among clusters [ 60 - 63 ]. For most of the engagement outcomes in this study, all clusters significantly differed, with 1-way ANOVA P <.001, with the exception being SR data ( P =.001). In addition, the 2-tailed t test P values showed that cluster 2 was significantly different from cluster 1 and cluster 3 in BP and weight data submission rates, whereas cluster 1 was significantly different from cluster 2 and cluster 3 in PA and sleep data submission rates. As for SR data submission rates, all 3 two-tailed t tests had P values >.001, meaning that there were no significant differences between any 2 of these clusters. Therefore, all 5 parameters used for clustering were separated into 3 groups based on the correlations of submission rates: 1 for BP and weight, 1 for PA and sleep, and 1 for SR data. PA and sleep data submission rates have a strong correlation because participants used the same device to record daily PA and sleeping conditions. SR data submission rates have a weak correlation with other parameters’ submission rates. Our previous research found that user retention in terms of submitting SR data was poorer than user retention in terms of using digital health devices, possibly because more manual operations are involved in the submission of SR data than other parameters or because the same questions were asked regularly, as noted by P027 in the Participant Engagement Outcomes subsection [ 64 ].

Other research that analyzed engagement with a diabetes support app found that user engagement was lower when more manual data entry was required [ 65 ]. In contrast to the other 2 groups of parameters, BP and weight data are collected using different devices. Whereas measuring BP requires using a BP monitor and manually synchronizing the data, measuring weight simply requires standing on the weight scale, and the data are automatically synchronized. Therefore, the manual operations involved in submitting BP and weight data are slightly different. However, the results showed a strong correlation between BP and weight because many participants preferred to measure both BP and weight together and incorporate taking these measurements into their daily routines. Research has indicated that if the use of a health care device becomes a regular routine, then participants will use it without consciously thinking about it [ 66 ]. Likewise, Yuan et al [ 67 ] note that integrating health apps into people’s daily activities and forming regular habits can increase people’s willingness to continue using the apps. However, participants using health care technology for long periods of time might become less receptive to exploring the system compared with using it based on the established methods to which they are accustomed [ 68 ]. In this study, many participants bundled their BP measurement with their weight measurement during their morning routine. Therefore, the engagement rates of interacting with these 2 devices were enhanced by each other. Future work could explore how to integrate additional measurements, such as monitoring SpO 2 level as well as self-reporting into this routine (eg, through prompting the user to submit these parameters while they are engaging with monitoring other parameters, such as BP and weight).

Relationship Between Engagement and Health and Well-Being Outcomes

Our third finding indicates that higher levels of engagement with digital health monitoring may result in better outcomes, such as symptom stabilization and increased PA levels. Milani et al [ 69 ] found that digital health care interventions can help people achieve BP control and improve hypertension control compared with usual care. In their study, users in the digital intervention group took an average of 4.2 readings a week. Compared with our study, this rate is lower than that of cluster 2 (5.7), the highly engaged user group, but higher than cluster 1 (2.5) and cluster 3 (2.9) rates. In our study, participants with a higher engagement rate experienced more stable BP, and for the majority of these participants (34/41, 83%), levels were maintained within the recommended thresholds of 140/90 mm Hg [ 70 ]. Many studies have shown that as engagement in digital diabetes interventions increases, patients will experience greater reductions in BG level compared with those with lower engagement [ 71 , 72 ]. However, in our study, BG levels in both the highly engaged user group (cluster 2) and the least engaged user group (cluster 1) increased in the later stages of the trial. Only the BG levels of the typical user group (cluster 3) decreased over time, which could be because the cluster 3 participants performed more PA in the later stages of the trial than during other time periods, as Figure 18 shows. Cluster 2, the highly engaged user group, maintained a relatively high level of PA during the trial period, although it continued to decline throughout the trial. Other research shows that more PA can also lead to better weight control and management [ 73 , 74 ], which could be 1 of the reasons why cluster 2 participants maintained their weight.

Limitations

There are some limitations to the research presented in this paper. First, although the sample size (n=60) was relatively large for a digital health study, the sample sizes for some parameters were small because not all participants monitored all parameters. Second, the participants were clustered based on weekly submissions of parameters only. If more features were included in clustering, such as submission intervals, participants could be grouped differently. It should also be pointed out that correlation is not a causality with respect to analyzing engagement rates with outcomes.

Conclusions

This study presents findings after the clustering of a data set that was generated from a longitudinal study of older adults using a digital health technology platform (ProACT) to self-manage multiple chronic health conditions. The highly engaged user group cluster (includes 17/54, 31% of users) had the lowest average age and highest frequency of submissions for every parameter. Engagement with digital health care technologies may also influence health and well-being outcomes (eg, symptoms and PA levels). The least engaged user group in our study had relatively poorer outcomes. However, the difference between the outcomes of the highly engaged user group and those of the typical user group is relatively small. There are 3 possible reasons for the correlations between the submission rates of parameters and devices. First, if 2 parameters are collected by the same device, they usually have a strong correlation, and users will engage with both equally. Second, the devices that involve fewer steps and parameters with less manual data entry will have a weak correlation with those devices that require more manual operations and data entry. Finally, participants’ daily routines also influence the correlations among devices; for example, in this study, many participants had developed a daily routine to weigh themselves after measuring their BP, which led to a strong correlation between BP and weight data submission rates. Future work should explore how to integrate the monitoring of additional parameters into a user’s routine and whether additional characteristics, such as the severity of disease or technical proficiency, impact engagement.

Acknowledgments

This work was part funded by the Integrated Technology Systems for Proactive Patient Centred Care (ProACT) project and has received funding from the European Union (EU)–funded Horizon 2020 research and innovation program (689996). This work was part funded by the EU’s INTERREG VA program, managed by the Special EU Programs Body through the Eastern Corridor Medical Engineering Centre (ECME) project. This work was part funded by the Scaling European Citizen Driven Transferable and Transformative Digital Health (SEURO) project and has received funding from the EU-funded Horizon 2020 research and innovation program (945449). This work was part funded by the COVID-19 Relief for Researchers Scheme set up by Ireland’s Higher Education Authority. The authors would like to sincerely thank all the participants of this research for their valuable time.

Conflicts of Interest

None declared.

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Abbreviations

Edited by T Leung, T de Azevedo Cardoso; submitted 05.02.23; peer-reviewed by B Chaudhry, M Peeples, A DeVito Dabbs; comments to author 12.09.23; revised version received 25.10.23; accepted 29.01.24; published 28.03.24.

©Yiyang Sheng, Raymond Bond, Rajesh Jaiswal, John Dinsmore, Julie Doyle. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 28.03.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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Choosing a Qualitative Research Approach

Associated data.

Editor's Note: The online version of this article contains a list of further reading resources and the authors' professional information .

The Challenge

Educators often pose questions about qualitative research. For example, a program director might say: “I collect data from my residents about their learning experiences in a new longitudinal clinical rotation. If I want to know about their learning experiences, should I use qualitative methods? I have been told that there are many approaches from which to choose. Someone suggested that I use grounded theory, but how do I know this is the best approach? Are there others?”

What Is Known

Qualitative research is the systematic inquiry into social phenomena in natural settings. These phenomena can include, but are not limited to, how people experience aspects of their lives, how individuals and/or groups behave, how organizations function, and how interactions shape relationships. In qualitative research, the researcher is the main data collection instrument. The researcher examines why events occur, what happens, and what those events mean to the participants studied. 1 , 2

Qualitative research starts from a fundamentally different set of beliefs—or paradigms—than those that underpin quantitative research. Quantitative research is based on positivist beliefs that there is a singular reality that can be discovered with the appropriate experimental methods. Post-positivist researchers agree with the positivist paradigm, but believe that environmental and individual differences, such as the learning culture or the learners' capacity to learn, influence this reality, and that these differences are important. Constructivist researchers believe that there is no single reality, but that the researcher elicits participants' views of reality. 3 Qualitative research generally draws on post-positivist or constructivist beliefs.

Qualitative scholars develop their work from these beliefs—usually post-positivist or constructivist—using different approaches to conduct their research. In this Rip Out, we describe 3 different qualitative research approaches commonly used in medical education: grounded theory, ethnography, and phenomenology. Each acts as a pivotal frame that shapes the research question(s), the method(s) of data collection, and how data are analyzed. 4 , 5

Choosing a Qualitative Approach

Before engaging in any qualitative study, consider how your views about what is possible to study will affect your approach. Then select an appropriate approach within which to work. Alignment between the belief system underpinning the research approach, the research question, and the research approach itself is a prerequisite for rigorous qualitative research. To enhance the understanding of how different approaches frame qualitative research, we use this introductory challenge as an illustrative example.

The clinic rotation in a program director's training program was recently redesigned as a longitudinal clinical experience. Resident satisfaction with this rotation improved significantly following implementation of the new longitudinal experience. The program director wants to understand how the changes made in the clinic rotation translated into changes in learning experiences for the residents.

Qualitative research can support this program director's efforts. Qualitative research focuses on the events that transpire and on outcomes of those events from the perspectives of those involved. In this case, the program director can use qualitative research to understand the impact of the new clinic rotation on the learning experiences of residents. The next step is to decide which approach to use as a frame for the study.

The table lists the purpose of 3 commonly used approaches to frame qualitative research. For each frame, we provide an example of a research question that could direct the study and delineate what outcomes might be gained by using that particular approach.

Methodology Overview

How You Can Start TODAY

• 1 Examine the foundations of the existing literature: As part of the literature review, make note of what is known about the topic and which approaches have been used in prior studies. A decision should be made to determine the extent to which the new study is exploratory and the extent to which findings will advance what is already known about the topic.
• 2 Find a qualitatively skilled collaborator: If you are interested in doing qualitative research, you should consult with a qualitative expert. Be prepared to talk to the qualitative scholar about what you would like to study and why . Furthermore, be ready to describe the literature to date on the topic (remember, you are asking for this person's expertise regarding qualitative approaches—he or she won't necessarily have content expertise). Qualitative research must be designed and conducted with rigor (rigor will be discussed in Rip Out No. 8 of this series). Input from a qualitative expert will ensure that rigor is employed from the study's inception.
• 3 Consider the approach: With a literature review completed and a qualitatively skilled collaborator secured, it is time to decide which approach would be best suited to answering the research question. Questions to consider when weighing approaches might include the following:
• • Will my findings contribute to the creation of a theoretical model to better understand the area of study? ( grounded theory )
• • Will I need to spend an extended amount of time trying to understand the culture and process of a particular group of learners in their natural context? ( ethnography )
• • Is there a particular phenomenon I want to better understand/describe? ( phenomenology )

What You Can Do LONG TERM

• 1 Develop your qualitative research knowledge and skills : A basic qualitative research textbook is a valuable investment to learn about qualitative research (further reading is provided as online supplemental material). A novice qualitative researcher will also benefit from participating in a massive online open course or a mini-course (often offered by professional organizations or conferences) that provides an introduction to qualitative research. Most of all, collaborating with a qualitative researcher can provide the support necessary to design, execute, and report on the study.
• 2 Undertake a pilot study: After learning about qualitative methodology, the next best way to gain expertise in qualitative research is to try it in a small scale pilot study with the support of a qualitative expert. Such application provides an appreciation for the thought processes that go into designing a study, analyzing the data, and reporting on the findings. Alternatively, if you have the opportunity to work on a study led by a qualitative expert, take it! The experience will provide invaluable opportunities for learning how to engage in qualitative research.

Supplementary Material

The views expressed in this article are those of the authors and do not necessarily reflect the official policy or position of the Uniformed Services University of the Health Sciences, the Department of the Navy, the Department of Defense, or the US government.

References and Resources for Further Reading

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Please note you do not have access to teaching notes, homelessness: challenges and opportunities in the “new normal”.

Mental Health and Social Inclusion

ISSN : 2042-8308

Article publication date: 29 March 2024

This paper – the final paper of a series of three – aims to discuss the implications of the findings from a service user needs assessment of people experiencing homelessness in the Northwest of England. It will expand on the previous paper by offering a more detailed analysis and discussion of the identified key themes and issues. The service user needs assessment was completed as part of a review of local service provision in the Northwest of England against the backdrop of the COVID-19 pandemic.

Design/methodology/approach

Semi-structured questionnaires were administered and used by health-care professionals to collect data from individuals accessing the Homeless and Vulnerable Adults Service (HVAS) in Bolton. The questionnaires included a section exploring Adverse Childhood Experiences. Data from 100 completed questionnaires were analysed to better understand the needs of those accessing the HVAS.

Multiple deprivations including extensive health and social care needs were identified within the cohort. Meeting these complex needs was challenging for both service users and service providers. This paper will explore key themes identified by the needs assessment and draw upon further comments from those who participated in the data-gathering process. The paper discusses the practicalities of responding to the complex needs of those with lived experience of homelessness. It highlights how a coordinated partnership approach, using an integrated service delivery model can be both cost-effective and responsive to the needs of those often on the margins of our society.

Research limitations/implications

Data collection during the COVID-19 pandemic presented a number of challenges. The collection period had to be extended whilst patient care was prioritised. Quantitative methods were used, however, this limited the opportunity for service user involvement and feedback. Future research could use qualitative methods to address this balance and use a more inclusive approach.

Practical implications

This study illustrates that the needs of the homeless population are broad and varied. Although the population themselves have developed different responses to their situations, their needs can only be fully met by a co-ordinated, multi-agency, partnership response. An integrated service model can help identify, understand, and meet the needs of the whole population and individuals within it to improve healthcare for a vulnerable population.

Social implications

This study highlighted new and important findings around the resilience of the homeless population and the significance of building protective factors to help combat the multiplicity of social isolation with both physical and mental health problems.

Originality/value

The discussion provides an opportunity to reflect on established views in relation to the nature and scope of homelessness. The paper describes a contemporary approach to tackling current issues faced by those experiencing homelessness in the current context of the COVID-19 pandemic. Recommendations for service improvements will include highlighting established good practices including embedding a more inclusive/participatory approach.

• Homelessness
• Social exclusion
• Health inequalities
• Mental health
• Partnerships

Acknowledgements

The authors received no financial support for the research, authorship and/or publication of this article. The authors wish to acknowledge the contributions made by those with lived experience who completed the survey. Recognition and thanks are also given to those involved in the delivery of services that seek to improve the lives of those who are homeless.

Woods, A. , Lace, R. , Dickinson, J. and Hughes, B. (2024), "Homelessness: challenges and opportunities in the “new normal”", Mental Health and Social Inclusion , Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/MHSI-02-2024-0032

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Copyright © 2024, Emerald Publishing Limited

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