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October 18, 2016
Part of the challenge of explaining clinical research to the public is describing the important points of a study without going into a detailed account of the study’s design. There are many different kinds of clinical studies, each with their own strengths and weaknesses, and no real shorthand way to explain them. Researchers sometimes don’t explicitly state the kind of study they’re talking about. To them, it’s obvious; they’ve been living and breathing this research for years, sometimes decades. But study design can often be difficult even for seasoned health and science communicators to understand.
The gold standard for proving that a treatment or medical approach works is a well-designed randomized controlled trial. This type of study allows researchers to test medical interventions by randomly assigning participants to treatment or control groups. The results can help determine if there’s a cause-and-effect relationship between the treatment and outcomes. But clinical researchers can’t always use this approach. For example, scientists can’t ethically study risky behaviors by asking people to start smoking or eating an unhealthy diet. And they can’t study the health effects of the environment by assigning people to live in different places.
Thus, researchers must often turn to some type of observational study, in which a population’s health or behaviors are observed and analyzed. These studies can’t prove cause and effect, but they can be useful for finding associations. Observational studies can also help researchers understand a situation and come up with hypotheses that can then be put to the test in clinical trials. These types of studies have been essential to understanding the genetic, infectious, environmental, and behavioral causes of disease.
We’ve developed a one-page guide to clarify the different kinds of clinical studies researchers use, to explain why researchers might use them, and to touch a little on each type’s strengths and weaknesses. We hope it can serve as a useful resource to explain clinical research, whether you’re describing the results of a study to the public or the design of a trial to a potential participant. Please take a look and share your thoughts with us by sending an email to [email protected] .
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Why participate in a clinical trial, what happens in a clinical trial or study, what happens when a clinical trial or study ends, what are the different phases of clinical trials, questions to ask before participating in clinical research, how do researchers decide who will participate, clinical research needs participants with diverse backgrounds.
By participating in clinical research, you can help scientists develop new medications and other strategies to treat and prevent disease. Many effective treatments that are used today, such as chemotherapy, cholesterol-lowering drugs, vaccines, and cognitive-behavioral therapy, would not exist without research participants. Whether you’re healthy or have a medical condition, people of all ages and backgrounds can participate in clinical trials. This article can help you learn more about clinical research, why people choose to participate, and how to get involved in a study.
Mr. Jackson is 73 years old and was just diagnosed with Alzheimer’s disease . He is worried about how it will affect his daily life. Will he forget to take his medicine? Will he forget his favorite memories, like the births of his children or hiking the Appalachian Trail? When Mr. Jackson talked to his doctor about his concerns, she told him about a clinical trial that is testing a possible new Alzheimer’s treatment. But Mr. Jackson has concerns about clinical trials. He does not want to feel like a lab rat or take the chance of getting a treatment that may not work or could make him feel worse. The doctor explained that there are both risks and benefits to being part of a clinical trial, and she talked with Mr. Jackson about research studies — what they are, how they work, and why they need volunteers. This information helped Mr. Jackson feel better about clinical trials. He plans to learn more about how to participate.
Clinical research is the study of health and illness in people. There are two main types of clinical research: observational studies and clinical trials.
Observational studies monitor people in normal settings. Researchers gather information from people and compare changes over time. For example, researchers may ask a group of older adults about their exercise habits and provide monthly memory tests for a year to learn how physical activity is associated with cognitive health . Observational studies do not test a medical intervention, such as a drug or device, but may help identify new treatments or prevention strategies to test in clinical trials.
Clinical trials are research studies that test a medical, surgical, or behavioral intervention in people. These trials are the primary way that researchers determine if a new form of treatment or prevention, such as a new drug, diet, or medical device (for example, a pacemaker), is safe and effective in people. Often, a clinical trial is designed to learn if a new treatment is more effective or has less harmful side effects than existing treatments.
Other aims of clinical research include:
Learn more about clinical research from MedlinePlus and ClinicalTrials.gov .
People volunteer for clinical trials and studies for a variety of reasons, including:
Whatever the motivation, when you choose to participate in a clinical trial, you become a partner in scientific discovery. Participating in research can help future generations lead healthier lives. Major medical breakthroughs could not happen without the generosity of clinical trial participants — young and old, healthy, or diagnosed with a disease.
Looking for clinical trials related to aging and age-related health conditions? Talk to your health care provider and use online resources to:
After you find one or more studies that you are interested in, the next step is for you or your doctor to contact the study research staff and ask questions. You can usually find contact information in the description of the study.
Let your health care provider know if you are thinking about joining a clinical trial. Your provider may want to talk to the research team to make sure the study is safe for you and to help coordinate your care.
Joining a clinical trial is a personal decision with potential benefits and some risks. Learn what happens in a clinical trial and how participant safety is protected . Read and listen to testimonials from people who decided to participate in research.
Here’s what typically happens in a clinical trial or study:
Before a clinical trial is designed and launched, scientists perform laboratory tests and often conduct studies in animals to test a potential intervention’s safety and effectiveness. If these studies show favorable results, the U.S. Food and Drug Administration (FDA) approves the intervention to be tested in humans. Learn more about how the safety of clinical trial participants is protected.
Once a clinical trial or study ends, the researchers analyze the data to determine what the findings mean and to plan the next steps. As a participant, you should be provided information before the study starts about how long it will last, whether you will continue receiving the treatment after the trial ends (if applicable), and how the results of the research will be shared. If you have specific questions about what will happen when the trial or study ends, ask the research coordinator or staff.
Clinical trials of drugs and medical devices advance through several phases to test safety, determine effectiveness, and identify any side effects. The FDA typically requires Phase 1, 2, and 3 trials to be conducted to determine if the drug or device can be approved for further use. If researchers find the intervention to be safe and effective after the first three phases, the FDA approves it for clinical use and continues to monitor its effects.
Each phase has a different purpose:
Clinical trials that test a behavior change, rather than a drug or medical device, advance through similar steps, but behavioral interventions are not regulated by the FDA. Learn more about clinical trials , including the types of trials and the four phases.
Choosing to participate in research is an important personal decision. If you are considering joining a trial or study, get answers to your questions and know your options before you decide. Here are questions you might ask the research team when thinking about participating.
To be eligible to participate, you may need to have certain characteristics, called inclusion criteria. For example, a clinical trial may need participants to have a certain stage of disease, version of a gene, or family history. Some trials require that participants have a study partner who can accompany them to clinic visits.
Participants with certain characteristics may not be allowed to participate in some trials. These characteristics are called exclusion criteria. They include factors such as specific health conditions or medications that could interfere with the treatment being tested.
Many volunteers must be screened to find enough people who are eligible for a trial or study. Generally, you can participate in only one clinical trial at a time, although this is not necessarily the case for observational studies. Different trials have different criteria, so being excluded from one trial does not necessarily mean you will be excluded from another.
When research only includes people with similar backgrounds, the findings may not apply to or benefit a broader population. The results of clinical trials and studies with diverse participants may apply to more people. That’s why research benefits from having participants of different ages, sexes, races, and ethnicities.
Researchers need older adults to participate in clinical research so that scientists can learn more about how new drugs, tests, and other interventions will work for them. Many older adults have health needs that are different from those of younger people. For example, as people age, their bodies may react differently to certain drugs. Older adults may need different dosages of a drug to have the intended result. Also, some drugs may have different side effects in older people than in younger individuals. Having older adults enrolled in clinical trials and studies helps researchers get the information they need to develop the right treatments for this age group.
Researchers know that it may be challenging for some older adults to join a clinical trial or study. For example, if you have multiple health problems, can you participate in research that is looking at only one condition? If you are frail or have a disability, will you be strong enough to participate? If you no longer drive, how can you get to the research site? Talk to the research coordinator or staff about your concerns. The research team may have already thought about some of the potential obstacles and have a plan to make it easier for you to participate.
Read more about diversity in clinical trials .
For more information about clinical trials.
Alzheimers.gov www.alzheimers.gov Explore the Alzheimers.gov website for information and resources on Alzheimer’s and related dementias from across the federal government.
Clinical Research Trials and You National Institutes of Health www.nih.gov/health-information/nih-clinical-research-trials-you
ClinicalTrials.gov www.clinicaltrials.gov
This content is provided by the NIH National Institute on Aging (NIA). NIA scientists and other experts review this content to ensure it is accurate and up to date.
Content reviewed: March 22, 2023
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There are many different types of research studies, and each has distinct strengths and weaknesses. In general, randomized trials and cohort studies provide the best information when looking at the link between a certain factor (like diet) and a health outcome (like heart disease).
These are studies done in laboratories on cells, tissue, or animals.
These studies examine the incidence of a certain outcome (disease or other health characteristic) in a specific group of people at one point in time. Surveys are often sent to participants to gather data about the outcome of interest.
These studies look at the characteristics of one group of people who already have a certain health outcome (the cases) and compare them with a similar group of people who do not have the outcome (the controls). An example may be looking at a group of people with heart disease and another group without heart disease who are similar in age, sex, and economic status, and comparing their intakes of fruits and vegetables to see if this exposure could be associated with heart disease risk.
These are observational studies that follow large groups of people over a long period of time, years or even decades, to find associations of an exposure(s) with disease outcomes. Researchers regularly gather information from the people in the study on several variables (like meat intake, physical activity level, and weight). Once a specified amount of time has elapsed, the characteristics of people in the group are compared to test specific hypotheses (such as a link between high versus low intake of carotenoid-rich foods and glaucoma, or high versus low meat intake and prostate cancer).
Two of the largest and longest-running cohort studies of diet are the Harvard-based Nurses’ Health Study and the Health Professionals Follow-up Study.
If you follow nutrition news, chances are you have come across findings from a cohort called the Nurses’ Health Study . The Nurses’ Health Study (NHS) began in 1976, spearheaded by researchers from the Channing Laboratory at the Brigham and Women’s Hospital, Harvard Medical School, and the Harvard T.H. Chan School of Public Health, with funding from the National Institutes of Health. It gathered registered nurses ages 30-55 years from across the U.S. to respond to a series of questionnaires. Nurses were specifically chosen because of their ability to complete the health-related, often very technical, questionnaires thoroughly and accurately. They showed motivation to participate in the long-term study that required ongoing questionnaires every two years. Furthermore, the group provided blood, urine, and other samples over the course of the study.
The NHS is a prospective cohort study, meaning a group of people who are followed forward in time to examine lifestyle habits or other characteristics to see if they develop a disease, death, or some other indicated outcome. In comparison, a retrospective cohort study would specify a disease or outcome and look back in time at the group to see if there were common factors leading to the disease or outcome. A benefit of prospective studies over retrospective studies is greater accuracy in reporting details, such as food intake, that is not distorted by the diagnosis of illness.
To date, there are three NHS cohorts: NHS original cohort, NHS II, and NHS 3. Below are some features unique to each cohort.
NHS – Original Cohort
From these three cohorts, extensive research has been published regarding the association of diet, smoking, physical activity levels, overweight and obesity, oral contraceptive use, hormone therapy, endogenous hormones, dietary factors, sleep, genetics, and other behaviors and characteristics with various diseases. In 2016, in celebration of the 40 th Anniversary of NHS, the American Journal of Public Health’s September issue was dedicated to featuring the many contributions of the Nurses’ Health Studies to public health.
In 1996, recruitment began for a new cross-generational cohort called GUTS (Growing Up Today Study) —children of nurses from the NHS II. GUTS is composed of 27,802 girls and boys who were between the ages of 9 and 17 at the time of enrollment. As the entire cohort has entered adulthood, they complete annual questionnaires including information on dietary intake, weight changes, exercise level, substance and alcohol use, body image, and environmental factors. Researchers are looking at conditions more common in young adults such as asthma, skin cancer, eating disorders, and sports injuries.
Like cohort studies, these studies follow a group of people over time. However, with randomized trials, the researchers intervene with a specific behavior change or treatment (such as following a specific diet or taking a supplement) to see how it affects a health outcome. They are called “randomized trials” because people in the study are randomly assigned to either receive or not receive the intervention. This randomization helps researchers determine the true effect the intervention has on the health outcome. Those who do not receive the intervention or labelled the “control group,” which means these participants do not change their behavior, or if the study is examining the effects of a vitamin supplement, the control group participants receive a placebo supplement that contains no active ingredients.
A meta-analysis collects data from several previous studies on one topic to analyze and combine the results using statistical methods to provide a summary conclusion. Meta-analyses are usually conducted using randomized controlled trials and cohort studies that have higher quality of evidence than other designs. A systematic review also examines past literature related to a specific topic and design, analyzing the quality of studies and results but may not pool the data. Sometimes a systematic review is followed by conducting a meta-analysis if the quality of the studies is good and the data can be combined.
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Different types of clinical research are used depending on what the researchers are studying. Below are descriptions of some different kinds of clinical research.
Treatment Research generally involves an intervention such as medication, psychotherapy, new devices, or new approaches to surgery or radiation therapy.
Prevention Research looks for better ways to prevent disorders from developing or returning. Different kinds of prevention research may study medicines, vitamins, vaccines, minerals, or lifestyle changes.
Diagnostic Research refers to the practice of looking for better ways to identify a particular disorder or condition.
Screening Research aims to find the best ways to detect certain disorders or health conditions.
Quality of Life Research explores ways to improve comfort and the quality of life for individuals with a chronic illness.
Genetic studies aim to improve the prediction of disorders by identifying and understanding how genes and illnesses may be related. Research in this area may explore ways in which a person’s genes make him or her more or less likely to develop a disorder. This may lead to development of tailor-made treatments based on a patient’s genetic make-up.
Epidemiological studies seek to identify the patterns, causes, and control of disorders in groups of people.
An important note: some clinical research is “outpatient,” meaning that participants do not stay overnight at the hospital. Some is “inpatient,” meaning that participants will need to stay for at least one night in the hospital or research center. Be sure to ask the researchers what their study requires.
Phases of clinical trials: when clinical research is used to evaluate medications and devices Clinical trials are a kind of clinical research designed to evaluate and test new interventions such as psychotherapy or medications. Clinical trials are often conducted in four phases. The trials at each phase have a different purpose and help scientists answer different questions.
Phase I trials Researchers test an experimental drug or treatment in a small group of people for the first time. The researchers evaluate the treatment’s safety, determine a safe dosage range, and identify side effects.
Phase II trials The experimental drug or treatment is given to a larger group of people to see if it is effective and to further evaluate its safety.
Phase III trials The experimental study drug or treatment is given to large groups of people. Researchers confirm its effectiveness, monitor side effects, compare it to commonly used treatments, and collect information that will allow the experimental drug or treatment to be used safely.
Phase IV trials Post-marketing studies, which are conducted after a treatment is approved for use by the FDA, provide additional information including the treatment or drug’s risks, benefits, and best use.
Examples of other kinds of clinical research Many people believe that all clinical research involves testing of new medications or devices. This is not true, however. Some studies do not involve testing medications and a person’s regular medications may not need to be changed. Healthy volunteers are also needed so that researchers can compare their results to results of people with the illness being studied. Some examples of other kinds of research include the following:
A long-term study that involves psychological tests or brain scans
A genetic study that involves blood tests but no changes in medication
A study of family history that involves talking to family members to learn about people’s medical needs and history.
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by Julia Belluz and Steven Hoffman
Today, the prestigious academic journal JAMA Internal Medicine published an article on the association between eating whole grains and having a lower risk of death from cardiovascular disease. Many news sources are going to have headlines like " Whole grains lead to heart-healthy benefits " and " Whole Grain Consumption Lowers Death Risk ." But you shouldn't believe them. While this latest work represents excellent science — a prospective cohort observational study, in scientific parlance — it's just one study. And when you look at a single study, you're getting only one piece of the puzzle, one interpretation of the research question, one idea about how to run a scientific experiment. In this case, the study population was not randomly assigned to eat more whole grains, which means we can't know whether the people who ate them are healthier because of their diet or because of other traits they share, like their age, ethnicity, smoking status, alcohol intake, physical activity levels, multivitamin use, and family medical history.
Whole grains probably won't save your life. (Michael Gottschalk/Photothek)
Studies can control for many possible " confounding factors " — or variables that may influence a particular outcome — but it's impossible to account for everything that may matter. For example, this study didn't account for key determinants of health like wealth and education, which may be more important for the health of whole grain eaters than what they eat. Besides, as the study authors themselves point out, theirs is not the only word on this matter. Their results match those found in the Iowa Women’s Health Study and the Norwegian County Study , but they did not fully align with other studies involving diabetics and healthy older people .
The grain study, like many other health studies you read about, is an excellent moment to think about one key insight that could help you live longer than whole grains (or red wine or coffee or chocolate) ever will: a study isn’t a study isn’t a study like any other. There are literally thousands of ways to design a study. When a news story suggests, “A new scientific study has found...” or a celebrity doctor begins a sentence with, “Studies show...”, you need to ask, “What kind of studies?” Because “studies” are not equally reliable, they all have different limitations, and they should not be acted on in the same manner — or even acted on at all. Here’s a quick guide to understanding study design that will help you navigate the often bewildering world of health research.
Much of health research — especially the kind that makes the news headlines — can be broken down into two basic types: observational and experimental. In observational studies, scientists observe and gather data on some phenomenon that’s already happening: patterns of olive oil consumption, who tends to take vitamin D supplements, how much people exercise, and so on. But they don’t intervene at all to change anything in people’s lives; they merely gather descriptive information on habits, beliefs, or events. With experimental research, on the other hand, scientists do intervene, or at least use statistical methods to mimic intervention: they give some people a drug, they perform an operation on others. In the best-designed experiments, study participants are randomly divided into at least two groups: those who get the intervention (i.e., treatment) and those who don’t (i.e., placebo). Random allocation ensures that the groups are statistically comparable with potential “confounding factors” equally distributed among them. The only difference between the groups is the intervention, which allows researchers to tease out what effect that intervention causes. This is why conclusions from experiments are generally considered to be more reliable and trustworthy.
Red wine probably won't save your life, either. (David Silverman/Getty Images News)
There are many different types of observational studies, but here are the four most common that you need to know about: cross-sectional surveys, cohort studies, case-control studies, and case reports. “ Cross-sectional surveys ” take a random sample of people and record information about them at one point in time. For example, researchers might survey randomly selected inhabitants of Washington, DC to figure out how many have heart disease (i.e., an epidemiological survey) or how they think about the quality of green space for outdoor exercise (i.e., a public opinion poll). “ Cohort studies ” are just like surveys but they track the same groups of people over an extended period of time. That’s why they are often called “longitudinal” and “prospective” studies. Instead of just gathering data on heart disease in Washington DC at one point in time, a cohort study would follow groups (or cohorts) of study participants over a period of, say, 10 years, and see how many people in each of the groups develop heart disease. This allows researchers to record changes in the health of the participants over time and compare the levels of health in different groups of people. “ Case-control studies ” are often called “retrospective studies.” That’s because researchers start with an end point and work backward, figuring out what might have caused that outcome. For example, researchers could take two groups of people who live in Washington, DC: those who have been diagnosed with heart disease and those who haven’t. They could then work backwards and survey the two groups about their earlier health behaviors to figure out what might have caused the disease to develop or not. They may ask about saturated fat consumption or exposure to disease-inducing viruses. From there, they would note any differences in risk factors or exposures that emerge between the two groups which can help suggest what may have led to heart disease in some people. “ Case reports ” are basically detailed stories about a particular patient’s medical history. If a doctor writes up case reports about a cluster of patients with the same condition or disease, this is a “case series.” Though these are considered the weakest kind of observational studies, they can still be very helpful for rare diseases and powerful for advocacy. Sometimes they can be a bellwether in medicine. Early case reports, for example, led to the tragic discovery that mothers who were taking thalidomide for morning sickness were having babies with missing limbs. These reports surfaced long before a randomized trial could ever be done — and spared thousands of babies. Read more: Why so many of the health articles you read are junk ; Stop Googling your health questions. Use these sites instead ; How to read a paper ; and the Vox cardstack on how to be a more savvy science reader.
3) Observational studies have limits you need to understand From a single observational study, researchers will only be able to suggest whether there’s an association between a risk like fat consumption and an outcome like heart disease — and not that one caused the other. That’s because the research participants were already eating fat or already had heart disease (or not) when the study began. What if people who eat lots of fat happen to be less health conscious? What if they are poorer and therefore more stressed? What if this particular group of fat-eaters just happened to be chubbier than those who stick to a low-fat diet? These things are called “confounding factors,” or the difficult-to-predict variables that are associated with both the cause (e.g., saturated fat) and potential effect (e.g., heart disease) under study. Sometimes confounding factors are knotty and wholly misleading. In 1991, the authors of a commentary published in the New England Journal of Medicine suggested that left-handed people had a higher risk of mortality. For their retrospective case-control study, researchers looked at death certificates from two counties in southern California and then asked family members of the deceased about their beloved ones’ handedness. They found that being left-handed is associated with dying younger. “The mean age at death in the right-handed sample was 75 years, as compared with a mean age at death of 66 years in the left-handers,” they wrote. After publication, the journal editor was inundated with angry letter-writers. That’s because the researchers failed to account for the cultural context: there was a time in the US when left-handed children were forced to become right-handed children. The reason there were few older left-handers was not because the hand you write with spells an early end, but because the would-be elderly lefties had converted when they were young and appeared as right-handed people in the study.
Now let’s move on to experimental research. There are two basic types: randomized controlled trials and quasi-experimental designs. “ Randomized controlled trials ” are considered the gold standard of medical evidence, though as you will probably surmise by now, they aren’t necessarily the best study design for every research question. The reason they’re so powerful, when they’re well done, is because they are designed to tease out cause-and-effect relationships; randomization means treatment groups are comparable, and the only difference between them is the intervention (i.e., whether they received the drug or not) so any difference in outcome between the two groups can be attributed to the intervention.
When these experiments are blinded, they’re even more powerful: blinding means either the study participants, the doctors, or both (“double-blinded”) do not know whether they are receiving/giving the real treatment or a placebo. So blinded studies account for any placebo effects that may arise.
Lastly, there’s a type of study design that lies somewhere between experimental and observational research: that’s the “ quasi-experiment .” These are essentially a type of unplanned or uncontrolled experiment that uses statistics and human ingenuity to mimic the conditions of an experiment. Scientists have found many ways of undertaking these. One example would be comparing tobacco consumption before and after a border town is subjected to new state smoking regulations with its neighboring town in a different state that keeps the old regulations. Another example would be to evaluate the effects of GPA-based university scholarships by comparing those students who were just above and just below the grade point cut-off for receiving them.
The classical hierarchy of evidence. (From the MS Trust Information )
Researchers often rank study designs in hierarchies (see above) to describe the relative weight of their conclusions. At the top of the hierarchy are syntheses of evidence that identify and integrate all sources of high-quality information relevant for a particular question coming from different contexts, settings, and methods. These reviews address that problem of the single study puzzle piece. Rather than relying on just one person's experience or even just one randomized controlled trial, synthesized evidence draws on multiple sources and weighs their contributions to arrive at a more fully-supported conclusion according to each study's rigor and relevance. This kind of research is regarded as the highest form of evidence — the king of all evidence if you will — and the best science to inform decision-making. The idea is that many studies, done on thousands of people and taken together as a whole, can get us closer to the truth than any single study or anecdote ever could. (That is, unless a single study or anecdote is the only evidence available.) Reviews are less biased than a selective sampling of smaller studies that they might summarize. Within synthesized evidence, the most reliable type for evaluating health claims are called "systematic reviews." These studies represent the best available syntheses of global evidence about the likely effects of different decisions, therapies and policies. not all systematic reviews are created equally, either. As their name suggests, systematic reviews use particular methods for finding helpful information, assembling it, and assessing its quality and applicability to the question you're interested in answering. Following this approach to the evidence — which is usually independently repeated at least twice by separate reviewers — reduces the bias that can creep into single studies. This process also helps to make sure results are not skewed or distorted by an individual author's preconceptions or cognitive biases. Finally, such transparency means that readers can know what the authors did to arrive at their conclusions and can easily evaluate the quality of the review itself. You can log into a place like the Cochrane Library , Health Systems Evidence , or PubMed Health and read systematic reviews about everything from the effects of acupuncture for migraines and premenstrual syndrome, to the efficacy of cranberry juice for bladder infections. The hard-working people behind these studies are even starting to translate their conclusions into "plain language summaries," written in the way most people actually speak. This means these reviews and databases are more accessible than ever before. But then again, not all systematic reviews are created equally, either. And systematic reviews are only a starting point. Even with the best available evidence from around the world at our disposal, we have to analyze it and apply it to our particular circumstances. A personal experience with the success or failure of a drug, like an allergic reaction, is more informative for you than the most rigorous study on the drug ever could be. Just remember that one person's experiences are merely anecdotes — the least helpful type of evidence — for others. And one study, like the latest on whole grains, is only one piece of the puzzle.
With Burden of Proof Julia Belluz (a journalist) and Steven Hoffman (an academic) join forces to tackle the most pressing health issues of our time — especially bugs, drugs, and pseudoscience thugs — and uncover the best science behind them. Have suggestions or comments? Email Belluz and Hoffman or Tweet us @juliaoftoronto and @shoffmania . You can see previous columns here .
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To be able to ask the right questions about a paper, you need to find out what type of research it is. Read the abstract of the paper to find out what the researchers were trying to find out, and which methods they used.
There are four main types of health research. Research that:
Often, research does not fit into neat categories. If you find the questions in your chosen category are not relevant to the paper you are reviewing, you use the back button to return here and choose a different category.
Some research uses a combination of different research methods. This is called mixed-methods research . Reviewing mixed-methods research can be difficult because different methods have different quality concerns. If the paper you wish to review uses mixed-methods, you could decide which method is the dominant method used in the research, and select the appropriate option below. For more information, see our introduction to mixed methods research .
Try to choose the category that best fits the research you are reviewing. If the research you are reviewing does not seem to fall under any of these categories, select Other types of research .
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Published on 3.9.2024 in Vol 26 (2024)
This is a member publication of University of Oxford (Jisc)
Authors of this article:
1 4YouandMe, Seattle, WA, United States
2 Department of Psychiatry, University of Oxford, Oxford, United Kingdom
3 Crohn's & Colitis Foundation, New York, NY, United States
4 Section of Urology and Renal Transplantation, Virginia Mason Francisan Health, Seattle, WA, United States
5 MindMed Inc, New York, NY, United States
6 Tufts University School of Medicine, Boston, MA, United States
7 Department of Paediatrics, University of Toronto, Toronto, ON, Canada
8 Department of Pediatric Hematology/Oncology, Schneider Children's Medical Center of Israel, Petach-Tikva, Israel
9 Department of Pediatrics, University of Toronto, Toronto, ON, Canada
10 Gasteroentology Unit, Oxford University Hospitals NHS Foundation Trust and Biomedical Research Centre, Oxford, United Kingdom
11 The Dr. Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
12 Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, United States
13 The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD, United States
14 Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada
15 Evidation Health Inc, Santa Mateo, CA, United States
16 Sage Bionetworks, Seattle, WA, United States
Sarah M Goodday, MSc, PhD
2901 3rd Ave
Seattle, WA, 98121
United States
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Background: Wearable digital health technologies and mobile apps (personal digital health technologies [DHTs]) hold great promise for transforming health research and care. However, engagement in personal DHT research is poor.
Objective: The objective of this paper is to describe how participant engagement techniques and different study designs affect participant adherence, retention, and overall engagement in research involving personal DHTs.
Methods: Quantitative and qualitative analysis of engagement factors are reported across 6 unique personal DHT research studies that adopted aspects of a participant-centric design. Study populations included (1) frontline health care workers; (2) a conception, pregnant, and postpartum population; (3) individuals with Crohn disease; (4) individuals with pancreatic cancer; (5) individuals with central nervous system tumors; and (6) families with a Li-Fraumeni syndrome affected member. All included studies involved the use of a study smartphone app that collected both daily and intermittent passive and active tasks, as well as using multiple wearable devices including smartwatches, smart rings, and smart scales. All studies included a variety of participant-centric engagement strategies centered on working with participants as co-designers and regular check-in phone calls to provide support over study participation. Overall retention, probability of staying in the study, and median adherence to study activities are reported.
Results: The median proportion of participants retained in the study across the 6 studies was 77.2% (IQR 72.6%-88%). The probability of staying in the study stayed above 80% for all studies during the first month of study participation and stayed above 50% for the entire active study period across all studies. Median adherence to study activities varied by study population. Severely ill cancer populations and postpartum mothers showed the lowest adherence to personal DHT research tasks, largely the result of physical, mental, and situational barriers. Except for the cancer and postpartum populations, median adherences for the Oura smart ring, Garmin, and Apple smartwatches were over 80% and 90%, respectively. Median adherence to the scheduled check-in calls was high across all but one cohort (50%, IQR 20%-75%: low-engagement cohort). Median adherence to study-related activities in this low-engagement cohort was lower than in all other included studies.
Conclusions: Participant-centric engagement strategies aid in participant retention and maintain good adherence in some populations. Primary barriers to engagement were participant burden (task fatigue and inconvenience), physical, mental, and situational barriers (unable to complete tasks), and low perceived benefit (lack of understanding of the value of personal DHTs). More population-specific tailoring of personal DHT designs is needed so that these new tools can be perceived as personally valuable to the end user.
Wearable digital health technologies (DHTs) [ 1 , 2 ] and mobile apps facilitate the remote, real-world assessment of health including objective signs of disease that are typically confined to health care visits and health care provider interpretation. These specific categories of DHTs, herein referred to as “personal DHTs,” hold promise for transforming health research through the new ability to capture high-resolution, high-frequency, in-the-moment health-related multimodal information in decentralized ways. Through the provision of personal DHTs in clinical care, individuals could be better empowered to navigate their health outside the health care system with greater accessibility, agency, and accuracy than currently possible [ 1 , 2 ]. One of the largest challenges in the future of digital health that involves the use of personal DHTs is end-user engagement. While direct comparisons of engagement in personal DHT research are challenging due to the heterogeneous reporting of retention and adherence factors, and a lack of consensus on a definition of “engagement” [ 3 - 6 ], accumulating evidence supports that so far engagement in the use of personal DHTs has been poor. Specifically, retention in personal DHT research studies and the use of health-related apps is low across diverse populations and applications [ 7 - 9 ]. Further, there is evidence of attrition biases in personal DHT research resulting in insufficient representation of minority populations [ 7 ]. In addition to poor retention, personal DHT research studies have low adherence to completing active app-based tasks resulting in large amounts of missing data. This missing data problem results in challenges in artificial intelligence models from insufficient volumes of data to follow individual patterns, and limits app-based context “label” data. This “label” data is crucial for validating passively collected information from personal DHTs, particularly given the early state of the field and as the utility of certain approaches such as knowledge graphs and large language models emerge.
Several personal DHTs health research studies have started to surface [ 7 - 12 ], resulting in the identification of barriers to engagement. These barriers include technical problems with the technology and in collecting the data, usability, privacy concerns, and digital literacy. Many of these barriers point to a need to retain a human element in the research process, and to include an aspect of co-designing with end users. Emerging personal DHT research studies that show better engagement retain some form of “human-in-the-loop” (regular contact with research staff) and co-design or end-user approach [ 11 - 15 ]. Among these studies, retention rates of 80% and higher have been observed, while average adherence to wearable device use and daily app surveys have been shown to be >90% and 70%, respectively [ 11 - 15 ].
The promise of digital health rests on the assumption that end users can be engaged in the long-term use of personal DHTs for health monitoring, yet this remains to be seen among most existing research applications. There have been increasing international calls for the inclusion of patients in the design and conduct of health research [ 16 - 18 ], and this seems particularly relevant for digital health research where the patient is the end user of these new remote tools. In this paper, we report on engagement across 6 unique personal DHT health research studies that adopted different aspects of a participant-centric design, but each with distinct population and design features. The objective is to describe how participant engagement techniques and different personal DHT designs affect participant adherence, retention, and overall engagement in personal DHT health research.
In total, 6 personal DHT research studies are included in this quantitative and qualitative analysis of engagement that span diverse populations including a frontline health care population (the stress and recovery in frontline health care workers study) [ 11 ]; a conception, pregnancy, and postpartum population (Better Understanding the Metamorphosis of Pregnancy [BUMP] study) [ 19 ]; and populations with different diseases including Crohn disease (stress in Crohn: forecasting symptom transitions study), Li-Fraumeni syndrome (stress and LFS: a feasibility study of wearable technologies to detect stress in families with LFS), and patients with pancreatic and central nervous system (CNS) tumors (help enable real-time observations [HERO] in pancreatic [PANC] and CNS tumors studies) [ 20 ].
All of these studies were conducted by 4YouandMe—a US-based nonprofit (charitable) organization. 4YouandMe specializes in open-source research into the application of personal DHTs for health and wellness [ 20 ]. 4YouandMe has a particular focus on leveraging personal DHTs to empower the patient in navigating their unique disease or life transitional period. These 6 studies were included in this analysis as they reflect all of the completed studies by 4YouandMe at the time of this analysis. Characteristics of these studies can be found in Table 1 and additional methodological detail can be found in Multimedia Appendix 1 . All studies involved the use of a bespoke study smartphone app built by 4YouandMe and the use of the Oura smart ring, the Garmin smartwatch, the Apple smartwatch, an Empatica smartwatch, and the Bodyport Cardiac Scale. Details of these devices can be found in Multimedia Appendix 2 ).
Study and population | Sample size | Age (years), median (IQR) | Active study time (months) | Recruitment | Devices | Average (SD) app daily burden | Compensation | Engagement strategy | |
Frontline health care workers | 365 | 33.0 (28.0-42.0) | 4-6 | Remote: Social media and health care organization newsletters | 5 (1.8) minutes | None (participants completing the study kept the wearable devices) | |||
Patients with Crohn disease | 195 (MSSM , N=139; Oxford, N=56) | MSSM (median 29, IQR 24-37), Oxford (median 39, IQR 32-50) | 6-9 | In-clinic: through inflammatory bowel disease clinics | 7.7 (1.0) minutes | Yes, participants could keep the ring or receive compensation based on points accumulated | |||
Patients with CNS tumors | 12 | 52 (43-56) | 7 | In-clinic: through cancer specialty clinics | 5.3 (2.1) minutes | None (participants completing the study kept the wearable devices) | |||
Patients with pancreatic cancer | 26 | 57 (53-65) | 1 to 14 months | In-clinic: through cancer specialty clinics | 3.1 (1.9) minutes | None (participants completing the study kept the wearable devices) | |||
Affected and unaffected family members of a proband with LFS | 49 | 39.0 (7.9-68.0) | 6 | In-clinic: through cancer specialty clinics | 2.3 (0.9) minutes | None | |||
Pregnant individuals (up to 15 weeks) | 524 | 33.0 (30-36) | Up to 12 months | Remote: through patient-provider portals, social media, and community health clinics | 5.0 (2.3) minutes | Yes, participants received compensation based on study points accumulated | |||
Individuals actively attempting to get pregnant | 273 | 34.0 (31-36) | Up to 6 months | Remote: through patient-provider portals, social media, and community health clinics | 3.8 (2.0) minutes | Yes, participants could keep the ring or receive compensation |
a MSSM: Mount Sinai School of Medicine.
b HERO-CNS: help enable real-time observations—central nervous system.
c CNS: central nervous system.
d HERO-PANC: help enable real-time observations—pancreatic cancer.
e n=24, 2 unknown.
f Until withdrawal, progression, death, or study completion (October 31, 2022).
g LFS: Li-Fraumeni syndrome.
h BUMP: Better Understanding the Metamorphosis of Pregnancy.
i BUMP-C: Better Understanding the Metamorphosis of Pregnancy—Conception.
All included studies were approved by the local institutional research ethics boards (REB) at their local sites ( Multimedia Appendix 1 ): stress and recovery in frontline health care workers study (institutional review board [IRB], Advarra [4UCOVID1901, Pro00043205]), BUMP study (IRB Advarra Pro00047893), stress in Crohn (Oxford site: Hampshire-A IRAS ID: 269286, Mount Sinai School of Medicine [MSSM] site: IRB of MSSM: GCO 19-1543 | IRB-19-02298), stress and LFS (Sick Kids: REB: 1000072240), HERO-CNS (John Hopkins Medicine IRB IRB00253818), and HERO-PANC (University Hospital Network REB: 20-5211).
Definitions of adherence in digital health research studies are heterogeneous [ 3 - 6 ]. Consistent criteria for adherence across all included studies were attempted. While many different wearable features could be used as the basis for the use of the device, features that were most reliably monitored were selected. For studies using the Oura smart ring, daily adherence was defined as at least one sleep data event present for the prior night. The Oura ring was only expected to be worn at night for many of the included studies, which is why sleep data were used as the indicator for adherence. For studies using the Garmin smartwatch, daily adherence was defined as step data present for that day. For the Empatica smartwatch, daily adherence was defined as at least one data event (worn properly in a day). Adherence to the Bodyport Cardiac Scale was defined as the proportion of days where a weight event was present divided by the total number of expected follow-up days. Adherence to in-app task completion was defined as the proportion of tasks completed when prompted in the app divided by the total number of tasks that should have been completed over study follow-up. For example, all included studies had a daily survey. In a study with a minimum of 4 months of follow-up expected from participants, the total number of expected daily surveys is approximately 120. For a weekly app survey, the total number of expected surveys for a 4-month study follow-up would be 16. Adherence to biweekly check-in calls was defined as the proportion of calls completed divided by the total number of expected calls over study follow-up. Medians and ranges are described since the adherence distributions were nonnormally distributed. All adherence estimations were performed only among retained participants.
Differences in adherence and retention by sociodemographic characteristics were estimated using χ 2 , Fisher exact, Mann-Whitney U , and ANOVA tests where appropriate among studies that have sufficient sample sizes (stress and recovery, BUMP, and stress in Crohn). Survival probabilities using the Kaplan-Meier approach were calculated to display the probability of retention over the course of each included study. Retention (total proportion of participants completing the study among all enrolled) is also reported. Additional information on how retention was calculated for each unique study can be found in Multimedia Appendix 3 .
Study design characteristics of all studies are described in Table 1 . All studies included the use of at least one wearable device plus a study app that involved daily, as well as intermittent surveys (daily question prompts, validated questionnaires) and active tasks (cognitive active or physical function tasks [eg, walk tests], video diaries). In all included studies, participants were required to use their own Android or iPhone smartphone for study activities. Recruitment mechanisms differed across studies with some including remote recruitment through digital advertisements on social media, professional organizations and newsletters, and patient portals (stress and recovery, and BUMP), while others recruited patients in-person through specialty clinics (stress in Crohn, HERO studies, and stress and LFS). The daily burden of app active tasks across studies ranged from 2 to 7 minutes. Study follow-up periods across studies ranged from 4 to 18 months. Across all studies except the stress and LFS study, participants were offered to keep some of the study wearable devices (most often the ring and the watch). Further, 2 studies included the option for modest financial compensation (BUMP and stress in Crohn).
All studies included an engagement strategy that centered around a biweekly phone check-in with a consistent engagement specialist that served the purpose of supporting participants, helping them with onboarding, resolving potential technological problems, and discussing and collecting study experience feedback. Additionally, all included studies implemented different strategies that focused on working with participants as co-designers. These strategies included making app changes that were driven by direct participant feedback during active follow-up, offering a “your data” section in the app that allowed participants to track key symptoms over time, hosting optional investigator-participant Zoom calls where participants could meet the study team, receive study updates, preliminary results, and could offer more feedback, and inviting participants to contribute to and be listed as coauthors on published work.
Median adherence in engagement phone check-in calls, wearable device use, daily app survey completion, and in-app active tasks can be found in Table 2 . Median adherence varied across study populations. The stress in Crohn–MSSM site had a lower adherence on the engagement check-in calls (50%) compared to other studies, many of which had 100% adherence on these calls ( Table 2 ). This study site is herein referred to as the low-engagement cohort. In this low-engagement cohort, median adherence to completing daily app surveys, to wearing the Empatica smartwatch, and to using the Bodyport Cardiac Scale were lower than all other study cohorts that included these studies’ activities (except the BUMP-postpartum cohort). Further, median adherence to using the Oura smart ring was lower in the low-engagement cohort compared to other cohorts except for the postpartum and severely ill cancer populations.
The HERO studies included the most severely ill participants including patients with active diagnoses of CNS and pancreatic tumors. Some HERO participants were undergoing chemotherapy, some had therapy-related complications, some had infections, and some had progressive, life-threatening tumor growth. While the total number of participants in these studies was low, these studies showed low adherence on the daily survey (<55%) and wearable device use (<65% HERO-CNS only). Interestingly, HERO-PANC participants exhibited high wearable device use median adherence (83.3%, IQR 51%-93.2%, Oura and 95.5%, IQR 75.2%-99.2%, Garmin), despite the health status of this population. Further, median adherence to in-app cognitive active tasks was higher among the HERO studies compared to most other studies. Engagement check-in call adherence was also high in the HERO studies. Among the BUMP postpartum cohort, there was consistently lower adherence on all study tasks except for the engagement check-in calls compared to other studies, particularly in comparison to the BUMP prenatal cohort. Specifically, median adherence to the Oura ring, Garmin smartwatch use, and the Bodyport Cardiac Scale in the BUMP-prenatal cohort compared to the BUMP postpartum cohort dropped from 87.2% (IQR 68.7%-96.7%) to 55% (IQR 5.5%-83.7%), 96.7% (IQR 82.9%-100%) to 62.5% (IQR 12.3%-96.4%), and 74.7% (IQR 52%-87.3%) to 33.1% (IQR 8.9%-67.7%), respectively ( Table 2 ).
Stress and recovery | BUMP-C | BUMP | BUMP-POST | SINC -MSSM | SINC-Oxford | HERO-CNS | HERO-PANC | Stress in LFS | |
Participants, n | 297 | 98 | 379 | 379 | 117 | 54 | 7 | 19 | 45 |
ES check-ins, median (IQR) | 75.0 (57.1-87.5) | 100.0 (87.9-100.0) | 100.0 (88.4-100.0) | 100.0 (100.0-100.0) | 50.0 (20.0-75.0) | 100.0 (90.9-100.0) | 85.7 (78.1-88.2) | 100.0 (100.0-100.0) | 60.0 (40.0-80.0) |
Oura ring, median (IQR) | 97.0 (86.0-100.0) | 90.6 (76.3-97.7) | 87.2 (68.7-96.7) | 55.0 (5.5-83.7) | 80.5 (37.1-92.4) | 98.9 (94.0-99.6) | 42.3 (32.0-58.2) | 83.3 (51.0-93.2) | — |
Garmin watch, median (IQR) | — | — | 96.7 (82.9-100.0) | 62.4 (12.3-96.4) | — | — | 63.3 (54.7-64.3) | 95.5 (75.2-99.2) | — |
Apple watch, median (IQR) | — | — | 98.1 (87.7-100.0) | 79.8 (32.4-96.3) | — | — | — | — | — |
Empatica watch, median (IQR) | — | — | — | — | 26.0 (6.2-64.1) | 72.5 (37.1-96.8) | — | — | 86.8 (66.7-95.6) |
Bodyport scale, median (IQR) | — | — | 74.7 (52.0-87.3) | 33.1 (8.9-67.7) | 38.5 (17.1-64.7) | — | — | 79.5 (52.7-88.4) | — |
Daily survey, median (IQR) | 75.4 (57.2-88.2) | 42.4 (24.6-69.7) | 60.1 (34.4-81.7) | 18.4 (1.0-47.6) | 27.9 (10.4-51.9) | 70.3 (41.9-84.0) | 53.3 (47.8-71.5) | 49.1 (20.2-83.4) | 62.5 (40.96-82.59) |
Reaction rime, median (IQR) | 88.9 (75.0-100.0) | 43.4 (24.3-72.8) | — | — | 30.4 (9.7-50.6) | 69.5 (46.6-89.3) | 59.0 (50.0-66.7) | 62.5 (20.9-86.6) | — |
Trail making, median (IQR) | 88.9 (71.1-100.0) | 46.5 (24.0-73.7) | — | — | 28.7 (9.4-50.0) | 71.6 (45.0-87.3) | 61.5 (52.1-76.5) | 38.1 (4.2-76.2) | 57.7 (36.8-72.0) |
EBT , median (IQR) | — | 30.1 (16.2-54.1) | 44.6 (22.6-73.9) | 6.5 (0.0-33.3) | 23.1 (9.1-44.4) | 32.1 (0.0-58.6) | — | — | — |
N-Back, median (IQR) | — | — | 51.4 (24.9-76.4) | 8.3 (0.0-44.4) | — | — | — | — | — |
Gait task, median (IQR) | — | — | 25.0 (0.0-60.0) | 0.0 (0.0-0.0) | — | — | 24.5 (18.8-62.8) | 36.0 (2.2-74.0) | — |
Walk test, median (IQR) | — | — | 14.3 (0.0-40.0) | 0.0 (0.0-0.0) | — | — | 23.1 (13.9-60.4) | 25.0 (7.8-49.5) | — |
Video diary, median (IQR) | — | 4.3 (0.0-27.7) | 8.3 (0.0-50.0) | 0.0 (0.0-0.0) | 5.6 (0.0-22.2) | 9.4 (0.0-35.1) | 25.0 (8.7-77.1) | 0.0 (0.0-37.5) | — |
a BUMP-C: Better Understanding the Metamorphosis of Pregnancy—Conception.
b BUMP: Better Understanding the Metamorphosis of Pregnancy.
c BUMP-POST: Better Understanding the Metamorphosis of Pregnancy—Postpartum.
d SINC: stress in Crohn.
e MSSM: Mount Sinai School of Medicine.
f HERO-CNS: help enable real-time observations—central nervous system.
g HERO-PANC: help enable real-time observations—pancreatic cancer.
h LFS: Li-Fraumeni syndrome.
i ES: engagement specialist.
j Not available.
k EBT: emotional bias test.
There were differences in adherence rates across different study activities. Adherence to wearable device use was consistently higher across studies compared to in-app activities, which is not surprising given the passive nature of these devices. Excluding the postpartum and HERO-CNS study, median adherence to Oura ring use was >80% across all studies, and as high as 99% (IQR 94.9%-99.6%; stress in Crohn-Oxford site; Table 2 ). There were also differences in adherence across specific wearable devices. Garmin and Apple smartwatch adherence was >95% in BUMP pregnant individuals and HERO-PANC participants, while median adherence for the Empatica Watch was lower among the studies that used this device (stress in Crohn-Oxford, 72.5%, IQR 37.1%-96.8%; stress in Crohn-MSSM, low-engagement cohort, 26%, IQR 6.2%-64.1%; and stress in LFS, 86.8%, IQR 0.7%-0.9%). Median adherence to the Bodyport Cardiac Scale was 74.7% (IQR 52%-87.3%) among BUMP pregnant individuals and 79.5% (IQR 52.7%-88.4%) in HERO-PANC participants ( Table 2 ). Excluding the postpartum and HERO study populations and the low-engagement cohort, in-app daily survey adherence was >60% for all studies ( Table 2 ). Finally, adherence to in-app active tasks was lower in general compared to other activities such as wearable device use or in-app surveys. Tasks that involved walking (gait and walk task) or speaking (video diaries) showed lower adherence compared to other active tasks (eg, cognitive and emotional bias tasks; Table 2 ).
There did not appear to be any meaningful difference in median adherence rates across study activities by study recruitment methods (in-clinic vs remote) or follow-up time. Further, 2 studies that included modest financial compensation in addition to engagement strategies showed higher adherence rates compared to some of the other studies (ie, BUMP and stress in Crohn), but the impact of compensation is difficult to disentangle from other study characteristics such as population differences, and these studies did not show superior adherence rates compared to the stress and recovery study that did not offer financial compensation.
The median proportion of participants retained in the study across the 6 studies was 77.2% (IQR 72.6%-88%; Table 3 ). The probability of staying in the study stayed above 80% for all studies during the first month of study participation and stayed above 50% for the entire active study period across all studies ( Multimedia Appendix 4 ).
Study | Proportion retained at study completion, retained/enrolled (%) |
Stress and recovery | 297/365 (81.4) |
BUMP-C | 134/187 (72.7) |
BUMP | 379/524 (72.3) |
Stress in Crohn-MSSM | 117/139 (84.2) |
Stress in Crohn-Oxford | 54/56 (96.4) |
HERO-CNS | 7/12 (58.3) |
HERO-PANC | 19/26 (73.1) |
Stress and LFS | 45/49 (91.8) |
b Only includes participants who were enrolled in the Better Understanding the Metamorphosis of Pregnancy—Conception-specific app.
c BUMP: Better Understanding the Metamorphosis of Pregnancy.
d MSSM: Mount Sinai School of Medicine.
e HERO-CNS: help enable real-time observations—central nervous system.
f HERO-PANC: help enable real-time observations—pancreatic cancer.
g Help enable real-time observations—pancreatic cancer has unique factors to consider when interpreting the proportion retained until study completion, since the study aimed to monitor patients until they developed progressive disease or died, or the study end date (October 31, 2022; see Multimedia Appendix 3 ).
Median adherence for the Oura smart ring, a smartwatch (Garmin, Apple, and Empatica), and the Bodyport Cardiac Scale was lower among younger participants compared to older participants across most studies ( Multimedia Appendix 5 ). Specifically, Oura smart ring adherence was significantly lower in those aged 18-25 years compared to those aged ≥26 years in the BUMP study ( P =.03) and stress in Crohn-MSSM studies ( P =.02), and was lower in the BUMP-C and stress and recover studies, but this difference was not statistically significant at P =.59 and P =.08, respectively. Median adherence for Apple smartwatch use was significantly lower in those aged 18-25 years compared to those aged ≥26 years in the BUMP study ( P =.02), while median adherence for Garmin smartwatch use was lower but not statistically significant ( P =.06). Median adherence for the Bodyport Cardiac Scale was significantly lower in those aged 18-25 years compared to those aged ≥26 years in BUMP ( P <.005) and stress in Crohn-MSSM ( P <.006).
In the BUMP study, Black or African American ethnicity had significantly higher median adherence to completing the in-app daily survey compared to other race or ethnicity groups ( P =.01). This trend was observed in the stress and recovery study ( P =.07) and the stress in Crohn-MSSM study ( P =.24), although the difference was not statistically significant. In contrast, median adherence to Oura smart ring, smartwatch, and Bodyport Cardiac Scale use was lower among Black or African American individuals compared to other race or ethnicity groups, although these differences were not statistically significant ( Multimedia Appendix 5 ).
Retention did not significantly differ by age group or gender ( Multimedia Appendix 6 ).
Retention likelihood was significantly different by race or ethnicity groups in BUMP-C ( P <.001) and BUMP ( P= .001). Specifically, participants of White ethnicity were more likely to stay in the study in both BUMP-C and BUMP, while participants reporting their race or ethnicity as either unknown or not reporting this item were less likely to be retained ( Multimedia Appendix 6 ).
Figure 1 describes key themes that impacted participant retention, adherence, and overall engagement that cut across all included studies. These themes include participant burden and forgetfulness, digital literacy, physical and mental barriers, personal and altruistic benefits, and privacy and confidentiality. Qualitative feedback from participants, research staff, and investigators across these 5 themes is summarized in Multimedia Appendix 7 . The top three barriers to engagement in active study tasks were (1) participant burden and in particular fatigue with the repetitiveness of tasks; (2) physical or mental and situational barriers that prevented the ability to complete tasks; and (3) personal and altruistic benefit, namely the perception that the use of the personal DHTs was not personally useful for a health benefit or a lack of understanding as to why and how certain features (eg, heart rate variability) could be useful to track for health benefit. Qualitative feedback from participants in the 2 cohorts demonstrating lower adherence (HERO-PANC and BUMP post partum) suggested that while participants were highly engaged, they were either too ill, distracted, or tired to complete many of the study activities while navigating a serious illness or the early postpartum period.
Evidence across 6 unique and diverse studies involving the longitudinal use of personal DHTs supports that participant-centric engagement strategies aid in participant retention and maintaining good adherence in some populations. These strategies centered around (1) human contact with an engagement specialist as often as every 2 weeks, (2) investigator-participant meetings during active study follow-up, (3) offering returned symptom data in the app, (4) inviting participants to contribute as coauthors in published work, and (5) real-time modifications to the study app based on participant feedback.
In the majority of included studies, the probability of staying in the study stayed above 90% for the first month and stayed above 50% for active study periods for all studies. Lower retention or adherence was observed among studies that included a severely ill cancer population and a postpartum population. Barriers to participation in these cohorts were largely the result of physical and situational roadblocks. Excluding studies of a severely ill and postpartum population and the low-engagement cohort in the stress in Crohn study, adherence to Oura smart ring and Garmin smartwatch use was 80% and as high as 99% in some cohorts, while adherence to the Bodyport Cardiac Scale was 75% in a pregnant population. This supports that different populations can successfully be engaged in the use of active app assessments and wearable devices in the long term with adequate support.
Retention and adherence rates observed in these studies are higher than typically reported by other personal DHT research studies [ 7 - 9 , 12 , 13 , 21 ]. For example, a review of 8 large app-based DHT research studies in the United States reported that the probability of staying in the study dropped to or below 50% after the first 4 weeks of participation for all included studies [ 7 ]. Further, across the 8 included studies in this review, >50% of participants did not engage with the app for at least 7 days. Another large app-based study in the United States, the Warfighter Analytics Using Smartphones for Health study that collected daily active and passive app data reported a median retention of 45.2% (38/84 days), while the probability of staying in the study hit 50% at approximately 5.5 weeks [ 10 ]. A large app-based study in the United Kingdom (cloudy with a chance of pain study) involving daily active app assessments reported that 64% of participants fell into the low engagement or no engagement categories after baseline [ 12 ]. The RADAR study [ 14 ], a multinational study involving active and passive assessments from an app, and a Fitbit reported comparable retention results among participants with major depression to those reported here. This study reported a retention rate of 54.6% for 43 weeks of study participation; however, the probability of staying in the study stayed above 75% for the first several months of participation (~6 months). While the active app assessments in this study only included assessments every 2 weeks as opposed to daily assessments, this study additionally included aspects of a participant-centric design, which may have contributed to the higher reported retention [ 15 ].
Taken together, in comparison to other published personal DHT research studies, the 6 studies included in this paper reflect higher levels of engagement. Importantly, the included studies in this analysis involved high burden designs in comparison to other studies that request, for example, weekly or biweekly active tasks of participants [ 14 ] or only involve the use of a smartwatch. Specifically, across the included studies here, participants were expected to complete on average 4.6 (SD 1.62) minutes a day of app activities in addition to continuously using multiple wearable devices.
While different variations of participant-centric strategies were used across the 6 included studies, a key common feature was a biweekly check-in call with an engagement specialist. These calls served the purpose of providing support and building rapport with participants, working through onboarding and technological issues with study devices, tracking adherence, and receiving study-related feedback from participants. Numerous challenges arise in the conduct of remote, personal DHT research, and without frequent check-in and semiregular data monitoring by research staff, knowledge of these issues is a black box. The most significant drop in retention in personal DHT research studies tends to be during the first few weeks of participation [ 7 ]. These early onboarding weeks are crucial in working with participants to ensure they can get into a rhythm of participation. The passive sensing nature of personal DHTs has much potential to inform new objective measures of health, however, are not always intuitively understood as personally important for unique diseases (eg, heart rate variability or phone screen time). Personal DHT studies allow for “light touch” research approaches that enable data collection without traditional research coordinator contact, but this may come with a cost that inadvertently creates a less engaging study environment for participants and limits the opportunity to help participants understand the value in their participation. Of the included 6 studies, 1 cohort had much lower engagement on the check-in calls (50% adherence) compared to other included studies and, in turn, consistently demonstrated lower adherence to study-related activities. Still, even with extensive engagement designs, populations that had physical, mental, and situational barriers to study task completion (ie, severely ill, postpartum mothers) showed lower adherence to wearable device use and active smartphone tasks compared to other study populations. Top reported barriers to engagement included participant burden, physical, mental, and situational barriers, and low perceived value of personal DHTs for health care. These engagement barriers have been reported in previous literature [ 8 , 9 ] relating to DHT research and in the use of DHT interventions. However, the conveyed importance of the perceived value of the approach among participants in the current analysis is noteworthy. Given the foreign nature of personal DHTs for many individuals, particularly older populations, further work is needed to co-design and educate end users on the potential value of self-monitoring unique health-related data.
Irrespective of the engagement approach, adherence to in-app surveys and tasks was lower than wearable device use, which is not surprising given the higher burden related to in-app activities. The self-reported information captured from frequent or momentary in-app assessments is extremely valuable as context information. This context information or “label” data is useful for validating objectively captured information, yet remains the most difficult to capture in sufficient detail. Further, certain in-app activity adherences were consistently lower than others. Namely, activities that required the user to be active (walk in a straight line or complete a video diary) were low across studies. Still, adherence to daily in-app surveys was >60% for all studies excluding the postpartum and HERO study populations.
This quantitative and qualitative analysis compared observational data across different digital health studies. However, no true comparison cohort that did not include engagement strategies was included. Therefore, the inferred casualty of participant check-ins with engagement specialists on retention and adherence rates cannot be not concluded. We are formally testing whether the biweekly check-in significantly increases adherence and retention in an ongoing study with an appropriate comparison arm without check-in support (NCT05753605). One of the included studies (stress and recovery) was conducted during the early 2020 COVID-19 pandemic. There is some evidence that engagement in research was higher during the early pandemic time periods [ 22 ]. It cannot be ruled out that the higher observed retention and adherence in this study compared to others was not due to this potential time period bias. The stress in the Crohn-Oxford site included a population of patients some of whom were already engaged in the use of web-based monitoring of symptoms. In turn, this could have contributed to the high retention and higher adherence observed at this site compared to the other stress in the Crohn-MSSM site. The results presented on barriers to engagement were primarily qualitative and collected from conversations with participants, research staff, and investigators across studies.
Globally, mobile apps are used for a variety of purposes in everyday life, while the use of smartwatches for activity monitoring is gaining increasing popularity. However, the use of these tools for health remains a challenge. These findings support that human support via phone and other participant-centric engagement strategies centered on giving back to participants and working with them as co-designers can support sufficient retention and adherence in personal DHT research across diverse populations. This has implications for the utility and potential necessity of a digital support worker in digital health care, as highlighted by others [ 23 ]. A power of personal DHTs is enabling the patient to be in control of their health through self-monitoring, but this new role comes with a responsibility. This important shift in role from doctor to patient outlines how crucial it is to include patients in the early design phase of personal DHT health research. Further work is needed to inform app designs that support habitual forming activities around task completion so that app-related activities become a part of participants’ daily routine and are perceived as personally valuable.
The stress and recovery study was supported in part by the Bill & Melinda Gates Foundation (INV-016651). The stress in Crohn study was funded by the Leona M. and Harry B. Helmsley Charitable Trust (1911-03376). The help enable real-time observation (HERO)–central nervus system study was funded by the Mark Foundation for Cancer Research through an ASPIRE award (19-024-ASP). The HERO–pancreatic cancer study was funded by the Mark Foundation for Cancer Research through an ASPIRE award (19-024-ASP), Pancreatic Cancer Canada, the Princess Margaret Cancer Foundation, and 4YouandMe. The Better Understanding the Metamorphosis of Pregnancy (BUMP) study was funded by 4YouandMe and Sema4 along with supplemental in-kind contributions from coalition partners (Evidation Health, Vector Institute, Cambridge Cognition, and Bodyport). The stress and LFS study was funded by in-kind contributions from 4YouandMe, SickKids Hospital, and the Vector Institute.
CB is a consultant for Depuy Synthes, Bionaut Labs, Galectin Therapeutics, Haystack Oncology, and Privo Technologies. CB is a cofounder of Belay Diagnostics and OrisDx. DRK is an officer, employee, and shareholder of MindMed; a consultant at Tempus, Nightware, and Limitless; and board member of Sonara. RPH is an advisory board member at Bristol Meyers Squibb. MH is an advisory board member for Servier, AnHeart, and Bayer; steering committee member for Novartis; honoraria from Novartis; data safety monitoring committee member for Advarra and Parexel. RG received a graduate scholarship from Pfizer and provided consulting or advisory roles for Astrazeneca, Tempus, Eisai, Incyte, Knight Therapeutics, Guardant Health, and Ipsen. The others declare no conflicts of interest.
Study descriptions.
Study wearable devices.
Retention calculations.
Probability of retaining in the study across studies.
Median adherence to study activities stratified by sociodemographic characteristics.
Sociodemographic differences in participants who were retained versus not retained.
Qualitative feedback from participants, research staff, and investigators surrounding barriers to engagement in digital health research, summarized across 6 unique studies.
Better Understanding the Metamorphosis of Pregnancy |
central nervous system |
digital health technology |
help enable real-time observation |
institutional review board |
Li-Fraumeni syndrome |
Mount Sinai School of Medicine |
pancreatic cancer |
research ethics board |
Edited by G Eysenbach, T de Azevedo Cardoso; submitted 06.03.24; peer-reviewed by C Godoy Jr; comments to author 05.04.24; revised version received 12.04.24; accepted 29.05.24; published 03.09.24.
©Sarah M Goodday, Emma Karlin, Alexa Brooks, Carol Chapman, Christiana Harry, Nelly Lugo, Shannon Peabody, Shazia Rangwala, Ella Swanson, Jonell Tempero, Robin Yang, Daniel R Karlin, Ron Rabinowicz, David Malkin, Simon Travis, Alissa Walsh, Robert P Hirten, Bruce E Sands, Chetan Bettegowda, Matthias Holdhoff, Jessica Wollett, Kelly Szajna, Kallan Dirmeyer, Anna Dodd, Shawn Hutchinson, Stephanie Ramotar, Robert C Grant, Adrien Boch, Mackenzie Wildman, Stephen H Friend. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 03.09.2024.
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 (ISSN 1438-8871), 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.
BMC Medical Education volume 24 , Article number: 967 ( 2024 ) Cite this article
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Despite the increasing recognition of the importance of research in undergraduate dental education, limited studies have explored the nature of undergraduate research activities in dental schools in the Middle East region. This study aimed to evaluate the research experience of final year dental students from three dental schools in the Middle East.
A descriptive, cross-sectional study was conducted among final-year dental students from three institutions, namely Jordan University of Science and Technology, University of Sharjah (UAE), and Oman Dental College. Participants were asked about the nature and scope of their research projects, the processes involved in the research, and their perceived benefits of engaging in research.
A total of 369 respondents completed the questionnaire. Cross-sectional studies represented the most common research type (50.4%), with public health (29.3%) and dental education (27.9%) being the predominant domains. More than half of research proposals were developed via discussions with instructors (55.0%), and literature reviews primarily utilized PubMed (70.2%) and Google Scholar (68.5%). Regarding statistical analysis, it was usually carried out with instructor’s assistance (45.2%) or using specialized software (45.5%). The students typically concluded their projects with a manuscript (58.4%), finding the discussion section most challenging to write (42.0%). The research activity was considered highly beneficial, especially in terms of teamwork and communication skills, as well as data interpretation skills, with 74.1% of students reporting a positive impact on their research perspectives.
The research experience was generally positive among surveyed dental students. However, there is a need for more diversity in research domains, especially in qualitative studies, greater focus on guiding students in research activities s, especially in manuscript writing and publication. The outcomes of this study could provide valuable insights for dental schools seeking to improve their undergraduate research activities.
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The importance of research training for undergraduate dental students cannot be overstressed and many reports have thoroughly discussed the necessity of incorporating research components in the dental curricula [ 1 , 2 , 3 , 4 ]. A structured research training is crucial to ensure that dental graduates will adhere to evidence-based practices and policies in their future career and are able to critically appraise the overwhelming amount of dental and relevant medical literature so that only rigorous scientific outcomes are adopted. Furthermore, a sound research background is imperative for dental graduates to overcome some of the reported barriers to scientific evidence uptake. This includes the lack of familiarity or uncertain applicability and the lack of agreement with available evidence [ 5 ]. There is even evidence that engagement in research activities can improve the academic achievements of students [ 6 ]. Importantly, many accreditation bodies around the globe require a distinct research component with clear learning outcomes to be present in the curriculum of the dental schools [ 1 ].
Research projects and courses have become fundamental elements of modern biomedical education worldwide. The integration of research training in biomedical academic programs has evolved over the years, reflecting the growing recognition of research as a cornerstone of evidence-based practice [ 7 ]. Notwithstanding the numerous opportunities presented by the inclusion of research training in biomedical programs, it poses significant challenges such as limited resources, varying levels of student preparedness, and the need for faculty development in research mentorship [ 8 , 9 ]. Addressing these challenges is essential to maximize the benefits of research training and to ensure that all students can engage meaningfully in research activities.
While there are different models for incorporating research training into biomedical programs, including dentistry, almost all models share the common goals of equipping students with basic research skills and techniques, critical thinking training and undertaking research projects either as an elective or a summer training course, or more commonly as a compulsory course required for graduation [ 2 , 4 , 10 ].
Dental colleges in the Middle East region are not an exception and most of these colleges are continuously striving to update their curricula to improve the undergraduate research component and cultivate a research-oriented academic teaching environment. Despite these efforts, there remains a significant gap in our understanding of the nature and scope of student-led research in these institutions, the challenges they face, and the perceived benefits of their research experiences. Furthermore, a common approach in most studies in this domain is to confine data collection to a single center from a single country, which in turn limits the value of the outcomes. Therefore, it is of utmost importance to conduct studies with representative samples and preferably multiple institutions in order to address the existing knowledge gaps, to provide valuable insights that can inform future curricular improvements and to support the development of more effective research training programs in dental education across the region. Accordingly, this study was designed and conducted to elucidate some of these knowledge gaps.
The faculty of dentistry at Jordan University of Science and Technology (JUST) is the biggest in Jordan and adopts a five-year bachelor’s program in dental surgery (BDS). The faculty is home to more than 1600 undergraduate and 75 postgraduate students. The college of dental medicine at the University of Sharjah (UoS) is also the biggest in the UAE, with both undergraduate and postgraduate programs, local and international accreditation and follows a (1 + 5) program structure, whereby students need to finish a foundation year and then qualify for the five-year BDS program. Furthermore, the UoS dental college applies an integrated stream-based curriculum. Finally, Oman Dental College (ODC) is the sole dental school in Oman and represents an independent college that does not belong to a university body.
The aim of this study was to evaluate the research experience of final year dental students from three major dental schools in the Middle East, namely JUST from Jordan, UoS from the UAE, and ODC from Oman. Furthermore, the hypothesis of this study was that research activities conducted at dental schools has no perceived benefit for final year dental students.
The rationale for selecting these three dental schools stems from the diversity in the dental curriculum and program structure as well as the fact that final year BDS students are required to conduct a research project as a prerequisite for graduation in the three schools. Furthermore, the authors from these dental schools have a strong scholarly record and have been collaborating in a variety of academic and research activities.
The current study is a population-based descriptive cross-sectional observational study. The study was conducted using an online self-administered questionnaire and targeted final-year dental students at three dental schools in the Middle East region: JUST from Jordan, UoS from the UAE, and ODC from Oman. The study took place in the period from January to June 2023.
For inclusion in the study, participants should have been final-year dental students at the three participating schools, have finished their research project and agreed to participate. Exclusion criteria included any students not in their final year, those who have not conducted or finished their research projects and those who refused to participate.
The study was approved by the institutional review board of JUST (Reference: 724–2022), the research ethics committee of the UoS (Reference: REC-22-02-22-3) as well as ODC (Reference: ODC-MA-2022-166). The study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [ 11 ]. The checklist is available as a supplementary file.
Sample size determination was based on previous studies with a similar design and was further confirmed with a statistical formula. A close look at the relevant literature reveals that such studies were either targeting a single dental or medical school or multiple schools and the sample size generally ranged from 158 to 360 [ 4 , 8 , 9 , 10 , 12 ]. Furthermore, to confirm the sample size, the following 2-step formula for finite population sample size calculation was used [ 13 ]:
Wherein Z is the confidence level at 95% =1.96, P is the population proportion = 0.5, and E is the margin of error = 0.05. Based on this formula, the resultant initial sample size was 384.
Wherein n is the initial sample size = 384, N is the total population size (total number of final year dental students in the 3 schools) = 443. Based on this formula, the adjusted sample size was 206.
An online, self-administered questionnaire comprising 13 questions was designed to assess the research experience of final year dental students in the participating schools. The questionnaire was initially prepared by the first three authors and was then reviewed and approved by the other authors. The questionnaire was developed following an extensive review of relevant literature to identify the most critical aspects of research projects conducted at the dental or medical schools and the most common challenges experienced by students with regards to research project design, research components, attributes, analysis, interpretation, drafting, writing, and presentation of the final outcomes.
The questionnaire was then pretested for both face and content validity. Face validity was assessed by a pilot study that evaluated clarity, validity, and comprehensiveness in a small cohort of 30 students. Content validity was assessed by the authors, who are all experienced academics with remarkable research profiles and experience in supervising undergraduate and postgraduate research projects. The authors critically evaluated each item and made the necessary changes whenever required. Furthermore, Cronbach’s alpha was used to assess the internal consistency/ reliability of the questionnaire and the correlation between the questionnaire items was found to be 0.79. Thereafter, online invitations along with the questionnaire were sent out to a total of 443 students, 280 from JUST, 96 from UoS and 67 from ODC, which represented the total number of final year students at the three schools. A first reminder was sent 2 weeks later, and a second reminder was sent after another 2 weeks.
In addition to basic demographic details, the questionnaire comprised questions related to the type of study conducted, the scope of the research project, whether the research project was proposed by the students or the instructors or both, the literature review part of the project, the statistical analysis performed, the final presentation of the project, the writing up of the resultant manuscript if applicable, the perceived benefits of the research project and finally suggestions to improve the research component for future students.
The outcomes of the study were the students’ research experience in terms of research design, literature review, data collection, analysis, interpretation and presentation, students’ perceived benefits from research, students’ perspective towards research in their future career and students’ suggestions to improve their research experience.
The exposures were the educational and clinical experience of students, research supervision by mentors and faculty members, and participation in extracurricular activities, while the predictors were the academic performance of students, previous research experience and self-motivation.
The collected responses were entered into a Microsoft Excel spreadsheet and analyzed using SPSS Statistics software, version 20.0 (SPSS Inc., Chicago, IL, USA). Descriptive data were presented as frequencies and percentages. For this study, only descriptive statistics were carried out as the aim was not to compare and contrast the three schools but rather to provide an overview of the research activities at the participating dental schools.
The heatmap generated to represent the answers for question 11 (perceived benefits of the research activity) was created using Python programming language (Python 3.11) and the pandas, seaborn, and matplotlib libraries. The heatmap was customized to highlight the count and percentage of responses in each component, with the highest values shown in red and the lowest values shown in blue.
Potentially eligible participants in this study were all final year dental students at the three dental schools (443 students, 280 from JUST, 96 from UoS and 67 from ODC). All potentially eligible participants were confirmed to be eligible and were invited to participate in the study.
The total number of participants included in the study, i.e. the total number of students who completed the questionnaire and whose responses were analyzed, was 369 (223 from JUST, 80 from UoS and 66 from ODC). The overall response rate was 83.3% (79.6% from JUST, 83.3% from UoS and 98.5% from ODC).
The highest proportion of participants were from JUST ( n = 223, 60.4%), followed by UoS ( n = 80, 21.7%), and then ODC ( n = 66, 17.9%). The majority of the participants were females ( n = 296, 80.4%), while males represented a smaller proportion ( n = 73, 19.6%). It is noteworthy that these proportions reflect the size of the cohorts in each college.
With regards to the type of study, half of final-year dental students in the 3 colleges participated in observational cross-sectional studies (i.e., population-based studies) ( n = 186, 50.4%), while literature review projects were the second most common type ( n = 83, 22.5%), followed by experimental studies ( n = 55, 14.9%). Longitudinal studies randomized controlled trials, and other types of studies (e.g., qualitative studies, case reports) were less common, with ( n = 5, 1.4%), ( n = 10, 2.7%), and ( n = 30, 8.1%) participation rates, respectively. Distribution of study types within each college is shown Fig. 1 .
Distribution in percent of study types within each college. JUST: Jordan University of Science and Technology, UOS: University of Sharjah, ODC: Oman Dental College
The most common scope of research projects among final-year dental students was in public health/health services ( n = 108, 29.3%) followed by dental education/attitudes of students or faculty ( n = 103, 27.9%) (Fig. 2 ). Biomaterials/dental materials ( n = 62, 16.8%) and restorative dentistry ( n = 41, 11.1%) were also popular research areas. Oral diagnostic sciences (oral medicine/oral pathology/oral radiology) ( n = 28, 7.6%), oral surgery ( n = 12, 3.2%) and other research areas ( n = 15, 4.1%) were less common among the participants. Thirty-two students (8.7%) were engaged in more than one research project.
Percentages of the scope of research projects among final-year dental students. JUST: Jordan University of Science and Technology, UOS: University of Sharjah, ODC: Oman Dental College
The majority of research projects were proposed through a discussion and agreement between the students and the instructor (55.0%). Instructors proposed the topic for 36.6% of the research projects, while students proposed the topic for the remaining 8.4% of the projects.
Most dental students (79.1%) performed the literature review for their research projects using internet search engines. Material provided by the instructor was used for the literature review by 15.5% of the students, while 5.4% of the students did not perform a literature review. More than half of the students ( n = 191, 51.7%) used multiple search engines in their literature search. The most popular search engines for literature review among dental students were PubMed (70.2% of cases) and Google Scholar (68.5% of cases). Scopus was used by 12.8% of students, while other search engines were used by 15.6% of students.
The majority of dental students ( n = 276, 74.8%) did not utilize the university library to gain access to the required material for their research. In contrast, 93 students (25.2%) reported using the university library for this purpose.
Dental students performed statistical analysis in their projects primarily by receiving help from the instructor ( n = 167, 45.2%) or using specialized software ( n = 168, 45.5%). A smaller percentage of students ( n = 34, 9.4%) consulted a professional statistician for assistance with statistical analysis. at the end of the research project, 58.4% of students ( n = 215) presented their work in the form of a manuscript or scientific paper. Other methods of presenting the work included PowerPoint presentations ( n = 80, 21.7%) and discussions with the instructor ( n = 74, 19.8%).
For those students who prepared a manuscript at the conclusion of their project, the most difficult part of the writing-up was the discussion section ( n = 155, 42.0%), followed by the methodology section ( n = 120, 32.5%), a finding that was common across the three colleges. Fewer students found the introduction ( n = 13, 3.6%) and conclusion ( n = 10, 2.7%) sections to be challenging. Additionally, 71 students (19.2%) were not sure which part of the manuscript was the most difficult to prepare (Fig. 3 ).
Percentages of the most difficult part reported by dental students during the writing-up of their projects. JUST: Jordan University of Science and Technology, UOS: University of Sharjah, ODC: Oman Dental College
The dental students’ perceived benefits from the research activity were evaluated across seven components, including literature review skills, research design skills, data collection and interpretation, manuscript writing, publication, teamwork and effective communication, and engagement in continuing professional development.
The majority of students found the research activity to be beneficial or highly beneficial in most of the areas, with the highest ratings observed in teamwork and effective communication, where 33.5% rated it as beneficial and 32.7% rated it as highly beneficial. Similarly, in the area of data collection and interpretation, 33.0% rated it as beneficial and 27.5% rated it as highly beneficial. In the areas of literature review skills and research design skills, 28.6% and 34.0% of students rated the research activity as beneficial, while 25.3% and 22.7% rated it as highly beneficial, respectively. Students also perceived the research activity to be helpful for the manuscript writing, with 27.9% rating it as beneficial and 19.2% rating it as highly beneficial.
When it comes to publication, students’ perceptions were more variable, with 22.0% rating it as beneficial and 11.3% rating it as highly beneficial. A notable 29.9% rated it as neutral, and 17.9% reported no benefit. Finally, in terms of engaging in continuing professional development, 26.8% of students rated the research activity as beneficial and 26.2% rated it as highly beneficial (Fig. 4 ).
Heatmap of the dental students’ perceived benefits from the research activity
The research course’s impact on students’ perspectives towards being engaged in research activities or pursuing a research career after graduation was predominantly positive, wherein 274 students (74.1%) reported a positive impact on their research perspectives. However, 79 students (21.5%) felt that the course had no impact on their outlook towards research engagement or a research career. A small percentage of students ( n = 16, 4.4%) indicated that the course had a negative impact on their perspective towards research activities or a research career after graduation.
Finally, when students were asked about their suggestions to improve research activities, they indicated the need for more training and orientation ( n = 127, 34.6%) as well as to allow more time for students to finish their research projects ( n = 87, 23.6%). Participation in competitions and more generous funding were believed to be less important factors to improve students` research experience ( n = 78, 21.2% and n = 63, 17.1%, respectively). Other factors such as external collaborations and engagement in research groups were even less important from the students` perspective (Fig. 5 ).
Precentages of dental students’ suggestions to improve research activities at their colleges
To the best of our knowledge, this report is the first to provide a comprehensive overview of the research experience of dental students from three leading dental colleges in the Middle East region, which is home to more than 50 dental schools according to the latest SCImago Institutions Ranking ® ( https://www.scimagoir.com ). The reasonable sample size and different curricular structure across the participating colleges enhanced the value of our findings not only for dental colleges in the Middle East, but also to any dental college seeking to improve and update its undergraduate research activities. However, it is noteworthy that since the study has included only three dental schools, the generalizability of the current findings would be limited, and the outcomes are preliminary in nature.
Cross-sectional (epidemiological) studies and literature reviews represented the most common types of research among our cohort of students, which can be attributed to the feasibility, shorter time and low cost required to conduct such research projects. On the contrary, longitudinal studies and randomized trials, both known to be time consuming and meticulous, were the least common types. These findings concur with previous reports, which demonstrated that epidemiological studies are popular among undergraduate research projects [ 4 , 10 ]. In a retrospective study, Nalliah et al. also demonstrated a remarkable increase in epidemiological research concurrent with a decline in the clinical research in dental students` projects over a period of 4 years [ 4 ]. However, literature reviews, whether systematic or scoping, were not as common in some dental schools as in our cohort. For instance, a report from Sweden showed that literature reviews accounted for less than 10% of total dental students` projects [ 14 ]. Overall, qualitative research was seldom performed among our cohort, which is in agreement with a general trend in dental research that has been linked to the low level of competence and experience of dental educators to train students in qualitative research, as this requires special training in social research [ 15 , 16 ].
In terms of the research topics, public health research, research in dental education and attitudinal research were the most prevalent among our respondents. In agreement with our results, research in health care appears common in dental students` projects [ 12 ]. In general, these research domains may reflect the underlying interests of the faculty supervisors, who, in our case, were actively engaged in the selection of the research topic for more than 90% of the projects. Other areas of research, such as clinical dentistry and basic dental research are also widely reported [ 4 , 10 , 14 , 17 ].
The selection of a research domain is a critical step in undergraduate research projects, and a systematic approach in identifying research gaps and selecting appropriate research topics is indispensable and should always be given an utmost attention by supervisors [ 18 ].
More than half of the projects in the current report were reasonably selected based on a discussion between the students and the supervisor, whereas 36% were selected by the supervisors. Otuyemi et al. reported that about half of undergraduate research topics in a Nigerian dental school were selected by students themselves, however, a significant proportion of these projects (20%) were subsequently modified by supervisors [ 19 ]. The autonomy in selecting the research topic was discussed in a Swedish report, which suggested that such approach can enhance the learning experience of students, their motivation and creativity [ 20 ]. Flexibility in selecting the research topic as well as the faculty supervisor, whenever feasible, should be offered to students in order to improve their research experience and gain better outcomes [ 12 ].
Pubmed and Google Scholar were the most widely used search engines for performing a literature review. This finding is consistent with recent reviews which classify these two search systems as the most commonly used ones in biomedical research despite some critical limitations [ 21 , 22 ]. It is noteworthy that students should be competent in critical appraisal of available literature to perform the literature review efficiently. Interestingly, only 25% of students used their respective university library`s access to the search engines, which means that most students retrieved only open access publications for their literature reviews, a finding that requires attention from faculty mentors to guide students to utilize the available library services to widen their accessibility to available literature.
Statistical analysis has classically been viewed as a perceived obstacle for undergraduate students to undertake research in general [ 23 , 24 ] and recent literature has highlighted the crucial need of biomedical students to develop necessary competencies in biostatistics during their studies [ 25 ]. One obvious advantage of conducting research in our cohort is that 45.5% of students used a specialized software to analyze their data, which means that they did have at least an overview of how data are processed and analyzed to reach their final results and inferences. Unfortunately, the remaining 54.5% of students were, partially or completely, dependent on the supervisor or a professional statistician for data analysis. It is noteworthy that the research projects were appropriately tailored to the undergraduate level, focusing on fundamental statistical analysis methods. Therefore, consulting a professional statistician for more complex analyses was done only if indicated, which explains the small percentage of students who consulted a professional statistician.
Over half of participating students (58.4%) prepared a manuscript at the end of their research projects and for these students, the discussion section was identified as the most challenging to prepare, followed by the methodology section. These findings can be explained by the students’ lack of knowledge and experience related to conducting and writing-up scientific research. The same was reported by Habib et al. who found dental students’ research knowledge to be less than that of medical students [ 26 ]. The skills of critical thinking and scientific writing are believed to be of paramount importance to biomedical students and several strategies have been proposed to enhance these skills especially for both English and non-English speaking students [ 27 , 28 , 29 ].
Dental students in the current study reported positive attitude towards research and found the research activity to be beneficial in several aspects of their education, with the most significant benefits in the areas of teamwork, effective communication, data collection and interpretation, literature review skills, and research design skills. Similar findings were reported by previous studies with most of participating students reporting a positive impact of their research experience [ 4 , 10 , 12 , 30 ]. Furthermore, 74% of students found that their research experience had a positive impact on their perspectives towards engagement in research in the future. This particular finding may be promising in resolving a general lack of interest in research by dental students, as shown in a previous report from one of the participating colleges in this study (JUST), which demonstrated that only 2% of students may consider a research career in the future [ 31 ].
Notably, only 11.3% of our students perceived their research experience as being highly beneficial with regards to publication. Students` attitudes towards publishing their research appear inconsistent in literature and ranges from highly positive rates in developed countries [ 4 ] to relatively low rates in developing countries [ 8 , 32 , 33 ]. This can be attributed to lack of motivation and poor training in scientific writing skills, a finding that has prompted researchers to propose strategies to tackle such a gap as mentioned in the previous section.
Finally, key suggestions by the students to improve the research experience were the provision of more training and orientation, more time to conduct the research, as well as participation in competitions and more funding opportunities. These findings are generally in agreement with previous studies which demonstrated that dental students perceived these factors as potential barriers to improving their research experience [ 8 , 10 , 17 , 30 , 34 ].
A major limitation of the current study is the inclusion of only three dental schools from the Middle East which my limit the generalizability and validity of the findings. Furthermore, the cross-sectional nature of the study would not allow definitive conclusions to be drawn as students’ perspectives were not evaluated before and after the research project. Potential confounders in the study include the socioeconomic status of the students, the teaching environment, previous research experience, and self-motivation. Moreover, potential sources of bias include variations in the available resources and funding to students’ projects and variations in the quality of supervision provided. Another potential source of bias is the non-response bias whereby students with low academic performance or those who were not motivated might not respond to the questionnaire. This potential source of bias was managed by sending multiple reminders to students and aiming for the highest response rate and largest sample size possible.
In conclusion, the current study evaluated the key aspects of dental students’ research experience at three dental colleges in the Middle East. While there were several perceived benefits, some aspects need further reinforcement and revision including the paucity of qualitative and clinical research, the need for more rigorous supervision from mentors with focus on scientific writing skills and research presentation opportunities. Within the limitations of the current study, these outcomes can help in designing future larger scale studies and provide valuable guidance for dental colleges to foster the research component in their curricula. Further studies with larger and more representative samples are required to validate these findings and to explore other relevant elements in undergraduate dental research activities.
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
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The authors would like to acknowledge final year dental students at the three participating colleges for their time completing the questionnaire.
No funding was received for this study.
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Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, P.O.Box: 27272, Sharjah, UAE
Mohammad S. Alrashdan & Sausan Al Kawas
Department of Oral Medicine and Oral Surgery, Faculty of Dentistry, Jordan University of Science and Technology, Irbid, Jordan
Mohammad S. Alrashdan
Department of Adult Restorative Dentistry, Oman Dental College, Muscat, Sultanate of Oman
Abubaker Qutieshat
Department of Restorative Dentistry, Dundee Dental Hospital & School, University of Dundee, Dundee, UK
Preventive and Restorative Dentistry Department, College of Dental Medicine, University of Sharjah, Sharjah, UAE
Mohamed El-Kishawi
Clinical Sciences Department, College of Dentistry, Ajman University, Ajman, UAE
Abdulghani Alarabi
Department of Prosthodontics, Faculty of Dentistry, University of Science and Technology, Irbid, Jordan
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M.A.: Conceptualization, data curation, project administration; supervision, validation, writing - original draft; writing - review and editing. A.Q: Conceptualization, data curation, project administration; writing - review and editing. M.E: Conceptualization, data curation, project administration; validation, writing - original draft; writing - review and editing. A.A.: data curation, writing - original draft; writing - review and editing. L.K.: Conceptualization, data curation, validation, writing - original draft; writing - review and editing. S.A: Conceptualization, writing - review and editing.
Correspondence to Mohammad S. Alrashdan .
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The current study was approved by the institutional review board of Jordan University of Science and Technology (Reference: 724–2022), the research ethics committee of the University of Sharjah (Reference: REC-22-02-22-3) and Oman Dental College (Reference: ODC-MA-2022-166).
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Alrashdan, M.S., Qutieshat, A., El-Kishawi, M. et al. Insights into research activities of senior dental students in the Middle East: A multicenter preliminary study. BMC Med Educ 24 , 967 (2024). https://doi.org/10.1186/s12909-024-05955-5
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Non-communicable diseases (NCDs) predispose households to exorbitant healthcare expenditures in health systems where there is no access to effective financial protection for healthcare. This study assessed the economic burden associated with the rising burden of type-2 diabetes (T2D) and hypertension comorbidity management, and its implications for healthcare seeking in urban Accra.
A convergent parallel mixed-methods study design was used. Quantitative sociodemographic and cost data were collected through survey from a random community-based sample of 120 adults aged 25 years and older and living with comorbid T2D and hypertension in Ga Mashie, Accra, Ghana in November and December 2022. The monthly economic cost of T2D and hypertension comorbidity care was estimated using a descriptive cost-of-illness analysis technique from the perspective of patients. Thirteen focus group discussions (FGDs) were conducted among community members with and without comorbid T2D and hypertension. The FGDs were analysed using deductive and inductive thematic approaches. Findings from the survey and qualitative study were integrated in the discussion.
Out of a total of 120 respondents who self-reported comorbid T2D and hypertension, 23 (19.2%) provided complete healthcare cost data. The direct cost of managing T2D and hypertension comorbidity constituted almost 94% of the monthly economic cost of care, and the median direct cost of care was US$19.30 (IQR:10.55–118.88). Almost a quarter of the respondents pay for their healthcare through co-payment and insurance jointly, and 42.9% pay out-of-pocket (OOP). Patients with lower socioeconomic status incurred a higher direct cost burden compared to those in the higher socioeconomic bracket. The implications of the high economic burden resulting from self-funding of healthcare were found from the qualitative study to be: 1) poor access to quality healthcare; (2) poor medication adherence; (3) aggravated direct non-medical and indirect cost; and (4) psychosocial support to help cope with the cost burden.
The economic burden associated with healthcare in instances of comorbid T2D and hypertension can significantly impact household budget and cause financial difficulty or impoverishment. Policies targeted at effectively managing NCDs should focus on strengthening a comprehensive and reliable National Health Insurance Scheme coverage for care of chronic conditions.
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Globally, non-communicable diseases (NCDs) lead to about 15 million premature deaths annually [ 1 , 2 ], and about eight in every ten deaths occur in low-and-middle-income countries (LMICs) [ 3 ]. World Health Organization (WHO) has projected that by 2025, NCDs will account for over 70% of all deaths globally, with more than 80% of the death occurring in developing countries [ 4 ]. Developing countries will incur NCDs related economic losses of US$21.3 trillion over the next two decades [ 5 ]. Existing literature indicates that diabetes, cancer, chronic lung diseases and cardiovascular diseases (CVD), alongside mental health, will cumulatively pose a global economic loss of 47 trillion US$ by 2030. This estimate is about 75% of the global gross domestic product (GDP) [ 6 ], which is projected to have disproportionate impacts on LMICs due to their fragile health systems. Approximately 10% of households globally are faced with high healthcare spending, of which the situation is projected to be worse in African countries [ 7 ]. In addition to Africa battling the attainment of universal health coverage (UHC) and financial risk protection schemes, over 2 billion people lack efficient, equitable and adequately funded healthcare systems [ 8 ]. Compared to high-income countries (HIC), the household financial burden of NCDs care in LMICs is much higher [ 9 , 10 ].
Evidence suggests that NCDs predispose households to a higher risk of health expenditure [ 11 ]. For instance, the mean household total costs per year in LMICs of CVD, cancers and diabetes were US$6055.99, US$3303.81 and US$1017.05 respectively [ 9 ]. The mean annual financial cost of managing one diabetic case at the outpatient clinic in Ghana was estimated at US$194.09 [ 12 ] and the mean healthcare management cost was US$38.68 [ 13 ]. Also, uncontrolled hypertension was found to be independent predictor of a higher cost of treatment in patients who died compared to those who survive in urban Ghana [ 14 ]. Excessive out-of-pocket (OOP) spending on healthcare services weakens households financially by wiping out savings and other durable resources, thereby plunging families into poverty [ 15 ]. Poor and vulnerable groups are least likely to obtain treatment for NCDs due to the high impact of OOP spending [ 16 , 17 ]. Meanwhile, there is growing evidence that governments’ expenditures on healthcare in SSA rarely focus on NCDs, suggesting that the costs of healthcare are passed on to patients [ 18 , 19 ]. Also, available evidence suggests there is poor coverage of NCD care by National Health Insurance Schemes [ 20 ], including Ghana. These phenomena hamper progress towards the attainment of UHC [ 11 ].
Comorbidity (co-existence of two or more conditions within an individual) is a growing public health challenge globally [ 21 ], substantially effecting individuals, carers and society [ 22 ]. Meanwhile, healthcare models in many LMICs have been designed to manage single health conditions rather than multiple conditions. Comparatively, individuals with comorbid chronic conditions often suffer higher rates of unplanned hospitalizations and frequent use of emergency services than those with single conditions [ 23 ]. In healthcare systems similar to Ghana where health insurance is ineffective and out of pocket payment as well as co-payments for healthcare is high, comorbidity exert more catastrophic healthcare expenditure on households [ 23 , 24 ]. Although the Ghana National Health Insurance Scheme (NHIS) benefit package is supposed to cover essential services like lab diagnosis and medicines, these often are not accessible to patients. The benefit routinely ends at catering for consultation fee. Consequently, most individuals with multiple chronic conditions become economically dependent on their relatives and support networks [ 23 , 24 ]. Also, the high healthcare cost drive people with NCDs to seek relatively more affordable alternative means of treatment (i.e., herbal and spiritual) to complement or completely replace orthodox medication [ 25 , 26 ].
There is a dearth of research on the effects of the healthcare-related economic burden of NCDs comorbidity on patients in Africa [ 27 , 28 ]. Although NCDs multimorbidity cause high financial burdens on households [ 29 , 30 ], the full extent of the economic burden that patients endure while seeking and receiving care is seldom reported. Costs incurred at each stage of the cascade of care (i.e., screening and diagnosis, treatment, management, and palliative care) include direct medical and non-medical costs, as well as indirect costs. These costs have implications for healthcare for people with NCDs, including comorbid T2D and hypertension [ 31 ]. Another major limitation in the literature is that, despite increasing scholarship on the economic burden caused by NCDs globally, most of the existing literature is from high-income countries and is disease specific [ 32 , 33 , 34 ].
As part of the ‘Contextual Awareness, Response and Evaluation: Diabetes in Ghana’ (CARE-Diabetes) project [ 35 ] (a mixed-methods study to generate a contextual understanding of T2D in an urban poor population), this study estimated the economic burden associated with T2D and hypertension multimorbidity in urban Ghana and discussed implications for interventions targeted at improving financial risk protection in vulnerable population in Ghana and other similar LMICs.
A convergent parallel mixed-methods study design was used. Quantitative and qualitative data were concurrently collected independently and analysed to assess the burden imposed by T2D and hypertension comorbidity, and its implications for healthcare. A descriptive cost-of-illness (COI) approach was used to estimate the economic burden of managing comorbid T2D and hypertension. The COI is a study method used to evaluate the economic burden imposed by an illness on individuals, institutions and/or society as a whole [ 36 ]. We further conducted focus group discussions (FGDs) to explore the cost burden implications for healthcare. Given that the CARE-Diabetes study focused on T2D, only the participants that self-reported an earlier diagnosis of T2D (index case) and co-occurrence of hypertension were used in this study.
The study was carried out in Ga Mashie, a densely populated impecunious urban setting comprising two indigenous communities, namely James Town and Ussher Town, located in the Greater Accra Region of Ghana. The mean monthly household income in the study setting is USD78.83, and about three-quarters of the population have attained up to Junior High School (or middle school) education and above [ 37 ]. The twin towns, i.e., James Town and Ussher Town, are indigenous communities with fishing, petty trading and other fishing-related activities being the main economic activities and primary sources of livelihood for community members. Health services are provided mainly by government hospitals including Ussher Town Polyclinic and the Korle-Bu Teaching Hospital, a tertiary-level healthcare facility located close by. Also, there are few private hospitals offering healthcare services to the residents. More details of the study settings can be found elsewhere [ 35 ].
Quantitative study.
This study was part of the CARE-Diabetes project[ 35 ], which had a target sample size of 1,242 adults aged ≥ 25 years within 959 households across 80 enumeration areas (EAs) of Ga Mashie. The sample size was determined on the ability to estimate the prevalence of T2D, and the sample was randomly selected from the 2021 population census [ 38 ]. The study excluded pregnant women or those who had given birth within the past six months as well as individuals who were unable to provide informed consent or had difficulty completing the survey, including those who were mentally incapacitated. All participants (n = 120) who self-reported T2D and hypertension were included in the present analysis.
Likewise, the qualitative study used data from the CARE-Diabetes project. This study used 13 focus group discussions (FGDs) with community members. The participants included men and women with T2D and hypertension comorbidity, and people caring for relatives with the comorbid conditions. The respondents were enlisted using three sampling techniques. Firstly, relying on T2D patients scheduled for appointment on NCD clinic day at the Ussher Hospital (the main public health facility serving the people of Ga Mashie), we identified people with T2D and recruited them for FGD on the first day of data collection. Secondly, using the people with T2D identified from the hospital as index, a snowball technique was used to identify and recruit community members with comorbid T2D and hypertension. The snowball process continued until the required number of participants for the 5 FGDs was reached. Thirdly, participant (caregivers) without comorbid T2D and hypertension (n = 8) were recruited using convenient sampling technique, whereby a community liaison guided the research team to select potential participants from across the community.
Quantitative.
Forty enumerators were recruited and trained to gather survey data on Open Data Kit (ODK) using mobile tablets in November and December 2022 [ 35 ]. Prior to data collection, the survey questionnaire was pretested in a different community outside Ga Mashie. Overall, 854 individuals completed the survey for the CARE-Diabetes project. Of this number, 120 (14%) self-reported co-morbid hypertension and T2D, all of whom were included in the present analysis.
Using pretested FGD guides, a total of 13 FGDs among community members with and without T2D and hypertension comorbidity were conducted from November to December, 2022 in the two predominant local dialects (Ga and Twi). The participants were different from those who participated in the survey. The topic guides were developed based on a literature review, and used to gather information on social norms, experiences, and attitudes regarding prevention, control, and care-seeking for T2D and hypertension comorbidity. Prior to the data collection, the topic guide was pretested in a different community. Copies of the FGD topic guides are attached to this manuscript as Supplementary files . The FGDs were led by trained research assistants. The training focused on the study guides and standard operating procedures (SOPs) for qualitative interviews. The total number of FGDs was considered sufficient for thematic saturation (i.e., no new information could be harnessed from interviews) [ 39 ]. The FGDs lasted for approximately one hour and were recorded digitally and detailed notes of the interactions were taken.
Quantitative analysis.
We generate a household wealth index using Principal Components Analysis (PCA) [ 40 ]. For the PCA, we selected and inputted into the model 15 out of the 23 assets, because they were reported to be owned by ≥ 5% but ≤ 95% of households. We also inputted into the PCA model whether the household had access to improved sources of drinking water, toilet facilities, gas or electricity as cooking fuels, and a separate room for the kitchen and the number of rooms in the household. We categorised the generated household wealth index into tertiles, specifically as ‘most poor’, ‘poor,’ and ‘least poor’.
Direct and indirect cost analyses were conducted using Microsoft Excel and STATA version 17. We adjusted for cluster and unequal probability survey design in the analysis by weighting. Direct medical cost was estimated by summing total cost incurred by people with comorbid T2D and hypertension on consultation, diagnostics and medication. Non-medical was estimated by summing the total cost of travel to and from hospital for comorbid T2D and hypertension medical care during the past one month. Total direct cost was estimated by summing the total direct medical and non-medical costs. The median and interquartile range were estimated. Indirect cost was estimated using the human capital approach (HCA). The HCA is a method commonly used to estimate lost productivity that results from disease, disability or premature death—which is an important component of the economic burden of chronic conditions [ 41 ]. Indirect cost was estimated by multiplying total productive hours lost (i.e., seeking comorbid T2D and hypertension care by patient and their caregiver). The national minimum wage per day of GHS13.53 for Ghana (US$1.00 equivalent to GHS8.58 (Bank of Ghana mean monthly interbank exchange rate, December 2022) was used to estimate value lost to productivity (Ministry of Finance, December 2022). The ratio of direct cost to income, by sex and socioeconomic status, was analysed. The mean economic cost of managing comorbid T2D and hypertension was estimated by dividing the sum of direct and indirect costs by the total participants. The robustness of cost estimates was tested through one-way and multi-way sensitivity analyses. This was done by varying critical cost components of the data which lacked certainty (i.e., medications and wages) by 3%, 8%, and 10% [ 42 ].
All FGDs were transcribed and translated into English by trained fieldworkers who also conducted/facilitated the interviews. Transcripts were analysed thematically using the framework approach [ 43 ]. By this, a deductive coding framework was developed jointly by three of the authors based on existing literature on the consequences of the direct cost of managing comorbid T2D and hypertension for healthcare [ 44 ]. The framework was expanded when new codes or themes emerged through joint deliberation and review of the transcripts by the three authors (inductive approach). All transcripts were loaded into QSR NVIVO Version 11 to facilitate data coding and analysis. The thematic coding was done by the first author (who was part of the joint review and has extensive experience in qualitative thematic analysis). One person did the coding because the involvement of three authors in the development of the coding framework allowed for consensus building on all the codes relative to its alignments with the respective themes . After coding, the three authors jointly reviewed the output, and resolved any discordance between codes and themes. The coding exclusively focused on the consequences of direct OOP cost in the management of T2D and hypertension comorbidity on patients’ healthcare. Data are reported following the Consolidated Criteria for Reporting Qualitative Research (COREQ) [ 45 ].
The findings from the qualitative and quantitative works were synthesized by categorizing the findings to identify complementary themes that correspond with the research questions about the economic cost burden (direct and indirect cost) and its consequences for healthcare for people with T2D and hypertension co-morbidity [ 46 ].
Survey data were gathered from 854 individuals in 629 households (household response rate of 66%; individual response rate of 69%). Of the 854 individuals who completed the survey, 120 (14%) self-reported comorbid T2D and hypertension, all of whom were included in the present analysis. However, the cost analysis included 23/120 (19.2%) comorbid T2D and hypertension individuals that provided completed healthcare cost data. Individuals who could not provide complete set of direct and indirect cost data were excluded in the economic burden analysis. As shown in Table 1 , many of the survey respondents were women (81.7%). More than half were ≥ 60 years, and most were unemployed (51.7%). Almost a quarter of the respondents reported that their healthcare was funded by co-payment and insurance jointly. A third reported funding their healthcare by insurance, whereas 42.9% reported funding solely out-of-pocket (OOP). Of the 94 participants of the FGDs, most were females (52.1%), almost two-third were widowed/single, and more than 56% were aged 25–49.
As presented in Table 2 , over 80% of the survey participants who provided complete direct and indirect costs information and were actually included in the economic cost analysis were females. The majority of the participants (60.9%) were employed, and most paid directly out-of-pocket for health care (42.9%).
As shown in Table 3. , the direct cost of managing T2D and hypertension comorbidity constituted almost 94% of the total economic cost of care, and the median monthly direct household cost of care was US$19.30 (IQR:10.55–118.88).
Further analysis of the proportion of direct cost to income, by patients’ socioeconomic status and sex, are presented in Table 4 . The absolute value of the mean direct cost for the poorest tertile was higher than the absolute value of the mean direct costs for the other wealth tertiles, although our sample size was too small to assess for statistical differences among groups. Also, men reported spending 122% of their income on healthcare compared to women (76.5%), although our sample size was too small to assess for statistical differences among groups. Furthermore, patients that paid for healthcare directly out of pocket spent over 100% of their income on care.
The findings presented above on the proportion of the income expended on the direct cost of healthcare demonstrate the huge cost burden posed on people with comorbid T2D and hypertension. The remaining results sections focus on the implications of this cost burden on healthcare seeking, from the perspectives of patients and their caregivers (those without T2D and hypertension).
The possible implications of the economic burden imposed by comorbid T2D and hypertension are classified into four broad themes and further elucidated in the subsequent sections of the results. These were: 1) poor access to quality healthcare; (2) poor medication adherence; (3) direct non-medical and indirect treatment cost aggravating burden; and (4) psychosocial support helps to cope with economic burden.
The high cost of managing T2D and hypertension comorbidity posed a huge burden for people living with these conditions. Most of the study respondents emphasized that availability and quality of healthcare were not a problem; however, affordability was a major hindrance to access. Thus, obtaining quality treatment was tied to the patient’s ability to pay for health services. Meanwhile, the extent of healthcare services offered depended on the patient’s ability to pay OOP at the point of seeking care. Even with the National Health Insurance Scheme (NHIS), patients were denied medication when they could not afford to pay OOP. The cost of healthcare services including labs, diagnostic tests, and certain medications often deter healthcare utilisation. Scheduled appointments were not adhered to due to the cost of health services.
“The healthcare provision is good, but it all depends on money. Treatment is not free, even though the health insurance covers part of the treatments, it does not cover most of the labs done by people living with T2D and hypertension.” (Man with comorbid T2D and hypertension )
“The main obstacle to accessing the services is the cost…The cost of the services, including lab, diagnostic tests, and medications, can be prohibitive. It prevents people from getting the care they need, even when they have an appointment scheduled.” (Woman with comorbid T2D and hypertension)
The inability to afford quality biomedical care led to plurality of healthcare, further complications and deteriorated conditions of patients. Some respondents shared experiences of the devastating consequences of their inability to meet the financial strains posed by direct and indirect costs of care. Due to the cost barrier to approved biomedical care, comorbid patients resorted to inferior treatment from multiple sources, which often worsen cost burden and health outcomes. That said, some patients noted that the use of complementary alternative medicines was also not cheap.
“They gave me the excuses that the health insurance does not cover the bills of the lab test. I resorted to using herbal medicine and going for prayers at different churches. After two years, I went to checkup on the same issue again at the hospital, and they realized the illness has worsened.” ( Woman with comorbid T2D and hypertension )
“Using Korle Bu hospital as an example, if you or any member of your family is admitted and you do not have the financial means to cater for the bills, I am sorry you will die. I have had a personal experience with them when my wife was admitted... Meanwhile herbal medicine is also not cheap” ( Man with comorbid T2D and hypertension )
Furthermore, the limited and unreliable NHIS coverage contributes to the direct cost burden. This is mainly because of a lack of knowledge on NHIS coverage by people with T2D and hypertension. Whereas some respondents believed that T2D and hypertension services were supposed to be free under the NHIS, others believed just a portion was covered. There was a widely held view among respondents that treatments for NCDs, particularly T2D and hypertension are supposed to be free under the NHIS. However, most medicines and services such as laboratory investigations were paid OOP.
“We were told that T2D and hypertension medicine is supposed to be free. All the health facilities in this community charge us for the service they render to us, none is free.” (Woman with comorbid T2D and hypertension)
“…we are told that insurance doesn’t cover the labs we do, and so we must pay. But it is through the lab result that diagnosis can be made, so they must review that aspect for us.” (Woman with comorbid T2D and hypertension
“The health insurance covers some of the diabetic’s drugs such as metformin, and some hypertensive drugs. But if the doctor prescribes specific one for you, you would be told it’s not available unless you pay out of pocket.” (Woman with comorbid T2D and hypertension)
According to some of the respondents with comorbid T2D and hypertension, the NHIS helped cover part of their hospital bills. However, patients bemoaned the limited and unreliable operations of the NHIS. They observed that medicines which were supposed to be free under the insurance were routinely sold to NHIS subscribers. The consequences were often devastating for those unable to co-pay. About three-quarters of the respondents (both those with and without comorbidity) accentuated the limited coverage of the NHIS and wondered what the relevance of subscribing to the NHIS was if their health needs could not freely or significantly be catered for.
“I heard the medication for T2D and hypertension was not to be sold, but right now if you don’t have money and you go to the hospital, you will die.” ( Man with comorbid T2D and hypertension )
“…We need a lot of medications, and they are expensive. If I don’t have money, I wouldn’t go to the hospital even though I have insurance… Last week I heard someone also confirm that the national health insurance is not working. (Woman with comorbid T2D and hypertension)
Even with the NHIS, patients with comorbid T2D and hypertension could not always get prescribed medications, even if they are supposedly entitled to them. People with T2D and hypertension comorbidity were compelled to pay a portion of the cost (i.e., co-payment) before being served with medication. Inability to afford healthcare results in patients not being attended to, affecting medication adherence. Thus, the cost of medication affects adherence to treatment regimens, as most patients manage their condition by heavily relying on financial support. The erratic financial support system for people with T2D and hypertension comorbidity led to non-adherence to treatment schedules. All respondents acknowledged that non-adherence to medication due to cost often led to dire complications like foot ulcers and cardiovascular diseases.
“…if you don’t have money, they will not sell the medicine to you, but in the health insurance it is supposed to be free, but they tell us it is not free, you must pay something. If you are not able to do so, your prescription will be given back to you.” ( Woman with comorbid T2D and hypertension )
“…My brother for instance takes injections twice a day; these drugs are very expensive…If he doesn’t get financial help, he skips the appointment. When he goes later after the default, he is sacked.” (Female without comorbid T2D and hypertension)
“Financial issues worry us a lot... When I run out of insulin, my legs will get swollen within four to five days and I will become very lean, which means the condition is becoming serious. Then my blood pressure will rise” (Man with comorbid T2D and hypertension)
Some caregivers highlighted the additional burden imposed by the indirect cost of managing T2D and hypertension on their relatives. This mainly relates to the special diets recommended by healthcare specialists. Furthermore, the devastating nature of comorbid T2D and hypertension rendered most patients incapacitated for productive ventures. A respondent with T2D and hypertension comorbidity observed that the negative effects of the conditions on work and productivity plunged most people living with the conditions into impoverishment, thereby affecting their livelihood as well as their dependents.
“I also think money is the only solution to their problem because they need to eat certain meals which are different from what everyone else in the family eats. So, they need money to be able to afford that kind of life.” ( Woman without comorbid T2D and hypertension)
“This disease causes one to spend a lot of money. Lacking financial means when one develops this disease renders the victim’s life miserable. Say you are the breadwinner of the family; developing this illness hinders you from working hence bring about hunger in your home.” ( Man with comorbid T2D and hypertension)
All study respondents emphasized the importance of social support in the management of their T2D and hypertension comorbidity. Specifically, the inability of family and friends to financially and emotionally support healthcare for people with comorbid T2D and hypertension resulted in non-adherence to the treatment regimen, thereby causing significant emotional and psychosocial burden, for example depression, anxiety, frustration, and confusion. The study respondents reiterated that there was no way they could have solely managed their comorbid condition without psychosocial and physical support from family and friends.
“If maybe I need money and family and friends do not have money to help, it makes me overthink, depressed, anxious, worried, unhappy, frustrated and confusion . I am told not to overthink, but it is something that has been disturbing me.” (Man with comorbid T2D and hypertension)
“…in fact, if you don’t have a strong family support, you would be humiliated because everything about diabetes and hypertension involve money…if you don’t have anyone in the family to support and always be close to you, you will deteriorate. Because at a point, if you don’t get support financially and physically, you will die from stress and depression.” (Man with comorbid T2D and hypertension)
“Sometimes my siblings help me, sometimes too they don’t help, so there are times I am not able to afford my medication. The Country’s economy is in bad state, so you cannot burden people with your financial challenges because they also have responsibilities.” (Woman with comorbid T2D and hypertension)
This study sought to understand and add to the limited literature available on the economic cost associated with the rising burden of T2D and hypertension comorbidity in the economically disadvantaged urban setting of Ga Mashie Accra and its implications for seeking healthcare. The study found a significant economic cost burden associated with management of T2D and hypertension comorbidity. Patients spent excessively more than their income on healthcare. Our findings are consistent with those of previous studies conducted in SSA that have reported high direct costs of managing chronic diseases [ 10 , 47 ], most specifically, T2D [ 48 , 49 , 50 , 51 ], hypertension [ 52 ], and comorbid T2D and hypertension [ 53 ].
Like other studies conducted in Ghana [ 13 , 54 ], evidence from this study emphasizes that the cost of managing T2D and hypertension comorbidity is high. Other studies in Ghana have reported that the cost of managing T2D can lead to catastrophic healthcare spending [ 49 , 55 ]. Although the estimated mean economic cost of managing comorbid T2D and hypertension [US$63.08 (95% CI:0.00- 145.35)] was analysed from a patient perspective, the cost is comparable to that reported in urban Kenya (US$38) which was analysed from a societal perspective [ 53 ]. This implies a higher burden of managing the comorbid condition in Ga Mashie compared to Kenya since the societal perspective estimates economic cost from a broader perspective comprising both patient and institutional costs. Overall, individuals with the comorbid condition spent almost 81% of their income on healthcare. This can be attributed to the poor healthcare seeking behaviour of people with NCDs in poverty-stricken urban communities of Ghana, whereby individuals seek healthcare in a worsened state and thus incur high cost of care [ 56 ].
The burden is aggravated by the fact that most comorbid T2D and hypertension patients are unemployed and rely heavily on financial and social support systems within the already impoverished community where income levels are generally low [ 37 ]. Hence, the economic cost burden imposed by the condition transcends the individual suffering from the disease. As shown by this study, the economic burden has far-reaching effects on healthcare. From the qualitative study, we found four main possible implications of the high economic burden on individual’s healthcare. Firstly, the cost burden affected access to care and treatment quality; secondly, the high cost affected medication adherence; thirdly, direct non-medical and indirect treatment cost add to the economic burden; and finally, lack of psychosocial support aggravates the economic burden. These themes are discussed below.
Firstly, the high healthcare cost impacts access to T2D and hypertension care and treatment quality among the poor urban community of Ga Mashie. In this study, the high-cost burden imposed by approved sources of care (health facilities) coupled with low socioeconomic status are barriers to access to quality comorbid T2D and hypertension care. Other studies conducted in Africa have reported the association between low socioeconomic status and limited access to treatment due to high cost [ 50 , 57 ], likewise other regions of the world [ 58 , 59 ].
Similar to available evidence on NCDs care and management across Africa [ 60 ], there are three main means through which people with T2D and hypertension in Ga Mashie seek healthcare and manage their condition. These are biomedical, ethnomedical (herbal) and faith/spiritual treatments. Often, biomedical treatment sources like government and private health facilities serve as the first point of call to persons with T2D and hypertension for diagnosis and medical education by health professionals. However, many comorbid T2D and hypertension patients in Ga Mashie consider biomedical treatment very expensive. The expenses incurred include consultation, diagnosis, medication, and other hospital bills. Meanwhile, evidence on biomedical therapy for NCDs globally indicates that most patients must take medication for the rest of their lives and on a regular basis [ 61 , 62 ]. Hence, borne out of desperation to lessen the economic burden through cheaper sources that promise rapid and permanent cure, patients resort to pluralistic means of combining biomedical, ethnomedical (herbal) and/or spiritual care, thereby compromising treatment quality.
A further possible implication of the high economic cost of biomedical treatment is that, not only does it serve as a barrier to accessing quality care but also to accessing biomedically approved medications, as people seek alternative means (i.e., herbal and spiritual) of treatment to complement or completely replace orthodox medication. Herbal drugs are perceived to be relatively more affordable than pharmaceutical drugs. This confirms the findings of other studies conducted in the African region [ 25 , 26 ]. Also, it is common in SSA that due to the high economic burden associated with managing T2D and hypertension, some people with T2D in poverty-stricken urban communities like Ga Mashie typically combine biomedical therapy with spiritual therapy, whereas others solely depend on spiritual/faith healing therapy as a cost-effective rapid measure to manage their T2D [ 63 , 64 ].
The economic burden of managing T2D in Ga Mashie is untenable for most of the patients in need of care. Bekele et al. reported that having health insurance is a strong predictor of access to screening of T2D and effective biomedical care [ 65 ]. In Ghana, the NHIS is the main strategy for delivering social protection. The NHIS Act (Act 850, 2012) exempts children under 18 years, lactating mothers, and the elderly over 70 years from premium payments. The exemptions aim to support the management of various ill-health conditions including NCDs. Although the NHIS targets everybody, principally the vulnerable, there is a plethora of evidence to show that due to the inability to afford premiums because of low socioeconomic status, segments of the population are not covered [ 66 , 67 ]. Our findings show low confidence in the NHIS due to its erratic and unreliable operations as well as inconsistent information on the insurance coverage. This pushes patients to seek healthcare outside the approved biomedical care system. The consequence of the cost barrier to reliable access to approved biomedical care is the inferiority of treatment sought from multiple sources often leading to an exacerbated cost burden and poor health outcomes.
Our findings are consistent with those of other studies that have found that non-adherence to treatment schedule and medication is endemic among people with T2D [ 68 ] and hypertension [ 69 ] in Ghana. They also corroborate other studies on diabetes in SSA that highlighted the high cost of biomedical medication, the absence of reliable health insurance cover for diabetes care [ 70 ], and the inability of patients to afford consultation fees and laboratory services [ 71 ] creating health system barriers for medical adherence among T2D patients. The cost barrier is fundamental to the non-adherence to prescribed medications among study participants. Thus, this study found that non-adherence to T2D medication occurs mainly because of patients' inability to afford direct medical and/or non-medical costs of treatment. Affordability is a real problem partly because most comorbid T2D and hypertension patients were found to be elderly and, thus, were not productively engaged for financial income. Hence, the majority of T2D patients rely heavily on social support for their healthcare needs.
Adherence to medication and treatment plans for patients in Ga Mashie critically depends on financial and social support from relatives and friends [ 72 , 73 ]. Our findings show that comorbid T2D and hypertension patients rely heavily on relatives to pay for direct medical and non-medical costs associated with care. Relatives support direct medical cost expenses like consultation, laboratory diagnosis, medication and other healthcare costs. Likewise, relatives and friends assist with non-medical expenses like transportation to and from the healthcare facilities as well as other subsistence costs. Consequently, erratic financial support from relatives and friends has implications for adherence to the systematic plan for their treatment therapy and, ultimately, health outcomes. Furthermore, adherence to biomedical treatment among T2D and hypertension comorbidity patients in poor urban communities like Ga Mashie depends on the type of treatment and cost [ 74 , 75 ]. By this, care providers routinely compromise healthcare quality to meet the financial strength of patients. Patients cannot afford the right dosage of medication required for effective management of their condition, hence the need to modify the treatment regimen.
Besides the direct medical cost of comorbid T2D and hypertension treatment, there are other costs which are often not extensively considered in the economic burden of NCD dialogues. These are direct non-medical (e.g., transportation costs to and from healthcare facilities and cost of dietary and nutritional therapy) and indirect costs (i.e., productive workdays lost due to health-seeking or health condition) of care. Akin to a study in south-eastern Tanzania that reported lived experiences of diabetes management among adults [ 75 ], this study found that the cost of transportation to and from health facilities imposes an additional cost burden on patients.
Similar to some studies in SSA [ 65 , 76 ], we found changes in the pattern of diet and nutritional arrangements for persons with T2D and hypertension comorbidity recommended by dieticians. It was widely observed among this study's respondents that adherence to dietary changes is an integral factor in the management of T2D and hypertension comorbidity due to its vital contribution to blood pressure and glycaemic control. However, the cost of purchasing suitable foods regularly is problematic, thereby preventing strict adherence to the recommended dietary patterns. Literature in Africa supports the observation made by this study that comorbid T2D and hypertension patients of low socioeconomic status find it challenging to adhere to recommended dietary plans because of the associated cost burden [ 77 ].
Furthermore, although the findings of this study show a minimal contribution of indirect cost to the cost profile, the far-reaching impact on patients’ livelihoods is devastating. The health condition of most people with comorbid T2D and hypertension prevented them from engaging in any meaningful productive work, thereby indirectly worsening the cost burden. Consequently, patients mostly rely on the benevolence of family and friends for the management of their illness and general subsistence. Given the low socioeconomic status of the people of Ga Mashie coupled with the catastrophic direct medical cost of treatment, these direct non-medical and indirect costs exacerbate the burden on patients.
The significant psychosocial burden imposed on people with NCDs cannot be underestimated [ 78 , 79 ]. Patients' inability to independently or substantially cater for themselves often poses psychological stress on them and their caregivers [ 60 ]. Like findings of a systematic review of experiences of people living with NCDs in Africa [ 60 ], the psychological changes T2D and hypertension comorbidity patients in Ga Mashie go through include depression, stress, guilt, anxiety, anger, confusion frustration, and fear of death. These adverse psychosocial experiences intangibly contribute to the cost burden and physical deterioration in underprivileged communities like Ga Mashie. This happens partly because the psychosocial burden imposed by the disease is often overlooked by health professionals notwithstanding its overwhelming consequences [ 80 ]. Social support is therefore the most viable option available for people living with the disease in Ga Mashie.
Consistent with prior literature on the experiences of people living with NCDs in Africa, the findings of this study show that primary caregivers and other family members as well as friends play significant roles in the healthcare and management of comorbid T2D and hypertension [ 65 , 81 , 82 ]. Particularly among the aged, there is always active support from partners, children, caregivers, and other family members in the management of the disease. The main psychosocial support provided includes financial, biological, emotional, spiritual, cultural, social, and mental. The support includes accompanying patients to health facilities and ensuring medical and dietary adherence. Respondents have attributed any semblance of good quality of life among people with T2D and hypertension comorbidity in Ga Mashie to the unwavering financial support from their families [ 83 ]. However, in the long run, the huge healthcare cost burden, loss of caregivers' productive hours, and disruption in family members’ routine socioeconomic activities lead to neglect of patients in a poor urban setting like Ga Mashie [ 60 ].
Although the NHIS coverage has greatly expanded in Ghana over the years, the current modalities still offer limited protection against high healthcare expenditure for patients with comorbid T2D and hypertension. To address the high-cost burden of managing T2D and hypertension comorbidity, population-based interventions aimed at eliminating the catastrophic healthcare expenditure and strengthening health systems for the provision of effective biomedical care for those affected are essential. Policies should crucially consider reform of the NHIS benefits packages for NCDs to improve its potency for financial risk protection and reliability of biomedical care, particularly for people with T2D and hypertension comorbidity. These should consider subsidies/exemptions on medication and sensitization on the consequences of medical pluralism and NHIS coverage.
The major strength of this study is the triangulation of quantitative and qualitative data source that promoted a richer understanding of the findings. However, the small sample of respondents who provided complete cost data for the quantitative analysis is a limitation which may have reduced the precision of our cost estimates, and hinders generalizability of the findings. Future studies intent on measuring the economic cost of comorbid NCDs should consider larger sample sizes. Also, although the CARE-Diabetes project’s survey participants were selected using rigorous multi-stage sampling approach, females constituted over 80% of the subset data used for this analysis, suggesting likelihood of a highly biased sampling method. However, this may also be ascribed to women being more conscious of their health status – as cases of comorbid T2D and hypertension were self-reported. For the qualitative study, the thematic coding was done by one person—an approach which may have compromised the analysis. However, we made cautious efforts to maintain the internal validity of the data by having three of the authors check the transcripts to resolve any discordance between codes and global/organizing themes. Furthermore, there may not necessarily be a direct relationship between the qualitative and quantitative results presented due to the different populations (of living with T2D and hypertension) used, and thus possible variations in the degree of disease burden across the two groups.
The economic burden of managing T2D and hypertension comorbidity is significant in deprived urban Ghana. The burden weighs heavily on household budgets, thereby negatively affecting health and healthcare seeking patterns of patients. To alleviate the economic burden of medical care and promote appropriate therapy, the NHIS should prioritize free/affordable medical care for patients with NCDs to facilitate the effective management of T2D and hypertension comorbidity. Future research should consider using a larger sample size for the cost analysis and consider assessing the catastrophic health expenditure associated with healthcare (proportion of healthcare expenditure to household monthly food and non-food spending).
The data and materials that support the findings of this study are available from the authors upon reasonable request.
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The work was supported by the Medical Research Council (MRC) through the United Kingdom Research and Innovation (UKRI), grant number MR/T029919/1. We are grateful to members of the CARE-Diabetes project team who helped execute this research work.
This research was funded by the United Kingdom Research and Innovation (UKRI)—Medical Research Council (MRC) through a Grant [reference MR/T029919/1]. The funder of the study had no role in the study design, data collection, data analysis, data interpretation, or writing of this manuscript.
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S,A., MA and H.H-B conceived the study. S.A., M.A., H.H-B and E.F. contributed to the methodology of the study. S.A., L.B., R.B.A., I.A.K., K.K., V.A-A, S.B.K., H.J., P.A., E.G. and D.K.A. contributed to the implementation of the study. SA and MA led the analyses with support from HHB, SKM and CGE. SA drafted the original manuscript with significant revisions from M.A., H.H-B, L.O., I.A.K., R.B.A., O.A.S., E.F., S.B.K., A.B., C.G-E, D.K.A., S.K.M., H.J., P.A., E.G. and K.K. All authors reviewed the final draft of the manuscript.
Correspondence to Samuel Amon .
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Amon, S., Aikins, M., Haghparast-Bidgoli, H. et al. Household economic burden of type-2 diabetes and hypertension comorbidity care in urban-poor Ghana: a mixed methods study. BMC Health Serv Res 24 , 1028 (2024). https://doi.org/10.1186/s12913-024-11516-9
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Scientists have long considered that the world’s most common sexually transmitted infection, human papillomavirus, or HPV, may be a driver of infertility.
Most research about HPV’s potential impact on fertility has focused on women. But in recent years, researchers have increasingly expanded their focus to include the infection’s association with male fertility.
A new study from Argentinian researchers has found that the strains of HPV considered high risk because of their links to cancer were not only more common than low-risk strains in a small study population of men, they also appeared to pose a greater threat to sperm quality.
The study, published Friday in Frontiers in Cellular and Infection Microbiology, found that high-risk HPV appears to suppress key components of the immune system in the male genital tract. This could hamper the body’s ability to clear HPV , a process that typically takes about six months to a year after infection, while raising the risk of other infections that may also compromise male fertility.
“Individuals often have no symptoms or signs, yet still carry HPV in the male genital tract,” said the study’s senior author, Virginia Rivero, a professor of immunology at the National University of Córdoba in Argentina.
A 2020 systematic review of 50 studies found that 21% of infertile men had HPV-positive semen, compared with 8% in the general male population. Even after accounting for female infertility, men with HPV in their semen had three-fold greater odds of being infertile than those without the virus.
There are over 200 known strains of HPV. The riskiest handful can cause multiple cancers, including, in the U.S., about 26,000 diagnoses in women and 21,000 in men each year, according to the Centers for Disease Control and Prevention. The most common HPV-driven malignancy is cervical cancer, with about 13,800 invasive cases annually. Research suggests that most people are unaware that the virus can also cause vulval, anal, throat, vaginal and penile cancer .
A vaccine for HPV has been available since 2006 , when it was initially recommended just for girls; the recommendation was expanded to boys in 2011. The current version, which is given in a two- or three-dose series, prevents nine of the riskiest HPV strains, including those that cause genital warts.
The CDC recommends routine HPV vaccination for all boys and girls at 11 or 12 years old — children can receive it at as young as age 9 — and for those through age 26 who were not previously fully vaccinated. Experts consider the vaccine exceptionally safe .
A CDC study published Thursday found that for adolescents born in 2007, about 65% were fully vaccinated for HPV by age 15, compared with 60% of those born in 2008. The CDC attributes this statistically significant difference to disruptions from the Covid pandemic, beginning when the younger group turned 12.
Vaccination at older ages typically provides less benefit, since so many people contract at least one strain of HPV after becoming sexually active. But the CDC suggests that people up to age 45 may still discuss potential vaccination with their doctors.
In her new study, Rivero and her colleagues studied the ejaculate samples of 205 men, none of whom were vaccinated for HPV. The men, who had a median age of 35, sought a fertility assessment or treatment for urinary-tract problems from 2018 to 2021 at a urology clinic in Argentina.
Thirty-nine, or 19%, of the men tested positive for HPV. Researchers were able to identify 20 men among them who had high-risk strains and seven men with low-risk HPV.
On the surface, the investigators didn’t find any notable differences in the semen quality between the men with either type of HPV and a group of 43 men who tested negative for the virus. When they examined the semen more closely with highly sensitive tools, they found clues suggesting how high-risk HPV strains might be influencing male infertility.
The men with high-risk HPV had a lower level of certain immune cells in their semen, suggesting the virus had hampered the body’s ability to fight it off. This suppression of immune cells might also have raised the men’s risk of other infections that could further compromise their ability to conceive.
There was also evidence that the sperm of the men with high-risk HPV were sustaining damage from what’s known as oxidative stress. This could explain why these men had a higher level of dead sperm compared with those who didn’t have the virus.
Dr. Eugenio Ventimiglia, a urologist at the Università Vita-Salute San Raffaele in Milan, Italy, said the new study, which he was not involved in, “provides insight into the biological mechanisms potentially linking HPV to male reproductive health issues.”
Nevertheless, he said its findings should be “interpreted cautiously.”
“Instead of conclusively proving a cause-effect relationship between HPV and male factor infertility, the study’s findings are more appropriately seen as generating hypotheses for further research,” Ventimiglia said.
Men’s HPV might also affect fertility in part by transmitting the virus into the woman’s reproductive tract; the virus might then harm the pregnancy at various stages, including before the fertilized egg implants in the womb. Couples receiving assisted reproductive technology have a greater chance of miscarriage if the man has HPV in his semen, researchers have found .
Research indicates that providing the HPV vaccine to men who are having trouble conceiving and who have an active HPV infection might help them clear the virus faster and potentially improve their chances of conceiving.
“Whatever other changes are thought to be associated with HPV, it should be noted that HPV infection is usually brief, as is the sperm lifespan,” said Dr. Marie-Hélène Mayrand, an epidemiologist and the chair of the obstetrics and gynecology department of University of Montreal. “This is reassuring that any effect, if found, would be brief and self-limited.” Mayrand was not involved in the new research.
Rivero advises that men struggling with fertility receive testing for HPV and other sexually transmitted infections that could affect their fertility. If positive for HPV, additional testing may be needed to identify specific strains.
The test results, Rivero said, could help men identify a potential driver of their infertility.
HPV vaccination rates among adolescent boys and men have been rising over the last decade. Recent research suggested that the HPV vaccine was linked to a drastically lower rate of head and neck cancers in men and adolescent boys.
It’s not yet known if the vaccine could protect men’s fertility.
“When a critical mass of boys and girls are vaccinated, it is likely that the transmission of the HPV genotypes covered by the vaccines will decrease.” Rivero said. “But the broader impact on fertility remains uncertain.”
Rivero said she hoped to see a larger study in the future that could lend more statistical heft to her findings. Her own lab plans to further study how simultaneous infections with HPV and other STIs might influence male fertility.
CORRECTION: (Aug. 23, 2024 10:50 a.m. ET) A previous version of this article misidentified Dr. Eugenio Ventimiglia. He is a urologist at the Università Vita-Salute San Raffaele, not an oncologist in the urology unit.
Benjamin Ryan is independent journalist specializing in science and LGBTQ coverage. He contributes to NBC News, The New York Times, The Guardian and Thomson Reuters Foundation and has also written for The Washington Post, The Nation, The Atlantic and New York.
Systematic Review
Please see our Study Design 101 tutorial for more information.
Meta-analysis
A quantitative method of combining the results of independent studies, which are drawn from the published literature, and synthesizing summaries and conclusions.
A review which endeavors to consider all published and unpublished material on a specific question. Studies that are judged methodologically sound are then combined quantitatively or qualitatively depending on their similarity.
Randomized Controlled Trial (RCT)
A clinical trial involving one or more new treatments and at least one control treatment with specified outcome measures for evaluating the intervention. The treatment may be a drug, device, or procedure. Controls are either placebo or an active treatment that is currently considered the "gold standard". If patients are randomized via mathmatical techniques then the trial is designated as a randomized controlled trial.
Cohort Study
In cohort studies, groups of individuals, who are initially free of disease, are classified according to exposure or non-exposure to a risk factor and followed over time to determine the incidence of an outcome of interest. In a prospective cohort study, the exposure information for the study subjects is collected at the start of the study and the new cases of disease are identified from that point on. In a retrospective cohort study, the exposure status was measured in the past and disease identification has already begun.
Case-control Study
Studies that start by identifying persons with and without a disease of interest (cases and controls, respectively) and then look back in time to find differences in exposure to risk factors.
Cross-sectional Study
Studies in which the presence or absence of disease or other health-related variables are determined in each member of a population at one particular time.
Levels of Evidence Pyramid created by Andy Puro, September 2014
An observational study is a study in which the investigator cannot control the assignment of treatment to subjects because the participants or conditions are not being directly assigned by the researcher.
In an experimental study , the investigators directly manipulate or assign participants to different interventions or environments.
Formal Trials versus Observational Studies (Ravi Thadhani, Harvard Medical School)
Study Designs (Centre for Evidence Based Medicine, University of Oxford)
Learn about Clinical Studies (ClinicalTrials.gov, National Institutes of Health)
Definitions taken from: Dawson B, Trapp R.G. (2004). Chapter 2. Study Designs in Medical Research. In Dawson B, Trapp R.G. (Eds), Basic & Clinical Biostatistics, 4e . Retrieved September 15, 2014 from http://accessmedicine.mhmedical.com/content.aspx?bookid=356&Sectionid=40086281.
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Lawrence mbuagbaw.
1 Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, ON Canada
2 Biostatistics Unit/FSORC, 50 Charlton Avenue East, St Joseph’s Healthcare—Hamilton, 3rd Floor Martha Wing, Room H321, Hamilton, Ontario L8N 4A6 Canada
3 Centre for the Development of Best Practices in Health, Yaoundé, Cameroon
Livia puljak.
4 Center for Evidence-Based Medicine and Health Care, Catholic University of Croatia, Ilica 242, 10000 Zagreb, Croatia
5 Department of Epidemiology and Biostatistics, School of Public Health – Bloomington, Indiana University, Bloomington, IN 47405 USA
6 Departments of Paediatrics and Anaesthesia, McMaster University, Hamilton, ON Canada
7 Centre for Evaluation of Medicine, St. Joseph’s Healthcare-Hamilton, Hamilton, ON Canada
8 Population Health Research Institute, Hamilton Health Sciences, Hamilton, ON Canada
Data sharing is not applicable to this article as no new data were created or analyzed in this study.
Methodological studies – studies that evaluate the design, analysis or reporting of other research-related reports – play an important role in health research. They help to highlight issues in the conduct of research with the aim of improving health research methodology, and ultimately reducing research waste.
We provide an overview of some of the key aspects of methodological studies such as what they are, and when, how and why they are done. We adopt a “frequently asked questions” format to facilitate reading this paper and provide multiple examples to help guide researchers interested in conducting methodological studies. Some of the topics addressed include: is it necessary to publish a study protocol? How to select relevant research reports and databases for a methodological study? What approaches to data extraction and statistical analysis should be considered when conducting a methodological study? What are potential threats to validity and is there a way to appraise the quality of methodological studies?
Appropriate reflection and application of basic principles of epidemiology and biostatistics are required in the design and analysis of methodological studies. This paper provides an introduction for further discussion about the conduct of methodological studies.
The field of meta-research (or research-on-research) has proliferated in recent years in response to issues with research quality and conduct [ 1 – 3 ]. As the name suggests, this field targets issues with research design, conduct, analysis and reporting. Various types of research reports are often examined as the unit of analysis in these studies (e.g. abstracts, full manuscripts, trial registry entries). Like many other novel fields of research, meta-research has seen a proliferation of use before the development of reporting guidance. For example, this was the case with randomized trials for which risk of bias tools and reporting guidelines were only developed much later – after many trials had been published and noted to have limitations [ 4 , 5 ]; and for systematic reviews as well [ 6 – 8 ]. However, in the absence of formal guidance, studies that report on research differ substantially in how they are named, conducted and reported [ 9 , 10 ]. This creates challenges in identifying, summarizing and comparing them. In this tutorial paper, we will use the term methodological study to refer to any study that reports on the design, conduct, analysis or reporting of primary or secondary research-related reports (such as trial registry entries and conference abstracts).
In the past 10 years, there has been an increase in the use of terms related to methodological studies (based on records retrieved with a keyword search [in the title and abstract] for “methodological review” and “meta-epidemiological study” in PubMed up to December 2019), suggesting that these studies may be appearing more frequently in the literature. See Fig. 1 .
Trends in the number studies that mention “methodological review” or “meta-
epidemiological study” in PubMed.
The methods used in many methodological studies have been borrowed from systematic and scoping reviews. This practice has influenced the direction of the field, with many methodological studies including searches of electronic databases, screening of records, duplicate data extraction and assessments of risk of bias in the included studies. However, the research questions posed in methodological studies do not always require the approaches listed above, and guidance is needed on when and how to apply these methods to a methodological study. Even though methodological studies can be conducted on qualitative or mixed methods research, this paper focuses on and draws examples exclusively from quantitative research.
The objectives of this paper are to provide some insights on how to conduct methodological studies so that there is greater consistency between the research questions posed, and the design, analysis and reporting of findings. We provide multiple examples to illustrate concepts and a proposed framework for categorizing methodological studies in quantitative research.
Any study that describes or analyzes methods (design, conduct, analysis or reporting) in published (or unpublished) literature is a methodological study. Consequently, the scope of methodological studies is quite extensive and includes, but is not limited to, topics as diverse as: research question formulation [ 11 ]; adherence to reporting guidelines [ 12 – 14 ] and consistency in reporting [ 15 ]; approaches to study analysis [ 16 ]; investigating the credibility of analyses [ 17 ]; and studies that synthesize these methodological studies [ 18 ]. While the nomenclature of methodological studies is not uniform, the intents and purposes of these studies remain fairly consistent – to describe or analyze methods in primary or secondary studies. As such, methodological studies may also be classified as a subtype of observational studies.
Parallel to this are experimental studies that compare different methods. Even though they play an important role in informing optimal research methods, experimental methodological studies are beyond the scope of this paper. Examples of such studies include the randomized trials by Buscemi et al., comparing single data extraction to double data extraction [ 19 ], and Carrasco-Labra et al., comparing approaches to presenting findings in Grading of Recommendations, Assessment, Development and Evaluations (GRADE) summary of findings tables [ 20 ]. In these studies, the unit of analysis is the person or groups of individuals applying the methods. We also direct readers to the Studies Within a Trial (SWAT) and Studies Within a Review (SWAR) programme operated through the Hub for Trials Methodology Research, for further reading as a potential useful resource for these types of experimental studies [ 21 ]. Lastly, this paper is not meant to inform the conduct of research using computational simulation and mathematical modeling for which some guidance already exists [ 22 ], or studies on the development of methods using consensus-based approaches.
Methodological studies occupy a unique niche in health research that allows them to inform methodological advances. Methodological studies should also be conducted as pre-cursors to reporting guideline development, as they provide an opportunity to understand current practices, and help to identify the need for guidance and gaps in methodological or reporting quality. For example, the development of the popular Preferred Reporting Items of Systematic reviews and Meta-Analyses (PRISMA) guidelines were preceded by methodological studies identifying poor reporting practices [ 23 , 24 ]. In these instances, after the reporting guidelines are published, methodological studies can also be used to monitor uptake of the guidelines.
These studies can also be conducted to inform the state of the art for design, analysis and reporting practices across different types of health research fields, with the aim of improving research practices, and preventing or reducing research waste. For example, Samaan et al. conducted a scoping review of adherence to different reporting guidelines in health care literature [ 18 ]. Methodological studies can also be used to determine the factors associated with reporting practices. For example, Abbade et al. investigated journal characteristics associated with the use of the Participants, Intervention, Comparison, Outcome, Timeframe (PICOT) format in framing research questions in trials of venous ulcer disease [ 11 ].
There is no clear answer to this question. Based on a search of PubMed, the use of related terms (“methodological review” and “meta-epidemiological study”) – and therefore, the number of methodological studies – is on the rise. However, many other terms are used to describe methodological studies. There are also many studies that explore design, conduct, analysis or reporting of research reports, but that do not use any specific terms to describe or label their study design in terms of “methodology”. This diversity in nomenclature makes a census of methodological studies elusive. Appropriate terminology and key words for methodological studies are needed to facilitate improved accessibility for end-users.
Methodological studies provide information on the design, conduct, analysis or reporting of primary and secondary research and can be used to appraise quality, quantity, completeness, accuracy and consistency of health research. These issues can be explored in specific fields, journals, databases, geographical regions and time periods. For example, Areia et al. explored the quality of reporting of endoscopic diagnostic studies in gastroenterology [ 25 ]; Knol et al. investigated the reporting of p -values in baseline tables in randomized trial published in high impact journals [ 26 ]; Chen et al. describe adherence to the Consolidated Standards of Reporting Trials (CONSORT) statement in Chinese Journals [ 27 ]; and Hopewell et al. describe the effect of editors’ implementation of CONSORT guidelines on reporting of abstracts over time [ 28 ]. Methodological studies provide useful information to researchers, clinicians, editors, publishers and users of health literature. As a result, these studies have been at the cornerstone of important methodological developments in the past two decades and have informed the development of many health research guidelines including the highly cited CONSORT statement [ 5 ].
Methodological studies can be found in most common biomedical bibliographic databases (e.g. Embase, MEDLINE, PubMed, Web of Science). However, the biggest caveat is that methodological studies are hard to identify in the literature due to the wide variety of names used and the lack of comprehensive databases dedicated to them. A handful can be found in the Cochrane Library as “Cochrane Methodology Reviews”, but these studies only cover methodological issues related to systematic reviews. Previous attempts to catalogue all empirical studies of methods used in reviews were abandoned 10 years ago [ 29 ]. In other databases, a variety of search terms may be applied with different levels of sensitivity and specificity.
In this section, we have outlined responses to questions that might help inform the conduct of methodological studies.
Q: How should I select research reports for my methodological study?
A: Selection of research reports for a methodological study depends on the research question and eligibility criteria. Once a clear research question is set and the nature of literature one desires to review is known, one can then begin the selection process. Selection may begin with a broad search, especially if the eligibility criteria are not apparent. For example, a methodological study of Cochrane Reviews of HIV would not require a complex search as all eligible studies can easily be retrieved from the Cochrane Library after checking a few boxes [ 30 ]. On the other hand, a methodological study of subgroup analyses in trials of gastrointestinal oncology would require a search to find such trials, and further screening to identify trials that conducted a subgroup analysis [ 31 ].
The strategies used for identifying participants in observational studies can apply here. One may use a systematic search to identify all eligible studies. If the number of eligible studies is unmanageable, a random sample of articles can be expected to provide comparable results if it is sufficiently large [ 32 ]. For example, Wilson et al. used a random sample of trials from the Cochrane Stroke Group’s Trial Register to investigate completeness of reporting [ 33 ]. It is possible that a simple random sample would lead to underrepresentation of units (i.e. research reports) that are smaller in number. This is relevant if the investigators wish to compare multiple groups but have too few units in one group. In this case a stratified sample would help to create equal groups. For example, in a methodological study comparing Cochrane and non-Cochrane reviews, Kahale et al. drew random samples from both groups [ 34 ]. Alternatively, systematic or purposeful sampling strategies can be used and we encourage researchers to justify their selected approaches based on the study objective.
Q: How many databases should I search?
A: The number of databases one should search would depend on the approach to sampling, which can include targeting the entire “population” of interest or a sample of that population. If you are interested in including the entire target population for your research question, or drawing a random or systematic sample from it, then a comprehensive and exhaustive search for relevant articles is required. In this case, we recommend using systematic approaches for searching electronic databases (i.e. at least 2 databases with a replicable and time stamped search strategy). The results of your search will constitute a sampling frame from which eligible studies can be drawn.
Alternatively, if your approach to sampling is purposeful, then we recommend targeting the database(s) or data sources (e.g. journals, registries) that include the information you need. For example, if you are conducting a methodological study of high impact journals in plastic surgery and they are all indexed in PubMed, you likely do not need to search any other databases. You may also have a comprehensive list of all journals of interest and can approach your search using the journal names in your database search (or by accessing the journal archives directly from the journal’s website). Even though one could also search journals’ web pages directly, using a database such as PubMed has multiple advantages, such as the use of filters, so the search can be narrowed down to a certain period, or study types of interest. Furthermore, individual journals’ web sites may have different search functionalities, which do not necessarily yield a consistent output.
Q: Should I publish a protocol for my methodological study?
A: A protocol is a description of intended research methods. Currently, only protocols for clinical trials require registration [ 35 ]. Protocols for systematic reviews are encouraged but no formal recommendation exists. The scientific community welcomes the publication of protocols because they help protect against selective outcome reporting, the use of post hoc methodologies to embellish results, and to help avoid duplication of efforts [ 36 ]. While the latter two risks exist in methodological research, the negative consequences may be substantially less than for clinical outcomes. In a sample of 31 methodological studies, 7 (22.6%) referenced a published protocol [ 9 ]. In the Cochrane Library, there are 15 protocols for methodological reviews (21 July 2020). This suggests that publishing protocols for methodological studies is not uncommon.
Authors can consider publishing their study protocol in a scholarly journal as a manuscript. Advantages of such publication include obtaining peer-review feedback about the planned study, and easy retrieval by searching databases such as PubMed. The disadvantages in trying to publish protocols includes delays associated with manuscript handling and peer review, as well as costs, as few journals publish study protocols, and those journals mostly charge article-processing fees [ 37 ]. Authors who would like to make their protocol publicly available without publishing it in scholarly journals, could deposit their study protocols in publicly available repositories, such as the Open Science Framework ( https://osf.io/ ).
Q: How to appraise the quality of a methodological study?
A: To date, there is no published tool for appraising the risk of bias in a methodological study, but in principle, a methodological study could be considered as a type of observational study. Therefore, during conduct or appraisal, care should be taken to avoid the biases common in observational studies [ 38 ]. These biases include selection bias, comparability of groups, and ascertainment of exposure or outcome. In other words, to generate a representative sample, a comprehensive reproducible search may be necessary to build a sampling frame. Additionally, random sampling may be necessary to ensure that all the included research reports have the same probability of being selected, and the screening and selection processes should be transparent and reproducible. To ensure that the groups compared are similar in all characteristics, matching, random sampling or stratified sampling can be used. Statistical adjustments for between-group differences can also be applied at the analysis stage. Finally, duplicate data extraction can reduce errors in assessment of exposures or outcomes.
Q: Should I justify a sample size?
A: In all instances where one is not using the target population (i.e. the group to which inferences from the research report are directed) [ 39 ], a sample size justification is good practice. The sample size justification may take the form of a description of what is expected to be achieved with the number of articles selected, or a formal sample size estimation that outlines the number of articles required to answer the research question with a certain precision and power. Sample size justifications in methodological studies are reasonable in the following instances:
For example, El Dib et al. computed a sample size requirement for a methodological study of diagnostic strategies in randomized trials, based on a confidence interval approach [ 40 ].
Q: What should I call my study?
A: Other terms which have been used to describe/label methodological studies include “ methodological review ”, “methodological survey” , “meta-epidemiological study” , “systematic review” , “systematic survey”, “meta-research”, “research-on-research” and many others. We recommend that the study nomenclature be clear, unambiguous, informative and allow for appropriate indexing. Methodological study nomenclature that should be avoided includes “ systematic review” – as this will likely be confused with a systematic review of a clinical question. “ Systematic survey” may also lead to confusion about whether the survey was systematic (i.e. using a preplanned methodology) or a survey using “ systematic” sampling (i.e. a sampling approach using specific intervals to determine who is selected) [ 32 ]. Any of the above meanings of the words “ systematic” may be true for methodological studies and could be potentially misleading. “ Meta-epidemiological study” is ideal for indexing, but not very informative as it describes an entire field. The term “ review ” may point towards an appraisal or “review” of the design, conduct, analysis or reporting (or methodological components) of the targeted research reports, yet it has also been used to describe narrative reviews [ 41 , 42 ]. The term “ survey ” is also in line with the approaches used in many methodological studies [ 9 ], and would be indicative of the sampling procedures of this study design. However, in the absence of guidelines on nomenclature, the term “ methodological study ” is broad enough to capture most of the scenarios of such studies.
Q: Should I account for clustering in my methodological study?
A: Data from methodological studies are often clustered. For example, articles coming from a specific source may have different reporting standards (e.g. the Cochrane Library). Articles within the same journal may be similar due to editorial practices and policies, reporting requirements and endorsement of guidelines. There is emerging evidence that these are real concerns that should be accounted for in analyses [ 43 ]. Some cluster variables are described in the section: “ What variables are relevant to methodological studies?”
A variety of modelling approaches can be used to account for correlated data, including the use of marginal, fixed or mixed effects regression models with appropriate computation of standard errors [ 44 ]. For example, Kosa et al. used generalized estimation equations to account for correlation of articles within journals [ 15 ]. Not accounting for clustering could lead to incorrect p -values, unduly narrow confidence intervals, and biased estimates [ 45 ].
Q: Should I extract data in duplicate?
A: Yes. Duplicate data extraction takes more time but results in less errors [ 19 ]. Data extraction errors in turn affect the effect estimate [ 46 ], and therefore should be mitigated. Duplicate data extraction should be considered in the absence of other approaches to minimize extraction errors. However, much like systematic reviews, this area will likely see rapid new advances with machine learning and natural language processing technologies to support researchers with screening and data extraction [ 47 , 48 ]. However, experience plays an important role in the quality of extracted data and inexperienced extractors should be paired with experienced extractors [ 46 , 49 ].
Q: Should I assess the risk of bias of research reports included in my methodological study?
A : Risk of bias is most useful in determining the certainty that can be placed in the effect measure from a study. In methodological studies, risk of bias may not serve the purpose of determining the trustworthiness of results, as effect measures are often not the primary goal of methodological studies. Determining risk of bias in methodological studies is likely a practice borrowed from systematic review methodology, but whose intrinsic value is not obvious in methodological studies. When it is part of the research question, investigators often focus on one aspect of risk of bias. For example, Speich investigated how blinding was reported in surgical trials [ 50 ], and Abraha et al., investigated the application of intention-to-treat analyses in systematic reviews and trials [ 51 ].
Q: What variables are relevant to methodological studies?
A: There is empirical evidence that certain variables may inform the findings in a methodological study. We outline some of these and provide a brief overview below:
Q: Should I focus only on high impact journals?
A: Investigators may choose to investigate only high impact journals because they are more likely to influence practice and policy, or because they assume that methodological standards would be higher. However, the JIF may severely limit the scope of articles included and may skew the sample towards articles with positive findings. The generalizability and applicability of findings from a handful of journals must be examined carefully, especially since the JIF varies over time. Even among journals that are all “high impact”, variations exist in methodological standards.
Q: Can I conduct a methodological study of qualitative research?
A: Yes. Even though a lot of methodological research has been conducted in the quantitative research field, methodological studies of qualitative studies are feasible. Certain databases that catalogue qualitative research including the Cumulative Index to Nursing & Allied Health Literature (CINAHL) have defined subject headings that are specific to methodological research (e.g. “research methodology”). Alternatively, one could also conduct a qualitative methodological review; that is, use qualitative approaches to synthesize methodological issues in qualitative studies.
Q: What reporting guidelines should I use for my methodological study?
A: There is no guideline that covers the entire scope of methodological studies. One adaptation of the PRISMA guidelines has been published, which works well for studies that aim to use the entire target population of research reports [ 71 ]. However, it is not widely used (40 citations in 2 years as of 09 December 2019), and methodological studies that are designed as cross-sectional or before-after studies require a more fit-for purpose guideline. A more encompassing reporting guideline for a broad range of methodological studies is currently under development [ 72 ]. However, in the absence of formal guidance, the requirements for scientific reporting should be respected, and authors of methodological studies should focus on transparency and reproducibility.
Q: What are the potential threats to validity and how can I avoid them?
A: Methodological studies may be compromised by a lack of internal or external validity. The main threats to internal validity in methodological studies are selection and confounding bias. Investigators must ensure that the methods used to select articles does not make them differ systematically from the set of articles to which they would like to make inferences. For example, attempting to make extrapolations to all journals after analyzing high-impact journals would be misleading.
Many factors (confounders) may distort the association between the exposure and outcome if the included research reports differ with respect to these factors [ 73 ]. For example, when examining the association between source of funding and completeness of reporting, it may be necessary to account for journals that endorse the guidelines. Confounding bias can be addressed by restriction, matching and statistical adjustment [ 73 ]. Restriction appears to be the method of choice for many investigators who choose to include only high impact journals or articles in a specific field. For example, Knol et al. examined the reporting of p -values in baseline tables of high impact journals [ 26 ]. Matching is also sometimes used. In the methodological study of non-randomized interventional studies of elective ventral hernia repair, Parker et al. matched prospective studies with retrospective studies and compared reporting standards [ 74 ]. Some other methodological studies use statistical adjustments. For example, Zhang et al. used regression techniques to determine the factors associated with missing participant data in trials [ 16 ].
With regard to external validity, researchers interested in conducting methodological studies must consider how generalizable or applicable their findings are. This should tie in closely with the research question and should be explicit. For example. Findings from methodological studies on trials published in high impact cardiology journals cannot be assumed to be applicable to trials in other fields. However, investigators must ensure that their sample truly represents the target sample either by a) conducting a comprehensive and exhaustive search, or b) using an appropriate and justified, randomly selected sample of research reports.
Even applicability to high impact journals may vary based on the investigators’ definition, and over time. For example, for high impact journals in the field of general medicine, Bouwmeester et al. included the Annals of Internal Medicine (AIM), BMJ, the Journal of the American Medical Association (JAMA), Lancet, the New England Journal of Medicine (NEJM), and PLoS Medicine ( n = 6) [ 75 ]. In contrast, the high impact journals selected in the methodological study by Schiller et al. were BMJ, JAMA, Lancet, and NEJM ( n = 4) [ 76 ]. Another methodological study by Kosa et al. included AIM, BMJ, JAMA, Lancet and NEJM ( n = 5). In the methodological study by Thabut et al., journals with a JIF greater than 5 were considered to be high impact. Riado Minguez et al. used first quartile journals in the Journal Citation Reports (JCR) for a specific year to determine “high impact” [ 77 ]. Ultimately, the definition of high impact will be based on the number of journals the investigators are willing to include, the year of impact and the JIF cut-off [ 78 ]. We acknowledge that the term “generalizability” may apply differently for methodological studies, especially when in many instances it is possible to include the entire target population in the sample studied.
Finally, methodological studies are not exempt from information bias which may stem from discrepancies in the included research reports [ 79 ], errors in data extraction, or inappropriate interpretation of the information extracted. Likewise, publication bias may also be a concern in methodological studies, but such concepts have not yet been explored.
In order to inform discussions about methodological studies, the development of guidance for what should be reported, we have outlined some key features of methodological studies that can be used to classify them. For each of the categories outlined below, we provide an example. In our experience, the choice of approach to completing a methodological study can be informed by asking the following four questions:
A methodological study may be focused on exploring sources of bias in primary or secondary studies (meta-bias), or how bias is analyzed. We have taken care to distinguish bias (i.e. systematic deviations from the truth irrespective of the source) from reporting quality or completeness (i.e. not adhering to a specific reporting guideline or norm). An example of where this distinction would be important is in the case of a randomized trial with no blinding. This study (depending on the nature of the intervention) would be at risk of performance bias. However, if the authors report that their study was not blinded, they would have reported adequately. In fact, some methodological studies attempt to capture both “quality of conduct” and “quality of reporting”, such as Richie et al., who reported on the risk of bias in randomized trials of pharmacy practice interventions [ 80 ]. Babic et al. investigated how risk of bias was used to inform sensitivity analyses in Cochrane reviews [ 81 ]. Further, biases related to choice of outcomes can also be explored. For example, Tan et al investigated differences in treatment effect size based on the outcome reported [ 82 ].
Methodological studies may report quality of reporting against a reporting checklist (i.e. adherence to guidelines) or against expected norms. For example, Croituro et al. report on the quality of reporting in systematic reviews published in dermatology journals based on their adherence to the PRISMA statement [ 83 ], and Khan et al. described the quality of reporting of harms in randomized controlled trials published in high impact cardiovascular journals based on the CONSORT extension for harms [ 84 ]. Other methodological studies investigate reporting of certain features of interest that may not be part of formally published checklists or guidelines. For example, Mbuagbaw et al. described how often the implications for research are elaborated using the Evidence, Participants, Intervention, Comparison, Outcome, Timeframe (EPICOT) format [ 30 ].
Sometimes investigators may be interested in how consistent reports of the same research are, as it is expected that there should be consistency between: conference abstracts and published manuscripts; manuscript abstracts and manuscript main text; and trial registration and published manuscript. For example, Rosmarakis et al. investigated consistency between conference abstracts and full text manuscripts [ 85 ].
In addition to identifying issues with reporting in primary and secondary studies, authors of methodological studies may be interested in determining the factors that are associated with certain reporting practices. Many methodological studies incorporate this, albeit as a secondary outcome. For example, Farrokhyar et al. investigated the factors associated with reporting quality in randomized trials of coronary artery bypass grafting surgery [ 53 ].
Methodological studies may also be used to describe methods or compare methods, and the factors associated with methods. Muller et al. described the methods used for systematic reviews and meta-analyses of observational studies [ 86 ].
Some methodological studies synthesize results from other methodological studies. For example, Li et al. conducted a scoping review of methodological reviews that investigated consistency between full text and abstracts in primary biomedical research [ 87 ].
Some methodological studies may investigate the use of names and terms in health research. For example, Martinic et al. investigated the definitions of systematic reviews used in overviews of systematic reviews (OSRs), meta-epidemiological studies and epidemiology textbooks [ 88 ].
In addition to the previously mentioned experimental methodological studies, there may exist other types of methodological studies not captured here.
Most methodological studies are purely descriptive and report their findings as counts (percent) and means (standard deviation) or medians (interquartile range). For example, Mbuagbaw et al. described the reporting of research recommendations in Cochrane HIV systematic reviews [ 30 ]. Gohari et al. described the quality of reporting of randomized trials in diabetes in Iran [ 12 ].
Some methodological studies are analytical wherein “analytical studies identify and quantify associations, test hypotheses, identify causes and determine whether an association exists between variables, such as between an exposure and a disease.” [ 89 ] In the case of methodological studies all these investigations are possible. For example, Kosa et al. investigated the association between agreement in primary outcome from trial registry to published manuscript and study covariates. They found that larger and more recent studies were more likely to have agreement [ 15 ]. Tricco et al. compared the conclusion statements from Cochrane and non-Cochrane systematic reviews with a meta-analysis of the primary outcome and found that non-Cochrane reviews were more likely to report positive findings. These results are a test of the null hypothesis that the proportions of Cochrane and non-Cochrane reviews that report positive results are equal [ 90 ].
Methodological reviews with narrow research questions may be able to include the entire target population. For example, in the methodological study of Cochrane HIV systematic reviews, Mbuagbaw et al. included all of the available studies ( n = 103) [ 30 ].
Many methodological studies use random samples of the target population [ 33 , 91 , 92 ]. Alternatively, purposeful sampling may be used, limiting the sample to a subset of research-related reports published within a certain time period, or in journals with a certain ranking or on a topic. Systematic sampling can also be used when random sampling may be challenging to implement.
Many methodological studies use a research report (e.g. full manuscript of study, abstract portion of the study) as the unit of analysis, and inferences can be made at the study-level. However, both published and unpublished research-related reports can be studied. These may include articles, conference abstracts, registry entries etc.
Some methodological studies report on items which may occur more than once per article. For example, Paquette et al. report on subgroup analyses in Cochrane reviews of atrial fibrillation in which 17 systematic reviews planned 56 subgroup analyses [ 93 ].
This framework is outlined in Fig. 2 .
A proposed framework for methodological studies
Methodological studies have examined different aspects of reporting such as quality, completeness, consistency and adherence to reporting guidelines. As such, many of the methodological study examples cited in this tutorial are related to reporting. However, as an evolving field, the scope of research questions that can be addressed by methodological studies is expected to increase.
In this paper we have outlined the scope and purpose of methodological studies, along with examples of instances in which various approaches have been used. In the absence of formal guidance on the design, conduct, analysis and reporting of methodological studies, we have provided some advice to help make methodological studies consistent. This advice is grounded in good contemporary scientific practice. Generally, the research question should tie in with the sampling approach and planned analysis. We have also highlighted the variables that may inform findings from methodological studies. Lastly, we have provided suggestions for ways in which authors can categorize their methodological studies to inform their design and analysis.
Abbreviations.
CONSORT | Consolidated Standards of Reporting Trials |
EPICOT | Evidence, Participants, Intervention, Comparison, Outcome, Timeframe |
GRADE | Grading of Recommendations, Assessment, Development and Evaluations |
PICOT | Participants, Intervention, Comparison, Outcome, Timeframe |
PRISMA | Preferred Reporting Items of Systematic reviews and Meta-Analyses |
SWAR | Studies Within a Review |
SWAT | Studies Within a Trial |
LM conceived the idea and drafted the outline and paper. DOL and LT commented on the idea and draft outline. LM, LP and DOL performed literature searches and data extraction. All authors (LM, DOL, LT, LP, DBA) reviewed several draft versions of the manuscript and approved the final manuscript.
This work did not receive any dedicated funding.
Ethics approval and consent to participate.
Not applicable.
Competing interests.
DOL, DBA, LM, LP and LT are involved in the development of a reporting guideline for methodological studies.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Here’s how you know
Probiotics are live microorganisms that are intended to have health benefits when consumed or applied to the body. They can be found in yogurt and other fermented foods, dietary supplements , and beauty products. Cases of severe or fatal infections have been reported in premature infants who were given probiotics, and the U.S. Food and Drug Administration (FDA) has warned health care providers about this risk.
Although people often think of bacteria and other microorganisms as harmful “germs,” many are actually helpful. Some bacteria help digest food, destroy disease-causing cells, or produce vitamins. Many of the microorganisms in probiotic products are the same as or similar to microorganisms that naturally live in our bodies.
Probiotics may contain a variety of microorganisms. The most common are bacteria that belong to groups called Lactobacillus and Bifidobacterium . Other bacteria may also be used as probiotics, and so may yeasts such as Saccharomyces boulardii .
Different types of probiotics may have different effects. For example, if a specific kind of Lactobacillus helps prevent an illness, that doesn’t necessarily mean that another kind of Lactobacillus or any of the Bifidobacterium probiotics would do the same thing.
No, prebiotics aren’t the same as probiotics. Prebiotics are nondigestible food components that selectively stimulate the growth or activity of desirable microorganisms.
Synbiotics are products that combine probiotics and prebiotics.
The 2012 National Health Interview Survey (NHIS) showed that about 4 million (1.6 percent) U.S. adults had used probiotics or prebiotics in the past 30 days. Among adults, probiotics or prebiotics were the third most commonly used dietary supplement other than vitamins and minerals. The use of probiotics by adults quadrupled between 2007 and 2012. The 2012 NHIS also showed that 300,000 children age 4 to 17 (0.5 percent) had used probiotics or prebiotics in the 30 days before the survey.
Probiotics may have a variety of effects in the body, and different probiotics may act in different ways.
Probiotics might:
Government regulation of probiotics in the United States is complex. Depending on a probiotic product’s intended use, the FDA might regulate it as a dietary supplement, a food ingredient, or a drug.
Many probiotics are sold as dietary supplements, which don’t require FDA approval before they are marketed. Dietary supplement labels may make claims about how the product affects the structure or function of the body without FDA approval, but they aren’t allowed to make health claims, such as saying the supplement lowers your risk of getting a disease, without the FDA’s consent.
If a probiotic is going to be marketed as a drug for treatment of a disease or disorder, it has to meet stricter requirements. It must be proven safe and effective for its intended use through clinical trials and be approved by the FDA before it can be sold.
The community of microorganisms that lives on us and in us is called the “microbiome,” and it’s a hot topic for research. The Human Microbiome Project, supported by the National Institutes of Health (NIH) from 2007 to 2016, played a key role in this research by mapping the normal bacteria that live in and on the healthy human body. With this understanding of a normal microbiome as the basis, researchers around the world, including many supported by NIH, are now exploring the links between changes in the microbiome and various diseases. They’re also developing new therapeutic approaches designed to modify the microbiome to treat disease and support health.
The National Center for Complementary and Integrative Health (NCCIH) is among the many agencies funding research on the microbiome. Researchers supported by NCCIH are studying the interactions between components of food and microorganisms in the digestive tract. The focus is on the ways in which diet-microbiome interactions may lead to the production of substances with beneficial health effects.
A great deal of research has been done on probiotics, but much remains to be learned about whether they’re helpful and safe for various health conditions.
Probiotics have shown promise for a variety of health purposes, including prevention of antibiotic-associated diarrhea (including diarrhea caused by Clostridium difficile ), prevention of necrotizing enterocolitis and sepsis in premature infants, treatment of infant colic , treatment of periodontal disease , and induction or maintenance of remission in ulcerative colitis .
However, in most instances, we still don’t know which probiotics are helpful and which are not. We also don’t know how much of the probiotic people would have to take or who would be most likely to benefit. Even for the conditions that have been studied the most, researchers are still working toward finding the answers to these questions.
The following sections summarize the research on probiotics for some of the conditions for which they’ve been studied.
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For more information, see the NCCIH fact sheet on irritable bowel syndrome .
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NCCIH sponsors a variety of research projects related to probiotics or the microbiome. In addition to the previously mentioned studies on diet-microbiome interactions in the digestive tract, recent topics include:
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NCCIH and the National Institutes of Health (NIH) provide tools to help you understand the basics and terminology of scientific research so you can make well-informed decisions about your health. Know the Science features a variety of materials, including interactive modules, quizzes, and videos, as well as links to informative content from Federal resources designed to help consumers make sense of health information.
Explaining How Research Works (NIH)
Know the Science: How To Make Sense of a Scientific Journal Article
Understanding Clinical Studies (NIH)
A service of the National Library of Medicine, PubMed® contains publication information and (in most cases) brief summaries of articles from scientific and medical journals. For guidance from NCCIH on using PubMed, see How To Find Information About Complementary Health Approaches on PubMed .
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For Consumers
5 Things To Know About Probiotics
For Health Care Providers
Risk of Invasive Disease in Preterm Infants Given Probiotics Formulated To Contain Live Bacteria or Yeast
Irritable Bowel Syndrome and Complementary Health Approaches
Probiotics - Systematic Reviews/Reviews/Meta-analyses (PubMed®)
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COMMENTS
There are various types of scientific studies such as experiments and comparative analyses, observational studies, surveys, or interviews. The choice of study type will mainly depend on the research question being asked. When making decisions, patients and doctors need reliable answers to a number of questions. Depending on the medical condition and patient's personal situation, the following ...
Types of study design. Medical research is classified into primary and secondary research. Clinical/experimental studies are performed in primary research, whereas secondary research consolidates available studies as reviews, systematic reviews and meta-analyses. Three main areas in primary research are basic medical research, clinical research ...
The article is based on a selective literature research on study types in medical research, as well as the authors' own experience. ... such as health services research, quality assurance, or clinical epidemiology. Figure 1 gives an overview of the different study types in medical research. Open in a separate window. Figure 1.
Public Health Research. This type of research can be one or a combination of the types of research mentioned above. Public health research tries to improve the health and well-being of people from a population-level perspective.
An observational study is a study in which the investigator cannot control the assignment of treatment to subjects because the participants or conditions are not directly assigned by the researcher.. Examines predetermined treatments, interventions, policies, and their effects; Four main types: case series, case-control studies, cross-sectional studies, and cohort studies
Clinical research is the comprehensive study of the safety and effectiveness of the most promising advances in patient care. Clinical research is different than laboratory research. It involves people who volunteer to help us better understand medicine and health. Lab research generally does not involve people — although it helps us learn ...
The term "health research," sometimes also called "medical research" or "clinical research," refers to research that is done to learn more about human health. Health research also aims to find better ways to prevent and treat disease. Health research is an important way to help improve the care and treatment of people worldwide.
Understanding Clinical Studies. Part of the challenge of explaining clinical research to the public is describing the important points of a study without going into a detailed account of the study's design. There are many different kinds of clinical studies, each with their own strengths and weaknesses, and no real shorthand way to explain them.
An observational study is a study in which the investigator cannot control the assignment of treatment to subjects because the participants or conditions are not being directly assigned by the researcher.. Examines predetermined treatments, interventions, policies, and their effects; Four main types: case-series, case-control, cross-sectional, and cohort studies
Clinical research is the study of health and illness in people. There are two main types of clinical research: observational studies and clinical trials. Read and share this infographic (PDF, 317K) to learn why researchers do different kinds of clinical studies. Observational studies monitor people in normal settings.
These are studies done in laboratories on cells, tissue, or animals. Strengths: Laboratories provide strictly controlled conditions and are often the genesis of scientific ideas that go on to have a broad impact on human health. They can help understand the mechanisms of disease. Weaknesses: Laboratory and animal studies are only a starting point.
on research methods in the health field, but also stimulate the reader ... We hope it will attract researchers to conduct further studies in the health field, be it a clinical trial or field epidemiology or study of health services. ix Health research methodology: A guide for training in research methods
Clinical research, also known as clinical trials, focuses on finding and testing new medicines (drugs), medical devices, or other medical treatments. Examples include: Developing a new test for breast cancer. Comparing two medications (drugs) to treat heart disease to see which one works better. Developing lenses to help people see more clearly ...
Making sense of this new information requires us to understand the different kinds of research. There are three main kinds of health research studies. These are preclinical, experimental, and epidemiologic research. Epidemiologic and preclinical studies often come first, followed by clinical trials involving human participants.
Research study design is a framework, or the set of methods and procedures used to collect and analyze data on variables specified in a particular research problem. Research study designs are of many types, each with its advantages and limitations. The type of study design used to answer a particular research question is determined by the ...
Below are descriptions of some different kinds of clinical research. Treatment Research generally involves an intervention such as medication, psychotherapy, new devices, or new approaches to ...
Observational vs Experimental vs RCT vs CCT. Observational studies. Here researchers observe the effect of a risk factor, diagnostic test or treatment without trying to influence what happens. Such studies are usually "retrospective" — the data are based on events that have already happened. Most workplace health research falls into this ...
The grain study, like many other health studies you read about, ... Now let's move on to experimental research. There are two basic types: randomized controlled trials and quasi-experimental ...
There are four main types of health research. Research that: Evaluates the impact of a treatment or other intervention. Researchers are often interested in finding out about the health effects of something, for example a new drug, health intervention or a change in the environment. To do this, researchers usually carry out trials.
University of Florida College of Public Health and Health Professions faculty conducted $48.5 million in research in fiscal year 2024, part of a record $1.26 billion in research expenditures at the university level, leading to new discoveries that are improving the lives of Floridians and beyond.
Investigate and monitor the impact of toxicological hazards on public health by conducting original research alongside prominent faculty practitioners.The PhD program in environmental health sciences enables you to analyze and solve real environmental health problems using advanced research methods and lab techniques in the biological, physical, and medical sciences.
Background: Evidence from health services research (HSR) is currently thinly spread through many journals, making it difficult for health services researchers, managers and policy-makers to find research on clinical practice guidelines and the appropriateness, process, outcomes, cost and economics of health care services. We undertook to develop and test search terms to retrieve from the ...
Background: Wearable digital health technologies and mobile apps (personal digital health technologies [DHTs]) hold great promise for transforming health research and care. However, engagement in personal DHT research is poor. Objective: The objective of this paper is to describe how participant engagement techniques and different study designs affect participant adherence, retention, and ...
In clinical research, our aim is to design a study which would be able to derive a valid and meaningful scientific conclusion using appropriate statistical methods. The conclusions derived from a research study can either improve health care or result in inadvertent harm to patients. Hence, this requires a well‐designed clinical research ...
Background Despite the increasing recognition of the importance of research in undergraduate dental education, limited studies have explored the nature of undergraduate research activities in dental schools in the Middle East region. This study aimed to evaluate the research experience of final year dental students from three dental schools in the Middle East. Methods A descriptive, cross ...
Non-communicable diseases (NCDs) predispose households to exorbitant healthcare expenditures in health systems where there is no access to effective financial protection for healthcare. This study assessed the economic burden associated with the rising burden of type-2 diabetes (T2D) and hypertension comorbidity management, and its implications for healthcare seeking in urban Accra.
High-risk strains of HPV, or human papillomavirus, may affect men's sperm quality, research finds. The high-risk strains of human papillomavirus that are linked to cancer appear to also pose a ...
Four main types: case-series, case-control, cross-sectional, and cohort studies. In an experimental study, the investigators directly manipulate or assign participants to different interventions or environments. Experimental studies that involve humans are called clinical trials. They fall into two categories: those with controls, and those ...
These studies can also be conducted to inform the state of the art for design, analysis and reporting practices across different types of health research fields, with the aim of improving research practices, and preventing or reducing research waste.
The community of microorganisms that lives on us and in us is called the "microbiome," and it's a hot topic for research. The Human Microbiome Project, supported by the National Institutes of Health (NIH) from 2007 to 2016, played a key role in this research by mapping the normal bacteria that live in and on the healthy human body.