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“Evidence-Based” vs. “Research-Based”: Understanding the Differences

Often, when reviewing resources, programs, or assessments, we might come across terms like “evidence-based” or “research-based.” These terms each tell us something about the resources that they describe and the evidence supporting them. Understanding each term’s meaning can help us make informed decisions when selecting and implementing resources.

So what do these terms mean, exactly?

Typically, the terms  Evidence-Based   Practices  or  Evidence-Based   Programs  refer to individual practices (for example, single lessons or in-class activities) or programs (for example, year-long curricula) that are considered effective based on scientific evidence. To deem a program or practice “evidence-based,” researchers will typically study the impact of the resource(s) in a controlled setting – for example, they may study differences in skill growth between students whose educators used the resources and students whose educators did not. If sufficient research suggests that the program or practice is effective, it may be deemed “evidence-based.”

Evidence-Informed  (or  Research-Based )  Practices  are practices that were developed based on the best research available in the field. This means that users can feel confident that the strategies and activities included in the program or practice have a strong scientific basis for their use. Unlike Evidence-Based Practices or Programs, Research-Based Practices have not been researched in a controlled setting.

What about assessment?

Terms like “evidence-based” and “research-based” are often used to describe  intervention activities,  like strategies or curricula designed to build skills in specific areas. But the process of measuring skills with assessment tools can be evidence-based as well. An assessment process can be considered  Evidence-Based Assessment  if:

• The choice of skills to be measured by the assessment was informed by research;
• The assessment method and measurement tools used are informed by scientific research and theory and meet the relevant standards for their intended uses; and
• The way that the assessment is implemented and interpreted is backed by research.

Using evidence-based assessment to guide or evaluate an intervention gives us confidence that the process is well-suited for our purpose, is grounded in scientific theory, and will be effective for our students.

What Standards Exist for Educational Assessments?

The process of Evidence-Based Assessment involves the use of a measurement tool that “meets the relevant standards for their intended uses.” What are the relevant standards, and how can we know if a tool meets them?

Some foundational standards for educational assessments, as compiled by experts in the educational, psychological, and assessment fields, include:

• Validity for an Intended Use:  the tool should have been researched to determine that it is valid, or appropriate, for the decisions we may make based on its results. Just like we wouldn’t use a math quiz to inform whether a student needs additional practice with reading comprehension, we shouldn’t use an assessment for purposes outside of those that research has deemed “valid.”
• Reliability:  the tool should have been researched to ensure that it meets expectations for reliability, or consistency. For example, researchers might explore whether the tool produces similar results if it is completed twice in a short period of time. Reliability can be explored via a variety of methods, depending on the measurement tool.
• Fairness:  the tool should have been researched to explore how fair, or unbiased, it is among different subgroups of students, such as subgroups based on race, ethnicity, or cultural background. Using a biased measurement tool can lead to biased decision-making and threaten our ability to provide equitable services.

Specific standards within each of these domains, and others, are compiled in the handbook, “Standards for Educational and Psychological Testing” (2014), written by the American Educational Research Association, the American Psychological Association, and the National Council on Measurement in Education. This handbook can be a useful companion when reviewing the specific evidence behind measurement tools.

In Conclusion

Terms like “evidence-based” or “research-based” are useful indicators of the type of evidence behind programs, practices, or assessments – however, they can only tell us so much about the specific research behind each tool. For situations where more information on a resource’s evidence base would be beneficial, it may be helpful to request research summaries or articles from the resource’s publisher for further review.

Further Reading

• Child Welfare Information Gateway. Evidence-Based Practice Definitions and Glossary .
• Hunsley, J., & Mash, E. J. (2007). Evidence-based assessment. Rev. Clin. Psychol., 3, 29-51 .
• Joint Committee on the Standards for Educational and Psychological Testing of the American Educational Research Association, the American Psychological Association, and the National Council on Measurement in Education (2014). Standards for Educational and Psychological Testing. The American Educational Research Association, the American Psychological Association, and the National Council on Measurement in Education .
• S. Department of Education (2016). Using Evidence to Strengthen Education Investments .

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Research-Based

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Research-based learning

In the context of this listing, research-based learning refers to involving learners directly in authentic research projects. This has an impact on a variety of levels. The learner potentially gains significant motivation as a result of their participation in real-life research. This has been seen to be the catalyst for learners to delve deeply into topics.

In addition, through their involvement in actual research, learners become knowledgeable about the nature of research and their role as researchers. Research-based learning techniques are introduced to learners in as many contexts as possible, in order to develop their skills of interpretation, analysis and application.

More detail:

IRIS outlines their vision for involving learners directly into research projects:

The Research Institute for Schools

Our vision: a transformation of the student and teacher experience of science. Being involved in real science inspires young people and is the best professional development for teachers.

Thanks to ever more powerful technology, today's school students can access top level scientific data, collaborate with scientists around the globe, process information at lightning speed and develop innovative experimental ideas. They can put an experiment in space and contribute to scientific discovery. IRIS helps students and their teachers do this.

From our work to-date, we find when sixth form students take part in research, greater numbers go on to study science at university and take up careers in science and engineering.

An excellent example of a research-based learning approach is the UK Institute for Research in Schools (IRIS: http://www.researchinschools.org ):

IRIS makes cutting edge research projects open to school students and their teachers so that they can experience the excitement and challenge of science. We do this by making data accessible to schools, providing teacher training and resources, and by lending out scientific research equipment.

Another example comes from Warwick University in the UK:

https://warwick.ac.uk/services/ldc/resource/rbl/whatis/

In Research-based learning, research is regarded as a theme which underpins teaching at a range of levels. As well as incorporating outcomes of research into curricula, it includes developing students' awareness of processes and methods of enquiry, and creating an inclusive culture of research involving staff and students.

CPOM helps school students become Earth Observation researchers

A new project launched by IRIS is offering students the chance to contribute to scientific understanding of the polar regions. Funded by the UK Space Agency, MELT will allow schools to monitor changes at the poles using Earth Observation data.

Experts… will be helping students to understand the latest satellite Earth Observation data and investigate events such as iceberg calving, where recent dramatic changes suggest that environmental conditions have changed.

Dr Hogg said: “There are really exciting opportunities for students to work with Earth Observation scientists on major changes. We used Sentinel-1 satellite data to watch a giant iceberg four times the size of London broke free from Antarctica’s Larsen-C ice shelf in 2017, and now students can use the same data to measure if new icebergs calve off some of the fastest flowing glaciers in the world!”

Web resources:

Professor Becky Parker Introduces IRIS

IRIS helps increase girls taking engineering degrees by 200%

Teaching Research Method Using a Student-Centred Approach? Critical Reflections on Practice 

Application:

The intention would be to use research-based learning in a variety of ways. The vision will go broader than science. With a belief in the importance of authentic contexts for learning, as much as possible self-directed learners will be encouraged to take up any opportunity for involvement in industry-based research, whatever the relevant area of knowledge.

Less self-directed learners are encouraged to develop data based on research into issues or challenges. The intention is to develop both a mindset of research, but also the skills of research.

Research-based learning is one of many 25 learning methodologies in the Learnlife learning paradigm toolkit . Learn more about the different ways to engage learners through the different learning methodologies .

Research-Based Teacher Education

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British Educational Research Association/BERA. (2014). Research and the teaching profession, final report of the BERA-RSA inquiry into the role of research in teacher education, BERA. Downloaded from: https://www.bera.ac.uk/project/research-and-teacher-education

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Toom, A., Kynäslahti, H., Krokfors, L., Jyrhämä, R., Byman, R., Stenberg, K., et al. (2010). Experiences of a research based approach to teacher education: Suggestions for future policies. European Journal of Education, 45 (2), Part II, 331–Part II, 344.

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Elaine Munthe

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Munthe, E. (2019). Research-Based Teacher Education. In: Peters, M. (eds) Encyclopedia of Teacher Education. Springer, Singapore. https://doi.org/10.1007/978-981-13-1179-6_53-1

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DOI : https://doi.org/10.1007/978-981-13-1179-6_53-1

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Evidence-Based Research Series-Paper 1: What Evidence-Based Research is and why is it important?

Affiliations.

• 1 Johns Hopkins Evidence-based Practice Center, Division of General Internal Medicine, Department of Medicine, Johns Hopkins University, Baltimore, MD, USA.
• 2 Digital Content Services, Operations, Elsevier Ltd., 125 London Wall, London, EC2Y 5AS, UK.
• 3 School of Nursing, McMaster University, Health Sciences Centre, Room 2J20, 1280 Main Street West, Hamilton, Ontario, Canada, L8S 4K1; Section for Evidence-Based Practice, Western Norway University of Applied Sciences, Inndalsveien 28, Bergen, P.O.Box 7030 N-5020 Bergen, Norway.
• 4 Department of Sport Science and Clinical Biomechanics, University of Southern Denmark, Campusvej 55, 5230, Odense M, Denmark; Department of Physiotherapy and Occupational Therapy, University Hospital of Copenhagen, Herlev & Gentofte, Kildegaardsvej 28, 2900, Hellerup, Denmark.
• 5 Musculoskeletal Statistics Unit, the Parker Institute, Bispebjerg and Frederiksberg Hospital, Copenhagen, Nordre Fasanvej 57, 2000, Copenhagen F, Denmark; Department of Clinical Research, Research Unit of Rheumatology, University of Southern Denmark, Odense University Hospital, Denmark.
• 6 Section for Evidence-Based Practice, Western Norway University of Applied Sciences, Inndalsveien 28, Bergen, P.O.Box 7030 N-5020 Bergen, Norway. Electronic address: [email protected].
• PMID: 32979491
• DOI: 10.1016/j.jclinepi.2020.07.020

Objectives: There is considerable actual and potential waste in research. Evidence-based research ensures worthwhile and valuable research. The aim of this series, which this article introduces, is to describe the evidence-based research approach.

Study design and setting: In this first article of a three-article series, we introduce the evidence-based research approach. Evidence-based research is the use of prior research in a systematic and transparent way to inform a new study so that it is answering questions that matter in a valid, efficient, and accessible manner.

Results: We describe evidence-based research and provide an overview of the approach of systematically and transparently using previous research before starting a new study to justify and design the new study (article #2 in series) and-on study completion-place its results in the context with what is already known (article #3 in series).

Conclusion: This series introduces evidence-based research as an approach to minimize unnecessary and irrelevant clinical health research that is unscientific, wasteful, and unethical.

Keywords: Clinical health research; Clinical trials; Evidence synthesis; Evidence-based research; Medical ethics; Research ethics; Systematic review.

Copyright © 2020 Elsevier Inc. All rights reserved.

Publication types

• Research Support, Non-U.S. Gov't
• Biomedical Research* / methods
• Biomedical Research* / organization & administration
• Clinical Trials as Topic / ethics
• Clinical Trials as Topic / methods
• Clinical Trials as Topic / organization & administration
• Ethics, Research
• Evidence-Based Medicine / methods*
• Needs Assessment
• Reproducibility of Results
• Research Design* / standards
• Research Design* / trends
• Systematic Reviews as Topic
• Treatment Outcome

Iowa Reading Research Center

Evidence-based vs. research-based interventions—update.

Have you ever wondered about the terms  evidence-based  and  research-based  interventions? Educators and schools often wrestle with these terms as they consider programs or intervention to implement.

Evidence-Based vs. Research-Based: What is the Difference?

Many publishers tout their programs as being research-based or evidence-based, and oftentimes people use those terms interchangeably.   Have you ever wondered if there was a difference between  evidence-based  and  research-based  and, if so, what that difference might be?

Dr. Sally Shaywitz, Co-Director of the Yale Center for Dyslexia and Creativity, provides a simple explanation in a brief  video clip  about the difference between the two terms and how they relate to reading programs. She discusses how research-based means there are theories behind it, but that they aren’t always proven true. She tells how evidence-based means there is efficacy to back it up.

We can help explain what Dr. Shaywitz meant by “efficacy” in several ways. First, it requires that the program was studied by researchers who were not involved in creating the program. In addition, the researchers cannot stand to profit from the outcomes. Finally, the study the researchers conducted should have the following characteristics:

• The program was compared to another type of program or a different kind of instruction.
• Improvements in students’ reading abilities were measured with valid and reliable instruments.
• There was a thorough description of how the program was implemented so that others could follow those same procedures and include the same elements.
• The effect sizes were reported, and those revealed an improvement that was significantly greater than any improvement in the comparison condition.

Understanding what creates an evidence base is helpful when thinking about intervention programs, particularly for those students with serious reading difficulties. As Dr. Shaywitz commented, “Our children’s reading is too important to be left to theoretical, but unproven, practices and methods. We must replace anecdotal and common, but not evidence-based practices with those that are proven; that is, they are evidence-based.”

Reviews of Intervention Programs

The  Iowa Department of Education  has commissioned a review of the evidence base for literacy interventions commonly used in the state. Studies that examined the effectiveness of these programs were evaluated against rigorous criteria such as those used by the  National Center on Intensive Interventions  (NCII) and the What Works Clearinghouse (WWC). The NCII maintains an  Academic Intervention Tools Chart  to summarize their reviews of individual studies on programs.

The  What Works Clearinghouse  (WWC), part of the Institute of Education Science, is another source for determining which programs and practices have  evidence of effectiveness . The WWC reviews research designed to answer the question, “What works in education?” You can visit the WWC and use the filters to locate reviews specific to reading skills.

• evidence-based practice
• interventions
• research-based practice

6 Spring Break Literacy Activities for Families

Research Basics

• What Is Research?
• Types of Research
• Secondary Research | Literature Review
• Developing Your Topic
• Primary vs. Secondary Sources
• Evaluating Sources
• Responsible Conduct of Research
• Additional Help

A good working definition of research might be:

Research is the deliberate, purposeful, and systematic gathering of data, information, facts, and/or opinions for the advancement of personal, societal, or overall human knowledge.

Based on this definition, we all do research all the time. Most of this research is casual research. Asking friends what they think of different restaurants, looking up reviews of various products online, learning more about celebrities; these are all research.

Formal research includes the type of research most people think of when they hear the term “research”: scientists in white coats working in a fully equipped laboratory. But formal research is a much broader category that just this. Most people will never do laboratory research after graduating from college, but almost everybody will have to do some sort of formal research at some point in their careers.

So What Do We Mean By “Formal Research?”

Casual research is inward facing: it’s done to satisfy our own curiosity or meet our own needs, whether that’s choosing a reliable car or figuring out what to watch on TV. Formal research is outward facing. While it may satisfy our own curiosity, it’s primarily intended to be shared in order to achieve some purpose. That purpose could be anything: finding a cure for cancer, securing funding for a new business, improving some process at your workplace, proving the latest theory in quantum physics, or even just getting a good grade in your Humanities 200 class.

What sets formal research apart from casual research is the documentation of where you gathered your information from. This is done in the form of “citations” and “bibliographies.” Citing sources is covered in the section "Citing Your Sources."

Formal research also follows certain common patterns depending on what the research is trying to show or prove. These are covered in the section “Types of Research.”

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• Last Updated: Dec 21, 2023 3:49 PM
• URL: https://guides.library.iit.edu/research_basics

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Methodology

• What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

A research design is a strategy for answering your   research question  using empirical data. Creating a research design means making decisions about:

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, other interesting articles, frequently asked questions about research design.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types.

• Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
• Descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

• Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalize your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study , your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question .

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

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As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method , you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample—by mail, online, by phone, or in person?

If you’re using a probability sampling method , it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method , how will you avoid research bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organizing and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymize and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarize your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarize your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Research-based Learning

If you have any questions about research-based learning or would like to discuss your course design, feel free to get in touch with us!

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Film: A Short History of Research-Based Learning

Research-based learning at the university of oldenburg.

Research-based learning is one of the highlights of teaching at the University of Oldenburg. Learning objectives inherent in research-based learning serve as a guideline for developing various degree programmes, modules and individual courses. 

What characterises research-based learning?

Research-based teaching and learning is a constructivist-based approach to learning in which students learn through research. Instructors support this process by teaching in a way that does not present topics as closed, but rather opens them up for students to research. Instructors and students shape the learning process together.

Research-based learning...

• encourages a high level of student participation
• ties in students' interest in topics
• enables collaborative learning
• promotes the ability to independently develop problem-solving strategies necessary for both research activities and professional practice
• promotes critical academic thinking through participating in the process of knowledge creation

Research-based learning process

The research-based learning approach is based on the idea that academic education consists of thinking beyond the curriculum and competences set therein, of questioning established knowledge and becoming active in the academic pursuits by developing an inquiring mind. When students learn through researching, they go through the typical stages of an open-ended research process, developing thus their academic and professional competences and expanding their knowledge independently. These stages include:

Developing a research question

Literature-based contextualisation within current research

Choosing a well-grounded theoretical approach and a research method

Conducting a methodological investigation or observation of a phenomenon

Evaluating and interpreting research data

Formulating and presenting results and critically reflecting on them

Teaching and Learning in Higher Education team offers consultations and inspiration for your teaching

The workshops offered by the Teaching and Learning in Higher Education team provide the opportunity to plan and discuss your course design. Participants can also get inspiration and tips on how to structure research-based learning in discussion with other instructors and the team of Teaching and Learning in Higher Education.

Instructors can also arrange consultations with our team at any time if they have questions or would like to discuss their course design and receive suggestions. Consultations are also available in English.

If you have any questions about research-based learning, feel free to get in touch with us! In English or in German!

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What Does ‘Evidence-Based’ Mean? A Study Finds Wide Variation.

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What makes an education intervention evidence-based?

Over the last 20 years, that question has moved front and center as more federal and state agencies require programs to show evidence of effectiveness, and more education leaders look for proof that interventions used in other districts will help their own students.

But a new analysis in the Review of Educational Research finds wide variation on the kind of evidence that is required to show an education intervention is effective. In fact, large research clearinghouses, set up to review the evidence bases of programs for practitioners, reach the same conclusion on less than a third of the education programs they review.

What that means is that a teacher or principal trying to choose a reading curriculum or tutoring program for students may find it recommended by one clearinghouse and rejected by another.

Researchers from George Washington and Northwestern universities analyzed the evidence standards for 10 common research clearinghouses, including the federal What Works Clearinghouse and the National Dropout Prevention Center in New York, part of the nonprofit Successful Practices Network. The majority of these groups are supported by public agencies or nonprofit foundations.

The team, led by postdoctoral researchers Mansi Wadhwa and Jingwen Zheng of George Washington, compared evidence reviews for nearly 1,360 pre-K-20 education programs and interventions whose evidence base had been reviewed by at least one of the 10 clearinghouses.

The study found 83 percent of the education programs reviewed had only ever been rated by one clearinghouse. Of the programs with multiple ratings, fewer than 1 in 3 had consistent ratings across clearinghouses.

The clearinghouses were more likely to agree about what didn’t work; more than 80 percent of programs with at least two similar reviews were deemed ineffective by both. Less than 18 percent of programs had at least two “effective” ratings, and many had mixed reviews.

For example, five different clearinghouses reviewed the evidence for Peer-Assisted Learning Strategies, a peer-tutoring program focused on math and reading. One clearinghouse recommended the program as a whole and another found it promising.

Two others reviewed the program separately for each subject; one recommended the math program, while the other didn’t find the program promising in either math or reading.

And the final clearinghouse reviewed the PALs program effectiveness on a variety of different outcomes, finding evidence to recommend it for some purposes and not for others.

One reason for the disagreement is that standards differ from clearinghouse to clearinghouse, on what kinds of outcomes can be used to judge program effectiveness, how large a sample of students must be studied, and for how long.

They also differ on whether studies must use randomized controlled experiments, in which students are randomly assigned to a study or control condition, or other designs. Randomized studies are generally considered the most rigorous, but they are difficult and expensive to conduct in educational settings.

“Because [research clearinghouses] do not agree on such criteria for acceptable evidence, and because they are important enough to lead to different judgments about program effectiveness, ‘evidence-based’ seems to be an idea [with limited use] despite [clearinghouses] being funded precisely to identify which programs are most evidence based,” they conclude.

Tough to build consensus

The nonprofit Successful Practices Network, one of the clearinghouses in the study which reviews research on issues like dropout recovery and career and technical education, doesn’t try to align how it defines evidence quality with other groups, according to Bill Dagget, the network’s founder.

“If you’re trying to define ‘evidence-based,’ it’s very difficult to incorporate any of the skills that are harder to measure,” like critical thinking, collaboration, or social-emotional development, Dagget said.

“When you begin to look at these broader skills, you can’t evaluate those with a written test. Typically you have got to do some type of rubric,” Dagget said. “The problem with that is any time you use a rubric, I don’t care how carefully you train, the people using them are always somewhat subjective.”

In a prior study , Jean Stockard, an emerita professor at the University of Oregon, found that half of the What Works Clearinghouse’s intervention reports were based on a single study. Stockard, who was not part of the new study in the journal Review of Educational Research, found that out of more than 120 different studies of one broadly used literacy program, Reading Mastery, those that included evidence beyond randomized controlled studies had more consistent and precise reviews.

The effects of education interventions often fade over time, and the researchers said there’s little agreement on how long and how much follow-up should be done on evaluations. Clearinghouses most often required researchers to follow up a year after an intervention is used, but some allowed shorter follow-ups.

While national research groups have begun to advocate for more researchers to verify a program’s effectiveness, “education research isn’t in limitless supply,” said Julie Brosnan of the National Student Support Accelerator at Stanford university, which collects and conducts research related to tutoring programs.

“For instance, to use tutoring as an example, it is neither feasible nor cost-effective to have every tutoring program engage in a randomized controlled trial to test effectiveness given that there is such a strong evidence base,” Brosnan said. “Education leaders need to ensure the program characteristics align with those that have evidence behind them, while also monitoring implementation and collecting ongoing data.”

Stockard and Brosnan agreed that to build an evidence base for a given program, studies need to include more details about how and for whom the program was used, as well as more analyses of multiple studies to tease out individual aspects of an intervention that may work for different groups of students.

“If the evidence isn’t the right evidence, then the study isn’t of much value,” Dagget said. “So the essential question is, what’s our purpose? Is our purpose to prepare kids for the next grade and the next test and the next level of education? Or is it to prepare them for the world beyond school?”

A version of this article appeared in the January 31, 2024 edition of Education Week as What Does ‘Evidence-Based’ Mean? A Study Finds Wide Variation

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Research-Based Curriculum

Review information about research-based curricula. A research-based early childhood curriculum is in line with current studies and best practices on how children develop and learn. It focuses on domain-specific, developmentally appropriate content and skills. A research-based curriculum is also content-rich. It offers a sequence of learning experiences based on children’s developmental progress. Education staff may use this resource to select and implement a research-based curriculum.

Download the PDF

Head Start Program Performance Standards 45 CFR §1302.32(a)(1) and §1302.35(d)(1): "Center-based and family child care programs must implement developmentally appropriate research-based early childhood curricula" that "are based on scientifically valid research." Home-based programs must "implement a developmentally appropriate research-based early childhood home-based curriculum."

What does "research-based" mean for early childhood curriculum?

• Is founded on solid research about child development and learning
• Promotes teaching and learning activities that are shown to have positive effects on child progress and outcomes
• Has descriptive research or evaluation reflecting child progress, but is lacking evidence from randomized control study

A research-based early childhood curriculum is consistent with research on how children develop and learn. It provides rich content and teaching practices that are shown to support children's learning and development. A research-based curriculum focuses on domain-specific, developmentally appropriate content and skills that contribute to children's later development in that domain. A research-based curriculum is also content-rich , meaning that it provides broad and varied experiences and activities that promote children's learning and development. A rich curriculum invites children to think deeply about content that interests them and builds on their prior knowledge and experiences. Finally, a research-based curriculum offers a sequence of learning experiences based on children's developmental progressions. There are both comprehensive research-based curricula that address all areas of the Head Start Early Learning Outcomes Framework (ELOF) and domain-specific curricula that the Head Start Program Performance Standards refer to as curricular enhancements, also sometimes referred to as curriculum supplements.

Why is a research-based curriculum important?

A research-based curriculum promotes domain-specific teaching practices that are effective in supporting positive child outcomes. A research-based curriculum must be appropriate for the ages, developmental levels, and cultural and linguistic backgrounds of the children enrolled in the program.

What does a research-based curriculum look like?

Read the following vignette to learn about how Sunny Days Early Head Start's research-based curriculum supports infants' and toddlers' social and emotional development.

Sunny Days Early Head Start uses a research-based infant and toddler curriculum. For social and emotional development, the curriculum focuses on developmentally appropriate goals to support the development of infants' and toddlers' relationships with adults and peers, emotional functioning, and sense of identity. The curriculum reflects relevant child development theories, such as attachment theory. For example, the curriculum describes how learning happens in the context of warm, responsive relationships. When adults respond warmly and appropriately to infants' and toddlers' cues, they develop trusting and secure relationships with adults. Children use trusted adults as a secure base from which to explore the environment.

Sunny Days Early Head Start's curriculum also describes children's developmental progressions, such as developing secure relationships with familiar adults. As part of this progression, young infants rely on the efforts of familiar adults to help them cope with stressful moments. Older toddlers are able to seek out familiar adults for comfort as needed. The curriculum then provides specific research-based practices to help infants and toddlers develop secure relationships with familiar adults. It recommends consistent routines, interactions, and communication with parents to learn about children's preferences and routines. The curriculum provides examples of how to interact positively and warmly with infants and toddlers (e.g., peek-a-boo), observe and respond to individual cues, and convey warmth and affection.

What do you learn about a research-based curriculum from this vignette?

• Sunny Days Early Head Start's curriculum focuses on developmentally appropriate, important goals in social and emotional development. The curriculum aligns with the sub-domains of the Early Learning Outcomes Framework (ELOF) in this area.
• The curriculum is guided by robust knowledge and theory of social and emotional development (e.g., attachment theory).
• The curriculum describes children's developmental progressions and offers concrete, research-based practices to help education staff build trusting relationships with infants and toddlers.

Resources to Support Your Work

Interactive Head Start Early Learning Outcomes Framework: Ages Birth to Five Select a domain and scroll to the bottom for a list of "Related Resources" to learn more about children's learning and development described in the ELOF and how to support it.

Planned Language Approach (PLA) The PLA is a comprehensive, systemic, research-based way for Head Start and Early Head Start programs to ensure optimal language and literacy services for children who speak English and for those who are dual language learners.

Head Start Early Learning Outcomes Framework (ELOF) Effective Practice Guides This set of resources provides research-based teaching practices in each of the ELOF domains and sub-domains.

« Go to Early Childhood Curriculum Resources

Resource Type: Publication

National Centers: Early Childhood Development, Teaching and Learning

Last Updated: July 25, 2023

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Psychological and Physical Approaches for Sleep Disorders: What the Science Says

Clinical Guidelines, Scientific Literature, Info for Patients:  Psychological and Physical Approaches for Sleep Disorders

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Current clinical practice guidelines from the  American Academy of Sleep Medicine (2021) recommend psychological and behavioral interventions in the treatment of chronic insomnia disorder in adults. 

• The American Academy of Sleep Medicine guidelines state: “We recommend that clinicians use multicomponent cognitive behavioral therapy for insomnia (CBT-I) for the treatment of chronic insomnia disorder in adults (strong recommendation). We suggest that clinicians use relaxation therapy as a single-component therapy for the treatment of chronic insomnia disorder in adults (conditional recommendation).” The authors of the guidelines also noted that there were fewer than three studies meeting their inclusion criteria for the use of cognitive therapy, paradoxical intention, mindfulness, biofeedback, and intensive sleep retraining; as a result, no recommendations were made about these treatments.

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• Clinical practice guidelines  issued by the American Academy of Sleep Medicine in 2021 recommend psychological and behavioral interventions for the treatment of chronic insomnia disorder in adults. The guidelines state: “We recommend that clinicians use multicomponent cognitive behavioral therapy for insomnia (CBT-I) for the treatment of chronic insomnia disorder in adults (strong recommendation).”
• A 2018 analysis of pooled data from 4 randomized controlled trials of 546 peri- and postmenopausal women with insomnia and bothersome vasomotor symptoms found that CBT-I produced the greatest reduction in Insomnia Severity Index (ISI) from baseline compared to an education control. 
• A  2014 randomized controlled trial  examined the comparative efficacy of cognitive behavioral therapy, tai chi, and a sleep seminar education control in 123 older adults with chronic and primary insomnia. The study found that cognitive behavioral therapy performed better than tai chi and sleep seminar education in remission of clinical insomnia. The cognitive behavioral therapy group also showed greater improvement in sleep quality, sleep parameters, fatigue, and depressive symptoms than the tai chi and sleep seminar education groups.

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• CBT-I is considered safe.

.header_greentext{color:green!important;font-size:24px!important;font-weight:500!important;}.header_bluetext{color:blue!important;font-size:18px!important;font-weight:500!important;}.header_redtext{color:red!important;font-size:28px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;font-size:28px!important;font-weight:500!important;}.header_purpletext{color:purple!important;font-size:31px!important;font-weight:500!important;}.header_yellowtext{color:yellow!important;font-size:20px!important;font-weight:500!important;}.header_blacktext{color:black!important;font-size:22px!important;font-weight:500!important;}.header_whitetext{color:white!important;font-size:22px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;}.Green_Header{color:green!important;font-size:24px!important;font-weight:500!important;}.Blue_Header{color:blue!important;font-size:18px!important;font-weight:500!important;}.Red_Header{color:red!important;font-size:28px!important;font-weight:500!important;}.Purple_Header{color:purple!important;font-size:31px!important;font-weight:500!important;}.Yellow_Header{color:yellow!important;font-size:20px!important;font-weight:500!important;}.Black_Header{color:black!important;font-size:22px!important;font-weight:500!important;}.White_Header{color:white!important;font-size:22px!important;font-weight:500!important;} Relaxation Techniques

There is a small amount of low-quality evidence that relaxation techniques by themselves can help with chronic insomnia.  Relaxation techniques may be recommended in certain situations, depending on individual preferences, health provider qualifications, and treatment availability. 

Current clinical practice guidelines from the American Academy of Sleep Medicine (2021) conditionally recommend relaxation therapy as a single-component therapy for the treatment of chronic insomnia disorder in adults. 

• Clinical guidelines from the American Academy of Sleep Medicine (2021) made a conditional recommendation to use relaxation therapy as a single-component therapy based on “a small body of low-quality evidence from five studies showing clinically meaningful improvements in one critical outcome, consideration that some patients prefer relaxation therapy, the fact that mental health providers are trained to deliver this form of treatment, and the potential for relaxation therapy to require only limited resources.”
• A 2018 systematic review looked at 27 studies of psychological interventions to try to improve sleep. The studies involved 2,776 college students who ranged from healthy sleepers to those with a diagnosed sleep disorder. About 22 percent of the studies investigated “relaxation, mindfulness, hypnotherapy” treatments. This review recommended cognitive behavioral therapy to improve sleep in college students. The review also found that relaxation approaches helped somewhat with sleep quality and sleep problems but especially with mental health. The authors recommended that “relaxation, mindfulness, hypnotherapy” treatments be combined with cognitive behavioral therapy as a way to enhance mental health benefits.
• Relaxation therapies for insomnia are considered safe.

.header_greentext{color:green!important;font-size:24px!important;font-weight:500!important;}.header_bluetext{color:blue!important;font-size:18px!important;font-weight:500!important;}.header_redtext{color:red!important;font-size:28px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;font-size:28px!important;font-weight:500!important;}.header_purpletext{color:purple!important;font-size:31px!important;font-weight:500!important;}.header_yellowtext{color:yellow!important;font-size:20px!important;font-weight:500!important;}.header_blacktext{color:black!important;font-size:22px!important;font-weight:500!important;}.header_whitetext{color:white!important;font-size:22px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;}.Green_Header{color:green!important;font-size:24px!important;font-weight:500!important;}.Blue_Header{color:blue!important;font-size:18px!important;font-weight:500!important;}.Red_Header{color:red!important;font-size:28px!important;font-weight:500!important;}.Purple_Header{color:purple!important;font-size:31px!important;font-weight:500!important;}.Yellow_Header{color:yellow!important;font-size:20px!important;font-weight:500!important;}.Black_Header{color:black!important;font-size:22px!important;font-weight:500!important;}.White_Header{color:white!important;font-size:22px!important;font-weight:500!important;} Yoga

Yoga has been shown to be helpful for sleep in several studies of cancer patients, women with sleep problems, and older adults and in individual studies of other population groups, including people with arthritis and women with menopause symptoms. However, a 2019 clinical practice guideline from the U.S. Department of Veterans Affairs and U.S. Department of Defense said there was insufficient evidence to recommend for or against yoga for treating insomnia.

• A  2020 systematic review and meta-analysis of 19 studies involving a total of 1,832 participants found positive effects of yoga in 16 randomized controlled trials, compared with the control group, in improving sleep quality among women using Pittsburgh Sleep Quality Index (PSQI); however, 2 studies revealed no effects of yoga compared to the control group in reducing insomnia among women using ISI. Seven studies revealed no evidence for effects of yoga compared with the control group in improving sleep quality for women with breast cancer using PSQI, while four studies revealed no evidence for the effects of yoga compared with the control group in improving the sleep quality for peri/postmenopausal women using PSQI.
• A  2020 secondary analysis of a randomized controlled trial involving 320 adults with chronic low-back pain and poor sleep quality prior to the intervention found modest but statistically significant improvements in sleep quality in the yoga (12 weekly yoga classes) and physical therapy groups.
• A  2019 systematic review of 11 studies that evaluated the use of yoga to manage stress and burnout in health care workers concluded that yoga is effective in improving physical problems and quality of sleep, as well as reducing stress levels and burnout. However, the authors of the review noted that it would be necessary to broaden the subject further and acquire more robust scientific evidence by designing and implementing research studies equipped with a solid methodological structure on bigger sample groups.
• A  2013 multicenter, randomized controlled trial evaluated the effect of yoga on sleep quality in 410 cancer survivors suffering from moderate or greater sleep disruption between 2 and 24 months after surgery, chemotherapy, and/or radiation therapy. The study found that compared with standard care, yoga participants demonstrated greater improvements in global sleep quality and subjective sleep quality, daytime dysfunction, wake after sleep onset, sleep efficiency, and medication use at postintervention.
• A  2022 randomized controlled trial  investigated the effects of yoga (duration of 20 weeks) on menopausal symptoms and sleep quality across menopause statuses in 208 women. Based on participant responses to questionnaires, the study found that yoga decreased menopausal symptoms, with the strongest effects noted in postmenopausal women, followed by perimenopausal women. In addition, yoga significantly improved sleep quality in postmenopausal and perimenopausal women after controlling for social support, depression, anxiety, stress, and menopausal symptoms; however, yoga did not affect sleep quality in premenopausal women.
• Yoga is generally considered a safe form of physical activity for healthy people when performed properly, under the guidance of a qualified instructor. However, as with other forms of physical activity, injuries can occur. 
• The most common injuries are sprains and strains, and the parts of the body most commonly injured are the knee or lower leg. Serious injuries are rare. The risk of injury associated with yoga is lower than that for higher impact physical activities.
• Hot yoga has special risks related to overheating and dehydration.
• Pregnant women, older adults, and people with health conditions should talk with their health care providers and the yoga instructor about their individual needs. They may need to avoid or modify some yoga poses and practices. 

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Results of several studies, using objective and subjective measures, have shown that tai chi may be helpful for people with sleep problems. However, a 2019 clinical practice guideline from the U.S. Department of Veterans Affairs and U.S. Department of Defense said there was insufficient evidence to recommend for or against using tai chi to treat insomnia.

• A 2020 systematic review and meta-analysis of 20 randomized controlled studies from 5 countries involving a total of 1,703 patients found that compared with nontherapeutic and other active treatments, tai chi has a positive effect on improving sleep quality. An in-depth analysis showed that 24-form and 8-form Yang-style tai chi had significant positive effects on sleep quality, as assessed by the Pittsburgh Sleep Quality Index (PSQI).
• A 2021 randomized controlled trial assigned 320 participants 60 years or older and with chronic insomnia to three groups: 12-week tai chi training, 12-week conventional exercise, and no intervention control. The study found that compared with the control group, the exercise and tai chi groups showed improved sleep efficiency, reductions of wake time after sleep onset, and reduced awakenings as measured by actigraphy. However, there were no significant differences between the exercise and tai chi groups.
• Tai chi appears to be safe. A 2019 meta-analysis of 24 studies (1,794 participants) found that the frequency of adverse events was similar for people doing tai chi, another active intervention, or no intervention. 

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A 2019 clinical practice guideline from the U.S. Department of Veterans Affairs and U.S. Department of Defense said there was not enough evidence to know whether mindfulness meditation is helpful for people with insomnia, and a 2021 clinical practice guideline from the American Academy of Sleep Medicine said there was not enough evidence to make recommendations on using mindfulness by itself for insomnia. 

• A 2022 review of 20 studies and 2,890 participants found that mindfulness-based stress reduction might be ineffective for improving sleep quality in people with insomnia, but the authors noted that the studies were small and showed bias.
• A   2019 systematic review and meta-analysis of 18 studies (1,654 total participants) found that mindfulness meditation practices improved sleep quality more than education-based treatments. However, the effects of mindfulness meditation approaches on sleep quality were no different than those of evidence-based treatments such as cognitive behavioral therapy and exercise.
• Results from a  2015 randomized controlled trial  involving 60 adults aged 75 years and over with chronic insomnia suggest that the mindfulness-based stress reduction program could be a useful treatment for chronic insomnia for this age group. 
• Meditation and mindfulness practices usually are considered to have few risks. 

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A 2022 review of 13 studies with 1,007 adult participants found that listening to music may lead to improved reports of sleep quality among people with insomnia. However, there was not enough good-quality evidence to determine the effect of listening to music on the severity of insomnia or the number of times a person wakes up. 

• A  2022 review of 13 studies with 1,007 adult participants found that listening to music may lead to improved reports of sleep quality among people with insomnia. However, there was not enough good-quality evidence to determine the effect of listening to music on the severity of insomnia or the number of times a person wakes up. The results showed that listening to music may slightly improve sleep-onset latency, sleep duration, sleep efficiency, and daytime effects.
• In general, research studies of music-based interventions do not show any negative effects. However, listening to music at too high a volume can contribute to noise-induced hearing loss. 
• Because music can be associated with strong memories or emotional reactions, some people may be distressed by exposure to specific pieces or types of music.

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A 2019 clinical practice guideline from the U.S. Department of Veterans Affairs and U.S. Department of Defense said there was not enough evidence to recommend for or against using acupuncture for insomnia, except for a weak recommendation for auricular acupuncture, which involves specific points on the outer ear. Results from some studies suggest that auricular acupuncture may help improve insomnia; however, many of the studies conducted on acupuncture for sleep disorders are small and are of low quality.

• A  2021 review of 11 studies and 775 participants suggested that acupuncture may help improve insomnia, but the studies were small, differed from each other in many ways (e.g., treatment dosage, acupoint selection), and judged to be low quality. 
• A  2019 clinical practice guideline from the U.S. Department of Veterans Affairs and U.S. Department of Defense said there was not enough evidence to recommend for or against using acupuncture for insomnia, except for a weak recommendation for auricular acupuncture, which involves specific points on the outer ear. 
• A  2020 evaluation of 7 systematic reviews (10,001 participants) on auricular acupuncture for insomnia found that the reviews suggested auricular acupuncture may be beneficial, but the quality of most of the reviews was low or critically low and the quality of the studies within the reviews was poor.
• Relatively few complications from using acupuncture have been reported.  However, complications have resulted from use of nonsterile needles and improper delivery of treatments.  When not delivered properly, acupuncture can cause serious adverse effects, including infections, punctured organs, and injury to the central nervous system. 

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• Cocchiara RA, Peruzzo M, Mannocci A, et al.  The use of yoga to manage stress and burnout in healthcare workers: a systematic review .  Journal of Clinical Medicine . 2019;8(3):284.
• Cui H, Wang Q, Pedersen M, et al.  The safety of tai chi: a meta-analysis of adverse events in randomized controlled trials .  Contemporary Clinical Trials . 2019;82:85-92. 
• Edinger JD, Arnedt JT, Bertisch SM, et al.  Behavioral and psychological treatments for chronic insomnia disorder in adults: an American Academy of Sleep Medicine clinical practice guideline .  Journal of Clinical Sleep Medicine . 2021;17(2):255-262.
• Friedrich A, Schlarb AA.  Let’s talk about sleep: a systematic review of psychological interventions to improve sleep in college students .  Journal of Sleep Research . 2018;27(1):4-22. 
• Guthrie KA, Larson JC, Ensrud KE, et al.  Effects of pharmacologic and nonpharmacologic interventions on insomnia symptoms and self-reported sleep quality in women with hot flashes: a pooled analysis of individual participant data from four MsFLASH trials .  Sleep . 2018;41(1):zsx190. 
• Huang J, Shen M, Qin X, et al.  Effectiveness of auricular acupuncture for insomnia: an overview of systematic reviews .  Evidence-Based Complementary and Alternative Medicine.  2020;2020:6920902.
• Irwin MR, Olmstead R, Carrillo C, et al.  Cognitive behavioral therapy vs. tai chi for late life insomnia and inflammatory risk: a randomized controlled comparative efficacy trial .  Sleep . 2014;37(9):1543-1552. 
• Jespersen KV, Pando-Naude V, Koenig J, et al.  Listening to music for insomnia in adults .  Cochrane Database of Systematic Reviews . 2022;8(8):CD010459.
• Li H, Chen J, Xu G, et al.  The effect of tai chi for improving sleep quality: a systematic review and meta-analysis .  Journal of Affective Disorders . 2020;274:1102-1112. 
• Mustian KM, Sprod LK, Janelsins M, et al.  Multicenter, randomized controlled trial of yoga for sleep quality among cancer survivors .  Journal of Clinical Oncology . 2013;31(26):3233-3241.
• Mysliwiec V, Martin JL, Ulmer CS, et al.  The management of chronic insomnia disorder and obstructive sleep apnea: synopsis of the 2019 U.S. Department of Veterans Affairs and U.S. Department of Defense Clinical Practice Guidelines .  Annals of Internal Medicine . 2020;172(5):325-336.
• Roseen EJ, Gerlovin H, Femia A, et al.  Yoga, physical therapy, and back pain education for sleep quality in low-income racially diverse adults with chronic low back pain: a secondary analysis of a randomized controlled trial .  Journal of General Internal Medicine . 2020;35(1):167-176. 
• Rusch HL, Rosario M, Levison LM, et al.  The effect of mindfulness meditation on sleep quality: a systematic review and meta-analysis of randomized controlled trials .  Annals of the New York Academy of Sciences.  2019;1445(1):5-16. 
• Siu PM, Yu AP, Tam BT, et al.  Effects of tai chi or exercise on sleep in older adults with insomnia: a randomized clinical trial .  JAMA Network Open . 2021;4(2):e2037199. 
• Susanti HD, Sonko I, Chang P-C, et al.  Effects of yoga on menopausal symptoms and sleep quality across menopause statuses: a randomized controlled trial .  Nursing and Health Sciences . 2022;24(2):368-379. 
• Wang W-L, Chen K-H, Pan Y-C, et al.  The effect of yoga on sleep quality and insomnia in women with sleep problems: a systematic review and meta-analysis .  BMC Psychiatry . 2020;20(1):195. 
• Zhang J-X, Liu X-H, Xie X-H, et al.  Mindfulness-based stress reduction for chronic insomnia in adults older than 75 years: a randomized, controlled, single-blind clinical trial .  Explore (NY).  2015;11(3):180-185. 
• Zhao F-Y, Fu Q-Q, Kennedy GA, et al.  Can acupuncture improve objective sleep indices in patients with primary insomnia? A systematic review and meta-analysis .  Sleep Medicine . 2021;80:244-259.

NCCIH Clinical Digest is a service of the National Center for Complementary and Integrative Health, NIH, DHHS. NCCIH Clinical Digest, a monthly e-newsletter, offers evidence-based information on complementary health approaches, including scientific literature searches, summaries of NCCIH-funded research, fact sheets for patients, and more.

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• heart health

Why Heart Disease Research Still Favors Men

Published in partnership with The Fuller Project , a nonprofit newsroom dedicated to the coverage of women’s issues around the world.

Katherine Fitzgerald had just arrived at the party. Before she could even get a drink, she threw up and broke out in a sweat. “I was dizzy. I couldn’t breathe. I had heart pain,” Fitzgerald says.

She knew she was having a heart attack.

What she didn’t know then was that the heart attack could have been prevented. Fitzgerald, a health-conscious, exercise-loving lawyer, should have been taking statin drugs to stop the buildup of plaque in her arteries that caused the heart attack and two others that followed.

Fitzgerald’s case illustrates a dangerous gap in medical care between men and women. While they are equally likely to suffer heart attacks, women are more likely to die from theirs. It’s one of the many symptoms of the medical system’s neglect of women .

Life-saving statins, like so many other medications, have been developed based on clinical trials that primarily recruited men. As a result, many women like Fitzgerald don’t receive prescriptions for the drugs that could help them the most, says Dr. Laxmi Mehta, director of Preventative Cardiology and Women’s Cardiovascular Health at The Ohio State University.

“There were a lot of trials. But women weren’t included as much,” says Mehta, who serves on the American Heart Association’s Research Goes Red Science Advisory Group. When women need treatment for heart conditions, she says, “we are assuming we are providing the best care based on data from men.”

Read More : What It Means if You Have Borderline High Cholesterol—And What to Do About It

More than 30 years ago, Congress directed the National Institutes of Health to include as many women as men in clinical trials. But while some progress has been made, equity remains elusive. And that’s dangerous for women. “Since 2000, women in the United States have reported total adverse events from approved medicines 52% more frequently than men, and serious or fatal events 36% more frequently,” research firm McKinsey & Company said in a report released in January .

Now, the Biden administration is taking a run at it.

Last year, the administration established a White House Initiative on Women’s Health Research and, in February, it announced it would be dedicating $100 million to the newly formed Advanced Research Projects Agency for Health (ARPA-H) to spearhead efforts to increase early stage research focusing on women.

“For far too long, scientific and biomedical research excluded women and undervalued the study of women’s health. The resulting research gaps mean that we know far too little about women’s health across women’s lifespans, and those gaps are even more prominent for women of color, older women, and women with disabilities,” Biden said in an executive order signed in March.

Heart disease should be a bright spot in this black hole of medical research. It was the recognition in the 1980s that heart disease was killing women at similar rates to men that kickstarted passage of the 1993 law requiring equity in clinical trials. The American Heart Association has spent decades funding research and leading awareness campaigns about women’s risks.

But gaps persist, says Dr. Martha Gulati, president of the American Society for Preventive Cardiology and a cardiologist at Cedars-Sinai Hospital in Los Angeles. “We don’t get represented in trials,” Gulati told a seminar sponsored by the Society for Women’s Health Research in February.

Read More : Why Are So Many Young People Getting Cancer?

One example: Dr. Safi Khan of West Virginia University and colleagues reviewed 60 trials of cholesterol-lowering drugs conducted between 1990 and 2018. Not even a third of the people enrolled—28.5%—were women, they reported in JAMA Network Open in 2020. The trials’ findings likely did not accurately represent the public as a whole, they say.

“Medical research is several steps behind on women and heart disease, and that is a major contributor to ongoing ignorance about the problem on the part of both the public and a range of medical professionals,” says Dr. Harmony Reynolds, a cardiologist at NYU Langone Health. “Everywhere along the way, there is different treatment for women, and there is some bias there.”

Statins have been widely described as wonder drugs , lowering the risk of major heart events such as heart attack or stroke by about 25% . Women are less likely than men to be offered these drugs . And when they do take them, women are more likely to stop using them because of perceived side effects. But no major study digs into the actual rate of side effects among females, or what might lie behind such differences.

Further studies might uncover additional benefits, says Dr. JoAnn Manson, professor of medicine at Harvard Medical School and Brigham and Women’s Hospital. There are hints that statins might lower a woman’s risk of dying from cancer , including ovarian cancer.

Failure of recognition

Fitzgerald was 60, had higher-than-optimal blood pressure, unhealthy cholesterol levels, and a family history of heart disease, says Reynolds, Fitzgerald’s new cardiologist. “Katherine had multiple risk factors. Many of my patients are told their blood pressure and cholesterol are ‘borderline’ when really they should be treated,” she says.

Doctors often blame women for failing to recognize their own heart disease symptoms, but the evidence shows medical professionals miss them, too. 

The symptoms of heart attacks in men are widely known: crushing chest pain, a telling sensation in the left arm, or sudden collapse. Women, on the other hand, often feel nausea, jaw pain, or lightheadedness,

Fitzgerald did recognize her symptoms. At the party where she suffered her first heart attack, she begged for an ambulance. But other guests, including a physician friend, said they didn’t think she needed medical attention.

When paramedics finally arrived, they, too, dismissed her fears and diagnosed a panic attack. They sent her home. “If I had been a man, there is no way the paramedic wouldn’t have taken me to the hospital and I wouldn’t be in the mess I am now,” Fitzgerald says.

Fitzgerald waited two days to visit an emergency room. By then, some of her heart muscle had died. She received two stents to hold open clogged arteries, but suffered two more heart attacks in the following months. She now stays out of the courtroom and sticks to less-stressful desk work.

“I take care of all these young women with heart attacks and I hear so many stories about people saying they were ignored,” says Reynolds.

Waiting for attention

The problem is not just anecdotal. Reynolds and colleagues studied the problem by looking at more than 29 million emergency room visits by people under 55 reporting chest pain. 

“In that study we show young women coming in with chest pains and they are waiting longer to be seen,” Reynolds says. “The women are waiting too long and women of color were waiting even longer. So we know there is some subtle bias there.”

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Doctors can use risk calculators to try to forecast a patient’s future likelihood of heart disease and treat accordingly. But Dr. Stephanie Faubion, medical director of the Menopause Society , says they do not work well for women.

“That is because we are still using those that were developed and made for men,” says Faubion, who is also director of the Mayo Clinic Center for Women’s Health in Jacksonville, Florida.

Women have many specific heart risks. They have smaller coronary arteries , thinner heart walls, and suffer more heart damage from diabetes. Pregnancy can raise risks in various ways. Autoimmune diseases such as rheumatoid arthritis also add heart disease risks, and women are far more likely than men to have these conditions. 

Women who start menstruation early, or who reach menopause early, have higher heart disease rates. Birth control pills can raise the risk for blood clots, strokes, and heart attacks.

Perhaps the most recent instance of women being left out of heart disease research can be seen in the trials of highly popular diabetes drugs such as semaglutide, sold under the brand names Ozempic and Wegovy .

The drugs cause dramatic weight loss, which made researchers wonder if they might lower heart disease rates, too. They do, according to several studies , and the U.S. Food and Drug Administration now approves their use to prevent heart disease.

But none of the weight-loss trials, published in prestigious medical journals such as the New England Journal of Medicine and the Journal of the American Medical Association , break out separate data on men and women. And while the weight-loss studies did include far more women than men, many of the follow-on heart disease trials did not.

“They report the sex. They report ‘we have this many men, this many women,’” says Faubion. “They didn’t disaggregate the data on sex so they don’t know if it works better, the same, or worse in women than it did in men.”

Dr. Robert Kushner, a professor of medicine at Northwestern University who led some of the weight-loss studies, says he was surprised at the discrepancy between the enrollment of women in the obesity trials of semaglutide—in which about three-quarters of volunteers were women—and in the heart disease trials, in which women represented fewer than 28% of participants.

He says researchers recruited people already being treated for heart disease. “Predominantly, the ones who are getting care and being seen around the world were men,” Kushner says.

Kushner says he has yet to analyze results in his trial of semaglutide and weight loss by sex.

Missing out on breakthroughs

Harvard Medical School’s Manson has been sounding the alarm on discrepancies in medical research for decades.

“Raising more questions is what leads to the major breakthroughs,” she says.

Yet she has been mostly ignored, even though she helped lead the largest-ever study looking specifically at women’s health—the Women’s Health Initiative, which involved more than 160,000 women over 15 years.

The study was initially designed to see if hormone therapy in women past menopause could reduce their rising rates of heart disease and breast cancer. It also later looked for evidence of effects on bone strength, other cancers, dementia and quality of life.

The first results were startling. The hormone therapy used in the trial raised the risk of breast cancer and failed to reduce heart disease.

Read More : Menopause Is Finally Going Mainstream

“Many clinicians stopped prescribing hormone therapy altogether. Many women tossed their pills and patches,” Manson says. When the trial started, an estimated 40% of menopausal women used hormone therapy. Now, Manson estimates, only about 4% do.

The study has since been shown to have been flawed. The average age of the women in the study was 63—well past menopause. And the hormone therapy used was a high-dose hormone distilled from horse estrogens.

Later studies have indicated that lower doses and different formulations such as patches, given to women as they start menopause, may be much less harmful while reducing hot flashes, sleep loss and other symptoms. “These formulations don’t go to the liver and should be safer,” Manson says. There’s also tantalizing evidence they may lower the risk of heart disease.

Meanwhile, the lack of data means that many women who would benefit from hormone therapy are not getting it, says Faubion. 

Back in 1993, it took the considerable efforts of Dr. Bernadine Healy, the first female director of the NIH, to persuade Congress to directly fund medical research on women and heart disease.

“They are just not going to do that again. It’s too expensive,” says Faubion.

Biden asked Congress for $12 billion to improve research planning and to set up a network of research centers to focus on women’s health. And NIH has encouraged requests for money to study women in particular.

But when Congress passed a last-minute spending bill in March, it kept health funding flat . The Republican-led House did not address Biden's request or allocate any cash for additional research into women's health.

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

Published on 5.4.2024 in Vol 26 (2024)

Assessing the Clinical Efficacy of a Virtual Reality Tool for the Treatment of Obesity: Randomized Controlled Trial

Authors of this article:

Original Paper

• Dimitra Anastasiadou 1, 2 , PhD   ; 
• Pol Herrero 2 , MSc   ; 
• Paula Garcia-Royo 2 , MSc   ; 
• Julia Vázquez-De Sebastián 2, 3 , MSc   ; 
• Mel Slater 4, 5, 6 , DSC   ; 
• Bernhard Spanlang 4 , PhD   ; 
• Elena Álvarez de la Campa 4 , PhD   ; 
• Andreea Ciudin 7, 8, 9 , MD, PhD   ; 
• Marta Comas 7, 8, 9 , PhD   ; 
• Josep Antoni Ramos-Quiroga 2, 10, 11, 12 , MD, PhD   ; 
• Pilar Lusilla-Palacios 2, 10, 11, 12 , MD, PhD  

1 Department of Clinical and Health Psychology, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain

2 Psychiatry, Mental Health and Addictions Research Group, Vall d´Hebron Research Institute, Barcelona, Spain

3 RE-FiT Barcelona Research Group, Vall d’Hebron Research Institute & Parc Sanitari Pere Virgili, Barcelona, Spain

4 Virtual Bodyworks S.L., Barcelona, Spain

5 The Institute of Neurosciences, Universitat de Barcelona, Barcelona, Spain

6 Department of Clinical Psychology and Psychobiology, Universitat de Barcelona, Barcelona, Spain

7 Endocrinology and Nutrition Department, Vall d’Hebron University Hospital, Barcelona, Spain

8 Vall d’Hebron Research Institute, Universitat Autònoma de Barcelona, Barcelona, Spain

9 Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, Madrid, Spain

10 Psychiatry Department, Vall d’Hebron University Hospital, Barcelona, Spain

11 Department of Psychiatry and Legal Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain

12 Biomedical Network Research Centre on Mental Health (CIBERSAM), Barcelona, Spain

Corresponding Author:

Dimitra Anastasiadou, PhD

Department of Clinical and Health Psychology, Universitat Autònoma de Barcelona

Cerdanyola del Vallès, Barcelona

Bellaterra, Barcelona, 08193

Phone: 34 935813854

Email: [email protected]

Background: Virtual reality (VR) interventions, based on cognitive behavioral therapy principles, have been proven effective as complementary tools in managing obesity and have been associated with promoting healthy behaviors and addressing body image concerns. However, they have not fully addressed certain underlying causes of obesity, such as a lack of motivation to change, low self-efficacy, and the impact of weight stigma interiorization, which often impede treatment adherence and long-term lifestyle habit changes. To tackle these concerns, this study introduces the VR self-counseling paradigm, which incorporates embodiment and body-swapping techniques, along with motivational strategies, to help people living with obesity effectively address some of the root causes of their condition.

Objective: This study aims to assess the clinical efficacy of ConVRself (Virtual Reality self-talk), a VR platform that allows participants to engage in motivational self-conversations.

Methods: A randomized controlled trial was conducted with 68 participants from the bariatric surgery waiting list from the obesity unit of the Vall d’Hebron University Hospital in Barcelona, Spain. Participants were assigned to 1 of 3 groups: a control group (CG), which only received treatment as usual from the obesity unit; experimental group 1 (EG1), which, after intensive motivational interviewing training, engaged in 4 sessions of VR-based self-conversations with ConVRself, and underwent embodiment and body-swapping techniques; and experimental group 2 (EG2), which engaged in 4 VR-based sessions led by a virtual counselor with a prerecorded discourse, and only underwent the embodiment technique. In the case of both EG1 and EG2, the VR interventions were assisted by a clinical researcher. Readiness to change habits, eating habits, and psychological variables, as well as adherence and satisfaction with ConVRself were measured at baseline, after the intervention, 1 week after the intervention, and 4 weeks after the intervention.

Results: Regarding the primary outcomes, EG1 (24/68, 35%) and EG2 (22/68, 32%) showed significant improvements in confidence to lose weight compared to the CG (22/68, 32%) at all assessment points (β=−.16; P =.02). Similarly, EG1 demonstrated a significant increase after the intervention in readiness to exercise more compared to the CG (β=−.17; P =.03). Regarding the secondary outcomes, EG1 participants showed a significant reduction in uncontrolled eating (β=.71; P =.01) and emotional eating (β=.29; P =.03) compared to the CG participants, as well as in their anxiety levels compared to EG2 and CG participants (β=.65; P =.01). In addition, participants from the experimental groups reported high adherence and satisfaction with the VR platform (EG1: mean 59.82, SD 4.00; EG2: mean 58.43, SD 5.22; d =0.30, 95% CI −0.30 to 0.89).

Conclusions: This study revealed that using VR self-conversations, based on motivational interviewing principles, may have benefits in helping people with obesity to enhance their readiness to change habits and self-efficacy, as well as reduce dysfunctional eating behaviors and anxiety.

Trial Registration: ClinicalTrials.gov NCT05094557; https://www.clinicaltrials.gov/study/NCT05094557

Introduction

Obesity is defined as a severe, chronic, complex, and multifactorial disease with detrimental effects on the individual’s physical and psychological health [ 1 ]. Various treatment options are currently available for obesity, including psychological interventions, behavioral interventions for lifestyle modification, pharmacotherapy, and bariatric surgery (BS) [ 2 , 3 ]. All these treatments are taken into consideration in a process of shared decision-making to generate a patient-centered plan [ 4 ].

According to the National Institute for Health and Care Excellence [ 5 ], BS is a treatment option available for people living with obesity with BMI >40 kg/m 2 (or BMI between 35 kg/m 2 and 40 kg/m 2 if relevant comorbidity is present that could be improved with weight loss). People living with obesity who undergo BS require rigorous and comprehensive preoperative and postoperative monitoring and support. This support should emphasize the adoption and maintenance of a healthy lifestyle, as well as the identification of any clinical or psychological barriers that may hinder adherence to postoperative treatment [ 5 ]. However, when assessing the long-term maintenance of weight loss in obesity management, including through BS procedures, achieving lasting and sustainable changes in body composition remains highly challenging [ 6 ]. Therefore, for more effective obesity management, it is beneficial to incorporate multicomponent psychological interventions that aim to improve health as a whole, foster self-efficacy and self-esteem, and prioritize sustainable goals chosen by the individual [ 2 ].

In this context, motivational interviewing (MI) has gained considerable recognition as an effective approach to enhance treatment adherence [ 7 ] and has been included in the most recent psychological and behavioral recommendations for obesity management [ 2 ]. Specifically, MI is a counseling technique based on a person-centered approach that aims to help individuals to identify the discrepancies between their goals and current circumstances and empower them to explore new alternatives toward behavior change, thereby enhancing motivation and resolving ambivalence [ 8 ].

Furthermore, in recent years, telemental health care has emerged as a rapid and efficient means of establishing different communication channels between patients and mental health professionals and has been proven capable of transforming the availability, accessibility, and efficacy of psychological treatments [ 9 , 10 ], particularly during the COVID-19 pandemic and beyond [ 11 ]. Recent research findings provide support for the acceptability, feasibility, and preliminary effectiveness of using new technologies in the treatment of eating and weight disorders [ 12 - 14 ]. Specifically, the integration of virtual reality (VR) into psychological interventions for these conditions holds great promise in addressing some of the factors associated with the development or maintenance of the disorders. Through the provision of multisensory experiences, extrinsic feedback, and opportunities for embodiment, psychological VR interventions, which are rooted primarily in cognitive behavioral therapy principles, have demonstrated effectiveness as complementary tools in managing obesity and have been associated with promoting behavior changes and weight reduction, reducing binge eating episodes, and addressing body image concerns [ 12 , 14 - 18 ]. While these interventions focus on correcting specific behaviors and provide patients with a safe context to practice eating, emotional, and relational management, they do not adequately address some of the root causes of obesity, such as a lack of motivation to change, low self-efficacy, and the impact of weight stigma interiorization, which are factors that often hinder treatment adherence and long-term maintenance of lifestyle habits [ 19 , 20 ].

The SOCRATES project (Self Conversation in Virtual Reality Embodiment to Enhance Healthier Lifestyles Among People with Obesity), funded by the European Commission (951930), has introduced the VR self-counseling paradigm, referred to as ConVRself (Virtual Reality self-talk), which uses embodiment and body-swapping techniques. On the one hand, embodiment enables participants to experience the perceptual illusion that the virtual body is their own [ 21 , 22 ]. On the other hand, body swapping allows embodying alternatingly between 2 virtual bodies (or avatars , eg, one representing the self and the other a counselor) and maintaining a conversation between these 2 embodied perspectives [ 23 - 25 ]. In this study, ConVRself is used as a solution to help people living with obesity explore some of the root causes of their condition. Drawing on MI principles, this approach allows participants to engage in self-counseling through motivational conversations with themselves. In particular, participants are immersed in a VR environment that resembles a counselor’s office. First, they embody an avatar that looks like themselves (a look-alike ), and from this perspective, they explain their problem, goals, and/or aspirations to a counselor, seated in front of them across a table. Once they have stated their problem, participants switch to embody the counselor’s avatar. In this new perspective, they listen to a playback of their own words and watch the related body gestures made during their speech. After having listened, they can respond from the counselor’s embodied perspective. In this way, participants engage in a self-conversation by adopting 2 different embodied perspectives and maintaining a conversation between them. To ensure that these conversations remain motivational, participants undergo intensive training in MI before engaging in the VR self-counseling experience. The objective of these virtual self-conversations has been to address the following challenges that people living with obesity face: (1) to raise awareness of their actual condition, (2) to better understand and address the impact of weight bias interiorization, and (3) to increase their self-efficacy by setting realistic goals in line with their values.

In this randomized controlled trial (RCT) based on the protocol described in the study by Anastasiadou et al [ 26 ], our primary objective was to evaluate the clinical efficacy of the ConVRself platform under 3 different conditions: one that uses the embodiment and body-swapping techniques together with the MI training, one only using the embodiment technique, and a group receiving treatment as usual (TAU).

We hypothesized that participants who used ConVRself with embodiment and body-swapping techniques, along with the MI training, would show greater improvement in the primary outcomes (motivation to lose weight and exercise more) as well as the secondary outcomes (lifestyle habits and psychological well-being) from baseline (T0) to the 3 postintervention assessments compared to participants who used ConVRself with only the embodiment element or the TAU group.

Ethical Considerations

This study was approved by the Clinical Research Ethics Committee and Research Projects Committee of the Vall d’Hebron University Hospital (VHUH). The study protocol was preregistered at ClinicalTrials.gov (NCT05094557) and published at BMJ Open [ 26 ]. Before enrollment in the study, written informed consent was obtained from all participants. To maintain confidentiality, each participant was assigned a numerical code. No compensation was provided for participating in the study.

Recruitment

Participants from the BS waiting list from the obesity unit of the VHUH within the national health system were assessed for eligibility between December 2021 and April 2023. To be eligible for inclusion, participants had to meet the following criteria: aged between 18 and 65 years; BMI ≥30 kg/m 2 ; receiving ambulatory treatment at the VHUH; not undergoing any other concurrent treatment specifically related to their obesity condition from other centers; possessing minimal digital skills, which means being able to use a digital device (smartphone, tablet, or computer) to make telephone calls and have video conversations via the internet, send or receive emails, and search for information about products and (health) services; demonstrable oral and written understanding of the Spanish language; and willingness to provide informed consent to participate. As stated in the usability study conducted by Anastasiadou et al [ 27 ], the BMI criterion was revised to include only a minimum threshold of ≥30 kg/m 2 for eligibility to participate in the study, contrary to the BMI criteria originally set in the study protocol (BMI between ≥30 kg/m 2 and ≤55 kg/m 2 ) [ 26 ]. Participants were not eligible if they met ≥1 of the following exclusion criteria: presence of an eating disorder in the last 2 years, nonstabilized severe mental disorder that could interfere with the successful implementation of the research protocol (ie, psychosis, depression with suicidal risk, alcohol or drug abuse, and psychotic or manic symptoms), intellectual disability or any major illness seriously affecting cognitive performance (ie, neurological disorders), and personal history of epilepsy (to avoid the potential risk of triggering seizures in this population). Of the 94 participants assessed for eligibility, 68 (72%) were recruited to participate in the study ( Figure 1 ). Details regarding the sample size calculation are available in the protocol (32).

We conducted an RCT with 3 parallel groups (experimental group 1 [EG1], experimental group 2 [EG2], and a control group [CG]) with a 1:1:1 allocation ratio. Measurements were carried out at 4 time points: T0, after the intervention (T1), 1 week after the intervention (T2), and 4 weeks after the intervention (T3).

Participant data were managed and automatically distributed using REDCap (Research Electronic Data Capture; Vanderbilt University) [ 28 , 29 ], hosted at the Vall d’Hebron Research Institute in Barcelona, Spain. The data collected at T0, T1, T2, and T3 were obtained through web-based self-report instruments completed by participants using REDCap. In addition, the research team members measured participants’ weight and height to calculate their BMI.

Potential eligible participants for the project were referred to the research team by health care professionals from the VHUH obesity unit. These participants received TAU provided by the obesity unit, which included regular medical, nutritional, and psychiatric follow-ups conducted by specialists at the hospital. Eligible participants were contacted via telephone by a research team member who provided information about the study. If they agreed to participate in the study, an appointment was scheduled at the hospital. During this appointment, a clinical interview was conducted by the team member to confirm the participants’ eligibility. Sociodemographic and clinical variables were also collected, and weight and height measurements were taken. Once the necessary data were collected, participants were randomly distributed in 1 of the 3 groups (EG1, EG2, and CG) using REDCap and were then informed about the outcome of the randomization. Next, they were asked to complete the web-based T0 assessment within the following week, which was managed by the automatic email distribution facilitated by REDCap. In addition, participants were asked to watch a 30-minute video with psychoeducational advice created by the research team. This video provided information about obesity and the promotion of healthy lifestyle habits. It was shared to ensure that all participants had a similar background concerning the concept of obesity and some knowledge about healthy lifestyle habits.

After the completion of the T0 assessment, participants received their assigned interventions based on their respective groups for a period of 10 weeks. The timeline of the assessments is depicted in Multimedia Appendix 1 . The T1 assessment took place during week 6, T2 assessment during week 7, and T3 assessment during week 10 using the same automatic email distribution as with the T0 assessment. Furthermore, REDCap implemented an automatic alert system to remind participants to complete the questionnaires if they had not done so at the scheduled time. Finally, participants who underwent BS during the course of the 4 exposures (for details, refer to the Experimental Groups subsection) were still requested to respond to the T1 assessment, even if they did not complete the full intervention.

Interventions

Cg participants.

After completing the T0 assessment, CG participants did not receive any intervention besides the TAU provided by the VHUH obesity unit and the psychoeducational video.

Experimental Groups

Interventions overview.

After completing the T0 assessment, EG1 participants underwent a 1-day MI training at the hospital facilities. The training lasted 4 hours and focused on developing basic MI skills. In addition, participants had an individual coaching session via telephone 1 week after the training. The initial in-person training session was led by an expert in MI (author PLP), while the follow-up sessions were carried out by the other members of the research team. At the end of the initial session, 3 photographs (2 from the front and 1 from the side) were taken of each participant to create their look-alike avatars, while their counselor’s avatar was designed according to each participant’s preferences concerning sex, age, and body shape. EG2 participants did not receive any MI training. Instead, they were invited to the hospital facilities where a research team member took photographs for the creation of their avatars.

Two weeks later, EG1 and EG2 participants engaged in 4 VR scenarios at the hospital facilities, assisted by a team clinical researcher. Scenarios were distributed in weekly sessions, each lasting 30 minutes, over a period of 4 weeks. After each exposure, satisfaction and adherence to the VR experiments were assessed for both groups using a semistructured interview designed by the research team. In addition, several self-report questionnaires were administered to the participants, including the readiness ruler (RR), the Suitability Evaluation Questionnaire (SEQ), and the Body Ownership Questionnaire (BOQ). For more detailed information, refer to the Measures subsection.

Specifically, the exposures of the 2 groups are detailed in the following subsections.

EG1 Participants

In each of the 4 scenarios, participants had a self-conversation using the embodiment and body-swapping techniques. Specifically, for exposures 1, 2, and 4, participants alternated between their look-alike avatar and the avatar of a counselor. During exposure 3, they alternated between their own avatar and an avatar representing their future self—a representation depicting their future self after adopting a healthier lifestyle 5 years from the present. When embodying the counselor and the future-self avatars, participants applied the MI techniques they had learned during the intensive training.

Exposure 1: Embodied Discussion About Problems and Solutions

The purpose of this scenario was to facilitate motivational self-conversations between the participant and their counselor about the lifestyle changes they planned to achieve in terms of eating healthier and being more physically active.

Exposure 2: Overcoming Self-Stigmatization

The objective of this intervention was to explore and address the participants’ subjective weight stigma experiences and their interiorization through motivational self-conversations between the participant and their counselor.

Exposure 3: Illustrating the Possibility of Autonomy

The objective of this intervention was to explore, through motivational self-conversations with participants’ future selves, how these future selves successfully achieve the goals that participants set in the present and to identify any barriers encountered during the process.

Exposure 4: Summing Up

Participants started their self-conversations by sharing the insights they gained from the previous exposures. In addition, they reflected on how these insights could be effectively implemented in their daily lives.

EG2 Participants

EG2 participants received a traditional counseling approach in a virtual setting. In all 4 exposures, participants were only embodied in their own look-alike avatars. First, participants engaged in a prerecorded discourse, conducted by a virtual counselor, that posed open-ended questions to which the participants responded. Second, the virtual counselor provided general and prerecorded advice that could be beneficial for the participants in promoting a healthier lifestyle.

For this exposure, the virtual counselor asked about the perceived barriers that participants faced when trying to adopt a healthier lifestyle and then provided practical recommendations to help overcome these barriers and facilitate the adoption of a healthier lifestyle. Examples of these recommendations are as follows:

Thank you for sharing your goals with me. Next, I’m going to give you some simple tips that can help improve your lifestyle and overall well-being. First of all, try to avoid miracle diets as none of them work in the medium and long term...Second, make healthy choices regarding your diet. Certain foods should be prioritized, others limited, and some replaced with healthier alternatives.

In this exposure, the participants shared their subjective experiences of body size discrimination, and the virtual counselor offered practical advice about how to deal with them. Some examples of the advice given are as follows:

Despite the enticing advertisements encouraging you to believe that image is everything, never forget that your appearance is just one aspect of who you are. Try to develop your sense of identity based on all the things you can do and the person you are deep within, despite inhabiting a larger body...Secondly, appreciate and take care of your body—a body that, when healthy, can accomplish many things.

In this exposure, the conversation focused on discussing the potential positive effects that people living with obesity may experience when they adopt healthier behaviors that prioritize their overall health, rather than solely focusing on weight. An example of such a conversation is as follows:

Given that many factors influence your health status, some of which are beyond your control, one important step you can take to promote good health while living in a larger body is to adopt healthy eating and exercise habits, along with activities that foster social support, without solely focusing on weight loss.

Participants engaged in a conversation during which the virtual counselor provided a general summary of the main concepts explained in the previous exposures.

Technical Features of the VR System

The VR system used in the study consisted of both hardware and software components. The VR hardware used was the Meta Quest 2, which is a stand-alone headset developed by Reality Labs (Meta Platforms, Inc). The main part of the hardware consists of a head-mounted display worn by the participants. This head-mounted display has a vision- and inertia-based inside-out tracking system that allows precise tracking of the user’s head movements. In addition, the Meta Quest 2 comes equipped with hand controllers that enable interaction within the virtual environment. Finally, the Meta Quest 2 also incorporates a built-in processor that generates stereoscopic images and spatialized audio. The headset runs the ConVRself application, which was developed using the Unity 3D development environment (Unity Technologies).

ConVRself Software

The VR software ConVRself, developed by Virtual Bodyworks SL, displays 3D scenarios and virtual human representations. This VR application enables participants to have self-conversations by embodying their look-alike avatar and another avatar alternatingly. The software generates scenarios that have 3 stages: calibration, tutorial, and experience. In the first stage, calibration, participants wore a VR headset and held VR controllers. The system used this setup to calculate an internal human representation that synchronized the movements of the embodied avatar with the participants’ own movements. During the next 2 stages, tutorial and experience, participants were immersed in the virtual environment, embodying their look-alike avatar from a first-person perspective. To enhance the sense of embodiment, they could see themselves reflected in a virtual mirror on their left. To watch a video showing how ConVRself works, please refer to the supplementary materials in the study by Anastasiadou et al [ 26 ]. Specifically in the tutorial stage, participants had to follow detailed audio instructions provided by the application to get used to the platform before the actual experience. In addition, during this stage, participants underwent the embodiment technique to foster the illusion that the virtual body represented their own. Finally, in the experience stage, participants engaged in different exposures.

To minimize the risks associated with COVID-19, the research team followed a safety protocol that included wearing masks, carrying out regular hand disinfection, and using the CX1 decontamination system (Cleanbox Technology) to clean the Meta Quest 2 headsets and controllers used in the study.

Primary Outcomes

The primary outcomes (motivation to lose weight and exercise more) were assessed using 2 measurement tools: the RR [ 8 ] and the Stages of Change Questionnaire for Weight Management (S-Weight) and Processes of Change Questionnaire for Weight Management (P-Weight) [ 30 ]. The RR is a visual analog scale ranging from 1 to 10 that assessed participants’ readiness for, confidence about, and perception of the importance of changing behavior with regard to 2 specific areas: (1) achieving a healthy weight and (2) exercising more. The S-Weight questionnaire consists of 5 mutually exclusive items that aim to allocate participants to 1 of the 5 stages of change in weight management according to the transtheoretical model: precontemplation, contemplation, preparation, action, and maintenance. The P-Weight questionnaire is a 5-point Likert scale (ranging from strongly disagree to strongly agree ) consisting of 34 items developed to assess 4 processes of change for weight management: (1) emotional re-evaluation (13 items), (2) weight management actions (7 items), (3) environmental restructuring (5 items), and (4) weight consequences evaluation (9 items). The scores of each subscale were summed to obtain a total score and were then transformed on a new scale ranging from 0 to 100. The Spanish version of P-Weight showed an adequate internal consistency (Cronbach α coefficients ranged from 0.78 to 0.96 in both individuals with normal weight and individuals with overweight and obesity) [ 30 ]. The Cronbach α values in this study for the P-Weight subscales were 0.74 for emotional re-evaluation, 0.74 for weight management actions, 0.76 for environmental restructuring, and 0.76 for weight consequences evaluation.

Secondary Outcomes

Eating habits.

The Three-Factor Eating Questionnaire–Revised 18 items (TFEQ-R18) [ 31 ] is a self-report questionnaire designed to measure 3 aspects of eating behavior: (1) cognitive restraint (CR; 6 items), (2) uncontrolled eating (UE; 9 items), and (3) emotional eating (EE; 3 items). Participants responded to each item on a 4-point Likert scale ranging from definitely true to definitely false . The total scores of each subscale were obtained by summing the scores of individual items. The Spanish version of the TFEQ-R18 [ 32 ] showed good internal consistency (Cronbach α coefficients ranged from 0.75 to 0.87) in a sample of young and healthy adults. The Cronbach α values in this study for each subscale were 0.59 for CR, 0.87 for UE, and 0.78 for EE.

The Eating Habits Questionnaire [ 33 ] is a self-report questionnaire with 37 items, each rated using a 5-point Likert scale ranging from never to always . This questionnaire measures eating habits across 8 different spheres: (1) sugar intake (4 items), (2) healthy eating (9 items), (3) physical activity (3 items), (4) diet caloric intake (5 items), (5) psychological well-being (3 items), (6) types of aliments (5 items), (7) knowledge and control (5 items), and (8) alcohol intake (2 items). A total score was obtained as the average of the scores from the 8 spheres. The Cronbach α coefficient for the complete questionnaire was 0.87 and ranged from 0.58 to 0.94 for the different spheres in a Spanish sample of adult participants living with overweight and obesity [ 33 ]. In this study, the Cronbach α value was 0.88.

Psychological Variables

Psychological functioning was estimated with the Hospital Anxiety and Depression Scale (HADS) [ 34 ]. The HADS is a self-report 14-item questionnaire (7 items for anxiety and 7 items for depression). Participants rated each item on a 4-point Likert scale to indicate the presence and severity of anxiety and depression symptoms. The total scores of each factor were obtained by summing the scores of individual items. The Spanish version showed high internal consistency, with a Cronbach α value of 0.86 for the 2 factors in a sample of patients and healthy controls [ 35 ]. The Cronbach α values in this study were 0.77 for the depression subscale and 0.79 for the anxiety subscale.

Body satisfaction was measured using the 10-item validated Spanish version of the Body Shape Questionnaire [ 36 ]. This self-report scale is rated using a 6-point Likert scale ranging from never to always . A total score was obtained by summing the scores of individual items. In this study, the Cronbach α value for this questionnaire was 0.89.

The Modified Weight Bias Internalization Scale (WBIS-M) [ 37 ] was used to assess weight bias interiorization. The WBIS-M is a self-report 11-item unidimensional scale rated using a 7-point scale ranging from strongly disagree to strongly agree . A total score was obtained as the sum of the scores of individual items. The Cronbach α coefficient for the complete questionnaire ranged from 0.93 to 0.94 in a sample of Spanish adults [ 37 ]. The Cronbach α value for this questionnaire in this study was 0.86.

The Cognitive Reserve Questionnaire [ 38 ] was used to measure participants’ cognitive reserve. This self-report questionnaire consists of 8 items that evaluate aspects generally related to cognitive reserve, such as educational status (own and parental), occupational status, completion of training courses, musical training, and language proficiency. The total score was obtained by summing the item scores. The Cronbach α value for this questionnaire in this study was 0.63.

Adherence and Satisfaction Regarding VR Experiments

To measure satisfaction, acceptance, and security regarding the use of the ConVRself platform, we used the SEQ [ 39 ]. The SEQ is a 14-item questionnaire, with 13 items rated on a 5-point Likert scale ranging from not at all to very much , as well as a last open-ended question where participants can provide suggestions and additional feedback. For the specific purposes of this study, the word “rehabilitation” in item 11 was replaced by “obesity treatment.” The total score was obtained by summing the scores of the first 13 items. Validation studies of the SEQ showed an acceptable internal consistency, with a Cronbach α value of 0.70 in samples of individuals with different physical pathologies. The Cronbach α value of the SEQ in this study was 0.61.

The BOQ evaluates the subjective illusion of body ownership in a VR context through a 7-point Likert scale ranging from strongly disagree to strongly agree . Specifically, the 4 questions of this scale were obtained from a previous study evaluating ConVRself [ 25 ]. The questionnaire assesses body ownership when (1) looking down at the virtual body, (2) observing oneself in a virtual mirror, (3) perceiving body movements, and (4) recognizing oneself. These questions were asked for the participants’ own avatar as well as the counselor’s avatar (except for the question regarding the self-recognition item, which was only asked for the participants’ own avatar). For EG2 participants, the questions were asked exclusively for their own avatar because they did not experience the body-swapping technique. For more information about particular items, please refer to the protocol published in the study by Anastasiadou et al [ 26 ].

Along with the questionnaires, a brief interview was conducted after each exposure to assess participants’ satisfaction with the VR experience and acceptability of ConVRself.

Statistical Analysis

Initial analyses involved comparisons of EG1, EG2, and CG participants on sociodemographic and clinical characteristics, adherence (dropout analysis), and assessment variables at the T0 level. Subsequent assessments examined those participants who completed the long-term follow-up assessment and those who did not on the same sociodemographic and clinical characteristics and outcome variables at T0. Depending on variable types or objective, various statistical tests were used: the Shapiro-Wilk test for assessing the normality of the distribution, a 1-way ANOVA with group as a factor for normally distributed variables, the Friedman test for variables with non-normal distributions, and the chi-square test for qualitative variables.

To handle missing data within questionnaires, passive multiple imputation was used. This approach updates total scores based on recent imputed values at the item level, thereby ensuring complete data for analysis [ 40 , 41 ].

Analyses for our primary and secondary outcomes were tested with 2-level hierarchical linear models (HLMs). These models were implemented with group (EG1, EG2, and CG) and time (T0, T1, T2, and T3 for the RR; T0, T1, and T3 for other variables) as fixed factors and participants nested within time as a random factor. For model adjustment, we used restricted maximum likelihood as the estimation method and the scaled identity as the error covariance structure. Potential moderators (such as age, sex, BMI at T0, time of treatment at the VHUH obesity unit, the presence of physical comorbidities, current mental illness, and Cognitive Reserve Questionnaire scores at T0) were examined. All covariables were grand mean centered.

An intent-to-treat (ITT) analysis was conducted using the available data of all participants for outcome estimation. This analysis, leveraging the ability of HLMs to integrate missing data, offers a more realistic, unbiased analysis compared to traditional methods [ 42 ]. To ensure the robustness of the results obtained from the primary and secondary ITT analyses, we also conducted a per-protocol analysis. This analysis included only participants who completed the RCT. Results from the per-protocol analysis were reported if they differed from the results of the aforementioned ITT analyses.

Effect sizes were calculated and reported using R 2 marginal (variance explained by the fixed effects) and R 2 conditional (variance explained by the entire model, both fixed and random effects) [ 43 , 44 ] for HLMs and Cohen d for comparison between the experimental groups in VR technique. Their magnitude was interpreted according to the Cohen guidelines where R 2 =0.01 or d ≤0.2 represents a small effect, R 2 =0.06 or d =0.5 represents a medium effect, and R 2 ≥0.14 or d ≥0.8 represents a large effect [ 45 ].

Analyses were conducted using SPSS (version 29.0; IBM Corp) and RStudio (version 2022.12.0; Posit Software, PBC), with the mice package from RStudio [ 46 ] for passive multiple imputation. A 2-tailed significance level of .05 was applied to all statistical tests.

Sample Description

As shown in Figure 1 , of the initial 94 participants assessed for the study, 19 (20%) were excluded before the randomization, resulting in 75 (80%) participants being enrolled and randomized. However, after receiving the T0 assessment, of the 75 participants, 7 (9%) decided not to continue participating; therefore, 68 (91%) participants completed the T0 assessment and were allocated to 1 of the 3 groups: EG1 (n=24, 35%), EG2 (n=22, 32%), and CG (n=22, 32%).

In EG1, of the 24 participants, 22 (92%) received the allocated intervention, of whom 16 (73%) completed the 4 exposures. At T1, 77% (17/22) responded; and at T2 and T3, 73% (16/22) responded. In EG2, of the 22 participants, 21 (95%) received the allocated intervention, of whom 13 (62%) completed the 4 exposures. At T1, 67% (14/21) responded; at T2, 57% (12/21); and at T3, 62% (13/21). Regarding the CG, of the 22 participants, at T1, 18 (82%) responded; at T2, 16 (73%); and at T3, 16 (73%). The adherence analysis did not reveal statistical differences among the groups ( χ 2 6 = 4.6; P =.60).

The sociodemographic and clinical characteristics of the sample are presented in Multimedia Appendix 2 . Participants had a mean age of 44.22 (SD 10.30) years, a mean BMI of 43.58 (SD 5.96) kg/m 2 , and had been receiving treatment at the VHUH obesity unit for an average of 21.62 (SD 11.18) months. Most of the participants were female individuals (54/68, 79%), Spanish citizens (51/68, 75%), employed either part time or full time (39/68, 57%), and lived with their family (50/68, 74%). Regarding clinical data, most of the participants had physical comorbidities (58/68, 85%), with pain and cardiovascular problems being the most prevalent, and no current mental illness (53/68, 78%). When comparing all groups on sociodemographic variables, significant differences were found regarding sex ( χ 2 2 =8.4; P =.02), the presence of current mental illness ( χ 2 2 =8.4; P =.02), and physical comorbidities ( χ 2 2 =6.0; P =.05).

Furthermore, there were no differences among the groups in any assessment variable at the T0 level ( P >.05). Descriptive results of the primary and secondary outcomes of all participants, and separately for each group, are presented in Multimedia Appendices 3 and 4 .

When comparing participants who completed the long-term follow-up assessments and those who did not, significant results were found regarding the following variables: (1) age (t 65 =−2.68; P =.009), (2) TFEQ-R18 CR (t 66 =−2.74; P =.008), (3) Eating Habits Questionnaire total score (t 64 =−3.23; P =.002), and (4) S-Weight ( χ 2 3 =16.3; P =.001). More precisely, participants with a higher mean age (46.4, SD 10.3) were more likely to complete the follow-up assessments than younger participants (mean age 39.7, SD 8.8). In addition, participants who completed all assessment points had significantly higher means in the TFEQ-R18 CR subscale and Eating Habits Questionnaire total score, and a higher percentage of them were at the maintenance stage (S-Weight), compared to those who did not complete all assessments.

The results of the analysis examining the effects of group condition and time are shown in Table 1 . The HLMs revealed significant results for some RR scales, while no effects were observed for group versus time and time for P-Weight and S-Weight.

a All hierarchical linear models were estimated with time and group as fixed effects; participant nested time as random effects; and age, sex, BMI at baseline, time of treatment at the obesity unit of the hospital, the presence of physical comorbidities, current mental illness, and Cognitive Reserve Questionnaire scores at baseline as covariables.

b Numbers presented are estimated values.

c R 2 marginal refers to the amount of variance explained by the fixed effects.

d R 2 conditional refers to the amount of variance explained by the entire model, both fixed and random effects.

e RR: readiness ruler.

f P-Weight: Processes of Change Questionnaire for Weight Management.

g S-Weight: Stages of Change Questionnaire for Weight Management.

h TFEQ-R18: Three-Factor Eating Questionnaire–Revised 18 items.

i HADS: Hospital Anxiety and Depression Scale.

j BSQ-10: Body Shape Questionnaire, 10-item version.

k WBIS-M: Modified Weight Bias Internalization Scale.

RR Analysis

First, regarding confidence to lose weight , the HLM revealed a significant group versus time effect (β=−.16; P =.02). Post hoc comparisons revealed that both EG1 and EG2 showed significant differences compared to the CG at T0 versus T1, T0 versus T2, and T0 versus T3. This notable increase in confidence to lose weight for both groups can be seen in Figure 2 . Second, a significant group versus time effect for readiness to exercise more (β=−.17; P =.03) was found, with post hoc comparisons showing a significant increase for EG1 compared to CG at T0 versus T2. The significant interaction effect is represented graphically in Figure 2 , where we can also see how the different group conditions evolve through the different time measures. Finally, participants from all groups had a significant improvement in their confidence to exercise more between T0 and T1 and between T0 and T3 (β=.55; P =.003) and in their readiness to lose weight between T0 and T1, T0 and T2, and T0 and T3 (β=.36; P =.01).

The HLMs revealed significant results for some TFEQ-R18 and HADS subscales, while no significant group versus time and time effects were found for the Eating Habits Questionnaire, Body Shape Questionnaire, and WBIS-M. The significant interaction effect for the TFEQ-R18 and HADS subscales is represented graphically in Multimedia Appendix 5 .

TFEQ-R18 Analysis

The HLMs revealed a significant time×group effect for the UE (β=.71; P =.01) and EE subscales (β=.29; P =.03). Post hoc comparisons revealed consistently lower levels of UE for EG1 versus CG across all time measures, and for EE, a reduction for EG1 versus CG at T0 versus T3.

HADS Analysis

A significant group versus time effect was found for the anxiety subscale (β=.65; P =.01). In particular, post hoc comparisons revealed greater reductions in anxiety levels between T0 and T1 for EG1 compared to EG2 and CG. For the depression subscale, only a significant time effect was found (β=1.07; P =.04), indicating a decrease in depression levels across all groups over time.

The HLM per-protocol analysis revealed consistent results with the ITT analysis, showing no significant differences between the 2 approaches.

Adherence and Satisfaction Regarding VR Experiments (SEQ and BOQ)

For the SEQ, the experimental groups demonstrated high suitability scores with the VR platform (EG1: mean 59.82, SD 4.00; EG2: mean 58.43, SD 5.22; d =0.30, 95% CI −0.30 to 0.89).

Regarding the BOQ, both groups displayed average positive agreement scores, showing higher mean in EG1 than in EG2. Specifically, from the perspective of their look-alike avatar, for item 1 (looking down at the virtual body), EG1 had a mean of 2.09 (SD 1.17), while EG2 had a mean of 0.05 (SD 2.12; d =1.18, 95% CI 0.53-1.81); for item 2 (observing oneself in a virtual mirror), EG1 scored a mean of 2.34 (SD 0.70), and EG2 scored a mean of 1.53 (SD 1.58; d =0.66, 95% CI 0.05-1.26); for item 3 (perceiving body movements), EG1 scored a mean of 2.56 (SD 0.45), and EG2 scored a mean of 2.14 (SD 0.97; d =0.56, 95% CI −0.54 to 1.14); and for item 4 (recognizing oneself), EG1 scored a mean of 1.53 (SD 1.31), and EG2 scored a mean of 1.36 (SD 1.57; d =0.12, 95% CI −0.47 to 0.71). From the counselor’s perspective, EG1 also demonstrated average agreement scores: (1) looking down at the virtual body: mean 1.72 (SD 1.25); (2) observing oneself in a virtual mirror: mean 1.99 (SD 0.81); and (3) perceiving body movements: mean 2.25 (SD 0.64).

Principal Findings

This study is focused on assessing the clinical efficacy of the ConVRself platform in tackling some of the root causes of obesity. The findings of this study confirmed our hypothesis, indicating that ConVRself with embodiment and body-swapping elements, along with MI training, significantly enhanced participants’ confidence to lose weight and readiness to perform physical exercise. In addition, this intervention proved effective in reducing dysfunctional eating behaviors and anxiety compared to the other groups. Overall, these findings are consistent with previous studies on the beneficial effects of positive, instructional, and motivational self-talk for performance [ 47 ].

The adherence analyses revealed that participants who had higher adherence to the treatment were those who reported, at T0, higher CR in relation to their eating habits, had healthier eating habits, and were in the maintenance stage of their change process, indicating a sustained commitment to lifestyle changes [ 8 ]. In line with these findings, a previous study showed that patients with obesity before BS who were more ready to limit food intake and were actively engaged in physical activity were more likely to adhere to dietary and physical activity recommendations after BS [ 48 ]. The dropout ratio of the study was 33.8% and was similar among the groups. This finding is aligned with another RCT that used VR psychological treatments for weight management [ 49 ], in which no differences between treatment conditions were found in the dropout rates.

As regards the sociodemographic information at T0, the average age closely aligns with findings from various studies involving patients who were on the BS waiting list or who had recently undergone surgery [ 50 ], as well as studies involving VR in the psychological management of obesity [ 51 ]. In addition, the uneven sex distribution in the study sample—79% (54/68) of the participants were female individuals—is common in studies with patients who have undergone BS [ 50 , 52 , 53 ], and also in VR interventions for weight management [ 49 , 51 ]. In fact, a recent systematic review that included 24 studies that used VR to treat obesity [ 15 ] reported that in 8 trials with both men and women, 93% of the sample were women. Several studies have explored this phenomenon [ 54 , 55 ], showing that women tend to experience higher body image dissatisfaction, psychological disturbances, and a greater desire to lose weight than men. This may explain why they are more frequently represented in clinical research studies.

Regarding clinical variables, consistent with previous literature [ 56 ], a high proportion of our sample (58/68, 85%) exhibited physical comorbidities, with pain, endocrine disorders, breathing problems, and cardiovascular problems being the most prevalent. These findings, together with the high prevalence of participants in our sample who were unemployed or on sick leave and with severe obesity (BMI: mean 43.58, SD 5.96 kg/m 2 ), further confirm the debilitating nature of the disease. In addition, the most prevalent mental disorders observed were anxiety (8/68, 12%) and depression (6/68, 9%). However, neither disorder reached significant levels of morbidity based on the recommended cutoff points of the HADS [ 57 ]. Interestingly, our results indicate lower levels of anxiety and depression compared to previous research on patients who have undergone BS [ 58 , 59 ].

Regarding the assessment of motivation to change, measured along 3 dimensions (importance, confidence, and readiness) over time, participants initially reported a high importance placed on losing weight or exercising more. This posed challenges in detecting significant changes over time, as they were predominantly situated in the action and maintenance phases, as indicated by the S-Weight scores. However, all 3 groups improved over time in confidence to exercise more and readiness to lose weight. This was likely influenced by their positive expectations related to the BS or their mere involvement in the study. The participants’ expectation of undergoing BS, in alignment with recent literature [ 53 ], potentially served as an additional motivator in their personal journey toward change [ 60 , 61 ]. Notably, most participants had already consulted with the nutritionist and endocrinologist of the obesity unit on multiple occasions and had received instructions on how to implement modifications to their diet and exercise routines in preparation for surgery. The adherence to these recommendations provided valuable insights into their likelihood of being eligible to undergo surgery soon. Consequently, the findings regarding changes in motivation to change over time remain inconclusive, making it difficult to draw specific conclusions regarding postintervention improvements in these aspects.

Regarding the group versus time effect on motivation to change, the results indicated that participants who used ConVRself reported a significant increase in their confidence to lose weight and a higher readiness to engage in exercise compared to the CG. These positive effects were maintained for a duration of between 1 and 4 weeks after the exposures. This finding is particularly encouraging for behavior change, given that, for a person to initiate and maintain a change process, they must believe that the change is important and have confidence in their ability to achieve it [ 8 ]. Notably, increased confidence is often associated with a greater propensity to adopt self-regulation skills, including control over eating behaviors and improvement in physical activity [ 62 ]. In this sense, our results derived from the TFEQ-R18 suggested improved control over eating behaviors over time among the ConVRself group, as evidenced by lower tendencies in EE and UE behaviors, compared to the CG. Previous studies employing VR interventions with people living with obesity have yielded similar results, consistently demonstrating increased self-efficacy and readiness to initiate behavior change [ 63 - 65 ].

Apart from the aforementioned improvement in eating control in EG1, the same group also experienced a notable reduction in anxiety from T0 to T1 compared to EG2 and CG. These findings align with the results of 2 RCTs conducted by Manzoni et al [ 64 , 65 ]. In these studies, it was demonstrated that a relaxation treatment augmented by VR was more effective in reducing anxiety and EE behaviors than traditional meditation interventions at 2-week and 3-month follow-ups among women living with obesity. It is worth mentioning that anxiety scores in EG1 decreased more after the intervention compared to EG2 and CG; however, this reduction was not sustained over time. We believe that this result can be attributed to the absence of ongoing psychological support after the intervention period with ConVRself. This finding emphasizes how patients can be highly sensitive to changes in the treatment process during the preoperative period and underscores the constant need for psychological support in all pre- and postoperative treatment phases [ 5 ]. In this context, similar to the successful application of VR in addressing various mental disorders, including anxiety [ 66 ], ConVRself has the potential to provide an additional benefit by enabling patients to cope with anxiety and develop strategies aligned with their values.

In line with our previous usability study [ 27 ], high SEQ scores on ConVRself indicated high usability and acceptance of the platform by people living with obesity, which means that the platform was well adapted to this population. In addition, as regards the body ownership of the avatars, the results obtained are similar to those reported in previous literature [ 25 , 27 ], which indicates that participants experienced a strong sense of body ownership over the virtual avatars. However, inconsistent outcomes were found with EG2 for the looking down at the virtual body item compared to the other body ownership evaluations. The likely reason for this anomaly is a technological problem. Whenever participants looked down while embodied in their look-alike avatars, they could only see their knees. This limited visibility of their legs was primarily due to their anatomy (body size and shape), which caused most of their legs to be out of view. EG2 participants, particularly, failed to infer that seeing their knees implied seeing their entire legs. Despite the avatar’s anatomy being the same for all participants, we believe that EG1 participants were more focused on engaging in self-conversation, while EG2 participants placed greater emphasis on the physical appearance of their avatars.

Limitations and Strengths

This study has several limitations. First, the high presence of physical comorbidities in the participants may have been influenced by the impact of delayed medical care and the exacerbation of conditions due to the COVID-19 pandemic [ 56 ]. This could potentially affect the generalizability of our results to the broader population. Second, a high dropout rate was observed, which made it challenging to achieve the expected adherence rate as stated in the study protocol [ 26 ]. This high dropout rate and the resulting smaller sample size may have impeded the detection of medium or small effects within the sample, particularly the potential differences among the groups. Third, the study design did not allow for a clear separation of the effects of ConVRself and the motivational training on the primary and secondary variables. The interpretations derived from our results could be attributed to either the effects of the motivational training or the virtual self-conversations, or, more likely, a combination of both. Fourth, the short follow-up period may limit the generalizability of the results. It becomes necessary to conduct a longer-term follow-up of the patients to observe whether the changes obtained with the ConVRself platform are sustained. Unfortunately, this was not feasible in this study due to time constraints imposed by the European SOCRATES project and the specific characteristics of our sample (patients on the BS waiting list). Furthermore, despite intensive basic training in MI, real competence in it demands constant and prolonged practice, potentially influencing the results. Finally, the high expectation of improvement from BS may have influenced the positive outcomes; therefore, it would be necessary to corroborate the results in a population with morbid obesity but without BS expectations.

The strengths of this study are its experimental design, specifically a study with 3 experimental groups and 4 assessment points, and the well-balanced distribution of the sample across groups. Moreover, we conducted a comprehensive evaluation of the participants’ health, including a clinical interview that considered both physical comorbidities and mental illness at T0. Furthermore, EG1 participants received intensive training before the VR intervention, which was led by an MI expert. In terms of statistical analysis, HLMs were used in conjunction with ITT analyses, enabling the inclusion of all available data from the study, including information from participants who dropped out. Finally, there are no previous studies on MI training aimed at patients rather than therapists, either in obesity or other medical fields. This opens up possibilities to train patients as experts, making them self-aware about their own condition and capable of self-motivation.

Conclusions

In conclusion, using VR self-conversations to address the root causes of obesity has demonstrated important benefits and can be safely applied, with no side effects, among this population. In particular, the VR self-conversation with novel techniques of embodiment and body-swapping was well received by EG1 participants and was effective in enhancing self-efficacy and readiness to change, as well as in reducing dysfunctional eating behaviors and anxiety, compared to the other groups. Despite the apparent complexity of the procedures (self-conversation with embodiment and body-swapping), participants were able to complete the exposures, and they engaged in meaningful self-conversations about their obesity-related challenges and potential solutions. In this regard, a future study will provide qualitative data (currently under analysis and subject to another publication) on the unfolding of the motivational self-conversation process.

As for future perspectives, our findings underscore the importance of incorporating innovative psychological interventions to promote overall well-being and facilitate improvements in eating behaviors and lifestyle beyond mere weight loss. Such integrated interventions are crucial not only during the preoperative phase but also for the long-term maintenance of positive outcomes after BS. Future research should be conducted with ConVRself as a treatment not only for people living with obesity but also for patients with mental disorders or addictive behaviors. The potential of enriching virtual self-conversation during moments of blockage in patients with artificial intelligence techniques presents an exciting future research line.

Acknowledgments

The authors would like to thank the rest of the SOCRATES consortium (Self Conversation in Virtual Reality Embodiment to Enhance Healthier Lifestyles Among People with Obesity) for their contributions to the project. In addition, they would like to thank the staff of the obesity unit and psychiatry department at the Vall d’Hebron University Hospital for their help during the recruitment process. Finally, the authors would like to thank all participants who contributed to this study. This study was funded by the European Union’s Horizon 2020 Research and Innovation Programme (951930). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. This study was also financially supported by the Serra Húnter Programme in the form of a grant awarded to DA.

Data Availability

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

Authors' Contributions

DA, MS, JARQ, and PLP conceived the study. DA, along with PH and PGR, wrote the manuscript. DA, JVDS, PH, and PGR conducted the recruitment during data collection. PLP led the motivational interviewing training for experimental group 1 participants. PGR conducted all statistical analyses and, with the support of PH, created all tables and figures for the study. JVDS and PLP provided valuable revisions during the manuscript writing process. BS, EAdlC, and MS adapted the virtual reality platform to the needs of people living with obesity and offered technical support during the study. AC provided support during the initial phases of the study, and MC contributed to the sample collection. Finally, BS and MS provided valuable feedback regarding previous revisions of the paper.

Conflicts of Interest

MS and BS are founders of Virtual Bodyworks SL, a spin-off company of the Universitat de Barcelona. EAdlC was employed by Virtual Bodyworks SL. All other authors declare no other conflicts of interest.

Procedure and timeline of the randomized controlled trial.

Sociodemographic and clinical characteristics of all participants, and separately for each group.

Descriptive statistics (mean and SD) on scales and subscales of the study divided into groups and time measures.

Stages of Change Questionnaires for Weight Management frequencies and percentages divided into time and groups.

Estimated means of secondary outcomes in group versus time effect (intent-to-treat analysis).

CONSORT checklist.

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Abbreviations

Edited by A Mavragani; submitted 03.08.23; peer-reviewed by N Farič, V Girishan Prabhu; comments to author 22.12.23; revised version received 12.01.24; accepted 30.01.24; published 05.04.24.

©Dimitra Anastasiadou, Pol Herrero, Paula Garcia-Royo, Julia Vázquez-De Sebastián, Mel Slater, Bernhard Spanlang, Elena Álvarez de la Campa, Andreea Ciudin, Marta Comas, Josep Antoni Ramos-Quiroga, Pilar Lusilla-Palacios. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 05.04.2024.

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

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Navy fires commander of biomedical research lab

The Navy fired the commanding officer of a Lima, Peru, based biomedical research lab on Friday, less than a year after she assumed command.

Capt. Abigail Y. Marter was relieved as head of Naval Medical Research Unit South “due to a loss of confidence in her ability to command,” the Navy said in a statement.

Such boilerplate language is often used by the Navy when first announcing the relief of commanding officers and other senior personnel.

Officials did not immediately respond to follow-up questions from Navy Times regarding the reasons for Marter’s firing.

“Navy commanding officers are held to the highest standards of personal and professional conduct,” the Navy said. “They are expected to uphold the highest standards of responsibility, reliability, and leadership, and the Navy holds them accountable when they fall short of meeting these standards.”

Cmdr. Michael Prouty has assumed temporary command of the unit, and Marter has been temporarily reassigned to Naval Medical Research Command.

A family nurse practitioner, Marter took command of the unit in July.

Formerly known as Naval Medical Research Unit 6, the command monitors and researches infectious diseases in Central and South America.

Its main hub is on a Peruvian naval base, but the command also runs a satellite lab on an air base in Honduras.

Geoff is the editor of Navy Times, but he still loves writing stories. He covered Iraq and Afghanistan extensively and was a reporter at the Chicago Tribune. He welcomes any and all kinds of tips at [email protected].

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