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• Research Objectives | Definition & Examples

Research Objectives | Definition & Examples

Published on July 12, 2022 by Eoghan Ryan . Revised on November 20, 2023.

Research objectives describe what your research is trying to achieve and explain why you are pursuing it. They summarize the approach and purpose of your project and help to focus your research.

Your objectives should appear in the introduction of your research paper , at the end of your problem statement . They should:

• Establish the scope and depth of your project
• Contribute to your research design
• Indicate how your project will contribute to existing knowledge

Table of contents

What is a research objective, why are research objectives important, how to write research aims and objectives, smart research objectives, other interesting articles, frequently asked questions about research objectives.

Research objectives describe what your research project intends to accomplish. They should guide every step of the research process , including how you collect data , build your argument , and develop your conclusions .

Your research objectives may evolve slightly as your research progresses, but they should always line up with the research carried out and the actual content of your paper.

Research aims

A distinction is often made between research objectives and research aims.

A research aim typically refers to a broad statement indicating the general purpose of your research project. It should appear at the end of your problem statement, before your research objectives.

Your research objectives are more specific than your research aim and indicate the particular focus and approach of your project. Though you will only have one research aim, you will likely have several research objectives.

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Research objectives are important because they:

• Establish the scope and depth of your project: This helps you avoid unnecessary research. It also means that your research methods and conclusions can easily be evaluated .
• Contribute to your research design: When you know what your objectives are, you have a clearer idea of what methods are most appropriate for your research.
• Indicate how your project will contribute to extant research: They allow you to display your knowledge of up-to-date research, employ or build on current research methods, and attempt to contribute to recent debates.

Once you’ve established a research problem you want to address, you need to decide how you will address it. This is where your research aim and objectives come in.

Step 1: Decide on a general aim

Your research aim should reflect your research problem and should be relatively broad.

Step 2: Decide on specific objectives

Break down your aim into a limited number of steps that will help you resolve your research problem. What specific aspects of the problem do you want to examine or understand?

Step 3: Formulate your aims and objectives

Once you’ve established your research aim and objectives, you need to explain them clearly and concisely to the reader.

You’ll lay out your aims and objectives at the end of your problem statement, which appears in your introduction. Frame them as clear declarative statements, and use appropriate verbs to accurately characterize the work that you will carry out.

The acronym “SMART” is commonly used in relation to research objectives. It states that your objectives should be:

• Specific: Make sure your objectives aren’t overly vague. Your research needs to be clearly defined in order to get useful results.
• Measurable: Know how you’ll measure whether your objectives have been achieved.
• Achievable: Your objectives may be challenging, but they should be feasible. Make sure that relevant groundwork has been done on your topic or that relevant primary or secondary sources exist. Also ensure that you have access to relevant research facilities (labs, library resources , research databases , etc.).
• Relevant: Make sure that they directly address the research problem you want to work on and that they contribute to the current state of research in your field.
• Time-based: Set clear deadlines for objectives to ensure that the project stays on track.

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.

Methodology

• Sampling methods
• 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

Research objectives describe what you intend your research project to accomplish.

They summarize the approach and purpose of the project and help to focus your research.

Your objectives should appear in the introduction of your research paper , at the end of your problem statement .

Your research objectives indicate how you’ll try to address your research problem and should be specific:

Once you’ve decided on your research objectives , you need to explain them in your paper, at the end of your problem statement .

Keep your research objectives clear and concise, and use appropriate verbs to accurately convey the work that you will carry out for each one.

I will compare …

A research aim is a broad statement indicating the general purpose of your research project. It should appear in your introduction at the end of your problem statement , before your research objectives.

Research objectives are more specific than your research aim. They indicate the specific ways you’ll address the overarching aim.

Scope of research is determined at the beginning of your research process , prior to the data collection stage. Sometimes called “scope of study,” your scope delineates what will and will not be covered in your project. It helps you focus your work and your time, ensuring that you’ll be able to achieve your goals and outcomes.

Defining a scope can be very useful in any research project, from a research proposal to a thesis or dissertation . A scope is needed for all types of research: quantitative , qualitative , and mixed methods .

To define your scope of research, consider the following:

• Budget constraints or any specifics of grant funding
• Your proposed timeline and duration
• Specifics about your population of study, your proposed sample size , and the research methodology you’ll pursue
• Any inclusion and exclusion criteria
• Any anticipated control , extraneous , or confounding variables that could bias your research if not accounted for properly.

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Published by Nicolas at March 21st, 2024 , Revised On March 12, 2024

The Ultimate Guide To Research Methodology

Research methodology is a crucial aspect of any investigative process, serving as the blueprint for the entire research journey. If you are stuck in the methodology section of your research paper , then this blog will guide you on what is a research methodology, its types and how to successfully conduct one. 

Table of Contents

What Is Research Methodology?

Research methodology can be defined as the systematic framework that guides researchers in designing, conducting, and analyzing their investigations. It encompasses a structured set of processes, techniques, and tools employed to gather and interpret data, ensuring the reliability and validity of the research findings. 

Research methodology is not confined to a singular approach; rather, it encapsulates a diverse range of methods tailored to the specific requirements of the research objectives.

Here is why Research methodology is important in academic and professional settings.

Facilitating Rigorous Inquiry

Research methodology forms the backbone of rigorous inquiry. It provides a structured approach that aids researchers in formulating precise thesis statements , selecting appropriate methodologies, and executing systematic investigations. This, in turn, enhances the quality and credibility of the research outcomes.

Ensuring Reproducibility And Reliability

In both academic and professional contexts, the ability to reproduce research outcomes is paramount. A well-defined research methodology establishes clear procedures, making it possible for others to replicate the study. This not only validates the findings but also contributes to the cumulative nature of knowledge.

Guiding Decision-Making Processes

In professional settings, decisions often hinge on reliable data and insights. Research methodology equips professionals with the tools to gather pertinent information, analyze it rigorously, and derive meaningful conclusions.

This informed decision-making is instrumental in achieving organizational goals and staying ahead in competitive environments.

Contributing To Academic Excellence

For academic researchers, adherence to robust research methodology is a hallmark of excellence. Institutions value research that adheres to high standards of methodology, fostering a culture of academic rigour and intellectual integrity. Furthermore, it prepares students with critical skills applicable beyond academia.

Enhancing Problem-Solving Abilities

Research methodology instills a problem-solving mindset by encouraging researchers to approach challenges systematically. It equips individuals with the skills to dissect complex issues, formulate hypotheses , and devise effective strategies for investigation.

Understanding Research Methodology

In the pursuit of knowledge and discovery, understanding the fundamentals of research methodology is paramount. 

Basics Of Research

Research, in its essence, is a systematic and organized process of inquiry aimed at expanding our understanding of a particular subject or phenomenon. It involves the exploration of existing knowledge, the formulation of hypotheses, and the collection and analysis of data to draw meaningful conclusions. 

Research is a dynamic and iterative process that contributes to the continuous evolution of knowledge in various disciplines.

Types of Research

Research takes on various forms, each tailored to the nature of the inquiry. Broadly classified, research can be categorized into two main types:

• Quantitative Research: This type involves the collection and analysis of numerical data to identify patterns, relationships, and statistical significance. It is particularly useful for testing hypotheses and making predictions.
• Qualitative Research: Qualitative research focuses on understanding the depth and details of a phenomenon through non-numerical data. It often involves methods such as interviews, focus groups, and content analysis, providing rich insights into complex issues.

Components Of Research Methodology

To conduct effective research, one must go through the different components of research methodology. These components form the scaffolding that supports the entire research process, ensuring its coherence and validity.

Research Design

Research design serves as the blueprint for the entire research project. It outlines the overall structure and strategy for conducting the study. The three primary types of research design are:

• Exploratory Research: Aimed at gaining insights and familiarity with the topic, often used in the early stages of research.
• Descriptive Research: Involves portraying an accurate profile of a situation or phenomenon, answering the ‘what,’ ‘who,’ ‘where,’ and ‘when’ questions.
• Explanatory Research: Seeks to identify the causes and effects of a phenomenon, explaining the ‘why’ and ‘how.’

Data Collection Methods

Choosing the right data collection methods is crucial for obtaining reliable and relevant information. Common methods include:

• Surveys and Questionnaires: Employed to gather information from a large number of respondents through standardized questions.
• Interviews: In-depth conversations with participants, offering qualitative insights.
• Observation: Systematic watching and recording of behaviour, events, or processes in their natural setting.

Data Analysis Techniques

Once data is collected, analysis becomes imperative to derive meaningful conclusions. Different methodologies exist for quantitative and qualitative data:

• Quantitative Data Analysis: Involves statistical techniques such as descriptive statistics, inferential statistics, and regression analysis to interpret numerical data.
• Qualitative Data Analysis: Methods like content analysis, thematic analysis, and grounded theory are employed to extract patterns, themes, and meanings from non-numerical data.

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Choosing a Research Method

Selecting an appropriate research method is a critical decision in the research process. It determines the approach, tools, and techniques that will be used to answer the research questions. 

Quantitative Research Methods

Quantitative research involves the collection and analysis of numerical data, providing a structured and objective approach to understanding and explaining phenomena.

Experimental Research

Experimental research involves manipulating variables to observe the effect on another variable under controlled conditions. It aims to establish cause-and-effect relationships.

Key Characteristics:

• Controlled Environment: Experiments are conducted in a controlled setting to minimize external influences.
• Random Assignment: Participants are randomly assigned to different experimental conditions.
• Quantitative Data: Data collected is numerical, allowing for statistical analysis.

Applications: Commonly used in scientific studies and psychology to test hypotheses and identify causal relationships.

Survey Research

Survey research gathers information from a sample of individuals through standardized questionnaires or interviews. It aims to collect data on opinions, attitudes, and behaviours.

• Structured Instruments: Surveys use structured instruments, such as questionnaires, to collect data.
• Large Sample Size: Surveys often target a large and diverse group of participants.
• Quantitative Data Analysis: Responses are quantified for statistical analysis.

Applications: Widely employed in social sciences, marketing, and public opinion research to understand trends and preferences.

Descriptive Research

Descriptive research seeks to portray an accurate profile of a situation or phenomenon. It focuses on answering the ‘what,’ ‘who,’ ‘where,’ and ‘when’ questions.

• Observation and Data Collection: This involves observing and documenting without manipulating variables.
• Objective Description: Aim to provide an unbiased and factual account of the subject.
• Quantitative or Qualitative Data: T his can include both types of data, depending on the research focus.

Applications: Useful in situations where researchers want to understand and describe a phenomenon without altering it, common in social sciences and education.

Qualitative Research Methods

Qualitative research emphasizes exploring and understanding the depth and complexity of phenomena through non-numerical data.

A case study is an in-depth exploration of a particular person, group, event, or situation. It involves detailed, context-rich analysis.

• Rich Data Collection: Uses various data sources, such as interviews, observations, and documents.
• Contextual Understanding: Aims to understand the context and unique characteristics of the case.
• Holistic Approach: Examines the case in its entirety.

Applications: Common in social sciences, psychology, and business to investigate complex and specific instances.

Ethnography

Ethnography involves immersing the researcher in the culture or community being studied to gain a deep understanding of their behaviours, beliefs, and practices.

• Participant Observation: Researchers actively participate in the community or setting.
• Holistic Perspective: Focuses on the interconnectedness of cultural elements.
• Qualitative Data: In-depth narratives and descriptions are central to ethnographic studies.

Applications: Widely used in anthropology, sociology, and cultural studies to explore and document cultural practices.

Grounded Theory

Grounded theory aims to develop theories grounded in the data itself. It involves systematic data collection and analysis to construct theories from the ground up.

• Constant Comparison: Data is continually compared and analyzed during the research process.
• Inductive Reasoning: Theories emerge from the data rather than being imposed on it.
• Iterative Process: The research design evolves as the study progresses.

Applications: Commonly applied in sociology, nursing, and management studies to generate theories from empirical data.

Research design is the structural framework that outlines the systematic process and plan for conducting a study. It serves as the blueprint, guiding researchers on how to collect, analyze, and interpret data.

Exploratory, Descriptive, And Explanatory Designs

Exploratory design.

Exploratory research design is employed when a researcher aims to explore a relatively unknown subject or gain insights into a complex phenomenon.

• Flexibility: Allows for flexibility in data collection and analysis.
• Open-Ended Questions: Uses open-ended questions to gather a broad range of information.
• Preliminary Nature: Often used in the initial stages of research to formulate hypotheses.

Applications: Valuable in the early stages of investigation, especially when the researcher seeks a deeper understanding of a subject before formalizing research questions.

Descriptive Design

Descriptive research design focuses on portraying an accurate profile of a situation, group, or phenomenon.

• Structured Data Collection: Involves systematic and structured data collection methods.
• Objective Presentation: Aims to provide an unbiased and factual account of the subject.
• Quantitative or Qualitative Data: Can incorporate both types of data, depending on the research objectives.

Applications: Widely used in social sciences, marketing, and educational research to provide detailed and objective descriptions.

Explanatory Design

Explanatory research design aims to identify the causes and effects of a phenomenon, explaining the ‘why’ and ‘how’ behind observed relationships.

• Causal Relationships: Seeks to establish causal relationships between variables.
• Controlled Variables : Often involves controlling certain variables to isolate causal factors.
• Quantitative Analysis: Primarily relies on quantitative data analysis techniques.

Applications: Commonly employed in scientific studies and social sciences to delve into the underlying reasons behind observed patterns.

Cross-Sectional Vs. Longitudinal Designs

Cross-sectional design.

Cross-sectional designs collect data from participants at a single point in time.

• Snapshot View: Provides a snapshot of a population at a specific moment.
• Efficiency: More efficient in terms of time and resources.
• Limited Temporal Insights: Offers limited insights into changes over time.

Applications: Suitable for studying characteristics or behaviours that are stable or not expected to change rapidly.

Longitudinal Design

Longitudinal designs involve the collection of data from the same participants over an extended period.

• Temporal Sequence: Allows for the examination of changes over time.
• Causality Assessment: Facilitates the assessment of cause-and-effect relationships.
• Resource-Intensive: Requires more time and resources compared to cross-sectional designs.

Applications: Ideal for studying developmental processes, trends, or the impact of interventions over time.

Experimental Vs Non-experimental Designs

Experimental design.

Experimental designs involve manipulating variables under controlled conditions to observe the effect on another variable.

• Causality Inference: Enables the inference of cause-and-effect relationships.
• Quantitative Data: Primarily involves the collection and analysis of numerical data.

Applications: Commonly used in scientific studies, psychology, and medical research to establish causal relationships.

Non-Experimental Design

Non-experimental designs observe and describe phenomena without manipulating variables.

• Natural Settings: Data is often collected in natural settings without intervention.
• Descriptive or Correlational: Focuses on describing relationships or correlations between variables.
• Quantitative or Qualitative Data: This can involve either type of data, depending on the research approach.

Applications: Suitable for studying complex phenomena in real-world settings where manipulation may not be ethical or feasible.

Effective data collection is fundamental to the success of any research endeavour. 

Designing Effective Surveys

Objective Design:

• Clearly define the research objectives to guide the survey design.
• Craft questions that align with the study’s goals and avoid ambiguity.

Structured Format:

• Use a structured format with standardized questions for consistency.
• Include a mix of closed-ended and open-ended questions for detailed insights.

Pilot Testing:

• Conduct pilot tests to identify and rectify potential issues with survey design.
• Ensure clarity, relevance, and appropriateness of questions.

Sampling Strategy:

• Develop a robust sampling strategy to ensure a representative participant group.
• Consider random sampling or stratified sampling based on the research goals.

Conducting Interviews

Establishing Rapport:

• Build rapport with participants to create a comfortable and open environment.
• Clearly communicate the purpose of the interview and the value of participants’ input.

Open-Ended Questions:

• Frame open-ended questions to encourage detailed responses.
• Allow participants to express their thoughts and perspectives freely.

Active Listening:

• Practice active listening to understand areas and gather rich data.
• Avoid interrupting and maintain a non-judgmental stance during the interview.

Ethical Considerations:

• Obtain informed consent and assure participants of confidentiality.
• Be transparent about the study’s purpose and potential implications.

Observation

1. participant observation.

Immersive Participation:

• Actively immerse yourself in the setting or group being observed.
• Develop a deep understanding of behaviours, interactions, and context.

Field Notes:

• Maintain detailed and reflective field notes during observations.
• Document observed patterns, unexpected events, and participant reactions.

Ethical Awareness:

• Be conscious of ethical considerations, ensuring respect for participants.
• Balance the role of observer and participant to minimize bias.

2. Non-participant Observation

Objective Observation:

• Maintain a more detached and objective stance during non-participant observation.
• Focus on recording behaviours, events, and patterns without direct involvement.

Data Reliability:

• Enhance the reliability of data by reducing observer bias.
• Develop clear observation protocols and guidelines.

Contextual Understanding:

• Strive for a thorough understanding of the observed context.
• Consider combining non-participant observation with other methods for triangulation.

Archival Research

1. using existing data.

Identifying Relevant Archives:

• Locate and access archives relevant to the research topic.
• Collaborate with institutions or repositories holding valuable data.

Data Verification:

• Verify the accuracy and reliability of archived data.
• Cross-reference with other sources to ensure data integrity.

Ethical Use:

• Adhere to ethical guidelines when using existing data.
• Respect copyright and intellectual property rights.

2. Challenges and Considerations

Incomplete or Inaccurate Archives:

• Address the possibility of incomplete or inaccurate archival records.
• Acknowledge limitations and uncertainties in the data.

Temporal Bias:

• Recognize potential temporal biases in archived data.
• Consider the historical context and changes that may impact interpretation.

Access Limitations:

• Address potential limitations in accessing certain archives.
• Seek alternative sources or collaborate with institutions to overcome barriers.

Common Challenges in Research Methodology

Conducting research is a complex and dynamic process, often accompanied by a myriad of challenges. Addressing these challenges is crucial to ensure the reliability and validity of research findings.

Sampling Issues

Sampling bias:.

• The presence of sampling bias can lead to an unrepresentative sample, affecting the generalizability of findings.
• Employ random sampling methods and ensure the inclusion of diverse participants to reduce bias.

Sample Size Determination:

• Determining an appropriate sample size is a delicate balance. Too small a sample may lack statistical power, while an excessively large sample may strain resources.
• Conduct a power analysis to determine the optimal sample size based on the research objectives and expected effect size.

Data Quality And Validity

Measurement error:.

• Inaccuracies in measurement tools or data collection methods can introduce measurement errors, impacting the validity of results.
• Pilot test instruments, calibrate equipment, and use standardized measures to enhance the reliability of data.

Construct Validity:

• Ensuring that the chosen measures accurately capture the intended constructs is a persistent challenge.
• Use established measurement instruments and employ multiple measures to assess the same construct for triangulation.

Time And Resource Constraints

Timeline pressures:.

• Limited timeframes can compromise the depth and thoroughness of the research process.
• Develop a realistic timeline, prioritize tasks, and communicate expectations with stakeholders to manage time constraints effectively.

Resource Availability:

• Inadequate resources, whether financial or human, can impede the execution of research activities.
• Seek external funding, collaborate with other researchers, and explore alternative methods that require fewer resources.

Managing Bias in Research

Selection bias:.

• Selecting participants in a way that systematically skews the sample can introduce selection bias.
• Employ randomization techniques, use stratified sampling, and transparently report participant recruitment methods.

Confirmation Bias:

• Researchers may unintentionally favour information that confirms their preconceived beliefs or hypotheses.
• Adopt a systematic and open-minded approach, use blinded study designs, and engage in peer review to mitigate confirmation bias.

Tips On How To Write A Research Methodology

Conducting successful research relies not only on the application of sound methodologies but also on strategic planning and effective collaboration. Here are some tips to enhance the success of your research methodology:

Tip 1. Clear Research Objectives

Well-defined research objectives guide the entire research process. Clearly articulate the purpose of your study, outlining specific research questions or hypotheses.

Tip 2. Comprehensive Literature Review

A thorough literature review provides a foundation for understanding existing knowledge and identifying gaps. Invest time in reviewing relevant literature to inform your research design and methodology.

Tip 3. Detailed Research Plan

A detailed plan serves as a roadmap, ensuring all aspects of the research are systematically addressed. Develop a detailed research plan outlining timelines, milestones, and tasks.

Tip 4. Ethical Considerations

Ethical practices are fundamental to maintaining the integrity of research. Address ethical considerations early, obtain necessary approvals, and ensure participant rights are safeguarded.

Tip 5. Stay Updated On Methodologies

Research methodologies evolve, and staying updated is essential for employing the most effective techniques. Engage in continuous learning by attending workshops, conferences, and reading recent publications.

Tip 6. Adaptability In Methods

Unforeseen challenges may arise during research, necessitating adaptability in methods. Be flexible and willing to modify your approach when needed, ensuring the integrity of the study.

Tip 7. Iterative Approach

Research is often an iterative process, and refining methods based on ongoing findings enhance the study’s robustness. Regularly review and refine your research design and methods as the study progresses.

Frequently Asked Questions

What is the research methodology.

Research methodology is the systematic process of planning, executing, and evaluating scientific investigation. It encompasses the techniques, tools, and procedures used to collect, analyze, and interpret data, ensuring the reliability and validity of research findings.

What are the methodologies in research?

Research methodologies include qualitative and quantitative approaches. Qualitative methods involve in-depth exploration of non-numerical data, while quantitative methods use statistical analysis to examine numerical data. Mixed methods combine both approaches for a comprehensive understanding of research questions.

How to write research methodology?

To write a research methodology, clearly outline the study’s design, data collection, and analysis procedures. Specify research tools, participants, and sampling methods. Justify choices and discuss limitations. Ensure clarity, coherence, and alignment with research objectives for a robust methodology section.

How to write the methodology section of a research paper?

In the methodology section of a research paper, describe the study’s design, data collection, and analysis methods. Detail procedures, tools, participants, and sampling. Justify choices, address ethical considerations, and explain how the methodology aligns with research objectives, ensuring clarity and rigour.

What is mixed research methodology?

Mixed research methodology combines both qualitative and quantitative research approaches within a single study. This approach aims to enhance the details and depth of research findings by providing a more comprehensive understanding of the research problem or question.

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Home » Research Methodology – Types, Examples and writing Guide

Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

Definition:

Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect , analyze , and interpret data to answer research questions or solve research problems . Moreover, They are philosophical and theoretical frameworks that guide the research process.

Structure of Research Methodology

Research methodology formats can vary depending on the specific requirements of the research project, but the following is a basic example of a structure for a research methodology section:

I. Introduction

• Provide an overview of the research problem and the need for a research methodology section
• Outline the main research questions and objectives

II. Research Design

• Explain the research design chosen and why it is appropriate for the research question(s) and objectives
• Discuss any alternative research designs considered and why they were not chosen
• Describe the research setting and participants (if applicable)

III. Data Collection Methods

• Describe the methods used to collect data (e.g., surveys, interviews, observations)
• Explain how the data collection methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or instruments used for data collection

IV. Data Analysis Methods

• Describe the methods used to analyze the data (e.g., statistical analysis, content analysis )
• Explain how the data analysis methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or software used for data analysis

V. Ethical Considerations

• Discuss any ethical issues that may arise from the research and how they were addressed
• Explain how informed consent was obtained (if applicable)
• Detail any measures taken to ensure confidentiality and anonymity

VI. Limitations

• Identify any potential limitations of the research methodology and how they may impact the results and conclusions

VII. Conclusion

• Summarize the key aspects of the research methodology section
• Explain how the research methodology addresses the research question(s) and objectives

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

This is a research methodology that involves the collection and analysis of numerical data using statistical methods. This type of research is often used to study cause-and-effect relationships and to make predictions.

Qualitative Research Methodology

This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.

Mixed-Methods Research Methodology

This is a research methodology that combines elements of both quantitative and qualitative research. This approach can be particularly useful for studies that aim to explore complex phenomena and to provide a more comprehensive understanding of a particular topic.

Case Study Research Methodology

This is a research methodology that involves in-depth examination of a single case or a small number of cases. Case studies are often used in psychology, sociology, and anthropology to gain a detailed understanding of a particular individual or group.

Action Research Methodology

This is a research methodology that involves a collaborative process between researchers and practitioners to identify and solve real-world problems. Action research is often used in education, healthcare, and social work.

Experimental Research Methodology

This is a research methodology that involves the manipulation of one or more independent variables to observe their effects on a dependent variable. Experimental research is often used to study cause-and-effect relationships and to make predictions.

Survey Research Methodology

This is a research methodology that involves the collection of data from a sample of individuals using questionnaires or interviews. Survey research is often used to study attitudes, opinions, and behaviors.

Grounded Theory Research Methodology

This is a research methodology that involves the development of theories based on the data collected during the research process. Grounded theory is often used in sociology and anthropology to generate theories about social phenomena.

Research Methodology Example

An Example of Research Methodology could be the following:

Research Methodology for Investigating the Effectiveness of Cognitive Behavioral Therapy in Reducing Symptoms of Depression in Adults

Introduction:

The aim of this research is to investigate the effectiveness of cognitive-behavioral therapy (CBT) in reducing symptoms of depression in adults. To achieve this objective, a randomized controlled trial (RCT) will be conducted using a mixed-methods approach.

Research Design:

The study will follow a pre-test and post-test design with two groups: an experimental group receiving CBT and a control group receiving no intervention. The study will also include a qualitative component, in which semi-structured interviews will be conducted with a subset of participants to explore their experiences of receiving CBT.

Participants:

Participants will be recruited from community mental health clinics in the local area. The sample will consist of 100 adults aged 18-65 years old who meet the diagnostic criteria for major depressive disorder. Participants will be randomly assigned to either the experimental group or the control group.

Intervention :

The experimental group will receive 12 weekly sessions of CBT, each lasting 60 minutes. The intervention will be delivered by licensed mental health professionals who have been trained in CBT. The control group will receive no intervention during the study period.

Data Collection:

Quantitative data will be collected through the use of standardized measures such as the Beck Depression Inventory-II (BDI-II) and the Generalized Anxiety Disorder-7 (GAD-7). Data will be collected at baseline, immediately after the intervention, and at a 3-month follow-up. Qualitative data will be collected through semi-structured interviews with a subset of participants from the experimental group. The interviews will be conducted at the end of the intervention period, and will explore participants’ experiences of receiving CBT.

Data Analysis:

Quantitative data will be analyzed using descriptive statistics, t-tests, and mixed-model analyses of variance (ANOVA) to assess the effectiveness of the intervention. Qualitative data will be analyzed using thematic analysis to identify common themes and patterns in participants’ experiences of receiving CBT.

Ethical Considerations:

This study will comply with ethical guidelines for research involving human subjects. Participants will provide informed consent before participating in the study, and their privacy and confidentiality will be protected throughout the study. Any adverse events or reactions will be reported and managed appropriately.

Data Management:

All data collected will be kept confidential and stored securely using password-protected databases. Identifying information will be removed from qualitative data transcripts to ensure participants’ anonymity.

Limitations:

One potential limitation of this study is that it only focuses on one type of psychotherapy, CBT, and may not generalize to other types of therapy or interventions. Another limitation is that the study will only include participants from community mental health clinics, which may not be representative of the general population.

Conclusion:

This research aims to investigate the effectiveness of CBT in reducing symptoms of depression in adults. By using a randomized controlled trial and a mixed-methods approach, the study will provide valuable insights into the mechanisms underlying the relationship between CBT and depression. The results of this study will have important implications for the development of effective treatments for depression in clinical settings.

How to Write Research Methodology

Writing a research methodology involves explaining the methods and techniques you used to conduct research, collect data, and analyze results. It’s an essential section of any research paper or thesis, as it helps readers understand the validity and reliability of your findings. Here are the steps to write a research methodology:

• Start by explaining your research question: Begin the methodology section by restating your research question and explaining why it’s important. This helps readers understand the purpose of your research and the rationale behind your methods.
• Describe your research design: Explain the overall approach you used to conduct research. This could be a qualitative or quantitative research design, experimental or non-experimental, case study or survey, etc. Discuss the advantages and limitations of the chosen design.
• Discuss your sample: Describe the participants or subjects you included in your study. Include details such as their demographics, sampling method, sample size, and any exclusion criteria used.
• Describe your data collection methods : Explain how you collected data from your participants. This could include surveys, interviews, observations, questionnaires, or experiments. Include details on how you obtained informed consent, how you administered the tools, and how you minimized the risk of bias.
• Explain your data analysis techniques: Describe the methods you used to analyze the data you collected. This could include statistical analysis, content analysis, thematic analysis, or discourse analysis. Explain how you dealt with missing data, outliers, and any other issues that arose during the analysis.
• Discuss the validity and reliability of your research : Explain how you ensured the validity and reliability of your study. This could include measures such as triangulation, member checking, peer review, or inter-coder reliability.
• Acknowledge any limitations of your research: Discuss any limitations of your study, including any potential threats to validity or generalizability. This helps readers understand the scope of your findings and how they might apply to other contexts.
• Provide a summary: End the methodology section by summarizing the methods and techniques you used to conduct your research. This provides a clear overview of your research methodology and helps readers understand the process you followed to arrive at your findings.

When to Write Research Methodology

Research methodology is typically written after the research proposal has been approved and before the actual research is conducted. It should be written prior to data collection and analysis, as it provides a clear roadmap for the research project.

The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.

The methodology should be written in a clear and concise manner, and it should be based on established research practices and standards. It is important to provide enough detail so that the reader can understand how the research was conducted and evaluate the validity of the results.

Applications of Research Methodology

Here are some of the applications of research methodology:

• To identify the research problem: Research methodology is used to identify the research problem, which is the first step in conducting any research.
• To design the research: Research methodology helps in designing the research by selecting the appropriate research method, research design, and sampling technique.
• To collect data: Research methodology provides a systematic approach to collect data from primary and secondary sources.
• To analyze data: Research methodology helps in analyzing the collected data using various statistical and non-statistical techniques.
• To test hypotheses: Research methodology provides a framework for testing hypotheses and drawing conclusions based on the analysis of data.
• To generalize findings: Research methodology helps in generalizing the findings of the research to the target population.
• To develop theories : Research methodology is used to develop new theories and modify existing theories based on the findings of the research.
• To evaluate programs and policies : Research methodology is used to evaluate the effectiveness of programs and policies by collecting data and analyzing it.
• To improve decision-making: Research methodology helps in making informed decisions by providing reliable and valid data.

Purpose of Research Methodology

Research methodology serves several important purposes, including:

• To guide the research process: Research methodology provides a systematic framework for conducting research. It helps researchers to plan their research, define their research questions, and select appropriate methods and techniques for collecting and analyzing data.
• To ensure research quality: Research methodology helps researchers to ensure that their research is rigorous, reliable, and valid. It provides guidelines for minimizing bias and error in data collection and analysis, and for ensuring that research findings are accurate and trustworthy.
• To replicate research: Research methodology provides a clear and detailed account of the research process, making it possible for other researchers to replicate the study and verify its findings.
• To advance knowledge: Research methodology enables researchers to generate new knowledge and to contribute to the body of knowledge in their field. It provides a means for testing hypotheses, exploring new ideas, and discovering new insights.
• To inform decision-making: Research methodology provides evidence-based information that can inform policy and decision-making in a variety of fields, including medicine, public health, education, and business.

Advantages of Research Methodology

Research methodology has several advantages that make it a valuable tool for conducting research in various fields. Here are some of the key advantages of research methodology:

• Systematic and structured approach : Research methodology provides a systematic and structured approach to conducting research, which ensures that the research is conducted in a rigorous and comprehensive manner.
• Objectivity : Research methodology aims to ensure objectivity in the research process, which means that the research findings are based on evidence and not influenced by personal bias or subjective opinions.
• Replicability : Research methodology ensures that research can be replicated by other researchers, which is essential for validating research findings and ensuring their accuracy.
• Reliability : Research methodology aims to ensure that the research findings are reliable, which means that they are consistent and can be depended upon.
• Validity : Research methodology ensures that the research findings are valid, which means that they accurately reflect the research question or hypothesis being tested.
• Efficiency : Research methodology provides a structured and efficient way of conducting research, which helps to save time and resources.
• Flexibility : Research methodology allows researchers to choose the most appropriate research methods and techniques based on the research question, data availability, and other relevant factors.
• Scope for innovation: Research methodology provides scope for innovation and creativity in designing research studies and developing new research techniques.

Research Methodology Vs Research Methods

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What is research methodology?

The basics of research methodology

Why do you need a research methodology, what needs to be included, why do you need to document your research method, what are the different types of research instruments, qualitative / quantitative / mixed research methodologies, how do you choose the best research methodology for you, frequently asked questions about research methodology, related articles.

When you’re working on your first piece of academic research, there are many different things to focus on, and it can be overwhelming to stay on top of everything. This is especially true of budding or inexperienced researchers.

If you’ve never put together a research proposal before or find yourself in a position where you need to explain your research methodology decisions, there are a few things you need to be aware of.

Once you understand the ins and outs, handling academic research in the future will be less intimidating. We break down the basics below:

A research methodology encompasses the way in which you intend to carry out your research. This includes how you plan to tackle things like collection methods, statistical analysis, participant observations, and more.

You can think of your research methodology as being a formula. One part will be how you plan on putting your research into practice, and another will be why you feel this is the best way to approach it. Your research methodology is ultimately a methodological and systematic plan to resolve your research problem.

In short, you are explaining how you will take your idea and turn it into a study, which in turn will produce valid and reliable results that are in accordance with the aims and objectives of your research. This is true whether your paper plans to make use of qualitative methods or quantitative methods.

The purpose of a research methodology is to explain the reasoning behind your approach to your research - you'll need to support your collection methods, methods of analysis, and other key points of your work.

Think of it like writing a plan or an outline for you what you intend to do.

When carrying out research, it can be easy to go off-track or depart from your standard methodology.

Tip: Having a methodology keeps you accountable and on track with your original aims and objectives, and gives you a suitable and sound plan to keep your project manageable, smooth, and effective.

With all that said, how do you write out your standard approach to a research methodology?

As a general plan, your methodology should include the following information:

• Your research method.  You need to state whether you plan to use quantitative analysis, qualitative analysis, or mixed-method research methods. This will often be determined by what you hope to achieve with your research.
• Explain your reasoning. Why are you taking this methodological approach? Why is this particular methodology the best way to answer your research problem and achieve your objectives?
• Explain your instruments.  This will mainly be about your collection methods. There are varying instruments to use such as interviews, physical surveys, questionnaires, for example. Your methodology will need to detail your reasoning in choosing a particular instrument for your research.
• What will you do with your results?  How are you going to analyze the data once you have gathered it?
• Advise your reader.  If there is anything in your research methodology that your reader might be unfamiliar with, you should explain it in more detail. For example, you should give any background information to your methods that might be relevant or provide your reasoning if you are conducting your research in a non-standard way.
• How will your sampling process go?  What will your sampling procedure be and why? For example, if you will collect data by carrying out semi-structured or unstructured interviews, how will you choose your interviewees and how will you conduct the interviews themselves?
• Any practical limitations?  You should discuss any limitations you foresee being an issue when you’re carrying out your research.

In any dissertation, thesis, or academic journal, you will always find a chapter dedicated to explaining the research methodology of the person who carried out the study, also referred to as the methodology section of the work.

A good research methodology will explain what you are going to do and why, while a poor methodology will lead to a messy or disorganized approach.

You should also be able to justify in this section your reasoning for why you intend to carry out your research in a particular way, especially if it might be a particularly unique method.

Having a sound methodology in place can also help you with the following:

• When another researcher at a later date wishes to try and replicate your research, they will need your explanations and guidelines.
• In the event that you receive any criticism or questioning on the research you carried out at a later point, you will be able to refer back to it and succinctly explain the how and why of your approach.
• It provides you with a plan to follow throughout your research. When you are drafting your methodology approach, you need to be sure that the method you are using is the right one for your goal. This will help you with both explaining and understanding your method.
• It affords you the opportunity to document from the outset what you intend to achieve with your research, from start to finish.

A research instrument is a tool you will use to help you collect, measure and analyze the data you use as part of your research.

The choice of research instrument will usually be yours to make as the researcher and will be whichever best suits your methodology.

There are many different research instruments you can use in collecting data for your research.

Generally, they can be grouped as follows:

• Interviews (either as a group or one-on-one). You can carry out interviews in many different ways. For example, your interview can be structured, semi-structured, or unstructured. The difference between them is how formal the set of questions is that is asked of the interviewee. In a group interview, you may choose to ask the interviewees to give you their opinions or perceptions on certain topics.
• Surveys (online or in-person). In survey research, you are posing questions in which you ask for a response from the person taking the survey. You may wish to have either free-answer questions such as essay-style questions, or you may wish to use closed questions such as multiple choice. You may even wish to make the survey a mixture of both.
• Focus Groups.  Similar to the group interview above, you may wish to ask a focus group to discuss a particular topic or opinion while you make a note of the answers given.
• Observations.  This is a good research instrument to use if you are looking into human behaviors. Different ways of researching this include studying the spontaneous behavior of participants in their everyday life, or something more structured. A structured observation is research conducted at a set time and place where researchers observe behavior as planned and agreed upon with participants.

These are the most common ways of carrying out research, but it is really dependent on your needs as a researcher and what approach you think is best to take.

It is also possible to combine a number of research instruments if this is necessary and appropriate in answering your research problem.

There are three different types of methodologies, and they are distinguished by whether they focus on words, numbers, or both.

➡️ Want to learn more about the differences between qualitative and quantitative research, and how to use both methods? Check out our guide for that!

If you've done your due diligence, you'll have an idea of which methodology approach is best suited to your research.

It’s likely that you will have carried out considerable reading and homework before you reach this point and you may have taken inspiration from other similar studies that have yielded good results.

Still, it is important to consider different options before setting your research in stone. Exploring different options available will help you to explain why the choice you ultimately make is preferable to other methods.

If proving your research problem requires you to gather large volumes of numerical data to test hypotheses, a quantitative research method is likely to provide you with the most usable results.

If instead you’re looking to try and learn more about people, and their perception of events, your methodology is more exploratory in nature and would therefore probably be better served using a qualitative research methodology.

It helps to always bring things back to the question: what do I want to achieve with my research?

Once you have conducted your research, you need to analyze it. Here are some helpful guides for qualitative data analysis:

➡️  How to do a content analysis

➡️  How to do a thematic analysis

➡️  How to do a rhetorical analysis

Research methodology refers to the techniques used to find and analyze information for a study, ensuring that the results are valid, reliable and that they address the research objective.

Data can typically be organized into four different categories or methods: observational, experimental, simulation, and derived.

Writing a methodology section is a process of introducing your methods and instruments, discussing your analysis, providing more background information, addressing your research limitations, and more.

Your research methodology section will need a clear research question and proposed research approach. You'll need to add a background, introduce your research question, write your methodology and add the works you cited during your data collecting phase.

The research methodology section of your study will indicate how valid your findings are and how well-informed your paper is. It also assists future researchers planning to use the same methodology, who want to cite your study or replicate it.

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A Comprehensive Guide to Methodology in Research

Table of Contents

Research methodology plays a crucial role in any study or investigation. It provides the framework for collecting, analyzing, and interpreting data, ensuring that the research is reliable, valid, and credible. Understanding the importance of research methodology is essential for conducting rigorous and meaningful research.

In this article, we'll explore the various aspects of research methodology, from its types to best practices, ensuring you have the knowledge needed to conduct impactful research.

What is Research Methodology?

Research methodology refers to the system of procedures, techniques, and tools used to carry out a research study. It encompasses the overall approach, including the research design, data collection methods, data analysis techniques, and the interpretation of findings.

Research methodology plays a crucial role in the field of research, as it sets the foundation for any study. It provides researchers with a structured framework to ensure that their investigations are conducted in a systematic and organized manner. By following a well-defined methodology, researchers can ensure that their findings are reliable, valid, and meaningful.

When defining research methodology, one of the first steps is to identify the research problem. This involves clearly understanding the issue or topic that the study aims to address. By defining the research problem, researchers can narrow down their focus and determine the specific objectives they want to achieve through their study.

How to Define Research Methodology

Once the research problem is identified, researchers move on to defining the research questions. These questions serve as a guide for the study, helping researchers to gather relevant information and analyze it effectively. The research questions should be clear, concise, and aligned with the overall goals of the study.

After defining the research questions, researchers need to determine how data will be collected and analyzed. This involves selecting appropriate data collection methods, such as surveys, interviews, observations, or experiments. The choice of data collection methods depends on various factors, including the nature of the research problem, the target population, and the available resources.

Once the data is collected, researchers need to analyze it using appropriate data analysis techniques. This may involve statistical analysis, qualitative analysis, or a combination of both, depending on the nature of the data and the research questions. The analysis of data helps researchers to draw meaningful conclusions and make informed decisions based on their findings.

Role of Methodology in Research

Methodology plays a crucial role in research, as it ensures that the study is conducted in a systematic and organized manner. It provides a clear roadmap for researchers to follow, ensuring that the research objectives are met effectively. By following a well-defined methodology, researchers can minimize bias, errors, and inconsistencies in their study, thus enhancing the reliability and validity of their findings.

In addition to providing a structured approach, research methodology also helps in establishing the reliability and validity of the study. Reliability refers to the consistency and stability of the research findings, while validity refers to the accuracy and truthfulness of the findings. By using appropriate research methods and techniques, researchers can ensure that their study produces reliable and valid results, which can be used to make informed decisions and contribute to the existing body of knowledge.

Steps in Choosing the Right Research Methodology

Choosing the appropriate research methodology for your study is a critical step in ensuring the success of your research. Let's explore some steps to help you select the right research methodology:

Identifying the Research Problem

The first step in choosing the right research methodology is to clearly identify and define the research problem. Understanding the research problem will help you determine which methodology will best address your research questions and objectives.

Identifying the research problem involves a thorough examination of the existing literature in your field of study. This step allows you to gain a comprehensive understanding of the current state of knowledge and identify any gaps that your research can fill. By identifying the research problem, you can ensure that your study contributes to the existing body of knowledge and addresses a significant research gap.

Once you have identified the research problem, you need to consider the scope of your study. Are you focusing on a specific population, geographic area, or time frame? Understanding the scope of your research will help you determine the appropriate research methodology to use.

Reviewing Previous Research

Before finalizing the research methodology, it is essential to review previous research conducted in the field. This will allow you to identify gaps, determine the most effective methodologies used in similar studies, and build upon existing knowledge.

Reviewing previous research involves conducting a systematic review of relevant literature. This process includes searching for and analyzing published studies, articles, and reports that are related to your research topic. By reviewing previous research, you can gain insights into the strengths and limitations of different methodologies and make informed decisions about which approach to adopt.

During the review process, it is important to critically evaluate the quality and reliability of the existing research. Consider factors such as the sample size, research design, data collection methods, and statistical analysis techniques used in previous studies. This evaluation will help you determine the most appropriate research methodology for your own study.

Formulating Research Questions

Once the research problem is identified, formulate specific and relevant research questions. These questions will guide your methodology selection process by helping you determine what type of data you need to collect and how to analyze it.

Formulating research questions involves breaking down the research problem into smaller, more manageable components. These questions should be clear, concise, and measurable. They should also align with the objectives of your study and provide a framework for data collection and analysis.

When formulating research questions, consider the different types of data that can be collected, such as qualitative or quantitative data. Depending on the nature of your research questions, you may need to employ different data collection methods, such as interviews, surveys, observations, or experiments. By carefully formulating research questions, you can ensure that your chosen methodology will enable you to collect the necessary data to answer your research questions effectively.

Implementing the Research Methodology

After choosing the appropriate research methodology, it is time to implement it. This stage involves collecting data using various techniques and analyzing the gathered information. Let's explore two crucial aspects of implementing the research methodology:

Data Collection Techniques

Data collection techniques depend on the chosen research methodology. They can include surveys, interviews, observations, experiments, or document analysis. Selecting the most suitable data collection techniques will ensure accurate and relevant data for your study.

Data Analysis Methods

Data analysis is a critical part of the research process. It involves interpreting and making sense of the collected data to draw meaningful conclusions. Depending on the research methodology, data analysis methods can include statistical analysis, content analysis, thematic analysis, or grounded theory.

Ensuring the Validity and Reliability of Your Research

In order to ensure the validity and reliability of your research findings, it is important to address these two key aspects:

Understanding Validity in Research

Validity refers to the accuracy and soundness of a research study. It is crucial to ensure that the research methods used effectively measure what they intend to measure. Researchers can enhance validity by using proper sampling techniques, carefully designing research instruments, and ensuring accurate data collection.

Ensuring Reliability in Your Study

Reliability refers to the consistency and stability of the research results. It is important to ensure that the research methods and instruments used yield consistent and reproducible results. Researchers can enhance reliability by using standardized procedures, ensuring inter-rater reliability, and conducting pilot studies.

A comprehensive understanding of research methodology is essential for conducting high-quality research. By selecting the right research methodology, researchers can ensure that their studies are rigorous, reliable, and valid. It is crucial to follow the steps in choosing the appropriate methodology, implement the chosen methodology effectively, and address validity and reliability concerns throughout the research process. By doing so, researchers can contribute valuable insights and advances in their respective fields.

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What is Research Methodology? Definition, Types, and Examples

Research methodology 1,2 is a structured and scientific approach used to collect, analyze, and interpret quantitative or qualitative data to answer research questions or test hypotheses. A research methodology is like a plan for carrying out research and helps keep researchers on track by limiting the scope of the research. Several aspects must be considered before selecting an appropriate research methodology, such as research limitations and ethical concerns that may affect your research.

The research methodology section in a scientific paper describes the different methodological choices made, such as the data collection and analysis methods, and why these choices were selected. The reasons should explain why the methods chosen are the most appropriate to answer the research question. A good research methodology also helps ensure the reliability and validity of the research findings. There are three types of research methodology—quantitative, qualitative, and mixed-method, which can be chosen based on the research objectives.

What is research methodology ?

A research methodology describes the techniques and procedures used to identify and analyze information regarding a specific research topic. It is a process by which researchers design their study so that they can achieve their objectives using the selected research instruments. It includes all the important aspects of research, including research design, data collection methods, data analysis methods, and the overall framework within which the research is conducted. While these points can help you understand what is research methodology, you also need to know why it is important to pick the right methodology.

Why is research methodology important?

Having a good research methodology in place has the following advantages: 3

• Helps other researchers who may want to replicate your research; the explanations will be of benefit to them.
• You can easily answer any questions about your research if they arise at a later stage.
• A research methodology provides a framework and guidelines for researchers to clearly define research questions, hypotheses, and objectives.
• It helps researchers identify the most appropriate research design, sampling technique, and data collection and analysis methods.
• A sound research methodology helps researchers ensure that their findings are valid and reliable and free from biases and errors.
• It also helps ensure that ethical guidelines are followed while conducting research.
• A good research methodology helps researchers in planning their research efficiently, by ensuring optimum usage of their time and resources.

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Types of research methodology.

There are three types of research methodology based on the type of research and the data required. 1

• Quantitative research methodology focuses on measuring and testing numerical data. This approach is good for reaching a large number of people in a short amount of time. This type of research helps in testing the causal relationships between variables, making predictions, and generalizing results to wider populations.
• Qualitative research methodology examines the opinions, behaviors, and experiences of people. It collects and analyzes words and textual data. This research methodology requires fewer participants but is still more time consuming because the time spent per participant is quite large. This method is used in exploratory research where the research problem being investigated is not clearly defined.
• Mixed-method research methodology uses the characteristics of both quantitative and qualitative research methodologies in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method.

What are the types of sampling designs in research methodology?

Sampling 4 is an important part of a research methodology and involves selecting a representative sample of the population to conduct the study, making statistical inferences about them, and estimating the characteristics of the whole population based on these inferences. There are two types of sampling designs in research methodology—probability and nonprobability.

• Probability sampling

In this type of sampling design, a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are:

• Systematic —sample members are chosen at regular intervals. It requires selecting a starting point for the sample and sample size determination that can be repeated at regular intervals. This type of sampling method has a predefined range; hence, it is the least time consuming.
• Stratified —researchers divide the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized, and then a sample can be drawn from each group separately.
• Cluster —the population is divided into clusters based on demographic parameters like age, sex, location, etc.
• Convenience —selects participants who are most easily accessible to researchers due to geographical proximity, availability at a particular time, etc.
• Purposive —participants are selected at the researcher’s discretion. Researchers consider the purpose of the study and the understanding of the target audience.
• Snowball —already selected participants use their social networks to refer the researcher to other potential participants.
• Quota —while designing the study, the researchers decide how many people with which characteristics to include as participants. The characteristics help in choosing people most likely to provide insights into the subject.

What are data collection methods?

During research, data are collected using various methods depending on the research methodology being followed and the research methods being undertaken. Both qualitative and quantitative research have different data collection methods, as listed below.

Qualitative research 5

• One-on-one interviews: Helps the interviewers understand a respondent’s subjective opinion and experience pertaining to a specific topic or event
• Document study/literature review/record keeping: Researchers’ review of already existing written materials such as archives, annual reports, research articles, guidelines, policy documents, etc.
• Focus groups: Constructive discussions that usually include a small sample of about 6-10 people and a moderator, to understand the participants’ opinion on a given topic.
• Qualitative observation : Researchers collect data using their five senses (sight, smell, touch, taste, and hearing).

Quantitative research 6

• Sampling: The most common type is probability sampling.
• Interviews: Commonly telephonic or done in-person.
• Observations: Structured observations are most commonly used in quantitative research. In this method, researchers make observations about specific behaviors of individuals in a structured setting.
• Document review: Reviewing existing research or documents to collect evidence for supporting the research.
• Surveys and questionnaires. Surveys can be administered both online and offline depending on the requirement and sample size.

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What are data analysis methods.

The data collected using the various methods for qualitative and quantitative research need to be analyzed to generate meaningful conclusions. These data analysis methods 7 also differ between quantitative and qualitative research.

Quantitative research involves a deductive method for data analysis where hypotheses are developed at the beginning of the research and precise measurement is required. The methods include statistical analysis applications to analyze numerical data and are grouped into two categories—descriptive and inferential.

Descriptive analysis is used to describe the basic features of different types of data to present it in a way that ensures the patterns become meaningful. The different types of descriptive analysis methods are:

• Measures of frequency (count, percent, frequency)
• Measures of central tendency (mean, median, mode)
• Measures of dispersion or variation (range, variance, standard deviation)
• Measure of position (percentile ranks, quartile ranks)

Inferential analysis is used to make predictions about a larger population based on the analysis of the data collected from a smaller population. This analysis is used to study the relationships between different variables. Some commonly used inferential data analysis methods are:

• Correlation: To understand the relationship between two or more variables.
• Cross-tabulation: Analyze the relationship between multiple variables.
• Regression analysis: Study the impact of independent variables on the dependent variable.
• Frequency tables: To understand the frequency of data.
• Analysis of variance: To test the degree to which two or more variables differ in an experiment.

Qualitative research involves an inductive method for data analysis where hypotheses are developed after data collection. The methods include:

• Content analysis: For analyzing documented information from text and images by determining the presence of certain words or concepts in texts.
• Narrative analysis: For analyzing content obtained from sources such as interviews, field observations, and surveys. The stories and opinions shared by people are used to answer research questions.
• Discourse analysis: For analyzing interactions with people considering the social context, that is, the lifestyle and environment, under which the interaction occurs.
• Grounded theory: Involves hypothesis creation by data collection and analysis to explain why a phenomenon occurred.
• Thematic analysis: To identify important themes or patterns in data and use these to address an issue.

How to choose a research methodology?

Here are some important factors to consider when choosing a research methodology: 8

• Research objectives, aims, and questions —these would help structure the research design.
• Review existing literature to identify any gaps in knowledge.
• Check the statistical requirements —if data-driven or statistical results are needed then quantitative research is the best. If the research questions can be answered based on people’s opinions and perceptions, then qualitative research is most suitable.
• Sample size —sample size can often determine the feasibility of a research methodology. For a large sample, less effort- and time-intensive methods are appropriate.
• Constraints —constraints of time, geography, and resources can help define the appropriate methodology.

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How to write a research methodology .

A research methodology should include the following components: 3,9

• Research design —should be selected based on the research question and the data required. Common research designs include experimental, quasi-experimental, correlational, descriptive, and exploratory.
• Research method —this can be quantitative, qualitative, or mixed-method.
• Reason for selecting a specific methodology —explain why this methodology is the most suitable to answer your research problem.
• Research instruments —explain the research instruments you plan to use, mainly referring to the data collection methods such as interviews, surveys, etc. Here as well, a reason should be mentioned for selecting the particular instrument.
• Sampling —this involves selecting a representative subset of the population being studied.
• Data collection —involves gathering data using several data collection methods, such as surveys, interviews, etc.
• Data analysis —describe the data analysis methods you will use once you’ve collected the data.
• Research limitations —mention any limitations you foresee while conducting your research.
• Validity and reliability —validity helps identify the accuracy and truthfulness of the findings; reliability refers to the consistency and stability of the results over time and across different conditions.
• Ethical considerations —research should be conducted ethically. The considerations include obtaining consent from participants, maintaining confidentiality, and addressing conflicts of interest.

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Frequently Asked Questions

Q1. What are the key components of research methodology?

A1. A good research methodology has the following key components:

• Research design
• Data collection procedures
• Data analysis methods
• Ethical considerations

Q2. Why is ethical consideration important in research methodology?

A2. Ethical consideration is important in research methodology to ensure the readers of the reliability and validity of the study. Researchers must clearly mention the ethical norms and standards followed during the conduct of the research and also mention if the research has been cleared by any institutional board. The following 10 points are the important principles related to ethical considerations: 10

• Participants should not be subjected to harm.
• Respect for the dignity of participants should be prioritized.
• Full consent should be obtained from participants before the study.
• Participants’ privacy should be ensured.
• Confidentiality of the research data should be ensured.
• Anonymity of individuals and organizations participating in the research should be maintained.
• The aims and objectives of the research should not be exaggerated.
• Affiliations, sources of funding, and any possible conflicts of interest should be declared.
• Communication in relation to the research should be honest and transparent.
• Misleading information and biased representation of primary data findings should be avoided.

Q3. What is the difference between methodology and method?

A3. Research methodology is different from a research method, although both terms are often confused. Research methods are the tools used to gather data, while the research methodology provides a framework for how research is planned, conducted, and analyzed. The latter guides researchers in making decisions about the most appropriate methods for their research. Research methods refer to the specific techniques, procedures, and tools used by researchers to collect, analyze, and interpret data, for instance surveys, questionnaires, interviews, etc.

Research methodology is, thus, an integral part of a research study. It helps ensure that you stay on track to meet your research objectives and answer your research questions using the most appropriate data collection and analysis tools based on your research design.

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• Research methodologies. Pfeiffer Library website. Accessed August 15, 2023. https://library.tiffin.edu/researchmethodologies/whatareresearchmethodologies
• Types of research methodology. Eduvoice website. Accessed August 16, 2023. https://eduvoice.in/types-research-methodology/
• The basics of research methodology: A key to quality research. Voxco. Accessed August 16, 2023. https://www.voxco.com/blog/what-is-research-methodology/
• Sampling methods: Types with examples. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/types-of-sampling-for-social-research/
• What is qualitative research? Methods, types, approaches, examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-qualitative-research-methods-types-examples/
• What is quantitative research? Definition, methods, types, and examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-quantitative-research-types-and-examples/
• Data analysis in research: Types & methods. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/data-analysis-in-research/#Data_analysis_in_qualitative_research
• Factors to consider while choosing the right research methodology. PhD Monster website. Accessed August 17, 2023. https://www.phdmonster.com/factors-to-consider-while-choosing-the-right-research-methodology/
• What is research methodology? Research and writing guides. Accessed August 14, 2023. https://paperpile.com/g/what-is-research-methodology/
• Ethical considerations. Business research methodology website. Accessed August 17, 2023. https://research-methodology.net/research-methodology/ethical-considerations/

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The Importance Of Research Objectives

Imagine you’re a student planning a vacation in a foreign country. You’re on a tight budget and need to draw…

Imagine you’re a student planning a vacation in a foreign country. You’re on a tight budget and need to draw up a pocket-friendly plan. Where do you begin? The first step is to do your research.

Before that, you make a mental list of your objectives—finding reasonably-priced hotels, traveling safely and finding ways of communicating with someone back home. These objectives help you focus sharply during your research and be aware of the finer details of your trip.

More often than not, research is a part of our daily lives. Whether it’s to pick a restaurant for your next birthday dinner or to prepare a presentation at work, good research is the foundation of effective learning. Read on to understand the meaning, importance and examples of research objectives.

Why Do We Need Research?

What are the objectives of research, what goes into a research plan.

Research is a careful and detailed study of a particular problem or concern, using scientific methods. An in-depth analysis of information creates space for generating new questions, concepts and understandings. The main objective of research is to explore the unknown and unlock new possibilities. It’s an essential component of success.

Over the years, businesses have started emphasizing the need for research. You’ve probably noticed organizations hiring research managers and analysts. The primary purpose of business research is to determine the goals and opportunities of an organization. It’s critical in making business decisions and appropriately allocating available resources.

Here are a few benefits of research that’ll explain why it is a vital aspect of our professional lives:

Expands Your Knowledge Base

One of the greatest benefits of research is to learn and gain a deeper understanding. The deeper you dig into a topic, the more well-versed you are. Furthermore, research has the power to help you build on any personal experience you have on the subject.

Keeps You Up To Date

Research encourages you to discover the most recent information available. Updated information prevents you from falling behind and helps you present accurate information. You’re better equipped to develop ideas or talk about a topic when you’re armed with the latest inputs.

Builds Your Credibility

Research provides you with a good foundation upon which you can develop your thoughts and ideas. People take you more seriously when your suggestions are backed by research. You can speak with greater confidence because you know that the information is accurate.

Sparks Connections

Take any leading nonprofit organization, you’ll see how they have a strong research arm supported by real-life stories. Research also becomes the base upon which real-life connections and impact can be made. It even helps you communicate better with others and conveys why you’re pursuing something.

Encourages Curiosity

As we’ve already established, research is mostly about using existing information to create new ideas and opinions. In the process, it sparks curiosity as you’re encouraged to explore and gain deeper insights into a subject. Curiosity leads to higher levels of positivity and lower levels of anxiety.

Well-defined objectives of research are an essential component of successful research engagement. If you want to drive all aspects of your research methodology such as data collection, design, analysis and recommendation, you need to lay down the objectives of research methodology. In other words, the objectives of research should address the underlying purpose of investigation and analysis. It should outline the steps you’d take to achieve desirable outcomes. Research objectives help you stay focused and adjust your expectations as you progress.

The objectives of research should be closely related to the problem statement, giving way to specific and achievable goals. Here are the four types of research objectives for you to explore:

General Objective

Also known as secondary objectives, general objectives provide a detailed view of the aim of a study. In other words, you get a general overview of what you want to achieve by the end of your study. For example, if you want to study an organization’s contribution to environmental sustainability, your general objective could be: a study of sustainable practices and the use of renewable energy by the organization.

Specific Objectives

Specific objectives define the primary aim of the study. Typically, general objectives provide the foundation for identifying specific objectives. In other words, when general objectives are broken down into smaller and logically connected objectives, they’re known as specific objectives. They help define the who, what, why, when and how aspects of your project. Once you identify the main objective of research, it’s easier to develop and pursue a plan of action.

Let’s take the example of ‘a study of an organization’s contribution to environmental sustainability’ again. The specific objectives will look like this:

To determine through history how the organization has changed its practices and adopted new solutions

To assess how the new practices, technology and strategies will contribute to the overall effectiveness

Once you’ve identified the objectives of research, it’s time to organize your thoughts and streamline your research goals. Here are a few effective tips to develop a powerful research plan and improve your business performance.

Set SMART Goals

Your research objectives should be SMART—Specific, Measurable, Achievable, Realistic and Time-constrained. When you focus on utilizing available resources and setting realistic timeframes and milestones, it’s easier to prioritize objectives. Continuously track your progress and check whether you need to revise your expectations or targets. This way, you’re in greater control over the process.

Create A Plan

Create a plan that’ll help you select appropriate methods to collect accurate information. A well-structured plan allows you to use logical and creative approaches towards problem-solving. The complexity of information and your skills are bound to influence your plan, which is why you need to make room for flexibility. The availability of resources will also play a big role in influencing your decisions.

Collect And Collate

After you’ve created a plan for the research process, make a list of the data you’re going to collect and the methods you’ll use. Not only will it help make sense of your insights but also keep track of your approach. The information you collect should be:

Logical, rigorous and objective

Can be reproduced by other people working on the same subject

Free of errors and highlighting necessary details

Current and updated

Includes everything required to support your argument/suggestions

Analyze And Keep Ready

Data analysis is the most crucial part of the process and there are many ways in which the information can be utilized. Four types of data analysis are often seen in a professional environment. While they may be divided into separate categories, they’re linked to each other.

Descriptive Analysis:

The most commonly used data analysis, descriptive analysis simply summarizes past data. For example, Key Performance Indicators (KPIs) use descriptive analysis. It establishes certain benchmarks after studying how someone has been performing in the past.

Diagnostic Analysis:

The next step is to identify why something happened. Diagnostic analysis uses the information gathered through descriptive analysis and helps find the underlying causes of an outcome. For example, if a marketing initiative was successful, you deep-dive into the strategies that worked.

Predictive Analysis:

It attempts to answer ‘what’s likely to happen’. Predictive analysis makes use of past data to predict future outcomes. However, the accuracy of predictions depends on the quality of the data provided. Risk assessment is an ideal example of using predictive analysis.

Prescriptive Analysis: 

The most sought-after type of data analysis, prescriptive analysis combines the insights of all of the previous analyses. It’s a huge organizational commitment as it requires plenty of effort and resources. A great example of prescriptive analysis is Artificial Intelligence (AI), which consumes large amounts of data. You need to be prepared to commit to this type of analysis.

Review And Interpret

Once you’ve collected and collated your data, it’s time to review it and draw accurate conclusions. Here are a few ways to improve the review process:

Identify the fundamental issues, opportunities and problems and make note of recurring trends if any

Make a list of your insights and check which is the most or the least common. In short, keep track of the frequency of each insight

Conduct a SWOT analysis and identify the strengths, weaknesses, opportunities and threats

Write down your conclusions and recommendations of the research

When we think about research, we often associate it with academicians and students. but the truth is research is for everybody who is willing to learn and enhance their knowledge. If you want to master the art of strategically upgrading your knowledge, Harappa Education’s Learning Expertly course has all the answers. Not only will it help you look at things from a fresh perspective but also show you how to acquire new information with greater efficiency. The Growth Mindset framework will teach you how to believe in your abilities to grow and improve. The Learning Transfer framework will help you apply your learnings from one context to another. Begin the journey of tactful learning and self-improvement today!

Explore Harappa Diaries to learn more about topics related to the THINK Habit such as  Learning From Experience ,  Critical Thinking  & What is  Brainstorming  to think clearly and rationally.

Formulating Research Aims and Objectives

Formulating research aim and objectives in an appropriate manner is one of the most important aspects of your thesis. This is because research aim and objectives determine the scope, depth and the overall direction of the research. Research question is the central question of the study that has to be answered on the basis of research findings.

Research aim emphasizes what needs to be achieved within the scope of the research, by the end of the research process. Achievement of research aim provides answer to the research question.

Research objectives divide research aim into several parts and address each part separately. Research aim specifies WHAT needs to be studied and research objectives comprise a number of steps that address HOW research aim will be achieved.

As a rule of dumb, there would be one research aim and several research objectives. Achievement of each research objective will lead to the achievement of the research aim.

Consider the following as an example:

Research title: Effects of organizational culture on business profitability: a case study of Virgin Atlantic

Research aim: To assess the effects of Virgin Atlantic organizational culture on business profitability

Following research objectives would facilitate the achievement of this aim:

• Analyzing the nature of organizational culture at Virgin Atlantic by September 1, 2022
• Identifying factors impacting Virgin Atlantic organizational culture by September 16, 2022
• Analyzing impacts of Virgin Atlantic organizational culture on employee performances by September 30, 2022
• Providing recommendations to Virgin Atlantic strategic level management in terms of increasing the level of effectiveness of organizational culture by October 5, 2022

Figure below illustrates additional examples in formulating research aims and objectives:

Formulation of research question, aim and objectives

Common mistakes in the formulation of research aim relate to the following:

1. Choosing the topic too broadly . This is the most common mistake. For example, a research title of “an analysis of leadership practices” can be classified as too broad because the title fails to answer the following questions:

a) Which aspects of leadership practices? Leadership has many aspects such as employee motivation, ethical behaviour, strategic planning, change management etc. An attempt to cover all of these aspects of organizational leadership within a single research will result in an unfocused and poor work.

b) An analysis of leadership practices in which country? Leadership practices tend to be different in various countries due to cross-cultural differences, legislations and a range of other region-specific factors. Therefore, a study of leadership practices needs to be country-specific.

c) Analysis of leadership practices in which company or industry? Similar to the point above, analysis of leadership practices needs to take into account industry-specific and/or company-specific differences, and there is no way to conduct a leadership research that relates to all industries and organizations in an equal manner.

Accordingly, as an example “a study into the impacts of ethical behaviour of a leader on the level of employee motivation in US healthcare sector” would be a more appropriate title than simply “An analysis of leadership practices”.

2. Setting an unrealistic aim . Formulation of a research aim that involves in-depth interviews with Apple strategic level management by an undergraduate level student can be specified as a bit over-ambitious. This is because securing an interview with Apple CEO Tim Cook or members of Apple Board of Directors might not be easy. This is an extreme example of course, but you got the idea. Instead, you may aim to interview the manager of your local Apple store and adopt a more feasible strategy to get your dissertation completed.

3. Choosing research methods incompatible with the timeframe available . Conducting interviews with 20 sample group members and collecting primary data through 2 focus groups when only three months left until submission of your dissertation can be very difficult, if not impossible. Accordingly, timeframe available need to be taken into account when formulating research aims and objectives and selecting research methods.

Moreover, research objectives need to be formulated according to SMART principle,

 where the abbreviation stands for specific, measurable, achievable, realistic, and time-bound.

Examples of SMART research objectives

At the conclusion part of your research project you will need to reflect on the level of achievement of research aims and objectives. In case your research aims and objectives are not fully achieved by the end of the study, you will need to discuss the reasons. These may include initial inappropriate formulation of research aims and objectives, effects of other variables that were not considered at the beginning of the research or changes in some circumstances during the research process.

John Dudovskiy

How To Choose Your Research Methodology

Qualitative vs quantitative vs mixed methods.

By: Derek Jansen (MBA). Expert Reviewed By: Dr Eunice Rautenbach | June 2021

Without a doubt, one of the most common questions we receive at Grad Coach is “ How do I choose the right methodology for my research? ”. It’s easy to see why – with so many options on the research design table, it’s easy to get intimidated, especially with all the complex lingo!

In this post, we’ll explain the three overarching types of research – qualitative, quantitative and mixed methods – and how you can go about choosing the best methodological approach for your research.

Overview: Choosing Your Methodology

Understanding the options – Qualitative research – Quantitative research – Mixed methods-based research

Choosing a research methodology – Nature of the research – Research area norms – Practicalities

1. Understanding the options

Before we jump into the question of how to choose a research methodology, it’s useful to take a step back to understand the three overarching types of research – qualitative , quantitative and mixed methods -based research. Each of these options takes a different methodological approach.

Qualitative research utilises data that is not numbers-based. In other words, qualitative research focuses on words , descriptions , concepts or ideas – while quantitative research makes use of numbers and statistics. Qualitative research investigates the “softer side” of things to explore and describe, while quantitative research focuses on the “hard numbers”, to measure differences between variables and the relationships between them.

Importantly, qualitative research methods are typically used to explore and gain a deeper understanding of the complexity of a situation – to draw a rich picture . In contrast to this, quantitative methods are usually used to confirm or test hypotheses . In other words, they have distinctly different purposes. The table below highlights a few of the key differences between qualitative and quantitative research – you can learn more about the differences here.

• Uses an inductive approach
• Is used to build theories
• Takes a subjective approach
• Adopts an open and flexible approach
• The researcher is close to the respondents
• Interviews and focus groups are oftentimes used to collect word-based data.
• Generally, draws on small sample sizes
• Uses qualitative data analysis techniques (e.g. content analysis , thematic analysis , etc)
• Uses a deductive approach
• Is used to test theories
• Takes an objective approach
• Adopts a closed, highly planned approach
• The research is disconnected from respondents
• Surveys or laboratory equipment are often used to collect number-based data.
• Generally, requires large sample sizes
• Uses statistical analysis techniques to make sense of the data

Mixed methods -based research, as you’d expect, attempts to bring these two types of research together, drawing on both qualitative and quantitative data. Quite often, mixed methods-based studies will use qualitative research to explore a situation and develop a potential model of understanding (this is called a conceptual framework), and then go on to use quantitative methods to test that model empirically.

In other words, while qualitative and quantitative methods (and the philosophies that underpin them) are completely different, they are not at odds with each other. It’s not a competition of qualitative vs quantitative. On the contrary, they can be used together to develop a high-quality piece of research. Of course, this is easier said than done, so we usually recommend that first-time researchers stick to a single approach , unless the nature of their study truly warrants a mixed-methods approach.

The key takeaway here, and the reason we started by looking at the three options, is that it’s important to understand that each methodological approach has a different purpose – for example, to explore and understand situations (qualitative), to test and measure (quantitative) or to do both. They’re not simply alternative tools for the same job. 

Right – now that we’ve got that out of the way, let’s look at how you can go about choosing the right methodology for your research.

2. How to choose a research methodology

To choose the right research methodology for your dissertation or thesis, you need to consider three important factors . Based on these three factors, you can decide on your overarching approach – qualitative, quantitative or mixed methods. Once you’ve made that decision, you can flesh out the finer details of your methodology, such as the sampling , data collection methods and analysis techniques (we discuss these separately in other posts ).

The three factors you need to consider are:

• The nature of your research aims, objectives and research questions
• The methodological approaches taken in the existing literature
• Practicalities and constraints

Let’s take a look at each of these.

Factor #1: The nature of your research

As I mentioned earlier, each type of research (and therefore, research methodology), whether qualitative, quantitative or mixed, has a different purpose and helps solve a different type of question. So, it’s logical that the key deciding factor in terms of which research methodology you adopt is the nature of your research aims, objectives and research questions .

But, what types of research exist?

Broadly speaking, research can fall into one of three categories:

• Exploratory – getting a better understanding of an issue and potentially developing a theory regarding it
• Confirmatory – confirming a potential theory or hypothesis by testing it empirically
• A mix of both – building a potential theory or hypothesis and then testing it

As a rule of thumb, exploratory research tends to adopt a qualitative approach , whereas confirmatory research tends to use quantitative methods . This isn’t set in stone, but it’s a very useful heuristic. Naturally then, research that combines a mix of both, or is seeking to develop a theory from the ground up and then test that theory, would utilize a mixed-methods approach.

Let’s look at an example in action.

If your research aims were to understand the perspectives of war veterans regarding certain political matters, you’d likely adopt a qualitative methodology, making use of interviews to collect data and one or more qualitative data analysis methods to make sense of the data.

If, on the other hand, your research aims involved testing a set of hypotheses regarding the link between political leaning and income levels, you’d likely adopt a quantitative methodology, using numbers-based data from a survey to measure the links between variables and/or constructs .

So, the first (and most important thing) thing you need to consider when deciding which methodological approach to use for your research project is the nature of your research aims , objectives and research questions. Specifically, you need to assess whether your research leans in an exploratory or confirmatory direction or involves a mix of both.

The importance of achieving solid alignment between these three factors and your methodology can’t be overstated. If they’re misaligned, you’re going to be forcing a square peg into a round hole. In other words, you’ll be using the wrong tool for the job, and your research will become a disjointed mess.

If your research is a mix of both exploratory and confirmatory, but you have a tight word count limit, you may need to consider trimming down the scope a little and focusing on one or the other. One methodology executed well has a far better chance of earning marks than a poorly executed mixed methods approach. So, don’t try to be a hero, unless there is a very strong underpinning logic.

Need a helping hand?

Factor #2: The disciplinary norms

Choosing the right methodology for your research also involves looking at the approaches used by other researchers in the field, and studies with similar research aims and objectives to yours. Oftentimes, within a discipline, there is a common methodological approach (or set of approaches) used in studies. While this doesn’t mean you should follow the herd “just because”, you should at least consider these approaches and evaluate their merit within your context.

A major benefit of reviewing the research methodologies used by similar studies in your field is that you can often piggyback on the data collection techniques that other (more experienced) researchers have developed. For example, if you’re undertaking a quantitative study, you can often find tried and tested survey scales with high Cronbach’s alphas. These are usually included in the appendices of journal articles, so you don’t even have to contact the original authors. By using these, you’ll save a lot of time and ensure that your study stands on the proverbial “shoulders of giants” by using high-quality measurement instruments .

Of course, when reviewing existing literature, keep point #1 front of mind. In other words, your methodology needs to align with your research aims, objectives and questions. Don’t fall into the trap of adopting the methodological “norm” of other studies just because it’s popular. Only adopt that which is relevant to your research.

Factor #3: Practicalities

When choosing a research methodology, there will always be a tension between doing what’s theoretically best (i.e., the most scientifically rigorous research design ) and doing what’s practical , given your constraints . This is the nature of doing research and there are always trade-offs, as with anything else.

But what constraints, you ask?

When you’re evaluating your methodological options, you need to consider the following constraints:

• Data access
• Equipment and software
• Your knowledge and skills

Let’s look at each of these.

Constraint #1: Data access

The first practical constraint you need to consider is your access to data . If you’re going to be undertaking primary research , you need to think critically about the sample of respondents you realistically have access to. For example, if you plan to use in-person interviews , you need to ask yourself how many people you’ll need to interview, whether they’ll be agreeable to being interviewed, where they’re located, and so on.

If you’re wanting to undertake a quantitative approach using surveys to collect data, you’ll need to consider how many responses you’ll require to achieve statistically significant results. For many statistical tests, a sample of a few hundred respondents is typically needed to develop convincing conclusions.

So, think carefully about what data you’ll need access to, how much data you’ll need and how you’ll collect it. The last thing you want is to spend a huge amount of time on your research only to find that you can’t get access to the required data.

Constraint #2: Time

The next constraint is time. If you’re undertaking research as part of a PhD, you may have a fairly open-ended time limit, but this is unlikely to be the case for undergrad and Masters-level projects. So, pay attention to your timeline, as the data collection and analysis components of different methodologies have a major impact on time requirements . Also, keep in mind that these stages of the research often take a lot longer than originally anticipated.

Another practical implication of time limits is that it will directly impact which time horizon you can use – i.e. longitudinal vs cross-sectional . For example, if you’ve got a 6-month limit for your entire research project, it’s quite unlikely that you’ll be able to adopt a longitudinal time horizon. 

Constraint #3: Money

As with so many things, money is another important constraint you’ll need to consider when deciding on your research methodology. While some research designs will cost near zero to execute, others may require a substantial budget .

Some of the costs that may arise include:

• Software costs – e.g. survey hosting services, analysis software, etc.
• Promotion costs – e.g. advertising a survey to attract respondents
• Incentive costs – e.g. providing a prize or cash payment incentive to attract respondents
• Equipment rental costs – e.g. recording equipment, lab equipment, etc.
• Travel costs
• Food & beverages

These are just a handful of costs that can creep into your research budget. Like most projects, the actual costs tend to be higher than the estimates, so be sure to err on the conservative side and expect the unexpected. It’s critically important that you’re honest with yourself about these costs, or you could end up getting stuck midway through your project because you’ve run out of money.

Constraint #4: Equipment & software

Another practical consideration is the hardware and/or software you’ll need in order to undertake your research. Of course, this variable will depend on the type of data you’re collecting and analysing. For example, you may need lab equipment to analyse substances, or you may need specific analysis software to analyse statistical data. So, be sure to think about what hardware and/or software you’ll need for each potential methodological approach, and whether you have access to these.

Constraint #5: Your knowledge and skillset

The final practical constraint is a big one. Naturally, the research process involves a lot of learning and development along the way, so you will accrue knowledge and skills as you progress. However, when considering your methodological options, you should still consider your current position on the ladder.

Some of the questions you should ask yourself are:

• Am I more of a “numbers person” or a “words person”?
• How much do I know about the analysis methods I’ll potentially use (e.g. statistical analysis)?
• How much do I know about the software and/or hardware that I’ll potentially use?
• How excited am I to learn new research skills and gain new knowledge?
• How much time do I have to learn the things I need to learn?

Answering these questions honestly will provide you with another set of criteria against which you can evaluate the research methodology options you’ve shortlisted.

So, as you can see, there is a wide range of practicalities and constraints that you need to take into account when you’re deciding on a research methodology. These practicalities create a tension between the “ideal” methodology and the methodology that you can realistically pull off. This is perfectly normal, and it’s your job to find the option that presents the best set of trade-offs.

Recap: Choosing a methodology

In this post, we’ve discussed how to go about choosing a research methodology. The three major deciding factors we looked at were:

• Exploratory
• Confirmatory
• Combination
• Research area norms
• Hardware and software
• Your knowledge and skillset

If you have any questions, feel free to leave a comment below. If you’d like a helping hand with your research methodology, check out our 1-on-1 research coaching service , or book a free consultation with a friendly Grad Coach.

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

You Might Also Like:

Very useful and informative especially for beginners

Nice article! I’m a beginner in the field of cybersecurity research. I am a Telecom and Network Engineer and Also aiming for PhD scholarship.

I find the article very informative especially for my decitation it has been helpful and an eye opener.

Hi I am Anna ,

I am a PHD candidate in the area of cyber security, maybe we can link up

The Examples shows by you, for sure they are really direct me and others to knows and practices the Research Design and prepration.

I found the post very informative and practical.

I’m the process of constructing my research design and I want to know if the data analysis I plan to present in my thesis defense proposal possibly change especially after I gathered the data already.

Thank you so much this site is such a life saver. How I wish 1-1 coaching is available in our country but sadly it’s not.

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Research Methodology

Methodology refers to the overarching strategy and rationale of your research. Developing your methodology involves studying the research methods used in your field and the theories or principles that underpin them, in order to choose the approach that best matches your research objectives. Methodology is the first step in planning a research project.

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Scientific method, qualitative research, experiments.

The scientific method is a step-by-step process used by researchers and scientists to determine if there is a relationship between two or more variables. Psychologists use this method to conduct psychological research, gather data, process information, and describe behaviors.

Learn More: Steps of the Scientific Method

Variables apply to experimental investigations. The independent variable is the variable the experimenter manipulates or changes. The dependent variable is the variable being tested and measured in an experiment, and is 'dependent' on the independent variable.

Learn More: Independent and Dependent Variables

When you perform a statistical test a p-value helps you determine the significance of your results in relation to the null hypothesis. A p-value less than 0.05 (typically ≤ 0.05) is statistically significant.

Learn More: P-Value and Statistical Significance

Qualitative research is a process used for the systematic collection, analysis, and interpretation of non-numerical data. Qualitative research can be used to gain a deep contextual understanding of the subjective social reality of individuals.

The experimental method involves the manipulation of variables to establish cause-and-effect relationships. The key features are controlled methods and the random allocation of participants into controlled and experimental groups.

Learn More: How the Experimental Method Works in Psychology

Frequent Asked Questions

What does p-value of 0.05 mean?

A p-value less than 0.05 (typically ≤ 0.05) is statistically significant. It indicates strong evidence against the null hypothesis, as there is less than a 5% probability the results have occurred by random chance rather than a real effect. Therefore, we reject the null hypothesis and accept the alternative hypothesis.

However, it is important to note that the p-value is not the only factor that should be considered when interpreting the results of a hypothesis test. Other factors, such as effect size, should also be considered.

Learn More: What A p-Value Tells You About Statistical Significance

What does z-score tell you?

A  z-score  describes the position of a raw score in terms of its distance from the mean when measured in standard deviation units. It is also known as a standard score because it allows the comparison of scores on different variables by standardizing the distribution. The z-score is positive if the value lies above the mean and negative if it lies below the mean.

Learn More: Z-Score: Definition, Calculation, Formula, & Interpretation

What is an independent vs dependent variable?

The independent variable is the variable the experimenter manipulates or changes and is assumed to have a direct effect on the dependent variable. For example, allocating participants to either drug or placebo conditions (independent variable) to measure any changes in the intensity of their anxiety (dependent variable).

Learn More : What are Independent and Dependent Variables?

What is the difference between qualitative and quantitative?

Quantitative data is numerical information about quantities and qualitative data is descriptive and regards phenomena that can be observed but not measured, such as language.

Learn More: What’s the difference between qualitative and quantitative research?

Explore Research Methodology

Criterion Validity: Definition & Examples

Convergent Validity: Definition and Examples

Content Validity in Research: Definition & Examples

Construct Validity In Psychology Research

Concurrent Validity In Psychology

Internal vs. External Validity In Psychology

Qualitative Research: Characteristics, Design, Methods & Examples

Demand Characteristics In Psychology: Definition, Examples & Control

Between-Subjects vs. Within-Subjects Study Design

Random Assignment in Psychology: Definition & Examples

Double-Blind Experimental Study And Procedure Explained

Observer Bias: Definition, Examples & Prevention

Sampling Bias: Types, Examples & How to Avoid It

What is The Null Hypothesis & When Do You Reject The Null Hypothesis

Between-Subjects Design: Overview & Examples

What Is A Case Control Study?

Case Study Research Method in Psychology

Cluster Sampling: Definition, Method and Examples

Cohort Study: Definition, Designs & Examples

Control Group vs Experimental Group

Convenience Sampling: Definition, Method and Examples

Confounding Variables in Psychology: Definition & Examples

Correlation in Psychology: Meaning, Types, Examples & coefficient

Controlled Experiment

Ethical Considerations In Psychology Research

Experimental Method In Psychology

Extraneous Variables In Research: Types & Examples

Ethnocentrism In Psychology: Examples, Disadvantages, & Cultural Relativism

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Home » Articles » Four Key Elements of a Successful Research Methodology

Four Key Elements of a Successful Research Methodology

Written by PortMA

• Experiential Measurement

Research methodology must be determined before actually beginning the research. You’ve heard the adage “Fail to Plan; Plan to Fail.” The research methodology is the most crucial step of the research design process. It’s the blueprint for the collection, measurement, and analysis of the data. Once completed, always keep the blueprint, or t he Methodology Brief available for easy reference. Research methodology may vary in form from one project to another, but should always incorporate the following four elements.

• Measurement Objectives
• Data Collection Processes
• Recommended Survey
• Reporting Plan

Research Methodology: Measurement Objectives

Measurement Objectives are the reasons for the research and the expected outcomes. The objectives are the “why” of the research. They should be clear and concise. Explain each measurement objective in detail. Be precise, so as not to leave any room for erroneous interpretation of the results.

Research Methodology: Data Collection

Data Collection methodology covers the logistics of the research. Determine how data should be collected. If there will be multiple data collection sources, the methodology should describe each source and how they fit together to make the big picture. Explain the pros and cons of each data collection source, especially if you are using any sources that are new to team members or if you expect to encounter problems with “buy in.”

Research Methodology: Survey

Base each question on at least one of the research objectives. Make a distinct connection between every survey question and the research objective. Don’t ask questions that don’t link directly to a research objective.

Research Methodology: Reporting Plan

Finally, always have a Reporting Plan. Explain how you plan to share the information gathered. Discuss the format in which you will deliver the reports ( e.g. , PowerPoint). Indicate how long the reports will be and what information each report will contain. Prepare a timeline with milestones and KPIs so everyone knows when to expect deliverables. Designing the research methodology may be the most important phase of any research project because it is the blueprint for all to follow. Don’t attempt to conduct viable research on a whim. The results could be extremely misleading and outright erroneous. The research methodology has everything that everyone needs to know about conducting the project, presented in a format that is referenceable  throughout a project.

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Davidson D, Ellis Paine A, Glasby J, et al. Analysis of the profile, characteristics, patient experience and community value of community hospitals: a multimethod study. Southampton (UK): NIHR Journals Library; 2019 Jan. (Health Services and Delivery Research, No. 7.1.)

Analysis of the profile, characteristics, patient experience and community value of community hospitals: a multimethod study.

Chapter 2 research objectives, questions and methodology.

In the light of the unfolding policy context and gaps within the existing literature outlined in Chapter 1 , and informed by conversations with key stakeholders (see Patient and public involvement ), this study aimed to provide a comprehensive analysis of the profile, characteristics, patient and carer experience and community engagement and value of community hospitals in contrasting local contexts. The specific objectives were to:

• construct a national database and develop a typology of community hospitals
• explore and understand the nature and extent of patients’ and carers’ experiences of community hospital care and services
• investigate the value of the interdependent relationship between community hospitals and their communities through in-depth case studies of community value (qualitative study) and analysis of Charity Commission data (quantitative study).

In meeting these aims and objectives, the study addressed three overarching research questions (each with an associated set of more specific subquestions as summarised in Table 1 ):

Research questions and objectives

• What is a community hospital? In addressing this question, we drew on existing definitions and conceptualisations of ‘community hospitals’ as outlined in Chapter 1 , Research on community hospitals . Although our emphasis here was primarily empirical and descriptive, we were nevertheless guided by, and sought to contribute to, theoretical debates on definitions of community hospitals and their place within wider health and care systems, drawing on concepts of rural health care, chronic disease and complex care burden, integrated care and clinical leadership.
• What are patients’ (and carers’) experiences of community hospitals? This element of the study was designed to contribute to the conceptualisation of the distinctive elements of community hospitals as understood through the ‘lived experiences’ of patients, rather than just satisfaction ratings. Here, we were influenced by prior analysis of the functional, technical and relational components of patient experience (e.g. environment and facilities, delivery of care, staff) alongside a more theoretical interest in the interpersonal, psychological and social dimensions of patient experience. Very early on in our study, through conversations with patient and public involvement (PPI) stakeholders, we recognised the importance of exploring and understanding the experience not only of patients but also of family carers, and hence we extended our initial question to include both patients’ and carers’ experiences.
• What does the community do for its community hospital, and what does the community hospital do for its community? In addressing this question, we drew on notions of voluntarism and participation and brought together thinking from the separate bodies of literature on volunteering, philanthropy and co-production. This led us to question not just the level of voluntary support for community hospitals but also the different forms it took, how this varies between and within communities, how it is encouraged, organised and managed, and what difference it makes (outcomes). We also drew on notions of social value, including existing typologies, that encouraged us to question different forms of value (e.g. economic, social, human, symbolic) and different stakeholder groups (e.g. staff, patients, communities).

Given the diversity of the questions, we do not set out to provide an over-riding hypothesis or unified theoretical framework for the study as a whole. Instead, these concepts, frameworks and debates served as ‘sensitising categories’, shaping our approach to study design as well as data collection and analysis. 71 We return to these in Chapter 8 and augment them with new concepts that emerged from our analysis.

In addressing these diverse questions, we adopted a multimethod approach with a convergent design. Quantitative methods were employed to provide breadth of understanding relating to the questions concerning ‘what’, ‘where’ and ‘how much’, whereas qualitative methods provided depth of understanding, particularly in relation to questions of ‘how’, ‘why’ and ‘to what effect’.

The research was conducted in three distinct (although temporally overlapping) phases, each with a number of different associated elements and research methods: (1) mapping (database construction and analysis through data set reconciliation and verification), (2) qualitative case studies (semistructured interviews, discovery interviews, focus groups) and (3) quantitative analysis of charity commission data. Table 1 summarises the study objectives, questions and research methods. Each of the three phases of research are discussed in turn through the following sections of this chapter, before the final sections discuss data integration, PPI and ethics.

• Phase 1: mapping and profiling community hospitals

Phase 1 of the research involved a national mapping exercise to address the first study question ‘what is a community hospital?’. It aimed to map the number and location of all hospitals in England to then provide a profile and definition of community hospitals. A database of characteristics would enable the profiling of community hospitals, inform a typology and support a sampling strategy for subsequent case studies. Data were collected from all four UK countries but, in accordance with the brief of the study, this report focuses on England. Reference is made to Scotland’s data as they were important in developing the methodology. The structure of the mapping comprised five elements:

• literature review – constructing a working definition: (see Chapter 1 )
• data set reconciliation – building a new database from multiple data sets
• database analysis – developing an initial classification of community hospitals with beds
• rapid telephone enquiry – refining the classification
• verification – checking and refining the database through internet searches.

The flow of activities is depicted in Figure 1 .

Structure of the national mapping exercise.

Literature review: constructing a working definition

We developed a working definition of a community hospital as drawn from the literature (and as outlined in Chapter 1 ):

• A hospital with < 100 beds serving a local population of up to 100,000 and providing direct access to GPs and local community staff.
• Typically GP led, or nurse led with medical support from local GPs.
• Services provided are likely to include inpatient care for older people, rehabilitation and maternity services, outpatient clinics and day care as well as minor injury and illness units, diagnostics and day surgery. The hospital may also be a base for the provision of outreach services by MDTs.
• Will not have a 24-hour A&E nor provide complex surgery. In addition, a specialist hospital (e.g. a children’s hospital, a hospice or a specialist mental health or learning disability hospital) would not be classified as a community hospital.

The initial enquiry was framed around a ‘classic’ community hospital. The term was drawn directly from the Community Hospital Association 2008 classification, 72 describing classic community hospitals as ‘local community hospitals with inpatient facilities’ (i.e. with beds) and as distinct from community care resource centres (without beds), community care homes (integrated health and social care campus) or rehabilitation units. Although the term ‘classic’ was initially helpful in setting the boundaries of the study, it presented ongoing problems, such as whether it described all community hospitals with beds or a subset within that. Throughout the study, therefore, we have adopted the term ‘community hospital’ and omitted the adjective ‘classic’. Our focus, however, has remained on community hospitals with beds.

Data reconciliation: building a new database from multiple data sets

There was no up-to-date comprehensive database of community hospitals in England. The NHS Benchmarking Network [URL: www.nhsbenchmarking.nhs.uk (accessed 8 October 2018)] membership database was not comprehensive and could not be used to populate our hospital-level database because the data were anonymised. For this reason, one of our first tasks was to compile a new database, by bringing together existing health-care data sets, each of which provided different fields of information needed to test our working definition and to map and profile community hospitals.

Two types of data sets were collected. Centrally available data sets formed the starting point for the mapping study, providing codified data (see Appendix 1 ). As none of these centrally available data sets provided a comprehensive picture, it was necessary to supplement them through extensive internet searching and by talking to people in the field, as well as drawing on the expertise of research team members.

The base year for major data sets was 2012/13. Data were difficult to access, not comprehensive and spread across a greater number of sources. Four data sets were used:

• Community Hospital Association databases of community hospitals (one from 2008 and another from 2013)
• Patient-Led Assessments of the Care Environment (PLACE) 2013 [replacing the former Patient Environment Action Team programme]
• Estates database – Estates Returns Information Collection (ERIC) 2012
• NHS Digital activity by site of treatment 2012/13.

Barriers to obtaining site and activity data included (1) specific difficulties in the period 2012/13 when primary care trusts (PCTs) were being disbanded and clinical commissioning groups (CCGs) were being established (with effect from 31 March 2013) and (2) processes and caution in NHS Digital associated with releasing patient-sensitive data (even though we had not requested patient-based data). Quality problems were associated with the ‘location of treatment’ code, which was central to our enquiry identifying community hospitals but did not appear to be well used in England, leading to examples of missing data and inconsistent labels (described under reconciliation and duplication). The code also lacked stability as it changed with each new NHS reconfiguration in England.

The core data set for England, supplied by NHS Digital, was a list of all hospitals in England, based on ‘site of treatment code.’ Figure 2 shows the relationship between national data sets.

The relationship between four England data sets. CHA, Community Hospitals Associations.

The new database, populated through our reconciliation of these various data sets, provided a census of community hospitals at 2012/13, which was updated to August 2015 (e.g. when a hospital closed and then redeveloped, formed a new hospital replacing two old community hospitals, closed beds on a temporary basis and changed its name).

Database analysis: developing an initial classification of community hospitals with beds

Although the focus of this report is on England, it is important to mention our work on mapping community hospitals in Scotland, as this was instrumental in developing our approach to classifying data for England. Data sets on community hospitals in Scotland [Information Services Division (ISD) and government community hospital data sets: community hospital, general hospital, long-stay/psychiatric hospital, small long-stay hospital] were both more accessible and more comprehensive, lending themselves to early analysis (see Appendix 2 ).

An initial classification of hospitals in England was developed, informed by categories set out by Estates (community hospital, general acute hospital, long-stay hospital, multiservice hospital, short-term non-acute hospital, specialist hospital, support facility, treatment centre) and PLACE (acute/specialist, community, mental health only, mixed acute and mental health/mental health, treatment centre). It was combined with specialty classifications based mainly on NHS Digital inpatient activity data and developed further through analysis of Community Hospitals Association (CHA) data and discussions within the study team ( Table 2 ).

Classification of all hospitals in England

Rapid telephone enquiry: refining the classification

Analysis of the Scotland data suggested that the code ‘GP specialty’ was a defining feature of community hospitals, but early analysis of the England data showed that this was less transferable. If we relied on GP specialty coding alone, many known community hospitals would be excluded from our database: not all community hospital inpatient beds in England were coded to GPs.

A short piece of empirical data collection was undertaken to understand the link between the specialty codes and practice and to test the working definition (based on the literature and on the Scottish data) that community hospitals were predominantly GP led. A telephone questionnaire was designed by the study team (see Appendix 3 ) and piloted through the CHA.

Seven hospitals from five specialty category codes (≥ 80% GP, < 80% GP and mixed specialties, general medicine, geriatric medicine, geriatric mixed specialties) were randomly selected. The test sample of 35 was reduced by four as a result of closure or conversion to nursing homes. The research team called the hospitals to gain contact details of the matron or ward manager ( n  = 20; the small sample size highlighting the difficulty of identifying leadership, especially when the community hospital is represented by a single ward), e-mailed the questionnaire, conducted telephone interviews with staff to complete the questionnaire (taking 10–20 minutes each), transcribed notes and returned the completed questionnaire to respondents ( n  = 12). Analysis of these telephone interviews gave us confidence in the specialty coding, while also confirming the need to be more expansive in our working definitions and categorisations.

Verification: checking and refining the database through internet searches

The mapping enquiry was finalised through five iterations of searching and checking. The CHA consulted its database and membership list (from both 2008 and 2013). A full internet search took place at two points, in February 2015 and August 2015, taking account of hospital closures and changes of function up to 2014/15, with further validation and amendments up to August 2015. By the end of the study, the 2012/13 data set, based on the NHS Digital Spine using ‘site of treatment code’, had been validated through a check of every potential community hospital. A total of 366 sites were examined through web-based and telephone enquiries, including 60 that were not present on the NHS Digital database (see Appendix 4 for the list of community hospitals with beds).

• Phase 2: case studies

In order to explore patient and carer experience of community hospitals and aspects of community engagement and value, we undertook qualitative case studies. Although the initial aim of the case studies was to address the second and third research questions, the findings also enabled new insights into the first study question of ‘what is a community hospital’.

The decision to adopt a comparative case study design 73 across multiple community hospital sites was influenced by three factors. First, given the gaps in the literature highlighted in Chapter 1 , it would be useful to uncover different aspects of the patient experience, community engagement and value of community hospitals and enable the identification and analysis of common themes (looking for similarities, differences and patterns) both within and across cases. 74 – 76 Second, it provides a suitable way of ‘exemplifying’ sites, 77 given the variety of ownership models and locations. Third, it is useful in enabling an examination of ‘complex social phenomena’, 78 and, in particular, the social, functional, interpersonal and psychological factors that shape patient experiences, as well as those that influence community engagement and value. Below, we summarise the approach to case study selection for work packages 2 and 3, before moving on to discuss the research elements used.

Selection of case study sites

In selecting case study sites, we adopted a ‘realist’ approach to sampling, 79 moving back and forth between categories identified from the literature as being important for patient experience and community value and our learning about the characteristics of community hospitals identified from the mapping exercise. In order to reflect the diversity of community hospitals (highlighted in the literature and mapping), we selected cases in contrasting locations with different numbers of beds, ranges of services, ownership/provision and levels of voluntary income and deprivation.

To allow for a particular focus on variations in voluntary support for community hospitals, hinted at through the national mapping exercise and identified as a particular gap in the existing literature, we selected pairs of hospitals across four Clinical Commissioning Group (CCG) areas with contrasting levels of voluntary income but similar levels of deprivation. This would allow for a good comparison within and between cases (e.g. why two community hospitals within one CCG area, with similar levels of deprivation, have contrasting levels of voluntary support, given that previous research has tended to suggest a strong negative correlation between deprivation and voluntary activity).

Using these criteria, we selected eight case studies of hospitals of different sizes, ages and service profiles located across England (although mostly concentrated in the south, reflecting the national pattern of community hospital development; see Figure 7 ) in areas of contrasting levels of deprivation. Six of the buildings were owned by, and their main inpatient service was provided by, the NHS. Two were owned by the NHS but their main inpatient services were provided by a community interest company (CIC). We added a ninth case study, owned by a charity, to increase diversity in terms of ownership/provision (as there were very few examples of independently owned community hospitals, it was not possible to identify a matched pair). Table 3 provides a summary of the nine case studies selected, according to the data that were available from the mapping exercise. Fuller qualitative descriptions are provided in Chapter 4 and Appendix 5 .

Profile of selected case studies

Case study data collection

The case studies involved seven research elements, as summarised in Table 4 . All elements were conducted over five visits to each case study. Across all case study sites and research methods, 241 people participated in the study through interviews and 130 people participated through 22 focus groups; a small number of people who participated in individual interviews also participated in focus groups (see Appendix 6 for full details).

Research elements and focus

Scoping visits were made to each of the case studies in order to build relationships with key stakeholders (primarily matrons and chairpersons of Leagues of Friends), gather background information on the hospitals and local communities, identify potential study participants and collect key documents and data. Documents selected included hospital histories, annual reports, local service information (when available) and media coverage. Reviewing these helped to provide a basic understanding of the cases prior to the main fieldwork visits and added to our profiling of each of the case study hospitals.

We also aimed to gather hospital-level data from patient-reported experience measures (PREMs) 80 and the revised Friends and Family Test (FFT). 81 However, none of the case study community hospitals collected PREMs data, as this had only recently been required of community providers. Although all sites collected FFT scores, we were able to access data for only seven of the nine case studies because, in the remaining two cases, the trust compiled data at trust rather than hospital level and it was not possible to disaggregate the data. Furthermore, the FFT data were not strictly comparable as some scores covered inpatient care only, whereas others covered both inpatient and outpatient care.

Local reference group

We established a local reference group (LRG) in each of our case studies to bring local people together to steer, support and inform the research at the local level. These LRGs comprised key members of hospital staff, the League of Friends, volunteers and local voluntary and community groups, some of whom had also been patients and/or carers. Their role was to help build a picture of the local context to inform subsequent data collection elements, build support for the study within the local community and reflect on emerging findings and their implications for local practice. There were two LRG meetings per case study during the local fieldwork stage: one at the start of the fieldwork period (which focused on mapping the community hospital services and community links) and one at the end (which focused on discussing the emerging findings and their potential implications). The first LRG meeting for CH3 and CH4 was joint (for convenience) but the second meeting was separate. Following completion of the fieldwork and analysis, each LRG received a report of the findings relating to their specific case study (i.e. alongside this national report, we produced nine local reports).

Semistructured interviews with staff, volunteers and community representatives

We conducted semistructured interviews with staff ( n  = 89 staff across the nine cases), community stakeholders ( n  = 20) and volunteers ( n  = 35). Although most of the interviews were with single respondents, some were with two or, very occasionally, three people (depending on respondent preferences). Respondents were selected through purposive sampling 79 guided by the scoping visits, the initial LRG and snowballing. Each of the interviews explored the profile of the hospital and the local context, perceptions of patient and carer experience, and community engagement and value. The emphasis placed on the different sets of questions, however, varied between the groups of respondents (e.g. more time was spent on community engagement and value within the community stakeholder interviews, although we still asked questions relating to hospital profile and perceptions of patient/carer experience). Interviews were nearly all conducted face to face, although a small number were conducted via telephone, at respondent preference. Interviews with staff, volunteers and stakeholders lasted, on average, 60 minutes. All were digitally recorded and later transcribed verbatim.

Discovery interviews with patients

Rather than focusing on satisfaction levels, or other quantifiable measures of experience, the study was concerned with exploring the lived experience of being a patient using community hospital services. Lessons from previous studies show that gathering experiences in the form of stories enhances their power and richness, 36 so we selected an experience-centred interview method 82 that drew on the principles of narrative approaches 83 and, particularly, discovery interviewing. 84 Narrative approaches invite respondents to tell their stories uninterrupted, rather than respond to predetermined questions, giving control to the ‘storyteller’. This approach can elicit richer and more complete accounts than other methods 85 , 86 because reflection enables respondents to contextualise, and connect to, different aspects of their experiences. Discovery interviewing helps to capture patients’ experiences of health care when there may be pathways or clinical interventions central to patient experience. 87 As such, after a general opening question, our interviews focused around a very open question inviting respondents to tell their story of being a patient at the community hospital. We followed this by asking respondents to consider a visual representation we had developed of factors found in previous research to have shaped patient experience, to prompt people’s memories and thoughts (see Appendix 7 for an example of the discovery interview).

Our aim was to interview six patients from each case study. Our final sample across all sites was 60 patients. The small sample size reflected the in-depth nature of the interviews. We sought, as far as possible, to select patients with a mix of demographics (particularly in terms of gender), care pathways (particularly in terms of step up/step down) and services used (inpatient/outpatient). Potential participants were identified by the hospital matron and/or lead clinician and/or service leads. Each was written to by the hospital with a request to participate in the study and was sent an information sheet and an opt-in consent form. Patients who were willing to participate sent their replies directly to the study team. Written consent was provided prior to the commencement of the interview. In line with the Mental Capacity Act 2005 Code of Practice, 88 we made provision for the appointment of consultees when potential respondents lacked the capacity to consent to participation in the study, although this was not utilised.

Although many of our respondents were current inpatients, we also spoke to some inpatients who had been recently discharged and to outpatients from a range of different clinics. Outpatients who agreed to participate tended to be those using services several times a week (e.g. renal patients) or over a longer period of time (e.g. those with chronic conditions), rather than one-off users. Interviews with patients lasted between 30 and 90 minutes, were digitally recorded (in all cases except for two because of respondent preference/requirements) and transcribed verbatim. At the end of the interviews, we asked respondents to complete a short pro forma to gather basic demographic and service information for analysis purposes.

Semistructured interviews with carers

Semistructured interviews were conducted with carers in order to explore their experience of using the community hospital as a carer of an inpatient. Our aim was to interview three carers per case study; in total we spoke to 28 carers across the nine sites. Carers were either related to, or close friends of, patients (either current or recent) at the hospital. In most cases, we interviewed carers of patients who had also been interviewed, but in some cases carers were not directly linked to patients involved in the study (indeed, some carers were reflecting on the experience of caring for a patient who had recently died).

The main focus of the interviews was on the experience of being a carer of someone at the hospital, with our initial question reflecting the narrative approach adopted for patients by asking respondents to tell us their story of using the hospital. In addition, as the respondents were typically local residents, we also asked questions about their perceptions of patient experience, about local support for, and engagement with, the hospital and of value. Interviews with carers lasted, on average, 60 minutes. All were digitally recorded and later transcribed verbatim.

Focus groups

We conducted focus groups with members of MDTs, volunteers and community stakeholders. Although we had anticipated conducting each of the three focus groups in each of the case study sites, this was not always possible owing to practical reasons; for example, in some of the case study sites there were very few volunteers, making it difficult to organise a focus group. We ran focus groups with MDTs in eight of the nine case studies, involving a total of 43 respondents; with volunteers in six of the case studies, involving a total of 33 respondents; and with community stakeholders in eight of the cases, involving 54 respondents. Individual focus group respondents were selected through purposive sampling. We worked with LRGs and other key contacts to identify potential participants, each of whom was written to and asked to participate.

The focus groups complemented the interviews, enabling the inclusion of a wider range of perspectives in the study and, in particular, allowing us to observe the emergence of discussion, consensus and dissonance among groups of participants. They lasted, on average, 90 minutes and were digitally recorded and transcribed in full.

Telephone interviews with managers and commissioners

We conducted telephone interviews to explore the views of senior managers of provider organisations and commissioners of community hospitals. The nine case studies were based in five CCG areas where the main inpatient services were provided by four NHS trusts and one integrated health and social care CIC. Our aim was to interview one respondent from each of the providers and CCGs. In total, we spoke to five provider and four CCG representatives. The interviews explored the strategic context for the community hospitals involved in the study, alongside the perceptions of these senior stakeholders of patient experience and the value of community hospitals. The interviews lasted, on average, 60 minutes and were digitally recorded and later transcribed in full.

Qualitative case study data analysis

We adopted a thematic approach to qualitative data analysis, aided by the use of NVivo 11 (QSR International, Warrington, UK) for data management and exploration. Our approach was both inductive, with themes emerging from the data, and deductive, framed by our research questions and ongoing reading of the literature. Initial themes and codes were developed after three members of the team (AEP, DD and NLM), who collectively had been responsible for the case study data collection, reviewed the transcripts. The emerging themes, codes and associated findings were discussed at wider study team meetings, at the LRG meetings for individual case studies and at annual learning events that brought together participants from across the case studies. A refined coding frame was then tested by the same three members of the research team each coding a sample of transcripts; this led to a further refinement of the codes, while also helping to ensure that each of the researchers was adopting a similar approach.

In this report, we focus in particular on across-case comparisons, highlighting themes that emerged across the case studies, emphasising key points of similarity and difference between the cases, as relevant. In addition, we have produced individual reports for each of the local case study sites that have shared findings from our within-case analysis, as relevant for each individual hospital. Comparative analysis, including of the paired cases, will be developed further in future research articles, in which a focus on more specific aspects of the study will allow more space for presentation of such work.

Throughout the analysis, unique identifiers were used for the transcripts/respondents to help ensure confidentiality and anonymity. Sites were assigned a number (e.g. CH1) and respondents given a letter: patient (P), family carer (CA), staff (S), volunteer (V), community stakeholder (CY) and senior manager or commissioner (T), with sequential numbering, date of interview and initials of researcher added to provide an audit trail. This basic coding method is used throughout the report (e.g. CH1, S01 represents the first staff member to be interviewed at the first community hospital case study site). It is worth noting, however, that, although respondents were identified by a key characteristic (e.g. patient or staff) and their transcripts labelled as such, the boundaries between these categories were not discrete: many community stakeholders, for example, had also been patients or carers, and many staff were also members of the local community.

• Phase 3: quantitative analysis of Charity Commission data

Collating data on charitable finance and volunteering support

The third phase of our research involved the quantitative analysis of data from the Charity Commission on voluntary income and volunteering for community hospitals across England. The aim of this activity was to examine charitable financial and volunteering support for community hospitals by investigating:

• variations in the likelihood that hospitals receive support through a formal organisational structure such as a League of Friends, and if so, variations in its scale (in financial terms) between communities
• uses of the funds raised (e.g. capital development, equipment, patient amenities).

We captured financial and volunteering data for registered charities from the Charity Commission (the Commission). The Commission holds details of organisations that have been recognised as charitable in law and that hold most of their assets in England, or have all or the majority of their trustees normally resident in England, or are companies incorporated in England. The data are described more fully in Appendix 9 .

Subject to a small number of exceptions, all charities in England with incomes of > £5000 must register with the Commission and submit financial statements consisting of trustees’ annual reports (returns) and annual accounts. The accounts of those charities whose income or expenditure exceeds a threshold of £25,000 are made available on the Commission’s website. 89 Charities that have income and expenditure of < £5000 a year have (since 2009) been exempted from the need to register. We identified 274 hospitals in England that satisfied the inclusion criteria for this research project ( Figure 3 ). We used the Charity Commission’s data to identify charities that support each of these hospitals, matching by name or through examining lists of charities registered in the locality where the hospital is based.

Community hospital and charities sampling frame.

We also directly approached eight non-registered charities (usually those with an income of < £5000 a year) that were known to have been established to support specific community hospitals, but received no usable data relating to them. Four hospitals in our data set were registered as charities themselves but were excluded from the analysis because they are exceptional cases of charitable action.

We found that 247 of these charities were registered in their own right (labelled ‘individual associated charities’ in Figure 3 ). The remainder were what is known as ‘linked’ charities, that is, entities associated with larger charitable organisations serving a NHS trust comprising several institutions. These ‘linked’ charities were excluded because it was not possible to disaggregate the support they provide to individual components of the trust. Financial information was available for the period from 1995 to 2014 (only small numbers of observations were available for years prior to that because digitisation of the register began only in the early 1990s).

Measurements

Financial information for at least 1 year between 1995 and 2014 was available for 245 charities in England, and this information formed the final sample for this part of the study. The number of non-zero financial reports to the Commission in each year ranged from 181 (1996) to 226 (2007). The data, covering the period to 2014, were the latest available at the time of analysis (2016). See Appendix 9 for full details of available charity reports by year. All financial information in this paper is presented at constant 2014 prices.

Using the Charity Commission website, we obtained copies of these accounts for those selected charities whose expenditure or income exceeded £25,000 in any one year. This gave data covering 358 separate financial years; the number of accounts available is shown in Table 5 .

Accounts for larger charities (income of > £25,000)

We focused on the period from 2008 to 2013, when between 41 and 91 charities of interest generated at least one such financial return. Numbers vary because an individual charity may or may not exceed the £25,000 threshold at which its accounts are presented via the Charity Commission’s website, depending on fluctuations in its finances.

Charity accounts aggregate income and expenditure figures into a small number of general categories. These provide relatively little detail on income and expenditure and may even aggregate quite different sources of expenditure within the same funding stream. As such, to probe income sources and the application of expenditure in more detail, data were captured from the notes to the accounts of these charities. The extensive income data that were generated (21,733 items) were categorised to provide useful insights into sources of income. Classifying the expenditure of charities was not undertaken because of the complexity of the data and the limits to the usefulness of such an exercise. Appendix 9 provides further details of the extraction, classification and analysis of income and expenditure data.

Contribution: number of volunteers and estimates of input

The Charity Commission guidelines 90 require charities to record their best estimates of the number of individual UK volunteers involved in the charity during the financial year, excluding trustees (see Appendix 9 ).

Before 2013, data on volunteer numbers were often sparse, but, since that date, efforts have been made to gather more detailed information. Approximately 73,000 charities had supplied between one and three non-zero returns of their volunteer counts in the three years between 2013 and 2015, including > 90% of our charities. We calculated the average number of volunteers for the period in question. To provide an upper-bound estimate, we also take the maximum value returned for each charity.

Volunteer hours were estimated using regular survey data (Home Office Citizenship Survey, 2001–10; Community Life survey, 2012 onwards). We take the average number of hours per week reported by those who say they have given unpaid help to organisations during the previous year. This is approximately 2.2 hours. This is a minimum estimate and it may be that the actual numbers are larger than these survey data would imply. If we make the assumption that these are probably fairly regular volunteers, a higher figure of 3.05 hours per week is given if we take the average number of hours reported by those who say they volunteer either at least once a week or more frequently, or at least monthly but less frequently than once a week.

There are no studies that would tell us with any certainty whether or not volunteers in these kinds of organisations put in more or fewer hours than the volunteering population generally. We then multiply these two estimates of time inputs by the average and maximum volunteer numbers, respectively, to give the number of hours contributed by volunteers over the course of the year (assuming 46 weeks of volunteering a year). These can be converted to full-time equivalent numbers by dividing by 37.5 (hours per working week) and 46 (weeks per working year).

Opinions differ on the best method for calculating a cash equivalent for the value of volunteer labour. The lowest is to use the national minimum wage; others might include an estimate of the replacement cost (i.e. what it would cost the organisation to employ people to do the same tasks if they had to pay them), but this assumes knowledge of the tasks being undertaken. The national minimum wage for the period for which we have the most comprehensive volunteering data (2013–15) was £6.50 per hour. 91

Data convergence and integration

Although the quantitative (phases 1 and 3) and qualitative (phase 2) data were collected separately, they could nevertheless be considered ‘integrated’ because the different research elements were explicitly related to each other within a single study and in such a way ‘as to be mutually illuminating, thereby producing findings that are greater than the sum of the parts’. 92 Data triangulation, convergence and integration occurred in a number of different ways, at different stages of the research.

In phase 1 of the research, a revised definition and set of characteristics captured within the database was used to support development of a typology and informed the case study sampling for phase 2. For phase 3, the database informed the sample of charities selected for analysing voluntary income and volunteering data and providing additional data fields to be linked to the Charity Commission data.

Although the national quantitative data provided breadth to the study, these were limited and left questions unanswered. The local qualitative data brought depth to the question ‘what is a community hospital’, by helping to build a picture of the history, context and change over time. Qualitative interviews in work packages 2 and 3 were conducted concurrently, and triangulation of data between stakeholder, volunteer, staff, carer and patient interviews helped validate findings and strengthen our understanding of patient and carer experiences and community engagement and value.

In addition, the combination of researchers working on more than one work package, reflexive team meetings and the involvement of different representations in the team [CHA, University of Birmingham and Crystal Blue Consulting (London, UK)] allowed for healthy dialogue, debate and analysis. Emerging findings from each phase of the research were, for example, shared through internal working papers and discussed regularly at whole project team meetings.

• Patient and public involvement

Our commitment to PPI ensured that patients, carers and the public were involved in this study before and during its conduct. PPI involvement in the study design was facilitated by one of the researchers (HT), who first consulted with 10 PPI members of the Swanage Health Forum, representing the League of Friends; a GP practice Patient Participation Group; Swanage Carers; Partnership for Older People’s Programme; Wayfinders; the Senior Forum; the Health and Wellbeing Board; Cancare; a public Governor for Dorset Healthcare NHS Trust; and a retired GP. This group provided an endorsement of the study’s proposed focus and methodology.

At the national level, 13 board members of CHA (four GPs, six nurses, two managers and one League of Friends member) co-produced the initial research proposal. Two members then became part of the study steering group, which met regularly throughout the study, supported the development of research materials and supporting documentation, helped facilitate access to potential case studies, contributed to the local and national reports and reviewed several drafts. We also engaged with approximately 100 delegates at three CHA annual conferences (presentations and workshops focused on working with findings) that included not only practitioners but members of community hospital Leagues of Friends.

In addition, a cross-study steering group, chaired by Professor Sir Lewis Ritchie, University of Aberdeen, provided guidance across all three Health Services and Delivery Research community hospital studies, with representation from the CHA, Attend (National League of Friends) and the Patients Association, alongside the three study teams. The steering group met seven times over the period of this study, offering opportunities to share findings and explore experiences between the studies.

As described in Local reference group , at the local level we established LRGs within each of our case study sites to bring local people together (hospital staff, volunteers and community members, a number of whom were patients and/or carers) to steer, support and inform the case study research. To facilitate cross-case learning, we brought together representatives from each of the LRGs three times to share experiences, identify best practice and network. Event themes reflected each of the three research questions, and the days offered time for case study representatives to work together, share across sites, hear from national experts, contribute to the ongoing development of the study and reflect on emerging findings and their implications.

• Ethics approval

Ethics approval was provided by the University of Birmingham, in line with the Department of Health and Social Care’s Research Governance Framework, for work package 1 (national mapping) and elements of work package 3 (quantitative charitable finance and volunteering support data). The university also provided sponsorship for the whole study. The qualitative case studies required full ethics review through the National Research Ethics Service as they involved interviews with patients and carers and interviews and focus groups with NHS staff, volunteers and community stakeholders. The Wales Research Ethics Committee 6 reviewed this research and provided a favourable ethics opinion (study reference number: 16/WA/0021).

Informed by key stakeholder engagement and a review of the policy context and existing literature, this study explored the profile, characteristics, patient and carer experience, community engagement and value of community hospitals in England through a multimethod approach. The research was conducted in three overlapping phases – mapping, case studies and Charity Commission data analysis – that, together, involved a range of qualitative and quantitative methods. Data for each phase were collected and analysed separately but iteratively, with emerging findings discussed regularly through a range of mechanisms, including whole project team meetings and internal working papers. We involved key national and local stakeholders throughout the study, from design, through to data collection and analysis, and reporting and dissemination.

Having framed the study (see Chapter 1 ) and described our research methodology (see Chapter 2 ), we now move on to share the findings. Chapters 3 – 7 describe the findings emerging from different elements of the study, and Chapter 8 brings those findings together and discusses them in relation to the wider literature and their significance for knowledge and practice.

• Cite this Page Davidson D, Ellis Paine A, Glasby J, et al. Analysis of the profile, characteristics, patient experience and community value of community hospitals: a multimethod study. Southampton (UK): NIHR Journals Library; 2019 Jan. (Health Services and Delivery Research, No. 7.1.) Chapter 2, Research objectives, questions and methodology.
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Aims and Objectives of Research Methodology

Meaning of research methodology.

Research methodology simply refers to the procedure or plan of action for conducting a research. It defines techniques and tools used to collect, process and analyze data regarding the research topic.

Research methodologies tell the systematic method for acquiring data and studying it for deriving out crucial findings. This is an important process that helps in solving problems and making business decisions. It enables management for properly organizing their efforts in a right direction for generating an idea.

Methodology of research indicates and influences the overall validity and reliability of whole research to be conducted. Methodology answers mainly two questions regarding research that are how the data used for study was acquired and how it was analyzed to derive out the findings.

Research methodologies are broadly classified into two main categories: Quantitative research methods and Qualitative research methods. Quantitative research is one which is based on quantitative terms and involves collection of numerical data, analyzing it and drawing conclusions using numbers. Qualitative research on other hand, is one which is done using non-numerical and unquantifiable elements like feelings, emotion, sound etc.

Aims and Objectives of Research methodology

Develops better Insight into Topic

Research methodology provides better familiarity with the research topic by properly explaining each concept associated with it. It aims at the proper analysis of every aspect and accurately portrays all findings of the project. 

Provides Systematic Structure

Research methodology eases the process of whole research to be done. It clearly defines the tools and techniques to be used for collecting, analyzing and interpreting the data to find out the solutions.

Enhance the Research Quality

It determines the reliability and validity of the whole research work. Research methodology tells accurate sources from where data should be taken for studying purpose which thereby improves the quality of research done.

Derive Better Solutions

Research methodology helps in deriving crucial findings for solving business problems. It performs an in-depth study of various projects, develops a better understanding and detects all problems.

Aids in Decision Making

Decision making is another important role played by research methodology. It supports management in organizing their efforts in generating a new idea. Research methodology by providing direction for various activities of the project helps managers for efficient decision making.

Inculcates logical and systematic Thinking

It develops the logical thinking ability of individuals. Research methodology evaluates every element of the project and highlights them in detail. It represents every aspect in a simplified manner which improves logical thinking.

Related posts:

• Project Report: Meaning, Contents, Objectives, Characteristics, Need
• Steps in Project Planning
• Advantages and Disadvantages of Decision Making
• Characteristics of Research Methodology
• Methodology of Operation Research
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This paper is in the following e-collection/theme issue:

Published on 12.4.2024 in Vol 26 (2024)

The Effectiveness of a Digital App for Reduction of Clinical Symptoms in Individuals With Panic Disorder: Randomized Controlled Trial

Authors of this article:

Original Paper

• KunJung Kim, MD   ; 
• Hyunchan Hwang, MD, PhD   ; 
• Sujin Bae, PhD   ; 
• Sun Mi Kim, MD, PhD   ; 
• Doug Hyun Han, MD, PhD  

Chung Ang University Hospital, Seoul, Republic of Korea

Corresponding Author:

Doug Hyun Han, MD, PhD

Chung Ang University Hospital

102 Heucsock ro

Seoul, 06973

Republic of Korea

Phone: 82 2 6299 3132

Fax:82 2 6299 3100

Email: [email protected]

Background: Panic disorder is a common and important disease in clinical practice that decreases individual productivity and increases health care use. Treatments comprise medication and cognitive behavioral therapy. However, adverse medication effects and poor treatment compliance mean new therapeutic models are needed.

Objective: We hypothesized that digital therapy for panic disorder may improve panic disorder symptoms and that treatment response would be associated with brain activity changes assessed with functional near-infrared spectroscopy (fNIRS).

Methods: Individuals (n=50) with a history of panic attacks were recruited. Symptoms were assessed before and after the use of an app for panic disorder, which in this study was a smartphone-based app for treating the clinical symptoms of panic disorder, panic symptoms, depressive symptoms, and anxiety. The hemodynamics in the frontal cortex during the resting state were measured via fNIRS. The app had 4 parts: diary, education, quest, and serious games. The study trial was approved by the institutional review board of Chung-Ang University Hospital (1041078-202112-HR-349-01) and written informed consent was obtained from all participants.

Results: The number of participants with improved panic symptoms in the app use group (20/25, 80%) was greater than that in the control group (6/21, 29%; χ 2 1 =12.3; P =.005). During treatment, the improvement in the Panic Disorder Severity Scale (PDSS) score in the app use group was greater than that in the control group ( F 1,44 =7.03; P =.01). In the app use group, the total PDSS score declined by 42.5% (mean score 14.3, SD 6.5 at baseline and mean score 7.2, SD 3.6 after the intervention), whereas the PDSS score declined by 14.6% in the control group (mean score 12.4, SD 5.2 at baseline and mean score 9.8, SD 7.9 after the intervention). There were no significant differences in accumulated oxygenated hemoglobin (accHbO 2 ) at baseline between the app use and control groups. During treatment, the reduction in accHbO 2 in the right ventrolateral prefrontal cortex (VLPFC; F 1,44 =8.22; P =.006) and the right orbitofrontal cortex (OFC; F 1,44 =8.88; P =.005) was greater in the app use than the control group.

Conclusions: Apps for panic disorder should effectively reduce symptoms and VLPFC and OFC brain activity in patients with panic disorder. The improvement of panic disorder symptoms was positively correlated with decreased VLPFC and OFC brain activity in the resting state.

Trial Registration: Clinical Research Information Service KCT0007280; https://cris.nih.go.kr/cris/search/detailSearch.do?seq=21448

Introduction

Panic disorder is a common and important disease in clinical practice that leads to a reduction of individual productivity and increased use of health care [ 1 ]. The lifetime prevalence of panic disorder in the general population is 4.8%, and 22.7% of people experience panic attacks [ 2 ]. The most common symptoms of panic disorder include palpitations, shortness of breath, chest pain, numbness of the hands and feet, and cardiorespiratory-type symptoms, in addition to fear of dying, sweating, tremors, dizziness, nausea, and chills [ 3 ]. The US Food and Drug Administration has currently only approved selective serotonin reuptake inhibitors (SSRIs) for the treatment of panic disorder [ 4 ]. However, it is clinically difficult to expect an improvement in symptoms using SSRIs alone in the acute phase; thus we treat patients with benzodiazepine, which can lead to dependence and withdrawal symptoms [ 5 , 6 ]. The most common side effects of SSRIs reported by patients are reduced sexual function, drowsiness, and weight gain [ 7 ], and clinicians may hesitate to use benzodiazepines due to dependence and withdrawal symptoms [ 8 ]. Cognitive behavioral therapy (CBT) is the most widely used nonpharmaceutical treatment for anxiety disorders [ 9 ]. Additional nonpharmaceutical treatments, such as group therapy and supportive psychotherapy, are also available for patients with panic disorder [ 10 , 11 ]. However, these treatments have the disadvantage of requiring face-to-face contact; therefore, other therapeutic alternatives should be offered to patients during pandemics such as COVID-19.

The definition of a digital therapeutic (DTx) is a therapeutic that delivers evidence-based interventions to prevent, manage, or treat a medical disorder or disease; DTxs are currently used in many areas [ 12 ]. This kind of medical and public health use of smartphones and digital technologies is also known as mobile health (mHealth). DTxs related to mental health medicine are actively used in various psychiatric disorders, such as insomnia, substance abuse, attention-deficit/hyperactivity disorder, and anxiety and depression, among others [ 13 ]. In particular, the use of Freespira, a panic disorder DTx, reduced panic symptoms, avoidance behaviors, and treatment costs in patients with panic disorder [ 14 ].

As brain imaging technology advances, a great deal of functional mapping information on the human brain has been accumulated from positron emission tomography (PET), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS). Among these technologies, fNIRS can measure brain activity in a noninvasive and safe manner through measuring changes in the hemoglobin oxygenation state of the human brain [ 15 ]. Various studies have been conducted using fNIRS and fMRI to reveal correlations between panic disorder and brain regions. For example, patients with panic disorder show increased activity in the inferior frontal cortex, hippocampus, cingulate (both anterior and posterior), and orbitofrontal cortex (OFC) [ 16 ]. Previously, we confirmed that patients with panic disorder during rest periods showed increased activity in the OFC [ 17 ].

In this study, we determined whether an app for panic disorder would improve panic disorder symptoms. In addition, we used fNIRS to confirm the association between changes in panic disorder symptoms and changes in activity in specific brain regions.

Participants

Patients who had experiences of panic attacks were recruited between March 1 and July 30, 2022, through billboard advertisements at our hospital. The inclusion criteria for the study were as follows: (1) age between 20 and 65 years, (2) diagnosis of panic disorder based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition and (3) ability to use apps without problems. The exclusion criteria were as follows: (1) a history of other psychiatric disorders, except for anxiety disorder, or substance dependence, except for habitual alcohol and tobacco use; and (2) a history of head trauma and chronic medical conditions. The research clinician assessed whether patients fulfilled the inclusion or exclusion criteria. Written informed consent was acquired from all participants at the first visit. This study has been registered with the Clinical Research Information Service (KCT0007280).

Assessment Scales for Anxiety Symptoms

The severity of panic symptoms was assessed using the Panic Disorder Severity Scale (PDSS). The PDSS was developed by Shear et al [ 18 ] in 1997. It is a 7-item instrument used to rate the overall severity of panic disorder and was validated in Korea by Lim et al [ 19 ] in 2001.

The anxiety symptoms of all participants were assessed using the clinician-based Hamilton Anxiety Scale (HAM-A) questionnaire and the participant-based Generalized Anxiety Disorder-7 (GAD-7) questionnaire. The HAM-A was developed by Hamilton in 1969 [ 20 ]. The 14-item version remains the most used outcome measure in clinical trials of treatments for anxiety disorders and was validated in Korea by Kim [ 21 ] in 2001.

The GAD-7 questionnaire, developed by Spitzer et al [ 22 ], is a 7-item self-report anxiety questionnaire designed to assess the patient’s health status during the previous 2 weeks. The GAD-7 was translated into the Korean language and is freely downloadable on the Patient Health Questionnaire website [ 23 ].

Hemodynamic Response of the Prefrontal Cortex

The hemodynamics in the frontal cortex during the resting state were measured using the fNIRS device (NIRSIT; OBELAB Inc). The NIRSIT has 24 laser diodes (sources) emitting light at 2 wavelengths (780 nm and 850 nm) and 32 photodetectors with a sampling rate of 8.138 Hz [ 24 ]. The distance between the source and photodetector is 15 mm. Based on the suggested suitable sensor-detector separation distance for measuring cortical hemodynamic changes, only 30-mm channels were analyzed in this study [ 25 ].

For our study, we used the 48-channel configuration ( Figure 1 ). The detected light signals in each wavelength were filtered with a band-pass filter (0.00 Hz-0.1 Hz) to reduce the effect of environmental noise–related light and body movements. In addition, channels with low-quality information (signal-to-noise ratio <30 dB) were removed from the hemodynamic analysis. The accumulated oxygenated hemoglobin (accHbO 2 ) values in the resting state represent the activation of the prefrontal cortex. In accordance with the theory that oxygenated hemoglobin has superior sensitivity and signal-to-noise ratio compared to deoxygenated hemoglobin data, only oxygenated hemoglobin were used for this analysis [ 26 - 28 ].

The means and SDs for accHbO 2 were calculated from regions of interest (ROIs) in the right and left dorsolateral prefrontal cortices (DLPFCs), right and left ventrolateral prefrontal cortices (VLPFCs), right and left frontopolar cortices (FPCs), and right and left orbitofrontal cortices (OFCs), based on Brodmann area 46. The right and left DLPFCs comprise channels 1, 2, 3, 5, 6, 11, 17, and 18 and channels 19, 20, 33, 34, 35, 38, 39, and 43, respectively. The right and left VLPFCs comprise channels 4, 9, and 10 and channels 40, 44, and 45, respectively. The right and left FPCs comprise channels 7, 8, 12, 13, 21, 22, 25, and 26 and channels 23, 24, 27, 28, 36, 37, 41, and 42, respectively. The right and left OFCs comprise channels 14, 15, 16, 29, and 30 and channels 31, 32, 46, 47, and 48, respectively ( Figure 1 ).

Digital App for Panic Disorder

The app for panic disorder is a smartphone-based app for treatment of clinical symptoms of panic disorder. The mobile app has 4 categories: diary, education, quest, and serious games. The diary category has three items: (1) assessment of daily psychological status, including mood and anxiety; (2) assessment of panic symptoms, including frequency and severity; and (3) consumption of medication, including regular medication and pro re nata medications. The education category has three items: (1) knowledge about panic disorders, (2) knowledge about medications for panic disorder, and (3) knowledge about panic disorder treatment, including CBT, breathing therapy, and positive thinking therapy. The quests include two treatments: (1) eye movement desensitization and reprocessing therapy and (2) positive thinking therapy. The serious games include two games: (1) a breathing game and (2) an exposure therapy game.

The diary, education, and serious games (ie, the breathing game and exposure therapy game) are important parts of CBT for panic disorder [ 29 - 32 ]. The efficacy of CBT for panic disorder has been examined in various randomized controlled trials [ 33 , 34 ]. Eye movement desensitization and reprocessing therapy are also known to help reduce panic symptoms [ 35 , 36 ]. We confirmed that the replacement of worry with different forms of positive ideation shows beneficial effects [ 37 ], so a similar type of positive thinking therapy can also be expected to show benefits. Multimedia Appendix 1 provides additional information on the app.

Ethical Considerations

The study trial was approved by the institutional review board of Chung-Ang University Hospital (1041078-202112-HR-349-01) and written informed consent was obtained from all participants. Participants received an explanation from the researchers that included an overview of the study and a description of the methodology and purpose before deciding to participate. Additionally, they were informed that participation was voluntary, informed about our confidentiality measures, given the option to withdraw, and informed about potential side effects and compensation. Participants in this study received ₩100,000 (US $75.50) as transportation reimbursement. Additionally, the various scales and fNIRS assessments were offered at no cost to the participants. The participants received the results of the tests in the form of a report via postal mail or email after the conclusion of the study. They also receive an explanatory document and consent form from the researchers that included contact information for any inquiries. If the participant agreed to take part in the study after understanding the consent form, the research proceeded. The participants’ personal information was not collected. Instead, a unique identifier was assigned to the collected data for the sole purpose of research management.

Study Procedure

A randomized and treatment-as-usual–controlled design was applied in this study. After screening, all participants with panic disorder were randomly assigned to the app use group or the control group. The randomization sequence in our design was generated using SPSS (version 24.0; IBM Corp), with a 1:1 allocation between groups. At baseline and after intervention, all patients with panic disorder were assessed with the PDSS for panic symptoms, the HAM-A for objective anxiety symptoms, and the GAD-7 for subjective anxiety symptoms. At baseline and after intervention, the hemodynamic response in all patients with panic disorder was assessed using NIRSIT. The app use group was asked to use the app for panic disorder 20 minutes per day, 5 times per week, for 4 weeks. The control group was asked to read short educational letters that were delivered via a social network service 5 times per week for 4 weeks. The short letters contained information about panic disorder and its treatment.

Demographic and Clinical Characteristics

After recruitment, 56 patients underwent eligibility assessments. A total of 6 individuals were excluded because they did not meet the inclusion criteria. The remaining patients were divided into 2 groups: 25 were assigned to the app use group and 21 to the control group, as 4 patients were excluded; contact was suddenly lost with 1 patient contact and 1 dropped out for personal reasons. In addition, 2 patients in the control group quit the study after reporting poor benefits from the short educational letters. Therefore, 25 people in the app use group and 21 people in the control group were analyzed. Figure 2 shows the Consolidated Standards of Reporting Trials (CONSORT) flowchart for participant flow through the trial.

There were no significant differences in age, sex ratio, years of education, marital status, employment status, or substance habits, including smoking and alcohol use, between the app use group and the control group ( Table 1 ).

b Chi-square.

There were no significant differences in HAM-A score, GAD-7 score, or PDSS score at baseline between the app use group and control group ( Table 1 ).

Comparison of Changes in Clinical Scales Between App Use Group and Control Group

The number of participants with improved panic symptoms in the app use group (20/25, 80%) was greater than in the control group (6/21, 29%; χ 2 1 =12.3; P =.005).

During the treatment period, the app use group showed greater improvement in PDSS score than the control group ( F 1,44 =7.03; P =.01). In the app use group, the PDSS score decreased by 42.5% (mean score 14.3, SD 6.5 at baseline and mean score 7.2, SD 3.6 after the intervention), while the score decreased by 14.6% in the control group (mean score 12.4, SD 5.2 at baseline and mean score 9.8, SD 7.9 after intervention) ( Figure 3 ).

During the treatment period, there were no significant differences in the change in HAM-A scores ( F 1,44 =2.83; P =.09) and GAD-7 scores ( F 1,44 =0.22; P =.64) between the app use group and control group ( Figure 3 ).

Comparison of Changes in accHbO 2 Values Between App Use Group and Control Group

There were no significant differences in accHbO 2 in the right (t 45 =0.84; P =.40) or left (t 45 =0.73; P =.46) DLPFCs, right (t 45 =1.04; P =.31) or left (t 45 =0.88; P =.39) VLPFCs, right (t 45 =-0.18; P =.86) or left (t 45 =1.85; P =.07) FPCs, or right (t 45 =0.33; P =.74) or left (t 45 =1.89; P =.07) OFCs in the app use and control groups at baseline.

During the treatment period, the app use group showed a greater reduction in accHbO 2 in the right VLPFC ( F 1,44 =8.22; P =.006) and right OFC ( F 1,44 =8.88; P =.005) compared to the control group ( Figure 1 ). During the treatment period, there were no significant differences in the change in accHbO 2 in the other ROIs between the app use and control groups.

Correlations Between the Changes in PDSS Scores and the Changes in accHbO 2

In all participants (ie, the app use group plus the control group), there was a positive correlation between the change in PDSS score and the change in accHbO 2 in the right VLPFC ( r =0.44; P =.002). In the app use group, there was a positive correlation between the change in PDSS score and the changes in accHbO 2 in the right VLPFC ( r =0.42; P =.04). However, in the control group, there was no significant correlation between the change in PDSS score and the change in accHbO 2 in the right VLPFC ( r =0.22; P =.16).

In all participants, there was a positive correlation between the change in PDSS score and the change in accHbO 2 in the right OFC ( r =0.44; P =.002). In both the app use group ( r =0.34; P =.09) and control group ( r =0.33; P =.13), there was no significant correlation between the change in PDSS score and the change in accHbO 2 in the right OFC ( Figure 4 ).

Principal Findings

This study showed that a digital app was effective for symptom reduction, as well as decreasing brain activity in the VLPFCs and OFCs, in patients with panic disorder. In addition, the panic disorder symptom improvement was positively correlated with decreased brain activity in the VLPFCs and OFCs in the resting state.

The digital app used in this trial proved to be effective in reducing panic symptoms when compared to the control group, as demonstrated by the reduction in the PDSS score. We believe that this is due to the combined effect of the 4 parts of the program, namely the diary, education, quest, and serious games. The diary component helps identify and correct faulty perceptions and enables cognitive reconstruction. The education component provides information about the nature and physiology of panic disorder. The breathing game helps the participant return to a relaxed condition, while the exposure therapy game allows the participant to experience agoraphobic situations in a safe environment, which helps cognitive restructuring. These are the important parts of CBT for panic disorder and have shown efficacy, as reported earlier [ 29 - 32 ]. The control group also received educational data, including the importance of keeping a diary of one’s panic symptoms and how to do it, as well as self-guided direction on breathing exercises, but failed to show a significant reduction of symptoms compared to the app use group. We think this is due to lack of proper feedback in the control group. The app shows real-time feedback on breathing exercises using breathing sounds, and a message was sent if the user of the program failed to use the program for more than 2 days. We know that the therapeutic effect is better when immediate feedback is provided to patients undergoing CBT treatment [ 38 ]. Therefore, we think that the decrease in PDSS score was smaller because the control group did not receive feedback from the app.

The control group also received educational data on diary recording, panic disorder information, and how to execute breathing therapy and exposure therapy. We measured their reduction in the PDSS score, but we found it was less than in the app use group due to a lack of proper daily management.

However, the app failed to lead to a difference in the reduction in anxiety, as defined by the HAM-A and GAD-7 scales, between the 2 groups. This is most likely due to a lack of power, as the trial was conducted as a pilot study. Other studies using CBT techniques or serious games have demonstrated reductions in anxiety symptoms in patients with panic disorder [ 14 ]. Likewise, this study showed a trend toward a reduction in anxiety symptoms, although this was not statistically significant, and future research with more participants may show that these kinds of programs are also effective in controlling anxiety.

Two major changes in brain activity were noted in the app use group, namely reductions in VLPFC and OFC activation. The functions of the OFC are varied and include control of inappropriate behavior and emotional responses, decision-making, and solving problems [ 39 , 40 ]. Abnormalities in the function of the OFC can cause problems in dealing with anxiety and show that it is deeply involved in the increasing the sense of fear in the fear response [ 17 ]. The results of this study confirm that OFC activity decreases as treatment progresses. This reinforces the results of a previous study, which showed that patients with panic disorder had increased OFC activity and that when the panic disorder was treated, the activity of the OFC was reduced, as indicated by decreased cerebral glucose metabolic rates [ 17 , 41 ].

The VLPFC is known to be associated with the amygdala and to maintain flexible attention and responses to environmental threats [ 42 , 43 ]. The amygdala is the backbone of the fear network, and the VLPFC is also known to be deeply involved in the processing of fear [ 43 - 45 ]. Several studies have shown increased activity in patients with panic disorder in the inferior frontal gyrus, which envelops the VLPFC, and other related regions, including the prefrontal cortex, hippocampus, and OFC [ 16 , 46 , 47 ]. After panic disorder treatment, such as with CBT, decreased amygdala and inferior frontal gyrus activation in fear situations was confirmed [ 48 , 49 ]. Through panic disorder treatment, inferior frontal gyrus activation decreased to a normal level; this happened because the treatment reduced fear cognition related to harm expectancy or attention to threats [ 49 - 51 ]. We consider that VLPFC activation increases to modulate the amygdala and decreases with treatment for panic disorder.

We believe that these reductions of brain activity in the VLPFC and OFC reflect how the app affected the patients. We know that overprediction of fear or panic is an important feature of anxiety disorders [ 52 ]. The app for panic disorder, including diary, education, quest, and serious game components, allowed users to correct their faulty perceptions about fear. As mentioned earlier, the VLPFC and OFC are related to fear management, so we can expect that activity of the VLPFC and OFC will be reduced through repeated app use as users learn how to deal with fear.

Limitations

This study has the following limitations: Most of the patients were effectively treated with alprazolam or other anxiolytics, such as SSRIs. Thus, treatment with antianxiety drugs may have influenced our results. Moreover, this study assessed changes immediately after app use. A long-term follow-up to evaluate the sustainability of the observed improvements would provide valuable insights into the effectiveness of the intervention over time. App use time could be easily tracked for the app use group; however, it was challenging to independently monitor the time the control group spent reading educational materials. Due to the limitations of available research tools, no investigation has been conducted on deep brain structures such as the amygdala, which is most closely related to panic disorders.

Conclusions

We believe that this app for panic disorder effectively reduces symptoms and noticeably impacts brain activity in specific areas. We observed a positive link between improvement in panic symptoms and decreased brain activity in the VLPFCs and OFCs in a resting state. These findings support the use of targeted interventions to determine the brain’s contribution to symptom relief. Further research should explore the duration of these positive effects and make digital therapy accessible to more individuals, thus unlocking its full potential in mental health care.

Data Availability

The data sets generated and analyzed during this study are not publicly available as they contain information that could compromise the privacy and consent of the research participants. However, the transformed data are available upon reasonable request from the authors.

Conflicts of Interest

None declared.

Digital app for panic disorder.

CONSORT-eHEALTH checklist (V 1.6.1).

• Roy-Byrne PP, Craske MG, Stein MB. Panic disorder. Lancet. Sep 16, 2006;368(9540):1023-1032. [ CrossRef ] [ Medline ]
• Kessler RC, Chiu WT, Jin R, Ruscio AM, Shear K, Walters EE. The epidemiology of panic attacks, panic disorder, and agoraphobia in the National Comorbidity Survey Replication. Arch Gen Psychiatry. Apr 2006;63(4):415-424. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Meuret AE, White KS, Ritz T, Roth WT, Hofmann SG, Brown TA. Panic attack symptom dimensions and their relationship to illness characteristics in panic disorder. J Psychiatr Res. Sep 2006;40(6):520-527. [ CrossRef ] [ Medline ]
• Locke A, Kirst N, Shultz CG. Diagnosis and management of generalized anxiety disorder and panic disorder in adults. Am Fam Physician. May 01, 2015;91(9):617-624. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Bruce SE, Vasile RG, Goisman RM, Salzman C, Spencer M, Machan JT, et al. Are benzodiazepines still the medication of choice for patients with panic disorder with or without agoraphobia? Am J Psychiatry. Aug 2003;160(8):1432-1438. [ CrossRef ] [ Medline ]
• Owen R, Tyrer P. Benzodiazepine dependence. A review of the evidence. Drugs. Apr 1983;25(4):385-398. [ CrossRef ] [ Medline ]
• Cascade E, Kalali AH, Kennedy SH. Real-world data on SSRI antidepressant side effects. Psychiatry (Edgmont). Feb 2009;6(2):16-18. [ FREE Full text ] [ Medline ]
• Breilmann J, Girlanda F, Guaiana G, Barbui C, Cipriani A, Castellazzi M, et al. Benzodiazepines versus placebo for panic disorder in adults. Cochrane Database Syst Rev. Mar 28, 2019;3(3):CD010677. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Schuurmans J, Comijs H, Emmelkamp PMG, Weijnen IJC, van den Hout M, van Dyck R. Long-term effectiveness and prediction of treatment outcome in cognitive behavioral therapy and sertraline for late-life anxiety disorders. Int Psychogeriatr. Dec 2009;21(6):1148-1159. [ CrossRef ] [ Medline ]
• Rufer M, Albrecht R, Schmidt O, Zaum J, Schnyder U, Hand I, et al. Changes in quality of life following cognitive-behavioral group therapy for panic disorder. Eur Psychiatry. Jan 2010;25(1):8-14. [ CrossRef ] [ Medline ]
• Shear MK, Houck P, Greeno C, Masters S. Emotion-focused psychotherapy for patients with panic disorder. Am J Psychiatry. Dec 2001;158(12):1993-1998. [ CrossRef ] [ Medline ]
• Digital health, digital medicine, digital therapeutics (DTx): What’s the difference. HealthXL. Oct 23, 2023. URL: https:/​/www.​healthxl.com/​blog/​digital-health-digital-medicine-digital-therapeutics-dtx-whats-the-difference [accessed 2023-03-19]
• Cho C, Lee H. Could digital therapeutics be a game changer in psychiatry? Psychiatry Investig. Feb 2019;16(2):97-98. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Kaplan A, Mannarino AP, Nickell PV. Evaluating the impact of Freespira on panic disorder patients' health outcomes and healthcare costs within the Allegheny Health Network. Appl Psychophysiol Biofeedback. Sep 2020;45(3):175-181. [ CrossRef ] [ Medline ]
• Hoshi Y. Functional near-infrared optical imaging: utility and limitations in human brain mapping. Psychophysiology. Jul 2003;40(4):511-520. [ CrossRef ] [ Medline ]
• Bystritsky A, Pontillo D, Powers M, Sabb FW, Craske MG, Bookheimer SY. Functional MRI changes during panic anticipation and imagery exposure. Neuroreport. Dec 21, 2001;12(18):3953-3957. [ CrossRef ] [ Medline ]
• Milad MR, Rauch SL. The role of the orbitofrontal cortex in anxiety disorders. Ann N Y Acad Sci. Dec 2007;1121(1):546-561. [ CrossRef ] [ Medline ]
• Shear MK, Brown TA, Barlow DH, Money R, Sholomskas DE, Woods SW, et al. Multicenter collaborative Panic Disorder Severity Scale. Am J Psychiatry. Nov 1997;154(11):1571-1575. [ CrossRef ] [ Medline ]
• Lim YJ, Yu BH, Kim JH. Korean Panic Disorder Severity Scale: construct validity by confirmatory factor analysis. Depress Anxiety. 2007;24(2):95-102. [ CrossRef ] [ Medline ]
• Maier W, Buller R, Philipp M, Heuser I. The Hamilton Anxiety Scale: reliability, validity and sensitivity to change in anxiety and depressive disorders. J Affect Disord. 1988;14(1):61-68. [ CrossRef ] [ Medline ]
• Kim CY. Psychiatric Assessment Instruments. Seoul, South Korea. Hana Psychiatric Publishing; 2001;99-101.
• Spitzer RL, Kroenke K, Williams JBW, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. May 22, 2006;166(10):1092-1097. [ CrossRef ] [ Medline ]
• Screener overview. PHQ Screeners. URL: https://www.phqscreeners.com/select-screener [accessed 2024-03-27]
• Choi J, Kim J, Hwang G, Yang J, Choi M, Bae H. Time-divided spread-spectrum code-based 400 fw-detectable multichannel fNIRS IC for portable functional brain imaging. IEEE J Solid-State Circuits. Feb 2016;51(2):484-495. [ FREE Full text ] [ CrossRef ]
• Yamamoto T, Maki A, Kadoya T, Tanikawa Y, Yamad Y, Okada E, et al. Arranging optical fibres for the spatial resolution improvement of topographical images. Phys Med Biol. Sep 21, 2002;47(18):3429-3440. [ CrossRef ] [ Medline ]
• Cui X, Bray S, Bryant DM, Glover GH, Reiss AL. A quantitative comparison of NIRS and fMRI across multiple cognitive tasks. Neuroimage. Feb 14, 2011;54(4):2808-2821. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Miyai I, Tanabe HC, Sase I, Eda H, Oda I, Konishi I, et al. Cortical mapping of gait in humans: a near-infrared spectroscopic topography study. Neuroimage. Nov 2001;14(5):1186-1192. [ CrossRef ] [ Medline ]
• Strangman G, Culver JP, Thompson JH, Boas DA. A quantitative comparison of simultaneous BOLD fMRI and NIRS recordings during functional brain activation. Neuroimage. Oct 2002;17(2):719-731. [ Medline ]
• Barlow DH, Craske MG, Cerny JA, Klosko JS. Behavioral treatment of panic disorder. Behav Ther. 1989;20(2):261-282. [ CrossRef ]
• Clark D, Salkovskis PM, Hackmann A, Wells A, Ludgate J, Gelder M. Brief cognitive therapy for panic disorder: a randomized controlled trial. J Consult Clin Psychol. Aug 1999;67(4):583-589. [ CrossRef ] [ Medline ]
• Telch MJ, Lucas JA, Schmidt NB, Hanna HH, LaNae Jaimez T, Lucas RA. Group cognitive-behavioral treatment of panic disorder. Behav Res Ther. Mar 1993;31(3):279-287. [ CrossRef ] [ Medline ]
• Bouchard S, Gauthier J, Laberge B, French D, Pelletier M, Godbout C. Exposure versus cognitive restructuring in the treatment of panic disorder with agoraphobia. Behav Res Ther. Mar 1996;34(3):213-224. [ CrossRef ] [ Medline ]
• Barlow D, Gorman JM, Shear MK, Woods SW. Cognitive-behavioral therapy, imipramine, or their combination for panic disorder: A randomized controlled trial. JAMA. May 17, 2000;283(19):2529-2536. [ CrossRef ] [ Medline ]
• Gould RA, Ott MW, Pollack MH. A meta-analysis of treatment outcome for panic disorder. Clin Psychol Rev. Jan 1995;15(8):819-844. [ CrossRef ]
• Faretta E. EMDR and cognitive behavioral therapy in the treatment of panic disorder: a comparison. J EMDR Prac Res. Jan 01, 2013;7(3):121-133. [ CrossRef ]
• Goldstein AJ, Feske U. Eye movement desensitization and reprocessing for panic disorder: a case series. J Anxiety Disord. 1994;8(4):351-362. [ CrossRef ]
• Eagleson C, Hayes S, Mathews A, Perman G, Hirsch CR. The power of positive thinking: pathological worry is reduced by thought replacement in generalized anxiety disorder. Behav Res Ther. Mar 2016;78:13-18. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Shapiro JR, Bauer S. Use of short message service (SMS)-based interventions to enhance low intensity CBT. Oxford guide to low intensity CBT interventions. In: Bennett-Levy J, editor. Oxford Guide to Low Intensity CBT Interventions. Oxford, UK. Oxford University Press; 2010;281-286.
• Schoenbaum G, Roesch M. Orbitofrontal cortex, associative learning, and expectancies. Neuron. Sep 01, 2005;47(5):633-636. [ FREE Full text ] [ CrossRef ] [ Medline ]
• O'Doherty J. Can't learn without you: predictive value coding in orbitofrontal cortex requires the basolateral amygdala. Neuron. Aug 28, 2003;39(5):731-733. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Nordahl TE, Stein MB, Benkelfat C, Semple WE, Andreason P, Zametkin A, et al. Regional cerebral metabolic asymmetries replicated in an independent group of patients with panic disorders. Biol Psychiatry. Nov 15, 1998;44(10):998-1006. [ CrossRef ] [ Medline ]
• Monk CS, Nelson EE, McClure EB, Mogg K, Bradley BP, Leibenluft E, et al. Ventrolateral prefrontal cortex activation and attentional bias in response to angry faces in adolescents with generalized anxiety disorder. Am J Psychiatry. Jun 2006;163(6):1091-1097. [ CrossRef ] [ Medline ]
• Hariri AR, Mattay VS, Tessitore A, Fera F, Weinberger DR. Neocortical modulation of the amygdala response to fearful stimuli. Biol Psychiatry. Mar 15, 2003;53(6):494-501. [ CrossRef ] [ Medline ]
• Gorman J, Kent JM, Sullivan GM, Coplan JD. Neuroanatomical hypothesis of panic disorder, revised. Am J Psychiatry. Apr 2000;157(4):493-505. [ CrossRef ] [ Medline ]
• Tian S, Huang F, Gao J, Li P, Ouyang X, Zhou S, et al. Ventrolateral prefrontal cortex is required for fear extinction in a modified delay conditioning paradigm in rats. Neuroscience. Aug 25, 2011;189:258-268. [ CrossRef ] [ Medline ]
• Ball TM, Ramsawh HJ, Campbell-Sills L, Paulus MP, Stein MB. Prefrontal dysfunction during emotion regulation in generalized anxiety and panic disorders. Psychol Med. Jul 2013;43(7):1475-1486. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Dresler T, Hindi Attar C, Spitzer C, Löwe B, Deckert J, Büchel C, et al. Neural correlates of the emotional Stroop task in panic disorder patients: an event-related fMRI study. J Psychiatr Res. Dec 2012;46(12):1627-1634. [ CrossRef ] [ Medline ]
• Beutel M, Stark R, Pan H, Silbersweig D, Dietrich S. Changes of brain activation pre-post short-term psychodynamic inpatient psychotherapy: an fMRI study of panic disorder patients. Psychiatry Res. Nov 30, 2010;184(2):96-104. [ CrossRef ] [ Medline ]
• Kircher T, Arolt V, Jansen A, Pyka M, Reinhardt I, Kellermann T, et al. Effect of cognitive-behavioral therapy on neural correlates of fear conditioning in panic disorder. Biol Psychiatry. Jan 01, 2013;73(1):93-101. [ CrossRef ] [ Medline ]
• Hofmann S. Cognitive processes during fear acquisition and extinction in animals and humans: implications for exposure therapy of anxiety disorders. Clin Psychol Rev. Feb 2008;28(2):199-210. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Bishop SJ. Neural mechanisms underlying selective attention to threat. Ann N Y Acad Sci. 2008;1129(1):141-152. [ CrossRef ] [ Medline ]
• Rachman S. The overprediction of fear: a review. Behav Res Ther. Sep 1994;32(7):683-690. [ CrossRef ] [ Medline ]

Abbreviations

Edited by A Mavragani; submitted 03.08.23; peer-reviewed by M Aksoy; comments to author 01.09.23; revised version received 11.09.23; accepted 08.03.24; published 12.04.24.

©KunJung Kim, Hyunchan Hwang, Sujin Bae, Sun Mi Kim, Doug Hyun Han. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 12.04.2024.

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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Original research article, associations between monitor-independent movement summary (mims) and fall risk appraisal combining fear of falling and physiological fall risk in community-dwelling older adults.

• 1 Department of Mechanical Engineering, University of Central Florida, Orlando, FL, United States
• 2 Disability, Aging and Technology Cluster, University of Central Florida, Orlando, FL, United States
• 3 College of Medicine, University of Central Florida, Orlando, FL, United States
• 4 School of Kinesiology and Rehabilitation Sciences, College of Health Professions and Sciences, University of Central Florida, Orlando, FL, United States
• 5 Department of Statistics and Data Science, University of Central Florida, Orlando, FL, United States
• 6 College of Nursing, University of Central Florida, Orlando, FL, United States

Introduction: Fall Risk Appraisal (FRA), a process that integrates perceived and objective fall risk measures, serves as a crucial component for understanding the incongruence between fear of falling (FOF) and physiological fall risk in older adults. Despite its importance, scant research has been undertaken to investigate how habitual physical activity (PA) levels, quantified in Monitor-Independent Movement Summary (MIMS), vary across FRA categories. MIMS is a device-independent acceleration summary metric that helps standardize data analysis across studies by accounting for discrepancies in raw data among research-grade and consumer devices.

Objective: This cross-sectional study explores the associations between MIMS (volume and intensity) and FRA in a sample of older adults in the United States.

Methods: We assessed FOF (Short Falls Efficacy Scale-International), physiological fall risk (balance: BTrackS Balance, leg strength: 30-s sit-to-stand test) and 7-day free-living PA (ActiGraph GT9X) in 178 community-dwelling older adults. PA volume was summarized as average daily MIMS (MIMS/day). PA intensity was calculated as peak 30-min MIMS (average of highest 30 non-consecutive MIMS minutes/day), representing a PA index of higher-intensity epochs. FRA categorized participants into following four groups: Rational (low FOF-low physiological fall risk), Irrational (high FOF-low physiological fall risk), Incongruent (low FOF-high physiological fall risk) and Congruent (high FOF-high physiological fall risk).

Results: Compared to rational group, average MIMS/day and peak 30-min MIMS were, respectively, 15.8% ( p = .025) and 14.0% ( p = .004) lower in irrational group, and 16.6% ( p = .013) and 17.5% ( p < .001) lower in congruent group. No significant differences were detected between incongruent and rational groups. Multiple regression analyses showed that, after adjusting for age, gender, and BMI (reference: rational), only irrational FRA was significantly associated with lower PA volume (β = −1,452.8 MIMS/day, p = .034); whereas irrational and congruent FRAs were significantly associated with lower “peak PA intensity” (irrational: β = −5.40 MIMS/day, p = .007; congruent: β = −5.43 MIMS/day, p = .004).

Conclusion: These findings highlight that FOF is a significant barrier for older adults to participate in high-intensity PA, regardless of their balance and strength. Therefore, PA programs for older adults should develop tailored intervention strategies (cognitive reframing, balance and strength exercises, or both) based on an individual’s FOF and physiological fall risk.

Introduction

In the United States (US), over 14 million adults aged 65 years or older fall each year ( Moreland et al., 2020 ; Kakara et al., 2023 ). According to the US Centers for Disease Control and Prevention, about 20% of falls in older adults cause serious injuries, which results in limited functional mobility, loss of independence, reduced quality of life, and premature death ( Ambrose et al., 2013 ). Fear of falling (FOF) has been recognized as an important psychological aspect associated with falls in older adults ( Jansen et al., 2021 ). However, studies report that many older adults might show a discrepancy between their FOF and physiological fall risk, known as maladaptive fall risk appraisal (FRA) ( Thiamwong et al., 2021a ), and such discrepancies can lead to adverse consequences. For example, individuals with low physiological fall risk but high FOF may overestimate their actual fall risk and restrict their daily activities, which can further lead to physical deconditioning and loss of muscle strength ( Deshpande et al., 2008 ). On the contrary, those with high physiological fall risk but low FOF may underestimate their actual fall risk and engage in unnecessary risky behavior beyond their physical capacity, making them even more vulnerable to falling ( Delbaere et al., 2010 ).

Therefore, FRA combining subjective and objective fall risk measures is important for understanding the discrepancy between FOF and physiological fall risk in older adults to inform more targeted interventions for fall prevention ( Thiamwong et al., 2020a ; Thiamwong et al., 2020b ). FRA is a two-dimensional fall risk assessment matrix that classifies older adults into four groups based on their FOF and physiological fall risk status ( Thiamwong, 2020 ). In FRA matrix, as shown in Figure 1 , two groups have their FOF level aligned with their physiological fall risk status, which are denoted as Rational (low FOF-low physiological fall risk) and Congruent (high FOF-high physiological fall risk). The other two groups show a mismatch between their FOF level and physiological fall risk status and are denoted as Incongruent (low FOF-high physiological fall risk) and Irrational (high FOF-low physiological fall risk).

Figure 1 . Fall Risk Appraisal (FRA) based on Fear of Falling (FOF) and physiological fall risk. Maladaptive FRA = mismatch between FOF and physiological fall risk; Adaptive FRA = FOF aligned with physiological fall risk.

Prior research has mostly focused on exploring the independent associations of FOF and objective fall risk measures with physical activity (PA) participation in older adults ( Gregg et al., 2000 ; Chan et al., 2007 ; Zijlstra et al., 2007 ; Heesch et al., 2008 ; Mendes da Costa et al., 2012 ). To date, only a small number of studies have investigated the combined effects of FOF and objective fall risk on PA engagement. For example, one study examined the joint associations of FOF and objective fall risk with everyday walking activities in older adults. This study used a four-group categorization from ( Delbaere et al., 2010 ), and found that the number of steps/day in their study sample was in accordance with objective fall risk rather than FOF ( Jansen et al., 2021 ). Another study examined accelerometry-based PA levels between FRA categories using the intensity cut-point approach and found that participants with high FOF accumulated significantly less time in moderate-to-vigorous PA (MVPA) compared to those with rational FRA, regardless of their balance performance ( Thiamwong et al., 2023 ). However, there exists a lack of evidence on how habitual PA levels, expressed in Monitor-Independent Movement Summary (MIMS) units, differ between FRA categories in older adults.

MIMS is used to summarize the acceleration measurements obtained on the x-, y-, and z-axes of wrist-worn activity monitors. This PA metric was first introduced in 2019 to summarize participant-level PA data for the 2011-2012 and 2013-2014 cycles of the US National Health and Nutrition Examination Survey (NHANES) ( John et al., 2019 ). The major benefit of using MIMS is that it is generated by a nonproprietary device–independent universal algorithm, allowing us to compare the total movement across studies regardless of the heterogeneity introduced by different brands, models and device types (such as consumer vs. research-grade) ( John et al., 2019 ). Similar to other traditional PA metrics such as steps/day or daily activity counts, PA volume can be expressed as daily MIMS (i.e., total MIMS unit accumulated per day) across valid days of assessment, where larger MIMS/day indicates higher daily PA volume ( Wolff-Hughes et al., 2014 ).

Traditionally, quantification of accelerometer-measured PA intensity has been predominantly based on minutes/day (or minutes/week) spent in MVPA, using either manufacturer-specific or device-specific cut points corresponding with ≥3 Metabolic Equivalents of Task (METs) ( Troiano et al., 2008 ). Recently, to establish an intensity-based expression for MIMS units, the concept of peak 30-min MIMS has been introduced ( Zheng et al., 2023 ). It is analogous to the concept of peak 30-min cadence, i.e., the average of 30 highest cadence (steps/minutes) values within a day, representing an individual’s best efforts ( Tudor-Locke et al., 2012 ). Similar to cadence (steps/minutes), MIMS/minutes values were shown to have a strong correlation with higher PA intensity ( John et al., 2019 ). Therefore, peak 30-min MIMS (i.e., the average of the highest 30 non-consecutive MIMS [minutes/day] values within a day) can be used as a measure of higher-intensity epochs across the PA monitoring period ( Zheng et al., 2023 ). Evaluating daily MIMS (volume) and peak 30-min MIMS (intensity) can facilitate a more comprehensive assessment of PA and its relationship with FRA.

Thus, the aim of this study is to investigate the associations between wrist-worn accelerometer-measured PA (expressed as daily MIMS and peak 30-min MIMS) and FRA in a sample of community-dwelling older adults. We are particularly interested in the question: “Which of the maladaptive FRA groups, i.e., Incongruent (low FOF-high physiological fall risk) and Irrational (high FOF-low physiological fall risk), differ more from the Rational (low FOF-low physiological fall risk) group in terms of habitual PA level?.” This will allow us to understand which of the two factors—FOF or physiological fall risk—has a stronger relationship with reduced PA participation among older adults.

Materials and methods

Study design and participants.

In this cross-sectional study, purposive sampling was used to recruit 178 community-dwelling older adults from the region of Central Florida, United States, between February 2021 and March 2023. The inclusion criteria were: i) 60 years of age or older; ii) being able to walk with or without an assistive device (but without the assistance of another person); iii) no marked cognitive impairment [i.e., Memory Impairment Screen score ≥5 ( Buschke et al., 1999 )], iv) fluency in English or Spanish, and v) living in their own homes or apartments. The exclusion criteria were: i) medical conditions that prevent PA engagement (e.g., shortness of breath, tightness in the chest, dizziness, or unusual fatigue at light exertion), ii) unable to stand on the balance plate, iii) currently receiving treatment from a rehabilitation facility, and iv) having medical implants (e.g., pacemakers). This study was approved by the Institutional Review Board at the University of Central Florida (Protocol No: 2189; 10 September 2020). All subjects provided written informed consent to participate. This cross-sectional assessment required one visit to the study site during which participants completed a demographic survey and anthropometric measurements, followed by assessments of FOF and physiological fall risk. At the end of the visit, each participant was fitted with a wrist-worn accelerometer for 7-day PA monitoring in free-living conditions.

Measurements

Fear of falling (fof).

FOF was assessed using the Short Falls Efficacy Scale-International (FES-I) questionnaire ( Yardley et al., 2005 ; Kempen et al., 2008 ). It is a 7-item, self-administered tool that uses a 4-point Likert scale to measure the level of concern about falling while performing seven activities (1 = not at all concerned to 4 = very concerned). The total scores ranged from 7 to 28. Short FES-I scores of 7–10 indicated low FOF, while scores of 11–28 indicated high FOF.

Physiological fall risk

Physiological fall risk was assessed using balance test and lower limb strength assessment. BTrackS Balance System (Balance Tracking Systems, San Diego, CA, United States) was used to measure static balance. This system includes a portable BTrackS Balance Plate and BTrackS Assess Balance Software running on a computer. It has shown high test–retest reliability (intraclass correlation coefficient, ICC = 0.83) and excellent validity (Pearson’s product-moment correlations, r > 0.90) in evaluating static balance ( Levy et al., 2018 ). The test protocol included four trials (each trial taking 20 s) with less than 10 s of inter-trial delays. During the trials, participants were asked to stand still on the BTrackS Balance Plate with their eyes closed, hands on their hips, and feet placed shoulder-width apart. BTrackS balance plate is an FDA-registered, lightweight force plate that measures center of pressure (COP) excursions during the static stance. The first trial was done for familiarization only. Results from the remaining three trials were used to calculate the average COP path length (in cm) across trials. COP path length is considered as a proxy measure for postural sway magnitude; thus, the larger the COP path length, the greater the postural sway is ( Goble et al., 2017 ). COP path length of 0–30  cm was used to indicate normal balance, while ≥31  cm indicated poor balance ( Thiamwong et al., 2021b ).

Lower limb strength was assessed using the 30-s sit-to-stand (STS) test, in accordance with the established protocol ( Yee et al., 2021 ; Choudhury et al., 2023 ). Participants were instructed to keep their arms folded across their chest, rise from a seated position on a chair to a standing posture and return to the sitting position as many times as possible within 30 s. The number of chair stands completed was counted and recorded. If a participant used his/her arms to stand, the test was stopped, and the score was recorded as zero. Age- and gender-specific STS normative scores were used as cut-offs to classify participants into below-average and average STS scores, as shown in Table 1 ( Rikli and Jones, 1999 ). A below-average STS score was indicative of a higher risk of fall. Meeting both normal balance and average STS score criteria was defined as low physiological fall risk, while not meeting either or both criteria was defined as high physiological fall risk.

Table 1 . Age and gender-specific below average scores for 30-s sit-to-stand test.

Fall risk appraisal (FRA) matrix

The FRA matrix was obtained using a combination of FOF and physiological fall risk status. Participants were grouped into the following four categories based on their FOF and physiological fall risk according to existing literature ( Thiamwong et al., 2020a ): i) Rational (low FOF-low physiological fall risk), ii) Irrational (high FOF-low physiological fall risk), iii) Incongruent (low FOF-high physiological fall risk), and iv) Congruent (high FOF-high physiological fall risk).

Physical activity (PA)

PA was assessed using ActiGraph GT9X Link (ActiGraph LLC., Pensacola, FL, United States), which contains a triaxial accelerometer with a dynamic range of ±8 gravitational units (g). The device was initialized to record acceleration data at 30  Hz sampling frequency. Participants wore the ActiGraph on their non-dominant wrists for seven consecutive days in free-living conditions. They were given instructions to wear it during waking hours and remove it only during sleeping, showering, swimming and medical imaging tests. After 7-day of PA monitoring, ActiGraph devices were collected from participants. Participants with ≥4 valid days were included in the analysis, and a day was considered valid if participants wore the device for at least 14 h or more ( Choudhury et al., 2023 ).

Raw acceleration data were downloaded as “.csv” files using ActiLife software v6.13.4 (ActiGraph LLC, Pensacola, FL, United States) and converted into MIMS units using MIMSunit package ( John et al., 2019 ) in R statistical software (R Core Team, Vienna, Austria). The data processing steps included: i) interpolating data to a consistent sampling rate (i.e., 100  Hz ) to account for inter-device variability in sampling rate, ii) extrapolating data to extend maxed-out signals to account for inter-device variability in dynamic range, iii) band-pass filtering to remove artifacts from acceleration signals that do not pertain to voluntary human movement, and iv) aggregation of processed signals from each axis into a sum of MIMS-units that represents the total amount of movement activity [details on MIMS-unit algorithm are published elsewhere ( John et al., 2019 )].

PA volume, denoted by daily MIMS (MIMS/day), was calculated by summing up all triaxial MIMS/minutes accumulated throughout a day and averaged across all valid days. PA intensity, expressed as peak 30-min MIMS, was obtained by (a) first rank ordering a participant’s triaxial MIMS/minutes values within each valid day, (b) calculating the average of the highest 30 MIMS/minutes values within each day, and (c) finally taking the average of the resulting MIMS/minutes values across all valid wear days.

Anthropometric measurements

Height (in cm) was measured using a stadiometer. Body mass (in kilograms) was measured using a digital scale with no shoes. Body mass index (BMI) was calculated as the weight (kg) divided by the square of height (m 2 ).

Statistical analyses

All statistical analyses were performed in R statistical software (version 4.1.2, R Core Team, Vienna, Austria) with statistical significance level set at .05. Descriptive characteristics of participants were summarized as mean (standard deviation, SD) for normally distributed continuous variables, as median (Interquartile Range, IQR) for non-normally distributed continuous variables, and as frequency (percentage) for categorical variables, stratified by FRA categories. The Shapiro-Wilk test was performed to check if a continuous variable followed a normal distribution. Differences across groups were examined using one-way analysis of variance (ANOVA) for normally distributed data and Kruskal–Wallis test for non-normally distributed data, with Bonferroni adjustment for post hoc comparisons.

Multiple linear regression was conducted for each outcome variable (i.e., daily MIMS and peak 30-min MIMS) using the four FRA groups—“Rational,” “Irrational,” “Incongruent” and “Congruent”—as explanatory variables, controlled by age, gender and BMI. A priori sample size calculation for multiple linear regression revealed that the minimum number of samples for 8 explanatory variables at a statistical power level of 0.8, α = 0.05, and a medium effect size (Cohen f 2 = 0.15) would be 108; therefore, our sample size (i.e., N = 178) had sufficient statistical power for multiple regression. The rational group (i.e., low FOF-low physiological fall risk) was selected as the reference group in the regression analysis.

Among 178 participants, 163 samples were included in the analyses, after retaining only those who had at least 4 days of valid PA data and completed both FOF and physiological fall risk assessments. The mean (SD) age of participants was 75.3 (7.1) years, and 73.6% of participants were in 60–79 years of age group ( n = 120) and 26.4% were above 80 years of age ( n = 43). Figure 2 shows the scatterplot of participants’ age (years) and FOF scores, stratified by physiological fall risk status. The proportion of participants with low FOF was 71.7% ( n = 86) in the 60–79 years of age group and 48.8% ( n = 21) in the ≥80 years of age group. The median (IQR) BMI of participants was 26.6 (6.3) kg/m 2 and majority of participants were female (79.1%). The median (IQR) Short FES-I score was 9 (5) and 34.4% of participants had high FOF. The median (IQR) COP path length was 27 (15) cm , and the median (IQR) sit-to-stand score was 13 (6) reps. 38.0% of participants had poor balance, 27.0% had below average lower limb strength, and 48.5% showed both poor balance and below average lower limb strength. Finally, 37.4% of participants were screened as rational ( n = 61), 14.2% were irrational ( n = 23), 28.2% were incongruent ( n = 46) and 20.2% were congruent ( n = 37). Table 2 summarizes the characteristics of study participants according to FRA categories.

Figure 2 . Scatterplot of Age (years) across Fear of Falling scores, stratified by physiological fall risk status. Low physiological fall risk = meeting both normal static balance cut-off and average sit-to-stand score cut-off. High physiological fall risk = not meeting normal static balance cut-off or average sit-to-stand score cut-off or both.

Table 2 . Participant characteristics stratified by Fall Risk Appraisal matrix.

In Figure 3A , the variations in average MIMS (MIMS/hours) over 24-h by FRA categories are shown. The average MIMS across all groups was in general low at night, then substantially increased during morning hours and gradually decreased as the day progressed and evening approached. In Figure 3B , the mean (line) and standard error (shaded area) of MIMS/hours for each FRA group is shown. Overall, rational group showed the highest average MIMS/hours across the day hours, while congruent had the lowest average MIMS/hours. Among maladaptive FRA groups, the peak was higher in incongruent group than their irrational counterparts, which indicates the potential role of FOF in limiting high-intensity PA participation.

Figure 3 . (A) Daily patterns of average MIMS per hour by Fall Risk Appraisal (FRA) groups. (B) Mean (line) and standard error (shaded area) of MIMS per hour for each FRA group.

The mean (SD) age in congruent group was 78.8 (7.6) years, which was higher than both rational (74.3 [5.8] years, p = .005) and incongruent (74.3 [7.0] years, p = .010) groups, as shown in Supplementary Figure S1 . This suggests that prevalence of high FOF, irrespective of balance performance and lower limb strength, may increase with advanced age. Also, the median (IQR) BMI in congruent group (28.9 [5.8]) kg/m 2 ) was higher in comparison to rational (24.9 [6.4] kg/m 2 , p = .001) and incongruent (26.9 [4.7] kg/m 2 , p = .018) groups (shown in Supplementary Figure S2 ), indicating that higher BMI in older adults may result in high FOF. However, no significant group differences were observed between rational and irrational groups in terms of age and BMI.

The mean (SD) daily MIMS in rational group was 10,408 (2,439) MIMS/day, which was 15.8% higher than irrational ( p = .025) and 16.6% higher than congruent ( p = .013) groups, as shown in Figure 4 . Also, the mean (SD) peak 30-min MIMS in rational group was 39.9 (8.3) MIMS/day, which was 14.0% higher than irrational ( p = .004) and 17.5% higher than congruent ( p < .001) groups ( Figure 5 ). Compared to rational group, incongruent participants showed no significant differences in PA volume and intensity, despite having poor balance and below average lower limb strength.

Figure 4 . Average daily MIMS (MIMS/day) across categories of Fall Risk Appraisal combining FOF and physiological fall risk, * p < .05.

Figure 5 . Peak 30-min MIMS per day across categories of Fall Risk Appraisal combining FOF and physiological fall risk, ** p < .01, *** p < .001.

Table 2 presents the regression models for daily MIMS. In comparison to reference group (i.e., rational), lower PA volume was associated with irrational ( β [SE] = −1,463.2 [687.7] MIMS/day, p = .035) and congruent ( β [SE] = −1,579.5 [582.9] MIMS/day, p = .007) FRAs in Model 1 (unadjusted). In model 2, after adjusting for age, gender and BMI, only irrational FRA was significantly associated with lower PA volume ( β [SE] = −1,476.41 [582.26] MIMS/day, p = .025; regression coefficients of covariates are presented in Supplementary Table S1 ).

Results for regression analysis for peak 30-min MIMS are presented in Table 3 . In model 1 (unadjusted), lower ‘peak PA intensity’ was associated with irrational ( β [SE] = −5.63 [1.99] MIMS/day, p = .005) and congruent FRAs ( β [SE] = −7.06 [1.76] MIMS/day, p < .001) compared to the reference group. In Model 2 ( Table 4 ), after adjusting for age, gender and BMI, both irrational and congruent FRAs were still significantly associated with lower “peak PA intensity”(irrational: β [SE] = −5.40 [1.97] MIMS/day, p = .007; congruent: β [SE] = −5.43 [1.86] MIMS/day, p = .004; regression coefficients of covariates are presented in Supplementary Table S2 ).

Table 3 . Association between Fall Risk Appraisal groups and average daily MIMS (MIMS/day) using linear regression. Model 2 was adjusted for age, gender and BMI.

Table 4 . Association between Fall Risk Appraisal groups and peak 30-min MIMS per day (MIMS/day) using linear regression. Model 2 was adjusted for age, gender and BMI.

This is the first study, to our knowledge, to evaluate the associations of FRA with daily MIMS and peak 30-min MIMS in a sample of community-dwelling US older adults. In general, both the volume and intensity of PA were highest in the rational group and lowest in the congruent group. In maladaptive FRA groups, high FOF (i.e., irrational FRA) was associated with lower PA volume and intensity compared to the reference group (i.e., rational FRA), but no significant differences were observed for high physiological fall risk (i.e., incongruent FRA).

Prior research has shown that FOF is associated with reduced PA levels in community-dwelling older adults using objectively measured PA data ( Jefferis et al., 2014 ; Choudhury et al., 2022 ). Our results broadly agree with it, showing that total daily PA volume was significantly lower in two high FOF groups (i.e., irrational and congruent) than the rational group. This suggests that regardless of balance performance and lower limb strength, low FOF was associated with high PA volume in our study sample. In linear regression analysis, after accounting for age, gender and BMI, reduced daily MIMS was significantly associated with irrational FRA, but not with congruent FRA. It can be attributed to the fact that the average age and BMI of congruent participants were higher than all other groups, and evidence suggests that that increasing older age and higher BMI contribute to lower PA levels in older adults ( Smith et al., 2015 ).

We did not observe any significant difference between two low FOF groups (i.e., rational and incongruent) in terms of daily PA volume. This suggests that, for maladaptive FRA, high physiological fall risk (not high FOF) had a stronger association with reduced daily PA accumulation in our study sample. In contrast to our findings, a recent study found that low physiological fall risk was more strongly associated with increased walking activity (steps/day) than low perceived fall risk in a sample of community-dwelling German older adults ( n = 294) ( Jansen et al., 2021 ). However, it should be noted that Jansen et al. used multiple independent risk factors (i.e., previous falls, balance impairment, gait impairment, and multimedication) to distinguish between high and low physiological fall risk, whereas they used only one tool (Short FES-I) to assess perceived fall risk. Furthermore, participants with low FOF and high physiological fall risk in that German older adult cohort ( Jansen et al., 2021 ) were relatively older than those in our study sample [mean (SD) age: 81.6 (5.5) years vs. 74.3 (7.0) years in our study]. Previous studies indicate that the likelihood of reduced participation in PA gradually increases with advanced age, because of age-related declines in muscle mass, muscle strength, and functional fitness (i.e., the physical capacity to perform activities of daily living independently and without the early onset of fatigue) ( Milanović et al., 2013 ; Westerterp, 2018 ; Suryadinata et al., 2020 ). Therefore, future research should examine how age-related functional declines mediate the relationship between maladaptive FRA and daily PA volume in older adults.

In our study, the peak PA intensity in both high FOF groups (i.e., irrational and congruent) was significantly lower than the rational group. Despite the differences in the PA metrics, this is in general agreement with the previous findings that showed older adults with irrational and congruent FRAs were more likely to spend less time in MVPA ( Thiamwong et al., 2023 ). Interestingly, after adjusting for confounders, the decrease in peak 30-min MIMS for irrational and congruent groups was almost equivalent in our study. This suggests that older adults with high FOF may restrict their participation in high intensity PA, irrespective of their physiological fall risk status. Our findings extend the previously reported association between PA intensity and FOF in older adults ( Sawa et al., 2020 ), highlighting the need to integrate cognitive behavioral therapy to reduce FOF in fall intervention programs.

For peak 30-min MIMS, we did not find any significant difference between two low FOF groups (i.e., rational and incongruent). This suggests that, similar to total PA volume, peak PA intensity was more strongly associated with high FOF (rather than high physiological fall risk) for maladaptive FRA in our sample. Unlike MVPA cut points that exclude PA intensities ≤3 METs or equivalent, peak 30-min MIMS considers acceleration magnitudes ranging from lower to higher peak efforts within a day, enabling comparison over the whole spectrum of PA intensity levels (e.g., light vs. vigorous) ( Zheng et al., 2023 ). Further research should investigate domains of peak PA efforts across different FRA groups, so that informed strategies can be developed to promote high-intensity PA participation according to the perceived and physiological risk of fall.

Based on the findings of our study, it can be conferred that the FRA assessment may be useful in designing customized PA interventions to promote an active lifestyle in older adults. For example, to increase PA participation in older adults with irrational FRA, cognitive behavioral therapy can be integrated into PA programs to improve their self-efficacy and sense of control over falling ( Tennstedt et al., 1998 ). For incongruent FRA, PA recommendations should include exercise regimens specifically designed to reduce physiological fall risk, such as high intensity balance and strength training, in addition to aerobic activities ( Sherrington et al., 2008 ). On the other hand, older adults with congruent FRA may benefit from PA programs that combine both balance and strength exercises, and cognitive behavioral therapy ( Brouwer et al., 2003 ).

A strength of our study is the use of MIMS metric to provide a comprehensive PA assessment (volume and intensity) enabling reliable, cross-study comparisons of our findings with other MIMS-based studies regardless of the device type, model or manufacturer. Furthermore, we used evidence-based cut-off points to determine FOF level (low vs. high FOF), balance status (poor vs. normal balance) and lower limb strength (below average vs. average strength) to categorize participants into FRA groups. However, our study has several limitations. First, to determine physiological fall risk status, we didn’t use the Physiological Profile Assessment ( Delbaere et al., 2010 ) or multiple independent risk factors ( Jansen et al., 2021 ), which might have led to different group formations than those studies. Instead, we used static balance and lower limb strength as physiological fall risk indicators. While balance and strength deficits are important predictors of falls in older adults, they might not account for all aspects of physiological fall risk (such as gait impairment, visual and sensory deficits, use of multi-medications etc.) ( Fabre et al., 2010 ). Second, it is to be noted that the balance performance measure (i.e., static balance) used in this study may not capture the full spectrum of an individual’s balance capabilities. There are different measures of balance performance, including static steady-state balance (i.e., the ability to maintain a steady position while standing or sitting), dynamic steady-state balance (i.e., the ability to maintain a steady position while performing postural transitions and walking), proactive balance (i.e., the ability to anticipate and mitigate a predicted postural disturbance), and reactive balance (i.e., the ability to recover a stable position following an unexpected postural disturbance) ( Shumway-Cook and Woollacott, 2007 ). Therefore, future studies may consider using more comprehensive assessments of balance performance in older adults to define physiological fall risk in FRA. Third, our study only considered FOF as the psychological fall risk measure in FRA and did not investigate other psychological constructs such as falls efficacy or balance confidence ( Moore et al., 2011 ). FOF and falls efficacy are two major fall-related psychological constructs in preventing and managing fall risks in older adults. It is to be noted that, though FOF and falls efficacy are correlated, they represent theoretically distinct concepts ( Hadjistavropoulos et al., 2011 ). FOF is defined as “the lasting concerns about falling that leads to an individual avoiding activities that one remains capable of performing.” Some common instruments for FOF measurement include FES-I, Short FES-I, Iconographical Falls Efficacy Scale (ICON-FES), Geriatric Fear of Falling Measure (GFFM), Survey of Activities and Fear of Falling in the Elderly (SAFE), Fear of Falling Avoidance Behaviour Questionnaire (FFABQ) etc., ( Soh et al., 2021 ). On the other hand, falls efficacy is defined as the perceived confidence in one’s ability to carry out activities of daily living without experiencing a fall ( Moore and Ellis, 2008 ). Existing instruments for measuring falls efficacy include Falls Efficacy Scale (FES), modified FES (MFES), Perceived Ability to Prevent and Manage Fall Risks (PAPMFR), and Perceived Ability to Manage Risk of Falls or Actual Falls (PAMF) ( Soh et al., 2021 ). Prior research has reported that, compared to FOF, falls efficacy shows stronger relationship with measures of basic and instrumental activities of daily living (ADL-IADL), and physical and social functioning ( Tinetti et al., 1994 ). Therefore, future studies should consider exploring the combined effects of falls efficacy and physiological fall risk measures on habitual PA level to determine whether FOF or fall efficacy should be considered as a target for PA interventions in older adults. Fourth, to date, there exists no established cut-offs for the MIMS metric to categorize total PA volume and intensity that correspond to meeting national PA guidelines, and it is still unknown how well MIMS/minute can estimate energy expenditure ( Vilar-Gomez et al., 2023 ). Our study just provided a first step toward the use of a standardized metric to associate PA behavior with FRA in a community-dwelling older adult sample in the US. Future studies should examine such associations in large, nationally representative populations to establish benchmark values for daily MIMS and peak 30-min MIMS in different FRA categories. Fifth, the cross-sectional design of the study didn’t allow us to determine a causal relationship between FRA and PA, so reverse and/or bidirectional causality might still be present. Sixth, although we controlled for age, gender, and BMI in the regression analyses, there remains the possibility of additional residual confounding [such as neuropsychological constructs that have been associated with FOF, which include depression, anxiety, neuroticism, attention, and executive function ( Delbaere et al., 2010 )]. Finally, our sample size was relatively small and 79% of participants were female. The generalizability of our findings might be restricted by the small, female dominant nature of our sample.

In conclusion, compared to rational FRA, the habitual PA level (daily MIMS and peak 30-min MIMS) was lower in both high FOF groups (i.e., irrational and congruent), but not in incongruent group. This suggests that, for maladaptive FRA in our study sample, high perceived fall risk had a stronger association with reduced PA level, rather than high physiological fall risk. When controlled for covariates, decrease in peak PA intensity remained significantly associated with irrational and congruent FRAs, indicating that older adults with high FOF performed PA at lower peak efforts, irrespective of their physiological fall status. Future prospective studies should focus on identifying the optimal habitual PA level (total PA volume and peak PA intensity) in accordance with an older adult’s FOF and physiological fall risk to better inform public health policies for sustainable, effective PA framework.

Data availability statement

The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by the Institutional Review Board, University of Central Florida. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

RC: Data curation, Formal Analysis, Investigation, Methodology, Software, Visualization, Writing–original draft. J-HP: Conceptualization, Funding acquisition, Investigation, Methodology, Resources, Supervision, Writing–review and editing. CB: Formal Analysis, Visualization, Writing–review and editing. MC: Data curation, Investigation, Writing–review and editing. DF: Conceptualization, Funding acquisition, Writing–review and editing. RX: Conceptualization, Funding acquisition, Writing–review and editing. JS: Conceptualization, Funding acquisition, Supervision, Writing–review and editing. LT: Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Writing–review and editing.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. The research was funded by the National Institute on Aging (R03AG06799) and the National Institute on Minority Health and Health Disparities (R01MD018025) of National Institutes of Health. This research also received financial support from the University of Central Florida CONNECT CENTRAL (Interdisciplinary research seed grant; AWD00001720 and AWD00005378).

Conflict of interest

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

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

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fragi.2024.1284694/full#supplementary-material

Ambrose, A. F., Paul, G., and Hausdorff, J. M. (2013). Risk factors for falls among older adults: a review of the literature. Maturitas 75 (1), 51–61. doi:10.1016/j.maturitas.2013.02.009

PubMed Abstract | CrossRef Full Text | Google Scholar

Brouwer, B. J., Walker, C., Rydahl, S. J., and Culham, E. G. (2003). Reducing fear of falling in seniors through education and activity programs: a randomized trial. J. Am. Geriatr. Soc. 51 (6), 829–834. doi:10.1046/j.1365-2389.2003.51265.x

Buschke, H., Kuslansky, G., Katz, M., Stewart, W. F., Sliwinski, M. J., Eckholdt, H. M., et al. (1999). Screening for dementia with the memory impairment screen. Neurology 52 (2), 231–238. doi:10.1212/wnl.52.2.231

Chan, B. K., Marshall, L. M., Winters, K. M., Faulkner, K. A., Schwartz, A. V., and Orwoll, E. S. (2007). Incident fall risk and physical activity and physical performance among older men: the Osteoporotic Fractures in Men Study. Am. J. Epidemiol. 165 (6), 696–703. doi:10.1093/aje/kwk050

Choudhury, R., Park, J. H., Banarjee, C., Thiamwong, L., Xie, R., and Stout, J. R. (2023). Associations of mutually exclusive categories of physical activity and sedentary behavior with body composition and fall risk in older women: a cross-sectional study. Int. J. Environ. Res. Public Health 20 (4), 3595. doi:10.3390/ijerph20043595

Choudhury, R., Park, J. H., Thiamwong, L., Xie, R., and Stout, J. R. (2022). Objectively measured physical activity levels and associated factors in older US women during the COVID-19 pandemic: cross-sectional study. JMIR Aging 5 (3), e38172. doi:10.2196/38172

Delbaere, K., Close, J. C., Brodaty, H., Sachdev, P., and Lord, S. R. (2010). Determinants of disparities between perceived and physiological risk of falling among elderly people: cohort study. Bmj 341, c4165. doi:10.1136/bmj

Deshpande, N., Metter, E. J., Lauretani, F., Bandinelli, S., Guralnik, J., and Ferrucci, L. (2008). Activity restriction induced by fear of falling and objective and subjective measures of physical function: a prospective cohort study. J. Am. Geriatr. Soc. 56 (4), 615–620. doi:10.1111/j.1532-5415.2007.01639.x

Fabre, J. M., Ellis, R., Kosma, M., and Wood, R. H. (2010). Falls risk factors and a compendium of falls risk screening instruments. J. Geriatr. Phys. Ther. 33 (4), 184–197. doi:10.1519/jpt.0b013e3181ff2a24

Goble, D. J., Hearn, M. C., and Baweja, H. S. (2017). Combination of BTrackS and Geri-Fit as a targeted approach for assessing and reducing the postural sway of older adults with high fall risk. Clin. Interv. Aging 12, 351–357. doi:10.2147/cia.S131047

Gregg, E. W., Pereira, M. A., and Caspersen, C. J. (2000). Physical activity, falls, and fractures among older adults: a review of the epidemiologic evidence. J. Am. Geriatr. Soc. 48 (8), 883–893. doi:10.1111/j.1532-5415.2000.tb06884.x

Hadjistavropoulos, T., Delbaere, K., and Fitzgerald, T. D. (2011). Reconceptualizing the role of fear of falling and balance confidence in fall risk. J. Aging Health 23 (1), 3–23. doi:10.1177/0898264310378039

Heesch, K. C., Byles, J. E., and Brown, W. J. (2008). Prospective association between physical activity and falls in community-dwelling older women. J. Epidemiol. Community Health 62 (5), 421–426. doi:10.1136/jech.2007.064147

Jansen, C. P., Klenk, J., Nerz, C., Todd, C., Labudek, S., Kramer-Gmeiner, F., et al. (2021). Association between everyday walking activity, objective and perceived risk of falling in older adults. Age Ageing 50 (5), 1586–1592. doi:10.1093/ageing/afab037

Jefferis, B. J., Iliffe, S., Kendrick, D., Kerse, N., Trost, S., Lennon, L. T., et al. (2014). How are falls and fear of falling associated with objectively measured physical activity in a cohort of community-dwelling older men? BMC Geriatr. 14, 114. doi:10.1186/1471-2318-14-114

John, D., Tang, Q., Albinali, F., and Intille, S. (2019). An open-source monitor-independent movement summary for accelerometer data processing. J. Meas. Phys. Behav. 2 (4), 268–281. doi:10.1123/jmpb.2018-0068

Kakara, R., Bergen, G., Burns, E., and Stevens, M. (2023). Nonfatal and fatal falls among adults aged ≥65 Years - United States, 2020-2021. MMWR Morb. Mortal. Wkly. Rep. 72 (35), 938–943. doi:10.15585/mmwr.mm7235a1

Kempen, G. I., Yardley, L., van Haastregt, J. C., Zijlstra, G. A., Beyer, N., Hauer, K., et al. (2008). The Short FES-I: a shortened version of the falls efficacy scale-international to assess fear of falling. Age Ageing 37 (1), 45–50. doi:10.1093/ageing/afm157

Levy, S. S., Thralls, K. J., and Kviatkovsky, S. A. (2018). Validity and reliability of a portable balance tracking system, BTrackS, in older adults. J. Geriatr. Phys. Ther. 41 (2), 102–107. doi:10.1519/jpt.0000000000000111

Mendes da Costa, E., Pepersack, T., Godin, I., Bantuelle, M., Petit, B., and Levêque, A. (2012). Fear of falling and associated activity restriction in older people. results of a cross-sectional study conducted in a Belgian town. Arch. Public Health 70 (1), 1. doi:10.1186/0778-7367-70-1

Milanović, Z., Pantelić, S., Trajković, N., Sporiš, G., Kostić, R., and James, N. (2013). Age-related decrease in physical activity and functional fitness among elderly men and women. Clin. Interv. Aging 8, 549–556. doi:10.2147/cia.S44112

Moore, D. S., and Ellis, R. (2008). Measurement of fall-related psychological constructs among independent-living older adults: a review of the research literature. Aging Ment. Health 12 (6), 684–699. doi:10.1080/13607860802148855

Moore, D. S., Ellis, R., Kosma, M., Fabre, J. M., McCarter, K. S., and Wood, R. H. (2011). Comparison of the validity of four fall-related psychological measures in a community-based falls risk screening. Res. Q. Exerc. Sport 82 (3), 545–554. doi:10.1080/02701367.2011.10599787

Moreland, B., Kakara, R., and Henry, A. (2020). Trends in nonfatal falls and fall-related injuries among adults aged ≥65 Years - United States, 2012-2018. MMWR Morb. Mortal. Wkly. Rep. 69 (27), 875–881. doi:10.15585/mmwr.mm6927a5

Rikli, R. E., and Jones, C. J. (1999). Functional fitness normative scores for community-residing older adults, ages 60-94. J. Aging Phys. Act. 7 (2), 162–181. doi:10.1123/japa.7.2.162

CrossRef Full Text | Google Scholar

Sawa, R., Asai, T., Doi, T., Misu, S., Murata, S., and Ono, R. (2020). The association between physical activity, including physical activity intensity, and fear of falling differs by fear severity in older adults living in the community. J. Gerontol. B Psychol. Sci. Soc. Sci. 75 (5), 953–960. doi:10.1093/geronb/gby103

Sherrington, C., Whitney, J. C., Lord, S. R., Herbert, R. D., Cumming, R. G., and Close, J. C. (2008). Effective exercise for the prevention of falls: a systematic review and meta-analysis. J. Am. Geriatr. Soc. 56 (12), 2234–2243. doi:10.1111/j.1532-5415.2008.02014.x

Shumway-Cook, A., and Woollacott, M. H. (2007). Motor control: translating research into clinical practice . United States: Lippincott Williams & Wilkins .

Google Scholar

Smith, L., Gardner, B., Fisher, A., and Hamer, M. (2015). Patterns and correlates of physical activity behaviour over 10 years in older adults: prospective analyses from the English Longitudinal Study of Ageing. BMJ Open 5 (4), e007423. doi:10.1136/bmjopen-2014-007423

Soh, S. L., Tan, C. W., Thomas, J. I., Tan, G., Xu, T., Ng, Y. L., et al. (2021). Falls efficacy: extending the understanding of self-efficacy in older adults towards managing falls. J. Frailty Sarcopenia Falls 6 (3), 131–138. doi:10.22540/jfsf-06-131

Suryadinata, R. V., Wirjatmadi, B., Adriani, M., and Lorensia, A. (2020). Effect of age and weight on physical activity. J. Public Health Res. 9 (2), 1840. doi:10.4081/jphr.2020.1840

Tennstedt, S., Howland, J., Lachman, M., Peterson, E., Kasten, L., and Jette, A. (1998). A randomized, controlled trial of a group intervention to reduce fear of falling and associated activity restriction in older adults. J. Gerontol. B Psychol. Sci. Soc. Sci. 53 (6), P384–P392. doi:10.1093/geronb/53b.6.p384

Thiamwong, L. (2020). A hybrid concept analysis of fall risk appraisal: integration of older adults' perspectives with an integrative literature review. Nurs. Forum 55 (2), 190–196. doi:10.1111/nuf.12415

Thiamwong, L., Huang, H. J., Ng, B. P., Yan, X., Sole, M. L., Stout, J. R., et al. (2020a). Shifting maladaptive fall risk appraisal in older adults through an in-home physio-fEedback and exercise pRogram (peer): a pilot study. Clin. Gerontol. 43 (4), 378–390. doi:10.1080/07317115.2019.1692120

Thiamwong, L., Ng, B. P., Kwan, R. Y. C., and Suwanno, J. (2021a). Maladaptive fall risk appraisal and falling in community-dwelling adults aged 60 and older: implications for screening. Clin. Gerontol. 44 (5), 552–561. doi:10.1080/07317115.2021.1950254

Thiamwong, L., Sole, M. L., Ng, B. P., Welch, G. F., Huang, H. J., and Stout, J. R. (2020b). Assessing fall risk appraisal through combined physiological and perceived fall risk measures using innovative Technology. J. Gerontol. Nurs. 46 (4), 41–47. doi:10.3928/00989134-20200302-01

Thiamwong, L., Stout, J. R., Park, J. H., and Yan, X. (2021b). Technology-based fall risk assessments for older adults in low-income settings: protocol for a cross-sectional study. JMIR Res. Protoc. 10 (4), e27381. doi:10.2196/27381

Thiamwong, L., Xie, R., Park, J. H., Choudhury, R., Malatyali, A., Li, W., et al. (2023). Levels of accelerometer-based physical activity in older adults with a mismatch between physiological fall risk and fear of falling. J. Gerontol. Nurs. 49 (6), 41–49. doi:10.3928/00989134-20230512-06

Tinetti, M. E., Mendes de Leon, C. F., Doucette, J. T., and Baker, D. I. (1994). Fear of falling and fall-related efficacy in relationship to functioning among community-living elders. J. Gerontol. 49 (3), M140–M147. doi:10.1093/geronj/49.3.m140

Troiano, R. P., Berrigan, D., Dodd, K. W., Mâsse, L. C., Tilert, T., and McDowell, M. (2008). Physical activity in the United States measured by accelerometer. Med. Sci. Sports Exerc. 40 (1), 181–188. doi:10.1249/mss.0b013e31815a51b3

Tudor-Locke, C., Brashear, M. M., Katzmarzyk, P. T., and Johnson, W. D. (2012). Peak stepping cadence in free-living adults: 2005-2006 NHANES. J. Phys. Act. Health 9 (8), 1125–1129. doi:10.1123/jpah.9.8.1125

Vilar-Gomez, E., Vuppalanchi, R., Gawrieh, S., Pike, F., Samala, N., and Chalasani, N. (2023). Significant dose-response association of physical activity and diet quality with mortality in adults with suspected NAFLD in a population study. Am. J. Gastroenterol. 118 (9), 1576–1591. doi:10.14309/ajg.0000000000002222

Westerterp, K. R. (2018). Changes in physical activity over the lifespan: impact on body composition and sarcopenic obesity. Obes. Rev. 19 (1), 8–13. doi:10.1111/obr.12781

Wolff-Hughes, D. L., Bassett, D. R., and Fitzhugh, E. C. (2014). Population-referenced percentiles for waist-worn accelerometer-derived total activity counts in U.S. youth: 2003 - 2006 NHANES. PLoS One 9 (12), e115915. doi:10.1371/journal.pone.0115915

Yardley, L., Beyer, N., Hauer, K., Kempen, G., Piot-Ziegler, C., and Todd, C. (2005). Development and initial validation of the falls efficacy scale-international (FES-I). Age Ageing 34 (6), 614–619. doi:10.1093/ageing/afi196

Yee, X. S., Ng, Y. S., Allen, J. C., Latib, A., Tay, E. L., Abu Bakar, H. M., et al. (2021). Performance on sit-to-stand tests in relation to measures of functional fitness and sarcopenia diagnosis in community-dwelling older adults. Eur. Rev. Aging Phys. Act. 18 (1), 1. doi:10.1186/s11556-020-00255-5

Zheng, P., Pleuss, J. D., Turner, D. S., Ducharme, S. W., and Aguiar, E. J. (2023). Dose-response association between physical activity (daily MIMS, peak 30-minute MIMS) and cognitive function among older adults: NHANES 2011-2014. J. Gerontol. A Biol. Sci. Med. Sci. 78 (2), 286–291. doi:10.1093/gerona/glac076

Zijlstra, G. A., van Haastregt, J. C., van Eijk, J. T., van Rossum, E., Stalenhoef, P. A., and Kempen, G. I. (2007). Prevalence and correlates of fear of falling, and associated avoidance of activity in the general population of community-living older people. Age Ageing 36 (3), 304–309. doi:10.1093/ageing/afm021

Keywords: falls, physical activity, accelerometry, aging, fear of falling, fall risk, MIMS

Citation: Choudhury R, Park J-H, Banarjee C, Coca MG, Fukuda DH, Xie R, Stout JR and Thiamwong L (2024) Associations between monitor-independent movement summary (MIMS) and fall risk appraisal combining fear of falling and physiological fall risk in community-dwelling older adults. Front. Aging 5:1284694. doi: 10.3389/fragi.2024.1284694

Received: 28 August 2023; Accepted: 20 March 2024; Published: 09 April 2024.

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

*Correspondence: Joon-Hyuk Park, [email protected]

This article is part of the Research Topic

Insights into Falls Efficacy and Fear of Falling