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Dissertations 4: methodology: methods.

• Introduction & Philosophy
• Methodology

Primary & Secondary Sources, Primary & Secondary Data

When describing your research methods, you can start by stating what kind of secondary and, if applicable, primary sources you used in your research. Explain why you chose such sources, how well they served your research, and identify possible issues encountered using these sources.  

Definitions  

There is some confusion on the use of the terms primary and secondary sources, and primary and secondary data. The confusion is also due to disciplinary differences (Lombard 2010). Whilst you are advised to consult the research methods literature in your field, we can generalise as follows:  

Secondary sources 

Secondary sources normally include the literature (books and articles) with the experts' findings, analysis and discussions on a certain topic (Cottrell, 2014, p123). Secondary sources often interpret primary sources.  

Primary sources 

Primary sources are "first-hand" information such as raw data, statistics, interviews, surveys, law statutes and law cases. Even literary texts, pictures and films can be primary sources if they are the object of research (rather than, for example, documentaries reporting on something else, in which case they would be secondary sources). The distinction between primary and secondary sources sometimes lies on the use you make of them (Cottrell, 2014, p123). 

Primary data 

Primary data are data (primary sources) you directly obtained through your empirical work (Saunders, Lewis and Thornhill 2015, p316). 

Secondary data 

Secondary data are data (primary sources) that were originally collected by someone else (Saunders, Lewis and Thornhill 2015, p316).   

Comparison between primary and secondary data   

Use  

Virtually all research will use secondary sources, at least as background information. 

Often, especially at the postgraduate level, it will also use primary sources - secondary and/or primary data. The engagement with primary sources is generally appreciated, as less reliant on others' interpretations, and closer to 'facts'. 

The use of primary data, as opposed to secondary data, demonstrates the researcher's effort to do empirical work and find evidence to answer her specific research question and fulfill her specific research objectives. Thus, primary data contribute to the originality of the research.    

Ultimately, you should state in this section of the methodology: 

What sources and data you are using and why (how are they going to help you answer the research question and/or test the hypothesis. 

If using primary data, why you employed certain strategies to collect them. 

What the advantages and disadvantages of your strategies to collect the data (also refer to the research in you field and research methods literature). 

Quantitative, Qualitative & Mixed Methods

The methodology chapter should reference your use of quantitative research, qualitative research and/or mixed methods. The following is a description of each along with their advantages and disadvantages. 

Quantitative research 

Quantitative research uses numerical data (quantities) deriving, for example, from experiments, closed questions in surveys, questionnaires, structured interviews or published data sets (Cottrell, 2014, p93). It normally processes and analyses this data using quantitative analysis techniques like tables, graphs and statistics to explore, present and examine relationships and trends within the data (Saunders, Lewis and Thornhill, 2015, p496). 

Qualitative research  

Qualitative research is generally undertaken to study human behaviour and psyche. It uses methods like in-depth case studies, open-ended survey questions, unstructured interviews, focus groups, or unstructured observations (Cottrell, 2014, p93). The nature of the data is subjective, and also the analysis of the researcher involves a degree of subjective interpretation. Subjectivity can be controlled for in the research design, or has to be acknowledged as a feature of the research. Subject-specific books on (qualitative) research methods offer guidance on such research designs.  

Mixed methods 

Mixed-method approaches combine both qualitative and quantitative methods, and therefore combine the strengths of both types of research. Mixed methods have gained popularity in recent years.  

When undertaking mixed-methods research you can collect the qualitative and quantitative data either concurrently or sequentially. If sequentially, you can for example, start with a few semi-structured interviews, providing qualitative insights, and then design a questionnaire to obtain quantitative evidence that your qualitative findings can also apply to a wider population (Specht, 2019, p138). 

Ultimately, your methodology chapter should state: 

Whether you used quantitative research, qualitative research or mixed methods. 

Why you chose such methods (and refer to research method sources). 

Why you rejected other methods. 

How well the method served your research. 

The problems or limitations you encountered. 

Doug Specht, Senior Lecturer at the Westminster School of Media and Communication, explains mixed methods research in the following video:

LinkedIn Learning Video on Academic Research Foundations: Quantitative

The video covers the characteristics of quantitative research, and explains how to approach different parts of the research process, such as creating a solid research question and developing a literature review. He goes over the elements of a study, explains how to collect and analyze data, and shows how to present your data in written and numeric form.

Link to quantitative research video

Some Types of Methods

There are several methods you can use to get primary data. To reiterate, the choice of the methods should depend on your research question/hypothesis. 

Whatever methods you will use, you will need to consider: 

why did you choose one technique over another? What were the advantages and disadvantages of the technique you chose? 

what was the size of your sample? Who made up your sample? How did you select your sample population? Why did you choose that particular sampling strategy?) 

ethical considerations (see also tab...)  

safety considerations  

validity  

feasibility  

recording  

procedure of the research (see box procedural method...).  

Check Stella Cottrell's book  Dissertations and Project Reports: A Step by Step Guide  for some succinct yet comprehensive information on most methods (the following account draws mostly on her work). Check a research methods book in your discipline for more specific guidance.  

Experiments 

Experiments are useful to investigate cause and effect, when the variables can be tightly controlled. They can test a theory or hypothesis in controlled conditions. Experiments do not prove or disprove an hypothesis, instead they support or not support an hypothesis. When using the empirical and inductive method it is not possible to achieve conclusive results. The results may only be valid until falsified by other experiments and observations. 

For more information on Scientific Method, click here . 

Observations 

Observational methods are useful for in-depth analyses of behaviours in people, animals, organisations, events or phenomena. They can test a theory or products in real life or simulated settings. They generally a qualitative research method.  

Questionnaires and surveys 

Questionnaires and surveys are useful to gain opinions, attitudes, preferences, understandings on certain matters. They can provide quantitative data that can be collated systematically; qualitative data, if they include opportunities for open-ended responses; or both qualitative and quantitative elements. 

Interviews  

Interviews are useful to gain rich, qualitative information about individuals' experiences, attitudes or perspectives. With interviews you can follow up immediately on responses for clarification or further details. There are three main types of interviews: structured (following a strict pattern of questions, which expect short answers), semi-structured (following a list of questions, with the opportunity to follow up the answers with improvised questions), and unstructured (following a short list of broad questions, where the respondent can lead more the conversation) (Specht, 2019, p142). 

This short video on qualitative interviews discusses best practices and covers qualitative interview design, preparation and data collection methods. 

Focus groups   

In this case, a group of people (normally, 4-12) is gathered for an interview where the interviewer asks questions to such group of participants. Group interactions and discussions can be highly productive, but the researcher has to beware of the group effect, whereby certain participants and views dominate the interview (Saunders, Lewis and Thornhill 2015, p419). The researcher can try to minimise this by encouraging involvement of all participants and promoting a multiplicity of views. 

This video focuses on strategies for conducting research using focus groups.  

Check out the guidance on online focus groups by Aliaksandr Herasimenka, which is attached at the bottom of this text box. 

Case study 

Case studies are often a convenient way to narrow the focus of your research by studying how a theory or literature fares with regard to a specific person, group, organisation, event or other type of entity or phenomenon you identify. Case studies can be researched using other methods, including those described in this section. Case studies give in-depth insights on the particular reality that has been examined, but may not be representative of what happens in general, they may not be generalisable, and may not be relevant to other contexts. These limitations have to be acknowledged by the researcher.     

Content analysis 

Content analysis consists in the study of words or images within a text. In its broad definition, texts include books, articles, essays, historical documents, speeches, conversations, advertising, interviews, social media posts, films, theatre, paintings or other visuals. Content analysis can be quantitative (e.g. word frequency) or qualitative (e.g. analysing intention and implications of the communication). It can detect propaganda, identify intentions of writers, and can see differences in types of communication (Specht, 2019, p146). Check this page on collecting, cleaning and visualising Twitter data.

Extra links and resources:  

Research Methods  

A clear and comprehensive overview of research methods by Emerald Publishing. It includes: crowdsourcing as a research tool; mixed methods research; case study; discourse analysis; ground theory; repertory grid; ethnographic method and participant observation; interviews; focus group; action research; analysis of qualitative data; survey design; questionnaires; statistics; experiments; empirical research; literature review; secondary data and archival materials; data collection. 

Doing your dissertation during the COVID-19 pandemic  

Resources providing guidance on doing dissertation research during the pandemic: Online research methods; Secondary data sources; Webinars, conferences and podcasts; 

• Virtual Focus Groups Guidance on managing virtual focus groups

5 Minute Methods Videos

The following are a series of useful videos that introduce research methods in five minutes. These resources have been produced by lecturers and students with the University of Westminster's School of Media and Communication. 

Case Study Research

Research Ethics

Quantitative Content Analysis 

Sequential Analysis 

Qualitative Content Analysis 

Thematic Analysis 

Social Media Research 

Mixed Method Research 

Procedural Method

In this part, provide an accurate, detailed account of the methods and procedures that were used in the study or the experiment (if applicable!). 

Include specifics about participants, sample, materials, design and methods. 

If the research involves human subjects, then include a detailed description of who and how many participated along with how the participants were selected.  

Describe all materials used for the study, including equipment, written materials and testing instruments. 

Identify the study's design and any variables or controls employed. 

Write out the steps in the order that they were completed. 

Indicate what participants were asked to do, how measurements were taken and any calculations made to raw data collected. 

Specify statistical techniques applied to the data to reach your conclusions. 

Provide evidence that you incorporated rigor into your research. This is the quality of being thorough and accurate and considers the logic behind your research design. 

Highlight any drawbacks that may have limited your ability to conduct your research thoroughly. 

You have to provide details to allow others to replicate the experiment and/or verify the data, to test the validity of the research. 

Bibliography

Cottrell, S. (2014). Dissertations and project reports: a step by step guide. Hampshire, England: Palgrave Macmillan.

Lombard, E. (2010). Primary and secondary sources.  The Journal of Academic Librarianship , 36(3), 250-253

Saunders, M.N.K., Lewis, P. and Thornhill, A. (2015).  Research Methods for Business Students.  New York: Pearson Education. 

Specht, D. (2019).  The Media And Communications Study Skills Student Guide . London: University of Westminster Press.  

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How to Analyse Secondary Data for a Dissertation

Secondary data refers to data that has already been collected by another researcher. For researchers (and students!) with limited time and resources, secondary data, whether qualitative or quantitative can be a highly viable source of data.  In addition, with the advances in technology and access to peer reviewed journals and studies provided by the internet, it is increasingly popular as a form of data collection.  The question that frequently arises amongst students however, is: how is secondary data best analysed?

The process of data analysis in secondary research

Secondary analysis (i.e., the use of existing data) is a systematic methodological approach that has some clear steps that need to be followed for the process to be effective.  In simple terms there are three steps:

• Step One: Development of Research Questions
• Step Two: Identification of dataset
• Step Three: Evaluation of the dataset.

Let’s look at each of these in more detail:

Step One: Development of research questions

Using secondary data means you need to apply theoretical knowledge and conceptual skills to be able to use the dataset to answer research questions.  Clearly therefore, the first step is thus to clearly define and develop your research questions so that you know the areas of interest that you need to explore for location of the most appropriate secondary data.

Step Two: Identification of Dataset

This stage should start with identification, through investigation, of what is currently known in the subject area and where there are gaps, and thus what data is available to address these gaps.  Sources can be academic from prior studies that have used quantitative or qualitative data, and which can then be gathered together and collated to produce a new secondary dataset.  In addition, other more informal or “grey” literature can also be incorporated, including consumer report, commercial studies or similar.  One of the values of using secondary research is that original survey works often do not use all the data collected which means this unused information can be applied to different settings or perspectives.

Key point: Effective use of secondary data means identifying how the data can be used to deliver meaningful and relevant answers to the research questions.  In other words that the data used is a good fit for the study and research questions.

Step Three: Evaluation of the dataset for effectiveness/fit

A good tip is to use a reflective approach for data evaluation.  In other words, for each piece of secondary data to be utilised, it is sensible to identify the purpose of the work, the credentials of the authors (i.e., credibility, what data is provided in the original work and how long ago it was collected).  In addition, the methods used and the level of consistency that exists compared to other works. This is important because understanding the primary method of data collection will impact on the overall evaluation and analysis when it is used as secondary source. In essence, if there is no understanding of the coding used in qualitative data analysis to identify key themes then there will be a mismatch with interpretations when the data is used for secondary purposes.  Furthermore, having multiple sources which draw similar conclusions ensures a higher level of validity than relying on only one or two secondary sources.

A useful framework provides a flow chart of decision making, as shown in the figure below.

Following this process ensures that only those that are most appropriate for your research questions are included in the final dataset, but also demonstrates to your readers that you have been thorough in identifying the right works to use.

Writing up the Analysis

Once you have your dataset, writing up the analysis will depend on the process used.  If the data is qualitative in nature, then you should follow the following process.

Pre-Planning

• Read and re-read all sources, identifying initial observations, correlations, and relationships between themes and how they apply to your research questions.
• Once initial themes are identified, it is sensible to explore further and identify sub-themes which lead on from the core themes and correlations in the dataset, which encourages identification of new insights and contributes to the originality of your own work.

Structure of the Analysis Presentation

Introduction.

The introduction should commence with an overview of all your sources. It is good practice to present these in a table, listed chronologically so that your work has an orderly and consistent flow. The introduction should also incorporate a brief (2-3 sentences) overview of the key outcomes and results identified.

The body text for secondary data, irrespective of whether quantitative or qualitative data is used, should be broken up into sub-sections for each argument or theme presented. In the case of qualitative data, depending on whether content, narrative or discourse analysis is used, this means presenting the key papers in the area, their conclusions and how these answer, or not, your research questions. Each source should be clearly cited and referenced at the end of the work. In the case of qualitative data, any figures or tables should be reproduced with the correct citations to their original source. In both cases, it is good practice to give a main heading of a key theme, with sub-headings for each of the sub themes identified in the analysis.

Do not use direct quotes from secondary data unless they are:

• properly referenced, and
• are key to underlining a point or conclusion that you have drawn from the data.

All results sections, regardless of whether primary or secondary data has been used should refer back to the research questions and prior works. This is because, regardless of whether the results back up or contradict previous research, including previous works shows a wider level of reading and understanding of the topic being researched and gives a greater depth to your own work.

Summary of results

The summary of the results section of a secondary data dissertation should deliver a summing up of key findings, and if appropriate a conceptual framework that clearly illustrates the findings of the work. This shows that you have understood your secondary data, how it has answered your research questions, and furthermore that your interpretation has led to some firm outcomes.

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• Dissertation
• What Is a Research Methodology? | Steps & Tips

What Is a Research Methodology? | Steps & Tips

Published on 25 February 2019 by Shona McCombes . Revised on 10 October 2022.

Your research methodology discusses and explains the data collection and analysis methods you used in your research. A key part of your thesis, dissertation, or research paper, the methodology chapter explains what you did and how you did it, allowing readers to evaluate the reliability and validity of your research.

It should include:

• The type of research you conducted
• How you collected and analysed your data
• Any tools or materials you used in the research
• Why you chose these methods
• Your methodology section should generally be written in the past tense .
• Academic style guides in your field may provide detailed guidelines on what to include for different types of studies.
• Your citation style might provide guidelines for your methodology section (e.g., an APA Style methods section ).

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Table of contents

How to write a research methodology, why is a methods section important, step 1: explain your methodological approach, step 2: describe your data collection methods, step 3: describe your analysis method, step 4: evaluate and justify the methodological choices you made, tips for writing a strong methodology chapter, frequently asked questions about methodology.

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Your methods section is your opportunity to share how you conducted your research and why you chose the methods you chose. It’s also the place to show that your research was rigorously conducted and can be replicated .

It gives your research legitimacy and situates it within your field, and also gives your readers a place to refer to if they have any questions or critiques in other sections.

You can start by introducing your overall approach to your research. You have two options here.

Option 1: Start with your “what”

What research problem or question did you investigate?

• Aim to describe the characteristics of something?
• Explore an under-researched topic?
• Establish a causal relationship?

And what type of data did you need to achieve this aim?

• Quantitative data , qualitative data , or a mix of both?
• Primary data collected yourself, or secondary data collected by someone else?
• Experimental data gathered by controlling and manipulating variables, or descriptive data gathered via observations?

Option 2: Start with your “why”

Depending on your discipline, you can also start with a discussion of the rationale and assumptions underpinning your methodology. In other words, why did you choose these methods for your study?

• Why is this the best way to answer your research question?
• Is this a standard methodology in your field, or does it require justification?
• Were there any ethical considerations involved in your choices?
• What are the criteria for validity and reliability in this type of research ?

Once you have introduced your reader to your methodological approach, you should share full details about your data collection methods .

Quantitative methods

In order to be considered generalisable, you should describe quantitative research methods in enough detail for another researcher to replicate your study.

Here, explain how you operationalised your concepts and measured your variables. Discuss your sampling method or inclusion/exclusion criteria, as well as any tools, procedures, and materials you used to gather your data.

Surveys Describe where, when, and how the survey was conducted.

• How did you design the questionnaire?
• What form did your questions take (e.g., multiple choice, Likert scale )?
• Were your surveys conducted in-person or virtually?
• What sampling method did you use to select participants?
• What was your sample size and response rate?

Experiments Share full details of the tools, techniques, and procedures you used to conduct your experiment.

• How did you design the experiment ?
• How did you recruit participants?
• How did you manipulate and measure the variables ?
• What tools did you use?

Existing data Explain how you gathered and selected the material (such as datasets or archival data) that you used in your analysis.

• Where did you source the material?
• How was the data originally produced?
• What criteria did you use to select material (e.g., date range)?

The survey consisted of 5 multiple-choice questions and 10 questions measured on a 7-point Likert scale.

The goal was to collect survey responses from 350 customers visiting the fitness apparel company’s brick-and-mortar location in Boston on 4–8 July 2022, between 11:00 and 15:00.

Here, a customer was defined as a person who had purchased a product from the company on the day they took the survey. Participants were given 5 minutes to fill in the survey anonymously. In total, 408 customers responded, but not all surveys were fully completed. Due to this, 371 survey results were included in the analysis.

Qualitative methods

In qualitative research , methods are often more flexible and subjective. For this reason, it’s crucial to robustly explain the methodology choices you made.

Be sure to discuss the criteria you used to select your data, the context in which your research was conducted, and the role you played in collecting your data (e.g., were you an active participant, or a passive observer?)

Interviews or focus groups Describe where, when, and how the interviews were conducted.

• How did you find and select participants?
• How many participants took part?
• What form did the interviews take ( structured , semi-structured , or unstructured )?
• How long were the interviews?
• How were they recorded?

Participant observation Describe where, when, and how you conducted the observation or ethnography .

• What group or community did you observe? How long did you spend there?
• How did you gain access to this group? What role did you play in the community?
• How long did you spend conducting the research? Where was it located?
• How did you record your data (e.g., audiovisual recordings, note-taking)?

Existing data Explain how you selected case study materials for your analysis.

• What type of materials did you analyse?
• How did you select them?

In order to gain better insight into possibilities for future improvement of the fitness shop’s product range, semi-structured interviews were conducted with 8 returning customers.

Here, a returning customer was defined as someone who usually bought products at least twice a week from the store.

Surveys were used to select participants. Interviews were conducted in a small office next to the cash register and lasted approximately 20 minutes each. Answers were recorded by note-taking, and seven interviews were also filmed with consent. One interviewee preferred not to be filmed.

Mixed methods

Mixed methods research combines quantitative and qualitative approaches. If a standalone quantitative or qualitative study is insufficient to answer your research question, mixed methods may be a good fit for you.

Mixed methods are less common than standalone analyses, largely because they require a great deal of effort to pull off successfully. If you choose to pursue mixed methods, it’s especially important to robustly justify your methods here.

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Next, you should indicate how you processed and analysed your data. Avoid going into too much detail: you should not start introducing or discussing any of your results at this stage.

In quantitative research , your analysis will be based on numbers. In your methods section, you can include:

• How you prepared the data before analysing it (e.g., checking for missing data , removing outliers , transforming variables)
• Which software you used (e.g., SPSS, Stata or R)
• Which statistical tests you used (e.g., two-tailed t test , simple linear regression )

In qualitative research, your analysis will be based on language, images, and observations (often involving some form of textual analysis ).

Specific methods might include:

• Content analysis : Categorising and discussing the meaning of words, phrases and sentences
• Thematic analysis : Coding and closely examining the data to identify broad themes and patterns
• Discourse analysis : Studying communication and meaning in relation to their social context

Mixed methods combine the above two research methods, integrating both qualitative and quantitative approaches into one coherent analytical process.

Above all, your methodology section should clearly make the case for why you chose the methods you did. This is especially true if you did not take the most standard approach to your topic. In this case, discuss why other methods were not suitable for your objectives, and show how this approach contributes new knowledge or understanding.

In any case, it should be overwhelmingly clear to your reader that you set yourself up for success in terms of your methodology’s design. Show how your methods should lead to results that are valid and reliable, while leaving the analysis of the meaning, importance, and relevance of your results for your discussion section .

• Quantitative: Lab-based experiments cannot always accurately simulate real-life situations and behaviours, but they are effective for testing causal relationships between variables .
• Qualitative: Unstructured interviews usually produce results that cannot be generalised beyond the sample group , but they provide a more in-depth understanding of participants’ perceptions, motivations, and emotions.
• Mixed methods: Despite issues systematically comparing differing types of data, a solely quantitative study would not sufficiently incorporate the lived experience of each participant, while a solely qualitative study would be insufficiently generalisable.

Remember that your aim is not just to describe your methods, but to show how and why you applied them. Again, it’s critical to demonstrate that your research was rigorously conducted and can be replicated.

1. Focus on your objectives and research questions

The methodology section should clearly show why your methods suit your objectives  and convince the reader that you chose the best possible approach to answering your problem statement and research questions .

2. Cite relevant sources

Your methodology can be strengthened by referencing existing research in your field. This can help you to:

• Show that you followed established practice for your type of research
• Discuss how you decided on your approach by evaluating existing research
• Present a novel methodological approach to address a gap in the literature

3. Write for your audience

Consider how much information you need to give, and avoid getting too lengthy. If you are using methods that are standard for your discipline, you probably don’t need to give a lot of background or justification.

Regardless, your methodology should be a clear, well-structured text that makes an argument for your approach, not just a list of technical details and procedures.

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 objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. interviews, experiments , surveys , statistical tests ).

In a dissertation or scientific paper, the methodology chapter or methods section comes after the introduction and before the results , discussion and conclusion .

Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

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Home » Secondary Data – Types, Methods and Examples

Secondary Data – Types, Methods and Examples

Table of Contents

Secondary Data

Definition:

Secondary data refers to information that has been collected, processed, and published by someone else, rather than the researcher gathering the data firsthand. This can include data from sources such as government publications, academic journals, market research reports, and other existing datasets.

Secondary Data Types

Types of secondary data are as follows:

• Published data: Published data refers to data that has been published in books, magazines, newspapers, and other print media. Examples include statistical reports, market research reports, and scholarly articles.
• Government data: Government data refers to data collected by government agencies and departments. This can include data on demographics, economic trends, crime rates, and health statistics.
• Commercial data: Commercial data is data collected by businesses for their own purposes. This can include sales data, customer feedback, and market research data.
• Academic data: Academic data refers to data collected by researchers for academic purposes. This can include data from experiments, surveys, and observational studies.
• Online data: Online data refers to data that is available on the internet. This can include social media posts, website analytics, and online customer reviews.
• Organizational data: Organizational data is data collected by businesses or organizations for their own purposes. This can include data on employee performance, financial records, and customer satisfaction.
• Historical data : Historical data refers to data that was collected in the past and is still available for research purposes. This can include census data, historical documents, and archival records.
• International data: International data refers to data collected from other countries for research purposes. This can include data on international trade, health statistics, and demographic trends.
• Public data : Public data refers to data that is available to the general public. This can include data from government agencies, non-profit organizations, and other sources.
• Private data: Private data refers to data that is not available to the general public. This can include confidential business data, personal medical records, and financial data.
• Big data: Big data refers to large, complex datasets that are difficult to manage and analyze using traditional data processing methods. This can include social media data, sensor data, and other types of data generated by digital devices.

Secondary Data Collection Methods

Secondary Data Collection Methods are as follows:

• Published sources: Researchers can gather secondary data from published sources such as books, journals, reports, and newspapers. These sources often provide comprehensive information on a variety of topics.
• Online sources: With the growth of the internet, researchers can now access a vast amount of secondary data online. This includes websites, databases, and online archives.
• Government sources : Government agencies often collect and publish a wide range of secondary data on topics such as demographics, crime rates, and health statistics. Researchers can obtain this data through government websites, publications, or data portals.
• Commercial sources: Businesses often collect and analyze data for marketing research or customer profiling. Researchers can obtain this data through commercial data providers or by purchasing market research reports.
• Academic sources: Researchers can also obtain secondary data from academic sources such as published research studies, academic journals, and dissertations.
• Personal contacts: Researchers can also obtain secondary data from personal contacts, such as experts in a particular field or individuals with specialized knowledge.

Secondary Data Formats

Secondary data can come in various formats depending on the source from which it is obtained. Here are some common formats of secondary data:

• Numeric Data: Numeric data is often in the form of statistics and numerical figures that have been compiled and reported by organizations such as government agencies, research institutions, and commercial enterprises. This can include data such as population figures, GDP, sales figures, and market share.
• Textual Data: Textual data is often in the form of written documents, such as reports, articles, and books. This can include qualitative data such as descriptions, opinions, and narratives.
• Audiovisual Data : Audiovisual data is often in the form of recordings, videos, and photographs. This can include data such as interviews, focus group discussions, and other types of qualitative data.
• Geospatial Data: Geospatial data is often in the form of maps, satellite images, and geographic information systems (GIS) data. This can include data such as demographic information, land use patterns, and transportation networks.
• Transactional Data : Transactional data is often in the form of digital records of financial and business transactions. This can include data such as purchase histories, customer behavior, and financial transactions.
• Social Media Data: Social media data is often in the form of user-generated content from social media platforms such as Facebook, Twitter, and Instagram. This can include data such as user demographics, content trends, and sentiment analysis.

Secondary Data Analysis Methods

Secondary data analysis involves the use of pre-existing data for research purposes. Here are some common methods of secondary data analysis:

• Descriptive Analysis: This method involves describing the characteristics of a dataset, such as the mean, standard deviation, and range of the data. Descriptive analysis can be used to summarize data and provide an overview of trends.
• Inferential Analysis: This method involves making inferences and drawing conclusions about a population based on a sample of data. Inferential analysis can be used to test hypotheses and determine the statistical significance of relationships between variables.
• Content Analysis: This method involves analyzing textual or visual data to identify patterns and themes. Content analysis can be used to study the content of documents, media coverage, and social media posts.
• Time-Series Analysis : This method involves analyzing data over time to identify trends and patterns. Time-series analysis can be used to study economic trends, climate change, and other phenomena that change over time.
• Spatial Analysis : This method involves analyzing data in relation to geographic location. Spatial analysis can be used to study patterns of disease spread, land use patterns, and the effects of environmental factors on health outcomes.
• Meta-Analysis: This method involves combining data from multiple studies to draw conclusions about a particular phenomenon. Meta-analysis can be used to synthesize the results of previous research and provide a more comprehensive understanding of a particular topic.

Secondary Data Gathering Guide

Here are some steps to follow when gathering secondary data:

• Define your research question: Start by defining your research question and identifying the specific information you need to answer it. This will help you identify the type of secondary data you need and where to find it.
• Identify relevant sources: Identify potential sources of secondary data, including published sources, online databases, government sources, and commercial data providers. Consider the reliability and validity of each source.
• Evaluate the quality of the data: Evaluate the quality and reliability of the data you plan to use. Consider the data collection methods, sample size, and potential biases. Make sure the data is relevant to your research question and is suitable for the type of analysis you plan to conduct.
• Collect the data: Collect the relevant data from the identified sources. Use a consistent method to record and organize the data to make analysis easier.
• Validate the data: Validate the data to ensure that it is accurate and reliable. Check for inconsistencies, missing data, and errors. Address any issues before analyzing the data.
• Analyze the data: Analyze the data using appropriate statistical and analytical methods. Use descriptive and inferential statistics to summarize and draw conclusions from the data.
• Interpret the results: Interpret the results of your analysis and draw conclusions based on the data. Make sure your conclusions are supported by the data and are relevant to your research question.
• Communicate the findings : Communicate your findings clearly and concisely. Use appropriate visual aids such as graphs and charts to help explain your results.

Examples of Secondary Data

Here are some examples of secondary data from different fields:

• Healthcare : Hospital records, medical journals, clinical trial data, and disease registries are examples of secondary data sources in healthcare. These sources can provide researchers with information on patient demographics, disease prevalence, and treatment outcomes.
• Marketing : Market research reports, customer surveys, and sales data are examples of secondary data sources in marketing. These sources can provide marketers with information on consumer preferences, market trends, and competitor activity.
• Education : Student test scores, graduation rates, and enrollment statistics are examples of secondary data sources in education. These sources can provide researchers with information on student achievement, teacher effectiveness, and educational disparities.
• Finance : Stock market data, financial statements, and credit reports are examples of secondary data sources in finance. These sources can provide investors with information on market trends, company performance, and creditworthiness.
• Social Science : Government statistics, census data, and survey data are examples of secondary data sources in social science. These sources can provide researchers with information on population demographics, social trends, and political attitudes.
• Environmental Science : Climate data, remote sensing data, and ecological monitoring data are examples of secondary data sources in environmental science. These sources can provide researchers with information on weather patterns, land use, and biodiversity.

Purpose of Secondary Data

The purpose of secondary data is to provide researchers with information that has already been collected by others for other purposes. Secondary data can be used to support research questions, test hypotheses, and answer research objectives. Some of the key purposes of secondary data are:

• To gain a better understanding of the research topic : Secondary data can be used to provide context and background information on a research topic. This can help researchers understand the historical and social context of their research and gain insights into relevant variables and relationships.
• To save time and resources: Collecting new primary data can be time-consuming and expensive. Using existing secondary data sources can save researchers time and resources by providing access to pre-existing data that has already been collected and organized.
• To provide comparative data : Secondary data can be used to compare and contrast findings across different studies or datasets. This can help researchers identify trends, patterns, and relationships that may not have been apparent from individual studies.
• To support triangulation: Triangulation is the process of using multiple sources of data to confirm or refute research findings. Secondary data can be used to support triangulation by providing additional sources of data to support or refute primary research findings.
• To supplement primary data : Secondary data can be used to supplement primary data by providing additional information or insights that were not captured by the primary research. This can help researchers gain a more complete understanding of the research topic and draw more robust conclusions.

When to use Secondary Data

Secondary data can be useful in a variety of research contexts, and there are several situations in which it may be appropriate to use secondary data. Some common situations in which secondary data may be used include:

• When primary data collection is not feasible : Collecting primary data can be time-consuming and expensive, and in some cases, it may not be feasible to collect primary data. In these situations, secondary data can provide valuable insights and information.
• When exploring a new research area : Secondary data can be a useful starting point for researchers who are exploring a new research area. Secondary data can provide context and background information on a research topic, and can help researchers identify key variables and relationships to explore further.
• When comparing and contrasting research findings: Secondary data can be used to compare and contrast findings across different studies or datasets. This can help researchers identify trends, patterns, and relationships that may not have been apparent from individual studies.
• When triangulating research findings: Triangulation is the process of using multiple sources of data to confirm or refute research findings. Secondary data can be used to support triangulation by providing additional sources of data to support or refute primary research findings.
• When validating research findings : Secondary data can be used to validate primary research findings by providing additional sources of data that support or refute the primary findings.

Characteristics of Secondary Data

Secondary data have several characteristics that distinguish them from primary data. Here are some of the key characteristics of secondary data:

• Non-reactive: Secondary data are non-reactive, meaning that they are not collected for the specific purpose of the research study. This means that the researcher has no control over the data collection process, and cannot influence how the data were collected.
• Time-saving: Secondary data are pre-existing, meaning that they have already been collected and organized by someone else. This can save the researcher time and resources, as they do not need to collect the data themselves.
• Wide-ranging : Secondary data sources can provide a wide range of information on a variety of topics. This can be useful for researchers who are exploring a new research area or seeking to compare and contrast research findings.
• Less expensive: Secondary data are generally less expensive than primary data, as they do not require the researcher to incur the costs associated with data collection.
• Potential for bias : Secondary data may be subject to biases that were present in the original data collection process. For example, data may have been collected using a biased sampling method or the data may be incomplete or inaccurate.
• Lack of control: The researcher has no control over the data collection process and cannot ensure that the data were collected using appropriate methods or measures.
• Requires careful evaluation : Secondary data sources must be evaluated carefully to ensure that they are appropriate for the research question and analysis. This includes assessing the quality, reliability, and validity of the data sources.

Advantages of Secondary Data

There are several advantages to using secondary data in research, including:

• Time-saving : Collecting primary data can be time-consuming and expensive. Secondary data can be accessed quickly and easily, which can save researchers time and resources.
• Cost-effective: Secondary data are generally less expensive than primary data, as they do not require the researcher to incur the costs associated with data collection.
• Large sample size : Secondary data sources often have larger sample sizes than primary data sources, which can increase the statistical power of the research.
• Access to historical data : Secondary data sources can provide access to historical data, which can be useful for researchers who are studying trends over time.
• No ethical concerns: Secondary data are already in existence, so there are no ethical concerns related to collecting data from human subjects.
• May be more objective : Secondary data may be more objective than primary data, as the data were not collected for the specific purpose of the research study.

Limitations of Secondary Data

While there are many advantages to using secondary data in research, there are also some limitations that should be considered. Some of the main limitations of secondary data include:

• Lack of control over data quality : Researchers do not have control over the data collection process, which means they cannot ensure the accuracy or completeness of the data.
• Limited availability: Secondary data may not be available for the specific research question or study design.
• Lack of information on sampling and data collection methods: Researchers may not have access to information on the sampling and data collection methods used to gather the secondary data. This can make it difficult to evaluate the quality of the data.
• Data may not be up-to-date: Secondary data may not be up-to-date or relevant to the current research question.
• Data may be incomplete or inaccurate : Secondary data may be incomplete or inaccurate due to missing or incorrect data points, data entry errors, or other factors.
• Biases in data collection: The data may have been collected using biased sampling or data collection methods, which can limit the validity of the data.
• Lack of control over variables: Researchers have limited control over the variables that were measured in the original data collection process, which can limit the ability to draw conclusions about causality.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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A Guide To Secondary Data Analysis

What is secondary data analysis? How do you carry it out? Find out in this post.  

Historically, the only way data analysts could obtain data was to collect it themselves. This type of data is often referred to as primary data and is still a vital resource for data analysts.   

However, technological advances over the last few decades mean that much past data is now readily available online for data analysts and researchers to access and utilize. This type of data—known as secondary data—is driving a revolution in data analytics and data science.

Primary and secondary data share many characteristics. However, there are some fundamental differences in how you prepare and analyze secondary data. This post explores the unique aspects of secondary data analysis. We’ll briefly review what secondary data is before outlining how to source, collect and validate them. We’ll cover:

• What is secondary data analysis?
• How to carry out secondary data analysis (5 steps)
• Summary and further reading

Ready for a crash course in secondary data analysis? Let’s go!

1. What is secondary data analysis?

Secondary data analysis uses data collected by somebody else. This contrasts with primary data analysis, which involves a researcher collecting predefined data to answer a specific question. Secondary data analysis has numerous benefits, not least that it is a time and cost-effective way of obtaining data without doing the research yourself.

It’s worth noting here that secondary data may be primary data for the original researcher. It only becomes secondary data when it’s repurposed for a new task. As a result, a dataset can simultaneously be a primary data source for one researcher and a secondary data source for another. So don’t panic if you get confused! We explain exactly what secondary data is in this guide . 

In reality, the statistical techniques used to carry out secondary data analysis are no different from those used to analyze other kinds of data. The main differences lie in collection and preparation. Once the data have been reviewed and prepared, the analytics process continues more or less as it usually does. For a recap on what the data analysis process involves, read this post . 

In the following sections, we’ll focus specifically on the preparation of secondary data for analysis. Where appropriate, we’ll refer to primary data analysis for comparison. 

2. How to carry out secondary data analysis

Step 1: define a research topic.

The first step in any data analytics project is defining your goal. This is true regardless of the data you’re working with, or the type of analysis you want to carry out. In data analytics lingo, this typically involves defining:

• A statement of purpose
• Research design

Defining a statement of purpose and a research approach are both fundamental building blocks for any project. However, for secondary data analysis, the process of defining these differs slightly. Let’s find out how.

Step 2: Establish your statement of purpose

Before beginning any data analytics project, you should always have a clearly defined intent. This is called a ‘statement of purpose.’ A healthcare analyst’s statement of purpose, for example, might be: ‘Reduce admissions for mental health issues relating to Covid-19′. The more specific the statement of purpose, the easier it is to determine which data to collect, analyze, and draw insights from.

A statement of purpose is helpful for both primary and secondary data analysis. It’s especially relevant for secondary data analysis, though. This is because there are vast amounts of secondary data available. Having a clear direction will keep you focused on the task at hand, saving you from becoming overwhelmed. Being selective with your data sources is key.

Step 3: Design your research process

After defining your statement of purpose, the next step is to design the research process. For primary data, this involves determining the types of data you want to collect (e.g. quantitative, qualitative, or both ) and a methodology for gathering them.

For secondary data analysis, however, your research process will more likely be a step-by-step guide outlining the types of data you require and a list of potential sources for gathering them. It may also include (realistic) expectations of the output of the final analysis. This should be based on a preliminary review of the data sources and their quality.

Once you have both your statement of purpose and research design, you’re in a far better position to narrow down potential sources of secondary data. You can then start with the next step of the process: data collection.

Step 4: Locate and collect your secondary data

Collecting primary data involves devising and executing a complex strategy that can be very time-consuming to manage. The data you collect, though, will be highly relevant to your research problem.

Secondary data collection, meanwhile, avoids the complexity of defining a research methodology. However, it comes with additional challenges. One of these is identifying where to find the data. This is no small task because there are a great many repositories of secondary data available. Your job, then, is to narrow down potential sources. As already mentioned, it’s necessary to be selective, or else you risk becoming overloaded.  

Some popular sources of secondary data include:  

• Government statistics , e.g. demographic data, censuses, or surveys, collected by government agencies/departments (like the US Bureau of Labor Statistics).
• Technical reports summarizing completed or ongoing research from educational or public institutions (colleges or government).
• Scientific journals that outline research methodologies and data analysis by experts in fields like the sciences, medicine, etc.
• Literature reviews of research articles, books, and reports, for a given area of study (once again, carried out by experts in the field).
• Trade/industry publications , e.g. articles and data shared in trade publications, covering topics relating to specific industry sectors, such as tech or manufacturing.
• Online resources: Repositories, databases, and other reference libraries with public or paid access to secondary data sources.

Once you’ve identified appropriate sources, you can go about collecting the necessary data. This may involve contacting other researchers, paying a fee to an organization in exchange for a dataset, or simply downloading a dataset for free online .

Step 5: Evaluate your secondary data

Secondary data is usually well-structured, so you might assume that once you have your hands on a dataset, you’re ready to dive in with a detailed analysis. Unfortunately, that’s not the case! 

First, you must carry out a careful review of the data. Why? To ensure that they’re appropriate for your needs. This involves two main tasks:

Evaluating the secondary dataset’s relevance

• Assessing its broader credibility

Both these tasks require critical thinking skills. However, they aren’t heavily technical. This means anybody can learn to carry them out.

Let’s now take a look at each in a bit more detail.  

The main point of evaluating a secondary dataset is to see if it is suitable for your needs. This involves asking some probing questions about the data, including:

What was the data’s original purpose?

Understanding why the data were originally collected will tell you a lot about their suitability for your current project. For instance, was the project carried out by a government agency or a private company for marketing purposes? The answer may provide useful information about the population sample, the data demographics, and even the wording of specific survey questions. All this can help you determine if the data are right for you, or if they are biased in any way.

When and where were the data collected?

Over time, populations and demographics change. Identifying when the data were first collected can provide invaluable insights. For instance, a dataset that initially seems suited to your needs may be out of date.

On the flip side, you might want past data so you can draw a comparison with a present dataset. In this case, you’ll need to ensure the data were collected during the appropriate time frame. It’s worth mentioning that secondary data are the sole source of past data. You cannot collect historical data using primary data collection techniques.

Similarly, you should ask where the data were collected. Do they represent the geographical region you require? Does geography even have an impact on the problem you are trying to solve?

What data were collected and how?

A final report for past data analytics is great for summarizing key characteristics or findings. However, if you’re planning to use those data for a new project, you’ll need the original documentation. At the very least, this should include access to the raw data and an outline of the methodology used to gather them. This can be helpful for many reasons. For instance, you may find raw data that wasn’t relevant to the original analysis, but which might benefit your current task.

What questions were participants asked?

We’ve already touched on this, but the wording of survey questions—especially for qualitative datasets—is significant. Questions may deliberately be phrased to preclude certain answers. A question’s context may also impact the findings in a way that’s not immediately obvious. Understanding these issues will shape how you perceive the data.  

What is the form/shape/structure of the data?

Finally, to practical issues. Is the structure of the data suitable for your needs? Is it compatible with other sources or with your preferred analytics approach? This is purely a structural issue. For instance, if a dataset of people’s ages is saved as numerical rather than continuous variables, this could potentially impact your analysis. In general, reviewing a dataset’s structure helps better understand how they are categorized, allowing you to account for any discrepancies. You may also need to tidy the data to ensure they are consistent with any other sources you’re using.  

This is just a sample of the types of questions you need to consider when reviewing a secondary data source. The answers will have a clear impact on whether the dataset—no matter how well presented or structured it seems—is suitable for your needs.

Assessing secondary data’s credibility

After identifying a potentially suitable dataset, you must double-check the credibility of the data. Namely, are the data accurate and unbiased? To figure this out, here are some key questions you might want to include:

What are the credentials of those who carried out the original research?

Do you have access to the details of the original researchers? What are their credentials? Where did they study? Are they an expert in the field or a newcomer? Data collection by an undergraduate student, for example, may not be as rigorous as that of a seasoned professor.  

And did the original researcher work for a reputable organization? What other affiliations do they have? For instance, if a researcher who works for a tobacco company gathers data on the effects of vaping, this represents an obvious conflict of interest! Questions like this help determine how thorough or qualified the researchers are and if they have any potential biases.

Do you have access to the full methodology?

Does the dataset include a clear methodology, explaining in detail how the data were collected? This should be more than a simple overview; it must be a clear breakdown of the process, including justifications for the approach taken. This allows you to determine if the methodology was sound. If you find flaws (or no methodology at all) it throws the quality of the data into question.  

How consistent are the data with other sources?

Do the secondary data match with any similar findings? If not, that doesn’t necessarily mean the data are wrong, but it does warrant closer inspection. Perhaps the collection methodology differed between sources, or maybe the data were analyzed using different statistical techniques. Or perhaps unaccounted-for outliers are skewing the analysis. Identifying all these potential problems is essential. A flawed or biased dataset can still be useful but only if you know where its shortcomings lie.

Have the data been published in any credible research journals?

Finally, have the data been used in well-known studies or published in any journals? If so, how reputable are the journals? In general, you can judge a dataset’s quality based on where it has been published. If in doubt, check out the publication in question on the Directory of Open Access Journals . The directory has a rigorous vetting process, only permitting journals of the highest quality. Meanwhile, if you found the data via a blurry image on social media without cited sources, then you can justifiably question its quality!  

Again, these are just a few of the questions you might ask when determining the quality of a secondary dataset. Consider them as scaffolding for cultivating a critical thinking mindset; a necessary trait for any data analyst!

Presuming your secondary data holds up to scrutiny, you should be ready to carry out your detailed statistical analysis. As we explained at the beginning of this post, the analytical techniques used for secondary data analysis are no different than those for any other kind of data. Rather than go into detail here, check out the different types of data analysis in this post.

3. Secondary data analysis: Key takeaways

In this post, we’ve looked at the nuances of secondary data analysis, including how to source, collect and review secondary data. As discussed, much of the process is the same as it is for primary data analysis. The main difference lies in how secondary data are prepared.

Carrying out a meaningful secondary data analysis involves spending time and effort exploring, collecting, and reviewing the original data. This will help you determine whether the data are suitable for your needs and if they are of good quality.

Why not get to know more about what data analytics involves with this free, five-day introductory data analytics short course ? And, for more data insights, check out these posts:

• Discrete vs continuous data variables: What’s the difference?
• What are the four levels of measurement? Nominal, ordinal, interval, and ratio data explained
• What are the best tools for data mining?

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Secondary research: definition, methods, & examples.

19 min read This ultimate guide to secondary research helps you understand changes in market trends, customers buying patterns and your competition using existing data sources.

In situations where you’re not involved in the data gathering process ( primary research ), you have to rely on existing information and data to arrive at specific research conclusions or outcomes. This approach is known as secondary research.

In this article, we’re going to explain what secondary research is, how it works, and share some examples of it in practice.

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What is secondary research?

Secondary research, also known as desk research, is a research method that involves compiling existing data sourced from a variety of channels . This includes internal sources (e.g.in-house research) or, more commonly, external sources (such as government statistics, organizational bodies, and the internet).

Secondary research comes in several formats, such as published datasets, reports, and survey responses , and can also be sourced from websites, libraries, and museums.

The information is usually free — or available at a limited access cost — and gathered using surveys , telephone interviews, observation, face-to-face interviews, and more.

When using secondary research, researchers collect, verify, analyze and incorporate it to help them confirm research goals for the research period.

As well as the above, it can be used to review previous research into an area of interest. Researchers can look for patterns across data spanning several years and identify trends — or use it to verify early hypothesis statements and establish whether it’s worth continuing research into a prospective area.

How to conduct secondary research

There are five key steps to conducting secondary research effectively and efficiently:

1.    Identify and define the research topic

First, understand what you will be researching and define the topic by thinking about the research questions you want to be answered.

Ask yourself: What is the point of conducting this research? Then, ask: What do we want to achieve?

This may indicate an exploratory reason (why something happened) or confirm a hypothesis. The answers may indicate ideas that need primary or secondary research (or a combination) to investigate them.

2.    Find research and existing data sources

If secondary research is needed, think about where you might find the information. This helps you narrow down your secondary sources to those that help you answer your questions. What keywords do you need to use?

Which organizations are closely working on this topic already? Are there any competitors that you need to be aware of?

Create a list of the data sources, information, and people that could help you with your work.

3.    Begin searching and collecting the existing data

Now that you have the list of data sources, start accessing the data and collect the information into an organized system. This may mean you start setting up research journal accounts or making telephone calls to book meetings with third-party research teams to verify the details around data results.

As you search and access information, remember to check the data’s date, the credibility of the source, the relevance of the material to your research topic, and the methodology used by the third-party researchers. Start small and as you gain results, investigate further in the areas that help your research’s aims.

4.    Combine the data and compare the results

When you have your data in one place, you need to understand, filter, order, and combine it intelligently. Data may come in different formats where some data could be unusable, while other information may need to be deleted.

After this, you can start to look at different data sets to see what they tell you. You may find that you need to compare the same datasets over different periods for changes over time or compare different datasets to notice overlaps or trends. Ask yourself: What does this data mean to my research? Does it help or hinder my research?

5.    Analyze your data and explore further

In this last stage of the process, look at the information you have and ask yourself if this answers your original questions for your research. Are there any gaps? Do you understand the information you’ve found? If you feel there is more to cover, repeat the steps and delve deeper into the topic so that you can get all the information you need.

If secondary research can’t provide these answers, consider supplementing your results with data gained from primary research. As you explore further, add to your knowledge and update your findings. This will help you present clear, credible information.

Primary vs secondary research

Unlike secondary research, primary research involves creating data first-hand by directly working with interviewees, target users, or a target market. Primary research focuses on the method for carrying out research, asking questions, and collecting data using approaches such as:

• Interviews (panel, face-to-face or over the phone)
• Questionnaires or surveys
• Focus groups

Using these methods, researchers can get in-depth, targeted responses to questions, making results more accurate and specific to their research goals. However, it does take time to do and administer.

Unlike primary research, secondary research uses existing data, which also includes published results from primary research. Researchers summarize the existing research and use the results to support their research goals.

Both primary and secondary research have their places. Primary research can support the findings found through secondary research (and fill knowledge gaps), while secondary research can be a starting point for further primary research. Because of this, these research methods are often combined for optimal research results that are accurate at both the micro and macro level.

Sources of Secondary Research

There are two types of secondary research sources: internal and external. Internal data refers to in-house data that can be gathered from the researcher’s organization. External data refers to data published outside of and not owned by the researcher’s organization.

Internal data

Internal data is a good first port of call for insights and knowledge, as you may already have relevant information stored in your systems. Because you own this information — and it won’t be available to other researchers — it can give you a competitive edge . Examples of internal data include:

• Database information on sales history and business goal conversions
• Information from website applications and mobile site data
• Customer-generated data on product and service efficiency and use
• Previous research results or supplemental research areas
• Previous campaign results

External data

External data is useful when you: 1) need information on a new topic, 2) want to fill in gaps in your knowledge, or 3) want data that breaks down a population or market for trend and pattern analysis. Examples of external data include:

• Government, non-government agencies, and trade body statistics
• Company reports and research
• Competitor research
• Public library collections
• Textbooks and research journals
• Media stories in newspapers
• Online journals and research sites

Three examples of secondary research methods in action

How and why might you conduct secondary research? Let’s look at a few examples:

1.    Collecting factual information from the internet on a specific topic or market

There are plenty of sites that hold data for people to view and use in their research. For example, Google Scholar, ResearchGate, or Wiley Online Library all provide previous research on a particular topic. Researchers can create free accounts and use the search facilities to look into a topic by keyword, before following the instructions to download or export results for further analysis.

This can be useful for exploring a new market that your organization wants to consider entering. For instance, by viewing the U.S Census Bureau demographic data for that area, you can see what the demographics of your target audience are , and create compelling marketing campaigns accordingly.

2.    Finding out the views of your target audience on a particular topic

If you’re interested in seeing the historical views on a particular topic, for example, attitudes to women’s rights in the US, you can turn to secondary sources.

Textbooks, news articles, reviews, and journal entries can all provide qualitative reports and interviews covering how people discussed women’s rights. There may be multimedia elements like video or documented posters of propaganda showing biased language usage.

By gathering this information, synthesizing it, and evaluating the language, who created it and when it was shared, you can create a timeline of how a topic was discussed over time.

3.    When you want to know the latest thinking on a topic

Educational institutions, such as schools and colleges, create a lot of research-based reports on younger audiences or their academic specialisms. Dissertations from students also can be submitted to research journals, making these places useful places to see the latest insights from a new generation of academics.

Information can be requested — and sometimes academic institutions may want to collaborate and conduct research on your behalf. This can provide key primary data in areas that you want to research, as well as secondary data sources for your research.

Advantages of secondary research

There are several benefits of using secondary research, which we’ve outlined below:

• Easily and readily available data – There is an abundance of readily accessible data sources that have been pre-collected for use, in person at local libraries and online using the internet. This data is usually sorted by filters or can be exported into spreadsheet format, meaning that little technical expertise is needed to access and use the data.
• Faster research speeds – Since the data is already published and in the public arena, you don’t need to collect this information through primary research. This can make the research easier to do and faster, as you can get started with the data quickly.
• Low financial and time costs – Most secondary data sources can be accessed for free or at a small cost to the researcher, so the overall research costs are kept low. In addition, by saving on preliminary research, the time costs for the researcher are kept down as well.
• Secondary data can drive additional research actions – The insights gained can support future research activities (like conducting a follow-up survey or specifying future detailed research topics) or help add value to these activities.
• Secondary data can be useful pre-research insights – Secondary source data can provide pre-research insights and information on effects that can help resolve whether research should be conducted. It can also help highlight knowledge gaps, so subsequent research can consider this.
• Ability to scale up results – Secondary sources can include large datasets (like Census data results across several states) so research results can be scaled up quickly using large secondary data sources.

Disadvantages of secondary research

The disadvantages of secondary research are worth considering in advance of conducting research :

• Secondary research data can be out of date – Secondary sources can be updated regularly, but if you’re exploring the data between two updates, the data can be out of date. Researchers will need to consider whether the data available provides the right research coverage dates, so that insights are accurate and timely, or if the data needs to be updated. Also, fast-moving markets may find secondary data expires very quickly.
• Secondary research needs to be verified and interpreted – Where there’s a lot of data from one source, a researcher needs to review and analyze it. The data may need to be verified against other data sets or your hypotheses for accuracy and to ensure you’re using the right data for your research.
• The researcher has had no control over the secondary research – As the researcher has not been involved in the secondary research, invalid data can affect the results. It’s therefore vital that the methodology and controls are closely reviewed so that the data is collected in a systematic and error-free way.
• Secondary research data is not exclusive – As data sets are commonly available, there is no exclusivity and many researchers can use the same data. This can be problematic where researchers want to have exclusive rights over the research results and risk duplication of research in the future.

When do we conduct secondary research?

Now that you know the basics of secondary research, when do researchers normally conduct secondary research?

It’s often used at the beginning of research, when the researcher is trying to understand the current landscape . In addition, if the research area is new to the researcher, it can form crucial background context to help them understand what information exists already. This can plug knowledge gaps, supplement the researcher’s own learning or add to the research.

Secondary research can also be used in conjunction with primary research. Secondary research can become the formative research that helps pinpoint where further primary research is needed to find out specific information. It can also support or verify the findings from primary research.

You can use secondary research where high levels of control aren’t needed by the researcher, but a lot of knowledge on a topic is required from different angles.

Secondary research should not be used in place of primary research as both are very different and are used for various circumstances.

Questions to ask before conducting secondary research

Before you start your secondary research, ask yourself these questions:

• Is there similar internal data that we have created for a similar area in the past?

If your organization has past research, it’s best to review this work before starting a new project. The older work may provide you with the answers, and give you a starting dataset and context of how your organization approached the research before. However, be mindful that the work is probably out of date and view it with that note in mind. Read through and look for where this helps your research goals or where more work is needed.

• What am I trying to achieve with this research?

When you have clear goals, and understand what you need to achieve, you can look for the perfect type of secondary or primary research to support the aims. Different secondary research data will provide you with different information – for example, looking at news stories to tell you a breakdown of your market’s buying patterns won’t be as useful as internal or external data e-commerce and sales data sources.

• How credible will my research be?

If you are looking for credibility, you want to consider how accurate the research results will need to be, and if you can sacrifice credibility for speed by using secondary sources to get you started. Bear in mind which sources you choose — low-credibility data sites, like political party websites that are highly biased to favor their own party, would skew your results.

• What is the date of the secondary research?

When you’re looking to conduct research, you want the results to be as useful as possible , so using data that is 10 years old won’t be as accurate as using data that was created a year ago. Since a lot can change in a few years, note the date of your research and look for earlier data sets that can tell you a more recent picture of results. One caveat to this is using data collected over a long-term period for comparisons with earlier periods, which can tell you about the rate and direction of change.

• Can the data sources be verified? Does the information you have check out?

If you can’t verify the data by looking at the research methodology, speaking to the original team or cross-checking the facts with other research, it could be hard to be sure that the data is accurate. Think about whether you can use another source, or if it’s worth doing some supplementary primary research to replicate and verify results to help with this issue.

We created a front-to-back guide on conducting market research, The ultimate guide to conducting market research , so you can understand the research journey with confidence.

In it, you’ll learn more about:

• What effective market research looks like
• The use cases for market research
• The most important steps to conducting market research
• And how to take action on your research findings

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Secondary Research: Definition, Methods and Examples.

In the world of research, there are two main types of data sources: primary and secondary. While primary research involves collecting new data directly from individuals or sources, secondary research involves analyzing existing data already collected by someone else. Today we’ll discuss secondary research.

One common source of this research is published research reports and other documents. These materials can often be found in public libraries, on websites, or even as data extracted from previously conducted surveys. In addition, many government and non-government agencies maintain extensive data repositories that can be accessed for research purposes.

LEARN ABOUT: Research Process Steps

While secondary research may not offer the same level of control as primary research, it can be a highly valuable tool for gaining insights and identifying trends. Researchers can save time and resources by leveraging existing data sources while still uncovering important information.

What is Secondary Research: Definition

Secondary research is a research method that involves using already existing data. Existing data is summarized and collated to increase the overall effectiveness of the research.

One of the key advantages of secondary research is that it allows us to gain insights and draw conclusions without having to collect new data ourselves. This can save time and resources and also allow us to build upon existing knowledge and expertise.

When conducting secondary research, it’s important to be thorough and thoughtful in our approach. This means carefully selecting the sources and ensuring that the data we’re analyzing is reliable and relevant to the research question . It also means being critical and analytical in the analysis and recognizing any potential biases or limitations in the data.

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Secondary research is much more cost-effective than primary research , as it uses already existing data, unlike primary research, where data is collected firsthand by organizations or businesses or they can employ a third party to collect data on their behalf.

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Secondary Research Methods with Examples

Secondary research is cost-effective, one of the reasons it is a popular choice among many businesses and organizations. Not every organization is able to pay a huge sum of money to conduct research and gather data. So, rightly secondary research is also termed “ desk research ”, as data can be retrieved from sitting behind a desk.

The following are popularly used secondary research methods and examples:

1. Data Available on The Internet

One of the most popular ways to collect secondary data is the internet. Data is readily available on the internet and can be downloaded at the click of a button.

This data is practically free of cost, or one may have to pay a negligible amount to download the already existing data. Websites have a lot of information that businesses or organizations can use to suit their research needs. However, organizations need to consider only authentic and trusted website to collect information.

2. Government and Non-Government Agencies

Data for secondary research can also be collected from some government and non-government agencies. For example, US Government Printing Office, US Census Bureau, and Small Business Development Centers have valuable and relevant data that businesses or organizations can use.

There is a certain cost applicable to download or use data available with these agencies. Data obtained from these agencies are authentic and trustworthy.

3. Public Libraries

Public libraries are another good source to search for data for this research. Public libraries have copies of important research that were conducted earlier. They are a storehouse of important information and documents from which information can be extracted.

The services provided in these public libraries vary from one library to another. More often, libraries have a huge collection of government publications with market statistics, large collection of business directories and newsletters.

4. Educational Institutions

Importance of collecting data from educational institutions for secondary research is often overlooked. However, more research is conducted in colleges and universities than any other business sector.

The data that is collected by universities is mainly for primary research. However, businesses or organizations can approach educational institutions and request for data from them.

5. Commercial Information Sources

Local newspapers, journals, magazines, radio and TV stations are a great source to obtain data for secondary research. These commercial information sources have first-hand information on economic developments, political agenda, market research, demographic segmentation and similar subjects.

Businesses or organizations can request to obtain data that is most relevant to their study. Businesses not only have the opportunity to identify their prospective clients but can also know about the avenues to promote their products or services through these sources as they have a wider reach.

Key Differences between Primary Research and Secondary Research

Understanding the distinction between primary research and secondary research is essential in determining which research method is best for your project. These are the two main types of research methods, each with advantages and disadvantages. In this section, we will explore the critical differences between the two and when it is appropriate to use them.

How to Conduct Secondary Research?

We have already learned about the differences between primary and secondary research. Now, let’s take a closer look at how to conduct it.

Secondary research is an important tool for gathering information already collected and analyzed by others. It can help us save time and money and allow us to gain insights into the subject we are researching. So, in this section, we will discuss some common methods and tips for conducting it effectively.

Here are the steps involved in conducting secondary research:

1. Identify the topic of research: Before beginning secondary research, identify the topic that needs research. Once that’s done, list down the research attributes and its purpose.

2. Identify research sources: Next, narrow down on the information sources that will provide most relevant data and information applicable to your research.

3. Collect existing data: Once the data collection sources are narrowed down, check for any previous data that is available which is closely related to the topic. Data related to research can be obtained from various sources like newspapers, public libraries, government and non-government agencies etc.

4. Combine and compare: Once data is collected, combine and compare the data for any duplication and assemble data into a usable format. Make sure to collect data from authentic sources. Incorrect data can hamper research severely.

4. Analyze data: Analyze collected data and identify if all questions are answered. If not, repeat the process if there is a need to dwell further into actionable insights.

Advantages of Secondary Research

Secondary research offers a number of advantages to researchers, including efficiency, the ability to build upon existing knowledge, and the ability to conduct research in situations where primary research may not be possible or ethical. By carefully selecting their sources and being thoughtful in their approach, researchers can leverage secondary research to drive impact and advance the field. Some key advantages are the following:

1. Most information in this research is readily available. There are many sources from which relevant data can be collected and used, unlike primary research, where data needs to collect from scratch.

2. This is a less expensive and less time-consuming process as data required is easily available and doesn’t cost much if extracted from authentic sources. A minimum expenditure is associated to obtain data.

3. The data that is collected through secondary research gives organizations or businesses an idea about the effectiveness of primary research. Hence, organizations or businesses can form a hypothesis and evaluate cost of conducting primary research.

4. Secondary research is quicker to conduct because of the availability of data. It can be completed within a few weeks depending on the objective of businesses or scale of data needed.

As we can see, this research is the process of analyzing data already collected by someone else, and it can offer a number of benefits to researchers.

Disadvantages of Secondary Research

On the other hand, we have some disadvantages that come with doing secondary research. Some of the most notorious are the following:

1. Although data is readily available, credibility evaluation must be performed to understand the authenticity of the information available.

2. Not all secondary data resources offer the latest reports and statistics. Even when the data is accurate, it may not be updated enough to accommodate recent timelines.

3. Secondary research derives its conclusion from collective primary research data. The success of your research will depend, to a greater extent, on the quality of research already conducted by primary research.

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In conclusion, secondary research is an important tool for researchers exploring various topics. By leveraging existing data sources, researchers can save time and resources, build upon existing knowledge, and conduct research in situations where primary research may not be feasible.

There are a variety of methods and examples of secondary research, from analyzing public data sets to reviewing previously published research papers. As students and aspiring researchers, it’s important to understand the benefits and limitations of this research and to approach it thoughtfully and critically. By doing so, we can continue to advance our understanding of the world around us and contribute to meaningful research that positively impacts society.

QuestionPro can be a useful tool for conducting secondary research in a variety of ways. You can create online surveys that target a specific population, collecting data that can be analyzed to gain insights into consumer behavior, attitudes, and preferences; analyze existing data sets that you have obtained through other means or benchmark your organization against others in your industry or against industry standards. The software provides a range of benchmarking tools that can help you compare your performance on key metrics, such as customer satisfaction, with that of your peers.

Using QuestionPro thoughtfully and strategically allows you to gain valuable insights to inform decision-making and drive business success. Start today for free! No credit card is required.

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What is Secondary Research?

Secondary research, also known as a literature review , preliminary research , historical research , background research , desk research , or library research , is research that analyzes or describes prior research. Rather than generating and analyzing new data, secondary research analyzes existing research results to establish the boundaries of knowledge on a topic, to identify trends or new practices, to test mathematical models or train machine learning systems, or to verify facts and figures. Secondary research is also used to justify the need for primary research as well as to justify and support other activities. For example, secondary research may be used to support a proposal to modernize a manufacturing plant, to justify the use of newly a developed treatment for cancer, to strengthen a business proposal, or to validate points made in a speech.

Why Is Secondary Research Important?

Because secondary research is used for so many purposes in so many settings, all professionals will be required to perform it at some point in their careers. For managers and entrepreneurs, regardless of the industry or profession, secondary research is a regular part of worklife, although parts of the research, such as finding the supporting documents, are often delegated to juniors in the organization. For all these reasons, it is essential to learn how to conduct secondary research, even if you are unlikely to ever conduct primary research.

Secondary research is also essential if your main goal is primary research. Research funding is obtained only by using secondary research to show the need for the primary research you want to conduct. In fact, primary research depends on secondary research to prove that it is indeed new and original research and not just a rehash or replication of somebody else’s work.

• Next: Literature Review >>
• Last Updated: Dec 21, 2023 3:46 PM
• URL: https://guides.library.iit.edu/litreview

• Open access
• Published: 14 May 2024

Non-pharmacological interventions to prevent PICS in critically ill adult patients: a protocol for a systematic review and network meta-analysis

• Xiaoying Sun 1 , 2 ,
• Qian Tao 2 ,
• Qing Cui 3 ,
• Yaqiong Liu 4 &
• Shouzhen Cheng   ORCID: orcid.org/0000-0002-5063-9473 2  

Systematic Reviews volume  13 , Article number:  132 ( 2024 ) Cite this article

16 Accesses

Metrics details

Postintensive care syndrome (PICS) is common in critically ill adults who were treated in the intensive care unit (ICU). Although comparative analyses between types of non-pharmacological measures and usual care to prevent PICS have been performed, it remains unclear which of these potential treatments is the most effective for prevention.

To obtain the best evidence for non-pharmaceutical interventions in preventing PICS, a systematic review and Bayesian network meta-analyses (NMAs) will be conducted by searching nine electronic databases for randomized controlled trials (RCTs). Two reviewers will carefully screen the titles, abstracts, and full-text papers to identify and extract relevant data. Furthermore, the research team will meticulously check the bibliographic references of the selected studies and related reviews to discover any articles pertinent to this research. The primary focus of the study is to examine the prevalence and severity of PICS among critically ill patients admitted to the ICU. The additional outcomes encompass patient satisfaction and adverse effects related to the preventive intervention. The Cochrane Collaboration’s risk-of-bias assessment tool will be utilized to evaluate the risk of bias in the included RCTs. To assess the efficacy of various preventative measures, traditional pairwise meta-analysis and Bayesian NMA will be used. To gauge the confidence in the evidence supporting the results, we will utilize the Confidence in NMA tool.

There are multiple non-pharmacological interventions available for preventing the occurrence and development of PICS. However, most approaches have only been directly compared to standard care, lacking comprehensive evidence and clinical balance. Although the most effective care methods are still unknown, our research will provide valuable evidence for further non-pharmacological interventions and clinical practices aimed at preventing PICS. The research is expected to offer useful data to help healthcare workers and those creating guidelines decide on the most effective path of action for preventing PICS in adult ICU patients.

Systematic review registration

PROSPERO CRD42023439343.

Graphical Abstract

Peer Review reports

Postintensive care syndrome (PICS) is an umbrella term used to define the general influence of severe disease on individuals who were treated in the intensive care unit (ICU), encompassing various physical (such as neuromuscular weakness and limitations in daily activities), psychological (such as anxiety, sadness, and post-traumatic stress disorder [PTSD]), and cognitive dysfunction [ 1 , 2 , 3 ]. These ailments impair everyday living and quality of life. A majority of adult patients who received treatment in the ICU encounter such impairments [ 4 , 5 , 6 ]. The significant progress made in the medical, scientific, and technological domains has led to a notable increase in survival among people admitted to the ICU in recent years [ 7 ]. However, although adults treated in the ICU have increased survival, their quality of life can be negatively affected by their time in the ICU.

Intensive care is the medical care provided to critically ill patients during a medical emergency or crisis, managing severe conditions of all disease types [ 8 ]. Infectious and noninfectious illnesses and injuries contribute significantly to the global burden, with an increasing trend over the years. The Global Burden of Disease project does not provide specific information on the burden of critical illness and global variation [ 9 , 10 , 11 ]. Figure  1 describes the burden of critical illness based on global overall expenditure, the aging trend, and the number of ICU beds. These data come from Our World in Data [ 12 ], the China Health Statistics Yearbook [ 13 ], and United Nations Aging data [ 14 ].

The burden of PICS is increasing. PICS, postintensive care syndrome

In the past 50 years, the number of patients admitted to the ICU has continuously increased, especially after the beginning of the COVID-19 pandemic [ 15 , 16 ]. This trend is evident from Fig.  1 A, which shows the growth of ICU bed capacity in China. The percentage of public health spending as a part of the gross domestic product for each country in 2019 is shown in Fig.  1 C. Developed countries invest more in the healthcare sector [ 17 ], which is likely closely related to their aging population and advancements in medical technology [ 18 ]. Figure  1 B illustrates the projected future extent of global aging, indicating that the global population of individuals aged 65 years or older is expected to double within the next three decades, reaching an estimated 1.6 billion by 2050. Concurrently, the number of people aged 80 and older is anticipated to reach 459 million. The increase in age in the global population has led to a higher risk of critical illnesses, as the aging population bears a heavier load of chronic diseases [ 19 ]. However, the spectrum of medical conditions managed in the ICU includes not only the exacerbation of chronic diseases but also burns, trauma, and infectious diseases, as detailed in Fig.  1 D. Moreover, our enhanced ability to treat formerly fatal conditions has led to higher demand for critical care services [ 20 ]. Consequently, PICS is also likely to increase with the growing number of adults treated in and discharged from the ICU.

Considering the substantial public health concerns arising from the consequences of PICS on quality of life, healthcare expenditures, and hospital readmissions, it is imperative to offer effective and feasible interventions to address this issue [ 21 ]. Assistance and support for patients in critical condition are potential interventions for improving outcomes related to PICS [ 22 ]. A recent study showed that administration of dexmedetomidine during the night as a preventive measure led to a substantial decrease in the incidence of PICS, as evidenced by a substantial reduction in psychological impairment during the 6-month monitoring period [ 23 ]. However, pharmacological treatments are often expensive and can pose a certain economic burden. Further, the use of sedative and anxiolytic drugs to treat patient symptoms is linked to delirium and negative physical and mental health consequences [ 24 ]. Consequently, there is an increasing focus on employing non-pharmacological approaches and establishing a more person-centered atmosphere within the ICU, aiming to benefit both patients and their families [ 25 ].

The current interventions for PICS that show the most potential involve non-pharmacological strategies [ 22 ]. The efficacy of early rehabilitation treatment, which consists of all physiotherapy, occupational therapy, and palliative care-related support, in managing PICS was explored through a systematic review [ 7 ], which showed that such treatment can lead to an improvement in short-term physical functioning but does not have any impact on mental or cognitive aspects. ICU diaries can reduce ICU-related psychological complications, such as ICU-related PTSD, depression, and anxiety [ 26 ]. However, results obtained from a randomized controlled trial (RCT) indicate that the use of ICU diaries alone does not provide any advantage over bedside education in reducing the symptoms of PTSD that are related to the stay in ICU [ 27 ]. Hence, it is still uncertain which non-pharmacological interventions are the most effective and preferred in preventing depression and anxiety, cognitive disorder, and physical function for adults with critical illness.

Despite the potential deleterious effects of PICS in terms of healthcare usage and caregiver burden and the increasing population of adults treated in and discharged from ICU, there is a lack of evidence-based practices for this specific group [ 28 ]. Although they provide indirect evidence to evaluate the confidence of treatment comparisons, network meta-analyses (NMAs) [ 29 ] have substantial advantages over conventional pairwise meta-analyses. NMAs allow for the evaluation of comparative effects that have not been directly compared in RCTs, potentially yielding more reliable and conclusive outcomes [ 30 ]. Hence, the study’s main goal is to use NMA to examine several non-pharmacological preventative treatments that addressed PICS in individuals treated in the ICU.

Methods/design

Criteria for eligibility.

Studies conducted during the ICU stay, as well as those extending from the ICU admission through to the post-discharge period, will be eligible for inclusion.

Participants

Adults (aged > 18 years) admitted to the ICU were included in the study. Gender, ethnicity, and nationality of participants will not be further restricted.

Type of study

Only RCTs providing comparisons of preventative strategies and other strategies or standard treatment for adult patients in ICUs with full-text publications will be included.

Intervention

Any non-pharmacological interventions to prevent PICS in critically ill patients. The potential interventions may encompass, but are not limited to the following:

Psychosocial programs

Follow-up service

Patient instructions

Exercise (e.g., strength and cardiovascular exercise)

Diary therapy

Environment control

Integrated therapy

Comparators

These are different types of non-pharmacological interventions or a control group; a control group is defined as a waiting list, usual/standard care, or a control condition that provided a brief educational leaflet.

Outcome measures

Studies must have assessed depression symptoms, anxiety symptoms, PTSD, cognitive status, sleep quality, pain, physical functioning, or quality of life, with detailed data available. Additionally, the evaluation of primary outcomes must use a comprehensive and specific scale, including but not limited to the following:

Primary outcomes

Depression: Hospital Anxiety and Depression Scales [ 31 ] and Hamilton Depression Rating Scale [ 32 ]

Anxiety: Hospital Anxiety and Depression Scales [ 31 ]

PTSD: The Impact of Event Scale-Revised [ 33 ] and the Davidson Trauma Scale [ 34 ]

Cognitive: The Confusion Assessment Method for the ICU [ 35 ] and Montreal Cognitive Assessment [ 36 ]

Sleep: Richards Campbell Sleep Questionnaire [ 37 ] and Pittsburgh Sleep Quality Index [ 38 ]

Pain: Numeric rating scale [ 39 ] and visual analog scale [ 40 ]

Physical functioning: The occurrence rate of ICU-acquired weakness and the evaluation through Medical Research Council scale scores [ 41 ] and activities of daily living [ 42 , 43 ]

Quality of life: Medical Outcomes Study 36-item short-form health survey [ 44 ] and European Quality of Life-5 Dimensions questionnaire [ 45 ]

Secondary outcomes

Any harms associated with the prevention intervention

Participant satisfaction

Search strategy

“Critical care,” “intensive care units,” “syndrome,” “symptom assessment,” “depression symptom,” “depression,” “anxiety,” “anxiety symptom,” “mental health,” “Posttraumatic Stress Disorder,” “cognitive dysfunction,” “delirium,” “sleep,” “sleep wake disorder,””sleep quality,””pain,””intensive care unit acquired weakness,” and “physical functioning” will be utilized as MeSH phrases or keywords. The following electronic databases will be search from inception to June 25, 2023: PubMed, Embase, CINAHL, Cochrane Central Register of Controlled Trials, Web of Science, PsycINFO, SinoMED, CNKI, and Wangfang. Example searches of PubMed can be found in Table  1 . Moreover, we will perform thorough reverse citation searches on all included studies and pertinent reviews to find any previously missed references. Additionally, to find recent articles that have mentioned the pertinent literature, we will do forward reference searching on Google Scholar. Finally, we will try to contact the authors of those studies for more information if the full text of certain sources is unavailable.

Study selection

This study will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses criteria, and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses flow diagram [ 46 ], shown in Fig.  2 , demonstrates the proposed research selection methods. The discovered studies will be imported into the online Rayyan literature management tool ( https://rayyan.qcri.org ) for additional analysis. Independent screening of the papers’ titles and abstracts will be performed by two reviewers. If either reviewer determines that an article meets the inclusion criteria, full texts will be obtained. Subsequently, both reviewers will independently assess the eligibility of each reference through a thorough examination of the full text. Any differences that cannot be settled via conversation will be brought to the attention of a third reviewer who will act as a mediator. Cohen’s kappa coefficient will be calculated to measure the inter-rater reliability. The reasons for excluding any studies will be carefully documented.

Data extraction

A standardized data extraction form is available as a supplemental file . Before the actual usage of the form, each member of the team will have the opportunity to test it. Two reviewers will independently perform data extraction. In the case of any inconsistencies, a third arbiter will be consulted to facilitate a discussion and achieve a consensus. Our inclusion criteria for data extraction include various aspects of the study, such as background data (first contributor and the time of publication), research design (setting, methods of sampling, randomization, allocations, and blinding), sample characteristics (inclusion and exclusion criteria, sample size, age, sex, and educational background, rates, or severity of PICS), intervention details (type, content, frequency, duration, provider, and control group), and primary and secondary outcomes (including measurement time points, assessment tools, and any negative effects connected to preventative measures). In cases where information is missing or requires further clarification, we will reach out to the corresponding author for additional details.

Risk of bias

Two individuals will independently determine the risk of bias. If a dispute or discrepancy cannot be settled via conversation, a third reviewer will help achieve an agreement. We will weigh the RCTs’ quality of methodology using the revised Cochrane risk-of-bias methodology for randomized trials [ 47 ]. The five domains of this tool are as follows: (1) risk of bias resulting from the randomization process, (2) risk of bias due to departure from the purpose of the intervention, (3) risk of bias due to lacking outcome data, (4) risk of bias in measuring of the outcome, and (5) risk of bias in selection of the presented result.

Data synthesis

Study results will be categorized and summarized based on the intervention type, detailing the methodologies and clinical attributes documented in the corresponding studies. The summary will include an exhaustive analysis of patient demographics, the reported outcomes, and a critical assessment of potential bias risks. In instances where a quantitative synthesis of research findings is infeasible, a narrative synthesis will elucidate the systematic reviews outcomes.

Assessment of transitivity

In NMA, the transitivity assumption is crucial, allowing for indirect comparisons between interventions via a common comparator [ 48 ]. Considering the inherent clinical and methodological diversity in systematic reviews, it is essential for researchers to determine whether such variability could significantly impact the transitivity. To identify potential intransitivity, we will scrutinize the distribution of known effect modifiers across all direct comparisons before conducting the NMA [ 49 ], including variables like age, gender, disease severity, and the duration of interventions. A comparable distribution of these factors suggests that the transitivity assumption holds. Conversely, if transitivity is compromised, the NMA results may be biased, warranting a more conservative interpretation.

• Network meta-analysis

Should the assumption of transitivity be deemed met, a random-effects NMA [ 50 ] will be executed employing vague priors within a Bayesian framework.

Detection of heterogeneity

Considering the anticipated variability in participant demographics, intervention methodologies, and outcome measurements, statistical heterogeneity is expected. In anticipation of inherent variability across the included studies, we will implement a random-effects model to mitigate the observed statistical heterogeneity. The deviance information criterion (DIC) will serve as our comparative metric for model selection, integrating considerations of model fit with complexity.

To explore the sources of heterogeneity, we will conduct network meta-regression, subgroup analyses, and sensitivity analyses [ 51 ]. Network meta-regression will be carried out to examine the impact of potential effect modifiers (e.g., average age of participants, baseline symptom scores) on the primary outcomes. The duration of interventions may be a significant factor affecting efficacy, and subgroup analyses will be performed to assess the influence of different intervention durations on the primary outcomes. Additionally, if a sufficient number of studies are available, we will conduct sensitivity analyses by excluding trials assessed to be at high risk of bias to ensure the robustness of the primary study results.

Assessment of inconsistency

When closed loops are present within the NMA framework, the node-splitting approach is employed to evaluate the consistency between direct and indirect evidence. p  > 0.05 in the node-splitting analysis is indicative of agreement between the two sources [ 52 ].

Assessment of publication bias

In instances where a treatment comparison encompasses over 10 studies, we will utilize a comparison-adjusted funnel plot to evaluate potential small-study effects and the likelihood of publication bias [ 53 ]. The symmetry of these plots will be systematically assessed via Egger’s test.

The overall strength of the evidence will be assessed while accounting for research limitations, imprecision, heterogeneity, indirectness, and publication bias using the Confidence in Network Meta-Analysis (CINeMA) method. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework is the foundation of CINeMA [ 54 ] and contains the following six dimensions: within-study bias, reporting bias, indirectness, imprecision, heterogeneity, and incoherence. The adoption of CINeMA boosts transparency and prevents the selective use of evidence in making judgments, thereby reducing the level of subjectivity.

Statistical analyses

All studies will be performed using the R-evolution software [ 55 ] version 4.3.0 and the gemtc package [ 56 ] version 1.0–1, which connects with JAGS version [ 57 ] 4.3.2 to perform a Markov chain Monte Carlo simulation (MCMC) [ 58 ]. We will configure 4 Markov chains, with executing a minimum of 20,000 iterations. The concordance between direct and indirect evidence will be ascertained through the node-splitting technique. Model convergence will be gauged using convergence diagnostic and trace density plots, with the potential scale reduction factor (PSRF) providing a metric for convergence adequacy—a PSRF close to 1 suggests satisfactory convergence. For continuous outcomes, the mean difference (MD) is utilized as the measure of effect, whereas for binary outcomes, the risk ratio (RR) is used, including its 95% confidence interval (CI). The area under the cumulative ranking curve (SUCRA), as determined from the ranking probability matrix generated by R software, will be calculated and the corresponding SUCRA curve plotted; a greater SUCRA value indicates an increased likelihood of a superior outcome ranking.

A network diagram will be created to visualize relationships between interventions [ 59 ]. Data processing will be executed utilizing network group commands. Subsequent to this, network evidence graphs will be generated [ 58 ]. In these visual representations, the magnitude of the nodes will be proportional to the sample sizes derived from the comparative analysis of interventions. The thickness of the edges will represent the volume of RCTs interlinking the interventions.

The ICU is a specialized hospital department dedicated to the intensive care and treatment of seriously ill patients. The recovery of patients treated in the ICU is crucial for their well-being, as well as for their families and society [ 60 ]. However, ICU patients experience a decline in immunological response and hormone disruption owing to the nature of their illnesses and the risk factors during ICU treatment [ 61 ]. This can lead to various symptoms, including sleep disturbance, anxiety, depression, cognitive impairment, and PTSD. Individuals can exhibit one or multiple symptoms of PICS [ 21 ], and they significantly impact the patient’s quality of life and impose additional economic and caregiving burdens on society. Current preventive measures for PICS in ICU patients mainly comprise pharmacological and non-pharmacological interventions. Non-pharmacological interventions primarily involve physical activity, ICU diaries, psychotherapy, health education, and comprehensive treatment [ 62 , 63 ]. However, there is no research evaluating the most effective non-pharmacological preventive measures. Therefore, this proposed study aims to compare the occurrence of PICS using an NMA approach to assess the effectiveness of various intervention measures.

The proposed systematic review and NMA aim to address the effectiveness of intervention measures in preventing PICS in adults treated in the ICU. Developing effective preventive interventions can help alleviate the social and economic burden of PICS by reducing new cases or alleviating symptoms in affected individuals. This systematic review will employ NMA to compare all non-pharmacological measures aimed at preventing PICS. The primary outcomes will include the incidence or relief of various PICS symptoms, such as depression, anxiety, PTSD, cognitive impairment, sleep disturbance, physical functional impairment, and pain. Secondary outcomes include participant satisfaction and the frequency of adverse events.

To the best of our knowledge, this will be the first systematic review and NMA to evaluate currently available non-pharmacological therapies for preventing PICS. The research findings will provide rankings in terms of treatment effectiveness and acceptability, which will contribute to evidence-based decision-making in the rehabilitation of ICU patients and further development of other non-pharmacological interventions. Furthermore, the methodology of this protocol is based on the Cochrane Handbook for Intervention Reviews [ 64 ], the PRISMA statement [ 46 , 65 ], and GRADE assessment [ 66 ], taking into account the risks of random errors and systematic errors.

The ability of our systematic review and NMA to draw conclusions about non-pharmacological interventions for PICS in individuals treated in the ICU may be limited by the available data, which could be considered a limitation of this study. However, despite this limitation, identifying the best available evidence from current research is still valuable. Additionally, we will search only Chinese and English databases and will not analyze articles in other languages, which may be another limitation. However, it is worth noting that the majority of high-quality studies are usually published in English and included in English databases, so our analysis is unlikely to omit important studies.

Some current trials may not have included patient preferences [ 67 ], but our study originates from previous research and uses existing outcome data for statistical analysis. Therefore, we hope individuals treated in the ICU can make their own choices combined with their circumstances while receiving prevention recommendations from doctors based on clinical evidence.

Availability of data and materials

The study is a systematic review.

Abbreviations

Confidence in Network Meta-Analysis

Grading of Recommendations Assessment, Development, and Evaluation

Iintensive care unit

• Postintensive care syndrome

Posttraumatic stress disorder

Randomized controlled trial

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

The deviance information criterion

Markov chain Monte Carlo simulation

Potential scale reduction factor

Mean difference

Confidence interval

Area under the cumulative ranking curve

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The visual abstract and icons in Figure 1B and D are from the following authors on icon font: Yang, Wang xiaoxia453, Ziji, Shenseng, Saori1994, Bafenzhongdewennuan, Shenxiawuyan, Viki-wei, Zhejiushixiaowang, Wendy-qinzi, Anniebaby11, and Xiaojiage. We sincerely appreciate their creativity and generosity in sharing their creations, which provided crucial elements for the design of the figures in this paper. Special thanks also go to the unDraw platform for offering free illustrations, which enhanced the clarity and visual appeal of the abstract. Figure 1A was created using genescloud tools, a free online platform for data analysis (URL: https://www.genescloud.cn ). We extend our gratitude to the genescloud team for providing such exceptional tools, which greatly facilitated the analysis of data and the production of figures in this study. Figure 1C is adapted from Esteban Ortiz-Ospina and Max Roser’s work “Healthcare Spending” (2017). The visualization was originally published online at OurWorldInData.org and retrieved from: https://ourworldindata.org/financing-healthcare . We appreciate the staff at OurWorldInData.org for providing this insightful chart.

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Xiaoying Sun, Qian Tao & Shouzhen Cheng

Department of Respiratory and Intensive Care Medicine, Shenzhen People’s Hospital, Shenzhen, 518020, China

School of Nursing, Suzhou Medical College, Soochow University, Suzhou, 215006, China

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XS and SC designed this study. Each author made a contribution to the protocol’s planning and creation. XS designed the graphics and wrote the initial draft, and all authors participated in revising the manuscript and approving and contributing to the final written manuscript. SC acted as the guarantor of the review.

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Sun, X., Tao, Q., Cui, Q. et al. Non-pharmacological interventions to prevent PICS in critically ill adult patients: a protocol for a systematic review and network meta-analysis. Syst Rev 13 , 132 (2024). https://doi.org/10.1186/s13643-024-02542-z

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Association of Remote Patient-Reported Outcomes and Step Counts With Hospitalization or Death Among Patients With Advanced Cancer Undergoing Chemotherapy: Secondary Analysis of the PROStep Randomized Trial

Authors of this article:

Original Paper

• Christopher R Manz 1 , MSHP, MD   ; 
• Emily Schriver 2, 3 , MS   ; 
• William J Ferrell 4, 5 , MPH   ; 
• Joelle Williamson 6 , MPH   ; 
• Jonathan Wakim 6 , BS   ; 
• Neda Khan 6, 7 , MHCI   ; 
• Michael Kopinsky 7 , BSE   ; 
• Mohan Balachandran 7 , MA, MS   ; 
• Jinbo Chen 5, 8 , PhD   ; 
• Mitesh S Patel 9 , MBA, MD   ; 
• Samuel U Takvorian 5, 6, 10 , MSHP, MD   ; 
• Lawrence N Shulman 5, 6, 10 , MD   ; 
• Justin E Bekelman 5, 10 , MD   ; 
• Ian J Barnett 8 * , PhD   ; 
• Ravi B Parikh 2, 4, 5, 10, 11 * , MPP, MD  

1 Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, United States

2 Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, United States

3 Penn Medicine Predictive Healthcare, University of Pennsylvania Health System, Philadelphia, PA, United States

4 Division of Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States

5 Penn Center for Cancer Care Innovation, Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, United States

6 Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States

7 Center for Health Care Innovation, Penn Medicine, Philadelphia, PA, United States

8 Department of Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States

9 Ascension, St. Louis, MO, United States

10 Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, United States

11 Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, United States

*these authors contributed equally

Corresponding Author:

Ravi B Parikh, MPP, MD

Division of Health Policy

Perelman School of Medicine

University of Pennsylvania

423 Guardian Drive

Philadelphia, PA, 19104

United States

Phone: 1 3524224285

Email: [email protected]

Background: Patients with advanced cancer undergoing chemotherapy experience significant symptoms and declines in functional status, which are associated with poor outcomes. Remote monitoring of patient-reported outcomes (PROs; symptoms) and step counts (functional status) may proactively identify patients at risk of hospitalization or death.

Objective: The aim of this study is to evaluate the association of (1) longitudinal PROs with step counts and (2) PROs and step counts with hospitalization or death.

Methods: The PROStep randomized trial enrolled 108 patients with advanced gastrointestinal or lung cancers undergoing cytotoxic chemotherapy at a large academic cancer center. Patients were randomized to weekly text-based monitoring of 8 PROs plus continuous step count monitoring via Fitbit (Google) versus usual care. This preplanned secondary analysis included 57 of 75 patients randomized to the intervention who had PRO and step count data. We analyzed the associations between PROs and mean daily step counts and the associations of PROs and step counts with the composite outcome of hospitalization or death using bootstrapped generalized linear models to account for longitudinal data.

Results: Among 57 patients, the mean age was 57 (SD 10.9) years, 24 (42%) were female, 43 (75%) had advanced gastrointestinal cancer, 14 (25%) had advanced lung cancer, and 25 (44%) were hospitalized or died during follow-up. A 1-point weekly increase (on a 32-point scale) in aggregate PRO score was associated with 247 fewer mean daily steps (95% CI –277 to –213; P <.001). PROs most strongly associated with step count decline were patient-reported activity (daily step change –892), nausea score (–677), and constipation score (524). A 1-point weekly increase in aggregate PRO score was associated with 20% greater odds of hospitalization or death (adjusted odds ratio [aOR] 1.2, 95% CI 1.1-1.4; P =.01). PROs most strongly associated with hospitalization or death were pain (aOR 3.2, 95% CI 1.6-6.5; P <.001), decreased activity (aOR 3.2, 95% CI 1.4-7.1; P =.01), dyspnea (aOR 2.6, 95% CI 1.2-5.5; P =.02), and sadness (aOR 2.1, 95% CI 1.1-4.3; P =.03). A decrease in 1000 steps was associated with 16% greater odds of hospitalization or death (aOR 1.2, 95% CI 1.0-1.3; P =.03). Compared with baseline, mean daily step count decreased 7% (n=274 steps), 9% (n=351 steps), and 16% (n=667 steps) in the 3, 2, and 1 weeks before hospitalization or death, respectively.

Conclusions: In this secondary analysis of a randomized trial among patients with advanced cancer, higher symptom burden and decreased step count were independently associated with and predictably worsened close to hospitalization or death. Future interventions should leverage longitudinal PRO and step count data to target interventions toward patients at risk for poor outcomes.

Trial Registration: ClinicalTrials.gov NCT04616768; https://clinicaltrials.gov/study/NCT04616768

International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2021-054675

Introduction

Patients with advanced cancer who receive chemotherapy often experience significant symptoms, declines in functional status, and hospitalization [ 1 - 3 ]. Patient-reported outcomes (PROs) measure symptom burden and well-being. In clinical trials, routine collection of PROs for patients with cancer undergoing treatment is associated with decreased acute care use and improved overall survival [ 4 , 5 ]. Similar to increased patient symptom burden, the decline in patient functional status often presages adverse events, hospitalizations, disease progression, and death [ 6 , 7 ]. While PROs are useful for monitoring changes in symptoms and reported activity levels, they do not provide objective measures of functional status. Step counts are a proxy measure of functional status and thus identify patients who are at a high risk of poor outcomes [ 6 ]. However, among people with advanced cancer undergoing treatment, the prognostic use of step count monitoring has never been explored [ 8 ].

Previously published studies in oncology demonstrate that lower step counts are associated with higher odds of adverse events, hospitalization, and death [ 6 , 9 - 11 ]. Yet, these studies have shortcomings that limit their generalizability to patients with advanced cancers. First, most studies were small (<50 patients), tracked step counts for less than 1 month, and focused only on patients receiving therapy with curative intent. Second, these studies measure associations between cross-sectional PROs or step counts and adverse outcomes. Few studies measured longitudinal PROs and step counts over several weeks, thus precluding evaluation of how patient-reported symptoms and objective measures of functional status interact and change over time.

Emerging value-based oncology models, including Medicare’s Enhancing Oncology Model that began in 2023, required measurement of electronic PROs. There is an urgent need to identify whether step count data complement PRO monitoring and improve early identification of patients with advanced cancer who are at risk of future adverse outcomes. The PROStep trial was a pragmatic, randomized controlled trial of patients with advanced gastrointestinal (GI) and lung cancers treated with intravenous chemotherapy [ 12 ]. Intervention patients received remote, longitudinal PRO surveys and step count monitoring. The objectives of this preplanned secondary analysis of the PROStep trial were to (1) evaluate the association between longitudinal PROs and step counts and (2) assess the association of PROs and step counts with the composite outcome of hospitalization or death. Our overarching hypothesis was that lower step counts would be associated with greater symptom burden measured by PROs and that higher symptom burden and lower step counts would be independently associated with subsequent hospitalization or death.

Study Design and Cohort

This is a preplanned secondary analysis of the PROStep randomized trial (ClinicalTrials.gov NCT04616768). The trial’s design, protocol, and main results have been previously published [ 13 ]. Briefly, PROStep tested the effect of clinician and patient-centered dashboards combining weekly PRO data, collected via text message, and step count monitoring, collected via a wearable accelerometer, on the primary outcomes of patient-reported clinician understanding of the patient’s symptoms and functional status. The study population consisted of 108 patients with stage IV GI or lung cancers undergoing intravenous cytotoxic chemotherapy at a tertiary academic cancer center between November 17, 2020, and June 17, 2021. Eligible patients were English-speaking, used a smartphone with SMS and Bluetooth capabilities, and received their primary oncology care at the study center. Patients undergoing monotherapy with checkpoint inhibitor therapy, targeted therapies (eg, cetuximab and trastuzumab), or oral chemotherapy without concurrent intravenous chemotherapy were excluded. Additionally, patients on active therapeutic interventional trials or confined to a wheelchair or bed were excluded.

Patients were electronically randomized in a 1:1:1 fashion, stratified by cancer type (GI or lung), to 1 of the 3 arms—(A) standard care (control), (B) PROStep intervention, or (C) PROStep intervention with active choice prompts (see Figure S1 in Multimedia Appendix 1 for CONSORT diagram). Patients randomized to the PROStep intervention received weekly 8-question text-based PRO surveys and passive step count monitoring via a wearable accelerometer (Fitbit Inspire HR; Google). PROs and step count level data were summarized in a dashboard and provided to the patient’s medical oncologist or advanced practitioner before a clinic visit. Patients in arm C also received an automated active choice text on the morning of each oncology visit, which prompted patients to discuss concerning symptoms with their oncologist.

In this preplanned secondary analysis, we evaluated the association between step counts and PROs among 57 of the 75 patients originally randomized to arms B or C who had any PRO and step count data; 18 of the 75 randomized patients did not have step counts and PRO data and could not be analyzed. As the primary analysis showed no difference in any outcome for arms B and C, study arms B and C were combined for this analysis. We then evaluated the independent associations between step count levels and PROs with subsequent hospitalization or death. The University of Pennsylvania Institutional Review Board approved the study and participants provided written or electronic informed consent during the trial.

PRO and Step Count Assessment

Symptoms were assessed using an 8-question, text-based PRO survey drawn from the PRO version of the National Cancer Institute’s Common Terminology Criteria for Adverse Events (CTCAE), scored on a 5-point Likert scale, from 0 (not present) to 4 (disabling; see Table S1 in Multimedia Appendix 1 ) [ 14 , 15 ]. PRO surveys were sent to participants weekly on Monday mornings at 10 AM via a text message on their mobile phones. For patients who did not respond to the PRO survey, automatic reminder alerts were sent on Tuesdays and Thursdays. To measure overall symptom burden, we aggregated the scores from the 8 PRO questions in an aggregate PRO score on a 0-32 scale.

Step counts were measured using the Fitbit Inspire HR. Patients enrolled in the trial using the Way2Health app and then were given Fitbits linked to the app so that they could submit step count data by opening the app. Fitbits had a 5-day memory, so the patients received a text reminder to synchronize their Fitbit 2 times per week as well as 2 days before a clinic visit unless the data were synchronized in the prior 24 hours. The Fitbit Inspire HR measured exact daily step counts; these were summarized on a weekly basis as average daily step counts each week. Only days in which the Fitbit was appropriately synced were used in the average step count calculation (ie, days with no Fitbit data were excluded from the step count calculation).

The primary outcome was a composite outcome of hospitalization or death, collected by the study research coordinator during the course of the trial for all enrolled patients. Associations between PROs and step counts were also assessed.

Statistical Analysis

We reported descriptive patient characteristics, mean adherence to weekly PRO surveys (number of completed weekly surveys divided by the number of weeks enrolled in the study), and mean weekly adherence to step count monitoring (percentage of weeks where step counts were available for >3 days in a given week). To determine the association between PROs and step counts in a given week, we modeled their concurrent association. To determine the association of change in PRO scores and the change in step counts in a given week, we calculated bootstrapped means with 1000 iterations using bootstrapped generalized linear models. To determine the association of the longitudinal measurement of composite PRO score and average weekly steps on the composite outcome of hospitalization or death, we used bootstrapped generalized linear models with multiple “outputation” to account for repeated measures from the longitudinal data [ 16 ]. To account for the decline in the degree of association between aggregate PRO score and average weekly steps as the time between activity data and the event increases, we assumed exponential decay between the time of the PRO or step count being recorded and the composite outcome. Specifically, we multiplied regression coefficients with an exponentially decaying term and used a grid search to determine the maximum likelihood exponential decay parameter estimate used for both PRO scores and Fitbit. Finally, to evaluate the trends in PRO scores and steps in the weeks leading up to the outcomes, we computed bootstrapped means. Analyses were performed using R (version 4.2.1; R Core Team) and Python (version 3.9.13; Python Software Foundation). Two-sided hypothesis testing with α=.05 was used to assess significance.

Ethical Considerations

This study underwent ethical review and was approved by the University of Pennsylvania Institutional Review Board (843616) and was registered on ClinicalTrials.gov (NCT04616768). Written informed consent was obtained from all participants in the trial. The study data were anonymized and deidentified. Participants in arms A, B, and C were compensated up to US $50 in gift cards upon completing their use surveys at 3 and 6 months after enrollment (US $25 each). Participants in arms B and C were permitted to keep their Fitbit as part of the trial (US $80 value).

Baseline Characteristics

The 57 patients had a mean age of 57 (SD 10.9) years, 24 (42%) were female, 49 (86%) were White, and 3 (5%) were Black. A total of 43 (75%) patients had advanced GI cancers and 14 (25%) had advanced lung cancer ( Table 1 ). A total of 79% (n=45) of patients completed 24 weeks of the study; the most common reasons for disenrollment were death (n=8) and voluntary drop out (n=4). Mean adherence to weekly PRO surveys was 77% (SD 29.7%), with 84% (n=48) of patients reporting PROs more than 50% of enrolled weeks. Mean weekly adherence to step count monitoring was 69% (SD 36.5%), with 70% (n=40) of patients recording step counts more than 50% of weeks enrolled in the study.

Associations Between PROs and Step Counts in a Given Week

In univariate analyses evaluating associations of PROs and step counts within a single week, a 1-point higher aggregate PRO score (out of 32) was associated with 150 fewer mean daily steps (95% CI –183 to –120 steps; P <.001) (Figure S2 in Multimedia Appendix 1 ). In a given week, 1-point higher scores (up to a score of 4) in shortness of breath ( P =.03), sadness ( P <.001), anxiety ( P <.001), patient-reported activity ( P <.001), nausea ( P <.001), and diarrhea ( P <.01) were associated with 112, 244, 125, 792, 564, and 248 fewer mean daily steps, respectively (Table S2 in Multimedia Appendix 1 ). Pain (–71 mean daily steps; P =.23), and constipation (–90; P =.06) were not independent factors. When examining between-PRO correlations, sadness and anxiety ( r =0.67) and shortness of breath and patient-reported activity ( r =0.64) were highly correlated. Patient-reported activity ( r =–0.41) and shortness of breath ( r =–0.36) were most correlated with decreased mean daily step count ( Figure 1 ).

Associations Between Longitudinal Changes in PROs and Step Counts

In univariate analyses assessing week-to-week changes in PROs and changes in step counts, changes in aggregate PRO score from the prior week to the current week were associated with stepwise increases or decreases in the mean daily step counts ( Figure 2 ). A 1-point increase in aggregate PRO score from the prior week was associated with a decrease of 247 mean daily steps (95% CI –277 to –213; P <.001). One-point increases in following PROs from the prior week had the strongest associations with decreased mean daily steps—patient-reported activity (mean daily step change –892, 95% CI –1050 to –758; P <.001), nausea (–677, 95% CI –770 to –588; P <.001), constipation (–524, 95% CI –614 to –431; P <.001), shortness of breath (–399, 95% CI –498 to –302; P <.01), pain (–304, 95% CI –409 to –204; P <.001), sadness (–382, 95% CI –462 to –302; P <.001), and anxiety (–125, 95% CI –213 to –42; P <.001) (Table S2 in Multimedia Appendix 1 ).

Associations Between PROs, Step Counts, and Hospitalization or Death

Among 57 patients, 21 (37%) patients were hospitalized and 8 (14%) died during the follow-up period. The rate of the composite outcome of hospitalization or death was 44% (n=25). On average, a 1-point increase in the aggregate PRO score was associated with a 20% increase in adjusted odds of hospitalization or death (adjusted odds ratio [aOR] 1.2, 95% CI 1.1-1.4; P =.01; Table 2 ). In a given week, a 1-point increase in patient-reported pain (aOR 3.2, 95% CI 1.6-6.5; P =.01), activity (aOR 3.2, 95% CI 1.4-7.1; P =.01), shortness of breath (aOR 2.6, 95% CI 1.2-5.5; P =.01), sadness (aOR 2.1, 95% CI 1.1-4.3; P =.03), and anxiety (aOR 2.7, 95% CI 1.3-5.6; P =.01) were most associated with increased odds of hospitalization or death. After adjusting for aggregate PRO score, a decrease in 1000 mean daily steps was associated with 16% increased odds of hospitalization or death (aOR 1.16, 95% CI 1.01-1.33; P =.03). Patients who were hospitalized or died had a progressive increase in aggregate PRO score and a decrease in mean daily steps in the 4 weeks leading up to the event, but patients who did not experience an event had no change in aggregate PRO score or mean daily steps in the prior 4 weeks ( Figure 3 ). Compared with baseline, mean daily step count decreased 7% (n=274/4112 steps), 9% (n=351/4112 steps), and 16% (n=667/4112 steps) in the 3, 2, and 1 weeks before hospitalization or death, respectively. Mean aggregate PRO score increased by 11% (n=0.8), 25% (n=1.9), and 36% (n=2.8), in the 3, 2, and 1 weeks before hospitalization or death, respectively.

a Each row represents a separate model composed of the symptom in the left column and step counts (in thousands) as the predictor variables and the composite outcome of hospitalization and death as the outcome.

b aOR: adjusted odds ratio.

c PRO: patient-reported outcome.

Principal Findings

In this preplanned secondary analysis of a randomized clinical trial among patients with incurable GI and lung cancers receiving chemotherapy, higher symptom burden measured by remote PROs was associated with lower daily step count. Moreover, higher symptom burden and decreased daily step counts were independently associated with an increased risk of hospitalization or death. PROs and step counts predictably worsened in the 4 weeks before hospitalization or death, whereas the individuals who did not experience hospitalization or death had stable PROs and step counts.

Prior studies among patients with cancer undergoing chemoradiation demonstrated that lower step counts, measured cross-sectionally over brief periods, are associated with subsequent acute care use and worse prognosis [ 6 , 9 - 11 ]. We build upon these findings by demonstrating that longitudinally measured PROs and step counts are predictive of hospitalization and death. PROs and step count predictably worsened in the 4 weeks before hospitalization and death. This indicates that among patients with advanced cancer receiving chemotherapy, longitudinal remotely monitored patient-generated health data may identify at-risk patients weeks before hospitalization or death—a window in which proactive interventions, including goals-of-care conversations, may improve outcomes or goal-concordant care. Importantly, decreasing patient-reported activity level was highly predictive of hospitalization or death (aOR 3.2, 95% CI 1.4-7.1) even when adjusting for step counts, suggesting that patient-reported activity may complement objective measures of step counts.

Our study is among the first to assess associations between longitudinal PROs and step counts and downstream use, among patients with cancer. We show that patients with higher symptom burden also experience fewer daily step counts in a given week. Furthermore, in longitudinal analyses, daily step counts decreased as symptom burden increased. Worsening nausea, constipation, shortness of breath, and sadness had the largest associations with decreased step count.

There is growing evidence to support routine collection of PROs, and Medicare’s Enhancing Oncology Model incentivizes PRO measurement. Our results may facilitate efforts to use these PRO data in predictive models by identifying those PROs most likely to contribute to adverse events. This study provides novel provocative data that step count monitoring independently identifies at-risk patients and could be used synergistically with PROs as part of targeted care delivery interventions to prevent acute care use or target supportive care interventions. Such interventions may include additional symptom support, home services like physical therapy, treatment modifications, palliative care consultation, or goals-of-care conversations. For instance, worsening step counts may prompt physical therapy interventions to improve functional status and perhaps reduce the risk of hospitalization. Policymakers may consider strategically incorporating activity monitoring with electronic PRO monitoring mandates to enhance risk prediction and stratification of high-risk populations. Future studies should examine how longitudinal PRO and step count data could identify decreasing chemotherapy tolerance, enabling earlier dose reduction that might improve quality of life and extend treatment tolerability. Future studies should also investigate whether longitudinal PRO and step count data predict disease progression or symptomatic disease, potentially prompting earlier imaging and switches to new therapy.

Limitations

This study has several limitations. It includes patients with only 2 types of cancer enrolled in a clinical trial at a single tertiary cancer center and all undergoing chemotherapy, which may enrich for a sicker population that may have the most pronounced changes in symptoms and step counts. Adherence to weekly PROs and step counts among patients in this analysis was 77% (SD 29.7%) and 69% (SD 37%), respectively, which is higher than the adherence reported in real-world studies [ 17 ]. However, we excluded patients who did not have PRO or step count data, and adherence in the full cohort was similar to the levels reported in these real-world studies [ 12 ]. Moreover, step counts could only be tracked if patients wore the wearable monitor—if patients were less likely to wear the monitors in certain circumstances (eg, when symptoms worsen), data missingness may be informative. A subsequent analysis will explore how data missingness can also be used to improve predictive power. The study also used an abbreviated sample of PRO- CTCAE questions targeted to patients with GI and lung cancers; findings may differ using the full PRO-CTCAE question panel and in patients with other malignancies. Finally, our sample size and overall number of outcomes were too small to disaggregate predictors of hospitalizations or death individually. We hope future prospective work with larger sample sizes will facilitate the disaggregation of these outcomes. Nonetheless, prediction of both death and hospitalizations near the end of life can be used to trigger interventions such as goals-of-care conversations that may enable better goal-concordant care.

Conclusions

This study of remote monitoring of PROs and step counts shows that changes in these measures predict hospitalization and death for patients with advanced cancer undergoing treatment. Future work should validate these findings in larger, more diverse populations and translate these results into interventions that can avoid acute care use and improve supportive care.

Acknowledgments

This work was supported by funding from the University of Pennsylvania Institute of Translational Medicine and Therapeutics (grant or award 400-4133-4-578849-xxxx-2446–4732) and National Institutes of Health (NIH) (grants 5T32CA092203-18 to CM and K08CA263541 to RBP). The funders had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit this paper for publication.

Data Availability

The data supporting the findings of this study are available upon request from the corresponding author. The data are not publicly available because of privacy concerns.

Conflicts of Interest

The authors are solely responsible for the design and conduct of this study, study analyses, and the drafting of this paper. CRM reports funding from Genentech and the American Cancer Society, outside the submitted work. MSP reports employment at Ascension.org and being owner of Catalyst Health, a behavior change and technology consulting firm, outside the submitted work. RBP reports receiving grants from the National Institutes of Health, Prostate Cancer Foundation, National Palliative Care Research Center, National Comprehensive Cancer Network Foundation, Conquer Cancer Foundation, Humana, Emerson Collective, and Veterans Health Administration; receiving personal fees and equity from GNS Healthcare, Thyme Care, and Onc.AI; receiving personal fees from the Cancer Study Group, Biofourmis, ConcertAI, CreditSuisse, Humana, Genetic Chemistry Therapeutics, and Nanology; filing a patent related to electronic health record–based predictive algorithms, receiving honoraria from Flatiron and Medscape; being an unpaid board member of the Coalition to Transform Advanced Care and American Cancer Society; and serving on a leadership consortium (unpaid) at the National Quality Forum, all outside the submitted work. JEB reports personal fees from Reimagine Care, Healthcare Foundry, and Astrazeneca and grants from Emerson Collective (co-investigator), Loxo@Lilly (co-investigator), and Gilead (co-investigator), outside the submitted work.

Consolidated Standards of Reporting Trials (CONSORT) diagram from PROStep trial; PRO survey: National Cancer Institute’s common terminology criteria for adverse events and Patient Reported Functional Status (PRFS) tool; associations between patient-reported symptoms and step counts in a given week; and association between patient-reported symptoms and mean daily step count.

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Abbreviations

Edited by A Mavragani; submitted 21.07.23; peer-reviewed by S Goldberg, P Innominato; comments to author 02.11.23; revised version received 09.11.23; accepted 29.01.24; published 17.05.24.

©Christopher R Manz, Emily Schriver, William J Ferrell, Joelle Williamson, Jonathan Wakim, Neda Khan, Michael Kopinsky, Mohan Balachandran, Jinbo Chen, Mitesh S Patel, Samuel U Takvorian, Lawrence N Shulman, Justin E Bekelman, Ian J Barnett, Ravi B Parikh. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 17.05.2024.

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.

Comparison of Soil Carbon Dynamics Between Restored Prairie and Agricultural Soils in the U.S. Midwest

Globally, soils hold more carbon than both the atmosphere and aboveground terrestrial biosphere combined. Changes in land use and land cover have the potential to alter soil carbon cycling throughout the soil profile, from the surface to meters deep, yet most studies focus only on the near surface impact ( 3 and C 4 photosynthetic pathway plant community composition. Comparative analysis of edaphic properties and soil carbon suggests that deep loess deposits in Nebraska permit enhanced water infiltration and SOC deposition to depths of ~100 cm in 60 years of prairie restoration. In Illinois, poorly drained, clay/lime rich soils on glacial till and a younger restored prairie age (15 years) restricted the influence of prairie restoration to the upper 30 cm. Comparing the δ 13 C values of SOC and SIC in each system demonstrated that SIC at each site is likely of lithogenic origin. This work indicates that the magnitude of influence of restoration management is dependent on edaphic properties inherited from geological and geomorphological controls. Future work should quantify root structures and redox properties to better understand the influence of rooting depth on soil carbon concentrations. Fast-cycling C dynamics can be assessed using continuous, in-situ CO 2 and O 2 soil gas concentration changes. The secondary objective of my thesis was to determine if manual, low temporal resolution gas sampling and analysis are a low cost and effective means of measuring soil O 2 and CO 2 , by comparing it with data from in-situ continuous (hourly) sensors. Manual analysis of soil CO 2 and O 2 from field replicates of buried gas collection cups resulted in measurement differences from the continuous sensors. Measuring CO2 concentration with manual methods often resulted in higher concentrations than hourly, continuous measurements across all sites. Additionally, O 2 concentrations measured by manual methods were higher than hourly values in the restored prairie and less in agricultural sites. A variety of spatial variability, pressure perturbations, calibration offsets, and system leakage influences on both analysis methods could cause the discrepancy.

NSF Grant 1331906

Degree type.

• Master of Science
• Earth, Atmospheric and Planetary Sciences

Campus location

• West Lafayette

Advisor/Supervisor/Committee Chair

Additional committee member 2, additional committee member 3, additional committee member 4, additional committee member 5, usage metrics.

• Environmental biogeochemistry
• Soil chemistry and soil carbon sequestration (excl. carbon sequestration science)