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How to Structure the Table of Contents for a Research Paper

How to Structure the Table of Contents for a Research Paper

4-minute read

  • 16th July 2023

So you’ve made it to the important step of writing the table of contents for your paper. Congratulations on making it this far! Whether you’re writing a research paper or a dissertation , the table of contents not only provides the reader with guidance on where to find the sections of your paper, but it also signals that a quality piece of research is to follow. Here, we will provide detailed instructions on how to structure the table of contents for your research paper.

Steps to Create a Table of Contents

  • Insert the table of contents after the title page.

Within the structure of your research paper , you should place the table of contents after the title page but before the introduction or the beginning of the content. If your research paper includes an abstract or an acknowledgements section , place the table of contents after it.

  • List all the paper’s sections and subsections in chronological order.

Depending on the complexity of your paper, this list will include chapters (first-level headings), chapter sections (second-level headings), and perhaps subsections (third-level headings). If you have a chapter outline , it will come in handy during this step. You should include the bibliography and all appendices in your table of contents. If you have more than a few charts and figures (more often the case in a dissertation than in a research paper), you should add them to a separate list of charts and figures that immediately follows the table of contents. (Check out our FAQs below for additional guidance on items that should not be in your table of contents.)

  • Paginate each section.

Label each section and subsection with the page number it begins on. Be sure to do a check after you’ve made your final edits to ensure that you don’t need to update the page numbers.

  • Format your table of contents.

The way you format your table of contents will depend on the style guide you use for the rest of your paper. For example, there are table of contents formatting guidelines for Turabian/Chicago and MLA styles, and although the APA recommends checking with your instructor for formatting instructions (always a good rule of thumb), you can also create a table of contents for a research paper that follows APA style .

  • Add hyperlinks if you like.

Depending on the word processing software you’re using, you may also be able to hyperlink the sections of your table of contents for easier navigation through your paper. (Instructions for this feature are available for both Microsoft Word and Google Docs .)

To summarize, the following steps will help you create a clear and concise table of contents to guide readers through your research paper:

1. Insert the table of contents after the title page.

2. List all the sections and subsections in chronological order.

3. Paginate each section.

4. Format the table of contents according to your style guide.

5. Add optional hyperlinks.

If you’d like help formatting and proofreading your research paper , check out some of our services. You can even submit a sample for free . Best of luck writing your research paper table of contents!

What is a table of contents?

A table of contents is a listing of each section of a document in chronological order, accompanied by the page number where the section begins. A table of contents gives the reader an overview of the contents of a document, as well as providing guidance on where to find each section.

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What should I include in my table of contents?

If your paper contains any of the following sections, they should be included in your table of contents:

●  Chapters, chapter sections, and subsections

●  Introduction

●  Conclusion

●  Appendices

●  Bibliography

Although recommendations may differ among institutions, you generally should not include the following in your table of contents:

●  Title page

●  Abstract

●  Acknowledgements

●  Forward or preface

If you have several charts, figures, or tables, consider creating a separate list for them that will immediately follow the table of contents. Also, you don’t need to include the table of contents itself in your table of contents.

Is there more than one way to format a table of contents?

Yes! In addition to following any recommendations from your instructor or institution, you should follow the stipulations of your style guide .

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How to Write a Table of Contents for Different Formats With Examples

11 December 2023

last updated

Rules that guide academic writing are specific to each paper format. However, some rules apply to all styles – APA, MLA, Chicago/Turabian, and Harvard. Basically, one of these rules is the inclusion of a Table of Contents (TOC) in an academic text, particularly long ones, like theses, dissertations, and research papers. When writing a TOC, students or researchers should observe some practices regardless of paper formats. Also, it includes writing the TOC on a new page after the title page, numbering the first-level and corresponding second-level headings, and indicating the page number of each entry. Hence, scholars need to learn how to write a table of contents in APA, MLA, Chicago/Turabian, and Harvard styles.

General Guidelines

When writing academic texts, such as theses, dissertations, and other research papers, students observe academic writing rules as applicable. Generally, the different paper formats – APA, MLA, Chicago/Turabian, and Harvard – have specific standards that students must follow in their writing. In this case, one of the rules is the inclusion of a Table of Contents (TOC) in the document. By definition, a TOC is a roadmap that scholars provide in their writing, outlining each portion of a paper. In other words, a TOC enables readers to locate specific information in documents or revisit favorite parts within written texts. Moreover, this part of academic papers provides readers with a preview of the paper’s contents.

How to write a table of contents

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Difference Between a Table of Contents and an Outline

In essence, a TOC is a description of first-level headings (topics) and second-level headings (subtopics) within the paper’s body. For a longer document, writers may also include third-level titles to make the text palatable to read. Ideally, the length of papers determines the depth that authors go into detailing their writing in TOCs. Basically, this feature means that shorter texts may not require third-level headings. In contrast, an essay outline is a summary of the paper’s main ideas with a hierarchical or logical structuring of the content. Unlike a TOC that only lists headings and subheadings, outlines capture these headings and then describe the content briefly under each one. As such, an outline provides a more in-depth summary of essay papers compared to a TOC.

How to Write a Table of Contents in APA

When writing a TOC in the APA format , writers should capture all the headings in the paper – first-level, second-level, and even third-level. Besides this information, they should also include an abstract, references, and appendices. Notably, while a TOC in the APA style has an abstract, this section is not necessary for the other formats, like MLA, Chicago/Turabian, and Harvard. Hence, an example of a Table of Contents written in the APA format is indicated below:

Example of a table of contents in APA

How to Write a Table of Contents in MLA

Unlike papers written in the APA style, MLA papers do not require a Table of Contents unless they are long enough. In this case, documents, like theses, dissertations, and books written in the MLA format should have a TOC. Even where a TOC is necessary, there is no specific method that a writer should use when writing it. In turn, the structure of the TOC is left to the writer’s discretion. However, when students have to include a TOC in their papers, the information they capture should be much more than what would appear in the APA paper . Hence, an example of writing a Table of Contents in MLA format is:

Example of a table of contents in MLA

In the case of writing a research paper, an example of a Table of Contents should be:

Example of a table of contents for a research paper in MLA

How to Write a Table of Contents in Chicago/Turabian

Like the MLA style, a Chicago/Turabian paper does not require writing a Table of Contents unless it is long enough. When a TOC is necessary, writers should capitalize on major headings. Additionally, authors do not need to add a row of periods (. . . . . . . .) between the heading entry and the page number. Moreover, the arrangement of the content should start with the first-level heading, then the second-level heading, and, finally, the third-level title, just like in the APA paper. In turn, all the information that precedes the introduction part should have lowercase Roman numerals. Also, the row of periods is only used for major headings. Hence, an example of writing a Table of Contents in a Chicago/Turabian paper is:

Example of a table of contents in Chicago/Turabian

How to Write a Table of Contents in Harvard

Like in the other formats, writing a Table of Contents in the Harvard style is captured by having the title “Table of Contents” at the center of the page, in the first line. Basically, it comes after the title page and captures all the sections and subsections of Harvard papers. In other words, writers must indicate first-level headings in a numbered list. Also, scholars should align titles to the left side and capitalize them. In turn, if there is a need to show second-level headings, authors should list them under corresponding first-level headings by using bullet points. However, it is essential for students not to disrupt the numbering of first-level headings. Moreover, writers should align second-level headings to the left side and indent them by half an inch and capitalize on this content. Hence, an example of writing a Table of Contents in a Harvard paper should appear as below:

Example of a table of contents in Harvard

A Table of Content (TOC) is an essential component of an academic paper , particularly for long documents, like theses, dissertations, and research papers. When students are writing a TOC, they should be careful to follow the applicable format’s rules and standards. Regardless of the format, writers should master the following tips when writing a TOC:

  • Write the TOC on a new page after the title page.
  • Indicate first-level headings of the document in a numbered list.
  • Indicate second-level headings under the corresponding first-level heading.
  • If applicable, indicate third-level headings under the corresponding second-level heading.
  • Write the page number for each heading.
  • Put the content in a two-column table.
  • Title the page with “Table of Contents.”

To Learn More, Read Relevant Articles

Mit essay prompts: free examples of writing assignments in 2024, how to cite a dictionary in mla 9: guidelines and examples.

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Table of Contents

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The Table of Contents should follow these guidelines:

  • ​All sections of the manuscript are listed in the Table of Contents except the Title Page, the Copyright Page, the Dedication Page, and the Table of Contents.
  • You may list subsections within chapters
  • Creative works are not exempt from the requirement to include a Table of Contents

Table of Contents Example

Here is an example of a Table of Contents page from the Template. Please note that your table of contents may be longer than one page.

Screenshot of Table of Contents page from Dissertation template

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How do I format a table of contents in MLA style?

Note: This post relates to content in the eighth edition of the MLA Handbook . For up-to-date guidance, see the ninth edition of the MLA Handbook .

Tables of contents may be formatted in a number of ways. In our publications, we sometimes list chapter numbers before chapter titles and sometimes list the chapter titles alone. We also sometimes list section heads beneath the chapter titles. After each chapter or heading title, the page number on which the chapter or section begins is provided. The following show examples from three of the MLA’s books.

From Elizabeth Brookbank and H. Faye Christenberry’s  MLA Guide to Undergraduate Research in Literature  (Modern Language Association of America, 2019):

From  Approaches to Teaching Bechdel’s  Fun Home, edited by Judith Kegan Gardiner (Modern Language Association of America, 2018):

From the  MLA Handbook , 8th ed. (Modern Language Association of America, 2016):

Need more information? Read about where to place a table of contents in your paper .

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Microsoft Word for Dissertations

  • Table of Contents
  • Introduction, Template, & Resources
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Automatic Table of Contents

An automatic Table of Contents relies on Styles to keep track of page numbers and section titles for you automatically. Microsoft Word can scan your document and find everything in the Heading 1 style and put that on the first level of your table of contents, put any Heading 2’s on the second level of your table of contents, and so on.

If you want an automatic table of contents you need to apply the Heading 1 style to all of your chapter titles and front matter headings (like “Dedication” and “Acknowledgements”).  All section headings within your chapters should use the Heading 2  style.  All sub-section headings should use  Heading 3 , etc....

If you have used Heading styles in your document, creating an automatic table of contents is easy.

  • Place your cursor where you want your table of contents to be.
  • On the References Ribbon, in the Table of Contents Group , click on the arrow next to the Table of Contents icon, and select  Custom Table of Contents .
  • We suggest that you set each level (Chapters, sections, sub-sections, aka TOC 1, TOC 2, TOC 3) to be single-spaced, with 12 points of space afterwards.  This makes each item in your ToC clump together if they're long enough to wrap to a second line, with the equivalent of a double space between each item, and makes the ToC easier to read and understand than if every line were double-spaced. See the video below for details.
  • If you want to change which headings appear in your Table of Contents, you can do so by changing the number in the Show levels: field. Select "1" to just include the major sections (Acknowledgements, List of Figures, Chapters, etc...).  Select "4" to include Chapters, sections, sub-sections, and sub-sub-sections.
  • Click OK to insert your table of contents.  

The table of contents is a snapshot of the headings and page numbers in your document, and does not automatically update itself as you make changes. At any time, you can update it by right-clicking on it and selecting Update field .  Notice that once the table of contents is in your document, it will turn gray if you click on it. This just reminds you that it is a special field managed by Word, and is getting information from somewhere else.

Modifying the format of your Table of Contents

The video below shows how to make your Table of Contents a little easier to read by formatting the spacing between items in your Table of Contents. You may recognize the "Modify Style" window that appears, which can serve as a reminder that you can use this window to modify more than just paragraph settings. You can modify the indent distance, or font, or tab settings for your ToC, just the same as you may have modified it for Styles. 

an image of the Modify Table of Contents window, where you can set Show Levels

By default, the Table of Contents tool creates the ToC by pulling in Headings 1 through 3. If you'd like to modify that -- to only show H1's, or to show Headings 1 through 4 -- then go to the References tab and select Custom Table of Contents .  In the window that appears, set Show Levels to "1" to only show Heading 1's in the Table of Contents, or set it to "4" to show Headings 1 through 4.

Bonus tip for updating fields like the Table of Contents

You'll quickly realize that all of the automatic Lists and Tables need to be updated occasionally to reflect any changes you've made elsewhere in the document -- they do not dynamically update by themselves. Normally, this means going to each field, right-clicking on it and selecting "Update Field". 

Alternatively, to update all fields throughout your document (Figure/Table numbers & Lists, cross-references, Table of Contents, etc...), just select "Print". This will cause Word to update everything in anticipation of printing. Once the print preview window appears, just cancel.

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Table of Contents Format

For Academic Papers

This table of contents is an essential part of writing a long academic paper, especially theoretical papers.

This article is a part of the guide:

  • Outline Examples
  • Example of a Paper
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Browse Full Outline

  • 1 Write a Research Paper
  • 2 Writing a Paper
  • 3.1 Write an Outline
  • 3.2 Outline Examples
  • 4.1 Thesis Statement
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  • 5.2 Abstract
  • 5.3 Introduction
  • 5.4 Methods
  • 5.5 Results
  • 5.6 Discussion
  • 5.7 Conclusion
  • 5.8 Bibliography
  • 6.1 Table of Contents
  • 6.2 Acknowledgements
  • 6.3 Appendix
  • 7.1 In Text Citations
  • 7.2 Footnotes
  • 7.3.1 Floating Blocks
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  • 8.2 Publication Bias
  • 8.3.1 Journal Rejection
  • 9.1 Article Writing
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It is usually not present in shorter research articles, since most empirical papers have similar structure .

A well laid out table of contents allows readers to easily navigate your paper and find the information that they need. Making a table of contents used to be a very long and complicated process, but the vast majority of word-processing programs, such as Microsoft WordTM and Open Office , do all of the hard work for you.

This saves hours of painstaking labor looking through your paper and makes sure that you have picked up on every subsection. If you have been using an outline as a basis for the paper, then you have a head start and the work on the table of contents formatting is already half done.

Whilst going into the exact details of how to make a table of contents in the program lies outside the scope of this article, the Help section included with the word-processing programs gives a useful series of tutorials and trouble-shooting guides.

That said, there are a few easy tips that you can adopt to make the whole process a little easier.

table of content of a research paper

The Importance of Headings

In the word processing programs, there is the option of automatically creating headings and subheadings, using heading 1, heading 2, heading 3 etc on the formatting bar. You should make sure that you get into the habit of doing this as you write the paper, instead of manually changing the font size or using the bold format.

Once you have done this, you can click a button, and the program will do everything for you, laying out the table of contents formatting automatically, based upon all of the headings and subheadings.

In Word, to insert a table of contents, first ensure that the cursor is where you want the table of contents to appear. Once you are happy with this, click 'Insert' on the drop down menu, scroll down to 'Reference,' and then across to 'Index and Tables'.

Click on the 'Table of Contents' tab and you are ready to click OK and go. OpenOffice is a very similar process but, after clicking 'Insert,' you follow 'Indexes and Tables' and 'Indexes and Tables' again.

The table of contents should appear after the title page and after the abstract and keywords, if you use them. As with all academic papers, there may be slight variations from department to department and even from supervisor to supervisor.

Check the preferred table of contents format before you start writing the paper , because changing things retrospectively can be a little more time consuming.

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How to Create a Table of Contents for Dissertation, Thesis or Paper & Examples

Dissertation Table of Contents

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A dissertation table of contents is a list of the chapters and sections included in a dissertation or thesis, along with their page numbers. It helps to navigate the document easily and locate specific information. Each chapter or section should be listed with its corresponding page number. The table of contents should be formatted according to the guidelines of the specific style guide being used, such as APA or MLA.

We would guess that students usually start working on the table of contents at the last minute. It is quite apparent and makes sense, as this is the list of chapters and sections with page locations. Do you think it's easy? 

From our experience, it can be quite tricky to organize everything according to APA, Chicago, or any other academic writing style. In this blog, we will discuss how to write a table of contents for a research paper , thesis or dissertation in Microsoft Word. We will create it together to guide students through the process. 

Also, here you will find examples of table of contents created by thesis writers at StudyCrumb . Let’s go!

What Is a Table of Contents: Definition

It is obvious that the table of contents (TOC) is an essential manuscript part you can’t skip. If you are dealing with a dissertation, thesis or research paper, you need to know how to build it in accordance with academic guidance. This is a detailed roadmap for your work and outlined structure you can follow for a research presentation. 

In case you are working on an essay or report, you may not include the table of contents, as it is a short academic text. But for the research paper, thesis or dissertation, table of contents is essential and required. It is possible to say the same about any Master’s project. It should be located between the dissertation abstract and introduction chapter. In most cases, it is about 2-3 pages long. 

Our expert dissertation writing service prepared a great template that can be used for your work. Make your research formatting easy with ready solutions!

Types of Table of Contents

How to choose which table of contents will fit your research paper, thesis, dissertation, or report best? Make a decision based on your work length. Some academic writing styles, such as APA paper format or MLA style , have specific formatting for this list. 

However, we will outline the most commonly used typology:

  • Single-level table of contents. At this type, we use only chapters. For instance, you will have an Introduction, Literature Review, methodology, and other chapters with page numbers. It can be used for shorter research work. For long writing forms like manuscripts, it can be too broad, and you will need to go into details.
  • Subdivided table of contents. The most frequently used form to organize the contents table. It will include not only chapters but also sections — a level 2 subheading for each part. It will help to be more specific about what to expect in each part of your research work.
  • Table of contents with multiple levels. This is a more divided structure, including subheadings with a level 3 for each section. Quite often, those subheadings can be rewritten or deleted during the last editing. It is essential to keep them in the right order.

Before you decide which type will work best for you, let us share with you some examples of each formatting style.

Example of Table of Contents With a Single Level

Introduction: The Misinformation Roots ………..…… 3 Literature Review .....................................….....………… 10 Research Methodology and Design ……................. 24 Results.............................................................................. 28 Discussion ....................................................................... 32

Sometimes, you will need to put an extra emphasis on subsections. Check this layout to see how your subheadings can be organized.

Example of Table of Contents Page with Subdivided Levels

Introduction: Information War ............……………….. 3       Background…………………………………….………..…… 4       Current State ……………………………………...…...…… 5       Defining Research Questions………………………. 9 Literature Review………………………...……………..……... 11       The Roots of Information Warfare ………....… 11        Information Wars …………………………….………..… 14        Cyber Wars Research ........................................ 17

If you are working on a lengthy, complex paper, this outline will suit your project most. It will help readers navigate through your document by breaking it down into smaller, more manageable sections.

Multi-Level Table of Contents Page Example

Introduction……………………………………………….......……….… 3       Emergence of Climate Change ………..……....….….. 3       Key Activist Groups in Climate Change .............. 5              Greenpeace International ………..…………......... 9              European Climate Foundation …….……………. 10              WWF ……………………………………….……….............. 11        Significant Movements ……………….………....……… 13 Literature Review ……………………………………......…………. 15

What Sections Should Be Included in a Table of Contents?

To start with, the scientific table of contents should include all chapters and its subheading. It is important to choose the formatting that will give your readers a full overview of your work from the very beginning. However, there are other chapters that you may miss constructing the 2-pager table. So, let's look at all you need to include:

  • Dissertation introduction
  • Literature review
  • Research methodology
  • Results section
  • Dissertation discussion
  • Conclusion of a thesis
  • Reference list. Mention a number of a page where you start listing your sources.
  • Appendices. For instance, if you have a data set, table or figure, include it in your research appendix .

This is how the ideal structured dissertation or research paper table of contents will look like. Remember that it still should take 2 pages. You need to choose the best formatting style to manage its length.

Tables, Figures, and Appendices in TOC

While creating a table of contents in a research paper, thesis or dissertation, you will need to include appendices in each case you have them. However, the formatting and adding tables and figures can vary based on the number and citation style. If you have more than 3 tables or figures, you may decide to have all of them at the end of your project. So, add them to the table of contents. 

Figures, graphics, and diagrams in research papers, dissertations and theses should be numbered. If you use them from another source, ensure that you make a proper citation based on the chosen style guide.

Appendix in Table of Contents Example

Appendix A. Row Data Set…………………………………… 41 Appendix B. IBR Data………………………………………….… 43 Appendix C. SPSS Data………………………………………… 44

What Shouldn't Be Included in a Table of Contents?

When creating a dissertation table of contents, students want to include everything they have in a document. However, some components should not be on this page. Here is what we are talking about:

  • Thesis acknowledgement
  • Paper abstract
  • The content list itself

Acknowledgement and abstract should be located before the content list, so there is no need to add them. You need to present a clear structure that will help your readers to navigate through the work and quickly find any requested information.

How to Create a Table of Contents for a Research Paper or Dissertation In Word?

It may look like working with this list can take a long. But we have one proposal for our users. Instead of writing a table of contents manually, create it automatically in Microsoft Word. You do not need any specific tech knowledge to do this. Let’s go through this process step-by-step and explain how to make a table of contents for a research paper or dissertation in a few clicks.

  • Open Home tab and choose the style for your table of contents (ToC next).
  • Apply heading 1 to your chapters, heading 2 to the subheading, and if needed heading 3 to the level 3 heading.
  • Next, you are going to create a research paper or PhD dissertation table of contents. Open References and choose ToC.
  • Choose the citation style for your work. For example, let’s choose APL for now. Meeting all style requirements (bold font, title formatting, numbers) is essential.
  • Define the number of levels for your dissertation or thesis table of contents. In case you want to have 3 levels, choose Automatic Table 2.
  • You are done! Click ok, and here is your page with listed chapters!

You see how easy it can be! Every time you make changes to your text or headings, it will be automatic.

Updating Your Table of Contents in MS Word

Table of contents of a research paper or dissertation is created, and you continue to edit your work until submission. It is common practice, and with MS Word, you can automate all the updates. 

Let’s outline this process in our step-by-step guide!

  • Right-click on your ToC in a document.
  • Update field section is next.
  • Choose “update ToC."
  • Here, you can update your entire ToC — choose an option that works the best for you!

As you may see, working with automated solutions is much easier when you write a dissertation which has manifold subsections. That is why it is better to learn how to work on MS Word with the content list meaning be able to manage it effectively.

Table of Contents Examples

From our experience, students used to think that the content list was quite a complicated part of the work. Even with automated solutions, you must be clear about what to include and how to organize formatting. To solve the problem and answer all your questions, use our research paper or dissertation contents page example. Our paper writers designed a sample table of contents to illustrate the best practices and various styles in formatting the work. 

Check our samples to find advanced options for organizing your own list.

Example of Table of Contents in Research Paper

Research Paper Table of Contents Example

As you can see, this contents page includes sections with different levels.

Thesis/Dissertation Table of Contents Example

Thesis/Dissertation Table of Contents Example

Have a question about your specific case? Check samples first, as we are sure you can get almost all the answers in our guides and sample sets. 

>> Read more: APA Format Table of Contents

Tips on Creating a Table of Contents

To finalize all that we shared on creating the table of contents page, let’s go through our tips list. We outline the best advice to help you with a dissertation table of contents.

  • Use automated solutions for creating a list of chapters for your report, research papers, or dissertations — it will save you time in the future.
  • Be clear with the formatting style you use for the research.
  • Choose the best level type of list based on the paper length.
  • Update a list after making changes to the text.
  • Check the page list before submitting the work.

Bottom Line on Making Table of Contents for Dissertations/ Papers

To summarize, working with a research paper, thesis or dissertation table of contents can be challenging. This article outlines how to create a table of contents in Word and how to update it appropriately. You can learn what to include in the content list, how long it can be, and where to locate it. Write your work using more than one table of contents sample we prepared for students. It is often easy to check how the same list was made for other dissertations before finalizing yours. We encourage you to learn how to create a list with pages automatically and update it. It will definitely make your academic life easier.

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How To Write a Table of Contents for Academic Papers

Posted by Rene Tetzner | Mar 17, 2021 | How To Get Published | 0 |

How To Write a Table of Contents for Academic Papers

How To Write a Table of Contents for Academic Papers Although every author begins a writing project with the best of intentions and an ideal outline in mind, it often proves difficult to stick to one’s initial plans as the text begins to unfold and its complexities grow in number and depth. Sometimes a document quickly exceeds the word limits for a project, and at others certain important aspects are neglected or turn out a good deal shorter than intended. Drafting a working table of contents for your writing project can provide an excellent tool for keeping your discussion on track and your text within length requirements as you write.

A working table of contents should begin with a title. This title may change as you draft your text, but a working title will help you focus your thoughts as you devise the headings and plan the content for the main parts, chapters, sections and subsections that should be added beneath it. All headings, whether numbered or not, should be formatted in effective and consistent ways that distinguish section levels and clearly indicate the overall structure of the text. These headings can also be altered as your writing advances, but they will provide an effective outline of what you need to discuss and the order in which you think the main topics should be presented. At this initial stage, it is also a good idea to write under each heading a brief summary of or rough notes about what you hope to include in that part of the document, and you can continue to add, adjust and move material around within and among the sections as your table of contents and ultimately your text progresses. Reminders of how long (measured in words, paragraphs or pages) the entire text and each of its parts should ideally be may also prove helpful.

table of content of a research paper

Once you have your annotated table of contents drafted, it will serve as an informative list of both content and order that can be productively consulted as you write. Assuming you construct your working table of contents as a computer file in the same program you intend to use for writing the entire document, you can also use the table of contents as a template for composing the text as a whole, replacing your rough notes under each heading with the formal text as you draft it. This practice lends an immediate physical presence to the guidance provided by your table of contents because you are literally working within that outline, which can be especially wise if you tend to run on or become distracted by new ideas as you write.

Finally, your working table of contents can become your final table of contents if one is required for your project. If you would like to use the working table of contents in this way and are also using it as a template, be sure to rename the file and save a separate copy before you begin adding the formal text of your document. Then you can simply delete your summaries and rough notes from the original table of contents to make your final one, leaving only the headings, to which you can add relevant page numbers as required.

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Example for Table of Contents

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Inhaltsverzeichnis

  • 1  Definition
  • 3 Examples for Your Thesis
  • 4 Master’s Thesis Examples
  • 5  Microsoft Word Tutorial
  • 6 In a Nutshell

 Definition

A table of contents example will help structure a long academic manuscript and a table of contents page is necessary for academic submission. The table of contents contains an organised listing of your manuscript’s chapters and sections with clearly marked (and accurate) page numbers. The aim of the table of contents is to allow the reader to flip easily to the section they require and to get a feel of your argument’s structure.

What comes first, table of contents or abstract?

If you are writing an academic paper, you have to take the order of your paper into account. Usually, the first sections of your thesis are the title page, cover page, acknowledgements and the abstract . After these pages, you place the table of contents. Be sure to check that all of the page numbers in your table of contents are correct.

What variations of table of content examples exist?

The table of contents can be displayed in the following formats:

  • Single level table of contents
  • Subdivided table of contents
  • Multi-level table of contents
  • Academic table of contents

You will find further details about what needs to be included inside of the table of contents on our blog.

Are references included in table of contents?

Yes. The references are included in the table of contents. You add them in as you would any other section of your thesis. Simply write the section in the table of contents with the corresponding page number. However, the acknowledgement for thesis   and the abstract are usually not included in the table of contents. However, check with your institution as this could be dependent upon the formatting that you’re required to follow.

How can I make a table of contents in Microsoft Word?

On Microsoft Word, you will find the function to create a table of contents under the ‘references’ tab. Click on the tab and select ‘table of contents’. You can use one that has been designed by Microsoft Word, or you can create a custom one by yourself. Scroll down for a full tutorial on Microsoft Word and creating a table of contents.

Examples for Your Thesis

Below, you will find different examples for table of contents, including a

  • Single level table of contents example
  • Subdivided table of contents example
  • Multi-level table of contents example

We will also show you with an example how the table of contents for a bachelor’s thesis could look like, as well as for a master’s thesis.

Advice for creating a good table of contents: A good table of contents must be easy to read and formatted accurately, containing quick reference pages for all figures and illustrations. A table of contents example will help you structure your own thesis, but remember to make it relevant to your discipline. Table of contents example structures can be created for different disciplines, such as social sciences, humanities and engineering.

The type and length of a table of contents example will depend on the manuscript. Some thesis’ are short, containing just several chapters, whilst others (like a PhD thesis) are as lengthy as a book. This length will dictate the amount of detail that goes into forming a table of contents example page and the amount of “levels” (or subdivisions) in each chapter.

Single Level Table of Contents Example

For shorter documents, a single level table of contents example can be used. This is a short and succinct table of contents example which utilises only single-level entries on sections or chapters. Remember, you’ll need to include properly formatted dots to lead the reader’s eye to the page number on the far right. The following table of contents example explores this basic structure:

Table-of-Contents-Example-Single-level-1

Subdivided Table of Contents E xample

A subdivided table of contents example is required for more lengthy papers, offering a subdivision of chapters and sections within chapters. These are more detailed and are recommended for higher-level dissertations like masters or PhD thesis’ (as well as some more detailed bachelor’s dissertations).

When formating subdivided table of contents example, ensure that chapters are listed in bold font and that subsections are not. It’s common (though not necessary) to denote each subsection by a number (1.1, etc.). You’ll also want to indent the subsections so that they can be read easily. The following table of contents example explores this structure:

Table-of-Contents-Example-Subdivided-table

Multi-level Table of Contents E xample

Adding additional levels to your table of contents is known as a multi-level table of contents example. These would be numbered onwards at 1.1.1, etc. Be aware that although you want to guide your reader through your manuscript, you should only highlight important areas of your manuscript, like sections and sub-sections, rather than random areas or thoughts in your manuscript. Creating too many levels will make your table of contents unnecessarily busy and too complex.

Table-of-Contents-Example-multi-level

Academic Table of Contents

All of the above can be used as an academic table of contents example. Often, each separate heading in an academic work needs to be both numbered and labelled in accordance with your preferred reference style (consult your department). The following table of contents example sections will illustrate a table of contents example for a bachelor thesis and a table of contents example for a master thesis.

Table of Contents Example: Bachelor’s Thesis

A bachelor’s degree thesis has no set word or page limit nationwide and will depend entirely on your university or department’s guidelines. However, you can expect a thesis under 60 pages of length at between 10,000 – 15,000 words. As such, you won’t be expected to produce a long and detailed table of contents example with multiple levels and subsections. This is because your main body is more limited in terms of word count. At most, you may find yourself using a subdivided table of contents similar to the table of contents example above.

A bachelor’s thesis table of contents example may be structured like so:

Table-of-Contents-Example-Bachelor-Thesis-1

This table of contents example may change depending on your discipline and thesis structure, but note that a single-level structure will often suffice. Subdivided structures like the table of contents example listed earlier will only be necessary when writing several chapters, like in a Master’s thesis.

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Master’s Thesis Examples

A master’s table of contents example is more complex than a bachelor’s thesis. This is because they average at about 80 pages with up to 40,000 words. Because this work is produced at a higher academic level, it normally includes a subdivision of chapters and subheadings, with a separate introduction and conclusion, as well as an abstract.

A table of contents example for a master’s thesis may then look something like this:

Table-of-Contents-Example-Master-Thesis

 Microsoft Word Tutorial

Creating a table of contents page with Microsoft Word is simple.

In a Nutshell

  • All theses are different. Various departments and disciplines follow different structures and rules. The table of contents example pages here will help you in general to format your document, but remember to consult your university guidelines
  • Consistency and accuracy are the most important things to remember. You need the correct page number and the same layout for each chapter. It’s no good combining single-level table of contents with a multi-level table of contents
  • Simply put, bachelor’s thesis’ generally follow a single-level table of contents example unless otherwise specified
  • Postgraduate thesis’ like master and PhD-level work generally require a more detailed subdivision table of contents example. This is because they deal with both more complex arguments and more words
  • Remember to include all aspects of your thesis within the table of contents. Pre-thesis material needs to be listed in Roman numerals and you need to include all back-matter as well, such as References and Bibliography

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  • Dissertation
  • Dissertation Table of Contents in Word | Instructions & Examples

Dissertation Table of Contents in Word | Instructions & Examples

Published on 15 May 2022 by Tegan George .

The table of contents is where you list the chapters and major sections of your thesis, dissertation, or research paper, alongside their page numbers. A clear and well-formatted table of contents is essential, as it demonstrates to your reader that a quality paper will follow.

The table of contents (TOC) should be placed between the abstract and the introduction. The maximum length should be two pages. Depending on the nature of your thesis, dissertation, or paper, there are a few formatting options you can choose from.

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Table of contents

What to include in your table of contents, what not to include in your table of contents, creating a table of contents in microsoft word, table of contents examples, updating a table of contents in microsoft word, other lists in your thesis, dissertation, or research paper, frequently asked questions about the table of contents.

Depending on the length of your document, you can choose between a single-level, subdivided, or multi-level table of contents.

  • A single-level table of contents only includes ‘level 1’ headings, or chapters. This is the simplest option, but it may be too broad for a long document like a dissertation.
  • A subdivided table of contents includes chapters as well as ‘level 2’ headings, or sections. These show your reader what each chapter contains.
  • A multi-level table of contents also further divides sections into ‘level 3’ headings. This option can get messy quickly, so proceed with caution. Remember your table of contents should not be longer than 2 pages. A multi-level table is often a good choice for a shorter document like a research paper.

Examples of level 1 headings are Introduction, Literature Review, Methodology, and Bibliography. Subsections of each of these would be level 2 headings, further describing the contents of each chapter or large section. Any further subsections would be level 3.

In these introductory sections, less is often more. As you decide which sections to include, narrow it down to only the most essential.

Including appendices and tables

You should include all appendices in your table of contents. Whether or not you include tables and figures depends largely on how many there are in your document.

If there are more than three figures and tables, you might consider listing them on a separate page. Otherwise, you can include each one in the table of contents.

  • Theses and dissertations often have a separate list of figures and tables.
  • Research papers generally don’t have a separate list of figures and tables.

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All level 1 and level 2 headings should be included in your table of contents, with level 3 headings used very sparingly.

The following things should never be included in a table of contents:

  • Your acknowledgements page
  • Your abstract
  • The table of contents itself

The acknowledgements and abstract always precede the table of contents, so there’s no need to include them. This goes for any sections that precede the table of contents.

To automatically insert a table of contents in Microsoft Word, be sure to first apply the correct heading styles throughout the document, as shown below.

  • Choose which headings are heading 1 and which are heading 2 (or 3!
  • For example, if all level 1 headings should be Times New Roman, 12-point font, and bold, add this formatting to the first level 1 heading.
  • Highlight the level 1 heading.
  • Right-click the style that says ‘Heading 1’.
  • Select ‘Update Heading 1 to Match Selection’.
  • Allocate the formatting for each heading throughout your document by highlighting the heading in question and clicking the style you wish to apply.

Once that’s all set, follow these steps:

  • Add a title to your table of contents. Be sure to check if your citation style or university has guidelines for this.
  • Place your cursor where you would like your table of contents to go.
  • In the ‘References’ section at the top, locate the Table of Contents group.
  • Here, you can select which levels of headings you would like to include. You can also make manual adjustments to each level by clicking the Modify button.
  • When you are ready to insert the table of contents, click ‘OK’ and it will be automatically generated, as shown below.

The key features of a table of contents are:

  • Clear headings and subheadings
  • Corresponding page numbers

Check with your educational institution to see if they have any specific formatting or design requirements.

Write yourself a reminder to update your table of contents as one of your final tasks before submitting your dissertation or paper. It’s normal for your text to shift a bit as you input your final edits, and it’s crucial that your page numbers correspond correctly.

It’s easy to update your page numbers automatically in Microsoft Word. Simply right-click the table of contents and select ‘Update Field’. You can choose either to update page numbers only or to update all information in your table of contents.

In addition to a table of contents, you might also want to include a list of figures and tables, a list of abbreviations and a glossary in your thesis or dissertation. You can use the following guides to do so:

  • List of figures and tables
  • List of abbreviations

It is less common to include these lists in a research paper.

All level 1 and 2 headings should be included in your table of contents . That means the titles of your chapters and the main sections within them.

The contents should also include all appendices and the lists of tables and figures, if applicable, as well as your reference list .

Do not include the acknowledgements or abstract   in the table of contents.

To automatically insert a table of contents in Microsoft Word, follow these steps:

  • Apply heading styles throughout the document.
  • In the references section in the ribbon, locate the Table of Contents group.
  • Click the arrow next to the Table of Contents icon and select Custom Table of Contents.
  • Select which levels of headings you would like to include in the table of contents.

Make sure to update your table of contents if you move text or change headings. To update, simply right click and select Update Field.

The table of contents in a thesis or dissertation always goes between your abstract and your introduction.

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How to Create Table of Contents for Research Paper?

table of content of a research paper

The table of contents is one of the most crucial components to include while writing a research paper, master’s thesis, or PhD dissertation.

Because it gives examiners a thorough and comprehensive list that they may use as a road map to go through each respective chapter, containing all relevant sections and subsections of material.

In this article, we will learn how to create table of contents for research paper? and learn what to include in table of contents with the help of examples and guide you how to create table of contents using Microsoft Word.

  • Table of Contents

What is Table of Contents?

A table of contents is a systematic list of the headings and subheadings within a research paper, along with their corresponding page numbers.

The chapters and significant sections of your thesis, dissertation, or research paper should be listed in the table of contents together with their corresponding page numbers. A clear, well-formatted table of contents is important because it shows the reader that a quality paper will follow.

The table of contents should be placed between the abstract and the introduction. The maximum length should be two pages. There are several formatting alternatives available depending on the type of your thesis, research paper or dissertation topic.

What to Include in Your Table of Contents?

  • Main Headings: Include the main sections or chapters of your research paper. These headings represent the major topics you will be addressing and should be descriptive enough to give readers an idea of the content covered in each section.
  • Subheadings: If your research paper is lengthy and consists of several subsections within each main heading, include subheadings in your table of contents. These subheadings provide a more detailed breakdown of the content and allow readers to locate specific information within a particular section.
  • Page Numbers: List the page numbers on which each heading and subheading can be found. This ensures that readers can quickly flip to the desired page and find the relevant information.

Your academic field and thesis length will determine how your table of contents is formatted. A methodical structure is used in some areas, such as the sciences, and includes suggested subheadings for methodology, data results, discussion, and conclusion.

On the other hand, humanities subjects are far more diverse. Regardless of the discipline you are working in, you must make an organized list of every chapter in the order that they occur, properly labelling the chapter subheadings.

Example: Table of Contents

The key features of a table of contents are:

  • Clear headings and subheadings
  • Corresponding page numbers

Check with your educational institution to see if they have any formatting or design requirements.

Example: Table of Contents

Table of Contents: Sample for a Short Dissertation

In a short dissertation, the table of contents serves as a roadmap for readers, outlining the main sections and subsections of the research paper.

Table of Contents: Sample for a Short Dissertation

It typically includes an introduction that sets the context, a literature review that analyzes existing scholarly works, a methodology section that describes the research design and data collection methods, a results and findings section that presents the research outcomes, and a conclusion that summarizes the key findings and implications.

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In a PhD dissertation, the table of contents provides a comprehensive overview of the entire research work. It encompasses various sections, starting with the title page and abstract, followed by acknowledgments and a detailed table of contents.

The contents include chapters such as introduction, literature review, methodology, results and findings, conclusion, and references. Additionally, there may be lists of figures and tables, as well as appendices containing supplementary materials.

This extensive table of contents helps readers navigate through the comprehensive research study and locate specific sections of interest.

Table of Contents: Sample for a PhD Dissertation

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Creating a Table of Contents in Microsoft Word

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  • A collection of built-in styles appears. Choose one of these, look at additional tables of contents on Office.com, or design your own table of contents.
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Creating a Table of Contents in Microsoft Word

The table of contents is added, listing all of the headings in the document in outline order, as well as the page number on which each heading occurs.

Update the Table of Contents

Make a note to yourself to update your table of contents as one of your final tasks before submitting your dissertation or paper. As you enter your final revisions, it’s common for your text to slightly change, but it’s critical that your page numbers still match.

In Microsoft Word, it’s simple to update your page numbers automatically. Simply choose “Update Field” from the context menu when you right-click the contents page. You have the option of updating your table of contents entirely or just the page numbers.

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

Published on 21.2.2024 in Vol 26 (2024)

Effects of eHealth Interventions on 24-Hour Movement Behaviors Among Preschoolers: Systematic Review and Meta-Analysis

Authors of this article:

Author Orcid Image

  • Shan Jiang 1 , MSc   ; 
  • Johan Y Y Ng 1 , PhD   ; 
  • Kar Hau Chong 2 , PhD   ; 
  • Bo Peng 1 , MSc   ; 
  • Amy S Ha 1 , PhD  

1 Department of Sports Science and Physical Education, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)

2 School of Health and Society and Early Start, Faculty of the Arts, Social Sciences and Humanities, University of Wollongong, Wollongong, Australia

Corresponding Author:

Amy S Ha, PhD

Department of Sports Science and Physical Education

The Chinese University of Hong Kong

G05 Kwok Sports Building, Shatin, N.T.

China (Hong Kong)

Phone: 852 3943 6083

Email: [email protected]

Background: The high prevalence of unhealthy movement behaviors among young children remains a global public health issue. eHealth is considered a cost-effective approach that holds great promise for enhancing health and related behaviors. However, previous research on eHealth interventions aimed at promoting behavior change has primarily focused on adolescents and adults, leaving a limited body of evidence specifically pertaining to preschoolers.

Objective: This review aims to examine the effectiveness of eHealth interventions in promoting 24-hour movement behaviors, specifically focusing on improving physical activity (PA) and sleep duration and reducing sedentary behavior among preschoolers. In addition, we assessed the moderating effects of various study characteristics on intervention effectiveness.

Methods: We searched 6 electronic databases (PubMed, Ovid, SPORTDiscus, Scopus, Web of Science, and Cochrane Central Register of Controlled Trials) for experimental studies with a randomization procedure that examined the effectiveness of eHealth interventions on 24-hour movement behaviors among preschoolers aged 2 to 6 years in February 2023. The study outcomes included PA, sleep duration, and sedentary time. A meta-analysis was conducted to assess the pooled effect using a random-effects model, and subgroup analyses were conducted to explore the potential effects of moderating factors such as intervention duration, intervention type, and risk of bias (ROB). The included studies underwent a rigorous ROB assessment using the Cochrane ROB tool. Moreover, the certainty of evidence was evaluated using the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) assessment.

Results: Of the 7191 identified records, 19 (0.26%) were included in the systematic review. The meta-analysis comprised a sample of 2971 preschoolers, which was derived from 13 included studies. Compared with the control group, eHealth interventions significantly increased moderate to vigorous PA (Hedges g =0.16, 95% CI 0.03-0.30; P =.02) and total PA (Hedges g =0.37, 95% CI 0.02-0.72; P =.04). In addition, eHealth interventions significantly reduced sedentary time (Hedges g =−0.15, 95% CI −0.27 to −0.02; P =.02) and increased sleep duration (Hedges g =0.47, 95% CI 0.18-0.75; P =.002) immediately after the intervention. However, no significant moderating effects were observed for any of the variables assessed ( P >.05). The quality of evidence was rated as “moderate” for moderate to vigorous intensity PA and sedentary time outcomes and “low” for sleep outcomes.

Conclusions: eHealth interventions may be a promising strategy to increase PA, improve sleep, and reduce sedentary time among preschoolers. To effectively promote healthy behaviors in early childhood, it is imperative for future studies to prioritize the development of rigorous comparative trials with larger sample sizes. In addition, researchers should thoroughly examine the effects of potential moderators. There is also a pressing need to comprehensively explore the long-term effects resulting from these interventions.

Trial Registration: PROSPERO CRD42022365003; http://tinyurl.com/3nnfdwh3

Introduction

Physical activity (PA), sedentary behavior (SB), and sleep are integrated as “24-hour movement behaviors” owing to the collective effect on daily movement patterns. The 24-hour movement paradigm acknowledges the possibility of categorizing these behaviors according to their intensity levels across a full day. This encompasses a diverse range of activities, including sleep; SB (eg, screen time, reclining, or lying down); and light, moderate, or vigorous PA [ 1 ]. Globally, the “24-hour movement behaviors” paradigm has already been recognized and adopted into movement guidelines [ 2 ]. In 2020, the World Health Organization (WHO) released guidelines on PA and SB that incorporate all 3 movement behaviors [ 3 ]. The health benefits of engaging in PA, getting the recommended sleep, and reducing sedentary time are well documented. Recent reviews have shown a positive association between PA; sleep; and a wide range of child outcomes related to mental health, cognition, and cardiometabolism [ 4 - 6 ]. In addition, it is worth mentioning that different domains of SB can have varying health effects. For instance, non–screen-based sedentary activities such as reading or studying have been associated with favorable cognitive development in children [ 7 ]. Conversely, screen-based sedentary time, also referred to as “screen time,” has been found to have adverse effects on health-related outcomes [ 8 ]. Moreover, prior research has indicated that imbalances in 24-hour movement behaviors—specifically, elevated sedentary screen time coupled with diminished levels of PA and sleep—could potentially increase the risk of depression [ 9 ] and result in poor health-related quality of life [ 10 ]. Therefore, any change in one of the movement behaviors may lead to a compensatory increase or decrease in one or both behaviors.

However, insufficient healthy levels of 24-hour movement behaviors in early childhood have remained one of the most critical global public health challenges [ 11 , 12 ]. According to the WHO guidelines [ 3 ], preschool children are recommended to engage in adequate daily PA, consisting of 180 minutes, with 60 minutes dedicated to moderate to vigorous PA (MVPA). In addition, they should ensure sufficient sleep, ranging from 10 to 13 hours, while limiting sedentary recreational screen time to no more than 60 minutes per day. Unfortunately, a significant proportion of preschoolers do not meet the PA guidelines (<50% across studies) [ 13 ]. Furthermore, previous studies have consistently demonstrated that preschoolers exceed the screen time recommendations set by the WHO. A comprehensive meta-analysis of 44 studies revealed that only 35.6% of children aged between 2 and 5 years met the guideline of limiting daily screen time to 1 hour. Moreover, when examining the integration of 24-hour movement behaviors [ 8 ], another meta-analysis discovered that only 13% of children worldwide adhere to all 3 behavior guidelines [ 14 ].

Preschoolers play a crucial role in laying the foundation for long-term physical health and overall well-being [ 15 , 16 ]. Improving PA levels, minimizing SB, and prioritizing quality sleep in young children have multiple benefits, including positively influencing their physical fitness [ 17 , 18 ], promoting the development of motor and cognitive skills [ 19 , 20 ], and preventing childhood obesity [ 21 ] and associated health issues [ 14 , 22 , 23 ]. Several studies have shown that these healthy behavior patterns can shape lifelong habits that extend from childhood through adolescence and into adulthood [ 5 , 24 ].

Although these statistics are concerning, attempts to address the issue through various interventions have yielded inconsistent findings [ 25 - 28 ]. For instance, a meta-analysis of PA intervention studies involving preschoolers revealed only small to moderate effects in enhancing PA, suggesting room for improvement in achieving the desired outcomes [ 29 ]. In a meta-analysis conducted by Fangupo et al [ 30 ], no intervention effect was observed on daytime sleep duration for young children. Interestingly, earlier research has also elucidated overflow effects stemming from interventions focusing on a specific behavior, impacting other behaviors that were not the primary target. A systematic review highlighted that interventions aimed at enhancing PA in children aged <5 years led to a reduction in screen time by approximately 32 minutes [ 31 ]. It is crucial to understand that as time is finite, the durations dedicated to PA, sedentary time, and sleep are interconnected within 24 hours. Thus, we need effective interventions for preschool children that holistically address all components of 24-hour movement behaviors.

eHealth broadly refers to a diverse array of information and communication technologies used to facilitate the delivery of health care [ 32 , 33 ]. The rapid evolution of digitalization in recent decades has led to the widespread adoption of eHealth in interventions [ 28 , 34 ]. Recent reviews [ 35 - 38 ] suggest that with the global proliferation of eHealth interventions, health promotion via these platforms is evolving to become more accessible and user-friendly, garnering acceptance among adolescents and adults. Previous reviews have underscored the effectiveness of these digital platforms in enhancing various movement behavior outcomes across diverse age groups, including children aged 6 to 12 years [ 39 ], adolescents [ 40 ], adults [ 41 ], and older adults [ 42 ]. Specifically, a meta-analysis indicated that eHealth interventions have successfully promoted PA among individuals with noncommunicable diseases [ 43 ]. Another review showed that computer, mobile, and wearable technologies have the potential to mitigate sedentary time effectively [ 41 ]. Previous studies have targeted different participant groups to investigate the impact of eHealth on sleep outcomes. Deng et al [ 44 ] conducted a meta-analysis demonstrating that eHealth interventions for adults with insomnia are effective in improving sleep and can be considered a promising treatment. Nevertheless, a review focusing on healthy adolescents found that there has not been any school-based eHealth interventions focusing on sleep outcomes [ 45 ].

Indeed, child-centered strategies such as gamification are used in some digital apps and have been shown to encourage children’s PA [ 46 - 48 ]. A considerable body of work has addressed the pivotal role of parental influence and role modeling in cultivating healthy lifestyle habits in children [ 49 , 50 ]. Physical literacy, a multidimensional concept encompassing various aspects of PA such as the affective, physical, cognitive, and behavioral dimensions, plays a vital role in enhancing PA engagement [ 51 ]. Ha et al [ 52 ] conducted a web-based parent-focused intervention, revealing that enhancing parents’ physical literacy can effectively support children’s participation in PA. By understanding and promoting physical literacy, parents can provide valuable support to their children, fostering a lifelong commitment to healthy and active lifestyles. Although eHealth interventions offer promise, there are conflicting findings regarding their impact, especially when they are parent supported and targeted at young children. A previous meta-analysis examining eHealth interventions targeted at parents found no significant impact on children’s BMI. In addition, no studies have included children aged <5 years [ 50 ]. Similarly, a recent systematic review observed that eHealth interventions aimed at parents showed no significant effectiveness in enhancing PA levels in young children [ 53 ]. However, the prevalence of digital device use in young children has become widespread. For instance, studies conducted in England (the United Kingdom), Estonia, and the United States have reported that, on average, 83% of children aged 5 years use a digital device at least once a week [ 54 ]. Research also revealed that in the United States, approximately three-fourths of children had their own mobile device by the age of 4 years, and nearly all children (96.6%) used mobile devices [ 55 ]. Consequently, there is an urgent need to harness the potential of digital platforms and explore whether they can effectively deliver interventions to preschoolers [ 56 ].

In previous research, there has been a lack of studies examining the effectiveness of eHealth behavior change interventions among preschoolers. Although a systematic review found a significant effect of digital health interventions on the PA of preschoolers [ 53 ], this review did not include sedentary time and sleep in its inclusion criteria, and there is a lack of conclusive statements owing to the insufficient number of studies, and no quantitative methods were available for synthesizing the evidence on the effectiveness of eHealth interventions. To our knowledge, no systematic review or meta-analysis has distinctly investigated the effects of eHealth interventions on 24-hour movement behaviors in preschoolers or the factors that may influence their implementation. Therefore, the aims of this study were (1) to assess the effectiveness of eHealth interventions on 24-hour movement behaviors (improving PA and sleep duration and decreasing sedentary time) and (2) to examine the moderating effects of study characteristics (eg, intervention duration, intervention type, and outcome measurement tools) on intervention effectiveness.

This review was registered with PROSPERO (CRD42022365003) and conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [ 57 ].

Eligibility Criteria

This review included trials with a randomization procedure that examined the outcomes of interventions using information and communication technology. These interventions targeted at least 1 movement behavior in preschool children aged 2 to 6 years. Studies were excluded if (1) the control groups received intervention using eHealth technology and (2) published in a non-English language. Full details are provided in Multimedia Appendix 1 [ 58 ].

Search and Selection

The following databases were systematically searched from inception to February 08, 2023: PubMed, Ovid, SPORTDiscus, Scopus, Web of Science, and Cochrane Central Register of Controlled Trials. We used the search terms “eHealth,” “Physical activity,” “Sedentary behavior,” “Sleep,” “preschooler,” and their Medical Subject Headings terms. The complete search strategy is described in Multimedia Appendix 2 [ 59 - 61 ]. A manual search of the reference lists of the included publications was performed to identify additional eligible studies for potential inclusion. Two independent reviewers (SJ and BP) screened the titles and abstracts and subsequent full-text articles for eligibility. Discrepancies that emerged during the selection process were effectively resolved through a discussion involving 3 authors (SJ, BP, and JYYN).

Data Extraction

A comprehensive data extraction form was developed (SJ) and refined (SJ and BP) based on the Cochrane Handbook for Systematic Reviews of Interventions [ 62 ]. Extracted information included bibliographic details (authors, title, journal, and year); study details (country, design, retention rate); participants’ characteristics (number of children and demographics); intervention type (parent supported, teacher led, or child centered), intervention’s theoretical basis, duration, delivery tool, and intensity; comparison (sample size, activity type); outcomes (behavioral variables with baseline and postintervention means with SDs), and measurement tools. Regarding the categorization of intervention types, we have established a clear classification. Specifically, in child-centered interventions, children are the direct beneficiaries, participating autonomously with less guidance from guardians. This can be accomplished using an exergaming system or designed mobile health games. In parent-supported interventions, parents are involved in educational programs and instructions that improve parents’ knowledge of preschoolers’ healthy movement behaviors. A teacher-led intervention involves supervising preschoolers’ PA during school time or participating in structured PA sessions aimed at improving healthy indicators. For data that were either incomplete or absent within the main text, we sought to reach out to the respective authors through email correspondence.

Risk of Bias

The included studies were assessed for risk of bias (ROB) using the revised Cochrane ROB2 tool [ 63 ]. The following domains of bias were assessed for each study: selection (random sequence generation and allocation concealment), performance and detection (masking of participants, personnel, and assessors), deviations from intended interventions, missing outcome data, measurement of the outcome, appropriateness of analysis (selection of the reported outcome), and bias arising from period and carryover effects (for crossover studies) [ 63 ]. The studies were ranked as low risk, some concerns, or high risk for each domain. The ROB was evaluated independently by 2 authors (SJ and BP). Any discrepancies were resolved through discussions with the author (JYYN).

Outcomes and Data Synthesis

Our outcome targeted any of the following movement behaviors: PA (MVPA and total PA), sedentary time (screen time and sitting time), or sleep duration. Meta-analysis was conducted in R (version 4.2.1; R Group for Statistical Computing) using the meta , metafor , and metareg packages [ 64 ]. A random-effects model (Hartung-Knapp method) was used to calculate pooled estimates (Hedges g , a type of standardized mean difference) to account for variations in participants and measurement methods of movement behavior outcomes [ 65 ]. Multimedia Appendix 3 [ 63 - 65 ] describes the processing of missing data. Hedges g and their corresponding variances were calculated using the pre- and postintervention mean scores and SDs. However, if some studies had changes in baseline and postintervention data or if there were significant differences in their baseline data [ 59 - 61 ], we used the within-group difference in means and their SDs for intervention and control groups to calculate the effect size. Values of 0.2, 0.5, and 0.8 represent small, moderate, and large effect sizes, respectively. A positive effect size indicated a beneficial effect on the intervention group compared with the control group. The between-group heterogeneity of the synthesized effect sizes was examined using the Cochran Q test and I 2 statistics. I 2 values of 25%, 50%, and 75% indicated low, moderate, and high levels of heterogeneity, respectively. Subgroup analyses were conducted based on the following factors: (1) intervention duration (0-3 months vs >3 months) and (2) type of intervention (child centered, parent focused, or teacher led). (3) Types of outcome measurement tools (objective vs self-reported) and (4) ROB (low risk, some concerns, or high risks).

Furthermore, we performed meta-regression analyses to examine the impact of potential moderators on the overall effect size. Potential moderators included 5 variables, as specified in the subgroup analyses, and 2 continuous variables (sample size and intervention length). These variables were selected based on existing evidence that highlights their significant moderating effects on eHealth interventions targeting movement behaviors [ 53 , 66 , 67 ]. Sensitivity analyses were performed using the leave-one-out method. Publication bias was visualized using funnel plot symmetry and quantified using the Eggertest score, for which P <.05 indicates a significant publication bias [ 68 ].

Quality Assessment of the Overall Evidence

GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) 2 criteria were used to assess the certainty of evidence for the effect of eHealth interventions on the targeted outcomes [ 69 , 70 ]. The GRADE assessment was completed using GRADEpro, and the quality of evidence was classified as high (≥4 points overall), moderate (3 points), low (2 points), or very low (≤1 point) [ 70 ].

Study Selection

The database search yielded 7140 records, with an additional 51 records identified from the reference lists of relevant systematic reviews. There were 64 articles screened for full text, and 45 articles were excluded. The reasons for exclusion are listed in Multimedia Appendix 4 . A total of 19 studies reporting the effectiveness of interventions on movement behaviors were included in the systematic review [ 17 , 59 - 61 , 71 - 85 ], and 13 studies were included in the meta-analysis [ 59 - 61 , 76 - 85 ]. The PRISMA flowchart of the study selection process is shown in Figure 1 and PRISMA checklists are in Multimedia Appendices 5 and 6 .

table of content of a research paper

Study Characteristics

The study characteristics are described in Table 1 . In the 19 studies, 2971 preschoolers from 6 regions were included. A total of 18 studies were conducted in high-income countries, and only 1 study was conducted in an upper middle–income country, according to the World Bank classification ( Multimedia Appendix 7 ) [ 86 ]. Most included studies were conducted during and after 2017. For the study design, 16 studies were 2-arm randomized controlled trials (RCTs), with 11 using a parallel group design [ 17 , 59 - 61 , 71 - 74 , 76 , 77 , 84 ], 2 being cluster RCTs [ 82 , 83 ], 2 pilot RCTs [ 79 , 81 ], and 1 crossover study [ 85 ]. The remaining 3 studies consisted of 2-arm experimental studies with a randomization procedure [ 75 , 78 , 80 ]. The sample size ranged from 34 preschoolers to 617 preschoolers. The study details are presented in Multimedia Appendices 8 and 9 [ 59 - 61 , 76 - 85 ].

a I: intervention.

b C: control.

c ECEC: early childhood education and care.

d PA: physical activity.

e SB: sedentary behavior.

f mHealth: mobile health.

g MINISTOP: mobile-based intervention intended to stop obesity in preschoolers.

h FMS: fundamental movement skills.

Intervention Details

The included studies used various delivery channels of eHealth technologies for the intervention. Seven studies used smartphone apps [ 59 - 61 , 74 ] and social media (Facebook and WhatsApp) [ 75 , 80 , 82 ]; 3 studies used an exergaming program [ 17 , 73 , 85 ]; 3 studies used the internet, with interventions including informational websites [ 83 , 84 ] and tablet computers [ 72 ]; and several studies used technology to dispatch reminders to exercise and send motivational messages encouraging persistence. Specifically, studies sent text messages and made telephone calls [ 71 , 76 - 79 , 81 ].

The intervention duration ranged from 1 week [ 78 ] to 36 months [ 77 ]. Seven studies had interventions that lasted >3 months [ 59 , 61 , 71 , 76 , 77 , 80 , 82 ]. Only 3 studies included follow-up assessment after intervention, with durations of 6 weeks [ 84 ], 3 months [ 72 ], and 6 months [ 60 ]. Regarding intervention types, this study consisted of 12 studies supported by parents [ 59 - 61 , 71 , 72 , 75 - 77 , 79 - 81 , 84 ], 3 studies led by teachers [ 78 , 82 , 83 ], and 4 studies involving eHealth interventions directed at children [ 17 , 73 , 74 , 85 ].

The comparison groups included a waitlist control group (n=4) [ 74 , 79 , 81 , 84 ], education as usual (n=7) [ 17 , 59 , 75 , 78 , 80 , 82 , 85 ], and an additional non-eHealth intervention (n=8) [ 59 - 61 , 71 - 73 , 76 , 77 ]. A total of 14 studies targeted PA [ 17 , 59 - 61 , 72 - 75 , 77 , 78 , 80 , 81 , 83 , 85 ], 12 studies targeted SB [ 59 - 61 , 71 , 76 - 80 , 82 , 84 , 85 ], and 4 studies targeted sleep duration [ 71 , 76 , 81 , 84 ]. Notably, no studies examined all 3 movement behaviors.

Meta-Analyses

Meta-analyses demonstrated that eHealth interventions produced significant improvements in MVPA (Hedges g =0.16, 95% CI 0.03-0.30; P =.02; 7/13, 54%) and total PA (Hedges g =0.37, 95% CI 0.02-0.72; P =.04; 2/13, 15%), as shown in Figure 2 A [ 77 , 78 , 80 - 83 , 85 ]. For SB outcomes, another meta-analysis showed a significant decrease (Hedges g =−0.15, 95% CI −0.27 to −0.02; P =.02; 8/13, 62%), as shown in Figure 2 B [ 76 - 80 , 82 , 84 , 85 ]. Finally, meta-analysis also showed that there were significant improvements in sleep duration (Hedges g =0.47, 95% CI 0.18-0.75; P <.01; 3/13, 23%), as shown in Figure 2 C [ 76 , 81 , 84 ].

Owing to the heterogeneity among the included studies, the mobile-based intervention intended to stop obesity in preschoolers (MINISTOP) project’s 3 studies solely reported the difference in pre-to-post comparison [ 60 , 61 , 76 ]. Consequently, their inclusion in the pooled analysis with other studies was deemed inappropriate. We analyzed a series of MINISTOP studies separately and presented the findings using a forest plot. The pooled analysis indicated that no significant change in MVPA (Hedges g =−0.03, 95% CI −0.15 to 0.09; P =.66; 3/6, 50%; Multimedia Appendix 10 [ 59 - 61 , 76 - 85 ]) was observed between the intervention and control groups. An intervention effect was found in reducing SB (Hedges g =0.02, 95% CI −0.13 to 0.16; P =.83; 3/6, 50%; Multimedia Appendix 10 ) immediately after the intervention, as indicated in Multimedia Appendix 10 . Nonetheless, this effect was not statistically significant. All the results showed negligible heterogeneity ( I 2 =0).

table of content of a research paper

Subgroup Analyses and Meta-Regression

Table 2 shows the subgroup analysis and meta-regression results of MVPA and sedentary time according to study characteristics. No significant moderating effects were observed for any of the variables assessed ( P >.05). The complete results of the subgroup analyses are presented in Multimedia Appendix 11 [ 59 - 61 , 76 - 85 ].

a MVPA: moderate to vigorous physical activity.

b N/A: not applicable.

c Teacher focused studies as a reference group.

Sensitivity Analyses and Publication Bias

Sensitivity analysis indicated that no individual study had an excessive influence on the results. The omitted meta-analytic estimates were not significantly different from those associated with the combined analysis, and all estimates were within the 95% CI. Forest plots of the sensitivity analysis for MVPA, sedentary time, and sleep are summarized in Multimedia Appendix 12 [ 59 - 61 , 76 - 85 ]. The significance of Egger’s test results provided evidence for asymmetry of the funnel plots (MVPA: t 5 =3.27; P =.02; Multimedia Appendix 13 ; sedentary time: t 6 =−3.37; P =.02; Multimedia Appendix 14 ). However, we could not distinguish chance from true asymmetry using the funnel plot asymmetry test because <10 studies were included in our meta-analysis [ 86 ].

ROB of Studies

Multimedia Appendix 15 [ 59 - 61 , 76 - 85 ] summarizes the overall ROB assessment for all the included papers. Six studies were considered to have a low ROB [ 59 , 74 , 76 , 77 , 79 , 85 ], and the remaining 13 were considered to have some concerns regarding the ROB [ 17 , 60 , 61 , 71 - 73 , 75 , 78 , 80 - 84 ]. Furthermore, 7 studies did not disclose randomization methods clearly [ 17 , 72 , 75 , 78 , 80 , 82 , 83 ], so they were rated as having some concerns about random sequence generation. All studies were rated as having a low risk for the measurement of outcomes based on the use of objective measurement tools or reliable questionnaires in each study. Four studies were rated as ‘some concerns’ of reporting bias because neither published study protocols nor registered trial records were presented [ 72 , 75 , 78 , 80 ].

Quality of the Evidence

The GRADE scores are shown in Multimedia Appendix 16 , and we deemed the overall quality of evidence to be moderate to low. The quality of evidence for MVPA and sedentary time outcomes was rated as “moderate,” considering the low ROB, absence of heterogeneity in participants’ outcomes, and high precision in results. As eHealth interventions are often combined with other intervention approaches, all evaluations of directness were assessed as “Indirectness.” There were high imprecisions with the sample size included in the study for total PA and sleep, which were graded as “Low.”

Principal Findings

This study systematically reviewed the effectiveness of eHealth interventions targeting 24-hour movement behaviors among preschool-aged children. Most studies assessed interventions aimed at increasing PA and decreasing SB. Few studies targeted sleep, and no studies have addressed a combination of all 24-hour movement behaviors. Overall, these studies showed trends supporting the effectiveness of eHealth interventions in increasing PA and sleep duration and reducing sedentary time immediately after the intervention; however, only short-term effects were found, and all trials were judged to be of low to moderate quality.

This review demonstrates a small positive effect of eHealth interventions targeting increases in preschooler’s MVPA (Hedges g =0.16) and total PA (Hedges g =0.37) immediately after the intervention. One possible explanation could be that eHealth interventions, while providing new opportunities for PA, might not be sufficient to result in significant overall activity increases. This might require expanding activity opportunities, extending new activity options, and enhancing broader activity strategies to achieve substantial benefits. Our findings echo the argument made in a previous study of young children that PA interventions had a small effect on MVPA [ 87 ]. Another meta-analysis found a positive impact of PA interventions with small to moderate effects on total PA (Hedges g =0.44) and moderate effects on MVPA (Hedges g =0.51) [ 29 ]. There is no conclusive explanation as to why MVPA and total PA were seen to have a smaller effect in our study, but this could be attributed to most interventions thus far concentrating on devising PA programs of diverse intensities without distinct objectives, including low-intensity PA, MVPA, and total PA (eg, activities such as outdoor active play and structured gross motor activity sessions in childcare environments). Moreover, our results are consistent with previous review findings that digital platforms can potentially increase PA among preschoolers [ 53 ]. Hence, future interventions should aim to optimize their effectiveness in increasing PA among young children. In addition, further research is warranted to investigate the mechanisms of the changes associated with these PA outcomes. This will help enhance the size and sustainability of the effects observed in eHealth interventions.

We found no significant improvement in MVPA for mobile app interventions (MINISTOP project). This is in contrast to a review of studies focusing on mobile apps and technologies, which highlighted the significant potential to enhance PA [ 88 ]. It is worth noting that the MINISTOP project aimed to reduce obesity as its primary outcome rather than targeting MVPA. In addition, studies concentrating solely on educating parents without implementing direct interventions for children have not achieved the desired enhancements in MVPA. Thus, we cannot draw conclusions about mobile apps because few intervention studies have used these means of communication for young children and their guardians. Given the small number of studies included in our meta-analysis, the positive, negative, and null findings of the individual studies may have attenuated the results. Thus, considering the popularity and cost-effectiveness of mobile apps in the new generation, future research should investigate the potential of using emerging and novel technologies, such as mobile health, for preschoolers.

Our meta-analysis suggests that eHealth interventions may be an effective strategy for decreasing sedentary time in preschoolers, although the magnitude of the effect was small (Hedges g =−0.15) and short term. Nonetheless, the significance should not be understated, given that many studies indicate that reduced sedentary time during childhood correlates with improved physical and mental health outcomes in subsequent years [ 16 , 21 , 89 ]. In the subgroup analysis, the effect of eHealth interventions on sedentary time varied depending on whether accelerometer or questionnaire measures were used. The questionnaire measures yielded higher levels of sedentary time, although this difference was not statistically significant. This observation aligns with findings from the existing literature, suggesting that questionnaire-based assessments tend to overestimate the actual sedentary time. For a more accurate evaluation of the impact of eHealth interventions, future research should consider using device-based measurement methods [ 90 ].

Interestingly, most eHealth interventions aimed to increase children’s PA and reduce sedentary time with parental support. Previous research has shown that parental and family involvement were among the key intervention components that encouraged significant improvement in children’s health behaviors and a decrease in sedentary time [ 91 , 92 ]. Likewise, Ha et al [ 49 ] found that parents’ physical literacy predicts children’s values toward PA, and concurrent interventions that target enhancing parents’ physical literacy for PA in the family context may be more effective in raising children’s PA values. However, our subgroup analysis showed no significant improvements in MVPA or reductions in sedentary time with the parent-supported interventions. This result also aligns with a prior review indicating that parent-directed digital interventions were ineffective in improving PA [ 53 ]. In that review, 8 studies, all published before 2020, primarily used digital platforms to convey health information and education to parents. Notably, in the wake of the COVID-19 pandemic, there has been a marked increase in research centered on leveraging technology to improve children’s PA, leading to more recent studies in 3 years [ 93 ]. Furthermore, the discourse regarding the comparative value of targeting either parents or children exclusively is not a novel debate within intervention research. In contrast to the review, our study featured a larger sample size and included a quantitative analysis of effect sizes in the interventions. These insights indicate that prevailing eHealth interventions, even with parental support, may fail to effectively engage preschoolers. Recognizing the reciprocal dynamics between parents and young children can offer insights for refining digital interventions. Therefore, preliminary research is imperative to comprehensively understand the perceptions, attitudes, and driving factors of parents. Recognizing the reciprocal dynamics between parents and young children is crucial in understanding how they influence their children’s PA and SB.

Intervention duration is also an essential component for conducting acceptable and highly effective interventions. Another subgroup analysis found that interventions with a duration of <3 months had a significantly greater effect on PA and sedentary time than those with a duration of >3 months, although the results were not significant. This notion is corroborated by another systematic review, which demonstrated the difficulty in sustaining long-term behavior change, potentially attributed to the diminishing effects of behavior change interventions mediated by digital technology [ 41 ].

The meta-analysis, involving 3 studies, revealed an immediate improvement in sleep duration following the intervention. Previous research has extensively examined the influence of sleep duration during the preschool years on physical, cognitive, and psychosocial development. For instance, the systematic review by Chaput et al [ 6 ] involving 25 studies revealed a correlation between shorter sleep duration and diminished emotion regulation in children aged 0 to 4 years. Recent findings also suggest that maintaining an extended sleep duration during the early preschool stages is significant for subsequent behavioral outcomes [ 24 ]. However, few studies have focused on effective interventions to improve sleep outcomes [ 45 , 94 ]. Consequently, further research is warranted to explore the impact of eHealth interventions on sleep outcomes among preschoolers.

Increasing awareness of the interconnected nature of 24-hour movement behaviors highlights their intrinsic interdependence [ 14 ]. However, none of the studies in our review specifically investigated the intervention effects on all 3 movement behaviors. Generally, conventional analytical methods do not adequately consider these indicators during analysis. Therefore, future research should explore alternative approaches, such as compositional analyses, to attain a more profound comprehension of whether an optimal equilibrium is present among SB, light PA, MVPA, and sleep [ 90 , 95 , 96 ]. Furthermore, most studies in our review examined the immediate postintervention effect. Consequently, insights into the enduring nature of alterations in 24-hour movement behaviors remain elusive. Further studies should include long-term follow-up assessments. In addition, it would be interesting to obtain more insights into the feasibility of incorporating wearable devices and apps into the design of eHealth interventions. This information could inform the design of wearables and apps that effectively enhance PA, diminish sedentary time, and enhance sleep, thereby maximizing their impact on public health. Moreover, the overall quality of the interventions was suboptimal, lacking thorough descriptions or proper execution in areas such as randomization, blinded outcome assessment, valid measurement of 24-hour movement behaviors, and adjusted differences between groups. In our meta-analysis, we observed that lower-quality studies exhibited a more pronounced positive impact on the targeted outcomes. Thus, it is essential to interpret the results cautiously, recognizing that there could be an overestimation of the effect of eHealth interventions in studies of lower quality owing to potential bias. This mirrors the findings from previous reviews on eHealth childhood PA [ 53 ] and behavior change interventions among adolescents [ 45 ].

Strengths and Limitations

This systematic review has some strengths. First, this study is the first meta-analysis to quantitatively assess the effects of previously conducted RCTs using eHealth interventions on 24-hour movement behaviors in preschoolers. Second, the review was conducted rigorously, encompassing comprehensive terms and using an extensive systematic search strategy. We focused on robust evidence from RCT studies, assessed the quality using the GRADE approach, and adhered to a preregistered protocol. This meticulous approach reduces the heterogeneity and provides a more precise estimation of the effects.

Nonetheless, several limitations of our study should be noted. First, the quality of the studies included in this review was generally low and lacked rigorous study designs. Second, the small number of studies discerned over the decade spanned by this meta-analysis underscores the nascent state of this research domain, even considering significant technological advancements and their widespread acceptance. Third, although we systematically screened relevant electronic databases to identify studies, the search was restricted to studies published in English. Finally, the lack of evidence regarding sustained effects beyond the immediate postintervention period underscores the need for extended follow-up. Future studies should strive to elucidate strategies for maintaining the intervention effects over the preschooler’s trajectory.

Future Research and Implications

This study highlights the significant avenues for future research. First, further research is warranted to develop eHealth interventions that yield larger effect sizes and higher quality, specifically in identifying effective 24-hour movement behaviors. It is worth noting that none of the eligible eHealth interventions addressed the comprehensive integration of 24-hour movement behaviors in preschoolers, despite the increasing recognition of the interdependence between PA, SB), and sleep. Second, many studies were conducted in Western and high-income countries, prompting the need for further exploration of the effectiveness of eHealth behavior change interventions in other country settings. Third, our study’s focus was primarily on the quantitative aspects of 24-hour movement behaviors, warranting future studies to also delve into the qualitative facets, such as motor skills and sleep quality. In addition, it is crucial to recognize the pivotal role of objective measurement tools in comprehending movement behaviors among young children. Given the sporadic and unstructured nature of preschoolers’ activities, it becomes challenging for parents and teachers to accurately discern shifts in MVPA and SB, even if they have occurred. This highlights the importance of using objective measurement tools for precise insights into these behaviors. Finally, future research in this field should prioritize broadening the focus and incorporate additional dimensions, such as physical, affective, and cognitive indicators. This approach may promote the holistic development of young children and contribute to advancements in the field of health outcomes. By considering these dimensions, researchers can also gain a comprehensive understanding of the various factors that influence children’s overall well-being and physical literacy development.

Given the multifaceted nature of intervention moderators, further research is warranted to establish optimal patterns of daily movement behaviors and to gain deeper insights into the mechanisms underlying change when addressing the amalgamation of 24-hour movement behaviors in preschoolers. Indeed, future interventions should also draw from the effective behavior change techniques used in single-behavior eHealth interventions and apply them to interventions targeting multiple healthy movement behaviors. Moreover, collaborative engagement with parents and teachers throughout both the developmental and implementation phases of these interventions will play a pivotal role in their success. In addition, capitalizing on emerging and novel technologies may offer a valuable avenue to enhance the effectiveness and feasibility of these interventions.

Conclusions

The findings suggest that eHealth interventions may hold promise in improving 24-hour movement behaviors, particularly by increasing PA, improving sleep duration, and reducing sedentary time among preschoolers. However, these effects were relatively modest and transient and were observed primarily immediately after the intervention. Furthermore, the overall quality of the evidence was rated as moderate to low. As a result, there is a pressing need for rigorous and high-quality research endeavors to develop eHealth interventions capable of effectively enhancing both the quantity and quality of 24-hour movement behaviors simultaneously. These interventions should strive to maintain their effects over extended periods.

Acknowledgments

The authors of this study would like to express their sincere gratitude to the authors who responded to their emails and generously provided detailed information and data regarding their studies. Their cooperation has been instrumental in advancing this study.

Data Availability

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

Authors' Contributions

SJ drafted the manuscript. SJ, ASH, and JYYN were responsible for the concept and design of the study. SJ and BP screened all abstracts full texts, extracted all data, performed the risk of bias, and conducted the quality assessment. SJ performed the statistical analyses. SJ, JYYN, KHC, and ASH critically revised the manuscript for important intellectual content. All authors participated in developing the review’s methodology, contributed to multiple manuscript drafts, and gave their approval for the final version.

Conflicts of Interest

None declared.

Eligibility criteria for study inclusion.

Search strategy.

Missing data processing.

Exclusion studies.

PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist.

PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) abstract checklist.

Number of studies included per country and income economy.

Summary of intervention details in the included studies.

Characteristics of the included studies including physical activity, sedentary behavior, and sleep outcomes.

Forest plot of the mobile-based intervention intended to stop obesity in preschoolers (MINISTOP) results.

Forest plots of the subgroup analyses of moderate to vigorous physical activity and sedentary behavior.

Sensitive analysis.

Moderate to vigorous physical activity bias funnel.

Sedentary behavior bias funnel.

Risk of bias.

GRADE (Grading of Recommendations Assessment, Development, and Evaluation) assessment results.

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Abbreviations

Edited by T de Azevedo Cardoso; submitted 19.09.23; peer-reviewed by W Liang, M Zhou, Y Zhang, EJ Buckler; comments to author 11.10.23; revised version received 04.11.23; accepted 18.01.24; published 21.02.24.

©Shan Jiang, Johan Y Y Ng, Kar Hau Chong, Bo Peng, Amy S Ha. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 21.02.2024.

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.

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

Published on 21.2.2024 in Vol 10 (2024)

Untapped Potential of Unobtrusive Observation for Studying Health Behaviors

Authors of this article:

Author Orcid Image

  • Jack S Benton, MSc, PhD   ; 
  • David P French, MSc, PhD  

Manchester Centre for Health Psychology, Division of Psychology and Mental Health, The University of Manchester, Manchester, United Kingdom

Corresponding Author:

Jack S Benton, MSc, PhD

Manchester Centre for Health Psychology

Division of Psychology and Mental Health

The University of Manchester

Oxford Road

Manchester, M13 9PL

United Kingdom

Phone: 44 0161 306 6000

Email: [email protected]

Improving the environment is an important upstream intervention to promote population health by influencing health behaviors such as physical activity, smoking, and social distancing. Examples of promising environmental interventions include creating high-quality green spaces, building active transport infrastructure, and implementing urban planning regulations. However, there is little robust evidence to inform policy and decision makers about what kinds of environmental interventions are effective and for which populations. In this viewpoint, we make the case that this evidence gap exists partly because health behavior research is dominated by obtrusive methods that focus on studying individual behavior and that are less suitable for understanding environmental influences. In contrast, unobtrusive observation can assess how behavior varies in different environmental contexts. It thereby provides valuable data relating to how environments affect the behavior of populations, which is often useful knowledge for effectively and equitably tackling population health challenges such as obesity and noncommunicable diseases. Yet despite a long history, unobtrusive observation methods are currently underused in health behavior research. We discuss how developing the use of video technology and automated computer vision techniques can offer a scalable solution for assessing health behaviors, facilitating a more thorough investigation of how environments influence health behaviors. We also reflect on the important ethical challenges associated with unobtrusive observation and the use of these emerging video technologies. By increasing the use of unobtrusive observation alongside other methods, we strongly believe this will improve our understanding of the influences of the environment on health behaviors.

Introduction

It is now widely recognized that features of the environment (eg, green spaces, transport systems, and land use) can shape human behavior [ 1 ]. This has led to increasing research and policy interest in the idea that environmental change can be used as an upstream intervention to influence health behaviors, such as physical activity, diet, smoking, and alcohol consumption [ 2 ]. Despite the intuitive appeal of this idea, there is a shortage of robust evidence on the effects of environmental interventions [ 3 , 4 ]. A key reason for this is that studies often rely on obtrusive methods for measuring health behavior, which require direct elicitation of information from participants through measures such as self-report (eg, questionnaires), wearable devices (eg, accelerometers), and clinical indicators of behavior (eg, heart rate).

In this viewpoint, we argue that increased use of unobtrusive methods, where measurement does not involve the elicitation of information from participants, is needed to accelerate progress in understanding how environments influence health behaviors. We make the case for unobtrusive observation and discuss the opportunities and ethical challenges associated with the application of video technology and automated computer vision techniques, which could unlock the untapped potential of these underused methods.

Limitations of Obtrusive Methods for Studying Health Behaviors

Studies on human behavior are dominated by traditional obtrusive methods that focus on understanding individual behavior, often overlooking the broader environmental context. For example, typical studies examining interventions to increase physical activity involve interventions delivered to individuals (eg, in primary care) and rely on obtrusive methods to measure physical activity (eg, self-report, pedometers, and accelerometers) [ 5 ]. These studies typically focus on individual behavior irrespective of location, rather than understanding how populations behave in environments, that is, place-based behaviors. However, if we are to effectively and equitably tackle population health challenges such as obesity and noncommunicable diseases, interventions must focus on changing the environmental determinants in populations instead of trying to change each person individually [ 6 ].

Research on 3 widely studied health behaviors (alcohol, smoking, and diet) illustrates the importance of focusing on environments. Studies using unobtrusive objective data on sales of alcoholic beverages [ 7 ], cigarettes [ 8 ], and unhealthy food [ 9 ] have tested the effects of policy and environmental interventions, often through evaluating “natural experiments,” that is, real-world interventions where the researcher has no control over the design and delivery of the intervention [ 10 ]. Fiscal measures (eg, taxation), policy and legislation (eg, smoke-free policies), and environmental changes (eg, food advertising) are all examples of population-level interventions for changing health behaviors that have been found to work quickly and are cost-effective based on studies using unobtrusive measures [ 11 , 12 ]. These studies have explicitly focused on how changes in environments affect population outcomes (eg, overall sales), rather than examining changes in individuals. However, there remains a skeptical attitude toward these kinds of population-level studies, reflecting the belief that associations on an individual level better reflect “true” causal relationships than those on a population level.

Studies of individuals using traditional obtrusive methods have also methodological biases, which arise because they rely on eliciting information from humans, all of whom have constraints in terms of time, attention, and capabilities. Even before a study has begun, the lengthy and burdensome recruitment process typically stretches from identifying and contacting potentially eligible participants, through eligibility assessment, to obtaining informed consent [ 13 ]. This substantial burden on participants reduces response rates and increases attrition, therefore producing sampling bias. Moreover, people from already disadvantaged populations (eg, ethnic minority groups and people with low literacy levels) are more likely to be deterred at each stage, albeit unintentionally [ 14 ]. This differential recruitment and attrition threatens the generalizability and equity of research findings.

Even if researchers manage to recruit individuals who are both willing and able to participate in research, obtrusive methods are prone to measurement reactivity. Reactivity effects of research participation include change due to being assessed, having views about the desirability of different possible research requirements, and deliberately or unwittingly trying to satisfy researchers [ 15 ]. Self-report, which has long been the dominant method for measuring human behavior, is particularly vulnerable to reactivity because it relies on introspection. Therefore, asking participants to self-report their behavior can lead to response biases from memory recall, cognitive difficulties, and social desirability [ 16 ]. Although these various biases are well known, researchers often overlook the extent to which research studies are unusual contexts and that participants may react in unexpected ways to what researchers ask them to do [ 17 ]. These biases, resulting from “research participation effects,” have the potential to affect study outcomes in ways that undermine the validity and representativeness of research findings.

Making the Case for Unobtrusive Observation

Unobtrusive methods have long been recommended to avoid these issues associated with humans taking part in research. In their influential book in 1966, Webb et al [ 18 ] argued that researchers rely too heavily on traditional obtrusive measures of data collection. They advocated for greater triangulation using both obtrusive (reactive) and unobtrusive (nonreactive) methods together to provide reassurance that research is robust to the different types of bias associated with each method of measurement. Webb et al [ 18 ] described four categories of unobtrusive methods: (1) physical traces, (2) archives, (3) simple observation (observing in natural settings), and (4) contrived observation (observing in controlled settings).

The method that we focus on in this paper is simple observation; specifically, nonparticipative observation of human behavior in the context in which it naturally occurs (hereafter referred to as “unobtrusive observation”). We have been involved in over 1000 hours of unobtrusive observation in natural experimental studies of built environment interventions (eg, a new sustainable park) on physical activity and other behaviors (eg, social interactions) [ 19 - 21 ]. Hence, our experiences derive from a positivist approach, producing quantitative data through systematic observation—a structured method of observation using a predefined coding system.

Unobtrusive observation has historically played a crucial role in various fields of study. For example, sociologists such as Whyte [ 22 ] and Jacobs [ 23 ] have used observation methods to investigate urban life and better understand how people socially interact in public spaces. Similarly, within urban design, unobtrusive observation has been a valuable tool to provide insights for designers to improve the quality of urban landscapes. This is exemplified in Gehl and Svarre’s [ 24 ] pioneering work where they observed public spaces and human behavior, and in studies on “desire paths,” which explore informal routes created by individuals seeking shortcuts rather than adhering to designated paths [ 25 ]. Ethnographic research on cultures, communities, and social practices also relies heavily on observation methods, often involving researchers immersing themselves in the communities they observe. Additionally, in ethology (the study of animal behavior), prominent researchers such as Lorenz [ 26 ] and Chivers and Goodall [ 27 ] have conducted extensive field observations to uncover insights into animal behavior and social structures. In health research, unobtrusive observation has been used to assess a range of health behaviors, including physical activity [ 28 ], smoking [ 29 ], suicidal behaviors [ 30 ], handwashing in clinical settings [ 31 ], and social distancing [ 32 ]. A common theme across all these observation approaches is that behavior is intricately tied to the environment, and a comprehensive understanding of behavior requires consideration of this contextual influence.

A unique strength of unobtrusive observation, in comparison to other unobtrusive methods, is its ability for fine-grained analysis of variations in health behaviors directly within the environments, or places, in which they occur. This provides us with strong insights into how people’s behavior is influenced by the microenvironments to which they are exposed. As a result, unobtrusive observation is particularly useful for evaluating the effectiveness of environmental interventions aimed at changing health behaviors. For example, Petticrew et al [ 29 ] used unobtrusive observation to evaluate the before-and-after impact of a Scottish legislative ban on smoking in public places, which allowed for the assessment of smoking behavior in great detail (eg, quantifying characteristics and behaviors of the smokers or nonsmokers, signage, and positioning of smoking materials) and in different environmental settings (eg, bars, bookmakers, and restaurants). In contrast, other unobtrusive measures, such as archival data, typically involve aggregated measures that make it more difficult for researchers to understand how people are exposed to specific environments of interest.

Observations can be conducted in many public settings, such as green spaces, public highways, shops, and bars, therefore providing real-world contexts for studying health behaviors and the impact of interventions designed to change them. Such studies can provide answers to important questions for policy and decision makers, for example: What kinds of green spaces best encourage physical activity? How can healthier food choices be promoted by changing physical microenvironments (eg, by altering the availability of unhealthy foods)? And how can smoke-free policies in public spaces influence smoking behaviors and secondhand smoke exposure? There is currently a small evidence base for these types of environmental interventions, suggestive of potentially large effects on health behaviors, but with considerable uncertainty and limited understanding of processes by which these outcomes are brought about.

Furthermore, as unobtrusive measures do not require explicit recruitment of participants, observations allow studies in a wide range of populations and settings. Therefore, unlike most traditional research that often fails to recruit participants from underserved groups (typically referred to as “hard-to-reach” groups) [ 33 ], using unobtrusive observation can produce valuable evidence in underserved populations where evidence is lacking but the need to improve health is the greatest. This is particularly important given that intervention effectiveness may differ between socioeconomically advantaged and disadvantaged populations [ 34 ].

Underused Method in Health Behavior Research

Despite these advantages, unobtrusive observation remains an underused method for studying health behaviors, even though it has been advocated for over half a century [ 18 ]. For example, in a recent systematic review of 116 studies that had an explicit focus on how public spaces influence physical activity, leisure activity, and social activity [ 35 ], one would expect that unobtrusive observation would be most appropriate because the focus is on the link between the environment (ie, public spaces) and behavior, rather than the person. Despite this, 95 (82%) of the studies included in this review used obtrusive methods, compared to 53 (46%) studies that used unobtrusive behavior observation. More importantly, of the 95 studies that used obtrusive methods, 57 (60%) studies relied on a single outcome measure to assess behavior, mostly relying on a questionnaire. This is particularly problematic because previous research suggests that relying on methods where participants complete measures in nonbehavioral contexts (eg, at home and in laboratories) may underestimate the importance of contextual factors [ 36 ]. Therefore, although this systematic review did not compare differences in findings between obtrusive and unobtrusive methods, relying on questionnaires may lead to inaccurate inferences about the relationship between these behaviors and environmental contexts. This example highlights the importance of triangulation between different methods to reduce the risk of threats to validity based on single-measure research. For example, unobtrusive observation is stronger at quantifying place-based behaviors to examine variation between different contexts (where the place is the subject of analysis), while obtrusive measures are necessary for individual-level longitudinal analysis and assessing intrinsic factors (where the person is the subject of analysis).

So why do researchers typically rely on obtrusive methods and overlook unobtrusive observation? An important factor is that unobtrusive observation does not conform to the traditional mainstays of ethical research that prioritize participants’ right to be informed and freely choose to participate in research. A decline in the number of observational studies reported in journals in several fields has been attributed, in part, to the impact of ethical regulation (eg, [ 37 ]). Researchers should, of course, always consider the ethical issues involved in the use of unobtrusive measures, balancing wider societal benefits derived from the research against possible harm to participants. Some specific guidelines have been developed to advise on the unique ethical issues raised by the use of unobtrusive observation (eg, [ 38 , 39 ]). These guidelines typically advise that observational research in public settings where those observed would expect to be observed by strangers, and from which no harm could be reasonably supposed to come, does generally not require consent. Nonetheless, researchers must engage with communities to understand any concerns specific to the sociocultural context they are studying and develop contextually specific solutions to minimize the risk of negative responses when conducting unobtrusive observation. For a more detailed discussion of these ethical challenges, see Clark [ 40 ].

Another barrier to the use of unobtrusive observation is the need to deploy in-person observers across multiple study sites, which involves substantial staffing, training, observation time, and data entry—all of which limit scalability. It is therefore perhaps unsurprising that researchers often choose the more convenient and familiar option of traditional obtrusive methods, such as questionnaires and surveys, which have become even more accessible with the rapid rise of digital methods.

Opportunities in Using Video Technology

With advancements in video technology, observation methods are beginning to use video recordings, which could help address the scalability issues associated with in-person observations and thereby increase the uptake of observational methods. Specifically, cameras can be used to collect video recordings in public spaces, which can then be watched and assessed (“coded”) by a researcher. Using cameras removes the need to recruit, train, deploy, and supervise in-person observers. Therefore, camera-based observations can overcome issues of observer availability, fatigue, and inattention; reduce risks to researchers from working alone and at night for prolonged periods during observations; reduce measurement reactivity associated with the physical presence of observers (JS Benton et al, unpublished data, 2024); and ultimately decrease costs. Furthermore, the ability to pause, rewind, and rewatch footage can improve the reliability of coding (JS Benton et al, unpublished data, 2024) and allow for more in-depth analysis compared with “live” in-person observations.

Although rare, there are examples of camera-based observation research, such as the use of closed-circuit television (CCTV) surveillance to assess bystander behavior in public spaces [ 41 ], traffic webcams to assess physical activity [ 42 ], drones to assess park use [ 43 ], and wearable video devices to assess behavior on sidewalks or streets [ 44 ]. The level of unobtrusiveness associated with these various camera-based approaches will depend on the research context. For example, a recent study found no evidence of participant reactivity to the deployment of fixed video cameras in public spaces where there was already existing CCTV surveillance (JS Benton et al, unpublished data, 2024). However, there may be an increased risk of reactivity in public spaces where cameras might be more conspicuous, for example, due to sociocultural norms.

It is difficult to ignore the emergence of new technologies, such as internet of things devices, artificial intelligence, laser tracking, and remote electroencephalography, which are opening up new avenues of unobtrusive measurement of human behaviors [ 45 , 46 ]. For example, researchers are beginning to capitalize on advances in computer vision to use deep learning models (a subset of machine learning) to automatically detect and recognize behaviors within video images. Examples of diverse applications for automated human behavior recognition include analysis of pedestrian behavior and crowds (eg, monitoring social distancing) [ 47 ], detecting when a person falls in a health care facility [ 48 ], and evaluating sports performance [ 49 ]. Developing such models for assessing health behaviors could dramatically reduce the labor, time, and cost needed to collect data at scale, over extended periods, and with increased consistency across video images compared with human observers.

Ethical Challenges in Using Video Technology

Capitalizing on these emerging video technologies creates new risks associated with recording images of people in public spaces, rather than just observing them. Privacy, consent, and confidentiality are all important challenges, which are entwined within data protection laws that researchers must comply with when processing video recordings of people in public spaces. Using computer vision models could address issues of privacy by eliminating the need for humans to watch video recordings once the models are developed and validated. However, less is known about the broader ethical and societal implications of this approach. Therefore, further work is required to establish responsible research practices for the use (and nonuse) of these techniques.

We recently attempted to provide recommendations on how camera-based research can be conducted ethically and in line with data protection requirements [ 50 ], drawing on our experiences in the United Kingdom of conducting 3 studies using fixed video cameras to assess observable health behaviors in public spaces. Examples of good practice include engaging with local communities to codevelop privacy and cybersecurity solutions to minimize the risk of negative responses; displaying privacy notice signs and participant information sheets to increase transparency and ensure compliance with data protection legislation; having clear reporting procedures in place for any observed illegal activities; and implementing robust cybersecurity measures to prevent personal data from being intentionally or unintentionally compromised (eg, using secure data storage solutions).

However, views on what makes this type of camera-based research ethical or not can change depending on the researcher’s positionality, context, and experience. For example, visual researchers in the United Kingdom are increasingly concerned about heightened ethical scrutiny and regulation [ 51 ], whereas in the United States, exemptions under the Code of Federal Regulations 46 allow for certain research activities to bypass extensive ethical oversight. It is therefore important to acknowledge differences in ethical standards across different jurisdictions and physical and sociocultural contexts, which will inevitably evolve over time in response to societal, technological, and cultural changes.

There are also important wider societal debates about the use of cameras in research, particularly concerning CCTV use, given its ubiquity in many urban spaces around the world. While the use of CCTV in research is on the rise [ 52 ], there are differences between using CCTV footage as an observational method in research and its broader application for public safety. A recent study explored the acceptability of using CCTV for research on suicide prevention, which found that there were positive public attitudes toward this approach [ 53 ]. Further research is needed to examine acceptability in different geographical and sociocultural settings and in other areas of health research.

Conclusions

Understanding how environments influence health behaviors requires a major change in research practices to address our overreliance on obtrusive methods that primarily focus on understanding individual behavior and that tend to overlook environmental influences. Unobtrusive observation can assess how environments affect the behavior of populations; yet despite a long history, it remains an underused method in health behavior research. Capitalizing on video technology and automated computer vision techniques could provide a scalable solution to increase the uptake of these methods. However, we must find a way to ensure that the scientific and societal benefits are maximized while protecting individual rights. By increasing the use of unobtrusive observation alongside other methods, we strongly believe that this will improve our understanding of the influences of the environment on health behaviors.

Acknowledgments

JSB is funded by a Wellcome Trust ISSF Fellowship (204796). The views expressed are those of the authors and not necessarily those of Wellcome Trust.

Authors' Contributions

JSB and DPF conceptualized the paper. JSB drafted this paper and DPF contributed to the revision of this paper. JSB and DPF read and approved the final version of this paper.

Conflicts of Interest

None declared.

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Abbreviations

Edited by A Mavragani; submitted 20.02.23; peer-reviewed by S Lin, C Calyx, J Polo, R Suminski, S Malden; comments to author 13.09.23; revised version received 03.10.23; accepted 16.12.23; published 21.02.24.

©Jack S Benton, David P French. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 21.02.2024.

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 JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on https://publichealth.jmir.org, as well as this copyright and license information must be included.

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

Published on 20.2.2024 in Vol 26 (2024)

Comparing the Effectiveness of the Blended Delivery Mode With the Face-to-Face Delivery Mode of Smoking Cessation Treatment: Noninferiority Randomized Controlled Trial

Authors of this article:

Author Orcid Image

Original Paper

  • Lutz Siemer 1, 2, 3 , PhD   ; 
  • Marcel E Pieterse 2 , PhD   ; 
  • Somaya Ben Allouch 4, 5 , PhD   ; 
  • Marloes G Postel 3 , PhD   ; 
  • Marjolein G J Brusse-Keizer 6, 7 , PhD  

1 School of Social Work, Saxion University of Applied Sciences, Enschede, Netherlands

2 Department of Psychology, Health and Technology, Centre for eHealth & Well-being Research - Behavioural, Management and Social Sciences, University of Twente, Enschede, Netherlands

3 Research Group Technology, Health & Care, Saxion University of Applied Sciences, Enschede, Netherlands

4 Digital Life Research Group, Amsterdam University of Applied Science, Amsterdam, Netherlands

5 Digital Interactions Lab (DIL), Informatics Institute, University of Amsterdam, Amsterdam, Netherlands

6 Medical School Twente, Medisch Spectrum Twente, Enschede, Netherlands

7 Health Technology & Services Research, Technical Medical (TechMed) Centre, University of Twente, Enschede, Netherlands

Corresponding Author:

Lutz Siemer, PhD

School of Social Work

Saxion University of Applied Sciences

M. H. Tromplaan 28

Enschede, 7513 AB

Netherlands

Phone: 31 657459469

Email: [email protected]

Background: Tobacco consumption is a leading cause of death and disease, killing >8 million people each year. Smoking cessation significantly reduces the risk of developing smoking-related diseases. Although combined treatment for addiction is promising, evidence of its effectiveness is still emerging. Currently, there is no published research comparing the effectiveness of blended smoking cessation treatments (BSCTs) with face-to-face (F2F) treatments, where web-based components replace 50% of the F2F components in blended treatment.

Objective: The primary objective of this 2-arm noninferiority randomized controlled trial was to determine whether a BSCT is noninferior to an F2F treatment with identical ingredients in achieving abstinence rates.

Methods: This study included 344 individuals who smoke (at least 1 cigarette per day) attending an outpatient smoking cessation clinic in the Netherlands. The participants received either a blended 50% F2F and 50% web-based BSCT or only F2F treatment with similar content and intensity. The primary outcome measure was cotinine-validated abstinence rates from all smoking products at 3 and 15 months after treatment initiation. Additional measures included carbon monoxide–validated point prevalence abstinence; self-reported point prevalence abstinence; and self-reported continuous abstinence rates at 3, 6, 9, and 15 months after treatment initiation.

Results: None of the 13 outcomes showed statistically confirmed noninferiority of the BSCT, whereas 4 outcomes showed significantly ( P <.001) inferior abstinence rates of the BSCT: cotinine-validated point prevalence abstinence rate at 3 months (difference 12.7, 95% CI 6.2-19.4), self-reported point prevalence abstinence rate at 6 months (difference 19.3, 95% CI 11.5-27.0) and at 15 months (difference 11.7, 95% CI 5.8-17.9), and self-reported continuous abstinence rate at 6 months (difference 13.8, 95% CI 6.8-20.8). The remaining 9 outcomes, including the cotinine-validated point prevalence abstinence rate at 15 months, were inconclusive.

Conclusions: In this high-intensity outpatient smoking cessation trial, the blended mode was predominantly less effective than the traditional F2F mode. The results contradict the widely assumed potential benefits of blended treatment and suggest that further research is needed to identify the critical factors in the design of blended interventions.

Trial Registration: Netherlands Trial Register 27150; https://onderzoekmetmensen.nl/nl/trial/27150

International Registered Report Identifier (IRRID): RR2-doi.org/10.1186/s12889-016-3851-x

Introduction

Tobacco’s global impact.

According to the World Health Organization [ 1 ], the tobacco epidemic is one of the biggest public health threats the world has ever faced: tobacco kills up to half of its consumers, which means >8 million people every year. Of these, >7 million deaths are because of direct tobacco consumption, whereas approximately 1.2 million are because of the exposure of nonsmokers to passive smoking [ 1 ]. The economic costs of tobacco consumption are considerable and include significant health costs of treating the disease caused by tobacco consumption and the loss of human capital through the morbidity and mortality attributable to tobacco consumption [ 1 ]. Smoking addiction is more prevalent in specific, often susceptible subpopulations, such as individuals in lower education or socioeconomic groups [ 2 ]. Approximately 80% of the world’s 1.1 billion smokers live in low- and middle-income countries, where the burden of tobacco-related diseases and deaths is the highest [ 1 ].

Smoking Cessation Progress

People who stop smoking greatly reduce their risk of disease and early death [ 3 ] and will have major immediate and long-term health benefits [ 4 , 5 ]. Among smokers who are aware of the dangers of tobacco and the benefits of quitting, most want to quit [ 1 ]. Compared with quitting without professional support, smoking cessation treatment can more than double the success rates of quitting attempts [ 1 ]; this ultimately results—for treatments comparable with those in this study—in estimated point prevalence abstinence rates of 28.4% (95% CI 21.3-35.5) for treatments with a total amount of contact time of 91 to 300 minutes and of 24.7% (95% CI 21.0-28.4) for treatments with >8 person-to-person treatment sessions (both intention-to-treat [ITT]; 6 months after the quit date [ 6 ]). Previous research in the hospital smoking cessation clinic where this study was conducted showed a 19% abstinence rate for comparable treatment in the target group of patients with chronic obstructive pulmonary disease at the 12-month follow-up [ 7 ].

Blended Treatment Evolution

In the past decades, a variety of effective interventions for smoking cessation have become available [ 8 , 9 ], including, more recently, eHealth services such as web-based interventions [ 10 , 11 ] or mobile phone–based interventions [ 12 , 13 ]. At present, traditional face-to-face (F2F) interventions, on the one hand, and both web-based and mobile phone–based interventions, on the other hand, are increasingly being developed into blended treatments. This development is consistent with the idea that blended treatment combines the best of both worlds [ 14 , 15 ], as the strengths of one type of treatment should compensate for the weaknesses of the other [ 14 - 21 ]. For example, personal attention from a professional in F2F treatment could compensate for the lack of personal contact in web-based treatment. In turn, one of the main features of web-based treatment is the possibility of being available anytime and anywhere, which could bridge the interval between sessions in F2F treatment.

A systematic review of randomized controlled trials (RCTs) by Erbe et al [ 17 ] on blended F2F and web-based interventions suggests that compared with stand-alone F2F therapy, blended therapy may save clinician time and lead to lower dropout rates and higher abstinence rates in patients with substance abuse. The authors concluded that for common mental health disorders, blended interventions are feasible and can be more effective than no-treatment controls, but more RCTs on the effectiveness of blended treatments compared with nonblended treatments are necessary.

Although promising, evidence available for blended deaddiction treatment is still emerging, addressing abuse of substances such as alcohol [ 22 ], cocaine, marijuana [ 23 ], or opioids [ 24 ]. For smoking cessation, we only found studies on the promising adjunctive use of smartphone apps with F2F contact [ 25 - 27 ]. To the best of our knowledge, there is no published research on the effectiveness of blended smoking cessation treatments (BSCTs) compared with F2F treatments, where in the blended treatment, the web-based components are not an adjunct but a substitute for specific F2F treatment components. Therefore, in this study, we present the results of an RCT comparing a blended 50% F2F and 50% web-based smoking cessation treatment with a traditional F2F treatment that was similar in content and intensity. The primary objective was to determine if a BSCT resulted in noninferior abstinence rates compared with an F2F treatment with identical ingredients. The rationale for choosing a noninferiority design was that we expected secondary benefits for the BSCT, such as lower costs, lower dropouts, and higher patient satisfaction, even if the BSCT only led to comparable abstinence rates.

This study reports the results of an unblinded 2-arm, parallel group, noninferiority RCT with 1:1 allocation using stratified randomization (nicotine dependency, internet skills, and quitting strategy).

The study was conducted at the outpatient smoking cessation clinic (Dutch: Stoppen met Roken Poli ) of the Medical Spectrum Twente Hospital in Enschede, the Netherlands. Enschede is a municipality and city in the east of the Netherlands with a population of 150,000 inhabitants. The estimated daily smoking prevalence in Enschede was 17.2% in 2017, which is approximately the same as the average (17.4%) in the Netherlands, which is one of the countries with the least number of smokers in Europe [ 28 ].

Trial Registration

The trial was registered in the Netherlands Trial Register on March 24, 2015 (Acronym: LiveSmokefree-Study; Title: Blended Smoking Cessation Treatment; new ID: NL5975; old ID: NTR5113), and a detailed protocol has been published previously [ 29 ].

Participants

We recruited participants between March 2015 and March 2019. The participants were self-referred to the treatment or were referred to the clinic by their general practitioner or hospital physician and were called by members of the research department to check for eligibility. Eligible participants were current daily smokers (eg, at least 1 cigarette, cigar, pipe, or e-cigarette per day [ 30 ]), those who were aged ≥18 years, those who had access to the internet (eg, email and websites), and those who were able to read and write Dutch. Eligible patients completed a questionnaire at the beginning of the study before being randomized.

Ethical Considerations

Consistent with the Dutch Medical Research Ethics Committee guidelines, this study was approved by the accredited Medical Research Ethics Committee Twente (P14-37/NL50944.044.14) and subsequently by the Board of Directors of Medisch Spectrum Twente Hospital. Before initiation, the trial was registered and a detailed protocol has been published previously [ 29 ].

A patient information letter outlining the burden of participation was distributed to all patients, and eligible patients attended an intake interview and signed a consent form.

We processed participants’ personal data in accordance with the Dutch Personal Data Protection Act. The data were collected in 2 ways as follows:

  • The data of the personal contacts were recorded on data collection forms and collected centrally at Medisch Spectrum Twente Hospital. The data manager of the study recorded all collected data in an Access 2007 (Microsoft Corp) database.
  • Most data were collected by Tactus Addiction Treatment, a regional addiction care organization with expertise in web-based treatment, using web-based questionnaires offered to both treatment groups.

Individual patients and caregivers had a log-in with a username and password secured by the Secure Sockets Layer. All data transferred between the patient’s PC and the application were encrypted and sent using the https protocol. All data were encrypted and stored on servers in secure data centers in the Netherlands. To further ensure data security, daily backups of the server were performed.

The participants did not receive any compensation for their participation in the study.

Interventions

The study interventions to be compared were a blended F2F treatment and web-based BSCT and an F2F treatment. Except for the differences in the mode of delivery (ie, F2F mode and web mode), both treatments had the same features as follows:

  • High-intensity treatment that comprised 10 sessions (20-minute contact time for each session, except for the first session, which lasts 50 minutes) and supportive pharmacotherapy, if needed, within a 6-month period with an expected quit date after about 3 months
  • Delivered by health care professionals in an outpatient cessation clinic
  • Concordant with the Dutch guidelines for tobacco addiction [ 31 ], fulfilling the requirements of the Dutch care module for smoking cessation [ 32 ]
  • Executed by counselors registered in the Dutch quality register of qualified smoking cessation counselors
  • Supporting 3 quitting strategies that patients could choose at the start of the treatment: (1) stop at once, (2) change gradually by increasing the number of daily activities that are performed smoke free, or (3) decrease smoking at regular intervals (eg, scheduled smoking reduction by 100%->75% and 75%->50%). The chosen quitting strategy did not generally influence the course of the treatment. The order, pace, duration, and intensity were the same for all strategies.

Both BSCT and F2F treatment covered 52 behavior change techniques (using behavior change technique taxonomy v1 of 93 hierarchically clustered techniques by Michie et al [ 33 ]) as shown in Table 1 .

a BSCT: blended smoking cessation treatment.

b F2F: face-to-face.

F2F treatment consisted of 10 F2F sessions delivered at an outpatient smoking cessation clinic. BSCT consisted of 5 F2F sessions at the outpatient clinic and 5 web-based sessions delivered via the web-based treatment platform Roken De Baas (which translates loosely as “in control of smoking”). During the RCT, the software had to be revised once, as the European General Data Protection Regulation became enforceable from May 25, 2018, which changed the appearance and handling but not the content of the interventions.

Both F2F treatment and BSCT consisted of counselor-dependent and counselor-independent components. The counselor-dependent web-based components of BSCT were interactive and relied on asynchronous communication (eg, email and SMS text messaging) between the counselor and patient. The counselor-independent components such as psychoeducational content or the smoking diary were used by the patients on their own and at their own time. In F2F treatment, these components were provided in a paper manual that clients took home. In BSCT, these components were accessible over the web. As such, both treatments were equivalent in terms of content and intensity. However, an additional benefit of BSCT was that the content of previous counselor-dependent components remained accessible as email and SMS text messaging correspondence saved on the web.

The characteristic feature of BSCT is an equal balance between F2F and web-based sessions, and the focus of the treatment was not supposed to be on the F2F mode or the web mode; in addition, there was a constantly alternating and interacting use of the F2F mode and web mode. Table 2 presents the order, timing, main features, duration, and modes of delivery of the treatment sessions for F2F treatment and BSCT. Although an even distribution was planned for BSCT with regard to the number of sessions, there was an uneven distribution for the duration of treatment because the first session (50 minutes of F2F mode) was longer than the remaining sessions (20 minutes of F2F mode or 20 minutes of web mode); therefore, BSCT patients spent 130 minutes in the F2F mode and 100 minutes in the web mode.

a F2F: face-to-face.

b BSCT: blended smoking cessation treatment.

c F2F mode: F2F sessions of BSCT.

d Web mode: web-based sessions of BSCT.

e CO: carbon monoxide.

f Cotinine measurement was only performed in patients who reported quitting smoking either in the 3-month follow-up questionnaire or during treatment to the counselor.

More information about both treatments can be found in the study protocol of the RCT [ 29 ] and in the description of the user experiences of BSCT [ 21 ]. The treatment fidelity of the counselors was not recorded. The adherence to the treatments was described elsewhere [ 34 , 35 ]; but, in brief, levels of adherence were comparable for BSCT and F2F treatment sessions. To provide an impression of the look and feel of the web interventions of BSCT, Multimedia Appendix 1 displays screenshots of the web-based sessions of BSCT.

For the primary objective (ie, effectiveness) of the analysis, the primary outcome for the ITT analysis of the treatments’ effectiveness in smoking cessation was the proportions of biochemically (ie, cotinine) validated point prevalence abstinence from all combustible tobacco products (eg, cigarettes, bags, cigars, and pipes) at 3 and 15 months after the start of the treatment. Additional outcomes were the proportions of carbon monoxide (CO)–validated point prevalence abstinence; self-reported point prevalence abstinence; and self-reported continuous abstinence at 3 (ie, shortly after the expected quit date), 6 (ie, end of treatment), and 9 and 15 (follow-up measurements) months. Applying the noninferiority margin justified in our protocol paper [ 29 ], BSCT was considered as noninferior if it resulted in abstinence rates that were <5% points lower than those of F2F treatment [ 29 ].

Measurements

Effectiveness.

Cotinine-validated and CO-validated abstinence measurements were used to measure biochemically validated point prevalence abstinence rates [ 36 - 38 ].

Cotinine measurement was performed at approximately the 3-month and 15-month follow-up (ie, shortly after the expected quit day, week 14; refer to Table 2 ) and at the 15-month follow-up only in patients who reported quitting either during the treatment to the counselor or in the 3-month or 15-month follow-up questionnaire. A 0.5 mL to 1 mL salivary sample was collected using a Salivette (Sarstedt AG and Co). Under supervision, patients chew on a cotton swab for 1 minute to stimulate the saliva flow rate. All saliva specimens were frozen until assayed and transported to the laboratory for the determination of cotinine levels using a gas chromatography technique. Abstinence was defined as having a salivary cotinine level <20 ng/mL [ 39 ].

The CO level was measured in all patients (independent of reporting quitting) at 3 months, at the last F2F treatment session at the hospital (for the BSCT group, the last F2F treatment session was at 5 months after the start of the treatment [week 22], and for the F2F treatment group, at 6 months after start of the treatment [week 26]; refer to Table 2 ), and in patients who reported quitting at 15 months together with the cotinine level. A breath CO level of 5 ppm was taken as the cutoff value between smokers and nonsmokers (≥5 ppm in smokers and <5 ppm in nonsmokers [ 40 ]). Breath CO levels were monitored using a piCO Smokerlyzer (Bedfont Instruments), a portable CO monitor.

Furthermore, self-reported point prevalence abstinence and self-reported continuous abstinence rates were measured at 3, 6, 9, and 15 months after treatment initiation. The measurement tool was a standardized questionnaire for Dutch tobacco research [ 30 ], which patients in both BSCT and F2F treatment completed over the web. Self-reported point prevalence abstinence rate was assessed by asking patients whether they had smoked ≥1 cigarette (eg, bags, cigars, and pipe) in the last 7 days, and the self-reported continuous abstinence rate was assessed by asking whether they had smoked since the current stop.

For each measurement during and after treatment, the participants were prompted twice via email and, in the absence of measurements, were additionally notified twice via telephone. If no measurement was available after 2 emails and 2 telephone calls, the participants were classified as lost to follow-up for the respective measurement and notified again for the next measurement.

Sample Size

For the RCT, we calculated the abstinence rates for 344 participants, assuming a long-term abstinence rate of 10% for those receiving F2F treatment [ 6 , 7 , 41 ] and—based on its expected benefits—15% for those receiving BSCT. If BSCT would lead to an abstinence rate not <5%, it would be considered as noninferior compared with F2F treatment. Therefore, 172 patients per group with a power of 80% and a Cronbach α of .025 were needed for this RCT (calculated using Power Analysis & Sample Size [NCSS Statistical Software]).

Randomization

We randomly allocated patients to either BSCT or F2F treatment using computerized randomization (Qminim Online Minimization). Randomization was performed at the individual level (allocation ratio 1:1). The minimization was stratified according to (1) the level of internet skills [ 42 ], (2) the level of nicotine dependence [ 30 ], and (3) the quitting strategy favored by the patient (eg, stop at once, gradual change, and scheduled reduced smoking; for details refer to the description in the Interventions section). The data used for minimization were collected using the baseline questionnaire, which was completed over the web by the patient after providing consent.

Owing to the nature of the treatment conditions, it was self-evidently impossible to blind the staff and patients involved in the study.

Statistical Methods

For both the BSCT group and the F2F treatment group, the patients’ demographic, smoking-related, and health-related characteristics at baseline were reported as means with SD for normally distributed continuous variables and as medians with IQR for nonnormally distributed continuous variables. Categorical variables were reported as numbers with corresponding percentages. To identify between-group differences, an independent 1-sided (1-tailed) t test or Mann-Whitney U test was performed as appropriate for continuous variables, and Pearson chi-square or Fisher exact test was performed for categorical variables.

As this was an ITT analysis, participants with missing data on smoking status were considered as smokers. The absolute and proportional abstinence rates in the treatment group were reported.

The noninferiority was analyzed by calculating the difference and the 95% CI of the observed difference in the abstinence rates and by comparing that to the previously defined noninferiority margin of 5% points [ 29 ]. In addition, the noninferiority analysis is illustrated in a forest chart.

To be able to compare the results of this study with those of other studies conducted using a more traditional RCT design, additional repeated measures analyses were conducted using generalized estimating equation to test for group, time, and group×time differences in abstinence rates.

All analyses were performed using the SPSS software (version 26.0; IBM Corp), except for the calculation of the CIs of the difference between abstinence rates, for which we used the web tool by VassarStats [ 43 ] for “The Confidence Interval For The Difference Between Two Independent Proportions.”

Participant Flow

Figure 1 shows the flow of participants throughout the study. A total of 344 patients were eligible for the study, provided written consent, and were randomized (smoking cessation treatment: BSCT, n=177; F2F treatment, n=177). Of 177 patients each in both groups, 167 (94.3%) patients of the BSCT group and all 177 (100%) of the F2F treatment group started treatment (ie, they received at least 1 session). Before the start of treatment, 151 (85.3%) of the 177 patients in the BSCT group and 175 (98.8%) of the 177 patients in the F2F treatment group completed the baseline questionnaire. Three months after starting treatment (ie, shortly after the expected quit date), of the 177 patients in the BSCT group, 14 (7.9%) who self-reported quitting were available for cotinine measurement, 68 (38.4%) were available for CO measurement, and 26 (14.6%) completed the follow-up questionnaire. Of the 177 patients in the F2F treatment group, 47 (26.5%) who reported quitting were available for cotinine measurement, 77 (43.5%) were available for CO measurement, and 47 (26.5%) completed the 3-month follow-up questionnaire. Of 177 patients in the BSCT group, 53 (29.9%) were available for the 5-month CO measurement and 18 (10.1%) completed the 6-month follow-up questionnaire. Of 177 patients in the F2F treatment group, 61 (34.4%) were available for the 6-month CO measurement and 53 (29.9%) completed the 6-month follow-up questionnaire. The 9-month follow-up questionnaire was completed by 20 (11.2%) patients of the BSCT group and 42 (23.7%) patients of the F2F treatment group. After 15 months of starting treatment, 9 (5.1%) of the 177 patients in the BSCT group who self-reported quitting were available for cotinine level measurement. A total of 16 (9%) patients in the BSCT group were available for CO level measurement and 7 (4%) completed the follow-up questionnaire. Of 177 patients in the F2F treatment group, 12 (6.8%) patients who reported quitting were available for cotinine level measurement, 15 (8.5%) were available for CO level measurement, and 31 (17.5%) completed the 15-month follow-up questionnaire.

table of content of a research paper

Baseline Characteristics

Table 3 shows that the baseline characteristics, including demographic, smoking-related, and health-related characteristics, were comparable between the participants in both groups.

c VET: vocational education and training.

d Internet skills: range 10-60; higher numbers indicate better skills.

e Nicotine dependency (Fagerström): range 0-10; higher numbers indicate higher nicotine dependency.

f Negative attitude toward quitting: range −12 to 0; lower numbers indicate a more negative attitude toward quitting smoking.

g Positive attitude toward quitting: range 0-12; higher numbers indicate a more positive attitude toward quitting smoking.

h Self-efficacy: range −12 to 12; higher numbers indicate higher self-efficacy related to smoking cessation.

i Readiness to quit: range 0-4; higher numbers indicate higher readiness to quit.

j Social support: range 0-5; higher numbers indicate more social support in smoking cessation.

k Social modeling: range 0-8; higher numbers indicate more smokers in the social environment.

l Use of alcohol: range 0-4; 0=Never, 1=1 time per month, 2=2-4 times per month, 3=2-3 times per week, and 4=≥4 times per week.

m Health-related complaints: range 0-40; higher numbers indicate poorer health status.

n MAP HSS: Maudsley Addiction Profile Health Symptoms Scale.

o Smoking-related complaints: range 0-64; higher numbers indicate more smoking-related complaints.

p Health- and smoking-related complaints: range 0-104; higher numbers indicate poorer health status and more smoking-related complaints.

q Depression, anxiety and stress: range 0-42; higher numbers indicate a higher level of depression, anxiety and stress.

r DASS: sum score of depression, anxiety and stress (range 0-126; higher numbers indicate a more negative emotional status).

s EQ-5D-3L: societal-based quantification of the patients’ health status (range 0-1; higher numbers indicate better health status).

t EQ VAS: visual analog scale for quality of life (range 0-100, higher numbers indicate better state of health).

Table 4 shows the results of effectiveness measurements at 3, 5 or 6, 9, and 15 months after the start of treatment. The cotinine-validated point prevalence abstinence shortly after the expected stop day (ie, 3 months after the treatment initiation) showed a significantly lower and inferior abstinence rate in the BSCT group (4.8%) than in the F2F treatment group (17.5%; difference of 12.7, 95% CI 6.2-19.4; P <.001). The differences found in the 15-month cotinine level measurement (difference of 1.5, 95% CI −3.5 to 6.4) and in all CO level measurements at 3 months (difference of 2.5, 95% CI −6.9 to 11.8), 5 or 6 months (difference 3.7, 95% CI −4.0 to 11.4), and 15 months (difference 0.7, 95% CI −4.9 to 6.7) were not substantial and inconclusive in terms of inferiority.

c Data not available.

d CO: carbon monoxide.

e Answer “no” to the questionnaire question “Have you smoked one or more cigarettes (bags, cigars, pipe) in the last 7 days?”

f Answer “no” to the questionnaire question “Have you smoked since the stop?”

Furthermore, we observed significantly lower and inferior abstinence rates in the BSCT group for self-reported point prevalence abstinence at 5 or 6 months (BSCT 7.8% vs F2F treatment 27.1%; difference 19.3, 95% CI 11.5-27.0; P <.001), for self-reported point prevalence abstinence at 15 months (BSCT 3% vs F2F treatment 14.7%; difference 11.7, 95% CI 5.8-17.9; P <.001), and for self-reported continuous abstinence at 5 or 6 months (BSCT 6% vs F2F treatment 19.8%; difference 13.8, 95% CI 6.8-20.8; P <.001). Significantly lower—but in terms of inferiority, inconclusive—abstinence rates in the BSCT group were found for self-reported point prevalence abstinence at 9 months (BSCT 11.4% vs F2F treatment 22%; difference 10.7, 95% CI 2.8-18.4; P =.009) and for self-reported continuous abstinence at 15 months (BSCT 1.8% vs F2F treatment 11.3%; difference 9.5, 95% CI 4.4-15.1; P <.001).

Figure 2 presents the 95% CIs of the differences between BSCT and F2F treatment groups for all abstinence outcome measures by applying the 5% points noninferiority margin. The forest plot illustrates the inferiority of BSCT with cotinine-validated point prevalence abstinence at 3 months, self-reported point prevalence abstinence at 6 and 15 months, and self-reported continuous abstinence at 6 months. For the remaining outcomes, the forest plot shows inconclusive results.

table of content of a research paper

The generalized estimating equation analysis showed significant differences ( P <.05) between both the groups with time and the group×time interaction for cotinine-validated point prevalence abstinence, self-reported point prevalence abstinence, and self-reported continuous abstinence rates. For the CO-validated point prevalence abstinence, a significant difference was found with time, but neither was there a difference between the groups nor a time×group interaction.

Principal Findings

This paper presents the results of an RCT comparing the effectiveness of a blended 50% F2F treatment and 50% web-based BSCT to F2F-only treatment with similar ingredients and intensity. Contrary to our expectations, the abstinence rates of the BSCT group were lower than those of the F2F group. For the primary outcome (ie, cotinine-validated point prevalence abstinence rate), applying the 5%-point noninferiority margin indicated inferiority of BSCT at 3 months, whereas the outcome at 15 months was inconclusive. Both results should be considered with caution as the statistical power to detect differences was limited owing to nonresponse. Furthermore, BSCT was found to be inferior in 3 of the secondary outcomes (ie, self-reported point prevalence abstinence rate, self-reported continuous abstinence rate at 6 months, and self-reported point prevalence abstinence rate at 15 months), whereas the remaining outcomes were inconclusive. Although most outcomes from the repeated measures analyses showed significantly lower abstinence rates for the blended treatment, all remaining outcomes were nonsignificant, further corroborating the inferiority of BSCT against F2F treatment.

Given that our results suggest that it is more likely that BSCT is inferior to F2F treatment, our study is not consistent with the higher abstinence rates reported in the literature [ 17 ] for blended treatments compared with F2F treatment. Explanations for this likely inferiority of BSCT require further study. As the patients’ demographic, smoking-related, and health-related characteristics were comparable in both treatment groups, these factors did not seem to play a role in this context. This also applies to adherence; as we found in previous analyses [ 35 ], adherence was comparable for both the groups. However, we know from qualitative analyses conducted as part of this RCT [ 21 ] that participants found the web-based components of BSCT to be rather unmotivating and not enjoyable, which may have resulted in BSCT patients making less use of the web-based components both during and after the treatment, and thus, may be a factor in the lower abstinence rates. The experience of patients in the BSCT suggests that the highly protocolized, equally balanced mix for blended treatment chosen in this study, with a fixed sequence of alternating F2F and web-based sessions, was too restrictive for blended treatment in practice [ 35 ], thus limiting tailoring to individual patient needs. Which intervention components should be offered when and in what form to achieve optimal treatment outcomes requires further investigation.

Furthermore, even if this cannot be supported by systematic observations and analyses, we believe that provider-related factors at the microlevel (eg, the treatment fidelity of counselors and therapist drift) and at the mesolevel (eg, the organization’s preexisting knowledge, routines, and leadership) should be considered more closely. A relevant factor could be that, in the development of BSCT, half of the counseling sessions of the F2F treatment established in the outpatient smoking cessation clinic were replaced by web-based tools from a web platform unfamiliar to the clinic and counselors. Therefore, counselors had half of their F2F intervention replaced and had to integrate the new web-based components into a new blended workflow. The preexisting routine and familiarity with the F2F treatment among counselors might have disadvantaged the quality of execution of the blended treatment. The normalization process theory [ 44 ] could provide valuable perspectives in this context. It posits that the unclear definition of BSCT’s meaningfulness (coherence) for counselors may have diminished their motivation and engagement (cognitive participation). Limited collective agency in BSCT’s implementation, owing to rigid protocols and insufficient reflective monitoring, may have further impeded its establishment in clinical practice. Understanding these barriers through targeted investigations could enhance the integration and efficacy of the BSCT.

Although not the focus of this analysis, we noticed that both treatments mostly showed lower abstinence rates than those reported in the literature for comparable treatments (ie, point prevalence abstinence rates of 28.4% for treatments with a total contact time of 91 to 300 minutes 6 months after the quit date [ 6 ]). At 9 months (ie, 6 months after the quit date), for F2F treatment, we found a self-reported point prevalence abstinence rate of 22% and a self-reported continuous abstinence rate of 13%. For BSCT, this rate was even lower with 11.4% for self-reported point prevalence abstinence and 7.8% for self-reported continuous abstinence. These relatively low abstinence rates could be because of the population characteristics (ie, patients in an outpatient smoking cessation clinic in a hospital context). Further analysis should investigate whether the sample differs from the general population in terms of known effectiveness predictors [ 45 ], such as, in this context, age, socioeconomic status, alcohol and drug use, health status, nicotine dependence, motivation to quit, or family status. However, a previous study by Christenhusz [ 7 ] in the same clinic with a comparable treatment for the specific target group of patients with chronic obstructive pulmonary disease showed much higher cotinine-validated abstinence rates (19%) compared with F2F treatment (5.7%) and BSCT (4.2%) at the 12-month follow-up. A more plausible explanation for this is the high dropout rate and missing data in this study. As we applied the common penalized imputation procedure (assuming missing=smoking [ 46 , 47 ]) to deal with missing data in our analyses, imputed quit rates will decrease proportionally to dropout rates.

A final point to consider is that toward the end of the RCT, the software of the web platform had to be updated because of legal changes, which temporarily caused accessibility problems. However, as only a few patients were affected by this and only toward the end of the RCT, these had no relevant influence on the results of this study.

Although blended treatment appears promising and reflects today’s digitalization of the lifestyle of patients and health care professionals [ 14 , 15 ], not every realization of blended treatment is automatically an improvement. This also underscores the need to answer the question Greenhalgh et al [ 48 ] raised earlier: “What explains the success of a blended treatment in one context and the failure of a comparable blended treatment in another context?” The likely inferiority of BSCT in this study indicates that the current realization of BSCT will have to be reconsidered, which may involve aspects such as the optimal balance and mix of F2F and web-based components or the use of synchronous versus asynchronous counseling within web-based components. Such a redesign process can be supported by an analysis using the normalization process theory [ 44 ] and guided by an eHealth development model such as the Center for eHealth Research Road map [ 49 ]; the nonadoption, abandonment, scale-up, spread, and sustainability framework [ 48 ]; or more practically by the “Fit for Blended Care” instrument [ 15 ], which is intended to support therapists and patients in deciding whether and how blended care can be established.

For the generalization of the results, it should be noted that this analysis referred to a hospital context and a blended treatment with a strict 50:50 ratio of web-based and F2F interventions. For example, hospital patients could be expected to have a higher disease burden and, possibly, owing to age, a lower eHealth literacy than the general population. The question arises whether the results would have been different in a healthier, younger population. In addition, as mentioned above, a fixed 50:50 ratio of web-based and F2F interventions was defined for BSCT, which did not consider the individual needs of patients or counselors. A blended treatment that is better tailored to the needs, characteristics, and skills of both the patients and the counselors could have led to better results [ 15 ]. We know from an earlier study by Siemer et al [ 21 ] that patients would have preferred to use a smartphone app instead of a web platform, for example, or that they would have liked to be free to choose the ratio and sequence of F2F and web-based interventions.

Limitations

A major limitation of this study was the high dropout rate at several follow-up time points, resulting in many missing values for both self-reported and biochemical measures, which had a major impact on the ITT analysis. According to the ITT procedure, all missing values for the outcomes were coded as smoking. As a result, both the biochemically validated outcomes and the self-reported outcomes of this study are likely to be overly conservative, which largely explains the relatively low abstinence rates found in both study groups compared with the existing literature.

Furthermore, because of the high dropout rate, we conducted an analysis of the factors associated with dropout. We identified 2 main predictors of dropout: having a smoking partner at baseline and lower mental health scores as indicated by the Depression Anxiety Stress Scale [ 50 ]. Both predictors are known to be associated with poorer treatment outcomes [ 51 ]. This finding suggests that neither of the interventions used in this study sufficiently reduced the barriers to successful intervention completion. Such nonrandom patterns of dropout pose a threat to the external validity of our findings as they suggest that our sample may not be fully representative of the wider population. However, it is notable that these attrition factors alone are unlikely to fully explain the relatively low quit rates observed in this trial compared with other similarly intensive interventions [ 7 ] as similar reasons for dropout are likely to occur in any smoking cessation trial sample. Nevertheless, the underrepresentation of participants with these risk factors at later follow-ups may have led to an overestimation of the effectiveness of our interventions, although it remains difficult to assess whether this occurred to a greater extent in this study than in other smoking cessation trials.

However, the most critical aspect is whether these predictors of attrition varied between the 2 treatment conditions [ 52 ]. Such differential attrition could compromise the internal validity of our findings, particularly in the noninferiority test comparing blended treatment with F2F treatment delivery. Owing to low cell counts of the two above-mentioned dropout predictors, a detailed analysis to examine the interaction effects of attrition predictors by treatment condition was not feasible. In addition, there were no consistent differences in the attrition rates between the study groups at any follow-up time points.

Another limitation to consider is the risk of bias in patient-reported outcome measures (PROMs), such as social desirability or recall bias, particularly given that the study relies in part on self-reported smoking cessation for an extended period of 15 months. The participants’ ability to accurately recall their smoking behavior could be impaired, especially in the context of continuous abstinence, which could bias the study results. In general, the lower quit rates in our study compared with the existing literature argue against a significant self-report bias, as PROMs tend to overestimate quit rates compared with biochemical validation. Furthermore, biased PROMs will only have affected the internal validity of this study if they occur differently in the 2 study groups. We have no indications of this, but we lack data to verify this statistically. Because the determination of noninferiority is ultimately based on applying the 5% margin, it can also be considered a weakness that this 5% margin is based only on our considerations, as stated in the protocol paper of this study [ 29 ]. However, a slightly higher or lower margin would have led to slight changes in the results but not fundamental changes in the conclusions.

In addition to the cotinine measurements, this study collected CO measurements at the last 3 follow-up points. However, the second of these CO measurements showed a 4-week difference between the groups: 5 months after baseline for the blended treatment group and 6 months for the F2F group. Assuming that relapse rates would be expected to increase with time, this difference should have favored the effectiveness of the blended treatment. However, our results at this time point show the opposite, further supporting our claim of inferiority of the blended treatment compared with the F2F treatment.

Another limitation of this study is that our data, which were designed to compare the 2 approaches of blended and F2F treatment, did not allow analyses at the level of treatment components within the 2 delivery modes. Nevertheless, studies comparing different blended protocols are warranted to enable the design of improved BSCT in the future.

A final limitation of this study is that, as is often the case in clinical studies [ 53 ], we have not recorded the treatment fidelity and therefore deviations from the treatment protocol favoring one of both modes of delivery may have biased our findings. Although we cannot rely on systematic observations, we have some reason to believe that the implementation and adoption of the innovative BSCT may have had a negative impact on the effectiveness of the BSCT compared with the usual F2F treatment. However, based on our data on adherence from previous papers [ 34 , 35 ] and the findings on satisfaction (not reported in this study), we found no indication of a fidelity issue.

Conclusions

In this analysis of an RCT comparing a BSCT with a comparable F2F treatment, we found predominant results indicating inferiority of the blended mode compared with the traditional F2F mode, exceeding a 5% margin in abstinence rate. This could not be explained by lower adherence. Further research is required on the critical factors involved in the design of blended interventions.

Acknowledgments

The authors would like to thank the directors, the staff of both Medisch Spectrum Twente Hospital and Tactus Addiction Treatment, and the patients of the outpatient smoking cessation clinic of the Medical Spectrum Twente Hospital.

During the revision process, the authors assessed the efficacy of artificial intelligence (AI) tools such as ChatGPT 4.0 (OpenAI) [ 54 ], DeepL Translator (DeepL SE) [ 55 ], and DeepL Write (DeepL SE) [ 56 ] in enhancing language use. The objective of the authors was to determine whether this approach could optimize the academic writing quality for nonnative English speakers. Where the AI did not alter the content, the authors refined the language by altering the phrasing based on AI suggestions.

Data Availability

The data underlying this paper are available in Data Archiving and Networked Services [ 57 ].

Authors' Contributions

LS, MGP, MGJB-K, MEP, and SBA initiated collaboration with the data provider, designed the study, and wrote the study protocol. LS conducted the literature search, monitored data collection, drafted the paper, and is the guarantor of the paper. LS, MGP, and MGJB-K conducted the trial. LS and MGJB-K wrote the statistical analysis plan. LS and MGP designed the data collection tools. LS, MGJB-K, and MEP analyzed the data. LS, MGP, MGJB-K, MEP, and SBA revised the draft paper.

Conflicts of Interest

None declared.

Screenshots of web sessions of the blended smoking cessation treatment.

CONSORT eHEALTH checklist.

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  • Blankers M, Smit ES, van der Pol P, de Vries H, Hoving C, van Laar M. The missing=smoking assumption: a fallacy in internet-based smoking cessation trials? Nicotine Tob Res. Jan 2016;18(1):25-33. [ CrossRef ] [ Medline ]
  • Greenhalgh T, Wherton J, Papoutsi C, Lynch J, Hughes G, A'Court C, et al. Beyond adoption: a new framework for theorizing and evaluating nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability of health and care technologies. J Med Internet Res. Nov 01, 2017;19(11):e367. [ FREE Full text ] [ CrossRef ] [ Medline ]
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  • Metse AP, Stockings E, Bailey J, Regan T, Bartlem K, Wolfenden L, et al. Rates of retention of persons with a mental health disorder in outpatient smoking cessation and reduction trials, and associated factors: protocol for a systematic review and meta-analysis. BMJ Open. Sep 04, 2019;9(9):e030646. [ FREE Full text ] [ CrossRef ] [ Medline ]
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Abbreviations

Edited by A Mavragani; submitted 06.03.23; peer-reviewed by L Harst, Z Ehtesham; comments to author 24.07.23; revised version received 04.11.23; accepted 29.12.23; published 20.02.24.

©Lutz Siemer, Marcel E Pieterse, Somaya Ben Allouch, Marloes G Postel, Marjolein G J Brusse-Keizer. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 20.02.2024.

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

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APA Format for Tables and Figures | Annotated Examples

Published on November 5, 2020 by Jack Caulfield . Revised on January 17, 2024.

A table concisely presents information (often numbers) in rows and columns. A figure is any other image or illustration you include in your text—anything from a bar chart to a photograph.

Tables and figures differ in terms of how they convey information, but APA Style presents them in a similar format—preceded by a number and title, and followed by explanatory notes (if necessary).

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Table of contents

Apa table format, apa figure format, numbering and titling tables and figures, formatting table and figure notes, where to place tables and figures, referring to tables and figures in the text, frequently asked questions about apa tables and figures.

Tables will vary in size and structure depending on the data you’re presenting, but APA gives some general guidelines for their design. To correctly format an APA table, follow these rules:

  • Table number in bold above the table.
  • Brief title, in italics and title case, below the table number.
  • No vertical lines.
  • Horizontal lines only where necessary for clarity.
  • Clear, concise labels for column and row headings.
  • Numbers consistently formatted (e.g. with the same number of decimal places).
  • Any relevant notes below the table.

An example of a table formatted according to APA guidelines is shown below.

Example of a table in APA format

The table above uses only four lines: Those at the top and bottom, and those separating the main data from the column heads and the totals.

Create your tables using the tools built into your word processor. In Word, you can use the “ Insert table ” tool.

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table of content of a research paper

Any images used within your text are called figures. Figures include data visualization graphics—e.g. graphs, diagrams, flowcharts—as well as things like photographs and artworks.

To correctly format an APA figure, follow these rules:

  • Figure number in bold above the figure.
  • Brief title, in italics and title case, under the figure number.
  • If necessary, clear labels and legends integrated into the image.
  • Any relevant notes below the figure.

An example of a figure formatted according to APA guidelines is shown below.

Example of a figure in APA format

Keep the design of figures as simple as possible. Use colors only where necessary, not just to make the image look more appealing.

For text within the image itself, APA recommends using a sans serif font (e.g. Arial) with a size between 8 and 14 points.

For other figures, such as photographs, you won’t need a legend; the figure consists simply of the image itself, reproduced at an appropriate size and resolution.

Each table or figure is preceded by a number and title.

Tables and figures are each numbered separately, in the order they are referred to in your text. For example, the first table you refer to is Table 1; the fourth figure you refer to is Figure 4.

The title should clearly and straightforwardly describe the content of the table or figure. Omit articles to keep it concise.

The table or figure number appears on its own line, in bold, followed by the title on the following line, in italics and title case.

Where a table or figure needs further explanation, notes should be included immediately after it. These are not your analysis of the data presented; save that for the main text.

There are three kinds of notes: general , specific , and probability . Each type of note appears in a new paragraph, but multiple notes of the same kind all appear in one paragraph.

Only include the notes that are needed to understand the table or figure. It may be that it is clear in itself, and has no notes, or only probability notes; be as concise as possible.

General notes

General notes come first. They are preceded by the word “ Note ” in italics, followed by a period. They include any explanations that apply to the table or figure as a whole and a citation if it was adapted from another source, and they end with definitions of any abbreviations used.

Specific notes

Specific notes refer to specific points in the table or figure. Superscript letters (a, b, c …) appear at the relevant points in the table or figure and at the start of each note to indicate what they refer to. They are used when it’s necessary to comment on a specific data point or term.

Probability notes

Probability notes give p -values for the data in the table or figure. They correspond to asterisks (and/or other symbols) in the table or figure.

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You have two options for the placement of tables and figures in APA Style:

  • Option 1: Place tables and figures throughout your text, shortly after the parts of the text that refer to them.
  • Option 2: Place them all together at the end of your text (after the reference list) to avoid breaking up the text.

If you place them throughout the text, note that each table or figure should only appear once. If you refer to the same table or figure more than once, don’t reproduce it each time—just place it after the paragraph in which it’s first discussed.

Align the table or figure with the text along the left margin. Leave a line break before and after the table or figure to clearly distinguish it from the main text, and place it on a new page if necessary to avoid splitting it across multiple pages.

Placement of tables in APA format

If you place all your tables and figures at the end, you should have one table or figure on each page. Begin with all your tables, then place all your figures afterwards.

Avoid making redundant statements about your tables and figures in your text. When you write about data from tables and figures, it should be to highlight or analyze a particular data point or trend, not simply to restate what is already clearly shown in the table or figure:

  • As Table 1 shows, there are 115 boys in Grade 4, 130 in Grade 5, and 117 in Grade 6 …
  • Table 1 indicates a notable preponderance of boys in Grade 5. It is important to take this into account because …

Additionally, even if you have embedded your tables and figures in your text, refer to them by their numbers, not by their position relative to the text or by description:

  • The table below shows…
  • Table 1 shows…
  • As can be seen in the image on page 4…
  • As can be seen in Figure 3…
  • The photograph of a bald eagle is an example of…
  • Figure 1 is an example of…

In an APA Style paper , use a table or figure when it’s a clearer way to present important data than describing it in your main text. This is often the case when you need to communicate a large amount of information.

Before including a table or figure in your text, always reflect on whether it’s useful to your readers’ understanding:

  • Could this information be quickly summarized in the text instead?
  • Is it important to your arguments?
  • Does the table or figure require too much explanation to be efficient?

If the data you need to present only contains a few relevant numbers, try summarizing it in the text (potentially including full data in an appendix ). If describing the data makes your text overly long and difficult to read, a table or figure may be the best option.

APA doesn’t require you to include a list of tables or a list of figures . However, it is advisable to do so if your text is long enough to feature a table of contents and it includes a lot of tables and/or figures.

A list of tables and list of figures appear (in that order) after your table of contents , and are presented in a similar way.

If you adapt or reproduce a table or figure from another source, you should include that source in your APA reference list . You should also acknowledge the original source in the note or caption for the table or figure.

Tables and figures you created yourself, based on your own data, are not included in the reference list.

In most styles, the title page is used purely to provide information and doesn’t include any images. Ask your supervisor if you are allowed to include an image on the title page before doing so. If you do decide to include one, make sure to check whether you need permission from the creator of the image.

Include a note directly beneath the image acknowledging where it comes from, beginning with the word “ Note .” (italicized and followed by a period). Include a citation and copyright attribution . Don’t title, number, or label the image as a figure , since it doesn’t appear in your main text.

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