Descriptive studies
OR=Odds ratio; RR=Relative risk; RCT= Randomized controlled trial; PPV: positive predictive value; NPV: negative predictive value; PLR: positive likelihood ratio; NLR: negative likelihood ratio; DOR: diagnostic odds ratio
A systematic review ends with the interpretation of results. At this stage, the results of the study are summarized and the conclusions are presented to improve clinical and therapeutic decision-making. A systematic review with or without meta-analysis provides the best evidence available in the hierarchy of evidence-based practice.[ 14 ] Using meta-analysis can provide explicit conclusions. Conceptually, meta-analysis is used to combine the results of two or more studies that are similar to the specific intervention and the similar outcomes. In meta-analysis, instead of the simple average of the results of various studies, the weighted average of studies is reported, meaning studies with larger sample sizes account for more weight. To combine the results of various studies, we can use two models of fixed and random effects. In the fixed-effect model, it is assumed that the parameters studied are constant in all studies, and in the random-effect model, the measured parameter is assumed to be distributed between the studies and each study has measured some of it. This model offers a more conservative estimate.[ 40 ]
Three types of homogeneity tests can be used: (1) forest plot, (2) Cochrane's Q test (Chi-squared), and (3) Higgins I 2 statistics. In the forest plot, more overlap between confidence intervals indicates more homogeneity. In the Q statistic, when the P value is less than 0.1, it indicates heterogeneity exists and a random-effect model should be used.[ 41 ] Various tests such as the I 2 index are used to determine heterogeneity, values between 0 and 100; the values below 25%, between 25% and 50%, and above 75% indicate low, moderate, and high levels of heterogeneity, respectively.[ 26 , 42 ] The results of the meta-analyzing study are presented graphically using the forest plot, which shows the statistical weight of each study with a 95% confidence interval and a standard error of the mean.[ 40 ]
The importance of meta-analyses and systematic reviews in providing evidence useful in making clinical and policy decisions is ever-increasing. Nevertheless, they are prone to publication bias that occurs when positive or significant results are preferred for publication.[ 43 ] Song maintains that studies reporting a certain direction of results or powerful correlations may be more likely to be published than the studies which do not.[ 44 ] In addition, when searching for meta-analyses, gray literature (e.g., dissertations, conference abstracts, or book chapters) and unpublished studies may be missed. Moreover, meta-analyses only based on published studies may exaggerate the estimates of effect sizes; as a result, patients may be exposed to harmful or ineffective treatment methods.[ 44 , 45 ] However, there are some tests that can help in detecting negative expected results that are not included in a review due to publication bias.[ 46 ] In addition, publication bias can be reduced through searching for data that are not published.
Systematic reviews and meta-analyses have certain advantages; some of the most important ones are as follows: examining differences in the findings of different studies, summarizing results from various studies, increased accuracy of estimating effects, increased statistical power, overcoming problems related to small sample sizes, resolving controversies from disagreeing studies, increased generalizability of results, determining the possible need for new studies, overcoming the limitations of narrative reviews, and making new hypotheses for further research.[ 47 , 48 ]
Despite the importance of systematic reviews, the author may face numerous problems in searching, screening, and synthesizing data during this process. A systematic review requires extensive access to databases and journals that can be costly for nonacademic researchers.[ 13 ] Also, in reviewing the inclusion and exclusion criteria, the inevitable mindsets of browsers may be involved and the criteria are interpreted differently from each other.[ 49 ] Lee refers to some disadvantages of these studies, the most significant ones are as follows: a research field cannot be summarized by one number, publication bias, heterogeneity, combining unrelated things, being vulnerable to subjectivity, failing to account for all confounders, comparing variables that are not comparable, just focusing on main effects, and possible inconsistency with results of randomized trials.[ 47 ] Different types of programs are available to perform meta-analysis. Some of the most commonly used statistical programs are general statistical packages, including SAS, SPSS, R, and Stata. Using flexible commands in these programs, meta-analyses can be easily run and the results can be readily plotted out. However, these statistical programs are often expensive. An alternative to using statistical packages is to use programs designed for meta-analysis, including Metawin, RevMan, and Comprehensive Meta-analysis. However, these programs may have limitations, including that they can accept few data formats and do not provide much opportunity to set the graphical display of findings. Another alternative is to use Microsoft Excel. Although it is not a free software, it is usually found in many computers.[ 20 , 50 ]
A systematic review study is a powerful and valuable tool for answering research questions, generating new hypotheses, and identifying areas where there is a lack of tangible knowledge. A systematic review study provides an excellent opportunity for researchers to improve critical assessment and evidence synthesis skills.
All authors contributed equally to this work.
Conflicts of interest.
There are no conflicts of interest.
Writing-a-literature-review-six-steps-to-get-you-from-start-to-finish.
Tanya Golash-Boza, Associate Professor of Sociology, University of California
February 03, 2022
Writing a literature review is often the most daunting part of writing an article, book, thesis, or dissertation. “The literature” seems (and often is) massive. I have found it helpful to be as systematic as possible when completing this gargantuan task.
Sonja Foss and William Walters* describe an efficient and effective way of writing a literature review. Their system provides an excellent guide for getting through the massive amounts of literature for any purpose: in a dissertation, an M.A. thesis, or preparing a research article for publication in any field of study. Below is a summary of the steps they outline as well as a step-by-step method for writing a literature review.
Step One: Decide on your areas of research:
Before you begin to search for articles or books, decide beforehand what areas you are going to research. Make sure that you only get articles and books in those areas, even if you come across fascinating books in other areas. A literature review I am currently working on, for example, explores barriers to higher education for undocumented students.
Step Two: Search for the literature:
Conduct a comprehensive bibliographic search of books and articles in your area. Read the abstracts online and download and/or print those articles that pertain to your area of research. Find books in the library that are relevant and check them out. Set a specific time frame for how long you will search. It should not take more than two or three dedicated sessions.
Step Three: Find relevant excerpts in your books and articles:
Skim the contents of each book and article and look specifically for these five things:
1. Claims, conclusions, and findings about the constructs you are investigating
2. Definitions of terms
3. Calls for follow-up studies relevant to your project
4. Gaps you notice in the literature
5. Disagreement about the constructs you are investigating
When you find any of these five things, type the relevant excerpt directly into a Word document. Don’t summarize, as summarizing takes longer than simply typing the excerpt. Make sure to note the name of the author and the page number following each excerpt. Do this for each article and book that you have in your stack of literature. When you are done, print out your excerpts.
Step Four: Code the literature:
Get out a pair of scissors and cut each excerpt out. Now, sort the pieces of paper into similar topics. Figure out what the main themes are. Place each excerpt into a themed pile. Make sure each note goes into a pile. If there are excerpts that you can’t figure out where they belong, separate those and go over them again at the end to see if you need new categories. When you finish, place each stack of notes into an envelope labeled with the name of the theme.
Step Five: Create Your Conceptual Schema:
Type, in large font, the name of each of your coded themes. Print this out, and cut the titles into individual slips of paper. Take the slips of paper to a table or large workspace and figure out the best way to organize them. Are there ideas that go together or that are in dialogue with each other? Are there ideas that contradict each other? Move around the slips of paper until you come up with a way of organizing the codes that makes sense. Write the conceptual schema down before you forget or someone cleans up your slips of paper.
Step Six: Begin to Write Your Literature Review:
Choose any section of your conceptual schema to begin with. You can begin anywhere, because you already know the order. Find the envelope with the excerpts in them and lay them on the table in front of you. Figure out a mini-conceptual schema based on that theme by grouping together those excerpts that say the same thing. Use that mini-conceptual schema to write up your literature review based on the excerpts that you have in front of you. Don’t forget to include the citations as you write, so as not to lose track of who said what. Repeat this for each section of your literature review.
Once you complete these six steps, you will have a complete draft of your literature review. The great thing about this process is that it breaks down into manageable steps something that seems enormous: writing a literature review.
I think that Foss and Walter’s system for writing the literature review is ideal for a dissertation, because a Ph.D. candidate has already read widely in his or her field through graduate seminars and comprehensive exams.
It may be more challenging for M.A. students, unless you are already familiar with the literature. It is always hard to figure out how much you need to read for deep meaning, and how much you just need to know what others have said. That balance will depend on how much you already know.
For people writing literature reviews for articles or books, this system also could work, especially when you are writing in a field with which you are already familiar. The mere fact of having a system can make the literature review seem much less daunting, so I recommend this system for anyone who feels overwhelmed by the prospect of writing a literature review.
*Destination Dissertation: A Traveler's Guide to a Done Dissertation
Image Credit/Source: Goldmund Lukic/Getty Images
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3 options to help structure your chapter.
By: Amy Rommelspacher (PhD) | Reviewer: Dr Eunice Rautenbach | November 2020 (Updated May 2023)
Writing the literature review chapter can seem pretty daunting when you’re piecing together your dissertation or thesis. As we’ve discussed before , a good literature review needs to achieve a few very important objectives – it should:
To achieve this, your literature review needs a well-thought-out structure . Get the structure of your literature review chapter wrong and you’ll struggle to achieve these objectives. Don’t worry though – in this post, we’ll look at how to structure your literature review for maximum impact (and marks!).
Deciding on the structure of your literature review should come towards the end of the literature review process – after you have collected and digested the literature, but before you start writing the chapter.
In other words, you need to first develop a rich understanding of the literature before you even attempt to map out a structure. There’s no use trying to develop a structure before you’ve fully wrapped your head around the existing research.
Equally importantly, you need to have a structure in place before you start writing , or your literature review will most likely end up a rambling, disjointed mess.
Importantly, don’t feel that once you’ve defined a structure you can’t iterate on it. It’s perfectly natural to adjust as you engage in the writing process. As we’ve discussed before , writing is a way of developing your thinking, so it’s quite common for your thinking to change – and therefore, for your chapter structure to change – as you write.
Like any other chapter in your thesis or dissertation, your literature review needs to have a clear, logical structure. At a minimum, it should have three essential components – an introduction , a body and a conclusion .
Let’s take a closer look at each of these.
Just like any good introduction, the introduction section of your literature review should introduce the purpose and layout (organisation) of the chapter. In other words, your introduction needs to give the reader a taste of what’s to come, and how you’re going to lay that out. Essentially, you should provide the reader with a high-level roadmap of your chapter to give them a taste of the journey that lies ahead.
Here’s an example of the layout visualised in a literature review introduction:
Your introduction should also outline your topic (including any tricky terminology or jargon) and provide an explanation of the scope of your literature review – in other words, what you will and won’t be covering (the delimitations ). This helps ringfence your review and achieve a clear focus . The clearer and narrower your focus, the deeper you can dive into the topic (which is typically where the magic lies).
Depending on the nature of your project, you could also present your stance or point of view at this stage. In other words, after grappling with the literature you’ll have an opinion about what the trends and concerns are in the field as well as what’s lacking. The introduction section can then present these ideas so that it is clear to examiners that you’re aware of how your research connects with existing knowledge .
The body of your literature review is the centre of your work. This is where you’ll present, analyse, evaluate and synthesise the existing research. In other words, this is where you’re going to earn (or lose) the most marks. Therefore, it’s important to carefully think about how you will organise your discussion to present it in a clear way.
The body of your literature review should do just as the description of this chapter suggests. It should “review” the literature – in other words, identify, analyse, and synthesise it. So, when thinking about structuring your literature review, you need to think about which structural approach will provide the best “review” for your specific type of research and objectives (we’ll get to this shortly).
There are (broadly speaking) three options for organising your literature review.
Organising the literature chronologically is one of the simplest ways to structure your literature review. You start with what was published first and work your way through the literature until you reach the work published most recently. Pretty straightforward.
The benefit of this option is that it makes it easy to discuss the developments and debates in the field as they emerged over time. Organising your literature chronologically also allows you to highlight how specific articles or pieces of work might have changed the course of the field – in other words, which research has had the most impact . Therefore, this approach is very useful when your research is aimed at understanding how the topic has unfolded over time and is often used by scholars in the field of history. That said, this approach can be utilised by anyone that wants to explore change over time .
For example , if a student of politics is investigating how the understanding of democracy has evolved over time, they could use the chronological approach to provide a narrative that demonstrates how this understanding has changed through the ages.
Here are some questions you can ask yourself to help you structure your literature review chronologically.
In some ways, chronology plays a part whichever way you decide to structure your literature review, because you will always, to a certain extent, be analysing how the literature has developed. However, with the chronological approach, the emphasis is very firmly on how the discussion has evolved over time , as opposed to how all the literature links together (which we’ll discuss next ).
The thematic approach to structuring a literature review means organising your literature by theme or category – for example, by independent variables (i.e. factors that have an impact on a specific outcome).
As you’ve been collecting and synthesising literature , you’ll likely have started seeing some themes or patterns emerging. You can then use these themes or patterns as a structure for your body discussion. The thematic approach is the most common approach and is useful for structuring literature reviews in most fields.
For example, if you were researching which factors contributed towards people trusting an organisation, you might find themes such as consumers’ perceptions of an organisation’s competence, benevolence and integrity. Structuring your literature review thematically would mean structuring your literature review’s body section to discuss each of these themes, one section at a time.
Here are some questions to ask yourself when structuring your literature review by themes:
PS – you can see an example of a thematically structured literature review in our literature review sample walkthrough video here.
The methodological option is a way of structuring your literature review by the research methodologies used . In other words, organising your discussion based on the angle from which each piece of research was approached – for example, qualitative , quantitative or mixed methodologies.
Structuring your literature review by methodology can be useful if you are drawing research from a variety of disciplines and are critiquing different methodologies. The point of this approach is to question how existing research has been conducted, as opposed to what the conclusions and/or findings the research were.
For example, a sociologist might centre their research around critiquing specific fieldwork practices. Their literature review will then be a summary of the fieldwork methodologies used by different studies.
Here are some questions you can ask yourself when structuring your literature review according to methodology:
Once you’ve completed the body section of your literature review using one of the structural approaches we discussed above, you’ll need to “wrap up” your literature review and pull all the pieces together to set the direction for the rest of your dissertation or thesis.
The conclusion is where you’ll present the key findings of your literature review. In this section, you should emphasise the research that is especially important to your research questions and highlight the gaps that exist in the literature. Based on this, you need to make it clear what you will add to the literature – in other words, justify your own research by showing how it will help fill one or more of the gaps you just identified.
Last but not least, if it’s your intention to develop a conceptual framework for your dissertation or thesis, the conclusion section is a good place to present this.
In the video below, we unpack a literature review chapter so that you can see an example of a thematically structure review in practice.
In this article, we’ve discussed how to structure your literature review for maximum impact. Here’s a quick recap of what you need to keep in mind when deciding on your literature review structure:
If you’re ready to get started, be sure to download our free literature review template to fast-track your chapter outline.
This post is an extract from our bestselling short course, Literature Review Bootcamp . If you want to work smart, you don't want to miss this .
Great work. This is exactly what I was looking for and helps a lot together with your previous post on literature review. One last thing is missing: a link to a great literature chapter of an journal article (maybe with comments of the different sections in this review chapter). Do you know any great literature review chapters?
I agree with you Marin… A great piece
I agree with Marin. This would be quite helpful if you annotate a nicely structured literature from previously published research articles.
Awesome article for my research.
I thank you immensely for this wonderful guide
It is indeed thought and supportive work for the futurist researcher and students
Very educative and good time to get guide. Thank you
Great work, very insightful. Thank you.
Thanks for this wonderful presentation. My question is that do I put all the variables into a single conceptual framework or each hypothesis will have it own conceptual framework?
Thank you very much, very helpful
This is very educative and precise . Thank you very much for dropping this kind of write up .
Pheeww, so damn helpful, thank you for this informative piece.
I’m doing a research project topic ; stool analysis for parasitic worm (enteric) worm, how do I structure it, thanks.
comprehensive explanation. Help us by pasting the URL of some good “literature review” for better understanding.
great piece. thanks for the awesome explanation. it is really worth sharing. I have a little question, if anyone can help me out, which of the options in the body of literature can be best fit if you are writing an architectural thesis that deals with design?
I am doing a research on nanofluids how can l structure it?
Beautifully clear.nThank you!
Lucid! Thankyou!
Brilliant work, well understood, many thanks
I like how this was so clear with simple language 😊😊 thank you so much 😊 for these information 😊
Insightful. I was struggling to come up with a sensible literature review but this has been really helpful. Thank you!
You have given thought-provoking information about the review of the literature.
Thank you. It has made my own research better and to impart your work to students I teach
I learnt a lot from this teaching. It’s a great piece.
I am doing research on EFL teacher motivation for his/her job. How Can I structure it? Is there any detailed template, additional to this?
You are so cool! I do not think I’ve read through something like this before. So nice to find somebody with some genuine thoughts on this issue. Seriously.. thank you for starting this up. This site is one thing that is required on the internet, someone with a little originality!
I’m asked to do conceptual, theoretical and empirical literature, and i just don’t know how to structure it
Asking questions are actually fastidious thing if you are not understanding anything fully, but this article presents good understanding yet.
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What is a literature review.
A literature review is much more than an annotated bibliography or a list of separate reviews of articles and books. It is a critical, analytical summary and synthesis of the current knowledge of a topic. Thus it should compare and relate different theories, findings, etc, rather than just summarize them individually. In addition, it should have a particular focus or theme to organize the review. It does not have to be an exhaustive account of everything published on the topic, but it should discuss all the significant academic literature and other relevant sources important for that focus.
This is meant to be a general guide to writing a literature review: ways to structure one, what to include, how it supplements other research. For more specific help on writing a review, and especially for help on finding the literature to review, sign up for a Personal Research Session .
The specific organization of a literature review depends on the type and purpose of the review, as well as on the specific field or topic being reviewed. But in general, it is a relatively brief but thorough exploration of past and current work on a topic. Rather than a chronological listing of previous work, though, literature reviews are usually organized thematically, such as different theoretical approaches, methodologies, or specific issues or concepts involved in the topic. A thematic organization makes it much easier to examine contrasting perspectives, theoretical approaches, methodologies, findings, etc, and to analyze the strengths and weaknesses of, and point out any gaps in, previous research. And this is the heart of what a literature review is about. A literature review may offer new interpretations, theoretical approaches, or other ideas; if it is part of a research proposal or report it should demonstrate the relationship of the proposed or reported research to others' work; but whatever else it does, it must provide a critical overview of the current state of research efforts.
Literature reviews are common and very important in the sciences and social sciences. They are less common and have a less important role in the humanities, but they do have a place, especially stand-alone reviews.
Types of Literature Reviews
There are different types of literature reviews, and different purposes for writing a review, but the most common are:
A literature review for a research report is often a revision of the review for a research proposal, which can be a revision of a stand-alone review. Each revision should be a fairly extensive revision. With the increased knowledge of and experience in the topic as you proceed, your understanding of the topic will increase. Thus, you will be in a better position to analyze and critique the literature. In addition, your focus will change as you proceed in your research. Some areas of the literature you initially reviewed will be marginal or irrelevant for your eventual research, and you will need to explore other areas more thoroughly.
Examples of Literature Reviews
See the series of Annual Reviews of *Subject* which are specifically devoted to literature review articles to find many examples of stand-alone literature reviews in the biomedical, physical, and social sciences.
Research report articles vary in how they are organized, but a common general structure is to have sections such as:
Here are some articles that illustrate variations on this theme. There is no need to read the entire articles (unless the contents interest you); just quickly browse through to see the sections, and see how each section is introduced and what is contained in them.
The Determinants of Undergraduate Grade Point Average: The Relative Importance of Family Background, High School Resources, and Peer Group Effects , in The Journal of Human Resources , v. 34 no. 2 (Spring 1999), p. 268-293.
This article has a standard breakdown of sections:
First Encounters of the Bureaucratic Kind: Early Freshman Experiences with a Campus Bureaucracy , in The Journal of Higher Education , v. 67 no. 6 (Nov-Dec 1996), p. 660-691.
This one does not have a section specifically labeled as a "literature review" or "review of the literature," but the first few sections cite a long list of other sources discussing previous research in the area before the authors present their own study they are reporting.
A literature review is an integrated analysis -- not just a summary-- of scholarly writings and other relevant evidence related directly to your research question. That is, it represents a synthesis of the evidence that provides background information on your topic and shows a association between the evidence and your research question.
A literature review may be a stand alone work or the introduction to a larger research paper, depending on the assignment. Rely heavily on the guidelines your instructor has given you.
Why is it important?
A literature review is important because it:
APA Style Blog - for those harder to find answers
Your literature review should be guided by your central research question. The literature represents background and research developments related to a specific research question, interpreted and analyzed by you in a synthesized way.
How many studies do you need to look at? How comprehensive should it be? How many years should it cover?
Make a list of the databases you will search.
Where to find databases:
Some questions to help you analyze the research:
Tips:
Run a free plagiarism check in 10 minutes, generate accurate citations for free.
Published on August 21, 2022 by Shona McCombes . Revised on July 18, 2023.
The discussion section is where you delve into the meaning, importance, and relevance of your results .
It should focus on explaining and evaluating what you found, showing how it relates to your literature review and paper or dissertation topic , and making an argument in support of your overall conclusion. It should not be a second results section.
There are different ways to write this section, but you can focus your writing around these key elements:
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What not to include in your discussion section, step 1: summarize your key findings, step 2: give your interpretations, step 3: discuss the implications, step 4: acknowledge the limitations, step 5: share your recommendations, discussion section example, other interesting articles, frequently asked questions about discussion sections.
There are a few common mistakes to avoid when writing the discussion section of your paper.
The AI-powered Citation Checker helps you avoid common mistakes such as:
Start this section by reiterating your research problem and concisely summarizing your major findings. To speed up the process you can use a summarizer to quickly get an overview of all important findings. Don’t just repeat all the data you have already reported—aim for a clear statement of the overall result that directly answers your main research question . This should be no more than one paragraph.
Many students struggle with the differences between a discussion section and a results section . The crux of the matter is that your results sections should present your results, and your discussion section should subjectively evaluate them. Try not to blend elements of these two sections, in order to keep your paper sharp.
The meaning of your results may seem obvious to you, but it’s important to spell out their significance for your reader, showing exactly how they answer your research question.
The form of your interpretations will depend on the type of research, but some typical approaches to interpreting the data include:
You can organize your discussion around key themes, hypotheses, or research questions, following the same structure as your results section. Alternatively, you can also begin by highlighting the most significant or unexpected results.
As well as giving your own interpretations, make sure to relate your results back to the scholarly work that you surveyed in the literature review . The discussion should show how your findings fit with existing knowledge, what new insights they contribute, and what consequences they have for theory or practice.
Ask yourself these questions:
Your overall aim is to show the reader exactly what your research has contributed, and why they should care.
Professional editors proofread and edit your paper by focusing on:
See an example
Even the best research has its limitations. Acknowledging these is important to demonstrate your credibility. Limitations aren’t about listing your errors, but about providing an accurate picture of what can and cannot be concluded from your study.
Limitations might be due to your overall research design, specific methodological choices , or unanticipated obstacles that emerged during your research process.
Here are a few common possibilities:
After noting the limitations, you can reiterate why the results are nonetheless valid for the purpose of answering your research question.
Based on the discussion of your results, you can make recommendations for practical implementation or further research. Sometimes, the recommendations are saved for the conclusion .
Suggestions for further research can lead directly from the limitations. Don’t just state that more studies should be done—give concrete ideas for how future work can build on areas that your own research was unable to address.
If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools!
Research bias
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In the discussion , you explore the meaning and relevance of your research results , explaining how they fit with existing research and theory. Discuss:
The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.
In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them.
In a thesis or dissertation, the discussion is an in-depth exploration of the results, going into detail about the meaning of your findings and citing relevant sources to put them in context.
The conclusion is more shorter and more general: it concisely answers your main research question and makes recommendations based on your overall findings.
If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.
McCombes, S. (2023, July 18). How to Write a Discussion Section | Tips & Examples. Scribbr. Retrieved September 3, 2024, from https://www.scribbr.com/dissertation/discussion/
Other students also liked, how to write a literature review | guide, examples, & templates, what is a research methodology | steps & tips, how to write a results section | tips & examples, what is your plagiarism score.
The introduction to a literature review serves as your reader’s guide through your academic work and thought process. Explore the significance of literature review introductions in review papers, academic papers, essays, theses, and dissertations. We delve into the purpose and necessity of these introductions, explore the essential components of literature review introductions, and provide step-by-step guidance on how to craft your own, along with examples.
In academic writing , the introduction for a literature review is an indispensable component. Effective academic writing requires proper paragraph structuring to guide your reader through your argumentation. This includes providing an introduction to your literature review.
It is imperative to remember that you should never start sharing your findings abruptly. Even if there isn’t a dedicated introduction section .
There are three main scenarios in which you need an introduction for a literature review:
It is crucial to customize the content and depth of your literature review introduction according to the specific format of your academic work.
In practical terms, this implies, for instance, that the introduction in an academic literature review paper, especially one derived from a systematic literature review , is quite comprehensive. Particularly compared to the rather brief one or two introductory sentences that are often found at the beginning of a literature review section in a standard academic paper. The introduction to the literature review chapter in a thesis or dissertation again adheres to different standards.
The introduction of an academic literature review paper, which does not rely on empirical data, often necessitates a more extensive introduction than the brief literature review introductions typically found in empirical papers. It should encompass:
In a standard 8000-word journal article, the literature review section typically spans between 750 and 1250 words. The first few sentences or the first paragraph within this section often serve as an introduction. It should encompass:
In some cases, you might include:
Some students choose to incorporate a brief introductory section at the beginning of each chapter, including the literature review chapter. Alternatively, others opt to seamlessly integrate the introduction into the initial sentences of the literature review itself. Both approaches are acceptable, provided that you incorporate the following elements:
Example 1: an effective introduction for an academic literature review paper.
To begin, let’s delve into the introduction of an academic literature review paper. We will examine the paper “How does culture influence innovation? A systematic literature review”, which was published in 2018 in the journal Management Decision.
The second example represents a typical academic paper, encompassing not only a literature review section but also empirical data, a case study, and other elements. We will closely examine the introduction to the literature review section in the paper “The environmentalism of the subalterns: a case study of environmental activism in Eastern Kurdistan/Rojhelat”, which was published in 2021 in the journal Local Environment.
The paper begins with a general introduction and then proceeds to the literature review, designated by the authors as their conceptual framework. Of particular interest is the first paragraph of this conceptual framework, comprising 142 words across five sentences:
Thus, the author successfully introduces the literature review, from which point onward it dives into the main concept (‘subalternity’) of the research, and reviews the literature on socio-economic justice and environmental degradation.
Numerous universities offer online repositories where you can access theses and dissertations from previous years, serving as valuable sources of reference. Many of these repositories, however, may require you to log in through your university account. Nevertheless, a few open-access repositories are accessible to anyone, such as the one by the University of Manchester . It’s important to note though that copyright restrictions apply to these resources, just as they would with published papers.
Phd thesis literature review chapter introduction, phd thesis literature review introduction.
The last example is the doctoral thesis Metacognitive strategies and beliefs: Child correlates and early experiences Chan, K. Y. M. (Author). 31 Dec 2020 . The author clearly conducted a systematic literature review, commencing the review section with a discussion of the methodology and approach employed in locating and analyzing the selected records.
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Humanities and Social Sciences Communications volume 11 , Article number: 1115 ( 2024 ) Cite this article
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The rapid expansion of information technology and the intensification of population aging are two prominent features of contemporary societal development. Investigating older adults’ acceptance and use of technology is key to facilitating their integration into an information-driven society. Given this context, the technology acceptance of older adults has emerged as a prioritized research topic, attracting widespread attention in the academic community. However, existing research remains fragmented and lacks a systematic framework. To address this gap, we employed bibliometric methods, utilizing the Web of Science Core Collection to conduct a comprehensive review of literature on older adults’ technology acceptance from 2013 to 2023. Utilizing VOSviewer and CiteSpace for data assessment and visualization, we created knowledge mappings of research on older adults’ technology acceptance. Our study employed multidimensional methods such as co-occurrence analysis, clustering, and burst analysis to: (1) reveal research dynamics, key journals, and domains in this field; (2) identify leading countries, their collaborative networks, and core research institutions and authors; (3) recognize the foundational knowledge system centered on theoretical model deepening, emerging technology applications, and research methods and evaluation, uncovering seminal literature and observing a shift from early theoretical and influential factor analyses to empirical studies focusing on individual factors and emerging technologies; (4) moreover, current research hotspots are primarily in the areas of factors influencing technology adoption, human-robot interaction experiences, mobile health management, and aging-in-place technology, highlighting the evolutionary context and quality distribution of research themes. Finally, we recommend that future research should deeply explore improvements in theoretical models, long-term usage, and user experience evaluation. Overall, this study presents a clear framework of existing research in the field of older adults’ technology acceptance, providing an important reference for future theoretical exploration and innovative applications.
Introduction.
In contemporary society, the rapid development of information technology has been intricately intertwined with the intensifying trend of population aging. According to the latest United Nations forecast, by 2050, the global population aged 65 and above is expected to reach 1.6 billion, representing about 16% of the total global population (UN 2023 ). Given the significant challenges of global aging, there is increasing evidence that emerging technologies have significant potential to maintain health and independence for older adults in their home and healthcare environments (Barnard et al. 2013 ; Soar 2010 ; Vancea and Solé-Casals 2016 ). This includes, but is not limited to, enhancing residential safety with smart home technologies (Touqeer et al. 2021 ; Wang et al. 2022 ), improving living independence through wearable technologies (Perez et al. 2023 ), and increasing medical accessibility via telehealth services (Kruse et al. 2020 ). Technological innovations are redefining the lifestyles of older adults, encouraging a shift from passive to active participation (González et al. 2012 ; Mostaghel 2016 ). Nevertheless, the effective application and dissemination of technology still depends on user acceptance and usage intentions (Naseri et al. 2023 ; Wang et al. 2023a ; Xia et al. 2024 ; Yu et al. 2023 ). Particularly, older adults face numerous challenges in accepting and using new technologies. These challenges include not only physical and cognitive limitations but also a lack of technological experience, along with the influences of social and economic factors (Valk et al. 2018 ; Wilson et al. 2021 ).
User acceptance of technology is a significant focus within information systems (IS) research (Dai et al. 2024 ), with several models developed to explain and predict user behavior towards technology usage, including the Technology Acceptance Model (TAM) (Davis 1989 ), TAM2, TAM3, and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al. 2003 ). Older adults, as a group with unique needs, exhibit different behavioral patterns during technology acceptance than other user groups, and these uniquenesses include changes in cognitive abilities, as well as motivations, attitudes, and perceptions of the use of new technologies (Chen and Chan 2011 ). The continual expansion of technology introduces considerable challenges for older adults, rendering the understanding of their technology acceptance a research priority. Thus, conducting in-depth research into older adults’ acceptance of technology is critically important for enhancing their integration into the information society and improving their quality of life through technological advancements.
Reviewing relevant literature to identify research gaps helps further solidify the theoretical foundation of the research topic. However, many existing literature reviews primarily focus on the factors influencing older adults’ acceptance or intentions to use technology. For instance, Ma et al. ( 2021 ) conducted a comprehensive analysis of the determinants of older adults’ behavioral intentions to use technology; Liu et al. ( 2022 ) categorized key variables in studies of older adults’ technology acceptance, noting a shift in focus towards social and emotional factors; Yap et al. ( 2022 ) identified seven categories of antecedents affecting older adults’ use of technology from an analysis of 26 articles, including technological, psychological, social, personal, cost, behavioral, and environmental factors; Schroeder et al. ( 2023 ) extracted 119 influencing factors from 59 articles and further categorized these into six themes covering demographics, health status, and emotional awareness. Additionally, some studies focus on the application of specific technologies, such as Ferguson et al. ( 2021 ), who explored barriers and facilitators to older adults using wearable devices for heart monitoring, and He et al. ( 2022 ) and Baer et al. ( 2022 ), who each conducted in-depth investigations into the acceptance of social assistive robots and mobile nutrition and fitness apps, respectively. In summary, current literature reviews on older adults’ technology acceptance exhibit certain limitations. Due to the interdisciplinary nature and complex knowledge structure of this field, traditional literature reviews often rely on qualitative analysis, based on literature analysis and periodic summaries, which lack sufficient objectivity and comprehensiveness. Additionally, systematic research is relatively limited, lacking a macroscopic description of the research trajectory from a holistic perspective. Over the past decade, research on older adults’ technology acceptance has experienced rapid growth, with a significant increase in literature, necessitating the adoption of new methods to review and examine the developmental trends in this field (Chen 2006 ; Van Eck and Waltman 2010 ). Bibliometric analysis, as an effective quantitative research method, analyzes published literature through visualization, offering a viable approach to extracting patterns and insights from a large volume of papers, and has been widely applied in numerous scientific research fields (Achuthan et al. 2023 ; Liu and Duffy 2023 ). Therefore, this study will employ bibliometric methods to systematically analyze research articles related to older adults’ technology acceptance published in the Web of Science Core Collection from 2013 to 2023, aiming to understand the core issues and evolutionary trends in the field, and to provide valuable references for future related research. Specifically, this study aims to explore and answer the following questions:
RQ1: What are the research dynamics in the field of older adults’ technology acceptance over the past decade? What are the main academic journals and fields that publish studies related to older adults’ technology acceptance?
RQ2: How is the productivity in older adults’ technology acceptance research distributed among countries, institutions, and authors?
RQ3: What are the knowledge base and seminal literature in older adults’ technology acceptance research? How has the research theme progressed?
RQ4: What are the current hot topics and their evolutionary trajectories in older adults’ technology acceptance research? How is the quality of research distributed?
Research method.
In recent years, bibliometrics has become one of the crucial methods for analyzing literature reviews and is widely used in disciplinary and industrial intelligence analysis (Jing et al. 2023 ; Lin and Yu 2024a ; Wang et al. 2024a ; Xu et al. 2021 ). Bibliometric software facilitates the visualization analysis of extensive literature data, intuitively displaying the network relationships and evolutionary processes between knowledge units, and revealing the underlying knowledge structure and potential information (Chen et al. 2024 ; López-Robles et al. 2018 ; Wang et al. 2024c ). This method provides new insights into the current status and trends of specific research areas, along with quantitative evidence, thereby enhancing the objectivity and scientific validity of the research conclusions (Chen et al. 2023 ; Geng et al. 2024 ). VOSviewer and CiteSpace are two widely used bibliometric software tools in academia (Pan et al. 2018 ), recognized for their robust functionalities based on the JAVA platform. Although each has its unique features, combining these two software tools effectively constructs mapping relationships between literature knowledge units and clearly displays the macrostructure of the knowledge domains. Particularly, VOSviewer, with its excellent graphical representation capabilities, serves as an ideal tool for handling large datasets and precisely identifying the focal points and hotspots of research topics. Therefore, this study utilizes VOSviewer (version 1.6.19) and CiteSpace (version 6.1.R6), combined with in-depth literature analysis, to comprehensively examine and interpret the research theme of older adults’ technology acceptance through an integrated application of quantitative and qualitative methods.
Web of Science is a comprehensively recognized database in academia, featuring literature that has undergone rigorous peer review and editorial scrutiny (Lin and Yu 2024b ; Mongeon and Paul-Hus 2016 ; Pranckutė 2021 ). This study utilizes the Web of Science Core Collection as its data source, specifically including three major citation indices: Science Citation Index Expanded (SCIE), Social Sciences Citation Index (SSCI), and Arts & Humanities Citation Index (A&HCI). These indices encompass high-quality research literature in the fields of science, social sciences, and arts and humanities, ensuring the comprehensiveness and reliability of the data. We combined “older adults” with “technology acceptance” through thematic search, with the specific search strategy being: TS = (elder OR elderly OR aging OR ageing OR senile OR senior OR old people OR “older adult*”) AND TS = (“technology acceptance” OR “user acceptance” OR “consumer acceptance”). The time span of literature search is from 2013 to 2023, with the types limited to “Article” and “Review” and the language to “English”. Additionally, the search was completed by October 27, 2023, to avoid data discrepancies caused by database updates. The initial search yielded 764 journal articles. Given that searches often retrieve articles that are superficially relevant but actually non-compliant, manual screening post-search was essential to ensure the relevance of the literature (Chen et al. 2024 ). Through manual screening, articles significantly deviating from the research theme were eliminated and rigorously reviewed. Ultimately, this study obtained 500 valid sample articles from the Web of Science Core Collection. The complete PRISMA screening process is illustrated in Fig. 1 .
Presentation of the data culling process in detail.
Raw data exported from databases often contain multiple expressions of the same terminology (Nguyen and Hallinger 2020 ). To ensure the accuracy and consistency of data, it is necessary to standardize the raw data (Strotmann and Zhao 2012 ). This study follows the data standardization process proposed by Taskin and Al ( 2019 ), mainly executing the following operations:
(1) Standardization of author and institution names is conducted to address different name expressions for the same author. For instance, “Chan, Alan Hoi Shou” and “Chan, Alan H. S.” are considered the same author, and distinct authors with the same name are differentiated by adding identifiers. Diverse forms of institutional names are unified to address variations caused by name changes or abbreviations, such as standardizing “FRANKFURT UNIV APPL SCI” and “Frankfurt University of Applied Sciences,” as well as “Chinese University of Hong Kong” and “University of Hong Kong” to consistent names.
(2) Different expressions of journal names are unified. For example, “International Journal of Human-Computer Interaction” and “Int J Hum Comput Interact” are standardized to a single name. This ensures consistency in journal names and prevents misclassification of literature due to differing journal names. Additionally, it involves checking if the journals have undergone name changes in the past decade to prevent any impact on the analysis due to such changes.
(3) Keywords data are cleansed by removing words that do not directly pertain to specific research content (e.g., people, review), merging synonyms (e.g., “UX” and “User Experience,” “aging-in-place” and “aging in place”), and standardizing plural forms of keywords (e.g., “assistive technologies” and “assistive technology,” “social robots” and “social robot”). This reduces redundant information in knowledge mapping.
Distribution power (rq1), literature descriptive statistical analysis.
Table 1 presents a detailed descriptive statistical overview of the literature in the field of older adults’ technology acceptance. After deduplication using the CiteSpace software, this study confirmed a valid sample size of 500 articles. Authored by 1839 researchers, the documents encompass 792 research institutions across 54 countries and are published in 217 different academic journals. As of the search cutoff date, these articles have accumulated 13,829 citations, with an annual average of 1156 citations, and an average of 27.66 citations per article. The h-index, a composite metric of quantity and quality of scientific output (Kamrani et al. 2021 ), reached 60 in this study.
The number of publications and citations are significant indicators of the research field’s development, reflecting its continuity, attention, and impact (Ale Ebrahim et al. 2014 ). The ranking of annual publications and citations in the field of older adults’ technology acceptance studies is presented chronologically in Fig. 2A . The figure shows a clear upward trend in the amount of literature in this field. Between 2013 and 2017, the number of publications increased slowly and decreased in 2018. However, in 2019, the number of publications increased rapidly to 52 and reached a peak of 108 in 2022, which is 6.75 times higher than in 2013. In 2022, the frequency of document citations reached its highest point with 3466 citations, reflecting the widespread recognition and citation of research in this field. Moreover, the curve of the annual number of publications fits a quadratic function, with a goodness-of-fit R 2 of 0.9661, indicating that the number of future publications is expected to increase even more rapidly.
A Trends in trends in annual publications and citations (2013–2023). B Overlay analysis of the distribution of discipline fields.
Figure 2B shows that research on older adults’ technology acceptance involves the integration of multidisciplinary knowledge. According to Web of Science Categories, these 500 articles are distributed across 85 different disciplines. We have tabulated the top ten disciplines by publication volume (Table 2 ), which include Medical Informatics (75 articles, 15.00%), Health Care Sciences & Services (71 articles, 14.20%), Gerontology (61 articles, 12.20%), Public Environmental & Occupational Health (57 articles, 11.40%), and Geriatrics & Gerontology (52 articles, 10.40%), among others. The high output in these disciplines reflects the concentrated global academic interest in this comprehensive research topic. Additionally, interdisciplinary research approaches provide diverse perspectives and a solid theoretical foundation for studies on older adults’ technology acceptance, also paving the way for new research directions.
A dual-map overlay is a CiteSpace map superimposed on top of a base map, which shows the interrelationships between journals in different domains, representing the publication and citation activities in each domain (Chen and Leydesdorff 2014 ). The overlay map reveals the link between the citing domain (on the left side) and the cited domain (on the right side), reflecting the knowledge flow of the discipline at the journal level (Leydesdorff and Rafols 2012 ). We utilize the in-built Z-score algorithm of the software to cluster the graph, as shown in Fig. 3 .
The left side shows the citing journal, and the right side shows the cited journal.
Figure 3 shows the distribution of citing journals clusters for older adults’ technology acceptance on the left side, while the right side refers to the main cited journals clusters. Two knowledge flow citation trajectories were obtained; they are presented by the color of the cited regions, and the thickness of these trajectories is proportional to the Z-score scaled frequency of citations (Chen et al. 2014 ). Within the cited regions, the most popular fields with the most records covered are “HEALTH, NURSING, MEDICINE” and “PSYCHOLOGY, EDUCATION, SOCIAL”, and the elliptical aspect ratio of these two fields stands out. Fields have prominent elliptical aspect ratios, highlighting their significant influence on older adults’ technology acceptance research. Additionally, the major citation trajectories originate in these two areas and progress to the frontier research area of “PSYCHOLOGY, EDUCATION, HEALTH”. It is worth noting that the citation trajectory from “PSYCHOLOGY, EDUCATION, SOCIAL” has a significant Z-value (z = 6.81), emphasizing the significance and impact of this development path. In the future, “MATHEMATICS, SYSTEMS, MATHEMATICAL”, “MOLECULAR, BIOLOGY, IMMUNOLOGY”, and “NEUROLOGY, SPORTS, OPHTHALMOLOGY” may become emerging fields. The fields of “MEDICINE, MEDICAL, CLINICAL” may be emerging areas of cutting-edge research.
Table 3 provides statistics for the top ten journals by publication volume in the field of older adults’ technology acceptance. Together, these journals have published 137 articles, accounting for 27.40% of the total publications, indicating that there is no highly concentrated core group of journals in this field, with publications being relatively dispersed. Notably, Computers in Human Behavior , Journal of Medical Internet Research , and International Journal of Human-Computer Interaction each lead with 15 publications. In terms of citation metrics, International Journal of Medical Informatics and Computers in Human Behavior stand out significantly, with the former accumulating a total of 1,904 citations, averaging 211.56 citations per article, and the latter totaling 1,449 citations, with an average of 96.60 citations per article. These figures emphasize the academic authority and widespread impact of these journals within the research field.
Countries and collaborations analysis.
The analysis revealed the global research pattern for country distribution and collaboration (Chen et al. 2019 ). Figure 4A shows the network of national collaborations on older adults’ technology acceptance research. The size of the bubbles represents the amount of publications in each country, while the thickness of the connecting lines expresses the closeness of the collaboration among countries. Generally, this research subject has received extensive international attention, with China and the USA publishing far more than any other countries. China has established notable research collaborations with the USA, UK and Malaysia in this field, while other countries have collaborations, but the closeness is relatively low and scattered. Figure 4B shows the annual publication volume dynamics of the top ten countries in terms of total publications. Since 2017, China has consistently increased its annual publications, while the USA has remained relatively stable. In 2019, the volume of publications in each country increased significantly, this was largely due to the global outbreak of the COVID-19 pandemic, which has led to increased reliance on information technology among the elderly for medical consultations, online socialization, and health management (Sinha et al. 2021 ). This phenomenon has led to research advances in technology acceptance among older adults in various countries. Table 4 shows that the top ten countries account for 93.20% of the total cumulative number of publications, with each country having published more than 20 papers. Among these ten countries, all of them except China are developed countries, indicating that the research field of older adults’ technology acceptance has received general attention from developed countries. Currently, China and the USA were the leading countries in terms of publications with 111 and 104 respectively, accounting for 22.20% and 20.80%. The UK, Germany, Italy, and the Netherlands also made significant contributions. The USA and China ranked first and second in terms of the number of citations, while the Netherlands had the highest average citations, indicating the high impact and quality of its research. The UK has shown outstanding performance in international cooperation, while the USA highlights its significant academic influence in this field with the highest h-index value.
A National collaboration network. B Annual volume of publications in the top 10 countries.
Analyzing the number of publications and citations can reveal an institution’s or author’s research strength and influence in a particular research area (Kwiek 2021 ). Tables 5 and 6 show the statistics of the institutions and authors whose publication counts are in the top ten, respectively. As shown in Table 5 , higher education institutions hold the main position in this research field. Among the top ten institutions, City University of Hong Kong and The University of Hong Kong from China lead with 14 and 9 publications, respectively. City University of Hong Kong has the highest h-index, highlighting its significant influence in the field. It is worth noting that Tilburg University in the Netherlands is not among the top five in terms of publications, but the high average citation count (130.14) of its literature demonstrates the high quality of its research.
After analyzing the authors’ output using Price’s Law (Redner 1998 ), the highest number of publications among the authors counted ( n = 10) defines a publication threshold of 3 for core authors in this research area. As a result of quantitative screening, a total of 63 core authors were identified. Table 6 shows that Chen from Zhejiang University, China, Ziefle from RWTH Aachen University, Germany, and Rogers from Macquarie University, Australia, were the top three authors in terms of the number of publications, with 10, 9, and 8 articles, respectively. In terms of average citation rate, Peek and Wouters, both scholars from the Netherlands, have significantly higher rates than other scholars, with 183.2 and 152.67 respectively. This suggests that their research is of high quality and widely recognized. Additionally, Chen and Rogers have high h-indices in this field.
Research knowledge base.
Co-citation relationships occur when two documents are cited together (Zhang and Zhu 2022 ). Co-citation mapping uses references as nodes to represent the knowledge base of a subject area (Min et al. 2021). Figure 5A illustrates co-occurrence mapping in older adults’ technology acceptance research, where larger nodes signify higher co-citation frequencies. Co-citation cluster analysis can be used to explore knowledge structure and research boundaries (Hota et al. 2020 ; Shiau et al. 2023 ). The co-citation clustering mapping of older adults’ technology acceptance research literature (Fig. 5B ) shows that the Q value of the clustering result is 0.8129 (>0.3), and the average value of the weight S is 0.9391 (>0.7), indicating that the clusters are uniformly distributed with a significant and credible structure. This further proves that the boundaries of the research field are clear and there is significant differentiation in the field. The figure features 18 cluster labels, each associated with thematic color blocks corresponding to different time slices. Highlighted emerging research themes include #2 Smart Home Technology, #7 Social Live, and #10 Customer Service. Furthermore, the clustering labels extracted are primarily classified into three categories: theoretical model deepening, emerging technology applications, research methods and evaluation, as detailed in Table 7 .
A Co-citation analysis of references. B Clustering network analysis of references.
The top ten nodes in terms of co-citation frequency were selected for further analysis. Table 8 displays the corresponding node information. Studies were categorized into four main groups based on content analysis. (1) Research focusing on specific technology usage by older adults includes studies by Peek et al. ( 2014 ), Ma et al. ( 2016 ), Hoque and Sorwar ( 2017 ), and Li et al. ( 2019 ), who investigated the factors influencing the use of e-technology, smartphones, mHealth, and smart wearables, respectively. (2) Concerning the development of theoretical models of technology acceptance, Chen and Chan ( 2014 ) introduced the Senior Technology Acceptance Model (STAM), and Macedo ( 2017 ) analyzed the predictive power of UTAUT2 in explaining older adults’ intentional behaviors and information technology usage. (3) In exploring older adults’ information technology adoption and behavior, Lee and Coughlin ( 2015 ) emphasized that the adoption of technology by older adults is a multifactorial process that includes performance, price, value, usability, affordability, accessibility, technical support, social support, emotion, independence, experience, and confidence. Yusif et al. ( 2016 ) conducted a literature review examining the key barriers affecting older adults’ adoption of assistive technology, including factors such as privacy, trust, functionality/added value, cost, and stigma. (4) From the perspective of research into older adults’ technology acceptance, Mitzner et al. ( 2019 ) assessed the long-term usage of computer systems designed for the elderly, whereas Guner and Acarturk ( 2020 ) compared information technology usage and acceptance between older and younger adults. The breadth and prevalence of this literature make it a vital reference for researchers in the field, also providing new perspectives and inspiration for future research directions.
Burst citation is a node of literature that guides the sudden change in dosage, which usually represents a prominent development or major change in a particular field, with innovative and forward-looking qualities. By analyzing the emergent literature, it is often easy to understand the dynamics of the subject area, mapping the emerging thematic change (Chen et al. 2022 ). Figure 6 shows the burst citation mapping in the field of older adults’ technology acceptance research, with burst citations represented by red nodes (Fig. 6A ). For the ten papers with the highest burst intensity (Fig. 6B ), this study will conduct further analysis in conjunction with literature review.
A Burst detection of co-citation. B The top 10 references with the strongest citation bursts.
As shown in Fig. 6 , Mitzner et al. ( 2010 ) broke the stereotype that older adults are fearful of technology, found that they actually have positive attitudes toward technology, and emphasized the centrality of ease of use and usefulness in the process of technology acceptance. This finding provides an important foundation for subsequent research. During the same period, Wagner et al. ( 2010 ) conducted theory-deepening and applied research on technology acceptance among older adults. The research focused on older adults’ interactions with computers from the perspective of Social Cognitive Theory (SCT). This expanded the understanding of technology acceptance, particularly regarding the relationship between behavior, environment, and other SCT elements. In addition, Pan and Jordan-Marsh ( 2010 ) extended the TAM to examine the interactions among predictors of perceived usefulness, perceived ease of use, subjective norm, and convenience conditions when older adults use the Internet, taking into account the moderating roles of gender and age. Heerink et al. ( 2010 ) adapted and extended the UTAUT, constructed a technology acceptance model specifically designed for older users’ acceptance of assistive social agents, and validated it using controlled experiments and longitudinal data, explaining intention to use by combining functional assessment and social interaction variables.
Then the research theme shifted to an in-depth analysis of the factors influencing technology acceptance among older adults. Two papers with high burst strengths emerged during this period: Peek et al. ( 2014 ) (Strength = 12.04), Chen and Chan ( 2014 ) (Strength = 9.81). Through a systematic literature review and empirical study, Peek STM and Chen K, among others, identified multidimensional factors that influence older adults’ technology acceptance. Peek et al. ( 2014 ) analyzed literature on the acceptance of in-home care technology among older adults and identified six factors that influence their acceptance: concerns about technology, expected benefits, technology needs, technology alternatives, social influences, and older adult characteristics, with a focus on differences between pre- and post-implementation factors. Chen and Chan ( 2014 ) constructed the STAM by administering a questionnaire to 1012 older adults and adding eight important factors, including technology anxiety, self-efficacy, cognitive ability, and physical function, based on the TAM. This enriches the theoretical foundation of the field. In addition, Braun ( 2013 ) highlighted the role of perceived usefulness, trust in social networks, and frequency of Internet use in older adults’ use of social networks, while ease of use and social pressure were not significant influences. These findings contribute to the study of older adults’ technology acceptance within specific technology application domains.
Recent research has focused on empirical studies of personal factors and emerging technologies. Ma et al. ( 2016 ) identified key personal factors affecting smartphone acceptance among older adults through structured questionnaires and face-to-face interviews with 120 participants. The study found that cost, self-satisfaction, and convenience were important factors influencing perceived usefulness and ease of use. This study offers empirical evidence to comprehend the main factors that drive smartphone acceptance among Chinese older adults. Additionally, Yusif et al. ( 2016 ) presented an overview of the obstacles that hinder older adults’ acceptance of assistive technologies, focusing on privacy, trust, and functionality.
In summary, research on older adults’ technology acceptance has shifted from early theoretical deepening and analysis of influencing factors to empirical studies in the areas of personal factors and emerging technologies, which have greatly enriched the theoretical basis of older adults’ technology acceptance and provided practical guidance for the design of emerging technology products.
Core keywords analysis.
Keywords concise the main idea and core of the literature, and are a refined summary of the research content (Huang et al. 2021 ). In CiteSpace, nodes with a centrality value greater than 0.1 are considered to be critical nodes. Analyzing keywords with high frequency and centrality helps to visualize the hot topics in the research field (Park et al. 2018 ). The merged keywords were imported into CiteSpace, and the top 10 keywords were counted and sorted by frequency and centrality respectively, as shown in Table 9 . The results show that the keyword “TAM” has the highest frequency (92), followed by “UTAUT” (24), which reflects that the in-depth study of the existing technology acceptance model and its theoretical expansion occupy a central position in research related to older adults’ technology acceptance. Furthermore, the terms ‘assistive technology’ and ‘virtual reality’ are both high-frequency and high-centrality terms (frequency = 17, centrality = 0.10), indicating that the research on assistive technology and virtual reality for older adults is the focus of current academic attention.
Using VOSviewer for keyword co-occurrence analysis organizes keywords into groups or clusters based on their intrinsic connections and frequencies, clearly highlighting the research field’s hot topics. The connectivity among keywords reveals correlations between different topics. To ensure accuracy, the analysis only considered the authors’ keywords. Subsequently, the keywords were filtered by setting the keyword frequency to 5 to obtain the keyword clustering map of the research on older adults’ technology acceptance research keyword clustering mapping (Fig. 7 ), combined with the keyword co-occurrence clustering network (Fig. 7A ) and the corresponding density situation (Fig. 7B ) to make a detailed analysis of the following four groups of clustered themes.
A Co-occurrence clustering network. B Keyword density.
Cluster #1—Research on the factors influencing technology adoption among older adults is a prominent topic, covering age, gender, self-efficacy, attitude, and and intention to use (Berkowsky et al. 2017 ; Wang et al. 2017 ). It also examined older adults’ attitudes towards and acceptance of digital health technologies (Ahmad and Mozelius, 2022 ). Moreover, the COVID-19 pandemic, significantly impacting older adults’ technology attitudes and usage, has underscored the study’s importance and urgency. Therefore, it is crucial to conduct in-depth studies on how older adults accept, adopt, and effectively use new technologies, to address their needs and help them overcome the digital divide within digital inclusion. This will improve their quality of life and healthcare experiences.
Cluster #2—Research focuses on how older adults interact with assistive technologies, especially assistive robots and health monitoring devices, emphasizing trust, usability, and user experience as crucial factors (Halim et al. 2022 ). Moreover, health monitoring technologies effectively track and manage health issues common in older adults, like dementia and mild cognitive impairment (Lussier et al. 2018 ; Piau et al. 2019 ). Interactive exercise games and virtual reality have been deployed to encourage more physical and cognitive engagement among older adults (Campo-Prieto et al. 2021 ). Personalized and innovative technology significantly enhances older adults’ participation, improving their health and well-being.
Cluster #3—Optimizing health management for older adults using mobile technology. With the development of mobile health (mHealth) and health information technology, mobile applications, smartphones, and smart wearable devices have become effective tools to help older users better manage chronic conditions, conduct real-time health monitoring, and even receive telehealth services (Dupuis and Tsotsos 2018 ; Olmedo-Aguirre et al. 2022 ; Kim et al. 2014 ). Additionally, these technologies can mitigate the problem of healthcare resource inequality, especially in developing countries. Older adults’ acceptance and use of these technologies are significantly influenced by their behavioral intentions, motivational factors, and self-management skills. These internal motivational factors, along with external factors, jointly affect older adults’ performance in health management and quality of life.
Cluster #4—Research on technology-assisted home care for older adults is gaining popularity. Environmentally assisted living enhances older adults’ independence and comfort at home, offering essential support and security. This has a crucial impact on promoting healthy aging (Friesen et al. 2016 ; Wahlroos et al. 2023 ). The smart home is a core application in this field, providing a range of solutions that facilitate independent living for the elderly in a highly integrated and user-friendly manner. This fulfills different dimensions of living and health needs (Majumder et al. 2017 ). Moreover, eHealth offers accurate and personalized health management and healthcare services for older adults (Delmastro et al. 2018 ), ensuring their needs are met at home. Research in this field often employs qualitative methods and structural equation modeling to fully understand older adults’ needs and experiences at home and analyze factors influencing technology adoption.
To gain a deeper understanding of the evolutionary trends in research hotspots within the field of older adults’ technology acceptance, we conducted a statistical analysis of the average appearance times of keywords, using CiteSpace to generate the time-zone evolution mapping (Fig. 8 ) and burst keywords. The time-zone mapping visually displays the evolution of keywords over time, intuitively reflecting the frequency and initial appearance of keywords in research, commonly used to identify trends in research topics (Jing et al. 2024a ; Kumar et al. 2021 ). Table 10 lists the top 15 keywords by burst strength, with the red sections indicating high-frequency citations and their burst strength in specific years. These burst keywords reveal the focus and trends of research themes over different periods (Kleinberg 2002 ). Combining insights from the time-zone mapping and burst keywords provides more objective and accurate research insights (Wang et al. 2023b ).
Reflecting the frequency and time of first appearance of keywords in the study.
An integrated analysis of Fig. 8 and Table 10 shows that early research on older adults’ technology acceptance primarily focused on factors such as perceived usefulness, ease of use, and attitudes towards information technology, including their use of computers and the internet (Pan and Jordan-Marsh 2010 ), as well as differences in technology use between older adults and other age groups (Guner and Acarturk 2020 ). Subsequently, the research focus expanded to improving the quality of life for older adults, exploring how technology can optimize health management and enhance the possibility of independent living, emphasizing the significant role of technology in improving the quality of life for the elderly. With ongoing technological advancements, recent research has shifted towards areas such as “virtual reality,” “telehealth,” and “human-robot interaction,” with a focus on the user experience of older adults (Halim et al. 2022 ). The appearance of keywords such as “physical activity” and “exercise” highlights the value of technology in promoting physical activity and health among older adults. This phase of research tends to make cutting-edge technology genuinely serve the practical needs of older adults, achieving its widespread application in daily life. Additionally, research has focused on expanding and quantifying theoretical models of older adults’ technology acceptance, involving keywords such as “perceived risk”, “validation” and “UTAUT”.
In summary, from 2013 to 2023, the field of older adults’ technology acceptance has evolved from initial explorations of influencing factors, to comprehensive enhancements in quality of life and health management, and further to the application and deepening of theoretical models and cutting-edge technologies. This research not only reflects the diversity and complexity of the field but also demonstrates a comprehensive and in-depth understanding of older adults’ interactions with technology across various life scenarios and needs.
To reveal the distribution of research quality in the field of older adults’ technology acceptance, a strategic diagram analysis is employed to calculate and illustrate the internal development and interrelationships among various research themes (Xie et al. 2020 ). The strategic diagram uses Centrality as the X-axis and Density as the Y-axis to divide into four quadrants, where the X-axis represents the strength of the connection between thematic clusters and other themes, with higher values indicating a central position in the research field; the Y-axis indicates the level of development within the thematic clusters, with higher values denoting a more mature and widely recognized field (Li and Zhou 2020 ).
Through cluster analysis and manual verification, this study categorized 61 core keywords (Frequency ≥5) into 11 thematic clusters. Subsequently, based on the keywords covered by each thematic cluster, the research themes and their directions for each cluster were summarized (Table 11 ), and the centrality and density coordinates for each cluster were precisely calculated (Table 12 ). Finally, a strategic diagram of the older adults’ technology acceptance research field was constructed (Fig. 9 ). Based on the distribution of thematic clusters across the quadrants in the strategic diagram, the structure and developmental trends of the field were interpreted.
Classification and visualization of theme clusters based on density and centrality.
As illustrated in Fig. 9 , (1) the theme clusters of #3 Usage Experience and #4 Assisted Living Technology are in the first quadrant, characterized by high centrality and density. Their internal cohesion and close links with other themes indicate their mature development, systematic research content or directions have been formed, and they have a significant influence on other themes. These themes play a central role in the field of older adults’ technology acceptance and have promising prospects. (2) The theme clusters of #6 Smart Devices, #9 Theoretical Models, and #10 Mobile Health Applications are in the second quadrant, with higher density but lower centrality. These themes have strong internal connections but weaker external links, indicating that these three themes have received widespread attention from researchers and have been the subject of related research, but more as self-contained systems and exhibit independence. Therefore, future research should further explore in-depth cooperation and cross-application with other themes. (3) The theme clusters of #7 Human-Robot Interaction, #8 Characteristics of the Elderly, and #11 Research Methods are in the third quadrant, with lower centrality and density. These themes are loosely connected internally and have weak links with others, indicating their developmental immaturity. Compared to other topics, they belong to the lower attention edge and niche themes, and there is a need for further investigation. (4) The theme clusters of #1 Digital Healthcare Technology, #2 Psychological Factors, and #5 Socio-Cultural Factors are located in the fourth quadrant, with high centrality but low density. Although closely associated with other research themes, the internal cohesion within these clusters is relatively weak. This suggests that while these themes are closely linked to other research areas, their own development remains underdeveloped, indicating a core immaturity. Nevertheless, these themes are crucial within the research domain of elderly technology acceptance and possess significant potential for future exploration.
Over the past decade, academic interest and influence in the area of older adults’ technology acceptance have significantly increased. This trend is evidenced by a quantitative analysis of publication and citation volumes, particularly noticeable in 2019 and 2022, where there was a substantial rise in both metrics. The rise is closely linked to the widespread adoption of emerging technologies such as smart homes, wearable devices, and telemedicine among older adults. While these technologies have enhanced their quality of life, they also pose numerous challenges, sparking extensive research into their acceptance, usage behaviors, and influencing factors among the older adults (Pirzada et al. 2022 ; Garcia Reyes et al. 2023 ). Furthermore, the COVID-19 pandemic led to a surge in technology demand among older adults, especially in areas like medical consultation, online socialization, and health management, further highlighting the importance and challenges of technology. Health risks and social isolation have compelled older adults to rely on technology for daily activities, accelerating its adoption and application within this demographic. This phenomenon has made technology acceptance a critical issue, driving societal and academic focus on the study of technology acceptance among older adults.
The flow of knowledge at the level of high-output disciplines and journals, along with the primary publishing outlets, indicates the highly interdisciplinary nature of research into older adults’ technology acceptance. This reflects the complexity and breadth of issues related to older adults’ technology acceptance, necessitating the integration of multidisciplinary knowledge and approaches. Currently, research is primarily focused on medical health and human-computer interaction, demonstrating academic interest in improving health and quality of life for older adults and addressing the urgent needs related to their interactions with technology. In the field of medical health, research aims to provide advanced and innovative healthcare technologies and services to meet the challenges of an aging population while improving the quality of life for older adults (Abdi et al. 2020 ; Wilson et al. 2021 ). In the field of human-computer interaction, research is focused on developing smarter and more user-friendly interaction models to meet the needs of older adults in the digital age, enabling them to actively participate in social activities and enjoy a higher quality of life (Sayago, 2019 ). These studies are crucial for addressing the challenges faced by aging societies, providing increased support and opportunities for the health, welfare, and social participation of older adults.
This study analyzes leading countries and collaboration networks, core institutions and authors, revealing the global research landscape and distribution of research strength in the field of older adults’ technology acceptance, and presents quantitative data on global research trends. From the analysis of country distribution and collaborations, China and the USA hold dominant positions in this field, with developed countries like the UK, Germany, Italy, and the Netherlands also excelling in international cooperation and research influence. The significant investment in technological research and the focus on the technological needs of older adults by many developed countries reflect their rapidly aging societies, policy support, and resource allocation.
China is the only developing country that has become a major contributor in this field, indicating its growing research capabilities and high priority given to aging societies and technological innovation. Additionally, China has close collaborations with countries such as USA, the UK, and Malaysia, driven not only by technological research needs but also by shared challenges and complementarities in aging issues among these nations. For instance, the UK has extensive experience in social welfare and aging research, providing valuable theoretical guidance and practical experience. International collaborations, aimed at addressing the challenges of aging, integrate the strengths of various countries, advancing in-depth and widespread development in the research of technology acceptance among older adults.
At the institutional and author level, City University of Hong Kong leads in publication volume, with research teams led by Chan and Chen demonstrating significant academic activity and contributions. Their research primarily focuses on older adults’ acceptance and usage behaviors of various technologies, including smartphones, smart wearables, and social robots (Chen et al. 2015 ; Li et al. 2019 ; Ma et al. 2016 ). These studies, targeting specific needs and product characteristics of older adults, have developed new models of technology acceptance based on existing frameworks, enhancing the integration of these technologies into their daily lives and laying a foundation for further advancements in the field. Although Tilburg University has a smaller publication output, it holds significant influence in the field of older adults’ technology acceptance. Particularly, the high citation rate of Peek’s studies highlights their excellence in research. Peek extensively explored older adults’ acceptance and usage of home care technologies, revealing the complexity and dynamics of their technology use behaviors. His research spans from identifying systemic influencing factors (Peek et al. 2014 ; Peek et al. 2016 ), emphasizing familial impacts (Luijkx et al. 2015 ), to constructing comprehensive models (Peek et al. 2017 ), and examining the dynamics of long-term usage (Peek et al. 2019 ), fully reflecting the evolving technology landscape and the changing needs of older adults. Additionally, the ongoing contributions of researchers like Ziefle, Rogers, and Wouters in the field of older adults’ technology acceptance demonstrate their research influence and leadership. These researchers have significantly enriched the knowledge base in this area with their diverse perspectives. For instance, Ziefle has uncovered the complex attitudes of older adults towards technology usage, especially the trade-offs between privacy and security, and how different types of activities affect their privacy needs (Maidhof et al. 2023 ; Mujirishvili et al. 2023 ; Schomakers and Ziefle 2023 ; Wilkowska et al. 2022 ), reflecting a deep exploration and ongoing innovation in the field of older adults’ technology acceptance.
Through co-citation analysis and systematic review of seminal literature, this study reveals the knowledge foundation and thematic progress in the field of older adults’ technology acceptance. Co-citation networks and cluster analyses illustrate the structural themes of the research, delineating the differentiation and boundaries within this field. Additionally, burst detection analysis offers a valuable perspective for understanding the thematic evolution in the field of technology acceptance among older adults. The development and innovation of theoretical models are foundational to this research. Researchers enhance the explanatory power of constructed models by deepening and expanding existing technology acceptance theories to address theoretical limitations. For instance, Heerink et al. ( 2010 ) modified and expanded the UTAUT model by integrating functional assessment and social interaction variables to create the almere model. This model significantly enhances the ability to explain the intentions of older users in utilizing assistive social agents and improves the explanation of actual usage behaviors. Additionally, Chen and Chan ( 2014 ) extended the TAM to include age-related health and capability features of older adults, creating the STAM, which substantially improves predictions of older adults’ technology usage behaviors. Personal attributes, health and capability features, and facilitating conditions have a direct impact on technology acceptance. These factors more effectively predict older adults’ technology usage behaviors than traditional attitudinal factors.
With the advancement of technology and the application of emerging technologies, new research topics have emerged, increasingly focusing on older adults’ acceptance and use of these technologies. Prior to this, the study by Mitzner et al. ( 2010 ) challenged the stereotype of older adults’ conservative attitudes towards technology, highlighting the central roles of usability and usefulness in the technology acceptance process. This discovery laid an important foundation for subsequent research. Research fields such as “smart home technology,” “social life,” and “customer service” are emerging, indicating a shift in focus towards the practical and social applications of technology in older adults’ lives. Research not only focuses on the technology itself but also on how these technologies integrate into older adults’ daily lives and how they can improve the quality of life through technology. For instance, studies such as those by Ma et al. ( 2016 ), Hoque and Sorwar ( 2017 ), and Li et al. ( 2019 ) have explored factors influencing older adults’ use of smartphones, mHealth, and smart wearable devices.
Furthermore, the diversification of research methodologies and innovation in evaluation techniques, such as the use of mixed methods, structural equation modeling (SEM), and neural network (NN) approaches, have enhanced the rigor and reliability of the findings, enabling more precise identification of the factors and mechanisms influencing technology acceptance. Talukder et al. ( 2020 ) employed an effective multimethodological strategy by integrating SEM and NN to leverage the complementary strengths of both approaches, thus overcoming their individual limitations and more accurately analyzing and predicting older adults’ acceptance of wearable health technologies (WHT). SEM is utilized to assess the determinants’ impact on the adoption of WHT, while neural network models validate SEM outcomes and predict the significance of key determinants. This combined approach not only boosts the models’ reliability and explanatory power but also provides a nuanced understanding of the motivations and barriers behind older adults’ acceptance of WHT, offering deep research insights.
Overall, co-citation analysis of the literature in the field of older adults’ technology acceptance has uncovered deeper theoretical modeling and empirical studies on emerging technologies, while emphasizing the importance of research methodological and evaluation innovations in understanding complex social science issues. These findings are crucial for guiding the design and marketing strategies of future technology products, especially in the rapidly growing market of older adults.
By analyzing core keywords, we can gain deep insights into the hot topics, evolutionary trends, and quality distribution of research in the field of older adults’ technology acceptance. The frequent occurrence of the keywords “TAM” and “UTAUT” indicates that the applicability and theoretical extension of existing technology acceptance models among older adults remain a focal point in academia. This phenomenon underscores the enduring influence of the studies by Davis ( 1989 ) and Venkatesh et al. ( 2003 ), whose models provide a robust theoretical framework for explaining and predicting older adults’ acceptance and usage of emerging technologies. With the widespread application of artificial intelligence (AI) and big data technologies, these theoretical models have incorporated new variables such as perceived risk, trust, and privacy issues (Amin et al. 2024 ; Chen et al. 2024 ; Jing et al. 2024b ; Seibert et al. 2021 ; Wang et al. 2024b ), advancing the theoretical depth and empirical research in this field.
Keyword co-occurrence cluster analysis has revealed multiple research hotspots in the field, including factors influencing technology adoption, interactive experiences between older adults and assistive technologies, the application of mobile health technology in health management, and technology-assisted home care. These studies primarily focus on enhancing the quality of life and health management of older adults through emerging technologies, particularly in the areas of ambient assisted living, smart health monitoring, and intelligent medical care. In these domains, the role of AI technology is increasingly significant (Qian et al. 2021 ; Ho 2020 ). With the evolution of next-generation information technologies, AI is increasingly integrated into elder care systems, offering intelligent, efficient, and personalized service solutions by analyzing the lifestyles and health conditions of older adults. This integration aims to enhance older adults’ quality of life in aspects such as health monitoring and alerts, rehabilitation assistance, daily health management, and emotional support (Lee et al. 2023 ). A survey indicates that 83% of older adults prefer AI-driven solutions when selecting smart products, demonstrating the increasing acceptance of AI in elder care (Zhao and Li 2024 ). Integrating AI into elder care presents both opportunities and challenges, particularly in terms of user acceptance, trust, and long-term usage effects, which warrant further exploration (Mhlanga 2023 ). These studies will help better understand the profound impact of AI technology on the lifestyles of older adults and provide critical references for optimizing AI-driven elder care services.
The Time-zone evolution mapping and burst keyword analysis further reveal the evolutionary trends of research hotspots. Early studies focused on basic technology acceptance models and user perceptions, later expanding to include quality of life and health management. In recent years, research has increasingly focused on cutting-edge technologies such as virtual reality, telehealth, and human-robot interaction, with a concurrent emphasis on the user experience of older adults. This evolutionary process demonstrates a deepening shift from theoretical models to practical applications, underscoring the significant role of technology in enhancing the quality of life for older adults. Furthermore, the strategic coordinate mapping analysis clearly demonstrates the development and mutual influence of different research themes. High centrality and density in the themes of Usage Experience and Assisted Living Technology indicate their mature research status and significant impact on other themes. The themes of Smart Devices, Theoretical Models, and Mobile Health Applications demonstrate self-contained research trends. The themes of Human-Robot Interaction, Characteristics of the Elderly, and Research Methods are not yet mature, but they hold potential for development. Themes of Digital Healthcare Technology, Psychological Factors, and Socio-Cultural Factors are closely related to other themes, displaying core immaturity but significant potential.
In summary, the research hotspots in the field of older adults’ technology acceptance are diverse and dynamic, demonstrating the academic community’s profound understanding of how older adults interact with technology across various life contexts and needs. Under the influence of AI and big data, research should continue to focus on the application of emerging technologies among older adults, exploring in depth how they adapt to and effectively use these technologies. This not only enhances the quality of life and healthcare experiences for older adults but also drives ongoing innovation and development in this field.
Based on the above research findings, to further understand and promote technology acceptance and usage among older adults, we recommend future studies focus on refining theoretical models, exploring long-term usage, and assessing user experience in the following detailed aspects:
Refinement and validation of specific technology acceptance models for older adults: Future research should focus on developing and validating technology acceptance models based on individual characteristics, particularly considering variations in technology acceptance among older adults across different educational levels and cultural backgrounds. This includes factors such as age, gender, educational background, and cultural differences. Additionally, research should examine how well specific technologies, such as wearable devices and mobile health applications, meet the needs of older adults. Building on existing theoretical models, this research should integrate insights from multiple disciplines such as psychology, sociology, design, and engineering through interdisciplinary collaboration to create more accurate and comprehensive models, which should then be validated in relevant contexts.
Deepening the exploration of the relationship between long-term technology use and quality of life among older adults: The acceptance and use of technology by users is a complex and dynamic process (Seuwou et al. 2016 ). Existing research predominantly focuses on older adults’ initial acceptance or short-term use of new technologies; however, the impact of long-term use on their quality of life and health is more significant. Future research should focus on the evolution of older adults’ experiences and needs during long-term technology usage, and the enduring effects of technology on their social interactions, mental health, and life satisfaction. Through longitudinal studies and qualitative analysis, this research reveals the specific needs and challenges of older adults in long-term technology use, providing a basis for developing technologies and strategies that better meet their requirements. This understanding aids in comprehensively assessing the impact of technology on older adults’ quality of life and guiding the optimization and improvement of technological products.
Evaluating the Importance of User Experience in Research on Older Adults’ Technology Acceptance: Understanding the mechanisms of information technology acceptance and use is central to human-computer interaction research. Although technology acceptance models and user experience models differ in objectives, they share many potential intersections. Technology acceptance research focuses on structured prediction and assessment, while user experience research concentrates on interpreting design impacts and new frameworks. Integrating user experience to assess older adults’ acceptance of technology products and systems is crucial (Codfrey et al. 2022 ; Wang et al. 2019 ), particularly for older users, where specific product designs should emphasize practicality and usability (Fisk et al. 2020 ). Researchers need to explore innovative age-appropriate design methods to enhance older adults’ usage experience. This includes studying older users’ actual usage preferences and behaviors, optimizing user interfaces, and interaction designs. Integrating feedback from older adults to tailor products to their needs can further promote their acceptance and continued use of technology products.
This study conducted a systematic review of the literature on older adults’ technology acceptance over the past decade through bibliometric analysis, focusing on the distribution power, research power, knowledge base and theme progress, research hotspots, evolutionary trends, and quality distribution. Using a combination of quantitative and qualitative methods, this study has reached the following conclusions:
Technology acceptance among older adults has become a hot topic in the international academic community, involving the integration of knowledge across multiple disciplines, including Medical Informatics, Health Care Sciences Services, and Ergonomics. In terms of journals, “PSYCHOLOGY, EDUCATION, HEALTH” represents a leading field, with key publications including Computers in Human Behavior , Journal of Medical Internet Research , and International Journal of Human-Computer Interaction . These journals possess significant academic authority and extensive influence in the field.
Research on technology acceptance among older adults is particularly active in developed countries, with China and USA publishing significantly more than other nations. The Netherlands leads in high average citation rates, indicating the depth and impact of its research. Meanwhile, the UK stands out in terms of international collaboration. At the institutional level, City University of Hong Kong and The University of Hong Kong in China are in leading positions. Tilburg University in the Netherlands demonstrates exceptional research quality through its high average citation count. At the author level, Chen from China has the highest number of publications, while Peek from the Netherlands has the highest average citation count.
Co-citation analysis of references indicates that the knowledge base in this field is divided into three main categories: theoretical model deepening, emerging technology applications, and research methods and evaluation. Seminal literature focuses on four areas: specific technology use by older adults, expansion of theoretical models of technology acceptance, information technology adoption behavior, and research perspectives. Research themes have evolved from initial theoretical deepening and analysis of influencing factors to empirical studies on individual factors and emerging technologies.
Keyword analysis indicates that TAM and UTAUT are the most frequently occurring terms, while “assistive technology” and “virtual reality” are focal points with high frequency and centrality. Keyword clustering analysis reveals that research hotspots are concentrated on the influencing factors of technology adoption, human-robot interaction experiences, mobile health management, and technology for aging in place. Time-zone evolution mapping and burst keyword analysis have revealed the research evolution from preliminary exploration of influencing factors, to enhancements in quality of life and health management, and onto advanced technology applications and deepening of theoretical models. Furthermore, analysis of research quality distribution indicates that Usage Experience and Assisted Living Technology have become core topics, while Smart Devices, Theoretical Models, and Mobile Health Applications point towards future research directions.
Through this study, we have systematically reviewed the dynamics, core issues, and evolutionary trends in the field of older adults’ technology acceptance, constructing a comprehensive Knowledge Mapping of the domain and presenting a clear framework of existing research. This not only lays the foundation for subsequent theoretical discussions and innovative applications in the field but also provides an important reference for relevant scholars.
To our knowledge, this is the first bibliometric analysis concerning technology acceptance among older adults, and we adhered strictly to bibliometric standards throughout our research. However, this study relies on the Web of Science Core Collection, and while its authority and breadth are widely recognized, this choice may have missed relevant literature published in other significant databases such as PubMed, Scopus, and Google Scholar, potentially overlooking some critical academic contributions. Moreover, given that our analysis was confined to literature in English, it may not reflect studies published in other languages, somewhat limiting the global representativeness of our data sample.
It is noteworthy that with the rapid development of AI technology, its increasingly widespread application in elderly care services is significantly transforming traditional care models. AI is profoundly altering the lifestyles of the elderly, from health monitoring and smart diagnostics to intelligent home systems and personalized care, significantly enhancing their quality of life and health care standards. The potential for AI technology within the elderly population is immense, and research in this area is rapidly expanding. However, due to the restrictive nature of the search terms used in this study, it did not fully cover research in this critical area, particularly in addressing key issues such as trust, privacy, and ethics.
Consequently, future research should not only expand data sources, incorporating multilingual and multidatabase literature, but also particularly focus on exploring older adults’ acceptance of AI technology and its applications, in order to construct a more comprehensive academic landscape of older adults’ technology acceptance, thereby enriching and extending the knowledge system and academic trends in this field.
The datasets analyzed during the current study are available in the Dataverse repository: https://doi.org/10.7910/DVN/6K0GJH .
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This research was supported by the Social Science Foundation of Shaanxi Province in China (Grant No. 2023J014).
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Shang, X., Liu, Z., Gong, C. et al. Knowledge mapping and evolution of research on older adults’ technology acceptance: a bibliometric study from 2013 to 2023. Humanit Soc Sci Commun 11 , 1115 (2024). https://doi.org/10.1057/s41599-024-03658-2
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Title: multimodal methods for analyzing learning and training environments: a systematic literature review.
Abstract: Recent technological advancements have enhanced our ability to collect and analyze rich multimodal data (e.g., speech, video, and eye gaze) to better inform learning and training experiences. While previous reviews have focused on parts of the multimodal pipeline (e.g., conceptual models and data fusion), a comprehensive literature review on the methods informing multimodal learning and training environments has not been conducted. This literature review provides an in-depth analysis of research methods in these environments, proposing a taxonomy and framework that encapsulates recent methodological advances in this field and characterizes the multimodal domain in terms of five modality groups: Natural Language, Video, Sensors, Human-Centered, and Environment Logs. We introduce a novel data fusion category -- mid fusion -- and a graph-based technique for refining literature reviews, termed citation graph pruning. Our analysis reveals that leveraging multiple modalities offers a more holistic understanding of the behaviors and outcomes of learners and trainees. Even when multimodality does not enhance predictive accuracy, it often uncovers patterns that contextualize and elucidate unimodal data, revealing subtleties that a single modality may miss. However, there remains a need for further research to bridge the divide between multimodal learning and training studies and foundational AI research.
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Impacts of pfas exposure on neurodevelopment: a comprehensive literature review.
2. materials and methods, 2.1. data sourcing, 2.2. exposure assessment, 2.3. outcomes, 2.4. covariates, 2.5. data extraction, 3.1. the intelligence quotient (iq), 3.2. attention-deficit hyperactivity disorder (adhd), 3.3. autism spectrum disorder (asd), 4. discussion, 5. conclusions, author contributions, conflicts of interest.
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First Author/Year/Country | Design | Sample Size | Age of Children | PFAS | Sample/ Measuring Method | Exposure Measure | Test Type and Indicator | Adjustment of Covariates | Conclusion |
---|---|---|---|---|---|---|---|---|---|
Carly V Goodman/2023/Canada [ ] | Cohort Study | n = 522 | Between 3 and 4 | PFOA, PFOS, and PFHxS | Plasma/ UHPLC–MS/MS | PFOA: 1.68 (1.10–2.50), PFOS: 4.97 (3.20–6.20), PFHxS: 1.09 (0.67–1.60) (µg/L) | Wechsler Preschool and Primary Scale of Intelligence, Third Edition (WPPSI-III), composite full-scale IQ (FSIQ), performance IQ (PIQ), and verbal IQ (VIQ) scores | Gestational week of blood sampling, maternal age, pre-pregnancy BMI, country of birth (Canadian born, foreign born), maternal level of education (trade school diploma or lower, bachelor’s degree or higher), parity (0, 1, 2+), maternal smoking during pregnancy (current smoker, former smoker, never smoked), study site, and the Home Observation Measurement of the Environment (HOME) score, a continuous measure of the quality of the child’s home environment | Each doubling of PFHxS levels corresponded to a reduction of 2.0 points (95% CI: −3.6, −0.5) in FSIQ and 2.9 points (95% CI: −4.7, −1.1) in PIQ in males. However, in females, PFHxS showed no association with FSIQ or PIQ. PFOA and PFOS were also linked to lower PIQ scores in males (PFOA: B = −2.8, 95% CI: −4.9, −0.7; PFOS: B = −2.6, 95% CI: −4.8, −0.5), while in females, they were slightly positively associated with PIQ, but not FSIQ |
Iben Have Beck/2023/Denmark [ ] | Cohort Study | n = 967 | 7 years old | PFOS, PFOA, PFHxS, PFNA, and PFDA | Serum/ LC–MS | PFOS: 4.61 (3.08–7.08), PFOA: 2.48 (1.58–3.49), PFHxS: 0.33 (0.21–0.50), PFNA: 0.57 (0.40–0.78), PFDA: 0.18 (0.13–0.24) (ng/mL) | Abbreviated version of the Danish WISC-V, Full-Scale Intelligence Quotient (FSIQ) score, and Verbal Comprehension Index (VCI) score | Maternal educational level, BMI, and sex | PFOS and PFNA exposure and FSIQ remained significant, with β coefficients of −1.7 (95% CI: −3.0, −0.3) and −1.7 (95% CI: −3.0, −0.4) |
Ann M Vuong/2019/United States [ ] | Cohort Study | n = 221 | 3 and 8 years old | PFOA, PFOS, PFHxS, and PFNA | Serum/ HPLC–MS/MS | PFOA: 2.4, PFOA: 3.9, PFHxS: 1.4, PFNA: 0.8 (ng/mL) | Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV) and Full Scale IQ (FSIQ) | Maternal sociodemographic, behavioral factors, and biological measurements of environmental chemical | Findings do not support that PFAS are adversely associated with cognitive function |
Hui Wang/2023/China [ ] | Cohort Study | n = 2031 | 4 years old | PFOA, PFOS, PFNA, PFUA, PFDA, PFHxS, PFBS, PFDoA, PFHpA, and PFOSA | Plasma/ HPLC–MS/MS | PFOA: 13.12 (9.36–15.50), PFOS: 11.3 (6.66–13.68), PFNA: 2.05 (1.27–2.49), PFDA: 2.16 (1.18–2.67), PFHxS: 0.62 (0.42–0.69) (ng/mL) | Wechsler Preschool and Primary Scales of Intelligence-Fourth Edition (WPPSI-IV) | Maternal age at delivery, maternal educational level, maternal pre-pregnancy body mass index, parity, maternal folic acid intake during pregnancy, maternal place of birth, maternal active/passive smoking status during pregnancy, maternal freshwater fish intake during pregnancy, and self-reported economic status | No significant associations between ln-transformed nine individual PFAS and child full scale IQ (FSIQ) or subscale IQ after adjusting for potential confounders |
Zeyan Liew/2018/Norway [ ] | Cohort Study | n = 1592 | 5 years old | PFOS, PFOA, PFHxS, PFNA, PFHpS, PFDA, and PFOSA | Plasma/ LC–MS/MS | PFOS: 28.10 (21.60–35.80), PFOA: 4.28 (3.51–5.49), PFHxS: 1.07 (0.76–1.38), PFNA: 0.46 (0.36–0.57), PFHpS: 0.37 (0.27–0.49), PFDA: 0.17 (0.14–0.22), PFOSA: 2.32 (1.38–4.16) (ng/mL) | Wechsler Primary and Preschool Scales of Intelligence–Revised (WPPSI-R) | Maternal age at delivery, parity, maternal IQ, socioeconomic status, maternal smoking during pregnancy, maternal alcohol consumption during pregnancy, maternal prepregnancy BMI, child’s sex | There is no reliable evidence establishing a connection between prenatal exposure to PFAS and IQ scores in children at the age of five |
Yan Wang/2015/United States [ ] | Cohort Study | n = 120 | 5 years old | PFHxS, PFOA, PFOS, PFNA, PFDeA, PFUnDA, PFDoDA, PFHpA, and PFHxA | Serum/ HPLC–MS/MS | PFHxS: 0.45 (0.35–0.57), PFOA: 2.00 (1.72–2.33), PFOS: 11.5 (10.2–13.07), PFNA: 1.33 (1.12–1.59), PFDeA: 0.39 (0.34–0.44), PFUnDA: 3.05 (2.37–3.94), PFDoDA: 0.29 (0.25–0.34) (ng/mL) | Full-Scale Intelligence Quotient (FSIQ), verbal IQ (VIQ) and performance IQ (PIQ) | Maternal age, maternal education, previous live births, family income, and maternal fish consumption during pregnancy | Exposure to two types of long-chain PFAS during pregnancy has been linked to lower IQ scores in children |
Maria H Harris/2018/United States [ ] | Cohort Study | n = 1226 | 3 years old | PFOA, PFOS, PFHxS, PFNA, MeFOSAA, and PFDeA | Plasma/ HPLC–MS/MS | PFOA: 4.4 (3.1–6.0), PFOS: 6.2 (4.2–9.7), PFHxS: 1.9 (1.2–3.4), PFNA: 1.5 (1.1–2.3), MeFOSAA: 0.3 (<LOD −0.6), PFDeA: 0.3 (0.2–0.5) (ng/mL) | Peabody Picture Vocabulary Test (PPVT-III), Wide Range Assessment of Visual Motor Abilities (WRAVMA), Kaufman Brief Intelligence Test (KBIT-2), and Visual Memory Index of the Wide Range Assessment of Memory and Learning (WRAML2) | Child sex, age at cognitive testing, maternal race/ethnicity, age, maternal and paternal education, socioeconomic status and maternal intelligence scores | Prenatal PFAS were associated with both better and worse cognitive scores |
Miranda J. Spratlen/2020/United States [ ] | Cohort Study | n = 110 | Children ages 3–7 years | PFOS, PFOA, PFHxS, PFNA, PFDS, PFBS, PFOSA, PFHxA, PFHpA, PFDA, PFUnDA, and PFDoDA | Plasma/ HPLC–MS/MS | PFOS: 6.27 (1.05, 33.7), PFOA: 2.37 (0.18, 8.14), PFNA: 0.45 (<LOQ, 10.3), PFHxS: 0.69 (<LOQ, 15.8), PFDS: 0.13 (<LOQ, 0.64) (ng/mL) | Bayley Scales of Infant Development (BSID-II), Mental Development Index (MDI), Psychomotor Development Index (PDI), and Wechsler Preschool and Primary Scale of Intelligence (WPPSI) | Maternal age; material hardship during pregnancy; pre-pregnancy BMI; maternal IQ; maternal race; maternal education; home smoking exposure; marital status; parity; child’s gestational age at birth; exact child age on test date; child’s sex; maternal demoralization score; and child breastfeeding history | Findings on prenatal PFAS exposure and child neurodevelopment are inconsistent |
Thea S. Skogheim/2020/Norway [ ] | Longitudinal Prospective Study | n = 944 | 3.5 years old | PFOA, PFNA, PFDA, PFUnDA, PFHxS, PFHpS, and PFOS | Plasma/ LC–MS/MS | PFOA: 2.50 (1.77–3.21), PFNA: 0.41 (0.29–0.53), PFDA: 0.15 (0.10–0.23), PFUnDA: 0.22 (0.14–0.32), PFHxS: 0.65 (0.46–0.88), PFHpS: 0.15 (0.10–0.20), PFOS: 11.51 (8.77–14.84) (ng/mL) | The Preschool Age Psychiatric Assessment interview, Child Development Inventory and Stanford–Binet (5th revision) | Maternal age, maternal education, maternal fish intake, parity, maternal ADHD symptoms, child sex, premature birth, birth weight, maternal BMI, maternal smoking, maternal alcohol consumption, maternal anxiety/depression and maternal iodine intake | No consistent evidence to conclude that prenatal exposure to PFAS are associated with cognitive dysfunctions in preschool children aged three and a half years |
Boya Zhang/2024/China [ ] | Cohort Study | n = 327 | 7 years old | PFHpA, PFOA, PFNA, PFDA, PFUnDA, PFDoDA, PFBS, PFHxS, PFHpS, PFOS, PFDS, and PFOSA | Serum/ UHPLC–MS/MS | PFHpA: 0.27 (0.23–0.30), PFOA: 3.51 (3.29–3.75), PFNA: 0.32 (0.28–0.36), PFDA: 0.86 (0.76–0.96), PFUnDA: 0.61 (0.57–0.65), PFDoDA: 0.13 (0.12–0.14), PFBS: 0.08 (0.07–0.09), PFHxS: 0.09 (0.08–0.10), PFHpS: 0.06 (0.05–0.07), PFOS: 2.10 (1.98–2.22) (ng/mL) | Wechsler Intelligence Scale for Children-Chinese Revised (WISC-CR) | Maternal age at delivery, parity, maternal educational level, child’s sex, annual household income, pet ownership, changes in marital status, pre-pregnancy BMI | Increased prenatal exposure to PFAS negatively affected the IQ of school-aged children |
First Author/Year/Country | Design | Sample Size | Age of Children | PFAS | Sample/Measuring Method | Exposure Measure | Test Type and Indicator | Adjustment of Covariates | Conclusion |
---|---|---|---|---|---|---|---|---|---|
Joan Forns/2020/Norway [ ] | Cross-Sectional Study | n = 518 | 3, 6, 12, and 24 months of age | PFOS and PFOA | Serum/ HPLC–MS/MS | PFOS: 20.19 (4.1–87.3), PFOA: 1.83 (0.5–5.1) (ng/mL) | Attention Syndrome Scale of the Child Behavior Checklist (CBCL-ADHD), Hyperactivity/Inattention Problems subscale of the Strengths and Difficulties Questionnaire (SDQ-Hyperactivity/Inattention), and ADHD Criteria of Diagnostic and Statistical Manual of Mental Disorders, 4th ed. (ADHD-DSM-IV) | Maternal prepregnancy body mass index, maternal age at delivery, maternal education, maternal smoking during pregnancy, maternal parity, duration of total breastfeeding, and child sex | Exposure to PFOS or PFOA early in life was not linked to ADHD during childhood, with odds ratios (ORs) varying between 0.96 (95% CI: 0.87, 1.06) and 1.02 (95% CI: 0.93, 1.11). Analysis using stratified models indicates that the impact of PFAS may vary based on the child’s sex and the mother’s level of education |
Louise Dalsager/2021/Denmark [ ] | Cohort Study | n = 1138 | 2.5–5 years old | PFHxS, PFOS, PFOA, PFNA, and PFDA | Serum/ LC–MS/MS | PFOS: 4.65 (11.22), PFOA: 2.43 (6.40), PFHxS: 0.32 (0.81), PFNA: 0.58 (1.24), PFDA: 0.18 (0.37), Median (95th percentile) (ng/mL) | Child Behavior Checklist 1.5–5 | Parity, maternal educational level, parental psychiatric diagnosis, child sex | No correlation has been found between PFAS levels in mothers or children and symptoms of ADHD |
Johanna Inhyang Kim/2023/South Korea [ ] | Prospective Cohort Study | n = 521 | 2, 4, and 8 years old | PFOA, PFNA, PFDA, PFUnDA, PFHxS, and PFOS | Serum/ HPLC–MS/MS | PFOA: 3.61 (1.91–6.72), PFNA: 0.99 (0.45–2.96), PFDA: 0.34 (0.12–0.94), PFUnDA: 0.45 (0.17–0.94), PFHxS: 1.01 (0.54–1.95), PFOS: 3.94 (1.80–7.47) (ng/mL | ADHD Rating Scale IV (ARS) | Mother’s age during pregnancy, mother’s educational attainment, father’s educational background, socioeconomic conditions, maternal smoking during pregnancy, use of assisted reproductive technologies, maternal stress levels during pregnancy | PFAS exposure at age 2 was associated with ADHD development at age 8 |
Ann M Vuong/2021/United States [ ] | Cohort Study | n = 240 | 5 and 8 years old | PFOA, PFHxS, PDNA, and PFOS | Serum/ HPLC–MS/MS | PFOA: 5.3 (1.7), PFOS: 12.8 (1.7), PFHxS: 1.5 (0.8), PFNA: 0.90 (1.5), mean (SD) (ng/mL) | The Behavioral Assessment System for Children-2 (BASC-2) and the Diagnostic Interview Schedule for Children–Young Child (DISC-YC) were used to evaluate ADHD symptoms and diagnostic criteria | Maternal age, race/ethnicity, education, family income, ln-maternal serum cotinine (ng/mL), maternal depression, marital status, maternal IQ, parity, and child sex | PFOS and PFNA were consistently linked to hyperactive-impulsive ADHD traits across two validated assessment tools |
Thea S. Skogheim/2021/Norway [ ] | Cohort Study | n = 821 | 3 years old | PFOA, PFNA, PFDA, PFUnDA, PFHxS, PFHpS, and PFOS | Plasma/ LC–MS/MS | PFOA: 2.46 (3.46–2.86), PFNA: 0.42 (0.20–0.49), PFDA: 0.19 (0.15–0.23) (ng/mL) | Adult ADHD Self-Report Scale (ASRS screener) | Child sex, birth weight, and small for gestational age (SGA); maternal age at delivery, education, parity, pre-pregnancy body mass index (BMI, kg/m ), self-reported smoking and alcohol intake during pregnancy, as well as FFQ-based estimates of seafood (g/day), and dietary iodine intake (μg/day) | Several PFAS (PFUnDA, PFDA, and PFOS) were inversely associated with odds of ADHD and/or ASD |
Sachiko Itoh/2022/Japan [ ] | Prospective Cohort Study | n = 770 | 8 years old | PFHxS, PFOS, PFHxA, PFHpA, PFOA, PFNA, PFDA, PFUnDA, PFDoDA, PFTrDA, and PFTeDA | Plasma/ UHPLC–MS/MS | PFHxS: 0.32 (0.22–0.41), PFOS: 6.66 (4.92–8.31), PFOA: 2.48 (1.50–3.00), PFNA: 1.16 (0.79–1.38), PFDA: 0.53 (0.34–0.62), PFUnDA: 1.37 (0.73–1.73), PFDoDA: 0.18 (0.12–0.23), PFTrDA: 0.35 (0.24–0.44) (ng/mL) | ADHD Rating Scale (ADHD-RS) | Age of the mother at delivery, number of previous pregnancies, level of education, body mass index before pregnancy, alcohol consumption during pregnancy, smoking habits during pregnancy, and the sex of the child | Higher the maternal PFAS levels, lower the risk of ADHD symptoms at 8 y of age |
Ilona Quaak/2016/The Netherlands [ ] | Cohort Study | n = 76 | 18 months | PFOA, PFOS, PFHxS, PFHpS, PFNA, PFDA, and PFUnDA | Plasma/ LC–MS/MS | PFOA: 905.6 (437.1), PFOS: 1583.6 (648.3), PFHxS: 140.0 (69.2), PFHpS: 35.6 (21.3), PFNA: 140.0 (61.8), PFDA: 52.2 (20.9), PFUnDA: 32.05 (11.9), Mean (SD) (ng/L) | Child Behavior Checklist 1.5–5 (CBCL) | Family history, educational level, smoking, alcohol use and illicit drug use during pregnancy | Prenatal exposure to PFAS showed no significant associations with ADHD scores |
Thea S. Skogheim/2020/Norway [ ] | Cohort Study | n = 944 | 3.5 years old | PFHpS, PFOS, PFHxS, PFOA, PFDA, PFUnDA, and PFNA | Plasma/ LC–MS/MS | PFOA: 2.61 (1.77–3.21), PFNA: 0.45 (0.29–0.53), PFDA: 0.19 (0.10–0.23), PFUnDA: 0.25 (0.05–0.32), PFHxS: 0.79 (0.46–0.88), PFHpS: 0.16 (0.10–0.20), PFOS: 12.32 (8.77–14.84), (ng/mL) | The Preschool Age Psychiatric Assessment interview, Child Development Inventory and Stanford–Binet (5th revision) | Maternal age, maternal education, maternal fish intake, parity, maternal ADHD symptoms, child sex, premature birth, birth weight, maternal BMI, maternal smoking, maternal alcohol consumption, maternal anxiety/depression and maternal iodine intake | Consistent evidence was not found to link prenatal PFAS exposure with ADHD symptoms or cognitive impairments in preschool children around three and a half years old |
Zeyan Liew/2015/United States [ ] | Cohort Study | n = 220 | Average 10.7 years old | PFOS, PFOA, PFHxS, PFHpS, PFNA, and PFDA | Plasma/ LC–MS/MS | PFOS: 26.80 (19.20, 35.00), PFOA: 4.06 (3.08, 5.50), PFHxS: 0.84 (0.61, 1.15), PFHpS: 0.30 (0.20, 0.40), PFNA: 0.42 (0.34, 0.52), PFDA: 0.15 (0.11, 0.20), (ng/mL) | ICD-10 codes F90.0 | Maternal age at delivery, socioeconomic status, maternal smoking, alcohol drinking during pregnancy, mother’s self-reported psychiatric illnesses, child’s birth year, child’s sex | Evidence does not consistently support a link between prenatal PFAS exposure and an increased risk of ADHD |
First Author/Year/Country | Design | Sample Size | Age of Children | PFAS | Sample/Measuring Method | Exposure Measure | Test Type and Indicator | Adjustment of Covariates | Conclusion |
---|---|---|---|---|---|---|---|---|---|
Thea S. Skogheim/2021/Norway [ ] | Cohort Study | n = 400 | 3 years old | PFOA, PFNA, PFDA, PFUnDA, PFHxS, PFHpS, and PFOS | Plasma/ LC–MS/MS | PFOA: 2.46 (3.46–2.86), PFNA: 0.42 (0.20–0.49), PFDA: 0.19 (0.15–0.23) (ng/mL) | Diagnoses of “pervasive developmental disorders” were identified using ICD-10 codes F84.0, F84.1, F84.5, F84.8, or F84.9 | Child’s sex, birth weight, and status as small for gestational age (SGA); maternal age at delivery, education level, number of previous births, pre-pregnancy body mass index (BMI, kg/m ), self-reported smoking and alcohol consumption during pregnancy, as well as estimates of seafood intake (g/day) and dietary iodine intake (μg/day) based on a food frequency questionnaire (FFQ). | An increased risk of Autism Spectrum Disorder (ASD) was observed in the second quartile of PFOA exposure [OR = 1.71 (95% CI: 1.20, 2.45)]. Conversely, PFUnDA, PFDA, and PFOS were associated with a reduced likelihood of ADHD, and the overall PFAS mixture showed a decreased risk of ASD [OR = 0.76 (95% CI: 0.64, 0.90)]. |
Jiwon Oh/2022/United States [ ] | Case–control Study | n = 551 | 2–5 years old | PFOS, PFHxS, PFNA, PFDA, PFPeA, PFUnDA, PFBS, PFHxA, MeFOSAA, and EtFOSAA | Serum/ HPLC–MS/MS | PFOA: 2.20 (0.91, 6.30), PFOS: 2.01 (0.81, 8.01), PFHxS: 0.59 (0.20, 3.05), PFNA: 0.71 (0.26, 2.49), PFDA: 0.14 (0.06, 0.49), PFPeA: 0.51 (0.20, 1.33), PFHpA: 0.23 (0.03, 1.00), PFUnDA: 0.03 (<LOD, 0.13), PFBS: <LOD (<LOD, 0.10), PFHxA: <LOD (<LOD, 0.43), MeFOSAA: 0.10 (<LOD, 1.56), EtFOSAA: <LOD (<LOD, 0.06) (ng/mL) | Mullen Scales of Early Learning (MSEL) and Vineland Adaptive Behavior Scales (VABS) are combined to generate an Early Learning Composite (Composite) score | Child’s sex, age at sampling, recruitment regional center; sampling year; gestational age at delivery, maternal factors, parity, breastfeeding duration, race/ethnicity, and socioeconomic status. | PFOA was linked to higher odds of ASD, with an odds ratio (OR) of 1.99 per log ng/mL increase (95% CI: 1.20, 3.29). PFHpA also showed increased odds of ASD with an OR of 1.61 (95% CI: 1.21, 2.13). Conversely, perfluoroundecanoic acid (PFUnDA) was associated with lower odds of ASD, showing an OR of 0.43 (95% CI: 0.26, 0.69). Additionally, mixtures of PFAS were associated with increased odds of ASD, with an average OR of 1.57 and a range from the 5th to 95th percentile of 1.16 to 2.13. |
Jiwon Oh/2021/United States [ ] | Cohort Study | n = 57 | 3 years old | PFOA, PFOS, PFHxS, PFNA, PFDA, PFUnDA, PFDoDA, MeFOSAA, and EtFOSAA | Serum/ Reverse-Phase LC–MS/MS | PFOA: 0.9 (0.3–2.3), PFOS: 3.0 (1.1–6.8), PFHxS 0.4 (0.2–1.6), PFNA 0.5 (0.2–1.0), PFDA 0.1 (<LOD −0.4), PFUnDA 0.1 (<LOD −0.3), PFDoDA: <LOD (<LOD −0.1), MeFOSAA: 0.1 (<LOD −0.8), EtFOSAA <LOD (<LOD-<LOD) (ng/mL) | Autism Diagnostic Observation Schedule (ADOS) and Mullen Scales of Early Learning (MSEL) | Child’s sex, birth year, maternal vitamin intake in the first month of pregnancy, maternal education, and homeownership. | PFOA and PFNA were positively associated with ASD risk, with relative risks (RR) of 1.20 (95% CI: 0.90, 1.61) and 1.24 (95% CI: 0.91, 1.69), respectively, for each 2-fold increase in concentration. In contrast, PFHxS was negatively associated with ASD risk, showing an RR of 0.88 (95% CI: 0.77, 1.01). |
Jeong Weon Choi/2024/United States [ ] | Cohort Study | n = 280 | 3 years old | PFHxS, PFOS, PFOA, PFNA, and PFDA | Serum/ Reverse-Phase LC–MS/MS | PFHxS: 0.45 (0.2–1.60), PFOS: 2.93 (1.10–7.00), PFOA: 0.87 (0.35–2.10), PFNA: 0.48 (0.20–1.00), PFDA 0.14 (<LOD −0.40) (ng/mL) | Autism Diagnostic Observation Schedule and Mullen Scales of Early Mullen Scales of Early Learning | Child sex, child age at assessment, year of birth, gestational age at delivery, maternal age at delivery, parity, maternal pre-pregnancy BMI, maternal race/ethnicity, maternal education, breastfeeding duration, homeownership, maternal smoking status during pregnancy, and child ASD outcome group. | PFOS, PFNA, and PFDA were associated with several behavioral problems among children diagnosed with ASD. |
Hyeong-Moo Shin/2020/United States [ ] | Case–control Study | n = 239 | 2–5 years old | PFOA, PFOS, PFHxS, and PFNA | Plasma/ Reverse-Phase HPLC–MS/MS | PFOA: 1.07 (0.37–3.40), PFOS: 3.10 (1.08–10.03), PFHxS: 0.50 (0.20–1.63), PFNA: 0.50 (<LOD −1.23) (ng/mL) | Mullen Scales of Early Learning (MSEL), the Vineland Adaptive Behavior Scales (VABS), Autism Diagnostic Interview-Revised (ADI-R), Autism Diagnostic Observation Schedules-Generic (ADOS-G) | Age and sex of the child at the time of assessment, year of birth, regional center of recruitment, number of previous pregnancies, gestational age at birth, maternal race/ethnicity, place of maternal birth, mother’s age at delivery, maternal BMI before pregnancy, vitamin intake around conception, duration of breastfeeding. | Increases in PFHxS and PFOS levels were tentatively connected to a higher risk of ASD diagnosis in children. For each nanogram per milliliter increase, PFHxS had an odds ratio of 1.46 (95% CI: 0.98, 2.18) and PFOS had an odds ratio of 1.03 (95% CI: 0.99, 1.08). |
Kristen Lyall/2018/United States [ ] | Case–control Stude | n = 553 | 15–19 weeks gestational age | Et-PFOSA, Me-PFOSA, PFDeA, PFHxS, PFNA, PFOA, PFOS, PFOSA | Serum/ Negative-ion Turbo Ion Spray–tandem mass spectrometry | Et-PFOSA: 0.68 (0.63, 0.73), Me-PFOSA: 1.14 (1.07, 1.23), PFDeA: 0.17 (0.16, 0.18), PFHxS: 1.39 (1.29, 1.49), PFNA: 0.60 (0.57, 0.63), PFOA: 3.58 (3.41, 3.76), PFOS: 17.5 (16.8, 18.3), PFOSA: 0.11 (0.10, 0.11) (ng/mL) | Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV-TR) criteria | Child sex, month and year of birth, maternal age, country of maternal birth, maternal race/ethnicity, parity, and maternal education. | While most PFAS prenatal concentrations were not significantly linked to ASD, notable inverse associations were observed for perfluorooctanoate (PFOA) and perfluorooctane sulfonate (PFOS). Specifically, the adjusted odds ratios for the highest versus lowest quartiles were 0.62 (95% CI: 0.41, 0.93) for PFOA and 0.64 (95% CI: 0.43, 0.97) for PFOS. |
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Currie, S.D.; Wang, J.-S.; Tang, L. Impacts of PFAS Exposure on Neurodevelopment: A Comprehensive Literature Review. Environments 2024 , 11 , 188. https://doi.org/10.3390/environments11090188
Currie SD, Wang J-S, Tang L. Impacts of PFAS Exposure on Neurodevelopment: A Comprehensive Literature Review. Environments . 2024; 11(9):188. https://doi.org/10.3390/environments11090188
Currie, Seth D., Jia-Sheng Wang, and Lili Tang. 2024. "Impacts of PFAS Exposure on Neurodevelopment: A Comprehensive Literature Review" Environments 11, no. 9: 188. https://doi.org/10.3390/environments11090188
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Alzheimer's disease (AD), is the most common form of dementia that affects the nervous system. In the past few years, non-invasive early AD diagnosis has become more popular as a way to improve patient care and treatment results. Imaging methods, electroencephalogram (EEG) tests, and sound evaluations are some of the new ways that researchers have looked into. This review covers 60 papers published from 2020. They are compared in terms of how they use basic deep learning models such as CNN, LSTM, Alex Net, Inception Net, VGG19, and ResNet to identify AD. But not many studies use more than one method together, like image and EEG, EEG and sounds, or images and sounds. The information from the Scopus database makes it easy to look at the newest information and work. This means that using more than one method to find AD isn't getting as much attention. Our review says that combining the best parts of each method in a mixed way could make Alzheimer's research much more useful and lead to better ways to diagnose. The paper talks about problems and opportunities in the field right now as well as possible study topics and issues for the future.
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The authors acknowledged the MIT Art, Design and Technology University, Pune, India for supporting the research work by providing the facilities.
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Deshpande, S., Kulkarni, N. Exploring Integration of Multimodal Deep Learning Approaches for Enhanced Alzheimer's Disease Diagnosis: A Review of Recent Literature. SN COMPUT. SCI. 5 , 852 (2024). https://doi.org/10.1007/s42979-024-03084-w
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A literature review is a survey of scholarly knowledge on a topic. Our guide with examples, video, and templates can help you write yours.
If your journal club has exactly this sort of team, then you should definitely write a review of the literature! In addition to critical thinking, a literature review needs consistency, for example in the choice of passive vs. active voice and present vs. past tense.
A literature review is a comprehensive analysis of existing research on a topic, identifying trends, gaps, and insights to inform new scholarly contributions. Read this comprehensive article to learn how to write a literature review, with examples.
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INTRODUCTION Whatever stage you are at in your academic life, you will have to review the literature and write about it. You will be asked to do this as a student when you write essays, dissertations and theses. Later, whenever you write an academic paper, there will usually be some element of literature review in the introduction. And if you have to write a grant application, you will be ...
Learn how to write a literature review in three straightforward steps. Includes free literature review templates and resources.
A literature review is a survey of scholarly sources on a specific topic. It provides an overview of current knowledge, allowing you to identify relevant theories, methods, and gaps in the existing research. There are five key steps to writing a literature review: Search for relevant literature. Evaluate sources. Identify themes, debates and gaps.
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How to write a literature review in 6 steps How do you write a good literature review? This step-by-step guide on how to write an excellent literature review covers all aspects of planning and writing literature reviews for academic papers and theses.
Writing a Literature Review. A literature review is a document or section of a document that collects key sources on a topic and discusses those sources in conversation with each other (also called synthesis). The lit review is an important genre in many disciplines, not just literature (i.e., the study of works of literature such as novels and ...
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Writing an effective literature review. In the Writer's Craft section we offer simple tips to improve your writing in one of three areas: Energy, Clarity and Persuasiveness. Each entry focuses on a key writing feature or strategy, illustrates how it commonly goes wrong, teaches the grammatical underpinnings necessary to understand it and ...
What are Literature Reviews? So, what is a literature review? "A literature review is an account of what has been published on a topic by accredited scholars and researchers. In writing the literature review, your purpose is to convey to your reader what knowledge and ideas have been established on a topic, and what their strengths and weaknesses are. As a piece of writing, the literature ...
This article provides a comprehensive guide on how to write a systematic review, a type of literature review that summarizes and synthesizes existing evidence on a topic.
How to Write a Literature Review. Step One: Decide on your areas of research: Before you begin to search for articles or books, decide beforehand what areas you are going to research. Make sure that you only get articles and books in those areas, even if you come across fascinating books in other areas.
Don't let a bad structure ruin your literature review. Learn how to structure your literature review and download our free template.
Your report, in addition to detailing the methods, results, etc. of your research, should show how your work relates to others' work. A literature review for a research report is often a revision of the review for a research proposal, which can be a revision of a stand-alone review. Each revision should be a fairly extensive revision.
A literature review requires the same style as any other piece of academic writing. That means no contractions or colloquialisms, concise language, formal tone, and an objective perspective at all times. To distinguish between your analysis and prior scholarly work in the field, use the past tense when discussing the previous research conducted ...
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The discussion section is where you delve into the meaning, importance, and relevance of your results. It should focus on explaining and evaluating what you found, showing how it relates to your literature review and paper or dissertation topic, and making an argument in support of your overall conclusion. It should not be a second results section.
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Through a systematic literature review and empirical study, Peek STM and Chen K, among others, identified multidimensional factors that influence older adults' technology acceptance.
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