google literature research

Stand on the shoulders of giants

Google Scholar provides a simple way to broadly search for scholarly literature. From one place, you can search across many disciplines and sources: articles, theses, books, abstracts and court opinions, from academic publishers, professional societies, online repositories, universities and other web sites. Google Scholar helps you find relevant work across the world of scholarly research.

How are documents ranked?

Google Scholar aims to rank documents the way researchers do, weighing the full text of each document, where it was published, who it was written by, as well as how often and how recently it has been cited in other scholarly literature.

Features of Google Scholar

• Search all scholarly literature from one convenient place
• Explore related works, citations, authors, and publications
• Locate the complete document through your library or on the web
• Keep up with recent developments in any area of research
• Check who's citing your publications, create a public author profile

Disclaimer: Legal opinions in Google Scholar are provided for informational purposes only and should not be relied on as a substitute for legal advice from a licensed lawyer. Google does not warrant that the information is complete or accurate.

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Search Help

Get the most out of Google Scholar with some helpful tips on searches, email alerts, citation export, and more.

Finding recent papers

Your search results are normally sorted by relevance, not by date. To find newer articles, try the following options in the left sidebar:

• click "Since Year" to show only recently published papers, sorted by relevance;
• click "Sort by date" to show just the new additions, sorted by date;
• click the envelope icon to have new results periodically delivered by email.

Locating the full text of an article

Abstracts are freely available for most of the articles. Alas, reading the entire article may require a subscription. Here're a few things to try:

• click a library link, e.g., "FindIt@Harvard", to the right of the search result;
• click a link labeled [PDF] to the right of the search result;
• click "All versions" under the search result and check out the alternative sources;
• click "Related articles" or "Cited by" under the search result to explore similar articles.

If you're affiliated with a university, but don't see links such as "FindIt@Harvard", please check with your local library about the best way to access their online subscriptions. You may need to do search from a computer on campus, or to configure your browser to use a library proxy.

Getting better answers

If you're new to the subject, it may be helpful to pick up the terminology from secondary sources. E.g., a Wikipedia article for "overweight" might suggest a Scholar search for "pediatric hyperalimentation".

If the search results are too specific for your needs, check out what they're citing in their "References" sections. Referenced works are often more general in nature.

Similarly, if the search results are too basic for you, click "Cited by" to see newer papers that referenced them. These newer papers will often be more specific.

Explore! There's rarely a single answer to a research question. Click "Related articles" or "Cited by" to see closely related work, or search for author's name and see what else they have written.

Searching Google Scholar

Use the "author:" operator, e.g., author:"d knuth" or author:"donald e knuth".

Put the paper's title in quotations: "A History of the China Sea".

You'll often get better results if you search only recent articles, but still sort them by relevance, not by date. E.g., click "Since 2018" in the left sidebar of the search results page.

To see the absolutely newest articles first, click "Sort by date" in the sidebar. If you use this feature a lot, you may also find it useful to setup email alerts to have new results automatically sent to you.

Note: On smaller screens that don't show the sidebar, these options are available in the dropdown menu labelled "Year" right below the search button.

Select the "Case law" option on the homepage or in the side drawer on the search results page.

It finds documents similar to the given search result.

It's in the side drawer. The advanced search window lets you search in the author, title, and publication fields, as well as limit your search results by date.

Select the "Case law" option and do a keyword search over all jurisdictions. Then, click the "Select courts" link in the left sidebar on the search results page.

Tip: To quickly search a frequently used selection of courts, bookmark a search results page with the desired selection.

Access to articles

For each Scholar search result, we try to find a version of the article that you can read. These access links are labelled [PDF] or [HTML] and appear to the right of the search result. For example:

A paper that you need to read

Access links cover a wide variety of ways in which articles may be available to you - articles that your library subscribes to, open access articles, free-to-read articles from publishers, preprints, articles in repositories, etc.

When you are on a campus network, access links automatically include your library subscriptions and direct you to subscribed versions of articles. On-campus access links cover subscriptions from primary publishers as well as aggregators.

Off-campus access

Off-campus access links let you take your library subscriptions with you when you are at home or traveling. You can read subscribed articles when you are off-campus just as easily as when you are on-campus. Off-campus access links work by recording your subscriptions when you visit Scholar while on-campus, and looking up the recorded subscriptions later when you are off-campus.

We use the recorded subscriptions to provide you with the same subscribed access links as you see on campus. We also indicate your subscription access to participating publishers so that they can allow you to read the full-text of these articles without logging in or using a proxy. The recorded subscription information expires after 30 days and is automatically deleted.

In addition to Google Scholar search results, off-campus access links can also appear on articles from publishers participating in the off-campus subscription access program. Look for links labeled [PDF] or [HTML] on the right hand side of article pages.

Anne Author , John Doe , Jane Smith , Someone Else

In this fascinating paper, we investigate various topics that would be of interest to you. We also describe new methods relevant to your project, and attempt to address several questions which you would also like to know the answer to. Lastly, we analyze …

You can disable off-campus access links on the Scholar settings page . Disabling off-campus access links will turn off recording of your library subscriptions. It will also turn off indicating subscription access to participating publishers. Once off-campus access links are disabled, you may need to identify and configure an alternate mechanism (e.g., an institutional proxy or VPN) to access your library subscriptions while off-campus.

Email Alerts

Do a search for the topic of interest, e.g., "M Theory"; click the envelope icon in the sidebar of the search results page; enter your email address, and click "Create alert". We'll then periodically email you newly published papers that match your search criteria.

No, you can enter any email address of your choice. If the email address isn't a Google account or doesn't match your Google account, then we'll email you a verification link, which you'll need to click to start receiving alerts.

This works best if you create a public profile , which is free and quick to do. Once you get to the homepage with your photo, click "Follow" next to your name, select "New citations to my articles", and click "Done". We will then email you when we find new articles that cite yours.

Search for the title of your paper, e.g., "Anti de Sitter space and holography"; click on the "Cited by" link at the bottom of the search result; and then click on the envelope icon in the left sidebar of the search results page.

First, do a search for your colleague's name, and see if they have a Scholar profile. If they do, click on it, click the "Follow" button next to their name, select "New articles by this author", and click "Done".

If they don't have a profile, do a search by author, e.g., [author:s-hawking], and click on the mighty envelope in the left sidebar of the search results page. If you find that several different people share the same name, you may need to add co-author names or topical keywords to limit results to the author you wish to follow.

We send the alerts right after we add new papers to Google Scholar. This usually happens several times a week, except that our search robots meticulously observe holidays.

There's a link to cancel the alert at the bottom of every notification email.

If you created alerts using a Google account, you can manage them all here . If you're not using a Google account, you'll need to unsubscribe from the individual alerts and subscribe to the new ones.

Google Scholar library

Google Scholar library is your personal collection of articles. You can save articles right off the search page, organize them by adding labels, and use the power of Scholar search to quickly find just the one you want - at any time and from anywhere. You decide what goes into your library, and we’ll keep the links up to date.

You get all the goodies that come with Scholar search results - links to PDF and to your university's subscriptions, formatted citations, citing articles, and more!

Library help

Find the article you want to add in Google Scholar and click the “Save” button under the search result.

Click “My library” at the top of the page or in the side drawer to view all articles in your library. To search the full text of these articles, enter your query as usual in the search box.

Find the article you want to remove, and then click the “Delete” button under it.

• To add a label to an article, find the article in your library, click the “Label” button under it, select the label you want to apply, and click “Done”.
• To view all the articles with a specific label, click the label name in the left sidebar of your library page.
• To remove a label from an article, click the “Label” button under it, deselect the label you want to remove, and click “Done”.
• To add, edit, or delete labels, click “Manage labels” in the left column of your library page.

Only you can see the articles in your library. If you create a Scholar profile and make it public, then the articles in your public profile (and only those articles) will be visible to everyone.

Your profile contains all the articles you have written yourself. It’s a way to present your work to others, as well as to keep track of citations to it. Your library is a way to organize the articles that you’d like to read or cite, not necessarily the ones you’ve written.

Citation Export

Click the "Cite" button under the search result and then select your bibliography manager at the bottom of the popup. We currently support BibTeX, EndNote, RefMan, and RefWorks.

Err, no, please respect our robots.txt when you access Google Scholar using automated software. As the wearers of crawler's shoes and webmaster's hat, we cannot recommend adherence to web standards highly enough.

Sorry, we're unable to provide bulk access. You'll need to make an arrangement directly with the source of the data you're interested in. Keep in mind that a lot of the records in Google Scholar come from commercial subscription services.

Sorry, we can only show up to 1,000 results for any particular search query. Try a different query to get more results.

Content Coverage

Google Scholar includes journal and conference papers, theses and dissertations, academic books, pre-prints, abstracts, technical reports and other scholarly literature from all broad areas of research. You'll find works from a wide variety of academic publishers, professional societies and university repositories, as well as scholarly articles available anywhere across the web. Google Scholar also includes court opinions and patents.

We index research articles and abstracts from most major academic publishers and repositories worldwide, including both free and subscription sources. To check current coverage of a specific source in Google Scholar, search for a sample of their article titles in quotes.

While we try to be comprehensive, it isn't possible to guarantee uninterrupted coverage of any particular source. We index articles from sources all over the web and link to these websites in our search results. If one of these websites becomes unavailable to our search robots or to a large number of web users, we have to remove it from Google Scholar until it becomes available again.

Our meticulous search robots generally try to index every paper from every website they visit, including most major sources and also many lesser known ones.

That said, Google Scholar is primarily a search of academic papers. Shorter articles, such as book reviews, news sections, editorials, announcements and letters, may or may not be included. Untitled documents and documents without authors are usually not included. Website URLs that aren't available to our search robots or to the majority of web users are, obviously, not included either. Nor do we include websites that require you to sign up for an account, install a browser plugin, watch four colorful ads, and turn around three times and say coo-coo before you can read the listing of titles scanned at 10 DPI... You get the idea, we cover academic papers from sensible websites.

That's usually because we index many of these papers from other websites, such as the websites of their primary publishers. The "site:" operator currently only searches the primary version of each paper.

It could also be that the papers are located on examplejournals.gov, not on example.gov. Please make sure you're searching for the "right" website.

That said, the best way to check coverage of a specific source is to search for a sample of their papers using the title of the paper.

Ahem, we index papers, not journals. You should also ask about our coverage of universities, research groups, proteins, seminal breakthroughs, and other dimensions that are of interest to users. All such questions are best answered by searching for a statistical sample of papers that has the property of interest - journal, author, protein, etc. Many coverage comparisons are available if you search for [allintitle:"google scholar"], but some of them are more statistically valid than others.

Currently, Google Scholar allows you to search and read published opinions of US state appellate and supreme court cases since 1950, US federal district, appellate, tax and bankruptcy courts since 1923 and US Supreme Court cases since 1791. In addition, it includes citations for cases cited by indexed opinions or journal articles which allows you to find influential cases (usually older or international) which are not yet online or publicly available.

Legal opinions in Google Scholar are provided for informational purposes only and should not be relied on as a substitute for legal advice from a licensed lawyer. Google does not warrant that the information is complete or accurate.

We normally add new papers several times a week. However, updates to existing records take 6-9 months to a year or longer, because in order to update our records, we need to first recrawl them from the source website. For many larger websites, the speed at which we can update their records is limited by the crawl rate that they allow.

Inclusion and Corrections

We apologize, and we assure you the error was unintentional. Automated extraction of information from articles in diverse fields can be tricky, so an error sometimes sneaks through.

Please write to the owner of the website where the erroneous search result is coming from, and encourage them to provide correct bibliographic data to us, as described in the technical guidelines . Once the data is corrected on their website, it usually takes 6-9 months to a year or longer for it to be updated in Google Scholar. We appreciate your help and your patience.

If you can't find your papers when you search for them by title and by author, please refer your publisher to our technical guidelines .

You can also deposit your papers into your institutional repository or put their PDF versions on your personal website, but please follow your publisher's requirements when you do so. See our technical guidelines for more details on the inclusion process.

We normally add new papers several times a week; however, it might take us some time to crawl larger websites, and corrections to already included papers can take 6-9 months to a year or longer.

Google Scholar generally reflects the state of the web as it is currently visible to our search robots and to the majority of users. When you're searching for relevant papers to read, you wouldn't want it any other way!

If your citation counts have gone down, chances are that either your paper or papers that cite it have either disappeared from the web entirely, or have become unavailable to our search robots, or, perhaps, have been reformatted in a way that made it difficult for our automated software to identify their bibliographic data and references. If you wish to correct this, you'll need to identify the specific documents with indexing problems and ask your publisher to fix them. Please refer to the technical guidelines .

Please do let us know . Please include the URL for the opinion, the corrected information and a source where we can verify the correction.

We're only able to make corrections to court opinions that are hosted on our own website. For corrections to academic papers, books, dissertations and other third-party material, click on the search result in question and contact the owner of the website where the document came from. For corrections to books from Google Book Search, click on the book's title and locate the link to provide feedback at the bottom of the book's page.

General Questions

These are articles which other scholarly articles have referred to, but which we haven't found online. To exclude them from your search results, uncheck the "include citations" box on the left sidebar.

First, click on links labeled [PDF] or [HTML] to the right of the search result's title. Also, check out the "All versions" link at the bottom of the search result.

Second, if you're affiliated with a university, using a computer on campus will often let you access your library's online subscriptions. Look for links labeled with your library's name to the right of the search result's title. Also, see if there's a link to the full text on the publisher's page with the abstract.

Keep in mind that final published versions are often only available to subscribers, and that some articles are not available online at all. Good luck!

Technically, your web browser remembers your settings in a "cookie" on your computer's disk, and sends this cookie to our website along with every search. Check that your browser isn't configured to discard our cookies. Also, check if disabling various proxies or overly helpful privacy settings does the trick. Either way, your settings are stored on your computer, not on our servers, so a long hard look at your browser's preferences or internet options should help cure the machine's forgetfulness.

Not even close. That phrase is our acknowledgement that much of scholarly research involves building on what others have already discovered. It's taken from Sir Isaac Newton's famous quote, "If I have seen further, it is by standing on the shoulders of giants."

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Reference management. Clean and simple.

Google Scholar: the ultimate guide

What is Google Scholar?

Why is google scholar better than google for finding research papers, the google scholar search results page, the first two lines: core bibliographic information, quick full text-access options, "cited by" count and other useful links, tips for searching google scholar, 1. google scholar searches are not case sensitive, 2. use keywords instead of full sentences, 3. use quotes to search for an exact match, 3. add the year to the search phrase to get articles published in a particular year, 4. use the side bar controls to adjust your search result, 5. use boolean operator to better control your searches, google scholar advanced search interface, customizing search preferences and options, using the "my library" feature in google scholar, the scope and limitations of google scholar, alternatives to google scholar, country-specific google scholar sites, frequently asked questions about google scholar, related articles.

Google Scholar (GS) is a free academic search engine that can be thought of as the academic version of Google. Rather than searching all of the indexed information on the web, it searches repositories of:

• universities
• scholarly websites

This is generally a smaller subset of the pool that Google searches. It's all done automatically, but most of the search results tend to be reliable scholarly sources.

However, Google is typically less careful about what it includes in search results than more curated, subscription-based academic databases like Scopus and Web of Science . As a result, it is important to take some time to assess the credibility of the resources linked through Google Scholar.

➡️ Take a look at our guide on the best academic databases .

One advantage of using Google Scholar is that the interface is comforting and familiar to anyone who uses Google. This lowers the learning curve of finding scholarly information .

There are a number of useful differences from a regular Google search. Google Scholar allows you to:

• copy a formatted citation in different styles including MLA and APA
• export bibliographic data (BibTeX, RIS) to use with reference management software
• explore other works have cited the listed work
• easily find full text versions of the article

Although it is free to search in Google Scholar, most of the content is not freely available. Google does its best to find copies of restricted articles in public repositories. If you are at an academic or research institution, you can also set up a library connection that allows you to see items that are available through your institution.

The Google Scholar results page differs from the Google results page in a few key ways. The search result page is, however, different and it is worth being familiar with the different pieces of information that are shown. Let's have a look at the results for the search term "machine learning.”

• The first line of each result provides the title of the document (e.g. of an article, book, chapter, or report).
• The second line provides the bibliographic information about the document, in order: the author(s), the journal or book it appears in, the year of publication, and the publisher.

Clicking on the title link will bring you to the publisher’s page where you may be able to access more information about the document. This includes the abstract and options to download the PDF.

To the far right of the entry are more direct options for obtaining the full text of the document. In this example, Google has also located a publicly available PDF of the document hosted at umich.edu . Note, that it's not guaranteed that it is the version of the article that was finally published in the journal.

Below the text snippet/abstract you can find a number of useful links.

• Cited by : the cited by link will show other articles that have cited this resource. That is a super useful feature that can help you in many ways. First, it is a good way to track the more recent research that has referenced this article, and second the fact that other researches cited this document lends greater credibility to it. But be aware that there is a lag in publication type. Therefore, an article published in 2017 will not have an extensive number of cited by results. It takes a minimum of 6 months for most articles to get published, so even if an article was using the source, the more recent article has not been published yet.
• Versions : this link will display other versions of the article or other databases where the article may be found, some of which may offer free access to the article.
• Quotation mark icon : this will display a popup with commonly used citation formats such as MLA, APA, Chicago, Harvard, and Vancouver that may be copied and pasted. Note, however, that the Google Scholar citation data is sometimes incomplete and so it is often a good idea to check this data at the source. The "cite" popup also includes links for exporting the citation data as BibTeX or RIS files that any major reference manager can import.

Pro tip: Use a reference manager like Paperpile to keep track of all your sources. Paperpile integrates with Google Scholar and many popular academic research engines and databases, so you can save references and PDFs directly to your library using the Paperpile buttons and later cite them in thousands of citation styles:

Although Google Scholar limits each search to a maximum of 1,000 results , it's still too much to explore, and you need an effective way of locating the relevant articles. Here’s a list of pro tips that will help you save time and search more effectively.

You don’t need to worry about case sensitivity when you’re using Google scholar. In other words, a search for "Machine Learning" will produce the same results as a search for "machine learning.”

Let's say your research topic is about self driving cars. For a regular Google search we might enter something like " what is the current state of the technology used for self driving cars ". In Google Scholar, you will see less than ideal results for this query .

The trick is to build a list of keywords and perform searches for them like self-driving cars, autonomous vehicles, or driverless cars. Google Scholar will assist you on that: if you start typing in the search field you will see related queries suggested by Scholar!

If you put your search phrase into quotes you can search for exact matches of that phrase in the title and the body text of the document. Without quotes, Google Scholar will treat each word separately.

This means that if you search national parks , the words will not necessarily appear together. Grouped words and exact phrases should be enclosed in quotation marks.

A search using “self-driving cars 2015,” for example, will return articles or books published in 2015.

Using the options in the left hand panel you can further restrict the search results by limiting the years covered by the search, the inclusion or exclude of patents, and you can sort the results by relevance or by date.

Searches are not case sensitive, however, there are a number of Boolean operators you can use to control the search and these must be capitalized.

• AND requires both of the words or phrases on either side to be somewhere in the record.
• NOT can be placed in front of a word or phrases to exclude results which include them.
• OR will give equal weight to results which match just one of the words or phrases on either side.

➡️ Read more about how to efficiently search online databases for academic research .

In case you got overwhelmed by the above options, here’s some illustrative examples:

Tip: Use the advanced search features in Google Scholar to narrow down your search results.

You can gain even more fine-grained control over your search by using the advanced search feature. This feature is available by clicking on the hamburger menu in the upper left and selecting the "Advanced search" menu item.

Adjusting the Google Scholar settings is not necessary for getting good results, but offers some additional customization, including the ability to enable the above-mentioned library integrations.

The settings menu is found in the hamburger menu located in the top left of the Google Scholar page. The settings are divided into five sections:

• Collections to search: by default Google scholar searches articles and includes patents, but this default can be changed if you are not interested in patents or if you wish to search case law instead.
• Bibliographic manager: you can export relevant citation data via the “Bibliography manager” subsection.
• Languages: if you wish for results to return only articles written in a specific subset of languages, you can define that here.
• Library links: as noted, Google Scholar allows you to get the Full Text of articles through your institution’s subscriptions, where available. Search for, and add, your institution here to have the relevant link included in your search results.
• Button: the Scholar Button is a Chrome extension which adds a dropdown search box to your toolbar. This allows you to search Google Scholar from any website. Moreover, if you have any text selected on the page and then click the button it will display results from a search on those words when clicked.

When signed in, Google Scholar adds some simple tools for keeping track of and organizing the articles you find. These can be useful if you are not using a full academic reference manager.

All the search results include a “save” button at the end of the bottom row of links, clicking this will add it to your "My Library".

To help you provide some structure, you can create and apply labels to the items in your library. Appended labels will appear at the end of the article titles. For example, the following article has been assigned a “RNA” label:

Within your Google Scholar library, you can also edit the metadata associated with titles. This will often be necessary as Google Scholar citation data is often faulty.

There is no official statement about how big the Scholar search index is, but unofficial estimates are in the range of about 160 million , and it is supposed to continue to grow by several million each year.

Yet, Google Scholar does not return all resources that you may get in search at you local library catalog. For example, a library database could return podcasts, videos, articles, statistics, or special collections. For now, Google Scholar has only the following publication types:

• Journal articles : articles published in journals. It's a mixture of articles from peer reviewed journals, predatory journals and pre-print archives.
• Books : links to the Google limited version of the text, when possible.
• Book chapters : chapters within a book, sometimes they are also electronically available.
• Book reviews : reviews of books, but it is not always apparent that it is a review from the search result.
• Conference proceedings : papers written as part of a conference, typically used as part of presentation at the conference.
• Court opinions .
• Patents : Google Scholar only searches patents if the option is selected in the search settings described above.

The information in Google Scholar is not cataloged by professionals. The quality of the metadata will depend heavily on the source that Google Scholar is pulling the information from. This is a much different process to how information is collected and indexed in scholarly databases such as Scopus or Web of Science .

➡️ Visit our list of the best academic databases .

Google Scholar is by far the most frequently used academic search engine , but it is not the only one. Other academic search engines include:

• Science.gov
• Semantic Scholar
• scholar.google.fr : Sur les épaules d'un géant
• scholar.google.es (Google Académico): A hombros de gigantes
• scholar.google.pt (Google Académico): Sobre os ombros de gigantes
• scholar.google.de : Auf den Schultern von Riesen

➡️ Once you’ve found some research, it’s time to read it. Take a look at our guide on how to read a scientific paper .

No. Google Scholar is a bibliographic search engine rather than a bibliographic database. In order to qualify as a database Google Scholar would need to have stable identifiers for its records.

No. Google Scholar is an academic search engine, but the records found in Google Scholar are scholarly sources.

No. Google Scholar collects research papers from all over the web, including grey literature and non-peer reviewed papers and reports.

Google Scholar does not provide any full text content itself, but links to the full text article on the publisher page, which can either be open access or paywalled content. Google Scholar tries to provide links to free versions, when possible.

The easiest way to access Google scholar is by using The Google Scholar Button. This is a browser extension that allows you easily access Google Scholar from any web page. You can install it from the Chrome Webstore .

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A Scholar's Guide to Google

• Google Scholar
• Google Books

Using Google Scholar

Google Scholar is a special version of Google specially designed for searching scholarly literature. It covers peer-reviewed papers, theses, books, preprints, abstracts and technical reports from all broad areas of research.

A Harvard ID and PIN are required for Google Scholar in order to access the full text of books, journal articles, etc. provided by licensed resources to which Harvard subscribes. Indviduals outside of Harvard may access Google Scholar directly at http://scholar.google.com/ , but they will not have access to the full text of articles provided by Harvard Library E-Resources .

Browsing Search Results

The following screenshots illustrate some of the features that accompany individual records in Google Scholar's results lists.

Find It@Harvard – Locates an electronic version of the work (when available) through Harvard's subscription library resources. If no electronic full text is available, a link to the appropriate HOLLIS Catalog record is provided for alternative formats.

Group of – Finds other articles included in this group of scholarly works, possibly preliminary, which you may be able to access. Examples include preprints, abstracts, conference papers or other adaptations.

Cited By – Identifies other papers that have cited articles in the group.

Related Articles - The list of related articles is ranked primarily by how similar these articles are to the original result, but also takes into account the relevance of each paper. Finding sets of related papers and books is often a great way for novices to get acquainted with a topic.

Cached - The "Cached" link is the snapshot that Google took of the page when they crawled the web. The page may have changed since that time and the cached page may reference images which are no longer available.

Web Search – Searches for information on the Web about this work using the Google search engine.

BL Direct – Purchase the full text of the article through the British Library. Once transferred into BL Direct, users can also link to the full collection of The British Library document supply content. Prices for the service are expressed in British pounds. Abstracts for some documents are provided.

The Advanced Search feature in Google Scholar allows researchers to limit their query to particular authors, publications, dates, and subject areas.  

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• University of Massachusetts Lowell
• University Libraries

Google Scholar Search Strategies

• About Google Scholar
• Manage Settings
• Enable My Library
• Google Scholar Library
• Cite from Google Scholar
• Tracking Citations
• Add Articles Manually
• Refine your Profile Settings

Using Google Scholar for Research

Google Scholar is a powerful tool for researchers and students alike to access peer-reviewed papers. With Scholar, you are able to not only search for an article, author or journal of interest, you can also save and organize these articles, create email alerts, export citations and more. Below you will find some basic search tips that will prove useful.

This page also includes information on Google Scholar Library - a resource that allows you to save, organize and manage citations - as well as information on citing a paper on Google Scholar.

Search Tips

• Locate Full Text
• Sort by Date
• Related Articles
• Court Opinions
• Email Alerts
• Advanced Search

Abstracts are freely available for most of the articles and UMass Lowell holds many subscriptions to journals and online resources. The first step is make sure you are affiliated with the UML Library on and off campus by Managing your Settings, under Library Links. 

When searching in Google Scholar here are a few things to try to get full text:

• click a library link, e.g., "Full-text @ UML Library", to the right of the search result;
• click a link labeled [PDF] to the right of the search result;
• click "All versions" under the search result and check out the alternative sources;
• click "More" under the search result to see if there's an option for full-text;
• click "Related articles" or "Cited by" under the search result to explore similar articles.

Your search results are normally sorted by relevance, not by date. To find newer articles, try the following options in the left sidebar:

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• PMID: 32279549
• DOI: 10.12968/bjon.2020.29.7.431

Undertaking a literature search can be a daunting prospect. Breaking the exercise down into smaller steps will make the process more manageable. This article suggests 10 steps that will help readers complete this task, from identifying key concepts to choosing databases for the search and saving the results and search strategy. It discusses each of the steps in a little more detail, with examples and suggestions on where to get help. This structured approach will help readers obtain a more focused set of results and, ultimately, save time and effort.

Keywords: Databases; Literature review; Literature search; Reference management software; Research questions; Search strategy.

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Understanding health behavior change by motivation and reward mechanisms: a review of the literature

The global rise of lifestyle-related chronic diseases has engendered growing interest among various stakeholders including policymakers, scientists, healthcare professionals, and patients, regarding the effective management of health behavior change and the development of interventions that facilitate lifestyle modification. Consequently, a plethora of health behavior change theories has been developed with the intention of elucidating the mechanisms underlying health behavior change and identifying key domains that enhance the likelihood of successful outcomes. Until now, only few studies have taken into account neurobiological correlates underlying health behavior change processes. Recent progress in the neuroscience of motivation and reward systems has provided further insights into the relevance of such domains. The aim of this contribution is to review the latest explanations of health behavior change initiation and maintenance based on novel insights into motivation and reward mechanisms. Based on a systematic literature search in PubMed, PsycInfo, and Google Scholar, four articles were reviewed. As a result, a description of motivation and reward systems (approach/wanting = pleasure; aversion/avoiding = relief; assertion/non-wanting = quiescence) and their role in health behavior change processes is presented. Three central findings are discussed: (1) motivation and reward processes allow to distinguish between goal-oriented and stimulus-driven behavior, (2) approach motivation is the key driver of the individual process of behavior change until a new behavior is maintained and assertion motivation takes over, (3) behavior change techniques can be clustered based on motivation and reward processes according to their functional mechanisms into facilitating (= providing external resources), boosting (= strengthening internal reflective resources) and nudging (= activating internal affective resources). The strengths and limitations of these advances for intervention planning are highlighted and an agenda for testing the models as well as future research is proposed.

1. Introduction

The prevalence of lifestyle-related chronic diseases has increased dramatically in the last decades. Chronic diseases were responsible for 71% of all deaths occurring worldwide in 2019 ( World Health Organisation [WHO], 2022a ), of which about one third are premature deaths, i.e., happening to people aged between 30 and 69 years ( World Health Organisation [WHO], 2022a ). Diseases of the circulatory system like stroke and ischaemic heart disease accounted for 30% of all deaths in 2019 in OECD countries, followed by cancer (24%), diseases of the respiratory system (10%) and diabetes (3%) ( Organisation for Economic Co-operation and Development [OECD], 2021 ). Individuals living with these conditions also face a major stress burden due to disability, in some cases already at young ages. Indeed, averaged across 26 OECD countries, more than one third of individuals aged 16 and over have been found to be living with longstanding illness or health problems ( Organisation for Economic Co-operation and Development [OECD], 2021 ). In addition, comorbidities (multimorbidity) as well as individual physical and emotional suffering frequently occur ( Stewart et al., 1989 ; Moussavi et al., 2007 ; de Ridder et al., 2008 ), reducing overall quality of life ( Maresova et al., 2019 ).

These numbers and trends can in part be traced back to rising rates of obesity, sedentary behavior and poor nutrition, as well as other metabolic risk factors for chronic diseases including tobacco use and harmful alcohol intake. In addition, as diseases and comorbidities accumulate in older age, countries’ aging populations further influence these numbers ( Zhou et al., 2016 ). Indeed, most countries in the world have experienced, and will experience great demographic transitions. It has been estimated that between 2015 and 2050, the number of individuals aged 60 years and older will nearly double from 12 to 22%, with two billion people aged above 60 years by 2050 ( World Health Organisation [WHO], 2022b ). At the same time, life expectancy has risen from 67.5 years in 2000 to 72.9 years in 2020 at the world’s average ( The World Bank, 2022 ). Based on these projections, it can be assumed that the total number of individuals with longstanding illnesses or health problems will continue to rise.

The treatment of chronic diseases is often lengthy and intense, and is frequently accompanied by a reduced ability to work ( Seuring et al., 2015 ). While this can reduce the quality of life in patients further ( Jing et al., 2018 ), it can also affect an individual’s household financial resources ( Seuring et al., 2015 ). In low income settings, tremendous costs for treatment can quickly drain savings ( World Health Organisation [WHO], 2022a ). This, in return, may perpetuate people’s conditions, as it has been found that poverty is closely linked with the prevalence of chronic diseases: vulnerable and socially disadvantaged people tend to get ill quicker and have lower life expectancy than people of higher social positions ( World Health Organisation [WHO], 2022a ). The main reasons for this phenomenon are that economically vulnerable individuals are at greater risk of being exposed to harmful products, such as tobacco, tend to have unhealthy diets, and, in some countries, cities or neighborhoods, have limited access to health services. In fact, the average life expectancy at birth of people with low income is 4.4 (women) to 8.6 (men) years lower than of people in the highest of five income groups ( Lampert et al., 2019 ).

These costs on individuals are accompanied by costs for the healthcare system and society as a whole. Health expenditure related to diabetes, for example, is at least 966 billion USD per year worldwide, which represents a 316% increase over the last 15 years ( International Diabetes Federation [IDF], 2021 ). In Germany, the cost burden for diabetes type 2 treatment has been calculated to be on average 1.8 times higher than for other diseases ( Ulrich et al., 2016 ). Multimorbidity typically incurs greater health care costs ( Rizzo et al., 2015 ), measured by the use of medication as well as emergency department presentations and hospital admissions ( Chan et al., 2002 ). For example, Schneider et al. (2009) found that older adults in the United States with three or more chronic conditions utilized on average 25 times more hospital bed-days and had on average 14.6 times more hospital admissions than older adults without any chronic condition. Furthermore, with one additional chronic condition in older adults, the health care utilization costs increase near exponentially ( Lehnert et al., 2011 ). In addition to these financial impacts, chronic conditions tend to dwell on non-tangible resources, e.g., through time and energy spent on disease management by the patient and family members ( Ellrodt et al., 1997 ; Korff et al., 1998 ; Wagner, 2000 ). These circumstances call for shifting the focus to health care measures that help to prevent and improve chronic conditions according to patient needs in a cost-effective way.

There is compelling evidence to suggest that lifestyle changes can significantly improve the conditions of chronic diseases. Studies have demonstrated the positive impact of increased exercise, healthier nutrition, reduced alcohol intake, smoking cessation, and relaxation techniques on a range of chronic conditions ( Ornish et al., 1990 ; Knowler et al., 2002 ; Savoye et al., 2007 ; Alert et al., 2013 ; Cramer et al., 2014 ; Morris et al., 2019 ). These health behaviors can decrease the major metabolic risk factors for chronic diseases and premature deaths, including blood pressure, blood glucose, blood lipids, and obesity ( World Health Organisation [WHO], 2022a ). Remarkably, the risk of developing type 2 diabetes is predominantly attributable to lifestyle-related factors rather than genetic risks ( Langenberg et al., 2014 ). Moreover, lifestyle changes could prevent up to 70% of strokes and cases of colon cancer, 80% of coronary heart diseases, and 90% of diabetes cases ( Willett, 2002 ). Such findings highlight the tremendous potential of lifestyle modification interventions for public health outcomes.

It is widely recognized that individuals encounter challenges when endeavoring to attain their lifestyle goals. This is not unexpected, given that lifestyle change necessitates a series of individual choices that often require postponement of immediate pleasure in favor of prospective long-term health gains (a.k.a. delayed gratification, present bias, hyperbolic discounting, etc., see Stroebe et al., 2008 , 2013 ; Hall and Fong, 2015 ). Despite these obvious difficulties, practitioners, politicians and stakeholders aim to engage patients in health behavior change ( Esch, 2018 ). How consistently individuals pursue health behavior changes depends largely on how well they can overcome their innate present bias and on their endowment with other resources, such as their knowledge about health behavior change consequences, their beliefs in their ability to succeed, their self-regulation skills, self-efficacy, internal locus of control, engagement and empowerment ( Cane et al., 2012 ; Cheng et al., 2016 ; Sheeran et al., 2016 ; Ludwig et al., 2020 ; Cardoso Barbosa et al., 2021 ). Hence, a thorough understanding of health behavior change and interventions to support health behavior change taking into account individuals’ resources are necessary.

Numerous health behavior change theories have been devised, with a primary emphasis on reflective resources and willpower ( Kwasnicka et al., 2016 ). However, there is a scarcity of research on domains that are supported by, or rooted in, neuroscientific evidence. Notably, recent advances in the neuroscience of motivation and reward systems have revealed new insights into the importance of such domains ( Michaelsen and Esch, 2021 , 2022 ).

The aim of this contribution is to provide an overview of the latest explanations of health behavior change initiation and maintenance based on novel insights to motivation and reward mechanisms. Based on a literature search in PubMed (22 hits), PsycInfo (39 hits), and Google Scholar using the term “motivation AND reward AND (‘behavior change’ OR ‘behavior modification’)” in titles and abstracts in January 2023, we identified four articles which discuss neurobiological mechanisms of reward and motivation in relation to health behavior change ( Letzen et al., 2019 ; Ludwig et al., 2020 ; Michaelsen and Esch, 2021 , 2022 ). These are integrated into the social psychological literature on behavior change, previously reviewed in Michaelsen and Esch (2021 , 2022) . The review is structured as follows: the next chapter presents a summary of behavior change theories as discussed in social and health psychology in order to provide thorough ground for the discussion of the role of motivation and reward processes in health behavior change. This is followed by a description of motivation and reward processes as recently discussed in neurobiological science. After this, three models are presented which take into account motivation and reward mechanisms in health behavior change and thereby combine the two strands of literature and present interesting avenues for future health behavior change intervention planning and implementation. A discussion of the review and future research is presented at the end of the article.

2. Behavior change theories in social and health psychology

A large number of theories aiming to explain health behavior change have been published in recent decades, most of them grounded in social and health psychology. These theories differ in the views of human nature they hold ( Bandura, 1989 ) as well as in what they consider to be the fundamental drivers of behavior and the resources necessary for behavior change.

Established theories are concerned with the determinants of and motives for initiation of behavior change, and some also take into account the domains that enhance the likelihood of maintaining a new behavior after initiation ( Kwasnicka et al., 2016 ). Among the leading theories are Bandura’s Social Cognitive Theory ( Bandura, 1989 ), Gollwitzer’s theory on Implementation Intentions ( Gollwitzer, 1999 ), and the Social Determination Theory by Ryan and Deci (2000) and Deci and Ryan (2008) . In Bandura’s Social Cognitive Theory, individuals are assumed to learn new behaviors not only through trial-and-error but also through copying the behavior of others. Based on the existence of role models, the performance of the new behavior is enhanced by outcome expectancies (individuals understand the potential outcomes of their behavior), self-efficacy (individuals believe that they can achieve their desired behavioral goal), and identification (individuals identify with certain aspects of the role model) ( Bandura, 1989 ). In Gollwitzer’s (1999) theory on Implementation Intentions, individuals are suggested to make plans for anticipated situations, in which their desired behavior is at risk. These plans (implementation intentions) are assumed to delegate the control of goal-directed responses over these critical situations when encountered. Another prominent behavior change theory has been published by Ryan and Deci (2000) and Deci and Ryan (2008) . According to their Self-Determination Theory, for behavior change to be successful, three basic psychological needs require fulfillment: autonomy (being the causal agent of one’s own life), competence (ability to master skills important to oneself) and relatedness (feeling connected to others). A number of other theories have each determined a small, inconsistent number of domains supposedly relevant for behavior change initiation.

In a systematic review on 100 behavior change maintenance theories, Kwasnicka et al. (2016) highlight a deficiency in theoretical elaboration regarding the process of maintenance after initial change present in the literature. Theories that are concerned with the behavior change maintenance describe several stages of a behavior change process and the resources necessary to progress from one stage to another. A widely used theory is the Transtheoretical Model ( Prochaska et al., 2008 ), according to which an individual’s change process starts at a precontemplation stage, and continues with the contemplation, planning, implementation, maintenance and termination stages. Similar processes have been suggested by other authors ( Weinstein and Sandman, 1992 , 2002 ; Gollwitzer, 1999 ; Rothman et al., 2004 ; Schwarzer et al., 2011 ). For example, Weinstein and Sandman (2002) emphasize the stage before precontemplation where individuals may be unaware of the issue (e.g., that change in diet could improve their health conditions) and Rothman et al.’s (2004) model adds a habit stage where individuals have automated the new behavior. Michaelsen and Esch (2021) have provided the first comprehensive synthesis of behavior change models, a flexible seven-stage behavior change process, which allows to systematically relate motivation and reward mechanisms to these stages. In their process, individuals may experience the stages unawareness, awareness, contemplation, planning, initiation, continued action, and maintenance. These stages are categorized into three phases of engagement, namely, non-engagement, motivational engagement, and executive engagement, in which individuals’ actions are driven by different types of motivation and reward processes ( Michaelsen and Esch, 2021 , 2022 ), as explained in more detail below.

3. Motivation and reward systems involved in behavior change processes

Michaelsen and Esch (2021) have described three types of motivational states (approach motivation, avoidance motivation, and assertion motivation) and their corresponding rewards (pleasure, relief, and quiescence) that seem to play key roles in health behavior change processes (see Figure 1 ).

Three types of motivation and reward. Esch (2022) ; copyright: ©2022 by the author (TE). Licensee MDPI, Basel, Switzerland.

3.1. Approach motivation

Approach motivation, also known as appetitive or incentive salience, is focused on stimuli or goals that are associated with positive and pleasurable experiences ( Bozarth, 1994 ; Esch and Stefano, 2004 ; Elliot et al., 2013 ). This type of motivation is linked to the wanting-system, reward expectation, performance, and action ( Esch, 2022 ). The attainment of a desired stimulus or goal typically produces a sense of pleasure or reward, which may or may not be noticeable depending on the intensity of the experience. The reward is not derived from the stimulus or goal itself, but from the psychological and neurobiological processes that occur when there is a positive anticipation and response to a stimulus or goal ( Berridge and Kringelbach, 2008 ; Schultz, 2015 ). While it is challenging to categorize experiences into specific types of motivational processes, it is generally agreed that individuals tend to assess stimuli as positive or negative ( Elliot et al., 2013 ). These assessments are frequently referred to as fundamental affective experiences and include emotions such as joy, pleasure, and excitement ( Schneirla, 1959 ; Cacioppo et al., 1999 ; Elliot et al., 2013 ; Lang and Bradley, 2013 ; Rolls, 2013 ). Therefore, the essence of approach motivation lies in the anticipation of obtaining a reward that is characterized by positive emotions.

The underlying physiological mechanisms of motivation occur in specific brain areas distinct from other sensory and cognitive areas ( Kringelbach, 2005 ; Esch, 2022 ). The approach motivation and reward system is commonly described as being embedded in the central nervous system (CNS), involves nerve cells that originate in the ventral tegmental area (VTA) and send projections to the frontal brain, specifically the nucleus accumbens (NAC), via the neurotransmitter dopamine ( Nestler, 2001 ; Nestler et al., 2001 ; Esch and Stefano, 2004 , 2010 ). The nucleus accumbens (NAC) plays a crucial role in the neural regulation of reward-seeking behavior by signaling the degree of effort necessary to acquire a reward and the desire to obtain it, thereby determining the appetitive motivational salience. Additionally, the ventral tegmental area-nucleus accumbens (VTA-NAC) pathway is responsible for measuring and regulating the rewarding aspects of an activity, transmitting pertinent information to other brain regions ( Esch and Stefano, 2004 ; Berridge, 2007 ; Smith et al., 2011 ; Esch, 2022 ). The magnitude of expected reward has been found to significantly influence the likelihood of an individual to retain and repeat a behavior ( Esch and Stefano, 2010 ). Furthermore, the hippocampus and amygdala have been identified as crucial components of the reward system, with the hippocampus serving as a gatekeeper for experiences to be recognized and stored in memory, while the amygdala assesses these experiences as either pleasurable or detrimental ( Esch and Stefano, 2004 ; Nestler and Malenka, 2004 ). The mesocortical dopamine pathway in the frontal cortex is also known to be involved in the evaluation of the “costs” and risks associated with the pursuit of rewards, ultimately shaping an individual’s behavioral response ( Esch and Stefano, 2010 ).

3.2. Avoidance motivation

The construct of avoidance motivation, also referred to as negatively-valenced fearful salience, pertains to the motivational system that drives the avoidance of punishment or potential harm, rather than the pursuit of reward. This type of motivation is intricately linked to the fight-flight-freeze response, which encompasses physiological and behavioral changes in response to perceived threat ( Bozarth, 1994 ; Esch and Stefano, 2004 ; Seymour et al., 2007 ; Esch, 2022 ). The phenomenon commonly known as avoidance behavior is typically evoked by an aversive or challenging stimulus, and elicits a motivated reaction of withdrawal, commonly manifested as the act of moving away from unpleasant conditions. It is noteworthy that avoidance behavior can be differentiated from punishment, which exerts a suppressing effect on the strength of the behavioral response (passive avoidance), and from negative reinforcement, which engenders an augmenting effect on the strength of the behavioral response (active avoidance) ( Schultz, 2015 ). In contrast to active reactions such as fighting or fleeing in response to a fear-inducing stimulus, there can also be the passive reactions of freezing ( Berridge, 2018 ). Emotions associated with avoidance motivation include anxiety, fear, and disgust ( Lang, 1995 ; Cacioppo et al., 1999 ; Watson et al., 1999 ; Elliot et al., 2013 ; Hirschberg and Manning, 2015 ; Esch, 2022 ).

Avoidance motivation is embedded in the stress system and involves increased sympathetic activity and the release of cortisol, adrenaline, opioids, and vasopressin ( Esch, 2022 ). This type of motivation is rooted in the lower limbic system, specifically the amygdala and hypothalamus. Upon the anticipation of an actual or imagined threat, two distinct pathways are instigated: one through the hypothalamus and pituitary gland, leading to the release of cortisol, and the other through the sympathetic nervous system, leading to the release of adrenaline ( Esch, 2022 ). The freeze reaction is also connected to the amygdala ( LeDoux, 1998 ). Successful avoidance can lead to relief, a positive, low-arousal emotion that can be experienced as relaxation or reward ( Levenson, 2011 ; Krisam et al., 2017 ; Esch, 2022 ). An incontrovertible interdependence between the approach and avoidance motivation systems exists, as akin brain regions are triggered during both relief and other positive affects ( Kim et al., 2007 ; Sangha, 2015 ).

3.3. Assertion motivation

The majority of research on motivation and reward does not differentiate between behavior driven by approach motivation and behavior driven by assertion motivation. In point of fact, these two categories of motivation are frequently confounded or amalgamated ( McCall and Singer, 2012 ), despite the divergent neurobiological mechanisms underlying them, their distinct loci in the brain, and their discrepant behavioral outcomes. Assertion motivation, or assertive salience, is linked to the “non-wanting” system and associated with inaction, acceptance, or contentment, homeostasis, and quiescence. It describes the motivation to maintain a certain condition or state ( McCall and Singer, 2012 ; Esch, 2022 ). Assertion motivation is different from approach motivation in terms of the emotions it evokes and the types of behavior it leads to McCall and Singer (2012) and Esch (2022) . Assertion motivation is associated with a lack of desire to change or move away from the current state, while approach motivation is associated with a desire to move toward something. Assertion motivation can be seen in instances where a person is content with their current situation, such as a newly habituated health behavior, and there is no inclination to change or move away from it.

Assertion motivation is linked to increased activity in the parasympathetic autonomous nervous system and is associated with neurotransmitters such as endogenous opiates, oxytocin, acetylcholine, serotonin, and endocannabinoids ( Esch, 2022 ). Brain areas involved in the activation of assertive motivation include the midbrain, vagus areas, cingulum, hippocampus, ventral striatum, hypothalamus, and pituitary gland ( Michaelsen and Esch, 2021 ). It is different from approach and avoidance motivation in terms of related affective qualities and behavioral outcomes and is not characterized by activation of dopaminergic activity.

4. Weaving together motivation and reward mechanisms with health behavior change theories

Weaving together psychological explanations of behavior change with neurobiological understandings of motivation and reward processes has produced three models explaining different aspects of behavior change. First, a model differentiating goal-directed and stimulus-driven behavior ( Michaelsen and Esch, 2021 ) will be explained. This is followed by the description of the Model of Engagement, that illustrates the role of the three types of motivation during a behavior change process ( Michaelsen and Esch, 2021 ). Finally, the behavior change resource model ( Michaelsen and Esch, 2022 ) that integrates the differentiation between goal-directed and stimulus-driven behavior with the Model of Engagement to explain the functional mechanisms of behavior change techniques is presented. The elaborations of Ludwig et al. (2020) concerning reward valuation and Letzen et al. (2019) on mesocorticolimbic function in behavior change are discussed within these sections.

4.1. Goal-directed and stimulus-driven behavior

Kwasnicka et al.’s (2016) systematic review revealed that existing health behavior change theories largely focus on cognitive resources deemed necessary for achieving behavior change. Their findings indicated that only 10% of the theories reviewed took into account the relevance of automatic responses to relevant cues or stimuli, which has been identified as a limitation to existing theories ( Van Cappellen et al., 2018 ). This is because the manifestation of health behaviors in daily life is often influenced by implicit emotions and non-cognitive motives, rather than reflective cognitive willpower, as various dual-process models have emphasized (e.g., Kahneman and Tversky, 1982 ; Strack and Deutsch, 2004 ; Hall and Fong, 2007 ; Marteau et al., 2012 ; Sheeran et al., 2013 ). Dual-process models of decision-making have been developed to differentiate between two regulatory systems in the brain: reflective (cognitive, conscious) and affective (impulsive, intuitive, automatic) antecedents of behavior ( Chaiken, 1980 ; Petty and Cacioppo, 2012 ). The reflective system is based on conscious deliberation and control, which requires subjective effort. It draws upon an individual’s knowledge of probabilities and values and is based on rules of language and logic. The key processes of the reflective system are volition and reasoning, which can be intentionally accessed. However, the reflective process is relatively slow ( Strack and Deutsch, 2004 ; Sheeran et al., 2013 ). The reflective system typically supersedes the automatic system, which is quicker and more effortless, and operates by utilizing stored associations acquired through experiences, responding to habits and impulses. Strack and Deutsch (2004) posit that the automatic system is a significant impulsive process that engenders activation, in which perceptual inputs stimulate elements in the associative memory, subsequently activating other related elements. This form of information processing is characterized by its rapidity and operation beyond conscious awareness, as noted in the extant literature ( Strack and Deutsch, 2004 ; Evans, 2010 ; Sheeran et al., 2013 ). While this view has garnered both commendation and condemnation from scholars ( Evans, 2018 ), it nevertheless represents a significant contribution to the comprehension of health behavior and behavior change. Furthermore, a widespread view stemming from dual-process models is that the more rapid component governs behavior.

In reference to dual-process models and the differentiation between controlled goals and autonomous goals (or unnoticed stimuli), Michaelsen and Esch (2021) present a neurobiologically informed model of stimulus-driven and goal-directed behavior. In stimulus-driven behavior, a stimulus activates automatic processes and leads to behavior without the individual having noticed the stimulus. Once a stimulus has undergone cognitive processing and been transformed into a goal, the ensuing behavior is referred to as goal-directed behavior. The authors posit that both varieties of stimuli are capable of inciting appetitive, aversive, or assertive salience by means of reward anticipation. In this way, motivational salience, or the ability to attract and hold attention, can lead to action and engagement without conscious thought or planning ( Ryan and Deci, 2000 ; Carver, 2009 ; Kruglanski et al., 2014 ; Berridge, 2018 ). Both unnoticed stimuli and those that are deliberately processed can result in the same actions and engagement. However, in goal-directed behavior, the individual is aware of their actions and is actively involved in the process, as noted by Michaelsen and Esch (2021) . Figure 2 illustrates the difference between stimulus-driven and goal-directed behavior in a simplified way.

Goal-directed and stimulus-driven behavior ( Michaelsen and Esch, 2021 ).

The model can be expanded by the theory proposed by Ludwig et al. (2020) , who propose an approach to achieve sustainable behavior change through a combination of theories and research on autonomous motivation, reinforcement learning and mindfulness. The authors argue that behavior change can occur through increased awareness of the reward value of specific actions, which drives future behavior, in addition to the commonly proposed “mental gap” mechanism. The stability of a behavior depends on changes in its reward value over time and the accessibility of more rewarding behaviors. The reward value of a behavior may depend on both external and internal factors, such as subjective experience and goal achievement. The authors suggest that bringing present-moment or mindful awareness to current behavior can update the reward value of habitual behaviors and lead to new learning. This approach involves direct, in-the-moment, curious awareness and is not reliant on reflective thought processes. An increased awareness about stimuli that engender change through increased reward value would shift individuals, in the above model, from stimulus-driven to goal-directed behavior.

4.2. Motivational engagement in behavior change processes

Based on the synthesis of multi-stage behavior change theories, Michaelsen and Esch (2021) have derived three different phases of engagement, based on the role of motivational processes involved during the stages of behavior change. During the first phase, called non-engagement phase, individuals are either unaware that behavior change may improve their health conditions, or they are aware but have no intention to change an aspect of their health behavior. During this phase, any motivational mechanisms are yet absent. Stimuli like new information about the health benefits of a certain behavior change may activate motivational processes so that individuals progress into the motivational engagement phase, which is comprised of the contemplation and planning stages.

The nature of the contemplative phase is contingent on the sort of motivational salience that is evoked by the stimulus. Should an individual be satisfied with their present state, assertive salience becomes operational. Here, the likelihood of perpetuating the present condition is linked to positive valence that instigates sensations of quiescence, stillness, and/or relaxation stemming from the discharge, such as that of endogenous opiates, oxytocin and related neurotransmitters, as well as parasympathetic activity. Such a state leads to a lack of behavioral activity, resulting in the cessation of the process of behavior change. In the event that an individual desires a change, either appetitive or aversive salience is elicited. When appetitive salience is activated, information undergoes processing by the mesocortical dopamine pathway in the frontal cortex, and a preference for a new behavior is set ( Esch and Stefano, 2010 ; Michaelsen and Esch, 2021 ). On the other hand, should aversive salience be activated, information is routed through the stress response pathways, namely, the hypothalamic-pituitary (-adrenal) axis and the (amygdalar-) sympathetic nervous system axis ( Esch and Stefano, 2010 ).

The planning stage is defined by cognitive, goal-directed action (see Figure 3 ). In order to plan, the actions of thinking, reflecting, and evaluating are involved, and, neurobiologically, the upper limbic level. The cognitive task of planning is propelled by either appetitive or aversive motivational salience and may culminate in an intention, or a series of intentions (a plan). Michaelsen and Esch (2021) contend that, owing to its cognitive underpinnings, planning can only transpire in goal-directed behavioral processes, and not in stimulus-driven behavioral processes. They posit that both stages of motivational engagement can be bypassed if the presented stimulus and the evoked motivational salience go unnoticed (i.e., are stimulus-driven).

Model of Engagement ( Michaelsen and Esch, 2021 ).

The third engagement phase is called executive engagement and consists of the stages initiation, continued action and maintenance ( Michaelsen and Esch, 2021 ). According to the authors, initiation is the behavioral consequence of a response-outcome mechanism, whereby an individual actively reacts to the appetitive or aversive motivational salience that ensues from the encounter (and processing) of a stimulus. This reaction is propelled by the anticipation of pleasurable feelings (in the case of positive stimuli) or relief (in the case of negative stimuli). The appraisal of experiences as pleasurable or unpleasurable takes place within the endogenous reward system (such as the amygdala), which also encompasses the establishment of associations between an experience and other stimuli ( Michaelsen and Esch, 2021 ). Upon fulfillment of the expectation of a positive experience, said experience engenders a memory that, in turn, spawns an anticipation of a reward from the same activity, thereby enhancing the likelihood of the behavior being reiterated ( Van Cappellen et al., 2018 ). This phenomenon is referred to as reward responsiveness ( Carver and White, 1994 ).

The process of recording memories of experiences, which includes the context in which they occurred, such as the location, time, and social companionship, entails the involvement of the hippocampus ( Nestler, 2001 ; Nestler et al., 2001 ; Esch and Stefano, 2004 , 2010 ). This type of learning can lead to a reciprocal effect: as time passes, associations between positive affect and stimuli that predict it, and memories of it, may endow those stimuli with appetitive salience, making them more likely to capture attention in the future ( Fredrickson and Joiner, 2018 ; Van Cappellen et al., 2018 ). The phenomenon of learning encompasses two critical components, namely conditioning and expectation. In the context of stimulus-driven and goal-directed behavior, the experience of reward is not contingent on whether the stimulus was subjected to cognitive processing to be transformed into a goal. According to Michaelsen and Esch (2021) , the initiation of a new behavior through the activation of endogenous reward triggers a learning process, wherein the association between the new behavior and the experienced positive affect fosters reward expectancy, potentially resulting in continued action. The present study posits that the maintenance of response-outcome associations between pleasurable stimuli and their predictive cues is enhanced by sustained behavioral engagement. In this context, the authors assert that the probability of repetitive behavior, and consequently the degree of engagement therein, is contingent upon the magnitude of endogenous reward elicited by the new behavior ( Michaelsen and Esch, 2021 ). Following the repeated enactment of stimulus-driven or goal-directed behavioral actions, individuals ultimately transition into a maintenance stage, characterized by a sustained operant learning process that leads to habit formation ( Schultz, 2015 ). During this stage, the behavior is executed with regularity, and the assertive salience driven by the motivation and reward systems remains active, thereby strengthening the habitually performed action ( Michaelsen and Esch, 2021 ). The experience of quiescence, calm or contentment associated with the activation of the parasympathetic nervous system and other down-regulatory pathways serves as a powerful motivator for the maintenance of newly adopted behaviors. This state of contentment engenders a state of “non-wanting” with regard to further modifications of behavior ( Michaelsen and Esch, 2021 ). This Model of Engagement is presented in Figure 3 .

The findings can be integrated with the idea of Letzen et al. (2019) , who incorporate putative neurobiological mechanisms contributing to motivation for pain self-management into the Motivational Model for Pain Self-Management ( Jensen et al., 2003 ). The authors propose that an altered function in the mesocorticolimbic function would inhibit behavior change. The goal of this updated model is to determine whether potential neurobiological deficiencies contributing to poor motivation feed into observed non-adherence among patients with chronic pain. The authors hypothesize that mesocorticolimbic function subserves treatment-related learning history, contingency processing, and cost/benefit analysis, and individuals with mesocorticolimbic dysfunction will have lower perceived importance of symptom self-management and poorer self-efficacy for symptom self-management. They also suggest that magnitude of mesocorticolimbic dysfunction will correlate with reported treatment motivation, so that greater dysfunction is associated with poorer readiness for change, and that self-reported treatment motivation moderates the relationship between pre-treatment mesocorticolimbic function and adherence ( Letzen et al., 2019 ). The article also suggests that practice of a pain management strategy will be associated with mesocorticolimbic activity via reinforcement, and individuals with high reinforcement from this practice will have greater motivation for future practice, leading to better adherence ( Letzen et al., 2019 ). While the authors do not discuss pain management behavior as a process, by relating their hypothesis to the Model of Engagement, we can derive that mesocorticolimbic dysfunction would inhibit the progress to the stages contemplation, planning, initiation and/or continued action, and individuals with mesocorticolimbic dysfunction facing these stages within their health behavior change process would need specific support to progress.

4.3. The behavior change resource model

4.3.1. three types of behavior change resources.

The resources individuals need to progress from one health behavior change stage to another, as suggested in a number of health behavior change theories, have been summarized by Cane et al. (2012) , Kwasnicka et al. (2016) , and Carey et al. (2019) . Recently, the resources that facilitate changes in health behavior have been classified by Michaelsen and Esch (2022) into two broad categories, namely the socio-environmental resources external to the individual, and the bio-psychological resources that pertain to the internal state of the individual, with both types being characterized by changeable and non-changeable factors. While behavior change techniques (BCTs) cannot be leveraged to address non-changeable factors such as the weather, their utility is geared to targeting changeable resources ( Michaelsen and Esch, 2022 ).

Based on the distinction between reflective and affective aspects, Michaelsen and Esch (2022) have established a categorization of resources according to how these resources are accessed or generated in the brain. As such, resources are either external (socio-environmental), or internal (bio-psychological), whereby the latter can be either reflective or affective. Reflective resources are accessed, generated or refined through deliberate and effortful cognitive processing, including but not limited to goal-setting and behavioral regulation. In contrast, affective resources, such as emotions and their reinforcing valences, may be promptly elicited by environmental stimuli without the need for volitional engagement. External resources, such as environmental context and material resources, can be externally provided ( Michaelsen and Esch, 2022 ). These three types of changeable resources are depicted in Figure 4 .

Three types of changeable resources ( Michaelsen and Esch, 2022 ).

4.3.2. Behavior change techniques

Behavior change theories provide a foundation for developing effective behavior change techniques (BCTs) to support individuals in modifying their behaviors. Such theories have been employed in diverse ways, including the integration of social interactions based on Bandura’s (1989) Social Cognitive Theory, and assisting patients in generating implementation intentions, drawing on Gollwitzer’s (1999) theory on Implementation Intentions [see Bélanger-Gravel et al. (2013) for a meta-analysis of BCTs based on Gollwitzer’s (1999) theory on Implementation Intentions]. The extant literature has primarily focused on employing behavior change techniques (BCTs) that enhance cognitive resources, such as nutritional or psychological counseling ( Ball et al., 2013 ), or create situations that promote behavior modification, such as supervised walking groups ( Kassavou et al., 2013 ) or financial incentives ( Lee et al., 2019 ). However, these techniques often fail to account for patients’ individual differences in needs and circumstances ( Cecchini et al., 2010 ). The majority of interventions geared toward behavioral change tend to be financially costly and hence, not sustainable over a prolonged period of time or feasible to offer to a wide populace ( Forster et al., 2011 ). Some interventions have also yielded adverse side effects. For instance, monetary rewards for weight loss have been shown to be effective until the remuneration is obtained; however, subsequent weeks have reported higher odds of weight gain ( Paul-Ebhohimhen and Avenell, 2008 ). In contrast to BCTs that mainly, or solely, address cognitive, rational, or circumstantial/environmental resources and domains, modern BCTs primarily build on individual behavioral responses to various motivational stimuli, including affective components of a behavioral decision. Examples are the use of wearables (e.g., Piwek et al., 2016 ) and other digital innovations (e.g., Priesterroth et al., 2019 ) as well as reminders (e.g., Orr and King, 2015 ) among various forms of nudging. Nudging can be understood as shaping decision contexts in a way that encourages a particular behavior (e.g., Hansen and Jespersen, 2013 ) in a playful way through the activation of affective processes in the brain ( Michaelsen and Esch, 2022 ).

Despite a rapid growth in the implementation of interventions, most of these interventions are only successful in the short term, and often fail to demonstrate a significant improvement in the medium and long term (e.g., Marteau et al., 2012 ; International Diabetes Federation [IDF], 2013 ; Ulrich et al., 2016 ; Sainsbury et al., 2019 ). One reason for this may be the lack of comprehensive theories that allow developing successful BCTs. Another reason may be the insufficient use of theories in intervention development. In a scoping review pertaining to nudging interventions, it was discovered that only a quarter of the studies under review took into consideration the purported working mechanisms underlying the effectiveness of the intervention, while three-quarters focused solely on demonstrating its efficacy ( Szaszi et al., 2018 ). The working mechanisms, which involve the connections between BCTs and the targeted domains or resources, i.e., the specific BCT that addresses a particular resource, were elucidated upon by Carey et al. (2019) . A detailed list of resources relevant to behavior change initiation is presented by Michie et al. (2005) , who identified 112 behavior change theories and clustered the domains of behavior change mentioned therein into 12 categories. This Theoretical Domains Framework has been validated by Cane et al. (2012) , who extended the number of categories to 14 domains: “knowledge,” “skills,” “social/professional role and identity,” “beliefs about capabilities,” “optimism,” “beliefs about consequences,” “reinforcement,” “intentions,” “goals,” “memory,” “attention and decision processes,” “environmental context and resources,” “social influences,” “emotion,” and “self-regulation”. Kwasnicka et al. (2016) have summarized the domains that have been presented relevant for behavior change maintenance in their reviewed maintenance theories into five overarching categories; “maintenance motives,” “self-regulation,” “resources,” “habit,” and “environmental and social influences.” These inhibit significant overlaps with Cane et al.’s (2012) 14 domains. An analysis of these resources and the BCTs they are targeted by is presented by Michaelsen and Esch (2022) , as is further explained below.

4.3.3. Clustering BCTs

Based on the triad of behavior change resources, BCTs can be clustered according to how they address these resources and can thereby be described as the functional mechanisms of BCTs. In this way, Michaelsen and Esch (2022) derived three types of BCTs, namely those, that provide external resources (facilitating), those which strengthen internal reflective resources (boosting) and those that activate internal affective resources (nudging).

4.3.3.1. Facilitating

BCTs that focus on providing external resources enable individuals to engage in a desired behavior. These resources, which fall under categories such as “environmental context and resources” and “social influences” in the Theoretical Domains Framework ( Michie et al., 2005 ; Cane et al., 2012 ), can be provided by the individual, another person, or an organization. Illustrative of the aforementioned interventions are strategies that enhance the availability of healthy food alternatives within workplace canteens ( Geaney et al., 2013 ), incentivization programs that offer monetary rewards ( Petry et al., 2013 ), modification of the physical environment through initiatives such as the establishment of public fitness trails ( Cohen et al., 2012 ), and social support mechanisms including the facilitation of assisted walking groups ( Kassavou et al., 2013 ). These techniques can help facilitate behavior change, but the new behavior may not be sustained once the external resources are removed. However, when an individual has established a routine or habit of a specific new behavior, and their motivation to continue is strong, the end of the availability of the BCT may lead to a similar behavior that can be implemented independently of the original BCT. As an example, the termination of an organized walking group may prompt the participants to either sustain their walking activity on an individual basis or establish autonomous walking groups.

4.3.3.2. Boosting

Internal reflective resources can be addressed by involving cognitive processes. BCTs which target theses resources are called boosts. These enjoyable tasks foster the building up or strengthening of internal reflective resources that can support health behavior change. Examples are “beliefs about capabilities,” “beliefs about consequences,” “intentions,” “goals,” and “behavioral regulation” ( Cane et al., 2012 ). These types of interventions may include self-monitoring techniques, such as keeping a diary or practicing mindfulness ( Shomaker et al., 2019 ) to improve attention and awareness. Additionally, interventions like health education ( Gigerenzer et al., 2007 ) and nutritional counseling ( Ball et al., 2013 ) can increase an individual’s understanding of the consequences of their behavior and lead to a willingness to change. There are also other examples of boosting interventions (see, e.g., Grüne-Yanoff and Hertwig, 2016 ) that can similarly lead to an increased readiness to change and intentional implementation of a desired behavior ( Michaelsen and Esch, 2022 ). Having executed the desired behavior by means of one’s own effort, thus, leads to an experience of self-efficacy and the related positive affect. This in turn, can act as a reinforcement to pursue the behavior again. The generated effects potentially persist beyond the intervention, if those resources have become sufficiently strong or stable ( Hertwig and Grüne-Yanoff, 2017 ) and the reward, e.g., through the self-efficacy experience, has been sufficiently intense and therefore been stored in memory.

4.3.3.3. Nudging

Nudges are interventions that guide people toward a certain behavior without limiting their freedom of choice (e.g., Thaler and Sunstein, 2008 ; Alemanno and Sibony, 2015 ; Halpern and Sanders, 2016 ). This is achieved by manipulating aspects of the environment to create cues, stimuli, or triggers that make the desired behavior more appealing. Nudging activates the emotional aspects of decision-making, making the behavior more attractive, enjoyable and intrinsically rewarding, while still allowing individuals to make their own choices ( Michaelsen and Esch, 2021 , 2022 ). Nudging does not require cognitive skills or external resources, but it activates non-conscious or automatic resources to compensate for the lack of external or reflective resources needed for behavior change ( van Gestel et al., 2020 ). Felsen and Reiner (2015) provided a neuroscientific explanation of how nudges exert their effects based on diffusion-to-bound models. In diffusion-to-bound models, it is assumed that a decision is made within a decision space bounded by the available choices. A decision variable that is comprised of multiple factors that influence the decision including current sensory stimulation, stored memory about past experience, and the subjective value of each option, moves further or closer to each bound depending on the strength of these factors until one bound is reached and the corresponding decision is made ( Felsen and Reiner, 2015 ). Nudges can be considered to shift the decision variable toward the bound of the preferred choice, i.e., making the preferred choice more likely ( Felsen and Reiner, 2015 ).

In a systematic review, nudging interventions have been shown to lead to medium size effects in behavior change ( Mertens et al., 2022 ). Examples are variations in the manner of presenting food items ( Bucher et al., 2016 ; Broers et al., 2017 ; van Gestel et al., 2020 ), reminders or reinforcement-based learning schemes ( Orr and King, 2015 ; Yom-Tov et al., 2017 ), lotteries ( Volpp et al., 2008 ), and point systems ( Priesterroth et al., 2019 ), all of which serve to augment the expectation of rewards. The underlying premise is that the magnitude of the anticipated reward is positively correlated with the likelihood of remembering and repeating it ( Esch and Stefano, 2004 ). These nudges are believed to only have temporary effects on behavior, as the increased motivation from the nudge is not sustained once the nudge is removed. For example, a study that used point-of-decision prompts to encourage stair use in a university dormitory found that the effects were not sustained once the prompts were removed ( Howie and Young, 2011 ). However, with frequent repetition, the behavior being nudged may become a habit that continues even after the nudge is removed because of neurobiological learning processes ( Verplanken and Aarts, 1999 ; Lieberoth et al., 2018 ; van Rookhuijzen et al., 2021 ).

4.3.4. Summary of the behavior change resource model

The classification of BCTs based on the behavior change resources they address, may be sufficient to define all existing BCTs and explain their functional mechanism. This means that any BCT, such as those listed in Michie et al. (2013) can be categorized as facilitating, boosting, or nudging. Michaelsen and Esch (2022) have defined resource-driven behavior change as a process that increases the likelihood of a preferred behavior by focusing on the resources needed for that particular behavior to occur. Resource-driven behavior change is accomplished via the implementation of one or a blend of three BCT types that provide external resources (facilitating), build up internal reflective resources (boosting) or activate internal affective resources (nudging). Upon achieving a certain level of efficacy, the BCTs can prompt the initiation or maintenance of a new behavior, which can subsequently yield a positive response (affect) as a reward. Such reward can serve as a cue or stimulus to augment resources, known as vantage resources ( Van Cappellen et al., 2018 ). Exemplifying this notion, a positive affect can function as a reinforcement, thereby acting as a subtle prompting mechanism (nudging), as the experience of a pleasurable affect is deemed vital in predicting the likelihood of subsequent behavioral engagement ( Michaelsen and Esch, 2022 ). Furthermore, successful implementation or repetition of the desired behavior can also reinforce other desirable cognitive and affective states, such as strengthening one’s belief in one’s own abilities (i.e., self-efficacy), which can serve as a boosting strategy. Neurobiologically, these emotional influences on reward experiences and subsequent decisions are mediated in the medial prefrontal cortex, as evidence from human and animal model studies indicates ( Euston et al., 2012 ). Therefore, the functional mechanisms of BCTs are not independent, but interrelated with neurobiological motivation and reward proceedings. Recognizing these multidirectional causal relationships, Michaelsen and Esch (2022) propose a new framework for understanding the functional mechanisms of BCTs, called the behavior change resource model (BCRM). The BCRM and its relation to the Model of Engagement is illustrated in Figure 5 .

Behavior change resource model and its relation to the Model of Engagement ( Michaelsen and Esch, 2021 , 2022 ).

5. Discussion

5.1. understanding health behavior change by motivation and reward mechanisms.

Despite being essential to enhance health, behavior change support is rarely covered by health care systems around the world ( Chauhan et al., 2017 ; Grabovac et al., 2019 ). It is therefore even more important to support the development of interventions, which are powerful in terms of efficiency and preservation of individuals’ autonomy in order to be applied in low-resource settings or independently of political decision-makers. Behavior change has been studied primarily from a social psychology perspective, focusing on cognitive, or reflective, resources and domains relevant to behavior change, including circumstantial/environmental aspects. Neurobiological advances in automatic functioning as well as motivation and reward systems, however, fit neatly into the discussion of how humans act and how behavior can be changed. Integrating motivation and reward mechanisms into the behavior change literature and presenting new models to understand behavior change potentially helps policy makers to identify the necessary and sufficient environmental, economic, and psychological conditions that make healthy choices possible and easy.

A framework with a similar purpose as the behavior change resource model is the behavior change wheel (BCW) ( Michie et al., 2011 ). The BCW is based on a review of 19 behavior change frameworks from various fields (e.g., health, environmental behavior). Its core is a “behavior system” with three essential conditions: capability, opportunity, and motivation. These three conditions can be interpreted as attributes of behavior change resources. They overlap slightly with the categorization of resources made by Michaelsen and Esch (2022) , in the sense that opportunity to behavior change is present when external resources are available, capability is fulfilled when the necessary internal reflective resources are strong enough, and motivation can be seen as an internal affective resource. In the BCW, motivation represents a psychological resource (referring to intrinsic vs. extrinsic motivation) and is not discussed or integrated in terms of its neurobiological underpinnings. In a second step, Michie et al. (2011) developed intervention functions, which are essentially a categorization of BCTs into nine groups: education, persuasion, incentivization, coercion, training, restriction, environmental restructuring, modeling and enablement. The three conditions are then linked to the intervention’s functions without a specific explanation of how the conditions relate to the interventions, i.e., their functional mechanisms are not explained.

5.2. Practical implications of the presented literature

The understanding of the role of motivational salience in health behavior change processes presented by the Model of Engagement could be applied to develop suitable cues and stimuli, e.g., nudges that direct people’s actions into their desired outcome. General examples are fruit placement experiments ( Wansink et al., 2011 ; Hansen et al., 2016 ) or goal formation through social comparison ( Custers and Aarts, 2005 , 2010 ). The findings can also be used in a more differentiated way. Considering the seven stages of the behavior change process, findings imply that different BCTs are required depending on where at the change process an individual is. At the unawareness stage, individuals are not aware that behavior change could contribute to their health status. Therefore, to move to the next stage, individuals require knowledge, insight or possibly a shift in health locus of control from external to more internal (i.e., perceiving the reward from one’s behavior as contingent on one’s own behavior, see Rotter, 1966 , or Cheng et al., 2016 for a meta-analysis on health locus of control and specific health behaviors). Knowledge can be provided, for example, through large-scale policy campaigns. Once an individual is aware that a change in behavior could positively affect their current or future health, a number of other resources may be required to spike interest in behavior change and to move into the motivational engagement phase. For example, hearing or reading about personal experiences from peers (e.g., friends, colleagues) could lead to goal formation. Thus, an individual could be incentivized to talk with peers about their health behavior goals and achievements. To move from the contemplation to the planning stage, information about various offers of health promotion courses could be beneficial. In general, the findings can be applied using these three steps:

• 1. Determining at which stage of their individual change process an individual is.
• 2. Identifying the resource(s) needed to reach the next relevant stage.
• 3. Selecting a BCT that targets the lacking, weak or inactive resource.

Step 1 can be done by applying motivational interviewing ( Rollnick and Miller, 1995 ) or the set of questions developed by Michie et al. (2005) . For step 2, the Theoretical Domains Framework by Cane et al. (2012) or any other framework that lists health behavior change resources, can be used. Once the lacking, weak or inactive resources for successful behavior change have been identified, one or more suitable BCTs can be selected and applied (step 3). Michaelsen and Esch (2022) provide guidance for the third step in their application guide of the BCRM. In this table, the potential target groups for each type of BCT, based on the seven-stage behavior change process described above, are explained and numerous examples for various settings and stakeholders are given. This can assist health/behavior therapists, intervention planners and patients in selecting appropriate measures to achieve the desired health behavior change.

From a public health perspective, the findings of the studies can also contribute to improve health literacy of specific patient groups, e.g., the chronically ill, or specific populations, such as vulnerable families, e.g., in low-income settings. By identifying the needs of these groups in relation to their health, knowing which type of motivation to foster and which resource to provide, strengthen, or activate with which measures, health behavior (e.g., diet) and disease management (e.g., regularly measuring blood sugar) can be improved. Thus, the findings can also be used in prevention and health promotion contexts and potentially help to close the gap in life expectancy between low- and high-income communities.

Furthermore, the findings have the potential to improve intervention effectiveness by better matching the goals of the intervention and the goals of the patients or individuals for whom they are developed. Interventions with a better fit promise better outcomes ( Michie and Prestwich, 2010 ; Prestwich et al., 2014 ; Beard et al., 2019 ; Carey et al., 2019 ) and could therefore be more cost-effective, thereby relying less on scarce financial resources of providers, such as health insurances, local governments, or states.

5.3. Avenues for future research

The results presented in this review are theoretical in nature and therefore require empirical verification. In addition, a number of aspects contained in the studies need to be explored further or discussed in more detail. Some of these points for future research are highlighted in the following.

First, for a number of research strands processed in this review, systematic rather than convenience literature searches could help to substantiate the claims made. While systematic literature searches have been conducted and reviews published on behavior change resources and BCTs (e.g., Cane et al., 2012 ; Prestwich et al., 2014 ; Kwasnicka et al., 2016 ), as well as on behavior change frameworks that served as a basis in the presented analyses (e.g., Kwasnicka et al., 2016 ; Carey et al., 2019 ), conducting new systematic searches and reviews could help to integrate the knowledge gained since the reviews were published. Especially the growing literature on single- or multi-system models (as alternatives to dual-process models) of behavior would benefit from a systematic overview and discussion of the advantages and disadvantages of the views published so far. On this basis, the BCRM potentially requires refinement. The literature on health promotion and behavior change is growing rapidly, so more up-to-date reviews could help to increase the granularity and accuracy of the findings. For example, a future systematic search of the behavior change resource literature could be done to map the resources identified in the literature to the three types of resources generated in this review. A comprehensive list of internal affective, internal reflective and external resources could be the result. This list could then be augmented by neurobiological analyses of the functional mechanisms proposed. In addition, a systematic search and analysis of empirically tested BCTs can result in a list and discussion of BCTs in the light of their functional mechanisms. Providing such lists would facilitate the application of the BCRM such that users could easily identify resources they need and the BCTs that help to address them.

Second, the Model of Engagement could be tested in the “real world” with patients, e.g., in primary care settings, through interviews that help patients to describe how they perceive their own behavior change process, at which stage they assume to be and what they require to move forwards. These descriptions are presumably very diverse and depend on patient characteristics, such as age, disease or cultural background. Qualitative interviews are potentially the right starting point for the development of a more general questionnaire to test the model in a larger population and with specific target groups.

Third, the BCRM, and aspects of it, is proposed based on several implicit hypotheses that need empirical verification. The first hypothesis is that it is possible to determine uniquely the stage in the behavior change process for each individual patient. This hypothesis could be tested through a new questionnaire that builds upon motivational interviewing ( Rollnick and Miller, 1995 ) or the set of questions developed by Michie et al. (2005) with specific reference to the stages developed in the Model of Engagement. The second hypothesis is that motivation and reward systems are required to process along the stages. This could be tested by interviewing individuals who have successfully progressed along their stage process with respect to their own description of their affective states (pleasure, relief, quiescence) that were present while progressing. The third hypothesis is that resources can uniquely be classified into internal affective, internal reflective and external resources. Neuroscientific methods such as brain imaging could be used to analyze the affective and motivational components associated with these resources and potentially involved in various BCTs. The fourth hypothesis is that certain BCTs influence resources through the three described functional mechanisms of facilitating, boosting, or nudging. Qualitative research methodologies may provide a means to expound upon the perceptions of individuals who have undergone specific interventions, in relation to the mechanisms that either support or impede their engagement in behavior change processes.

Future research should explore the specific functional mechanisms of BCTs in more detail. So far, the literature presents only a general understanding of the functional mechanisms of BCTs. The BCRM should be subject to further scrutiny by investigating the intricate affective processes that underlie nudging interventions, through the assessment of affective states before, during, and after decision-making. Gaining insight into the neurobiological mechanisms that underpin the three functional components of the BCRM, and their respective roles in determining motivational salience and reward intensity, would undoubtedly enhance the scientific knowledge base and prove invaluable in the development and implementation of future interventions in everyday settings.

Finally, the application process of the three steps with patients or communities could be accompanied by research on its applicability, feasibility and effectiveness to optimize the model and its features for future use.

6. Conclusion

Previous theories of health behavior change have overemphasized either cognitive, rational, or relational aspects, while largely neglecting the emotional-affective or motivational processes involved in behavior change. Recent literature has integrated neuroscientific evidence and evidence-informed models into the explanations of how health behavior can be changed, short-term and long-term. Thereby, classifications of behavior change resources and behavior change techniques have been developed and the mechanisms of behavior change techniques have been explained. All in all, the literature has potential to be enriched by more neuroscientific evidence, e.g., more details of the functional mechanisms of health behavior change techniques for particular behavior change resources. Other interesting avenues for future research have been described in this review.

Author contributions

MM was responsible for initial literature search, article screening, interpretation of the existing research, conducting the analysis, as well as writing, and critical revision of the manuscript. TE provided support from the idea through the conception and design of the review and also provided suggestions for revising the manuscript. Both authors contributed to the article and approved the submitted version.

Conflict of interest

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

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

• Alemanno A., Sibony A. -L. (2015). Nudge and the law: A European perspective. London: Bloomsbury Publishing. [ Google Scholar ]
• Alert M. D., Rastegar S., Foret M., Slipp L., Jacquart J., Macklin E., et al. (2013). The effectiveness of a comprehensive mind body weight loss intervention for overweight and obese adults: A pilot study. Complement. Ther. Med. 21 286–293. 10.1016/j.ctim.2013.05.005 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ball L., Johnson C., Desbrow B., Leveritt M. (2013). General practitioners can offer effective nutrition care to patients with lifestyle-related chronic disease. J. Prim. Health Care 5 59–69. [ PubMed ] [ Google Scholar ]
• Bandura A. (1989). “ Social cognitive theory ,” in Annals of child development: Vol. 6. Six theories of child development , ed. Vasta R. (Stamford, CT: JAI Press; ), 1–60. [ Google Scholar ]
• Beard E., West R., Lorencatto F., Gardner B., Michie S., Owens L., et al. (2019). What do cost-effective health behaviour-change interventions contain? A comparison of six domains. PLoS One 14 : e0213983 . 10.1371/journal.pone.0213983 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bélanger-Gravel A., Godin G., Amireault S. (2013). A meta-analytic review of the effect of implementation intentions on physical activity. Health Psychol. Rev. 7 23–54. 10.1080/17437199.2011.560095 [ CrossRef ] [ Google Scholar ]
• Berridge K. C. (2007). The debate over dopamine’s role in reward: The case for incentive salience. Psychopharmacology 191 391–431. 10.1007/s00213-006-0578-x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Berridge K. C. (2018). Evolving concepts of emotion and motivation. Front. Psychol. 9 : 1647 . 10.3389/fpsyg.2018.01647 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Berridge K. C., Kringelbach M. L. (2008). Affective neuroscience of pleasure: Reward in humans and animals. Psychopharmacology 199 457–480. 10.1007/s00213-008-1099-6 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bozarth M. (1994). “ Pleasure systems in the brain ,” in Pleasure: The politics and the reality , ed. Warburton D. M. (Hoboken, NJ: John Wiley & Sons; ). [ Google Scholar ]
• Broers V. J. V., de Breucker C., van den Broucke S., Luminet O. (2017). A systematic review and meta-analysis of the effectiveness of nudging to increase fruit and vegetable choice. Eur. J. Public Health 27 912–920. 10.1093/eurpub/ckx085 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bucher T., Collins C., Rollo M. E., McCaffrey T. A., Vlieger N., de, et al. (2016). Nudging consumers towards healthier choices: A systematic review of positional influences on food choice. Br. J. Nutr. 115 2252–2263. 10.1017/S0007114516001653 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cacioppo J. T., Gardner W. L., Berntson G. G. (1999). The affect system has parallel and integrative processing components: Form follows function. J. Pers. Soc. Psychol. 76 839–855. 10.1037/0022-3514.76.5.839 [ CrossRef ] [ Google Scholar ]
• Cane J., O’Connor D., Michie S. (2012). Validation of the theoretical domains framework for use in behaviour change and implementation research. Implement. Sci. 7 : 37 . 10.1186/1748-5908-7-37 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cardoso Barbosa H., de Queiroz Oliveira J. A., Moreira da Costa J., Melo Santos R. P., et al. (2021). Empowerment-oriented strategies to identify behavior change in patients with chronic diseases: An integrative review of the literature. Patient Educ. Counsel. 104 689–702. 10.1016/j.pec.2021.01.011 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Carey R. N., Connell L. E., Johnston M., Rothman A. J., de Bruin M., Kelly M. P., et al. (2019). Behavior change techniques and their mechanisms of action: A synthesis of links described in published intervention literature. Ann. Behav. Med. 53 693–707. 10.1093/abm/kay078 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Carver C. S. (2009). Threat sensitivity, incentive sensitivity, and the experience of relief. J. Pers. 77 125–138. 10.1111/j.1467-6494.2008.00540.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Carver C. S., White T. L. (1994). Behavioral inhibition, behavioral activation, and affective responses to impending reward and punishment: The BIS/BAS scales. J. Pers. Soc. Psychol. 67 319–333. 10.1037//0022-3514.67.2.319 [ CrossRef ] [ Google Scholar ]
• Cecchini M., Sassi F., Lauer J. A., Lee Y. Y., Guajardo-Barron V., Chisholm D. (2010). Tackling of unhealthy diets, physical inactivity, and obesity: Health effects and cost-effectiveness. Lancet 376 1775–1784. 10.1016/S0140-6736(10)61514-0 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Chaiken S. (1980). Heuristic versus systematic information processing and the use of source versus message cues in persuasion. J. Pers. Soc. Psychol. 39 752–766. 10.1037/0022-3514.39.5.752 [ CrossRef ] [ Google Scholar ]
• Chan D. K. Y., Chong R., Basilikas J., Mathie M., Hung W. T. (2002). Survey of major chronic iIlnesses and hospital admissions via the emergency department in a randomized older population in Randwick, Australia. Emerg. Med. 14 387–392. 10.1046/j.1442-2026.2002.00343.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Chauhan B. F., Jeyaraman M. M., Mann A. S., Lys J., Skidmore B., Sibley K. M., et al. (2017). Behavior change interventions and policies influencing primary healthcare professionals’ practice-an overview of reviews. Implement. Sci. 12 : 3 . 10.1186/s13012-016-0538-8 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cheng C., Cheung M. W. L., Lo B. C. Y. (2016). Relationship of health locus of control with specific health behaviours and global health appraisal: A meta-analysis and effects of moderators. Health Psychol. Rev. 10 460–477. 10.1080/17437199.2016.1219672 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cohen D., Marsh T., Williamson S., Golinelli D., McKenzie T. L. (2012). Impact and cost-effectiveness of family fitness zones: A natural experiment in urban public parks. Health Place 18 39–45. 10.1016/j.healthplace.2011.09.008 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cramer H., Lauche R., Haller H., Steckhan N., Michalsen A., Dobos G. (2014). Effects of yoga on cardiovascular disease risk factors: A systematic review and meta-analysis. Int. J. Cardiol. 173 170–183. 10.1016/j.ijcard.2014.02.017 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Custers R., Aarts H. (2005). Positive affect as implicit motivator: On the nonconscious operation of behavioral goals. J. Pers. Soc. Psychol. 89 129–142. 10.1037/0022-3514.89.2.129 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Custers R., Aarts H. (2010). The unconscious will: How the pursuit of goals operates outside of conscious awareness. Science 329 47–50. 10.1126/science.1188595 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• de Ridder D., Geenen R., Kuijer R., van Middendorp H. (2008). Psychological adjustment to chronic disease. Lancet 372 246–255. 10.1016/S0140-6736(08)61078-8 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Deci E. L., Ryan R. M. (2008). Self-determination theory: A macrotheory of human motivation, development, and health. Can. Psychol. 49 182–185. 10.1037/a0012801 [ CrossRef ] [ Google Scholar ]
• Elliot A. J., Eder A. B., Harmon-Jones E. (2013). Approach–avoidance motivation and emotion: Convergence and divergence. Emot. Rev. 5 308–311. 10.1177/1754073913477517 [ CrossRef ] [ Google Scholar ]
• Ellrodt G., Cook D. J., Lee J., Cho M., Hunt D., Weingarten S. (1997). Evidence-based disease management. JAMA 278 1687–1692. 10.1001/jama.1997.03550200063033 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Esch T. (2018). OpenNotes, patient narratives, and their transformative effects on patient-centered care. NEJM (Catalyst). Available online at: https://catalyst.nejm.org/doi/abs/10.1056/CAT.18.0078 (accessed January 25, 2023). [ Google Scholar ]
• Esch T. (2022). The ABC model of happiness-neurobiological aspects of motivation and positive mood, and their dynamic changes through practice, the course of life . Biology 11 : 843 . 10.3390/biology11060843 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Esch T., Stefano G. B. (2004). The neurobiology of pleasure, reward processes, addiction and their health implications. Neuro Endocrinol. Lett. 25 235–251. [ PubMed ] [ Google Scholar ]
• Esch T., Stefano G. B. (2010). Endogenous reward mechanisms and their importance in stress reduction, exercise and the brain. AMS 6 447–455. 10.5114/aoms.2010.14269 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Euston D. R., Gruber A. J., McNaughton B. L. (2012). The role of medial prefrontal cortex in memory and decision making. Neuron 76 1057–1070. 10.1016/j.neuron.2012.12.002 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Evans J. (2010). Intuition and reasoning: A dual-process perspective. Psychol. Inq. 21 313–326. [ Google Scholar ]
• Evans J. (2018). “ Dual process theory: Perspectives and problems ,” in Dual process theory 2.0 , ed. De Neys W. (Oxforshire: Routledge/Taylor & Francis Group; ), 137–155. [ Google Scholar ]
• Felsen G., Reiner P. B. (2015). What can neuroscience contribute to the debate over nudging? Rev. Philos. Psychol. 6 469–479. 10.1007/s13164-015-0240-9 [ CrossRef ] [ Google Scholar ]
• Forster M., Veerman J. L., Barendregt J. J., Vos T. (2011). Cost-effectiveness of diet and exercise interventions to reduce overweight and obesity. Int. J. Obes. 35 1071–1078. [ PubMed ] [ Google Scholar ]
• Fredrickson B. L., Joiner T. (2018). Reflections on positive emotions and upward spirals. Perspect. Psychol. Sci. 13 194–199. 10.1177/1745691617692106 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Geaney F., Di Scotto Marrazzo J., Kelly C., Fitzgerald A. P., Harrington J. M., Kirby A., et al. (2013). The food choice at work study: Effectiveness of complex workplace dietary interventions on dietary behaviours and diet-related disease risk - study protocol for a clustered controlled trial. Trials 14 : 370 . 10.1186/1745-6215-14-370 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gigerenzer G., Gaissmaier W., Kurz-Milcke E., Schwartz L. M., Woloshin S. (2007). Helping doctors and patients make sense of health statistics. Psychol. Sci. Public Interest 8 53–96. 10.1111/j.1539-6053.2008.00033.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gollwitzer P. M. (1999). Implementation intentions: Strong effects of simple plans. Am. Psychol. 54 493–503. 10.1037/0003-066X.54.7.493 [ CrossRef ] [ Google Scholar ]
• Grabovac I., Smith L., Stefanac S., Haider S., Cao C., Waldhoer T., et al. (2019). Health care providers’ advice on lifestyle modification in the US population: Results from the NHANES 2011-2016. Am. J. Med. 132 489–497.e1. 10.1016/j.amjmed.2018.11.021 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Grüne-Yanoff T., Hertwig R. (2016). Nudge versus boost: How coherent are policy and theory? Minds Mach. 26 149–183. 10.1007/s11023-015-9367-9 [ CrossRef ] [ Google Scholar ]
• Hall P. A., Fong G. T. (2007). Temporal self-regulation theory: A model for individual health behavior. Health Psychol. Rev. 1 6–52. 10.1080/17437190701492437 [ CrossRef ] [ Google Scholar ]
• Hall P. A., Fong G. T. (2015). Temporal self-regulation theory: A neurobiologically informed model for physical activity behavior. Front. Hum. Neurosci. 9 : 117 . 10.3389/fnhum.2015.00117 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Halpern D., Sanders M. (2016). Nudging by government: Progress, impact, & lessons learned. Behav. Sci. Policy 2 52–65. 10.1353/bsp.2016.0015 [ CrossRef ] [ Google Scholar ]
• Hansen P. G., Jespersen A. M. (2013). Nudge and the manipulation of choice: A framework for the responsible use of the nudge approach to behaviour change in public policy. Eur. J. Risk Regul. 4 3–28. 10.1017/S1867299X00002762 [ CrossRef ] [ Google Scholar ]
• Hansen P. G., Skov L. R., Jespersen A. M., Skov K. L., Schmidt K. (2016). Apples versus brownies: A field experiment in rearranging conference snacking buffets to reduce short-term energy intake. J. Foodserv. Bus. Res. 19 122–130. 10.1080/15378020.2016.1129227 [ CrossRef ] [ Google Scholar ]
• Hertwig R., Grüne-Yanoff T. (2017). Nudging and boosting: Steering or empowering good decisions. Perspect. Psychol. Sci. 12 973–986. 10.1177/1745691617702496 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hirschberg J., Manning C. D. (2015). Advances in natural language processing. Science 349 261–266. 10.1126/science.aaa8685 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Howie E. K., Young D. R. (2011). Step it up: A multicomponent intervention to increase stair use in a university residence building. Am. J. Health Promot. 26 2–5. 10.4278/ajhp.091106-ARB-357 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• International Diabetes Federation [IDF] (2013). IDF diabetes , 6th Edn. Belgium: International Diabetes Federation. [ Google Scholar ]
• International Diabetes Federation [IDF] (2021). IDF diabetes , 10th Edn. Belgium: International Diabetes Federation. [ Google Scholar ]
• Jensen M. P., Nielson W. R., Kerns R. D. (2003). Toward the development of a motivational model of pain self-management. J. Pain 4 477–492. 10.1016/S1526-5900(03)00779-X [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jing X., Chen J., Dong Y., Han D., Zhao H., Wang X., et al. (2018). Related factors of quality of life of type 2 diabetes patients: A systematic review and meta-analysis. Health Qual. Life Outcomes 16 : 189 . 10.1186/s12955-018-1021-9 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kahneman D., Tversky A. (1982). The psychology of preferences. Sci. Am. 246 160–173. 10.1038/scientificamerican0182-160 [ CrossRef ] [ Google Scholar ]
• Kassavou K., Turner A., French D. (2013). Do interventions to promote walking in groups increase physical activity? A meta-analysis. Int. J. Behav. Nutr. Phys. Act. 10 : 18 . 10.1186/1479-5868-10-18 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kim J., Lee S., Park K., Hong I., Song B., Son G., et al. (2007). Amygdala depotentiation and fear extinction. Proc. Natl. Acad. Sci. U.S.A. 104 20955–20960. 10.1073/pnas.0710548105 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Knowler W. C., Barrett-Connor E., Fowler S. E., Hamman R. F., Lachin J. M., Walker E. A., et al. (2002). Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Eng. J. Med. 346 393–403. 10.1056/NEJMoa012512 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Korff M., Gruman J., Schaefer J., Curry S. J., Wagner E. H. (1998). Collaborative management of chronic illness. Ann. Int. Med. 127 1097–1102. 10.7326/0003-4819-127-12-199712150-00008 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kringelbach M. L. (2005). The human orbitofrontal cortex: Linking reward to hedonic experience. Nat. Rev. Neurosci. 6 691–702. 10.1038/nrn1747 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Krisam M., Meder B., Philipsborn P., von (2017). Nudging in der primärprävention: Eine übersicht und perspektiven für deutschland. Gesundheitswesen 79 117–123. [ PubMed ] [ Google Scholar ]
• Kruglanski A. W., Chernikova M., Rosenzweig E., Kopetz C. (2014). On motivational readiness. Psychol. Rev. 121 367–388. 10.1037/a0037013 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kwasnicka D., Dombrowski S. U., White M., Sniehotta F. F. (2016). Theoretical explanations for maintenance of behavior change: A systematic review of behavior theories. Health Psychol. Rev. 10 1–39. 10.1080/17437199.2016.1151372 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lampert T., Hoebel J., Kroll L. E. (2019). Soziale unterschiede in der mortalität und lebenserwartung in deutschland. Aktuelle situation und trends. J. Health Monit. 4 3–15. 10.25646/5868 [ CrossRef ] [ Google Scholar ]
• Lang P. J. (1995). The emotion probe: Studies of motivation and attention. Am. Psychol. 50 372–385. 10.1037/0003-066X.50.5.372 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lang P. J., Bradley M. M. (2013). Appetitive and defensive motivation: Goal-directed or goal-determined? Emot. Rev. 5 230–234. 10.1177/1754073913477511 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Langenberg C., Sharp S. J., Franks P. W., Scott R. A., Deloukas P., Forouhi N. G., et al. (2014). Gene-lifestyle interaction and type 2 diabetes: The EPIC interact case-cohort study. PLoS Med. 11 : e1001647 . 10.1371/journal.pmed.1001647 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• LeDoux J. (1998). Fear and the brain: Where have we been, and where are we going? Biol. Psychiatry 44 1229–1238. 10.1016/S0006-3223(98)00282-0 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lee Y., Mozaffarian D., Sy S., Huang Y., Liu J., Wilde P. E., et al. (2019). Cost-effectiveness of financial incentives for improving diet and health through medicare and medicaid: A microsimulation study. PLoS Med. 16 : e1002761 . 10.1371/journal.pmed.1002761 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lehnert T., Heider D., Leicht H., Heinrich S., Corrieri S., Luppa M., et al. (2011). Review: Health care utilization and costs of elderly persons with multiple chronic conditions. Med. Care Res. Rev. 68 387–420. 10.1177/1077558711399580 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Letzen J. E., Seminowicz D. A., Campbell C. M., Finan P. H. (2019). Exploring the potential role of mesocorticolimbic circuitry in motivation for and adherence to chronic pain self-management interventions. Neurosci. Biobehav. Rev. 98 10–17. 10.1016/j.neubiorev.2018.12.011 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Levenson R. W. (2011). Basic emotion questions. Emot. Rev. 3 379–386. 10.1177/1754073911410743 [ CrossRef ] [ Google Scholar ]
• Lieberoth A., Holm Jensen N., Bredahl T. (2018). Selective psychological effects of nudging, gamification and rational information in converting commuters from cars to buses: A controlled field experiment. Transp. Res. Part F Traffic Psychol. Behav. 55 246–261. 10.1016/j.trf.2018.02.016 [ CrossRef ] [ Google Scholar ]
• Ludwig V. U., Brown K. W., Brewer J. A. (2020). Self-regulation without force: Can awareness leverage reward to drive behavior change? Perspect. Psychol. Sci. 15 1382–1399. 10.1177/1745691620931460 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Maresova P., Javanmardi E., Barakovic S., Barakovic Husic J., Tomsone S., Krejcar O., et al. (2019). Consequences of chronic diseases and other limitations associated with old age - a scoping review. BMC Public Health 19 : 1431 . 10.1186/s12889-019-7762-5 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Marteau T. M., Hollands G. J., Fletcher P. C. (2012). Changing human behavior to prevent disease: The importance of targeting automatic processes. Science 337 1492–1495. 10.1126/science.1226918 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• McCall C., Singer T. (2012). The animal and human neuroendocrinology of social cognition, motivation and behavior. Nat. Neurosci. 15 681–688. 10.1038/nn.3084 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mertens S., Herberz M., Hahnel U. J. J., Brosch T. (2022). The effectiveness of nudging: A meta-analysis of choice architecture interventions across behavioral domains. Proc. Natl. Acad. Sci. U.S.A. 119 : e2107346118 . 10.1073/pnas.2107346118 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Michaelsen M. M., Esch T. (2021). Motivation and reward mechanisms in health behavior change processes. Brain Res. 1757 : 147309 . 10.1016/j.brainres.2021.147309 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Michaelsen M. M., Esch T. (2022). Functional mechanisms of health behavior change techniques: A conceptual review. Front. Psychol. 13 : 725644 . 10.3389/fpsyg.2022.725644 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Michie S., Prestwich A. (2010). Are interventions theory-based? Development of a theory coding scheme. Health Psychol. 29 1–8. 10.1037/a0016939 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Michie S., Johnston M., Abraham C., Lawton R., Parker D., Walker A. (2005). Making psychological theory useful for implementing evidence based practice: A consensus approach. BMJ Qual. Saf. 14 26–33. 10.1136/qshc.2004.011155 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Michie S., Richardson M., Johnston M., Abraham C., Francis J., Hardeman W., et al. (2013). The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: Building an international consensus for the reporting of behavior change interventions. Ann. Behav. Med. 46 81–95. 10.1007/s12160-013-9486-6 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Michie S., van Stralen M. M., West R. (2011). The behaviour change wheel: A new method for characterising and designing behaviour change interventions. Implement. Sci. 6 : 42 . 10.1186/1748-5908-6-42 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Morris E., Jebb S., Aveyard P. (2019). Type 2 diabetes: Treating not managing. Lancet Diab. Endocrinol. 7 326–327. [ PubMed ] [ Google Scholar ]
• Moussavi S., Chatterji S., Verdes E., Tandon A., Patel V., Ustun B. (2007). Depression, chronic diseases, and decrements in health: Results from the world health surveys. Lancet 370 851–858. 10.1016/S0140-6736(07)61415-9 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nestler E. J. (2001). Molecular basis of long-term plasticity underlying addiction. Nat. Rev. Neurosci. 2 119–128. 10.1038/35053570 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nestler E. J., Malenka R. C. (2004). The addicted brain. Sci. Am. 290 78–85. 10.1038/scientificamerican0304-78 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nestler E. J., Malenka R. C., Hyman S. (2001). Molecular basis of neuropharmacology. New York, NY: McGraw-Hill Medical. [ Google Scholar ]
• Organisation for Economic Co-operation and Development [OECD] (2021). Health at a glance 2021. Berlin: OECD Publishing. 10.1787/ae3016b9-en [ CrossRef ] [ Google Scholar ]
• Ornish D., Brown S. E., Billings J. H., Scherwitz L. W., Armstrong W. T., Ports T. A., et al. (1990). Can lifestyle changes reverse coronary heart disease? The lifestyle heart trial. Lancet 336 129–133. 10.1016/0140-6736(90)91656-U [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Orr J. A., King R. J. (2015). Mobile phone SMS messages can enhance healthy behaviour: A meta-analysis of randomised controlled trials. Health Psychol. Rev. 9 397–416. 10.1080/17437199.2015.1022847 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Paul-Ebhohimhen V., Avenell A. (2008). Systematic review of the use of financial incentives in treatments for obesity and overweight. Obes. Rev. 9 355–367. 10.1111/j.1467-789X.2007.00409.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Petry N. M., Cengiz E., Wagner J. A., Hood K. K., Carria L., Tamborlane W. V. (2013). Incentivizing behaviour change to improve diabetes care. Diab. Obes. Metab. 15 1071–1076. 10.1111/dom.12111 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Petty R. E., Cacioppo J. T. (2012). “ The elaboration likelihood model of persuasion ,” in Communication and persuasion: Central and peripheral routes to attitude change , ed. Petty R. E. (Berlin: Springer; ), 1–24. 10.1007/978-1-4612-4964-1_1 [ CrossRef ] [ Google Scholar ]
• Piwek L., Ellis D. A., Andrews S., Joinson A. (2016). The rise of consumer health wearables: Promises and barriers. PLoS Med. 13 : e1001953 . 10.1371/journal.pmed.1001953 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Prestwich A., Sniehotta F. F., Whittington C., Dombrowski S. U., Rogers L., Michie S. (2014). Does theory influence the effectiveness of health behavior interventions? Meta-analysis. Health Psychol. 33 465–474. 10.1037/a0032853 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Priesterroth L., Grammes J., Holtz K., Reinwarth A., Kubiak T. (2019). Gamification and behavior change techniques in diabetes self-management apps. J. Diab. Sci. Technol. 13 954–958. 10.1177/1932296818822998 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Prochaska J. O., Redding C. A., Evers K. E. (2008). “ The transtheoretical model and stages of change ,” in Health behavior and health education: Theory, research, and practice , 4th Edn, eds Glanz K., Rimer B. K., Viswanath K. (Hoboken, NJ: Jossey-Bass; ), 97–121. [ Google Scholar ]
• Rizzo J. A., Chen J., Gunnarsson C. L., Naim A., Lofland J. H. (2015). Adjusting for comorbidities in cost of illness studies. J. Med. Econ. 18 12–28. 10.3111/13696998.2014.969434 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rollnick S., Miller W. R. (1995). What is motivational interviewing? Behav. Cogn. Psychother. 23 325–334. 10.1017/S135246580001643X [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rolls E. T. (2013). What are emotional states, and why do we have them? Emot. Rev. 5 241–247. 10.1177/1754073913477514 [ CrossRef ] [ Google Scholar ]
• Rothman A. J., Baldwin A. S., Hertel A. W. (2004). “ Self-regulation and behavior change: Disentangling behavioral initiation and behavioral maintenance ,” in Handbook of self-regulation: Research, theory, and applications , eds Baumeister R. F., Vohs K. D. (New York, NY: Guilford Press; ), 130–148. 10.1186/s12889-020-09111-8 [ CrossRef ] [ Google Scholar ]
• Rotter J. B. (1966). Generalized expectancies for internal versus external control of reinforcement (No. 1). Psychol. Monogr. Gen. Appl. 80 1–28. [ PubMed ] [ Google Scholar ]
• Ryan R. M., Deci E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am. Psychol. 55 68–78. 10.1037//0003-066x.55.1.68 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sainsbury K., Evans E. H., Pedersen S., Marques M. M., Teixeira P. J., Lähteenmäki L., et al. (2019). Attribution of weight regain to emotional reasons amongst European adults with overweight and obesity who regained weight following a weight loss attempt. Eat. Weight Disord. 24 351–361. 10.1007/s40519-018-0487-0 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sangha S. (2015). Plasticity of fear and safety neurons of the amygdala in response to fear extinction. Front. Behav. Neurosci. 9 : 354 . 10.3389/fnbeh.2015.00354 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Savoye M., Shaw M., Dziura J., Tamborlane W. V., Rose P., Guandalini C., et al. (2007). Effects of a weight management program on body composition and metabolic parameters in overweight children a randomized controlled trial. JAMA 297 2697–2704. 10.1001/jama.297.24.2697 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schneider K. M., O’Donnell B. E., Dean D. (2009). Prevalence of multiple chronic conditions in the United States’ medicare population. Health Qual. Life Outcomes 7 : 82 . 10.1186/1477-7525-7-82 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schneirla T. C. (1959). “ An evolutionary and developmental theory of biphasic processes underlying approach and withdrawal ,” in Nebraska symposium on motivation , Vol. 7 ed. Jones M. R. (Lincoln, NE: University Nebraska Press; ), 1–42. 10.1177/000306517001800210 [ CrossRef ] [ Google Scholar ]
• Schultz W. (2015). Neuronal reward and decision signals: From theories to data. Physiol. Rev. 95 853–951. 10.1152/physrev.00023.2014 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schwarzer R., Lippke S., Luszczynska A. (2011). Mechanisms of health behavior change in persons with chronic illness or disability: The health action process approach (HAPA). Rehabil. Psychol. 56 161–170. 10.1037/a0024509 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Seuring T., Archangelidi O., Suhrcke M. (2015). The economic costs of type 2 diabetes: A global systematic review. Pharmacoeconomics 33 811–831. 10.1007/s40273-015-0268-9 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Seymour B., Singer T., Dolan R. (2007). The neurobiology of punishment. Nat. Rev. Neurosci. 8 300–311. 10.1038/nrn2119 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sheeran P., Gollwitzer P. M., Bargh J. A. (2013). Nonconscious processes and health. Health Psychol. 32 460–473. 10.1037/a0029203 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sheeran P., Maki A., Montanaro E., Avishai A., Bryan A., Klein W., et al. (2016). The impact of changing attitudes, norms, and self-efficacy on health-related intentions and behavior: A meta-analysis. Health Psychol. 35 1178–1188. 10.1037/hea0000387 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Shomaker L. B., Pivarunas B., Annameier S. K., Gulley L., Quaglia J., Brown K. W., et al. (2019). One-year follow-up of a randomized controlled trial piloting a mindfulness-based group intervention for adolescent insulin resistance. Front. Psychol. 10 : 1040 . 10.3389/fpsyg.2019.01040 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Smith K. S., Berridge K. C., Aldridge J. W. (2011). Disentangling pleasure from incentive salience and learning signals in brain reward circuitry. Proc. Natl. Acad. Sci. U.S.A. 108 E255–E264. 10.1073/pnas.1101920108 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Stewart A. L., Greenfield S., Hays R. D., Wells K., Rogers W. H., Berry S. D., et al. (1989). Functional status and well-being of patients with chronic conditions. Results from the medical outcomes study. JAMA 262 907–913. [ PubMed ] [ Google Scholar ]
• Strack F., Deutsch R. (2004). Reflective and impulsive determinants of social behavior. Pers. Soc. Psychol. Rev. 8 220–247. 10.1207/s15327957pspr0803_1 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Stroebe W., Mensink W., Aarts H., Schut H., Kruglanski A. W. (2008). Why dieters fail: Testing the goal conflict model of eating. J. Exp. Soc. Psychol. 44 26–36. 10.1016/j.jesp.2007.01.005 [ CrossRef ] [ Google Scholar ]
• Stroebe W., van Koningsbruggen G. M., Papies E. K., Aarts H. (2013). Why most dieters fail but some succeed: A goal conflict model of eating behavior. Psychol. Rev. 120 110–138. 10.1037/a0030849 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Szaszi B., Palinkas A., Palfi B., Szollosi A., Aczel B. (2018). A systematic scoping review of the choice architecture movement: Toward understanding when and why nudges work. J. Behav. Decis. Mak. 31 355–366. 10.1002/bdm.2035 [ CrossRef ] [ Google Scholar ]
• Thaler R. H., Sunstein C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. New Haven, CT: Yale University Press. [ Google Scholar ]
• The World Bank (2022). Data - Life expectancy at birth. Available online at: https://data.worldbank.org/indicator/SP.DYN.LE00.IN (accessed January 25, 2023). [ Google Scholar ]
• Ulrich S., Holle R., Wacker M., Stark R., Icks A., Thorand B., et al. (2016). Cost burden of type 2 diabetes in Germany: Results from the population-based KORA studies. BMJ Open 6 : e012527 . 10.1136/bmjopen-2016-012527 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Van Cappellen P., Rice E. L., Catalino L. I., Fredrickson B. L. (2018). Positive affective processes underlie positive health behaviour change. Psychol. Health 33 77–97. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• van Gestel L. C., Adriaanse M. A., de Ridder D. T. D. (2020). Beyond discrete choices - investigating the effectiveness of a proximity nudge with multiple alternative options. Front. Psychol. 11 : 1211 . 10.3389/fpsyg.2020.01211 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• van Rookhuijzen M., de Vet E., Adriaanse M. A. (2021). The effects of nudges: One-shot only? Exploring the temporal spillover effects of a default nudge. Front. Psychol. 12 : 683262 . 10.3389/fpsyg.2021.683262 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Verplanken B., Aarts H. (1999). Habit, attitude, and planned behaviour: Is habit an empty construct or an interesting case of goal-directed automaticity? Eur. Rev. Soc. Psychol. 10 101–134. 10.1080/14792779943000035 [ CrossRef ] [ Google Scholar ]
• Volpp K. G., John L. K., Troxel A. B., Norton L., Fassbender J., Loewenstein G. (2008). Financial incentive–based approaches for weight loss: A randomized trial. JAMA 300 2631–7. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Wagner E. H. (2000). The role of patient care teams in chronic disease management. BMJ 320 569–572. 10.1136/bmj.320.7234.569 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wansink B., Just D., Smith L. (2011). Move the fruit: Putting fruit in new bowls and new places doubles lunchroom sales. J. Nutr. Educ. Behav. 43 : S1 . 10.1016/j.jneb.2011.03.013 [ CrossRef ] [ Google Scholar ]
• Watson D., Wiese D., Vaidya J., Tellegen A. (1999). The two general activation systems of affect: Structural findings, evolutionary considerations, and psychobiological evidence. J. Pers. Soc. Psychol. 76 820–838. 10.1037/0022-3514.76.5.820 [ CrossRef ] [ Google Scholar ]
• Weinstein N. D., Sandman P. M. (1992). A model of the precaution adoption process: Evidence from home radon testing. Health Psychol. 11 170–180. 10.1037/0278-6133.11.3.170 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Weinstein N. D., Sandman P. M. (2002). “ The precaution adoption process model and its application ,” in Emerging Theories in Health Promotion Practice and Research , eds DiClemente R. J., Crosby R. A., Kegler Michelle C. (Hoboken, NJ: Jossey-Bass; ), 16–39. [ Google Scholar ]
• Willett W. C. (2002). Balancing life-style and genomics research for disease prevention. Science 296 695–698. 10.1126/science.1071055 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• World Health Organisation [WHO] (2022a). Fact sheets - noncommunicable diseases. Available online at: https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases (accessed January 25, 2023). [ Google Scholar ]
• World Health Organisation [WHO] (2022b). Fact sheets - aging and health. Available online at: https://www.who.int/news-room/fact-sheets/detail/ageing-and-health (accessed January 25, 2023). [ Google Scholar ]
• Yom-Tov E., Feraru G., Kozdoba M., Mannor S., Tennenholtz M., Hochberg I. (2017). Encouraging physical activity in patients with diabetes: Intervention using a reinforcement learning system. J. Med. Int. Res. 19 : e338 . 10.2196/jmir.7994 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Zhou B., Lu Y., Hajifathalian K., Bentham J., Di Cesare M., Danaei G., et al. (2016). Worldwide trends in diabetes since 1980: A pooled analysis of 751 population-based studies with 4⋅4 million participants. Lancet 387 1513–1530. 10.1016/S0140-6736(16)00618-8 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

• Open access
• Published: 03 June 2024

Patients’ expectations surrounding revision total hip arthroplasty: a literature review

• Omar Mohammad   ORCID: orcid.org/0000-0002-3054-2578 1 ,
• Shahril Shaarani 2 ,
• Adnan Mohammad 3 &
• Sujith Konan 2  

Arthroplasty volume  6 , Article number:  28 ( 2024 ) Cite this article

Metrics details

Revision total hip arthroplasties (RTHA) are associated with a higher complication rate than primary total hip arthroplasties (THA), and therefore it is important for patients to have realistic expectations regarding outcomes. The aim of this literature review was to gather and summarize the available evidence on patients’ expectations following RTHA.

A literature search was conducted in PubMed, PsycINFO, Cochrane, Google Scholar, Web of Science and Embase from inception to November 2023. Articles assessing patient expectations for RTHA were included. Methodological quality was assessed by two independent reviewers using the National Heart, Lung and Blood Institute (NIH) study quality assessment tool for observational cohort and cross-sectional studies. A qualitative analysis was performed involving the summarization of study characteristics and outcomes.

The search strategy generated 7,450 references, of which 5 articles met the inclusion criteria. Methodological quality scores ranged from 7–10. Patients had high expectations concerning future walking ability, pain and implant longevity relative to actual postoperative outcomes. A significant positive correlation was found between fulfilled expectations of pain and walking ability and patient satisfaction ( r  = 0.46–0.47). Only two studies assessed the fulfillment of patient expectations. Great variability was seen in the measurement of expectations.

Patients undergoing RTHA appeared to have high expectations for pain and functionality compared to postoperative outcomes. However, there was a paucity of high-quality data in this area, limiting the accuracy of the conclusion. Further research is needed, that emphasizes developing a sound theoretical framework for expectations, allowing for the consistent implementation of valid measurement tools for patient expectations.

Introduction

Total hip arthroplasty (THA) is a cost-effective procedure for improving a patient’s quality of life (QOL), pain, and function when conservative therapies have failed [ 1 , 2 , 3 ]. Despite the widely recognized success of THA, there is a certain level of risk that may necessitate a revision procedure. The incidence of revisions is on the rise and is projected to increase by 31% by 2030 in England & Wales, UK [ 4 ].

When compared to primary THA, revision THA (RTHA) is associated with higher rates of short- and long-term complications, elevated mortality rates, lower satisfaction, and smaller improvements in functional outcomes [ 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. Whether patients undergo either primary or RTHA, they largely expect a reduction in pain and an improvement in both function and quality of life [ 12 , 13 , 14 , 15 ]. In the preoperative period, it is important to assess these expectations, to ensure that patients have a realistic perspective of the outcomes of the operation and are not dissatisfied. Aside from technical factors and the quality of existing bone, patient factors may also partially explain the less favourable outcomes of RTHA relative to primary THA [ 8 ].

There is a growing body of literature across a variety of medical specialties linking clinical outcomes with patients’ expectations and satisfaction. Patient satisfaction has been shown to lead to higher compliance and attendance for monitoring and follow-up care [ 16 ], which are integral factors in optimizing prosthesis longevity. Furthermore, patients’ expectations are strongly correlated with satisfaction, with satisfied patients having their expectations fulfilled [ 17 ] and unrealistic expectations being correlated with dissatisfaction [ 18 ]. This has led to increasing emphasis on measures of quality of life and patients’ feelings of satisfaction [ 19 , 20 ]. Therefore, as a reflection of this shift in emphasis, it has become essential to gain a better understanding of patients’ expectations.

Although patient expectations have been widely discussed in current primary THA research [ 17 , 18 , 21 ], there is an apparent sparsity in the RTHA literature. This literature review therefore aimed to comprehensively assess all relevant studies evaluating the expectations of patients undergoing RTHA, and how this in turn relates to post-operative outcomes where possible.

Materials and methods

Search strategy.

A comprehensive electronic literature search was performed in the following databases: PubMed, The Cochrane Library, Google Scholar, PsychINFO, Web of Science and Embase to identify eligible studies published until the 7th November 2023. Search terms were derived from MeSh terms in PubMed and free text terms relating to (1) hip arthroplasty, (2) revision and (3) expectations/expectancies (Table  1 ). Although Haanstra et al. offered distinct definitions for expectations and expectancies as being “cognitions regarding probable future events” and “the act or state of expecting” [ 22 ], the current literature uses the two terms interchangeably to show that an individual is “expecting something to occur in the future”. Therefore, whilst they are different concepts, no distinction was acknowledged between the two.

Inclusion criteria

The individual search results from each database were combined barring duplicates, and the remaining titles and abstracts were then screened against the inclusion criteria found below.

The studies had to meet the following inclusion criteria to be eligible:

The study included revision THA patients;

Patients’ expectations were assessed;

The study had to be written in English;

The patients were adults > 18 years of age.

If an article assessed both primary and RTHA groups but failed to report the data separately for each group, the study was excluded, as we would not be able to extract the relevant data.

Two reviewers (OM and SRS) independently assessed the full text articles, based on the title and abstract, against the inclusion criteria. If there was any uncertainty regarding the eligibility of a study the full text was examined. The results of the search are shown in Table  2 .

Data extraction and methodological quality assessment

The same two reviewers extracted relevant data from the included studies using a standardized data extraction form (Table  3 ). The form included information on study design, study population, follow-up period, measurement of expectations and outcome measurements. Moreover, data on the strength of the relationship between expectations and outcomes was extracted where possible (e.g., P -values and correlation coefficients).

Furthermore, the methodological quality of the selected studies was assessed using the National Heart, Lung and Blood Institute (NIH) study quality assessment tool for observational cohort and cross-sectional studies [ 26 ]. Each study was judged on key concepts for internal validity, such as sample size, exposure/outcome measurement and compatibility of the groups. There were fourteen questions in total, for which studies could score a maximum of 14 points in sum. If there was any disagreement between the two reviewers, it was agreed that a discussion would be held to reach a point of consensus. This did not occur.

Data analysis

Due to the heterogeneity of the measurement of patients’ expectations in the studies identified, it was not possible to statistically pool the data in a meta-analysis. Instead, a qualitative analysis was performed involving the summarization of study characteristics and outcomes, as well as a methodological assessment using the NIH quality assessment tool. Studies were noted as poor quality if they scored 0–4, fair if they scored 5–10 and good if they scored 11–14 out of 14 questions [ 27 ].

Study selection process

The literature search retrieved a total of 7,450 records. After removal of duplicates ( n  = 162), records not in English ( n  = 382), non-human studies ( n  = 251) and studies not on adults aged > 18 ( n  = 1,876), a total of 4,779 papers remained. After screening of the titles and abstracts, 4,742 studies were excluded, as they either did not assess patient expectations, did not include revision THA or were review articles. This left a total of 37 studies for further investigation. After full-text assessment, a further 32 articles were excluded, leaving 5 articles that met all the inclusion criteria [ 12 , 14 , 23 , 24 , 25 ] and were subsequently included in this review (Fig.  1 ).

Flowchart of literature search and selection process

Study characteristics

Five cohort studies were included in this review. The sample size ranged from 60 to 320 participants. Four studies only included RTHA [ 12 , 14 , 23 , 25 ] and one included both primary and RTHA [ 24 ]. In the assessment of expectations, two studies utilized a single item measurement which utilized either a three-point Likert-scale [ 23 ] or a six-point Likert-scale [ 25 ], two studies implemented a two-item instrument utilizing either a 4-point Likert scale [ 12 ] or a close-ended multiple-choice format [ 24 ]. One study modified the pre-existing Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC scale—a validated instrument) to assess patients’ expectations of pain, stiffness and physical function in 6 months after the revision operation [ 14 ]. Overall, no validated instruments were used in the assessment of patients’ expectations in revision THA across all studies.

Methodological quality

The average quality score was 9 out of 14 (range 7–9) (Table  2 ). As expected, the lowest scoring items were:

“Were the outcome assessors blinded to the exposure status of the participants?” —due to all studies having utilized a self-reported questionnaire;

“For exposures that can vary in amount or level, did the study examine different levels of the exposure as related to the outcome”;

“Was the exposure(s) assessed more than once over time?”—as the exposure was a single revision THA.

Other notable methodological shortcomings were the common lack of sample size justification and often absent statistical analyses of confounding variables.

Expectations

The measurement of patient expectations varied across the studies included in this review. Two studies focused on revision longevity expectations [ 23 , 24 ]. Barrack et al. implemented a single postoperative question concerning implant longevity and scaled responses using a 3-point Likert scale. Hellman et al. also measured implant longevity expectations using a single retrospective question and graded responses with close-ended multiple-choice questions.

One study prospectively measured the expectations of future pain and walking ability utilizing two questions scaled via a 4-point Likert scale [ 12 ]. One study assessed patients’ expectations of pain, stiffness and physical function utilizing the modified WOMAC scale [ 14 ]. These were measured prospectively and used a 5-point Likert scale. Two studies examined fulfillment of patients’ expectations after surgery [ 12 , 25 ]. Eisler et al. postoperatively assessed fulfillment of expectations with two questions and utilized a 4-point Likert scale. Zhang et al. used one postoperative question with a 6-point Likert scale. Only one study measured how this in turn correlated with patient satisfaction [ 12 ].

Patients’ expectations of pain were measured in two studies. Eisler et al. found that 92% of patients expected to have no pain or to have much less pain, and only 8% expected a slight reduction in pain. Haddad et al. reported an average score of 7.4/25 (CI 6.2–8.6) for pain, with a lower score conferring a low expectation of pain.

Function was assessed in two studies [ 12 , 14 ]. Eisler et al. noted that 82% of patients expected the same walking ability as after the first THA or markedly improved walking ability, 15%, slightly improved and 3%, no difference in walking ability. Haddad et al. reported an average expectation of 28.1/85 (CI 24.0–32.2) for physical activity, with a lower score indicating a higher expectation of function. Additionally, only Haddad et al. assessed expectations on stiffness, with an average expectation of 3.5/10 (CI 3.0–4.0) for stiffness, with a lower score indicating a lower expectation for stiffness.

Fulfilled expectations

Eisler et al. found that 55 and 69% of patients had fulfilled expectations regarding walking ability and pain. Furthermore, fulfilled expectations about pain and walking ability demonstrated a modest positive correlation with satisfaction ( r  = 0.46–0.47). The absence of complications was the only predictor of fulfilled pain expectations during the postoperative hospital period (odds ratio (OR) 4.8; 95% confidence interval (CI) 1.1–20.8). Zhang et al. found that at 6 months postoperatively, distressed patients had significantly lower rates of fulfilled expectations compared to non-distressed patients (64.5% vs. 94.1%, P  = 0.027). At 2 years postoperatively, this was no longer significantly different (63.6% vs. 79.3%, P  = 0.342).

Implant longevity

Two studies assessed patients’ expectations concerning the longevity of their revision THA [ 23 , 24 ]. Barrack et al. found that most patients, regardless of original implant longevity, expected their revision to last longer. In patients in whom the primary THA lasted < 5 years: 77% expected revision to last longer and in those where the primary lasted 5–10 years: 76% expected revision to last longer. If the primary lasted 10–15 years: 69% expected the revision to last longer and in those where the primary THA lasted > 15 years: 62% expected the revision to last longer. Hellman et al. found that 35% of patients expected the revision to last for the rest of their lives.

This review found that RTHA patients tend to have unrealistically high expectations regarding pain relief, improvement in movement, and implant longevity. Furthermore, distressed patients are less likely to have their expectations fulfilled postoperatively in the short term [ 25 ]. Given poorer outcomes with revision surgery versus primary THA, these expectations are unlikely to be fulfilled and may result in patient dissatisfaction [ 8 , 12 , 14 ]. Only one study [ 12 ] assessed how fulfillment of these expectations correlated with postoperative satisfaction, revealing a moderate positive correlation with expectations of pain and walking ability. However, overall, there is a paucity of research concerning expectations following RTHA procedures, despite the higher risk of complications [ 28 ]. Additionally, there is significant variability in the way expectations are measured.

Important areas that need to be addressed in future research include (1) The theoretical framework of expectations; (2) the measurement of expectations; (3) the correlation of psychological and other demographic factors and (4) the relationship between fulfilled expectations and satisfaction.

Firstly, none of the papers in this review provided a definition of patient expectations. The absence of a consistent theoretical framework for expectations lends itself to an increased propensity for the heterogeneous use of terminology and measurements. If left unaddressed, this can lead to research plagued by discontinuity and poor methodological quality. In the past, several reviews [ 29 , 30 , 31 ] have acknowledged patient expectations as being a complex multifaceted construct. Kravitz [ 31 ] made a distinct delineation between value (reflecting the patient’s wishes/hopes) and probability expectations (the likelihood that an event will occur). Furthermore, Bandura [ 32 ] separated efficacy from outcome expectations. Given the different perspectives on expectations, it is necessary to utilize a consistent framework to allow for accurate classification and subsequent assessment. For example, Hobbs et al. [ 33 ] successfully utilized the International Classification of Functioning, Disability and Health (ICF) framework to classify patients’ expectations in primary THA. This involved assigning patients’ expectations to one of three domains: activity limitations, impairments to bodily function and structure, and participation restrictions. It was found that patients generally focused more on the recovery of valued activities rather than the reversal of their functional impairment. In future RTHA research investigating patient expectations, researchers should aim to map their findings to each of the core ICF constructs. If performed consistently, this has the potential to lead to more uniformity of definitions, better integration of data amongst different studies and improved validation of measurement instruments. Additionally, this method could be used to ascertain whether certain expectation domains, e.g., impairment, activity limitations or participation restriction expectations are predictors of patient reported outcome measures (PROMs).

As mentioned previously, the lack of a consistent theoretical framework for patient expectations has likely contributed to the absence of a valid and standardized measurement tool. This prevents the effective integration and comparison of data across studies [ 22 ]. Each study in this review implemented a unique instrument that was only used for one investigation. They often lacked a rationale behind their development, or data on reliability and validity, which limits the credibility of evidence collected. This issue has affected both primary THA research and research in other fields such as psychotherapy, where, for example, Constantino et al. [ 34 ] reported that the majority (67%) of measurements were of poor quality. A possible strategy may be to either adapt an already well-established patient-reported outcome tool (such as the WOMAC) or use a theory-guided approach, with testing in independent samples to gather data on reliability, construct validity and predictive validity. Alternatively, the Hospital for Special Surgery Total Hip Replacement Expectations Survey (HSS-HRES) could be used for RTHA patients. This survey is a well-validated 18-question expectations survey that is graded on a 5-point Likert scale and has been used effectively in past THA research [ 35 ]. Regardless, future researchers should aim to use a validated instrument.

Additionally, half of the studies included in this review measured patients’ expectations in the postoperative period. This is not optimal and increases the risk of bias, as the patients may not be able to accurately recall their preoperative expectations due to the time elapsed [ 36 ]. Furthermore, since patient dissatisfaction is secondary to a disequilibrium between expectations and fulfilled expectations [ 37 ], patients may therefore alter their expectations to match their current status, to prevent dissatisfaction [ 38 ]. A Canadian study in 2006, reported this phenomenon regarding total knee arthroplasty, where 35% of patients over- or underestimated their preoperative level of functioning [ 39 ]. However, there is another issue purported by Haanstra et al. which pertains to the timing of expectation measurement [ 22 ]. Given that patients’ expectations are likely to be widely influenced by their doctor, it is possible that the longer the patient is in contact with them and the later their expectations are measured, the more realistic and reliable they may be. Currently no investigation has measured the influence of time of measurement, but it is a variable to keep in mind, which could be offset by collecting data at different time points.

Moreover, only one study in this review collected data in the pre- and postoperative period to assess the percentage of fulfilled expectations, and only this study analyzed the correlation between fulfilled expectations and satisfaction [ 12 ]. Whilst expectations are an important preoperative factor, it is the fulfillment of these expectations that has been shown to be the more significant determinant of patient-reported outcomes and satisfaction [ 40 ]. High expectations are not inherently detrimental, but unrealistic expectations are [ 40 ]. Therefore, it is important to assess the percentage of patients with fulfilled expectations, as this information can be used to foster realistic, high expectations through effective preoperative education.

If patients are to be measured in the postoperative period, the length of the follow-up period needs to be addressed, as it may influence findings. Barlow et al. found that expectations may take up to two years post-surgery before they are fulfilled, due to function having the potential to improve for up to two years, alluding to the existence of a timing bias [ 41 ].

Finally, half of the available literature did not include a multivariate analysis of confounding variables such as age, gender, ethnicity and preoperative education level despite their influence on patient expectations [ 35 , 42 ]. Furthermore, psychological factors (depression, optimism and catastrophizing), which may interact with expectations or treatment outcomes, were rarely analyzed [ 22 ]. Future research should try to delineate these factors for further consideration.

A promising area of focus for future research is the consenting process. Patient recall of the consenting process, and the relevant risks and outcomes, is frequently poor [ 43 ]. A recent study demonstrated that patients undergoing THA, who were consented with the generic consent form, only recalled 0.67 risks four weeks after surgery. In contrast, those who were given a surgery-specific consent form, recalled 1.43 risks on average [ 44 ]. This surgery-specific consent form listed potential adverse events alongside appropriate explanations. With regards to RTHA, this could be implemented with the addition of a section on postoperative outcomes. This would help to ensure that patients have a better comprehension of the procedure and retain more information. This may, therefore, lead to more realistic expectations that can be fulfilled.

This study has limitations that need to be considered. Firstly, a meta-analysis was not possible due to the heterogeneity in the papers included and the poor standard of reporting. And so, we performed a qualitative analysis. However, a thorough, definitive analysis of the data is not possible using this method. Secondly, only a limited number of studies were available for review, due to the lack of research in this area. As a result, there are limited data available to analyse, which may not fully represent patient expectations. The data were also relatively old, with only 2 references being < 10 years ago. Patient expectations may have improved since then with changes in perioperative information. Therefore, the strength of conclusions made in the paper may not be accurate and should be taken with caution. Although a limitation, this highlights a clear deficit in current research that needs to be addressed.

As conclusions from RTHA literature are limited, we can look at adjacent literature concerning total knee arthroplasty (TKA), to better understand what patients tend to expect with a joint replacement procedure. Similarly, TKA patients have been shown to have unrealistically high expectations regarding postoperative pain, function and recovery [ 45 ]. Moreover, patient satisfaction has been shown to be highly correlated with expectation fulfillment [ 45 ]. Recent research has demonstrated improvements in patients’ WOMAC pain and satisfaction scores at over 1 year post operation in TKA patients, by setting realistic expectations [ 46 ]. Although a different procedure/patient demographic, these findings are similar to the current evidence base for RTHA and reinforce the importance of setting appropriate baseline patient expectations through perioperative counselling, to foster better PROMs.

A definitive conclusion is limited by the sparse data available. However, the current literature demonstrates that revision THA patients tend to have unrealistic expectations with regards to pain relief, function and implant longevity. Realistic patient education prior to surgery is necessary to avoid expectation/outcome mismatch and hence dissatisfaction. Nevertheless, this review demonstrates the lack of adequate research on patients’ expectations in revision THA, both in terms of absolute numbers, and methodological quality. More research is needed, which utilizes a standardized approach in assessment, in order to foster a better understanding of the relationship between patient expectations and postoperative outcome measures. Only then, can this information be effectively applied clinically to improve the outcome of revision THAs. We suggest counselling of patients before surgery and using a procedure-specific consent. As to collection of pre- and postoperative data—postoperative data should be collected at different points of time as the patients’ outcomes improve with time and so will the outcome and expectations. Patients-reported outcomes are a better tool to assess the patient outcomes.

Availability of data and materials

Not applicable.

Abbreviations

Total Hip Arthroplasty

Revision Total Hip Arthroplasty

National Heart, Lung and Blood Institute

Western Ontario and McMaster Universities Arthritis Index

Patient reported outcome measure

Nilsdotter A-K, Isaksson F. Patient relevant outcome 7 years after total hip replacement for OA - a prospective study. BMC Musculoskelet Disord. 2010;11(1):47. https://doi.org/10.1186/1471-2474-11-47 .

Article   PubMed   PubMed Central   Google Scholar  

Bachmeier CJM, March LM, Cross MJ, Lapsley HM, Tribe KL, Courtenay BG, Brooks PM. A comparison of outcomes in osteoarthritis patients undergoing total hip and knee replacement surgery. Osteoarthritis Cartilage. 2001;9(2):137–46. https://doi.org/10.1053/joca.2000.0369 .

Article   CAS   PubMed   Google Scholar  

Pivec R, Johnson AJ, Mears SC, Mont MA. Hip arthroplasty. Lancet. 2012;380(9855):1768–77. https://doi.org/10.1016/S0140-6736(12)60607-2 .

Article   PubMed   Google Scholar  

Patel A, Pavlou G, Mújica-Mota RE, Toms AD. The epidemiology of revision total knee and hip arthroplasty in England and Wales: a comparative analysis with projections for the United States. A study using the National Joint Registry dataset. Bone Joint J. 2015;97-B(8):1076–81. https://doi.org/10.1302/0301-620X.97B8.35170 .

Saleh KJ, Celebrezze M, Kassim R, Dykes DC, Gioe TJ, Callaghan JJ, Salvati EA. Functional outcome after revision hip arthroplasty: a metaanalysis. Clin Orthop Relat Res. 2003;416:254–64. https://doi.org/10.1097/01.blo.0000093006.90435.43 .

Article   Google Scholar  

Mahomed NN, Barrett JA, Katz JN, Phillips CB, Losina E, Lew RA, Guadagnoli E, Harris WH, Poss R, Baron JA. Rates and outcomes of primary and revision total hip replacement in the United States medicare population. J Bone Joint Surg Am. 2003;85(1):27–32. https://doi.org/10.2106/00004623-200301000-00005 .

de Thomasson E, Guingand O, Terracher R, Mazel C. Perioperative complications after total hip revision surgery and their predictive factors. A series of 181 consecutive procedures. Rev Chir Orthop Reparatrice Mot. 2001;87(5):477–88.

Google Scholar  

Mahomed N, Katz JN. Revision total hip arthroplasty Indications and outcomes. Arthritis Rheum. 1996;39(12):1939–50. https://doi.org/10.1002/art.1780391202 .

Pellicci PM, Wilson PD, Sledge CB, Salvati EA, Ranawat CS, Poss R. Revision total hip arthroplasty. Clin Orthop Relat Res. 1982;170:34–41.

Lie SA, Havelin LI, Furnes ON, Engesæter LB, Vollset SE. Failure rates for 4762 revision total hip arthroplasties in the Norwegian Arthroplasty Register. J Bone Joint Surg Br. 2004;86-B(4):504–9. https://doi.org/10.1302/0301-620X.86B4.14799 .

Ballard WT, Callaghan JJ, Johnston RC. Revision of total hip arthroplasty in octogenarians. J Bone Joint Surg Am. 1995;77(4):585–9. https://doi.org/10.2106/00004623-199504000-00012 .

Eisler T, Svensson O, Tengström A, Elmstedt E. Patient expectation and satisfaction in revision total hip arthroplasty. J Arthroplasty. 2002;17(4):457–62. https://doi.org/10.1054/arth.2002.31245 .

Greiner W, Weijnen T, Nieuwenhuizen M, Oppe S, Badia X, Busschbach J, Buxton M, Dolan P, Kind P, Krabbe P, Ohinmaa A, Parkin D, Roset M, Sintonen H, Tsuchiya A, de Charro F. A single European currency for EQ-5D health states. Results from a six-country study. Eur J Health Econ. 2003;4(3):222–31. https://doi.org/10.1007/s10198-003-0182-5 .

Haddad FS, Garbuz DS, Chambers GK, Jagpal TJ, Masri BA, Duncan CP. The expectations of patients undergoing revision hip arthroplasty. J Arthroplasty. 2001;16(1):87–91. https://doi.org/10.1054/arth.2001.17937 .

Mancuso CA, Jout J, Salvati EA, Sculco TP. Fulfillment of patients’ expectations for total hip arthroplasty. J Bone Joint Surg Am. 2009;91(9):2073–8. https://doi.org/10.2106/JBJS.H.01802 .

Linder-Pelz S. Social psychological determinants of patient satisfaction: a test of five hypothesis. Soc Sci Med. 1982;16(5):583–9. https://doi.org/10.1016/0277-9536(82)90312-4 .

Mancuso CA, Salvati EA, Johanson NA, Peterson MG, Charlson ME. Patients’ expectations and satisfaction with total hip arthroplasty. J Arthroplasty. 1997;12(4):387–96. https://doi.org/10.1016/s0883-5403(97)90194-7 .

Mahomed NN, Liang MH, Cook EF, Daltroy LH, Fortin PR, Fossel AH, Katz JN. The importance of patient expectations in predicting functional outcomes after total joint arthroplasty. J Rheumatol. 2002;29(6):1273–9.

PubMed   Google Scholar  

Katz JN, Phillips CB, Poss R, Harrast JJ, Fossel AH, Liang MH, Sledge CB. The validity and reliability of a total hip arthroplasty outcome evaluation questionnaire. J Bone Joint Surg Am. 1995;77(10):1528–34. https://doi.org/10.2106/00004623-199510000-00007 .

Liang MH, Katz JN, Phillips C, Sledge C, Cats-Baril W. The total hip arthroplasty outcome evaluation form of the American Academy of Orthopaedic Surgeons. Results of a nominal group process. The American Academy of Orthopaedic Surgeons Task Force on Outcome Studies. J Bone Joint Surg Am. 1991;73(5):639–46.

Burton KE, Wright V, Richards J. Patients’ expectations in relation to outcome of total hip replacment surgery. Ann Rheum Dis. 1979;38(5):471–4. https://doi.org/10.1136/ard.38.5.471 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Haanstra TM, van den Berg T, Ostelo RW, Poolman RW, Jansma IP, Cuijpers P, de Vet HC. Systematic review: do patient expectations influence treatment outcomes in total knee and total hip arthroplasty? Health Qual Life Outcomes. 2012;10(1):152. https://doi.org/10.1186/1477-7525-10-152 .

Barrack RL, McClure JT, Burak CF, Clohisy JC, Parvizi J, Hozack W. Revision total hip arthroplasty: the patient’s perspective. Clin Orthop Relat Res. 2006;453:173–7. https://doi.org/10.1097/01.blo.0000246537.67500.50 .

Hellman EJ, Feinberg JR, Capello WN. When is total hip arthroplasty a failure? The patients’ perspective. Iowa Orthop J. 1996;16:113–7.

CAS   PubMed   PubMed Central   Google Scholar  

Zhang S, Tay DK, Pang HN, Lo NN, Yeo SJ, Liow MH. Preoperative mental distress is associated with poorer physical improvements after revision total hip arthroplasty. J Orthop. 2023;35:18–23. https://doi.org/10.1016/j.jor.2022.10.007 .

National Heart, Lung, and Blood Institute. Study quality assessment tools. 2019. https://www.nhlbi.nih.gov/health-topics/study-quality-assessment-tools .

Bagias C, Sukumar N, Weldeselassie Y, Oyebode O, Saravanan P. Cord blood adipocytokines and body composition in early childhood: a systematic review and meta-analysis. Int J Environ Res Public Health. 2021;18(4):1897. https://doi.org/10.3390/ijerph18041897 .

Robinson AH, Palmer CR, Villar RN. Is revision as good as primary hip replacement? A comparison of quality of life. J Bone Joint Surg Br. 1999;81(1):42–5. https://doi.org/10.1302/0301-620x.81b1.8728 .

Crow R, Gage H, Hampson S, Hart J, Kimber A, Thomas H. The role of expectancies in the placebo effect and their use in the delivery of health care: a systematic review. Health Technol Assess. 1999;3(3):1–96.

Bialosky JE, Bishop MD, Cleland JA. Individual expectation: an overlooked, but pertinent, factor in the treatment of individuals experiencing musculoskeletal pain. Phys Ther. 2010;90(9):1345–55. https://doi.org/10.2522/ptj.20090306 .

Kravitz RL. Patients’ expectations for medical care: an expanded formulation based on review of the literature. Med Care Res Rev. 1996;53(1):3–27. https://doi.org/10.1177/107755879605300101 .

Bandura A. Self-efficacy: toward a unifying theory of behavioral change. Adv Behav Res Ther. 1978;1(4):139–61. https://doi.org/10.1016/0146-6402(78)90002-4 .

Hobbs N, Dixon D, Rasmussen S, Judge A, Dreinhöfer KE, Günther K-P, Dieppe P. Patient preoperative expectations of total hip replacement in European orthopedic centers. Arthritis Care Res. 2011;63(11):1521–7. https://doi.org/10.1002/acr.20596 .

Constantino MJ, Arnkoff DB, Glass CR, Ametrano RM, Smith JZ. Expectations. J Clin Psychol. 2011;67(2):184–92. https://doi.org/10.1002/jclp.20754 .

Mancuso CA, Graziano S, Briskie LM, Peterson MG, Pellicci PM, Salvati EA, Sculco TP. Randomized trials to modify patients’ preoperative expectations of hip and knee arthroplasties. Clin Orthop Relat Res. 2008;466(2):424–31. https://doi.org/10.1007/s11999-007-0052-z .

Coughlin SS. Recall bias in epidemiologic studies. J Clin Epidemiol. 1990;43(1):87–91. https://doi.org/10.1016/0895-4356(90)90060-3 .

Appleton-Knapp SL, Krentler KA. Measuring student expectations and their effects on satisfaction: the importance of managing student expectations. J Mark Educ. 2006;28(3):254–64. https://doi.org/10.1177/0273475306293359 .

Schwartz CE, Andresen EM, Nosek MA, Krahn GL, RRTC Expert Panel on Health Status Measurement. Response shift theory: important implications for measuring quality of life in people with disability. Arch Phys Med Rehabil. 2007;88(4):529–36. https://doi.org/10.1016/j.apmr.2006.12.032 .

Razmjou H, Yee A, Ford M, Finkelstein JA. Response shift in outcome assessment in patients undergoing total knee arthroplasty. J Bone Joint Surg Am. 2006;88(12):2590–5. https://doi.org/10.2106/JBJS.F.00283 .

Hafkamp FJ, Gosens T, de Vries J, den Oudsten BL. Do dissatisfied patients have unrealistic expectations? A systematic review and best-evidence synthesis in knee and hip arthroplasty patients. EFORT Open Rev. 2020;5(4):226–40. https://doi.org/10.1302/2058-5241.5.190015 .

Barlow T, Clark T, Dunbar M, Metcalfe A, Griffin D. The effect of expectation on satisfaction in total knee replacements: a systematic review. Springerplus. 2016;5(1):167. https://doi.org/10.1186/s40064-016-1804-6 .

Scott CEH, Bugler KE, Clement ND, MacDonald D, Howie CR, Biant LC. Patient expectations of arthroplasty of the hip and knee. J Bone Joint Surg Br. 2012;94(7):974–81. https://doi.org/10.1302/0301-620X.94B7.28219 .

Saigal R, Clark AJ, Scheer JK, Smith JS, Bess S, Mummaneni PV, McCarthy IM, Hart RA, Kebaish KM, Klineberg EO, Deviren V, Schwab F, Shaffrey CI, Ames CP. Adult spinal deformity patients recall fewer than 50% of the risks discussed in the informed consent process preoperatively and the recall rate worsens significantly in the postoperative period. Spine. 2015;40(14):1079–85. https://doi.org/10.1097/BRS.0000000000000964 .

Pomeroy E, Shaarani S, Kenyon R, Cashman J. Patient recall of informed consent at 4 weeks after total hip replacement with standardized versus procedure-specific consent forms. J Patient Saf. 2021;17(6):e575–81. https://doi.org/10.1097/PTS.0000000000000412 .

Noble PC, Conditt MA, Cook KF, Mathis KB. The John Insall Award: patient expectations affect satisfaction with total knee arthroplasty. Clin Orthop Relat Res. 2006;452:35–43. https://doi.org/10.1097/01.blo.0000238825.63648.1e .

Nam HS, Yoo HJ, Ho JPY, Kim YB, Lee YS. Preoperative education on realistic expectations improves the satisfaction of patients with central sensitization after total knee arthroplasty: a randomized-controlled trial. Knee Surg Sports Traumatol Arthrosc. 2023;31(11):4705–15. https://doi.org/10.1007/s00167-023-07487-9 .

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Omar Mohammad

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O.M. was involved in the literature search, analysis and evaluation of data and writing all sections of the paper; S.R.S. was involved in the literature search and analysis of the data. SRS contributed to the editing of the written sections; A.M. was involved in the literature search and the editing of written sections; S.K. was involved in the topic of the review and overview of the project. All authors read and approved the final manuscript.

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Mohammad, O., Shaarani, S., Mohammad, A. et al. Patients’ expectations surrounding revision total hip arthroplasty: a literature review. Arthroplasty 6 , 28 (2024). https://doi.org/10.1186/s42836-024-00250-6

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Received : 29 November 2023

Accepted : 18 March 2024

Published : 03 June 2024

DOI : https://doi.org/10.1186/s42836-024-00250-6

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