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Literature Review, Systematic Review and Meta-analysis

Literature reviews can be a good way to narrow down theoretical interests; refine a research question; understand contemporary debates; and orientate a particular research project. It is very common for PhD theses to contain some element of reviewing the literature around a particular topic. It’s typical to have an entire chapter devoted to reporting the result of this task, identifying gaps in the literature and framing the collection of additional data.

Systematic review is a type of literature review that uses systematic methods to collect secondary data, critically appraise research studies, and synthesise findings. Systematic reviews are designed to provide a comprehensive, exhaustive summary of current theories and/or evidence and published research (Siddaway, Wood & Hedges, 2019) and may be qualitative or qualitative. Relevant studies and literature are identified through a research question, summarised and synthesized into a discrete set of findings or a description of the state-of-the-art. This might result in a ‘literature review’ chapter in a doctoral thesis, but can also be the basis of an entire research project.

Meta-analysis is a specialised type of systematic review which is quantitative and rigorous, often comparing data and results across multiple similar studies. This is a common approach in medical research where several papers might report the results of trials of a particular treatment, for instance. The meta-analysis then statistical techniques to synthesize these into one summary. This can have a high statistical power but care must be taken not to introduce bias in the selection and filtering of evidence.

Whichever type of review is employed, the process is similarly linear. The first step is to frame a question which can guide the review. This is used to identify relevant literature, often through searching subject-specific scientific databases. From these results the most relevant will be identified. Filtering is important here as there will be time constraints that prevent the researcher considering every possible piece of evidence or theoretical viewpoint. Once a concrete evidence base has been identified, the researcher extracts relevant data before reporting the synthesized results in an extended piece of writing.

Literature Review: GO-GN Insights

Sarah Lambert used a systematic review of literature with both qualitative and quantitative phases to investigate the question “How can open education programs be reconceptualised as acts of social justice to improve the access, participation and success of those who are traditionally excluded from higher education knowledge and skills?”

“My PhD research used systematic review, qualitative synthesis, case study and discourse analysis techniques, each was underpinned and made coherent by a consistent critical inquiry methodology and an overarching research question. “Systematic reviews are becoming increasingly popular as a way to collect evidence of what works across multiple contexts and can be said to address some of the weaknesses of case study designs which provide detail about a particular context – but which is often not replicable in other socio-cultural contexts (such as other countries or states.) Publication of systematic reviews that are done according to well defined methods are quite likely to be published in high-ranking journals – my PhD supervisors were keen on this from the outset and I was encouraged along this path. “Previously I had explored social realist authors and a social realist approach to systematic reviews (Pawson on realist reviews) but they did not sufficiently embrace social relations, issues of power, inclusion/exclusion. My supervisors had pushed me to explain what kind of realist review I intended to undertake, and I found out there was a branch of critical realism which was briefly of interest. By getting deeply into theory and trying out ways of combining theory I also feel that I have developed a deeper understanding of conceptual working and the different ways theories can be used at all stagesof research and even how to come up with novel conceptual frameworks.”

Useful references for Systematic Review & Meta-Analysis: Finfgeld-Connett (2014); Lambert (2020); Siddaway, Wood & Hedges (2019)

Research Methods Handbook Copyright © 2020 by Rob Farrow; Francisco Iniesto; Martin Weller; and Rebecca Pitt is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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Systematic Reviews and Meta-Analysis: A Guide for Beginners

Affiliation.

  • 1 Department of Pediatrics, Advanced Pediatrics Centre, PGIMER, Chandigarh. Correspondence to: Prof Joseph L Mathew, Department of Pediatrics, Advanced Pediatrics Centre, PGIMER Chandigarh. [email protected].
  • PMID: 34183469
  • PMCID: PMC9065227
  • DOI: 10.1007/s13312-022-2500-y

Systematic reviews involve the application of scientific methods to reduce bias in review of literature. The key components of a systematic review are a well-defined research question, comprehensive literature search to identify all studies that potentially address the question, systematic assembly of the studies that answer the question, critical appraisal of the methodological quality of the included studies, data extraction and analysis (with and without statistics), and considerations towards applicability of the evidence generated in a systematic review. These key features can be remembered as six 'A'; Ask, Access, Assimilate, Appraise, Analyze and Apply. Meta-analysis is a statistical tool that provides pooled estimates of effect from the data extracted from individual studies in the systematic review. The graphical output of meta-analysis is a forest plot which provides information on individual studies and the pooled effect. Systematic reviews of literature can be undertaken for all types of questions, and all types of study designs. This article highlights the key features of systematic reviews, and is designed to help readers understand and interpret them. It can also help to serve as a beginner's guide for both users and producers of systematic reviews and to appreciate some of the methodological issues.

Publication types

  • Meta-Analysis
  • Meta-Analysis as Topic*
  • Research Design
  • Systematic Reviews as Topic*

Systematic Reviews and Meta Analysis

  • Getting Started
  • Guides and Standards
  • Review Protocols
  • Databases and Sources
  • Randomized Controlled Trials
  • Controlled Clinical Trials
  • Observational Designs
  • Tests of Diagnostic Accuracy
  • Software and Tools
  • Where do I get all those articles?
  • Collaborations
  • EPI 233/528
  • Countway Mediated Search
  • Risk of Bias (RoB)

Cochrane Handbook

The Cochrane Handbook isn't set down to be a standard, but it has become the de facto standard for planning and carrying out a systematic review. Chapter 6, Searching for Studies, is most helpful in planning your review.

Scoping Reviews, JBI Manual for Evidence Synthesis

The Joanna Briggs Institute provides extensive guidance for their authors in producing both systematic and scoping reviews. Their chapter on scoping reviews provides a succinct overview of the scoping review process. JBI maintains a page with other materials for scoping reviewers.

Methods Guide for Effectiveness and Comparative Effectiveness Reviews

Very good chapters on conducting a review, most of which were published as articles in the Journal of Clincal Epidemiology.

Institutes of Medicine Standards for Systematic Reviews

The IOM standards promote objective, transparent, and scientifically valid systematic reviews. They address the entire systematic review process, from locating, screening, and selecting studies for the review, to synthesizing the findings (including meta-analysis) and assessing the overall quality of the body of evidence, to producing the final review report.

Systematic Reviews: CRD's Guidance for Undertaking Reviews in Health Care

Provides a succinct outline for carrying out systematic reviews and well as details about constructing a protocol, testing for bias, and other aspects of the review process. Includes examples.

Systematic reviews to support evidence-based medicine how to review and apply findings of healthcare research

Khan, K., & Royal Society of Medicine. 2nd ed,  2013. London [England]: Hodder Annold. [Harvard ID required]

Systematic reviews to answer health care questions

Nelson, H. (2014). Philadelphia: Wolters Kluwer Health/Lippincott Williams & Wilkins. [Harvard ID required]

Systematic Review Toolbox

Not a guide or standard but a clearinghouse for all things systematic review. Check here for templates, reporting standards, screening tools, risk of bias assessment, etc.

Reporting Standards: PRISMA and MOOSE

You will improve the quality of your review by adhering to the standards below. Using the approriate standard can reassure editors and reviewers that you have conscienciously carried out your review.

http://www.prisma-statement.org/ The Preferred Reporting Items for Systematic Reviews and Meta-Analyses is an evidence-based minimum set of items for reporting in systematic reviews and meta-analyses. A 27-item checklist,  PRISMA  focuses on randomized trials but can also be used as a basis for reporting systematic reviews of other types of research, particularly evaluations of interventions. PRISMA may also be useful for critical appraisal of published systematic reviews, although it is not a quality assessment instrument to gauge the quality of a systematic review.

Consider using PRISMA-P when completing your protocol. PRISMA-P is a 17-item checklist for elements considered essential in protocol for a systematic review or meta-analysis. The documentation contains an excellent rationale for completing a protocol, too.

Use PRISMA-ScR, a 20-item checklist, for reporting scoping reviews. The documentation provides a clear overview of scoping reviews.

Further Reading:

Moher D, Liberati A, Tetzlaff J, Altman DG; PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009 Jul 21;6(7):e1000097. Epub 2009 Jul 21. PubMed PMID: 19621072 .  

Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JP, Clarke M, Devereaux PJ, Kleijnen J, Moher D. The PRISMA statement for reporting  systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. PLoS Med. 2009 Jul 21;6(7):e1000100. Epub 2009 Jul 21. PubMed PMID: 19621070 . 

Shamseer L, Moher D, Clarke M, Ghersi D, Liberati A, Petticrew M, Shekelle P, Stewart LA; PRISMA-P Group. Preferred reporting items for systematic review andmeta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. BMJ. 2015 Jan 2;349:g7647. doi: 10.1136/bmj.g7647. PubMed PMID: 25555855 .

Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, Shekelle P, Stewart LA; PRISMA-P Group. Preferred reporting items for systematic review andmeta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev. 2015 Jan 1;4:1. doi: 10.1186/2046-4053-4-1. PubMed PMID: 25554246 .

Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, Moher D, Peters MDJ, Horsley T, Weeks L, Hempel S, Akl EA, Chang C, McGowan J, Stewart L, Hartling L, Aldcroft A, Wilson MG, Garritty C, Lewin S, Godfrey CM, Macdonald MT, Langlois EV, Soares-Weiser K, Moriarty J, Clifford T, Tunçalp Ö, Straus SE. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann Intern Med. 2018 Oct 2;169(7):467-473. doi: 10.7326/M18-0850. Epub 2018 Sep 4. PMID: 30178033 .

Also published in the Annals of Internal Medicine, BMJ, and the Journal of Clinical Epidemiology.

MOOSE Guidelines

http://www.consort-statement.org/Media/Default/Downloads/Other%20Instruments/MOOSE%20Statement%202000.pdf Meta-analysis of Observational Studies in Epidemiology checklist contains specifications for reporting of meta-analyses of observational studies in epidemiology. Editors will expect you to follow and cite this checklist.  It refers to the  Newcastle-Ottawa Scale for assessing the quality of non-randomized studies, a method of rating each observational study in your meta-analysis.

Stroup DF, Berlin JA, Morton SC, Olkin I, Williamson GD, Rennie D, Moher D, Becker BJ, Sipe TA, Thacker SB. Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group. JAMA. 2000 Apr 19;283(15):2008-12. PubMed PMID:  10789670 .

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  • Last Updated: Feb 14, 2024 2:47 PM
  • URL: https://guides.library.harvard.edu/meta-analysis
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Systematic reviews vs meta-analysis: what’s the difference?

Posted on 24th July 2023 by Verónica Tanco Tellechea

""

You may hear the terms ‘systematic review’ and ‘meta-analysis being used interchangeably’. Although they are related, they are distinctly different. Learn more in this blog for beginners.

What is a systematic review?

According to Cochrane (1), a systematic review attempts to identify, appraise and synthesize all the empirical evidence to answer a specific research question. Thus, a systematic review is where you might find the most relevant, adequate, and current information regarding a specific topic. In the levels of evidence pyramid , systematic reviews are only surpassed by meta-analyses. 

To conduct a systematic review, you will need, among other things: 

  • A specific research question, usually in the form of a PICO question.
  • Pre-specified eligibility criteria, to decide which articles will be included or discarded from the review. 
  • To follow a systematic method that will minimize bias.

You can find protocols that will guide you from both Cochrane and the Equator Network , among other places, and if you are a beginner to the topic then have a read of an overview about systematic reviews.

What is a meta-analysis?

A meta-analysis is a quantitative, epidemiological study design used to systematically assess the results of previous research (2) . Usually, they are based on randomized controlled trials, though not always. This means that a meta-analysis is a mathematical tool that allows researchers to mathematically combine outcomes from multiple studies.

When can a meta-analysis be implemented?

There is always the possibility of conducting a meta-analysis, yet, for it to throw the best possible results it should be performed when the studies included in the systematic review are of good quality, similar designs, and have similar outcome measures.

Why are meta-analyses important?

Outcomes from a meta-analysis may provide more precise information regarding the estimate of the effect of what is being studied because it merges outcomes from multiple studies. In a meta-analysis, data from various trials are combined and generate an average result (1), which is portrayed in a forest plot diagram. Moreover, meta-analysis also include a funnel plot diagram to visually detect publication bias.

Conclusions

A systematic review is an article that synthesizes available evidence on a certain topic utilizing a specific research question, pre-specified eligibility criteria for including articles, and a systematic method for its production. Whereas a meta-analysis is a quantitative, epidemiological study design used to assess the results of articles included in a systematic-review. 

Remember: All meta-analyses involve a systematic review, but not all systematic reviews involve a meta-analysis.

If you would like some further reading on this topic, we suggest the following:

The systematic review – a S4BE blog article

Meta-analysis: what, why, and how – a S4BE blog article

The difference between a systematic review and a meta-analysis – a blog article via Covidence

Systematic review vs meta-analysis: what’s the difference? A 5-minute video from Research Masterminds:

  • About Cochrane reviews [Internet]. Cochranelibrary.com. [cited 2023 Apr 30]. Available from: https://www.cochranelibrary.com/about/about-cochrane-reviews
  • Haidich AB. Meta-analysis in medical research. Hippokratia. 2010;14(Suppl 1):29–37.

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Methodology

  • Systematic Review | Definition, Example, & Guide

Systematic Review | Definition, Example & Guide

Published on June 15, 2022 by Shaun Turney . Revised on November 20, 2023.

A systematic review is a type of review that uses repeatable methods to find, select, and synthesize all available evidence. It answers a clearly formulated research question and explicitly states the methods used to arrive at the answer.

They answered the question “What is the effectiveness of probiotics in reducing eczema symptoms and improving quality of life in patients with eczema?”

In this context, a probiotic is a health product that contains live microorganisms and is taken by mouth. Eczema is a common skin condition that causes red, itchy skin.

Table of contents

What is a systematic review, systematic review vs. meta-analysis, systematic review vs. literature review, systematic review vs. scoping review, when to conduct a systematic review, pros and cons of systematic reviews, step-by-step example of a systematic review, other interesting articles, frequently asked questions about systematic reviews.

A review is an overview of the research that’s already been completed on a topic.

What makes a systematic review different from other types of reviews is that the research methods are designed to reduce bias . The methods are repeatable, and the approach is formal and systematic:

  • Formulate a research question
  • Develop a protocol
  • Search for all relevant studies
  • Apply the selection criteria
  • Extract the data
  • Synthesize the data
  • Write and publish a report

Although multiple sets of guidelines exist, the Cochrane Handbook for Systematic Reviews is among the most widely used. It provides detailed guidelines on how to complete each step of the systematic review process.

Systematic reviews are most commonly used in medical and public health research, but they can also be found in other disciplines.

Systematic reviews typically answer their research question by synthesizing all available evidence and evaluating the quality of the evidence. Synthesizing means bringing together different information to tell a single, cohesive story. The synthesis can be narrative ( qualitative ), quantitative , or both.

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Systematic reviews often quantitatively synthesize the evidence using a meta-analysis . A meta-analysis is a statistical analysis, not a type of review.

A meta-analysis is a technique to synthesize results from multiple studies. It’s a statistical analysis that combines the results of two or more studies, usually to estimate an effect size .

A literature review is a type of review that uses a less systematic and formal approach than a systematic review. Typically, an expert in a topic will qualitatively summarize and evaluate previous work, without using a formal, explicit method.

Although literature reviews are often less time-consuming and can be insightful or helpful, they have a higher risk of bias and are less transparent than systematic reviews.

Similar to a systematic review, a scoping review is a type of review that tries to minimize bias by using transparent and repeatable methods.

However, a scoping review isn’t a type of systematic review. The most important difference is the goal: rather than answering a specific question, a scoping review explores a topic. The researcher tries to identify the main concepts, theories, and evidence, as well as gaps in the current research.

Sometimes scoping reviews are an exploratory preparation step for a systematic review, and sometimes they are a standalone project.

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A systematic review is a good choice of review if you want to answer a question about the effectiveness of an intervention , such as a medical treatment.

To conduct a systematic review, you’ll need the following:

  • A precise question , usually about the effectiveness of an intervention. The question needs to be about a topic that’s previously been studied by multiple researchers. If there’s no previous research, there’s nothing to review.
  • If you’re doing a systematic review on your own (e.g., for a research paper or thesis ), you should take appropriate measures to ensure the validity and reliability of your research.
  • Access to databases and journal archives. Often, your educational institution provides you with access.
  • Time. A professional systematic review is a time-consuming process: it will take the lead author about six months of full-time work. If you’re a student, you should narrow the scope of your systematic review and stick to a tight schedule.
  • Bibliographic, word-processing, spreadsheet, and statistical software . For example, you could use EndNote, Microsoft Word, Excel, and SPSS.

A systematic review has many pros .

  • They minimize research bias by considering all available evidence and evaluating each study for bias.
  • Their methods are transparent , so they can be scrutinized by others.
  • They’re thorough : they summarize all available evidence.
  • They can be replicated and updated by others.

Systematic reviews also have a few cons .

  • They’re time-consuming .
  • They’re narrow in scope : they only answer the precise research question.

The 7 steps for conducting a systematic review are explained with an example.

Step 1: Formulate a research question

Formulating the research question is probably the most important step of a systematic review. A clear research question will:

  • Allow you to more effectively communicate your research to other researchers and practitioners
  • Guide your decisions as you plan and conduct your systematic review

A good research question for a systematic review has four components, which you can remember with the acronym PICO :

  • Population(s) or problem(s)
  • Intervention(s)
  • Comparison(s)

You can rearrange these four components to write your research question:

  • What is the effectiveness of I versus C for O in P ?

Sometimes, you may want to include a fifth component, the type of study design . In this case, the acronym is PICOT .

  • Type of study design(s)
  • The population of patients with eczema
  • The intervention of probiotics
  • In comparison to no treatment, placebo , or non-probiotic treatment
  • The outcome of changes in participant-, parent-, and doctor-rated symptoms of eczema and quality of life
  • Randomized control trials, a type of study design

Their research question was:

  • What is the effectiveness of probiotics versus no treatment, a placebo, or a non-probiotic treatment for reducing eczema symptoms and improving quality of life in patients with eczema?

Step 2: Develop a protocol

A protocol is a document that contains your research plan for the systematic review. This is an important step because having a plan allows you to work more efficiently and reduces bias.

Your protocol should include the following components:

  • Background information : Provide the context of the research question, including why it’s important.
  • Research objective (s) : Rephrase your research question as an objective.
  • Selection criteria: State how you’ll decide which studies to include or exclude from your review.
  • Search strategy: Discuss your plan for finding studies.
  • Analysis: Explain what information you’ll collect from the studies and how you’ll synthesize the data.

If you’re a professional seeking to publish your review, it’s a good idea to bring together an advisory committee . This is a group of about six people who have experience in the topic you’re researching. They can help you make decisions about your protocol.

It’s highly recommended to register your protocol. Registering your protocol means submitting it to a database such as PROSPERO or ClinicalTrials.gov .

Step 3: Search for all relevant studies

Searching for relevant studies is the most time-consuming step of a systematic review.

To reduce bias, it’s important to search for relevant studies very thoroughly. Your strategy will depend on your field and your research question, but sources generally fall into these four categories:

  • Databases: Search multiple databases of peer-reviewed literature, such as PubMed or Scopus . Think carefully about how to phrase your search terms and include multiple synonyms of each word. Use Boolean operators if relevant.
  • Handsearching: In addition to searching the primary sources using databases, you’ll also need to search manually. One strategy is to scan relevant journals or conference proceedings. Another strategy is to scan the reference lists of relevant studies.
  • Gray literature: Gray literature includes documents produced by governments, universities, and other institutions that aren’t published by traditional publishers. Graduate student theses are an important type of gray literature, which you can search using the Networked Digital Library of Theses and Dissertations (NDLTD) . In medicine, clinical trial registries are another important type of gray literature.
  • Experts: Contact experts in the field to ask if they have unpublished studies that should be included in your review.

At this stage of your review, you won’t read the articles yet. Simply save any potentially relevant citations using bibliographic software, such as Scribbr’s APA or MLA Generator .

  • Databases: EMBASE, PsycINFO, AMED, LILACS, and ISI Web of Science
  • Handsearch: Conference proceedings and reference lists of articles
  • Gray literature: The Cochrane Library, the metaRegister of Controlled Trials, and the Ongoing Skin Trials Register
  • Experts: Authors of unpublished registered trials, pharmaceutical companies, and manufacturers of probiotics

Step 4: Apply the selection criteria

Applying the selection criteria is a three-person job. Two of you will independently read the studies and decide which to include in your review based on the selection criteria you established in your protocol . The third person’s job is to break any ties.

To increase inter-rater reliability , ensure that everyone thoroughly understands the selection criteria before you begin.

If you’re writing a systematic review as a student for an assignment, you might not have a team. In this case, you’ll have to apply the selection criteria on your own; you can mention this as a limitation in your paper’s discussion.

You should apply the selection criteria in two phases:

  • Based on the titles and abstracts : Decide whether each article potentially meets the selection criteria based on the information provided in the abstracts.
  • Based on the full texts: Download the articles that weren’t excluded during the first phase. If an article isn’t available online or through your library, you may need to contact the authors to ask for a copy. Read the articles and decide which articles meet the selection criteria.

It’s very important to keep a meticulous record of why you included or excluded each article. When the selection process is complete, you can summarize what you did using a PRISMA flow diagram .

Next, Boyle and colleagues found the full texts for each of the remaining studies. Boyle and Tang read through the articles to decide if any more studies needed to be excluded based on the selection criteria.

When Boyle and Tang disagreed about whether a study should be excluded, they discussed it with Varigos until the three researchers came to an agreement.

Step 5: Extract the data

Extracting the data means collecting information from the selected studies in a systematic way. There are two types of information you need to collect from each study:

  • Information about the study’s methods and results . The exact information will depend on your research question, but it might include the year, study design , sample size, context, research findings , and conclusions. If any data are missing, you’ll need to contact the study’s authors.
  • Your judgment of the quality of the evidence, including risk of bias .

You should collect this information using forms. You can find sample forms in The Registry of Methods and Tools for Evidence-Informed Decision Making and the Grading of Recommendations, Assessment, Development and Evaluations Working Group .

Extracting the data is also a three-person job. Two people should do this step independently, and the third person will resolve any disagreements.

They also collected data about possible sources of bias, such as how the study participants were randomized into the control and treatment groups.

Step 6: Synthesize the data

Synthesizing the data means bringing together the information you collected into a single, cohesive story. There are two main approaches to synthesizing the data:

  • Narrative ( qualitative ): Summarize the information in words. You’ll need to discuss the studies and assess their overall quality.
  • Quantitative : Use statistical methods to summarize and compare data from different studies. The most common quantitative approach is a meta-analysis , which allows you to combine results from multiple studies into a summary result.

Generally, you should use both approaches together whenever possible. If you don’t have enough data, or the data from different studies aren’t comparable, then you can take just a narrative approach. However, you should justify why a quantitative approach wasn’t possible.

Boyle and colleagues also divided the studies into subgroups, such as studies about babies, children, and adults, and analyzed the effect sizes within each group.

Step 7: Write and publish a report

The purpose of writing a systematic review article is to share the answer to your research question and explain how you arrived at this answer.

Your article should include the following sections:

  • Abstract : A summary of the review
  • Introduction : Including the rationale and objectives
  • Methods : Including the selection criteria, search method, data extraction method, and synthesis method
  • Results : Including results of the search and selection process, study characteristics, risk of bias in the studies, and synthesis results
  • Discussion : Including interpretation of the results and limitations of the review
  • Conclusion : The answer to your research question and implications for practice, policy, or research

To verify that your report includes everything it needs, you can use the PRISMA checklist .

Once your report is written, you can publish it in a systematic review database, such as the Cochrane Database of Systematic Reviews , and/or in a peer-reviewed journal.

In their report, Boyle and colleagues concluded that probiotics cannot be recommended for reducing eczema symptoms or improving quality of life in patients with eczema. Note Generative AI tools like ChatGPT can be useful at various stages of the writing and research process and can help you to write your systematic review. However, we strongly advise against trying to pass AI-generated text off as your own work.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

  • Student’s  t -distribution
  • Normal distribution
  • Null and Alternative Hypotheses
  • Chi square tests
  • Confidence interval
  • Quartiles & Quantiles
  • Cluster sampling
  • Stratified sampling
  • Data cleansing
  • Reproducibility vs Replicability
  • Peer review
  • Prospective cohort study

Research bias

  • Implicit bias
  • Cognitive bias
  • Placebo effect
  • Hawthorne effect
  • Hindsight bias
  • Affect heuristic
  • Social desirability bias

A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a thesis, dissertation , or research paper , in order to situate your work in relation to existing knowledge.

A literature review is a survey of credible sources on a topic, often used in dissertations , theses, and research papers . Literature reviews give an overview of knowledge on a subject, helping you identify relevant theories and methods, as well as gaps in existing research. Literature reviews are set up similarly to other  academic texts , with an introduction , a main body, and a conclusion .

An  annotated bibliography is a list of  source references that has a short description (called an annotation ) for each of the sources. It is often assigned as part of the research process for a  paper .  

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

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  • Open access
  • Published: 17 February 2024

Pain assessment tools in adults with communication disorders: systematic review and meta-analysis

  • Álvaro Sabater-Gárriz 1 , 2 , 3 , 4 ,
  • Jesús Molina-Mula 2 , 4 ,
  • Pedro Montoya 3 , 4 &
  • Inmaculada Riquelme 2 , 3 , 4  

BMC Neurology volume  24 , Article number:  66 ( 2024 ) Cite this article

291 Accesses

Metrics details

Verbal communication is the "gold standard" for assessing pain. Consequently, individuals with communication disorders are particularly vulnerable to incomplete pain management. This review aims at identifying the current pain assessment instruments for adult patients with communication disorders.

A systematic review with meta-analysis was conducted on PubMed, PEDRO, EBSCOhost, VHL and Cochrane databases from 2011 to 2023 using MeSH terms “pain assessment, “nonverbal communication” and “communication disorders” in conjunction with additional inclusion criteria: studies limited to humans, interventions involving adult patients, and empirical investigations.

Fifty articles were included in the review. Seven studies report sufficient data to perform the meta-analysis. Observational scales are the most common instruments to evaluate pain in individuals with communication disorders followed by physiological measures and facial recognition systems. While most pain assessments rely on observational scales, current evidence does not strongly endorse one scale over others for clinical practice. However, specific observational scales appear to be particularly suitable for identifying pain during certain potentially painful procedures, such as suctioning and mobilization, in these populations. Additionally, specific observational scales appear to be well-suited for certain conditions, such as mechanically ventilated patients.

Conclusions

While observational scales dominate pain assessment, no universal tool exists for adults with communication disorders. Specific scales exhibit promise for distinct populations, yet the diverse landscape of tools hampers a one-size-fits-all solution. Crucially, further high-quality research, offering quantitative data like reliability findings, is needed to identify optimal tools for various contexts. Clinicians should be informed to select tools judiciously, recognizing the nuanced appropriateness of each in diverse clinical situations.

Trial registration

This systematic review is registered in PROSPERO (International prospective register of systematic reviews) with the ID: CRD42022323655 .

Peer Review reports

Introduction

Verbal communication is regarded as the "gold standard" for pain assessment [ 1 ], which is necessary for optimal management [ 2 ]. Since pain can be challenging to recognize by professionals, who frequently assess it based on their clinical impression, people with difficulties in verbal communication are particularly vulnerable to reduced or incomplete pain management [ 3 , 4 , 5 , 6 ]. Communication disorders affect people of all ages, although the prevalence and complexity of these conditions increase with age [ 7 ]. Thus, pain in people with communication difficulties due to dementia, intellectual disabilities or neurological conditions has been classically underestimated and, therefore, poorly treated [ 8 , 9 ]. Moreover, many hospitalized people also experience temporary limitations in ability to communicate in situations such as recovering from anesthesia or being intubated [ 10 ].

Under-treated pain can result in both physical and psychological complications [ 11 , 12 ]. However, evaluating pain in individuals with communication disorders is often viewed as a challenging and time-consuming task by healthcare professionals [ 13 , 14 ]. Many of these professionals often report inadequate education and limited experience in dealing with patients in pain during their medical training, particularly in relation to vulnerable groups [ 14 , 15 ]. Thus, a reliable and validated technique for pain evaluation in patients who are unable to self-report is urgently needed [ 16 ].

A multitude of observational tools is available to assess pain in this population, but there is not a clear consensus about the one to choose [ 11 , 17 ]. Furthermore, these solutions are often considered to provide subjective, observer-dependent data [ 18 , 19 , 20 ], and some of these are only valid for a specific group of patients and context of care [ 21 ]. One way or another, there is an open debate about the usefulness of the non-verbal behaviors considered in these tools, as many of them can be non-specific or non-pain sensitive [ 17 ] or may determine secondary physiological indicators [ 18 ]. In order to address these issues, the clinical community is beginning to measure physiological signs that potentially can reflect pain, such as heart rate changes and heart rate variability, skin conductance and perfusion, changes in oxygen saturation, brain activity, pupil reactivity to light and expression of salivary metabolites, to cite a few [ 18 , 22 , 23 , 24 , 25 , 26 ]. However, it needs to be pointed out that many of them are considered to lack sensitivity and specificity and cannot be used independently [ 27 ].

Taking all this into account, and due to the lack of evidence-based guidelines for pain assessment in the adult population [ 28 ], the main objective of this systematic review was to identify the different pain assessment methods currently used in adult patients with either permanent or temporary inability to communicate in any way. Specifically, we aimed at mapping and categorize existing instruments to evaluate pain in people with communication problems from which to commission primary research. Furthermore, the assessment of pain in people with communication problems was carried out through three constructs: pressure pain, suctioning pain and mobilization pain. These constructs could be included in the meta-analysis because they contained pre and post results or two comparison groups.

Material and methods

A systematic mapping review with meta-analysis of pain assessment instruments in adult patients (≥18 years old) with communication disorders was performed. The PRISMA international standards were followed, as well as the Cochrane recommendations. This systematic review is registered in PROSPERO (International prospective register of systematic reviews) with the ID CRD42022323655.

Search strategy

The bibliographic search was conducted from January 2021 to August 2023 in the following databases: Pubmed, PEDRO, Virtual Health Library (VHL), Cochrane and EBSCOhost (includes the following databases: CINAHL®Complet, Psychology & Behavioral Sciences Collection, Academic Search Complete, APA PsycInfo, Abstracts in Social Gerontology, MLA International Bibliography, APA PsycArticles and E-Journals. The search formulation was based on DeCS/MeSH Descriptors and free terms using Boolean operators and, in some cases, truncation to obtain the maximum number of compatible results and prevent loss of information. The Boolean combination was: (Pain assessment) AND (communication disorder OR non verbal communication).

According to Price's Law and Cochrane recommendations, the search was limited to results in the English language, interventions involving adult patients, and a publication period from 2011 to 2021. A secondary review was conducted in August 2023, encompassing publications from 2021 to 2023 to identify any additional clinical trials published during the analysis period. Additionally, some of the previously used terms were recognized and utilized as MeSH terms by the PubMed search engine: pain, pain assessment, communication disorders, nonverbal communication. Finally, a targeted snowball search strategy was implemented to include relevant studies that, due to the chosen publication period or other criteria, did not initially align with the search strategy but still provided valuable information related to the review's objectives. All identified studies were imported into the Mendeley bibliographic manager (Elsevier, London, England) with the intention of removing any duplicate entries.

Selection criteria

The following inclusion criteria were followed in this systematic review: a) studies limited to humans; b) studies limited to patients over 18 years of age; c) studies limited to patients with an inability to self-report d) studies with control group or pre- and post- measurements that analyze or propose an assessment system that evaluates any behavioral (identifiable through observation) or physiological (identifiable through the measurement of any physiological parameter) responses related to a painful stimulus.

The exclusion criteria were a) No inability to self-report; b) No pain assessment models; c) Infant or neonate patients; d) Opinion pieces; e) Letters to the editor; f) Descriptive study protocols; g) Linguistic validations.

Data collection

Two researchers (AS-G and IR) independently performed the selection and critical reading. In case of disagreement, a third investigator (JM) was consulted.

The selection of articles proceeded through four phases:

Identification: This phase involved searching different databases with subsequent elimination of duplicates.

Screening: Articles were evaluated based on their titles.

Selection: The eligibility of articles was assessed based on abstracts.

Inclusion: Potentially eligible studies were selected based on a critical reading of the full text.

The results were compiled in an Excel datasheet that included: title, author/s, year of publication, country of publication, financing, article source, study design, recruitment, sample (with demographic and clinical data), follow-up, measures, interventions, risk of bias, conclusions, and limitations.

Finally, an Excel table was created to categorize the analytical papers for assessing the feasibility of the meta-analysis (MA). The analytical coding table included the following variables: study code, title, year, author, assessment instrument, construct, pre-measurement (mean and SD), post-measurement (mean and SD), and sample size. In instances where complete data for the pre-post measurements were not available, requests were made to the authors ( n =5).

Assessment of risk of bias

The risks of bias of each study were assessed using the Cochrane Collaboration Tool as guidance [ 29 ]. This tool evaluates bias across seven specific domains: random sequence generation (selection bias), allocation concealment (selection bias), blinding of participants and personnel (performance bias), blinding of outcome assessment (detection bias), incomplete outcome data (attrition bias), selective reporting (reporting bias), and other bias. Each domain was categorized as "low risk," "high risk," or "moderate or uncertain risk." The overall risk was determined by weighing the risks observed in the various studies.

Analysis and synthesis

Qualitative synthesis.

A qualitative analysis was conducted to assess the strength of the relationship between the variables and various pain assessment methods described in patients with communication disorders. This analysis allowed us to filter and interpret the data for the meta-analysis. Some studies were not included in the meta-analysis due to the heterogeneity of the data or the absence of relevant outcome measures. The methodological quality of all seven studies included in the meta-analysis was assessed using the Critical Appraisal Skills Program tool, Spanish version (CASPe) [ 30 ]. Studies that achieved a score of 7 or higher were considered of sufficient quality for inclusion in both the review and meta-analysis. Each study's level of evidence, as determined by the CASPe score, was further categorized by the Scottish Intercollegiate Guidelines Network (SIGN) [ 31 ], along with its corresponding degree of recommendation.

We also provide the reliability findings from the studies, reporting measures such as Cronbach's alpha, kappa, or ICC. In the case of ICC, the interpretations are as follows: ICC < 0.5 = poor reliability, ICC 0.5-0.75 = moderate reliability, ICC 0.75-0.9 = good reliability, ICC > 0.90 = excellent reliability [ 32 ].

Quantitative synthesis

When two or more outcome measures evaluated the same construct using similar instruments, the study was eligible for inclusion in a meta-analysis. The 'Meta-Essentials' Excel tool was used to conduct the meta-analysis [ 33 ]. Effect sizes were calculated by extracting pre-post sample sizes, means, and standard deviations (SD) from the selected studies. This was achieved by using the effect size or magnitude of the results, acknowledging the limitation that sometimes, even if the studies used the same construct, they might use similar but not identical scales. Dividing by a standard deviation allows studies that have applied different scales to measure the same construct or variable (e.g., measurement of pain) to express their results in a common metric (standard units). The quantification of results in a common metric is an essential requirement for applying subsequent statistical analysis techniques. Given the considerable diversity of scales and instruments used to measure the same variable in the phenomenon under study, the use of the standardized mean difference addresses the problem of heterogeneity in measurement instruments, enabling the statistical synthesis of the meta-analysis [ 34 , 35 , 36 ].

Despite the potential risk of introducing significant variability (heterogeneity), this approach was employed in an exploratory manner to offer additional insight into the overall landscape of current primary research and the prevailing state of measures used to assess pain in individuals with communication problems.

For continuous data, standardized mean differences (SMD) and 95% confidence intervals (CI) were calculated by dividing the mean of pre- and post- groups by the pooled SD. The SMD of the means proposed by Cohen in each study were weighted by the inverse of their variance to obtain the pooled index of the magnitude of the effect. Due to the heterogeneous nature of the selected studies, a random effects model was used. Finally, heterogeneity was evaluated using the inferential Q test proposed by Cochran, Pq test, Tau (T) square Tau \({T}^{2}\) and the \({I}^{2}\) hetero-geneity index with 95% CI. Heterogeneity was considered high or considerable when \({I}^{2}\) was >75% [ 37 ].

The asymmetries in the distribution of effect sizes, potentially resulting from publication bias or other forms of bias, were examined using two different approaches: Begg's strategy and Egger's test.

A sensitivity analysis was performed to test the influence of possible outliers and visualize the trends in the results. The thresholds for the interpretation of effect sizes were as follows: 0.1, small; 0.3, moderate; 0.5, large;0.7, very large; and 0.9, extremely large [ 33 ]. P < 0.05 was considered to indicate statistical significance.

It is important to note that for those studies that could not be incorporated into the meta-analysis due to either insufficient data or the utilization of different assessment instruments, solely a qualitative analysis was conducted ( n =38).

Search results

The comprehensive search was completed in August 2023, yielding a total of 345 studies, of which 253 remained after eliminating duplicates. Once the eligibility criteria were applied and the abstracts were reviewed, the number of studies was reduced to 76 for subsequent full-text reading. Finally, 50 studies were included in the systematic review. Among them, twenty-two (44%) were clinical trials and were further examined to determine if they were suitable for inclusion in a meta-analysis. The distribution of the remaining studies was as follows: n =12 (24%) observational/descriptive; n =8 (16%) systematic reviews; n =5 (10%) linguistic validation; n =1 (2%) psychometric validation; n =1 (2%) secondary data analysis; n =1 (2%) scale validation. Ultimately, 8 studies provided enough data to perform the meta-analysis. Figure 1 illustrates the flowchart of the review based on the PRISMA criteria [ 38 ].

figure 1

Flowchart. Selection process

Description of included studies

The sample of participants in the included studies consisted of 1,054,982 individuals. The mean age of the participants was 63.71 years (SD= 12.20). Among the 50 selected studies, it should be noted that only 2 had the presence of a control group ( n =45), referring to a group of individuals who were assessed without receiving a painful stimuli/procedure. Table S1 (see Supplementary material , table S1) presents the main characteristics of the selected studies.

Of all the studies, 36% ( n =18) were conducted in North America, 34% ( n =17) were conducted in Europe, 16% ( n =8) were conducted in Asia, 6% ( n =3) were performed in Oceania and 2% ( n =1) in South America. Regarding the development of the studies, 28% ( n =14) were multicentric.

Regarding the pain assessment systems used in the studies included in this review, the vast majority used observational scales 80% ( n =40). Besides, the 4% ( n =2) of the studies used computerized facial recognition technologies (Electronic Pain Assessment Tool -ePAT) and 16% ( n =8) employed the evaluation of different physiological parameters such as brain activity, cardiac activity, muscle activity, respiratory activity, sweating or conductance of the skin. Tables S2, S3 and S4 (see Supplementary material , tables S2-S4) provide detailed information about the different systems used to assess pain.

Classifying the studies by the characteristics of the patients, 29.54% ( n =13) focused on elderly patients with dementia, 22.73% ( n =10) on patients with mechanical ventilation, 12% ( n =6) on patients with brain damage, 8% ( n =4) on patients with cerebral palsy, 6% ( n =3) on elderly patients with communication disorders,6% ( n =3) on patients with intellectual disability, 6% ( n =3) on critical patients, 6% ( n =3) on patients with aphasia post-stroke, 2% ( n =1) on cancer patients, 2% ( n =1) on patients with acute pain, and 2% ( n =1) on patients in a vegetative state/minimal consciousness.

Regarding the painful procedure assessed (factor), the review showed great heterogeneity. Most of the studies [24% ( n =12)], assessed pain produced by mobilization or transfer of patients or by tracheal aspiration [20% ( n =10)]. Further, the 16% ( n =8) of the studies assessed pain due to a routine assessment, 16% ( n =8) due to routine activities, 10% ( n =5) due to painful pressure (pain produced by direct pressure on the skin with a pressure algometer) or by puncture 6% ( n =3) (2 injections, 1 puncture with neuropen), 4% ( n =2) due to movement (nonspecific), and 4% ( n =2) due to walking among others.

Reliability findings

Out of all the selected studies utilizing observational scales, a total of 27 studies (67.5%) reported reliability results (detailed in the Supplementary material , table S5). Given the diversity in the types of scales employed across these studies, as well as the variations in the populations under assessment and the methods of reliability evaluation, we have categorized the studies to facilitate the synthesis and comparative analysis of their results (Table 1 ).

The methodological quality of all the 7 studies included in the meta-analysis according to CASPe and SIGN, is specified in table S6 (see Supplementary material , table S6 ).

Quantitative analysis

Three meta-analyses were performed among 7 studies. Specifically, variables such as Pressure pain, (pain produced by a direct pressure on the skin with a pressure algometer) (Table 2 ), Suctioning Pain (pain produced by a tracheal suctioning) (Table 3 ) and Mobilization Pain (pain produced by a postural change or transference) (Table 4 ) were analyzed quantitatively. Pressure pain was assessed with PCSLACII and NCS (Nociception Coma Scale); Suctioning pain was assessed with ESCID, BPS and CPOT; and Mobilization pain was assessed with ESCID and BPS.

The effect size has been moderate or large in the studies included in the meta-analysis. We highlight the effect size of López-López C et. al., 2018 of -0.4 (moderate) and of Al Darwish ZQ, et. al., 2016 of 0.67 (large). The rest has a small effect between 0.1 and 0.3

​The effect size findings reveal that a large number of comparisons fall into the small and moderate magnitude category, which can generate errors in the interpretation of the results based on the p value of the different studies and therefore, take your conclusions with caution.

The results showed low heterogeneity in all the analyzed variables ( \({I}^{2}\) = 0% for all variables) and there were no statistically significant changes on the outcomes of the different assessment tools between pre-pain and post-pain assessments [( p >0.05); Pressure ( \({I}^{2}\) = 0%; Z=0.82; p =0.207), suctioning ( \({I}^{2}\) = 0%; Z=-1.42; p =0.079), mobilization ( \({I}^{2}\) = 0%; Z=-1.49 p =0.069)]. Thus, despite the to the lack of significance and the absence of heterogeneity, the meta-analysis cannot conclude the usefulness of any of the scales under study to statistically differentiate pre- and-post pain using the cited variables.

Risk of bias assessment

High risk of bias was found in 15 studies: Lautenbacher et al [ 40 ], López-López et al [ 41 ], Benromano et al (a&b) [ 24 , 25 ], Al Darwish et al [ 26 ], Le et al [ 42 ], Linde et al [ 43 ], Rahu et al [ 44 ], Chatelle et al [ 45 ], Meir et al [ 46 ], Jeitziner et al [ 47 ], Vázquez et al [ 48 ], Arbour et al [ 49 ], Thé et al [ 50 ] and Poulsen et al [ 51 ] ; unclear risk of bias was found in 6 studies: Atee et al [ 52 ], Rahu et al [ 53 ], Roulin et al [ 54 ], Shinde et al [ 55 ], Latorre-Marco et al [ 56 ] and Chanques et al [ 57 ]. Only one study had a low risk of bias (Soares et al., 2018) [ 58 ]. According to the ROBINS-I tool (Risk Of Bias tool to assess Non-randomized Studies of Interventions), the areas that were most likely to increase the risk of bias were random sequence generation and blinding of participants and personnel, while bias due to selective reporting of result as had the lowest risk (Fig. 2 ).

figure 2

Risk of bias assessment: Overall risk of bias A Risk of bias summary B

Our review revealed a wide array of pain assessment tools designed for non-communicative patients, ranging from physiological variables to observational scales. Among these tools, observational scales are the most commonly employed instruments for evaluating pain in individuals with communication disorders. The diversity of methods poses a challenge in designating a single scale as the gold standard for pain assessment in adults with communication disorders. Nevertheless, specific observational scales appear to be particularly suitable for identifying pain during certain potentially painful procedures, such as suctioning and mobilization, in these populations. Additionally, specific observational scales appear to be well-suited for particular conditions, notably in the case of mechanically ventilated patients.

Evidence underscores the importance of using observational tools since relying solely on self-reports is inadequate for assessing pain in patients with communicative disorders [ 59 ]. Our study revealed a wide variety of studies employing different scales, often with small sample sizes and a high risk of bias. This diversity hinders a comprehensive and reliable analysis, resulting in a low level of confidence according to this systematic review and analytical study. Indeed, the meta-analysis showed low results when examining pain changes before and after three painful procedures.

Nonetheless, our meta-analyses identified consistent trends in the effectiveness of specific scales used in pain assessments during certain procedures, such as mobilization and aspiration. These procedures should be monitored for pain in these vulnerable populations. While these findings may not be universally applicable, they do suggest promising avenues for further research.

Other tools that employ a combination of specific facial codes and common pain behaviors [ 60 ] have demonstrated favorable reliability properties [ 61 ]. Nonetheless, to the best of our knowledge, there are no studies concerning the correlation of their scores with those obtained from other assessment tools. In addition, this systematic review has unveiled a range of physiological measures, reflecting efforts to utilize objective markers for pain evaluation. However, even in environments with readily available access to these instruments, such as ICUs, the use of observational scales remains more prevalent [ 13 , 62 , 63 , 64 ]. While this review did not yield sufficient data to assess their reliability properties, these measures may emerge as an alternative or complement to behavioral scales. They warrant further consideration in future studies to ensure a multidimensional approach to pain assessment [ 27 ].

This review has several limitations. The use of effect size in similar but not identical instruments introduces an important element of variability in the meta-analysis that can compromise heterogeneity even if analyzing the same construct. This is not an exclusive difficulty of meta-analysis, since the wide variety of characteristics inherent to the study subjects makes it necessary to design a uniform protocol, carry out a rigorous process of subject selection and perform a careful analysis of the influence on the results of extreme cases. Moreover, this aspect has been seen in previous systematic reviews, which also concluded that no single scale could be universally recommended [ 65 , 66 ]. Furthermore, not being able to report all the confidence intervals before the absence of data provided by the authors, of the included studies, represents a reproducibility bias of the meta-analysis. This means that it is not possible to fully determine the impact of the findings.

In conclusion, the predominant method of pain assessment in adults with communication disorders involves the use of observational scales, with certain scales demonstrating promising psychometric properties for specific populations. Nevertheless, the existing diversity in assessment tools and study designs prevents the selection of a universally suitable scale for evaluating pain across all adults with communication disorders.

Current evidence does not strongly favor one scale over others for clinical practice. To enhance their recommendation in clinical guidelines, further research with more rigorous study designs is imperative. In this regard, we acknowledge the existence of at least two major groups [ 67 , 68 ] that are conducting psychometric tests on items from various observational scales and analyzing those items that best predict clinicians' evaluations of pain intensity, in order to provide tools with high reliability and validity, such as the Pain Intensity Measure for Persons with Dementia and the Pain Assessment in Impaired Cognition (PAIC-15 scale).

It is advisable to carry out studies of diagnostic accuracy (STARD) and prognosis (REMARK) to, based on this review, establish the instruments that offer the most sensitivity and specificity.

Moreover, there is a need for exploration of alternative instruments that can complement the information provided by behavioral scales, including facial recognition systems or physiological signals. Such exploration can help mitigate the observer-dependent, subjective nature of observational assessment systems.

Availability of data and materials

The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors thank Mónica Vázquez Clatayud, Zainab Aldarwish and Ruth Defrin for providing us with unpublished data from their studies to perform this meta-analysis.

This research was funded by MCIN/AEI/10.13039/501100011033, Spain, grant PID2020-114967GA-I00.

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Additional file 1:.

Table S1. Description of selected studies [ 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 ]. Table S2. Population, pain observational scales and painful procedure of the included studies.  Table S3. Facial recognition measures, population and painful procedure of the included studies.  Table S4. Physiological measures, population and painful procedure of the included studies.  Table S5. Reliability findings of pain observational scales included in the systematic review.  Table S6. Methodological quality of the studies included in the meta-analysis.

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Sabater-Gárriz, Á., Molina-Mula, J., Montoya, P. et al. Pain assessment tools in adults with communication disorders: systematic review and meta-analysis. BMC Neurol 24 , 66 (2024). https://doi.org/10.1186/s12883-024-03539-w

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‘It depends’: what 86 systematic reviews tell us about what strategies to use to support the use of research in clinical practice

  • Annette Boaz   ORCID: orcid.org/0000-0003-0557-1294 1 ,
  • Juan Baeza 2 ,
  • Alec Fraser   ORCID: orcid.org/0000-0003-1121-1551 2 &
  • Erik Persson 3  

Implementation Science volume  19 , Article number:  15 ( 2024 ) Cite this article

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The gap between research findings and clinical practice is well documented and a range of strategies have been developed to support the implementation of research into clinical practice. The objective of this study was to update and extend two previous reviews of systematic reviews of strategies designed to implement research evidence into clinical practice.

We developed a comprehensive systematic literature search strategy based on the terms used in the previous reviews to identify studies that looked explicitly at interventions designed to turn research evidence into practice. The search was performed in June 2022 in four electronic databases: Medline, Embase, Cochrane and Epistemonikos. We searched from January 2010 up to June 2022 and applied no language restrictions. Two independent reviewers appraised the quality of included studies using a quality assessment checklist. To reduce the risk of bias, papers were excluded following discussion between all members of the team. Data were synthesised using descriptive and narrative techniques to identify themes and patterns linked to intervention strategies, targeted behaviours, study settings and study outcomes.

We identified 32 reviews conducted between 2010 and 2022. The reviews are mainly of multi-faceted interventions ( n  = 20) although there are reviews focusing on single strategies (ICT, educational, reminders, local opinion leaders, audit and feedback, social media and toolkits). The majority of reviews report strategies achieving small impacts (normally on processes of care). There is much less evidence that these strategies have shifted patient outcomes. Furthermore, a lot of nuance lies behind these headline findings, and this is increasingly commented upon in the reviews themselves.

Combined with the two previous reviews, 86 systematic reviews of strategies to increase the implementation of research into clinical practice have been identified. We need to shift the emphasis away from isolating individual and multi-faceted interventions to better understanding and building more situated, relational and organisational capability to support the use of research in clinical practice. This will involve drawing on a wider range of research perspectives (including social science) in primary studies and diversifying the types of synthesis undertaken to include approaches such as realist synthesis which facilitate exploration of the context in which strategies are employed.

Peer Review reports

Contribution to the literature

Considerable time and money is invested in implementing and evaluating strategies to increase the implementation of research into clinical practice.

The growing body of evidence is not providing the anticipated clear lessons to support improved implementation.

Instead what is needed is better understanding and building more situated, relational and organisational capability to support the use of research in clinical practice.

This would involve a more central role in implementation science for a wider range of perspectives, especially from the social, economic, political and behavioural sciences and for greater use of different types of synthesis, such as realist synthesis.

Introduction

The gap between research findings and clinical practice is well documented and a range of interventions has been developed to increase the implementation of research into clinical practice [ 1 , 2 ]. In recent years researchers have worked to improve the consistency in the ways in which these interventions (often called strategies) are described to support their evaluation. One notable development has been the emergence of Implementation Science as a field focusing explicitly on “the scientific study of methods to promote the systematic uptake of research findings and other evidence-based practices into routine practice” ([ 3 ] p. 1). The work of implementation science focuses on closing, or at least narrowing, the gap between research and practice. One contribution has been to map existing interventions, identifying 73 discreet strategies to support research implementation [ 4 ] which have been grouped into 9 clusters [ 5 ]. The authors note that they have not considered the evidence of effectiveness of the individual strategies and that a next step is to understand better which strategies perform best in which combinations and for what purposes [ 4 ]. Other authors have noted that there is also scope to learn more from other related fields of study such as policy implementation [ 6 ] and to draw on methods designed to support the evaluation of complex interventions [ 7 ].

The increase in activity designed to support the implementation of research into practice and improvements in reporting provided the impetus for an update of a review of systematic reviews of the effectiveness of interventions designed to support the use of research in clinical practice [ 8 ] which was itself an update of the review conducted by Grimshaw and colleagues in 2001. The 2001 review [ 9 ] identified 41 reviews considering a range of strategies including educational interventions, audit and feedback, computerised decision support to financial incentives and combined interventions. The authors concluded that all the interventions had the potential to promote the uptake of evidence in practice, although no one intervention seemed to be more effective than the others in all settings. They concluded that combined interventions were more likely to be effective than single interventions. The 2011 review identified a further 13 systematic reviews containing 313 discrete primary studies. Consistent with the previous review, four main strategy types were identified: audit and feedback; computerised decision support; opinion leaders; and multi-faceted interventions (MFIs). Nine of the reviews reported on MFIs. The review highlighted the small effects of single interventions such as audit and feedback, computerised decision support and opinion leaders. MFIs claimed an improvement in effectiveness over single interventions, although effect sizes remained small to moderate and this improvement in effectiveness relating to MFIs has been questioned in a subsequent review [ 10 ]. In updating the review, we anticipated a larger pool of reviews and an opportunity to consolidate learning from more recent systematic reviews of interventions.

This review updates and extends our previous review of systematic reviews of interventions designed to implement research evidence into clinical practice. To identify potentially relevant peer-reviewed research papers, we developed a comprehensive systematic literature search strategy based on the terms used in the Grimshaw et al. [ 9 ] and Boaz, Baeza and Fraser [ 8 ] overview articles. To ensure optimal retrieval, our search strategy was refined with support from an expert university librarian, considering the ongoing improvements in the development of search filters for systematic reviews since our first review [ 11 ]. We also wanted to include technology-related terms (e.g. apps, algorithms, machine learning, artificial intelligence) to find studies that explored interventions based on the use of technological innovations as mechanistic tools for increasing the use of evidence into practice (see Additional file 1 : Appendix A for full search strategy).

The search was performed in June 2022 in the following electronic databases: Medline, Embase, Cochrane and Epistemonikos. We searched for articles published since the 2011 review. We searched from January 2010 up to June 2022 and applied no language restrictions. Reference lists of relevant papers were also examined.

We uploaded the results using EPPI-Reviewer, a web-based tool that facilitated semi-automation of the screening process and removal of duplicate studies. We made particular use of a priority screening function to reduce screening workload and avoid ‘data deluge’ [ 12 ]. Through machine learning, one reviewer screened a smaller number of records ( n  = 1200) to train the software to predict whether a given record was more likely to be relevant or irrelevant, thus pulling the relevant studies towards the beginning of the screening process. This automation did not replace manual work but helped the reviewer to identify eligible studies more quickly. During the selection process, we included studies that looked explicitly at interventions designed to turn research evidence into practice. Studies were included if they met the following pre-determined inclusion criteria:

The study was a systematic review

Search terms were included

Focused on the implementation of research evidence into practice

The methodological quality of the included studies was assessed as part of the review

Study populations included healthcare providers and patients. The EPOC taxonomy [ 13 ] was used to categorise the strategies. The EPOC taxonomy has four domains: delivery arrangements, financial arrangements, governance arrangements and implementation strategies. The implementation strategies domain includes 20 strategies targeted at healthcare workers. Numerous EPOC strategies were assessed in the review including educational strategies, local opinion leaders, reminders, ICT-focused approaches and audit and feedback. Some strategies that did not fit easily within the EPOC categories were also included. These were social media strategies and toolkits, and multi-faceted interventions (MFIs) (see Table  2 ). Some systematic reviews included comparisons of different interventions while other reviews compared one type of intervention against a control group. Outcomes related to improvements in health care processes or patient well-being. Numerous individual study types (RCT, CCT, BA, ITS) were included within the systematic reviews.

We excluded papers that:

Focused on changing patient rather than provider behaviour

Had no demonstrable outcomes

Made unclear or no reference to research evidence

The last of these criteria was sometimes difficult to judge, and there was considerable discussion amongst the research team as to whether the link between research evidence and practice was sufficiently explicit in the interventions analysed. As we discussed in the previous review [ 8 ] in the field of healthcare, the principle of evidence-based practice is widely acknowledged and tools to change behaviour such as guidelines are often seen to be an implicit codification of evidence, despite the fact that this is not always the case.

Reviewers employed a two-stage process to select papers for inclusion. First, all titles and abstracts were screened by one reviewer to determine whether the study met the inclusion criteria. Two papers [ 14 , 15 ] were identified that fell just before the 2010 cut-off. As they were not identified in the searches for the first review [ 8 ] they were included and progressed to assessment. Each paper was rated as include, exclude or maybe. The full texts of 111 relevant papers were assessed independently by at least two authors. To reduce the risk of bias, papers were excluded following discussion between all members of the team. 32 papers met the inclusion criteria and proceeded to data extraction. The study selection procedure is documented in a PRISMA literature flow diagram (see Fig.  1 ). We were able to include French, Spanish and Portuguese papers in the selection reflecting the language skills in the study team, but none of the papers identified met the inclusion criteria. Other non- English language papers were excluded.

figure 1

PRISMA flow diagram. Source: authors

One reviewer extracted data on strategy type, number of included studies, local, target population, effectiveness and scope of impact from the included studies. Two reviewers then independently read each paper and noted key findings and broad themes of interest which were then discussed amongst the wider authorial team. Two independent reviewers appraised the quality of included studies using a Quality Assessment Checklist based on Oxman and Guyatt [ 16 ] and Francke et al. [ 17 ]. Each study was rated a quality score ranging from 1 (extensive flaws) to 7 (minimal flaws) (see Additional file 2 : Appendix B). All disagreements were resolved through discussion. Studies were not excluded in this updated overview based on methodological quality as we aimed to reflect the full extent of current research into this topic.

The extracted data were synthesised using descriptive and narrative techniques to identify themes and patterns in the data linked to intervention strategies, targeted behaviours, study settings and study outcomes.

Thirty-two studies were included in the systematic review. Table 1. provides a detailed overview of the included systematic reviews comprising reference, strategy type, quality score, number of included studies, local, target population, effectiveness and scope of impact (see Table  1. at the end of the manuscript). Overall, the quality of the studies was high. Twenty-three studies scored 7, six studies scored 6, one study scored 5, one study scored 4 and one study scored 3. The primary focus of the review was on reviews of effectiveness studies, but a small number of reviews did include data from a wider range of methods including qualitative studies which added to the analysis in the papers [ 18 , 19 , 20 , 21 ]. The majority of reviews report strategies achieving small impacts (normally on processes of care). There is much less evidence that these strategies have shifted patient outcomes. In this section, we discuss the different EPOC-defined implementation strategies in turn. Interestingly, we found only two ‘new’ approaches in this review that did not fit into the existing EPOC approaches. These are a review focused on the use of social media and a review considering toolkits. In addition to single interventions, we also discuss multi-faceted interventions. These were the most common intervention approach overall. A summary is provided in Table  2 .

Educational strategies

The overview identified three systematic reviews focusing on educational strategies. Grudniewicz et al. [ 22 ] explored the effectiveness of printed educational materials on primary care physician knowledge, behaviour and patient outcomes and concluded they were not effective in any of these aspects. Koota, Kääriäinen and Melender [ 23 ] focused on educational interventions promoting evidence-based practice among emergency room/accident and emergency nurses and found that interventions involving face-to-face contact led to significant or highly significant effects on patient benefits and emergency nurses’ knowledge, skills and behaviour. Interventions using written self-directed learning materials also led to significant improvements in nurses’ knowledge of evidence-based practice. Although the quality of the studies was high, the review primarily included small studies with low response rates, and many of them relied on self-assessed outcomes; consequently, the strength of the evidence for these outcomes is modest. Wu et al. [ 20 ] questioned if educational interventions aimed at nurses to support the implementation of evidence-based practice improve patient outcomes. Although based on evaluation projects and qualitative data, their results also suggest that positive changes on patient outcomes can be made following the implementation of specific evidence-based approaches (or projects). The differing positive outcomes for educational strategies aimed at nurses might indicate that the target audience is important.

Local opinion leaders

Flodgren et al. [ 24 ] was the only systemic review focusing solely on opinion leaders. The review found that local opinion leaders alone, or in combination with other interventions, can be effective in promoting evidence‐based practice, but this varies both within and between studies and the effect on patient outcomes is uncertain. The review found that, overall, any intervention involving opinion leaders probably improves healthcare professionals’ compliance with evidence-based practice but varies within and across studies. However, how opinion leaders had an impact could not be determined because of insufficient details were provided, illustrating that reporting specific details in published studies is important if diffusion of effective methods of increasing evidence-based practice is to be spread across a system. The usefulness of this review is questionable because it cannot provide evidence of what is an effective opinion leader, whether teams of opinion leaders or a single opinion leader are most effective, or the most effective methods used by opinion leaders.

Pantoja et al. [ 26 ] was the only systemic review focusing solely on manually generated reminders delivered on paper included in the overview. The review explored how these affected professional practice and patient outcomes. The review concluded that manually generated reminders delivered on paper as a single intervention probably led to small to moderate increases in adherence to clinical recommendations, and they could be used as a single quality improvement intervention. However, the authors indicated that this intervention would make little or no difference to patient outcomes. The authors state that such a low-tech intervention may be useful in low- and middle-income countries where paper records are more likely to be the norm.

ICT-focused approaches

The three ICT-focused reviews [ 14 , 27 , 28 ] showed mixed results. Jamal, McKenzie and Clark [ 14 ] explored the impact of health information technology on the quality of medical and health care. They examined the impact of electronic health record, computerised provider order-entry, or decision support system. This showed a positive improvement in adherence to evidence-based guidelines but not to patient outcomes. The number of studies included in the review was low and so a conclusive recommendation could not be reached based on this review. Similarly, Brown et al. [ 28 ] found that technology-enabled knowledge translation interventions may improve knowledge of health professionals, but all eight studies raised concerns of bias. The De Angelis et al. [ 27 ] review was more promising, reporting that ICT can be a good way of disseminating clinical practice guidelines but conclude that it is unclear which type of ICT method is the most effective.

Audit and feedback

Sykes, McAnuff and Kolehmainen [ 29 ] examined whether audit and feedback were effective in dementia care and concluded that it remains unclear which ingredients of audit and feedback are successful as the reviewed papers illustrated large variations in the effectiveness of interventions using audit and feedback.

Non-EPOC listed strategies: social media, toolkits

There were two new (non-EPOC listed) intervention types identified in this review compared to the 2011 review — fewer than anticipated. We categorised a third — ‘care bundles’ [ 36 ] as a multi-faceted intervention due to its description in practice and a fourth — ‘Technology Enhanced Knowledge Transfer’ [ 28 ] was classified as an ICT-focused approach. The first new strategy was identified in Bhatt et al.’s [ 30 ] systematic review of the use of social media for the dissemination of clinical practice guidelines. They reported that the use of social media resulted in a significant improvement in knowledge and compliance with evidence-based guidelines compared with more traditional methods. They noted that a wide selection of different healthcare professionals and patients engaged with this type of social media and its global reach may be significant for low- and middle-income countries. This review was also noteworthy for developing a simple stepwise method for using social media for the dissemination of clinical practice guidelines. However, it is debatable whether social media can be classified as an intervention or just a different way of delivering an intervention. For example, the review discussed involving opinion leaders and patient advocates through social media. However, this was a small review that included only five studies, so further research in this new area is needed. Yamada et al. [ 31 ] draw on 39 studies to explore the application of toolkits, 18 of which had toolkits embedded within larger KT interventions, and 21 of which evaluated toolkits as standalone interventions. The individual component strategies of the toolkits were highly variable though the authors suggest that they align most closely with educational strategies. The authors conclude that toolkits as either standalone strategies or as part of MFIs hold some promise for facilitating evidence use in practice but caution that the quality of many of the primary studies included is considered weak limiting these findings.

Multi-faceted interventions

The majority of the systematic reviews ( n  = 20) reported on more than one intervention type. Some of these systematic reviews focus exclusively on multi-faceted interventions, whilst others compare different single or combined interventions aimed at achieving similar outcomes in particular settings. While these two approaches are often described in a similar way, they are actually quite distinct from each other as the former report how multiple strategies may be strategically combined in pursuance of an agreed goal, whilst the latter report how different strategies may be incidentally used in sometimes contrasting settings in the pursuance of similar goals. Ariyo et al. [ 35 ] helpfully summarise five key elements often found in effective MFI strategies in LMICs — but which may also be transferrable to HICs. First, effective MFIs encourage a multi-disciplinary approach acknowledging the roles played by different professional groups to collectively incorporate evidence-informed practice. Second, they utilise leadership drawing on a wide set of clinical and non-clinical actors including managers and even government officials. Third, multiple types of educational practices are utilised — including input from patients as stakeholders in some cases. Fourth, protocols, checklists and bundles are used — most effectively when local ownership is encouraged. Finally, most MFIs included an emphasis on monitoring and evaluation [ 35 ]. In contrast, other studies offer little information about the nature of the different MFI components of included studies which makes it difficult to extrapolate much learning from them in relation to why or how MFIs might affect practice (e.g. [ 28 , 38 ]). Ultimately, context matters, which some review authors argue makes it difficult to say with real certainty whether single or MFI strategies are superior (e.g. [ 21 , 27 ]). Taking all the systematic reviews together we may conclude that MFIs appear to be more likely to generate positive results than single interventions (e.g. [ 34 , 45 ]) though other reviews should make us cautious (e.g. [ 32 , 43 ]).

While multi-faceted interventions still seem to be more effective than single-strategy interventions, there were important distinctions between how the results of reviews of MFIs are interpreted in this review as compared to the previous reviews [ 8 , 9 ], reflecting greater nuance and debate in the literature. This was particularly noticeable where the effectiveness of MFIs was compared to single strategies, reflecting developments widely discussed in previous studies [ 10 ]. We found that most systematic reviews are bounded by their clinical, professional, spatial, system, or setting criteria and often seek to draw out implications for the implementation of evidence in their areas of specific interest (such as nursing or acute care). Frequently this means combining all relevant studies to explore the respective foci of each systematic review. Therefore, most reviews we categorised as MFIs actually include highly variable numbers and combinations of intervention strategies and highly heterogeneous original study designs. This makes statistical analyses of the type used by Squires et al. [ 10 ] on the three reviews in their paper not possible. Further, it also makes extrapolating findings and commenting on broad themes complex and difficult. This may suggest that future research should shift its focus from merely examining ‘what works’ to ‘what works where and what works for whom’ — perhaps pointing to the value of realist approaches to these complex review topics [ 48 , 49 ] and other more theory-informed approaches [ 50 ].

Some reviews have a relatively small number of studies (i.e. fewer than 10) and the authors are often understandably reluctant to engage with wider debates about the implications of their findings. Other larger studies do engage in deeper discussions about internal comparisons of findings across included studies and also contextualise these in wider debates. Some of the most informative studies (e.g. [ 35 , 40 ]) move beyond EPOC categories and contextualise MFIs within wider systems thinking and implementation theory. This distinction between MFIs and single interventions can actually be very useful as it offers lessons about the contexts in which individual interventions might have bounded effectiveness (i.e. educational interventions for individual change). Taken as a whole, this may also then help in terms of how and when to conjoin single interventions into effective MFIs.

In the two previous reviews, a consistent finding was that MFIs were more effective than single interventions [ 8 , 9 ]. However, like Squires et al. [ 10 ] this overview is more equivocal on this important issue. There are four points which may help account for the differences in findings in this regard. Firstly, the diversity of the systematic reviews in terms of clinical topic or setting is an important factor. Secondly, there is heterogeneity of the studies within the included systematic reviews themselves. Thirdly, there is a lack of consistency with regards to the definition and strategies included within of MFIs. Finally, there are epistemological differences across the papers and the reviews. This means that the results that are presented depend on the methods used to measure, report, and synthesise them. For instance, some reviews highlight that education strategies can be useful to improve provider understanding — but without wider organisational or system-level change, they may struggle to deliver sustained transformation [ 19 , 44 ].

It is also worth highlighting the importance of the theory of change underlying the different interventions. Where authors of the systematic reviews draw on theory, there is space to discuss/explain findings. We note a distinction between theoretical and atheoretical systematic review discussion sections. Atheoretical reviews tend to present acontextual findings (for instance, one study found very positive results for one intervention, and this gets highlighted in the abstract) whilst theoretically informed reviews attempt to contextualise and explain patterns within the included studies. Theory-informed systematic reviews seem more likely to offer more profound and useful insights (see [ 19 , 35 , 40 , 43 , 45 ]). We find that the most insightful systematic reviews of MFIs engage in theoretical generalisation — they attempt to go beyond the data of individual studies and discuss the wider implications of the findings of the studies within their reviews drawing on implementation theory. At the same time, they highlight the active role of context and the wider relational and system-wide issues linked to implementation. It is these types of investigations that can help providers further develop evidence-based practice.

This overview has identified a small, but insightful set of papers that interrogate and help theorise why, how, for whom, and in which circumstances it might be the case that MFIs are superior (see [ 19 , 35 , 40 ] once more). At the level of this overview — and in most of the systematic reviews included — it appears to be the case that MFIs struggle with the question of attribution. In addition, there are other important elements that are often unmeasured, or unreported (e.g. costs of the intervention — see [ 40 ]). Finally, the stronger systematic reviews [ 19 , 35 , 40 , 43 , 45 ] engage with systems issues, human agency and context [ 18 ] in a way that was not evident in the systematic reviews identified in the previous reviews [ 8 , 9 ]. The earlier reviews lacked any theory of change that might explain why MFIs might be more effective than single ones — whereas now some systematic reviews do this, which enables them to conclude that sometimes single interventions can still be more effective.

As Nilsen et al. ([ 6 ] p. 7) note ‘Study findings concerning the effectiveness of various approaches are continuously synthesized and assembled in systematic reviews’. We may have gone as far as we can in understanding the implementation of evidence through systematic reviews of single and multi-faceted interventions and the next step would be to conduct more research exploring the complex and situated nature of evidence used in clinical practice and by particular professional groups. This would further build on the nuanced discussion and conclusion sections in a subset of the papers we reviewed. This might also support the field to move away from isolating individual implementation strategies [ 6 ] to explore the complex processes involving a range of actors with differing capacities [ 51 ] working in diverse organisational cultures. Taxonomies of implementation strategies do not fully account for the complex process of implementation, which involves a range of different actors with different capacities and skills across multiple system levels. There is plenty of work to build on, particularly in the social sciences, which currently sits at the margins of debates about evidence implementation (see for example, Normalisation Process Theory [ 52 ]).

There are several changes that we have identified in this overview of systematic reviews in comparison to the review we published in 2011 [ 8 ]. A consistent and welcome finding is that the overall quality of the systematic reviews themselves appears to have improved between the two reviews, although this is not reflected upon in the papers. This is exhibited through better, clearer reporting mechanisms in relation to the mechanics of the reviews, alongside a greater attention to, and deeper description of, how potential biases in included papers are discussed. Additionally, there is an increased, but still limited, inclusion of original studies conducted in low- and middle-income countries as opposed to just high-income countries. Importantly, we found that many of these systematic reviews are attuned to, and comment upon the contextual distinctions of pursuing evidence-informed interventions in health care settings in different economic settings. Furthermore, systematic reviews included in this updated article cover a wider set of clinical specialities (both within and beyond hospital settings) and have a focus on a wider set of healthcare professions — discussing both similarities, differences and inter-professional challenges faced therein, compared to the earlier reviews. These wider ranges of studies highlight that a particular intervention or group of interventions may work well for one professional group but be ineffective for another. This diversity of study settings allows us to consider the important role context (in its many forms) plays on implementing evidence into practice. Examining the complex and varied context of health care will help us address what Nilsen et al. ([ 6 ] p. 1) described as, ‘society’s health problems [that] require research-based knowledge acted on by healthcare practitioners together with implementation of political measures from governmental agencies’. This will help us shift implementation science to move, ‘beyond a success or failure perspective towards improved analysis of variables that could explain the impact of the implementation process’ ([ 6 ] p. 2).

This review brings together 32 papers considering individual and multi-faceted interventions designed to support the use of evidence in clinical practice. The majority of reviews report strategies achieving small impacts (normally on processes of care). There is much less evidence that these strategies have shifted patient outcomes. Combined with the two previous reviews, 86 systematic reviews of strategies to increase the implementation of research into clinical practice have been conducted. As a whole, this substantial body of knowledge struggles to tell us more about the use of individual and MFIs than: ‘it depends’. To really move forwards in addressing the gap between research evidence and practice, we may need to shift the emphasis away from isolating individual and multi-faceted interventions to better understanding and building more situated, relational and organisational capability to support the use of research in clinical practice. This will involve drawing on a wider range of perspectives, especially from the social, economic, political and behavioural sciences in primary studies and diversifying the types of synthesis undertaken to include approaches such as realist synthesis which facilitate exploration of the context in which strategies are employed. Harvey et al. [ 53 ] suggest that when context is likely to be critical to implementation success there are a range of primary research approaches (participatory research, realist evaluation, developmental evaluation, ethnography, quality/ rapid cycle improvement) that are likely to be appropriate and insightful. While these approaches often form part of implementation studies in the form of process evaluations, they are usually relatively small scale in relation to implementation research as a whole. As a result, the findings often do not make it into the subsequent systematic reviews. This review provides further evidence that we need to bring qualitative approaches in from the periphery to play a central role in many implementation studies and subsequent evidence syntheses. It would be helpful for systematic reviews, at the very least, to include more detail about the interventions and their implementation in terms of how and why they worked.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Before and after study

Controlled clinical trial

Effective Practice and Organisation of Care

High-income countries

Information and Communications Technology

Interrupted time series

Knowledge translation

Low- and middle-income countries

Randomised controlled trial

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Johnson MJ, May CR. Promoting professional behaviour change in healthcare: what interventions work, and why? A theory-led overview of systematic reviews. BMJ Open. 2015;5:e008592. https://doi.org/10.1136/bmjopen-2015-008592 .

Metz A, Jensen T, Farley A, Boaz A, et al. Is implementation research out of step with implementation practice? Pathways to effective implementation support over the last decade. Implement Res Pract. 2022;3:1–11. https://doi.org/10.1177/26334895221105585 .

May CR, Finch TL, Cornford J, Exley C, et al. Integrating telecare for chronic disease management in the community: What needs to be done? BMC Health Serv Res. 2011;11:1–11. https://doi.org/10.1186/1472-6963-11-131 .

Harvey G, Rycroft-Malone J, Seers K, Wilson P, et al. Connecting the science and practice of implementation – applying the lens of context to inform study design in implementation research. Front Health Serv. 2023;3:1–15. https://doi.org/10.3389/frhs.2023.1162762 .

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Acknowledgements

The authors would like to thank Professor Kathryn Oliver for her support in the planning the review, Professor Steve Hanney for reading and commenting on the final manuscript and the staff at LSHTM library for their support in planning and conducting the literature search.

This study was supported by LSHTM’s Research England QR strategic priorities funding allocation and the National Institute for Health and Care Research (NIHR) Applied Research Collaboration South London (NIHR ARC South London) at King’s College Hospital NHS Foundation Trust. Grant number NIHR200152. The views expressed are those of the author(s) and not necessarily those of the NIHR, the Department of Health and Social Care or Research England.

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AB led the conceptual development and structure of the manuscript. EP conducted the searches and data extraction. All authors contributed to screening and quality appraisal. EP and AF wrote the first draft of the methods section. AB, JB and AF performed result synthesis and contributed to the analyses. AB wrote the first draft of the manuscript and incorporated feedback and revisions from all other authors. All authors revised and approved the final manuscript.

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Boaz, A., Baeza, J., Fraser, A. et al. ‘It depends’: what 86 systematic reviews tell us about what strategies to use to support the use of research in clinical practice. Implementation Sci 19 , 15 (2024). https://doi.org/10.1186/s13012-024-01337-z

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Systematic Reviews and Meta-Analysis: A Guide for Beginners

Joseph l. mathew.

Department of Pediatrics, Advanced Pediatrics Centre, PGIMER, Chandigarh, India

Systematic reviews involve the application of scientific methods to reduce bias in review of literature. The key components of a systematic review are a well-defined research question, comprehensive literature search to identify all studies that potentially address the question, systematic assembly of the studies that answer the question, critical appraisal of the methodological quality of the included studies, data extraction and analysis (with and without statistics), and considerations towards applicability of the evidence generated in a systematic review. These key features can be remembered as six ‘A’; Ask, Access, Assimilate, Appraise, Analyze and Apply. Meta-analysis is a statistical tool that provides pooled estimates of effect from the data extracted from individual studies in the systematic review. The graphical output of meta-analysis is a forest plot which provides information on individual studies and the pooled effect. Systematic reviews of literature can be undertaken for all types of questions, and all types of study designs. This article highlights the key features of systematic reviews, and is designed to help readers understand and interpret them. It can also help to serve as a beginner’s guide for both users and producers of systematic reviews and to appreciate some of the methodological issues.

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Effect of exercise for depression: systematic review and network meta-analysis of randomised controlled trials

Linked editorial.

Exercise for the treatment of depression

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  • Peer review
  • Michael Noetel , senior lecturer 1 ,
  • Taren Sanders , senior research fellow 2 ,
  • Daniel Gallardo-Gómez , doctoral student 3 ,
  • Paul Taylor , deputy head of school 4 ,
  • Borja del Pozo Cruz , associate professor 5 6 ,
  • Daniel van den Hoek , senior lecturer 7 ,
  • Jordan J Smith , senior lecturer 8 ,
  • John Mahoney , senior lecturer 9 ,
  • Jemima Spathis , senior lecturer 9 ,
  • Mark Moresi , lecturer 4 ,
  • Rebecca Pagano , senior lecturer 10 ,
  • Lisa Pagano , postdoctoral fellow 11 ,
  • Roberta Vasconcellos , doctoral student 2 ,
  • Hugh Arnott , masters student 2 ,
  • Benjamin Varley , doctoral student 12 ,
  • Philip Parker , pro vice chancellor research 13 ,
  • Stuart Biddle , professor 14 15 ,
  • Chris Lonsdale , deputy provost 13
  • 1 School of Psychology, University of Queensland, St Lucia, QLD 4072, Australia
  • 2 Institute for Positive Psychology and Education, Australian Catholic University, North Sydney, NSW, Australia
  • 3 Department of Physical Education and Sport, University of Seville, Seville, Spain
  • 4 School of Health and Behavioural Sciences, Australian Catholic University, Strathfield, NSW, Australia
  • 5 Department of Clinical Biomechanics and Sports Science, University of Southern Denmark, Odense, Denmark
  • 6 Biomedical Research and Innovation Institute of Cádiz (INiBICA) Research Unit, University of Cádiz, Spain
  • 7 School of Health and Behavioural Sciences, University of the Sunshine Coast, Petrie, QLD, Australia
  • 8 School of Education, University of Newcastle, Callaghan, NSW, Australia
  • 9 School of Health and Behavioural Sciences, Australian Catholic University, Banyo, QLD, Australia
  • 10 School of Education, Australian Catholic University, Strathfield, NSW, Australia
  • 11 Australian Institute of Health Innovation, Macquarie University, Macquarie Park, NSW, Australia
  • 12 Children’s Hospital Westmead Clinical School, University of Sydney, Westmead, NSW, Australia
  • 13 Australian Catholic University, North Sydney, NSW, Australia
  • 14 Centre for Health Research, University of Southern Queensland, Springfield, QLD, Australia
  • 15 Faculty of Sport and Health Science, University of Jyvaskyla, Jyvaskyla, Finland
  • Correspondence to: M Noetel m.noetel{at}uq.edu.au (or @mnoetel on Twitter)
  • Accepted 15 January 2024

Objective To identify the optimal dose and modality of exercise for treating major depressive disorder, compared with psychotherapy, antidepressants, and control conditions.

Design Systematic review and network meta-analysis.

Methods Screening, data extraction, coding, and risk of bias assessment were performed independently and in duplicate. Bayesian arm based, multilevel network meta-analyses were performed for the primary analyses. Quality of the evidence for each arm was graded using the confidence in network meta-analysis (CINeMA) online tool.

Data sources Cochrane Library, Medline, Embase, SPORTDiscus, and PsycINFO databases.

Eligibility criteria for selecting studies Any randomised trial with exercise arms for participants meeting clinical cut-offs for major depression.

Results 218 unique studies with a total of 495 arms and 14 170 participants were included. Compared with active controls (eg, usual care, placebo tablet), moderate reductions in depression were found for walking or jogging (n=1210, κ=51, Hedges’ g −0.62, 95% credible interval −0.80 to −0.45), yoga (n=1047, κ=33, g −0.55, −0.73 to −0.36), strength training (n=643, κ=22, g −0.49, −0.69 to −0.29), mixed aerobic exercises (n=1286, κ=51, g −0.43, −0.61 to −0.24), and tai chi or qigong (n=343, κ=12, g −0.42, −0.65 to −0.21). The effects of exercise were proportional to the intensity prescribed. Strength training and yoga appeared to be the most acceptable modalities. Results appeared robust to publication bias, but only one study met the Cochrane criteria for low risk of bias. As a result, confidence in accordance with CINeMA was low for walking or jogging and very low for other treatments.

Conclusions Exercise is an effective treatment for depression, with walking or jogging, yoga, and strength training more effective than other exercises, particularly when intense. Yoga and strength training were well tolerated compared with other treatments. Exercise appeared equally effective for people with and without comorbidities and with different baseline levels of depression. To mitigate expectancy effects, future studies could aim to blind participants and staff. These forms of exercise could be considered alongside psychotherapy and antidepressants as core treatments for depression.

Systematic review registration PROSPERO CRD42018118040.

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Introduction

Major depressive disorder is a leading cause of disability worldwide 1 and has been found to lower life satisfaction more than debt, divorce, and diabetes 2 and to exacerbate comorbidities, including heart disease, 3 anxiety, 4 and cancer. 5 Although people with major depressive disorder often respond well to drug treatments and psychotherapy, 6 7 many are resistant to treatment. 8 In addition, access to treatment for many people with depression is limited, with only 51% treatment coverage for high income countries and 20% for low and lower-middle income countries. 9 More evidence based treatments are therefore needed.

Exercise may be an effective complement or alternative to drugs and psychotherapy. 10 11 12 13 14 In addition to mental health benefits, exercise also improves a range of physical and cognitive outcomes. 15 16 17 Clinical practice guidelines in the US, UK, and Australia recommend physical activity as part of treatment for depression. 18 19 20 21 But these guidelines do not provide clear, consistent recommendations about dose or exercise modality. British guidelines recommend group exercise programmes 20 21 and offer general recommendations to increase any form of physical activity, 21 the American Psychiatric Association recommends any dose of aerobic exercise or resistance training, 20 and Australian and New Zealand guidelines suggest a combination of strength and vigorous aerobic exercises, with at least two or three bouts weekly. 19

Authors of guidelines may find it hard to provide consistent recommendations on the basis of existing mainly pairwise meta-analyses—that is, assessing a specific modality versus a specific comparator in a distinct group of participants. 12 13 22 These meta-analyses have come under scrutiny for pooling heterogeneous treatments and heterogenous comparisons leading to ambiguous effect estimates. 23 Reviews also face the opposite problem, excluding exercise treatments such as yoga, tai chi, and qigong because grouping them with strength training might be inappropriate. 23 Overviews of reviews have tried to deal with this problem by combining pairwise meta-analyses on individual treatments. A recent such overview found no differences between exercise modalities. 13 Comparing effect sizes between different pairwise meta-analyses can also lead to confusion because of differences in analytical methods used between meta-analysis, such as choice of a control to use as the referent. Network meta-analyses are a better way to precisely quantify differences between interventions as they simultaneously model the direct and indirect comparisons between interventions. 24

Network meta-analyses have been used to compare different types of psychotherapy and pharmacotherapy for depression. 6 25 26 For exercise, they have shown that dose and modality influence outcomes for cognition, 16 back pain, 15 and blood pressure. 17 Two network meta-analyses explored the effects of exercise on depression: one among older adults 27 and the other for mental health conditions. 28 Because of the inclusion criteria and search strategies used, these reviews might have been under-powered to explore moderators such as dose and modality (κ=15 and κ=71, respectively). To resolve conflicting findings in existing reviews, we comprehensively searched randomised trials on exercise for depression to ensure our review was adequately powered to identify the optimal dose and modality of exercise. For example, a large overview of reviews found effects on depression to be proportional to intensity, with vigorous exercise appearing to be better, 13 but a later meta-analysis found no such effects. 22 We explored whether recommendations differ based on participants’ sex, age, and baseline level of depression.

Given the challenges presented by behaviour change in people with depression, 29 we also identified autonomy support or behaviour change techniques that might improve the effects of intervention. 30 Behaviour change techniques such as self-monitoring and action planning have been shown to influence the effects of physical activity interventions in adults (>18 years) 31 and older adults (>60 years) 32 with differing effectiveness of techniques in different populations. We therefore tested whether any intervention components from the behaviour change technique taxonomy were associated with higher or lower intervention effects. 30 Other meta-analyses found that physical activity interventions work better when they provide people with autonomy (eg, choices, invitational language). 33 Autonomy is not well captured in the taxonomy for behaviour change technique. We therefore tested whether effects were stronger in studies that provided more autonomy support to patients. Finally, to understand the mechanism of intervention effects, such as self-confidence, affect, and physical fitness, we collated all studies that conducted formal mediation analyses.

Our findings are presented according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Network Meta-analyses (PRISMA-NMA) guidelines (see supplementary file, section S0; all supplementary files, data, and code are also available at https://osf.io/nzw6u/ ). 34 We amended our analysis strategy after registering our review; these changes were to better align with new norms established by the Cochrane Comparing Multiple Interventions Methods Group. 35 These norms were introduced between the publication of our protocol and the preparation of this manuscript. The largest change was using the confidence in network meta-analysis (CINeMA) 35 online tool instead of the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) guidelines and adopting methods to facilitate assessments—for example, instead of using an omnibus test for all treatments, we assessed publication bias for each treatment compared with active controls. We also modelled acceptability (through dropout rate), which was not predefined but was adopted in response to a reviewer’s comment.

Eligibility criteria

To be eligible for inclusion, studies had to be randomised controlled trials that included exercise as a treatment for depression and included participants who met the criteria for major depressive disorder, either clinician diagnosed or identified through participant self-report as exceeding established clinical thresholds (eg, scored >13 on the Beck depression inventory-II). 36 Studies could meet these criteria when all the participants had depression or when the study reported depression outcomes for a subgroup of participants with depression at the start of the study.

We defined exercise as “planned, structured and repetitive bodily movement done to improve or maintain one or more components of physical fitness.” 37 Unlike recent reviews, 12 22 we included studies with more than one exercise arm and multifaceted interventions (eg, health and exercise counselling) as long as they contained a substantial exercise component. These trials could be included because network meta-analysis methods allows for the grouping of those interventions into homogenous nodes. Unlike the most recent Cochrane review, 12 we also included participants with physical comorbidities such as arthritis and participants with postpartum depression because the Diagnostic Statistical Manual of Mental Health Disorders , fifth edition, removed the postpartum onset specifier after that analysis was completed. 23 Studies were excluded if interventions were shorter than one week, depression was not reported as an outcome, and data were insufficient to calculate an effect size for each arm. Any comparison condition was included, allowing us to quantify the effects against established treatments (eg, selective serotonin reuptake inhibitors (SSRIs), cognitive behavioural therapy), active control conditions (usual care, placebo tablet, stretching, educational control, and social support), or waitlist control conditions. Published and unpublished studies were included, with no restrictions on language applied.

Information sources

We adapted the search strategy from the most recent Cochrane review, 12 adding keywords for yoga, tai chi, and qigong, as they met our definition for exercise. We conducted database searches, without filters or date limits, in The Cochrane Library via CENTRAL, SPORTDiscus via Embase, and Medline, Embase, and PsycINFO via Ovid. Searches of the databases were conducted on 17 December 2018 and 7 August 2020 and last updated on 3 June 2023 (see supplementary file section S1 for full search strategies). We assessed full texts of all included studies from two systematic reviews of exercise for depression. 12 22

Study selection and data collection

To select studies, we removed duplicate records in Covidence 38 and then screened each title and abstract independently and in duplicate. Conflicts were resolved through discussion or consultation with a third reviewer. The same methods were used for full text screening.

We used the Extraction 1.0 randomised controlled trial data extraction forms in Covidence. 38 Data were extracted independently and in duplicate, with conflicts resolved through discussion with a third reviewer.

For each study, we extracted a description of the interventions, including frequency, intensity, and type and time of each exercise intervention. Using the Compendium of Physical Activities, 39 we calculated the energy expenditure dose of exercise for each arm as metabolic equivalents of task (METs) min/week. Two authors evaluated each exercise intervention using the Behaviour Change Taxonomy version 1 30 for behaviour change techniques explicitly described in each exercise arm. They also rated the level of autonomy offered to participants, on a scale from 1 (no choice) to 10 (full autonomy). We also extracted descriptions of the other arms within the randomised trials, including other treatment or control conditions; participants’ age, sex, comorbidities, and baseline severity of depressive symptoms; and each trial’s location and whether or not the trial was funded.

Risk of bias in individual studies

We used Cochrane’s risk of bias tool for randomised controlled trials. 40 Risk of bias was rated independently and in duplicate, with conflicts resolved through discussion with a third reviewer.

Summary measures and synthesis

For main and moderation analyses, we used bayesian arm based multilevel network meta-analysis models. 41 All network meta-analytical approaches allow users to assess the effects of treatments against a range of comparisons. The bayesian arm based models allowed us to also assess the influence of hypothesised moderators, such as intensity, dose, age, and sex. Many network meta-analyses use contrast based methods, comparing post-test scores between study arms. 41 Arm based meta-analyses instead describe the population-averaged absolute effect size for each treatment arm (ie, each arm’s change score). 41 As a result, the summary measure we used was the standardised mean change from baseline, calculated as standardised mean differences with correction for small studies (Hedges’ g). In keeping with the norms from the included studies, effect sizes describe treatment effects on depression, such that larger negative numbers represent stronger effects on symptoms. Using National Institute for Health and Care Excellence guidelines, 42 we standardised change scores for different depression scales (eg, Beck depression inventory, Hamilton depression rating scale) using an internal reference standard for each scale (for each scale, the average of pooled standard deviations at baseline) reported in our meta-analysis. Because depression scores generally show regression to the mean, even in control conditions, we present effect sizes as improvements beyond active control conditions. This convention makes our results comparable to existing, contrast based meta-analyses.

Active control conditions (usual care, placebo tablet, stretching, educational control, and social support) were grouped to increase power for moderation analyses, for parsimony in the network graph, and because they all showed similar arm based pooled effect sizes (Hedges’ g between −0.93 and −1.00 for all, with no statistically significant differences). We separated waitlist control from these active control conditions because it typically shows poorer effects in treatment for depression. 43

Bayesian meta-analyses were conducted in R 44 using the brms package. 45 We preregistered informative priors based on the distributional parameters of our meta-analytical model. 46 We nested effects within arms to manage dependency between multiple effect sizes from the same participants. 46 For example, if one study reported two self-reported measures of depression, or reported both self-report and clinician rated depression, we nested these effect sizes within the arm to account for both pieces of information while controlling for dependency between effects. 46 Finally, we compared absolute effect sizes against a standardised minimum clinically important difference, 0.5 standard deviations of the change score. 47 From our data, this corresponded to a large change in before and after scores (Hedges’ g −1.16), a moderate change compared with waitlist control (g −0.55), or a small benefit when compared with active controls (g −0.20). For credibility assessments comparing exercise modalities, we used the netmeta package 48 and CINeMA. 49 We also used netmeta to model acceptability, comparing the odds ratio for drop-out rate in each arm.

Additional analyses

All prespecified moderation and sensitivity analyses were performed. We moderated for participant characteristics, including participants’ sex, age, baseline symptom severity, and presence or absence of comorbidities; duration of the intervention (weeks); weekly dose of the intervention; duration between completion of treatment and measurement, to test robustness to remission (in response to a reviewer’s suggestion); amount of autonomy provided in the exercise prescription; and presence of each behaviour change technique. As preregistered, we moderated for behaviour change techniques in three ways: through meta-regression, including all behaviour change techniques simultaneously for primary analysis; including one behaviour change technique at a time (using 99% credible intervals to somewhat control for multiple comparisons) in exploratory analyses; and through meta-analytical classification and regression trees (metaCART), which allowed for interactions between moderating variables (eg, if goal setting combined with feedback had synergistic effects). 50 We conducted sensitivity analyses for risk of bias, assessing whether studies with low versus unclear or high risk of bias on each domain showed statistically significant differences in effect sizes.

Credibility assessment

To assess the credibility of each comparison against active control, we used CINeMA. 35 49 This online tool was designed by the Cochrane Comparing Multiple Interventions Methods Group as an adaptation of GRADE for network meta-analyses. 35 In line with recommended guidelines, for each comparison we made judgements for within study bias, reporting bias, indirectness, imprecision, heterogeneity, and incoherence. Similar to GRADE, we considered the evidence for comparisons to show high confidence then downgraded on the basis of concerns in each domain, as follows:

Within study bias —Comparisons were downgraded when most of the studies providing direct evidence for comparisons were unclear or high risk.

Reporting bias —Publication bias was assessed in three ways. For each comparison with at least 10 studies 51 we created funnel plots, including estimates of effect sizes after removing studies with statistically significant findings (ie, worst case estimates) 52 ; calculated an s value, representing how strong publication bias would need to be to nullify meta-analytical effects 52 ; and conducted a multilevel Egger’s regression test, indicative of small study bias. Given these tests are not recommended for comparisons with fewer than 10 studies, 51 those comparisons were considered to show “some concerns.”

Indirectness — Our primary population of interest was adults with major depression. Studies were considered to be indirect if they focused on one sex only (>90% male or female), participants with comorbidities (eg, heart disease), adolescents and young adults (14-20 years), or older adults (>60 years). We flagged these studies as showing some concerns if one of these factors was present, and as “major concerns” if two of these factors were present. Evidence from comparisons was classified as some concerns or major concerns using majority rating for studies directly informing the comparison.

Imprecision — As per CINeMA, we used the clinically important difference of Hedges’ g=0.2 to ascribe a zone of equivalence, where differences were not considered clinically significant (−0.2<g<0.2). Studies were flagged as some concerns for imprecision if the bounds of the 95% credible interval extended across that zone, and they were flagged as major concerns if the bounds extended to the other side of the zone of equivalence (such that effects could be harmful).

Heterogeneity — Prediction intervals account for heterogeneity differently from credible intervals. 35 As a result, CINeMA accounts for heterogeneity by assessing whether the prediction intervals and the credible intervals lead to different conclusions about clinical significance (using the same zone of equivalence from imprecision). Comparisons are flagged as some concerns if the prediction interval crosses into, or out of, the zone of equivalence once (eg, from helpful to no meaningful effect), and as major concerns if the prediction interval crosses the zone twice (eg, from helpful and harmful).

Incoherence — Incoherence assesses whether the network meta-analysis provides similar estimates when using direct evidence (eg, randomised controlled trials on strength training versus SSRI) compared with indirect evidence (eg, randomised controlled trials where either strength training or SSRI uses waitlist control). Incoherence provides some evidence the network may violate the assumption of transitivity: that the only systematic difference between arms is the treatment, not other confounders. We assessed incoherence using two methods: Firstly, a global design-by-treatment interaction to assess for incoherence across the whole network, 35 49 and, secondly, separating indirect and direct evidence (SIDE method) for each comparison through netsplitting to see whether differences between those effect estimates were statistically significant. We flagged comparisons as some concerns if either no direct comparisons were available or direct and indirect evidence gave different conclusions about clinical significance (eg, from helpful to no meaningful effect, as per imprecision and heterogeneity). Again, we classified comparisons as major concerns if the direct and indirect evidence changed the sign of the effect or changed both limits of the credible interval. 35 49

Patient and public involvement

We discussed the aims and design of this study with members of the public, including those who had experienced depression. Several of our authors have experienced major depressive episodes, but beyond that we did not include patients in the conduct of this review.

Study selection

The PRISMA flow diagram outlines the study selection process ( fig 1 ). We used two previous reviews to identify potentially eligible studies for inclusion. 12 22 Database searches identified 18 658 possible studies. After 5505 duplicates had been removed, two reviewers independently screened 13 115 titles and abstracts. After screening, two reviewers independently reviewed 1738 full text articles. Supplementary file section S2 shows the consensus reasons for exclusion. A total of 218 unique studies described in 246 reports were included, totalling 495 arms and 14 170 participants. Supplementary file section S3 lists the references and characteristics of the included studies.

Fig 1

Flow of studies through review

Network geometry

As preregistered, we removed nodes with fewer than 100 participants. Using this filter, most interventions contained comparisons with at least four other nodes in the network geometry ( fig 2 ). The results of the global test design-by-treatment interaction model were not statistically significant, supporting the assumption of transitivity (χ 2 =94.92, df=75, P=0.06). When net-splitting was used on all possible combinations in the network, for two out of the 120 comparisons we found statistically significant incoherence between direct and indirect evidence (SSRI v waitlist control; cognitive behavioural therapy v tai chi or qigong). Overall, we found little statistical evidence that the model violated the assumption of transitivity. Qualitative differences were, however, found for participant characteristics between different arms (see supplementary file, section S4). For example, some interventions appeared to be prescribed more frequently among people with severe depression (eg, 7/16 studies using SSRIs) compared with other interventions (eg, 1/15 studies using aerobic exercise combined with therapy). Similarly, some interventions appeared more likely to be prescribed for older adults (eg, mean age, tai chi=59 v dance=31) or women (eg, per cent female: dance=88% v cycling=53%). Given that plausible mechanisms exist for these systematic differences (eg, the popularity of tai chi among older adults), 53 there are reasons to believe that allocation to treatment arms would be less than perfectly random. We have factored these biases in our certainty estimates through indirectness ratings.

Fig 2

Network geometry indicating number of participants in each arm (size of points) and number of comparisons between arms (thickness of lines). SSRI=selective serotonin reuptake inhibitor

Risk of bias within studies

Supplementary file section S5 provides the risk of bias ratings for each study. Few studies explicitly blinded participants and staff ( fig 3 ). As a result, overall risk of bias for most studies was unclear or high, and effect sizes could include expectancy effects, among other biases. However, sensitivity analyses suggested that effect sizes were not influenced by any risk of bias criteria owing to wide credible intervals (see supplementary file, section S6). Nevertheless, certainty ratings for all treatments arms were downgraded owing to high risk of bias in the studies informing the comparison.

Fig 3

Risk of bias summary plot showing percentage of included studies judged to be low, unclear, or high risk across Cochrane criteria for randomised trials

Synthesis of results

Supplementary file section S7 presents a forest plot of Hedges’ g values for each study. Figure 4 shows the predicted effects of each treatment compared with active controls. Compared with active controls, large reductions in depression were found for dance (n=107, κ=5, Hedges’ g −0.96, 95% credible interval −1.36 to −0.56) and moderate reductions for walking or jogging (n=1210, κ=51, g −0.63, −0.80 to −0.46), yoga (n=1047, κ=33, g=−0.55, −0.73 to −0.36), strength training (n=643, κ=22, g=−0.49, −0.69 to −0.29), mixed aerobic exercises (n=1286, κ=51, g=−0.43, −0.61 to −0.25), and tai chi or qigong (n=343, κ=12, g=−0.42, −0.65 to −0.21). Moderate, clinically meaningful effects were also present when exercise was combined with SSRIs (n=268, κ=11, g=−0.55, −0.86 to −0.23) or aerobic exercise was combined with psychotherapy (n=404, κ=15, g=−0.54, −0.76 to −0.32). All these treatments were significantly stronger than the standardised minimum clinically important difference compared with active control (g=−0.20), equating to an absolute g value of −1.16. Dance, exercise combined with SSRIs, and walking or jogging were the treatments most likely to perform best when modelling the surface under the cumulative ranking curve ( fig 4 ). For acceptability, the odds of participants dropping out of the study were lower for strength training (n=247, direct evidence κ=6, odds ratio 0.55, 95% credible interval 0.31 to 0.99) and yoga (n=264, κ=5, 0.57, 0.35 to 0.94) than for active control. The rate of dropouts was not significantly different from active control in any other arms (see supplementary file, section S8).

Fig 4

Predicted effects of different exercise modalities on major depression compared with active controls (eg, usual care), with 95% credible intervals. The estimate of effects for the active control condition was a before and after change of Hedges’ g of −0.95 (95% credible interval −1.10 to −0.79), n=3554, κ =113. Colour represents SUCRA from most likely to be helpful (dark purple) to least likely to be helpful (light purple). SSRI=selective serotonin reuptake inhibitor; SUCRA=surface under the cumulative ranking curve

Consistent with other meta-analyses, effects were moderate for cognitive behaviour therapy alone (n=712, κ=20, g=−0.55, −0.75 to −0.37) and small for SSRIs (n=432, κ=16, g=−0.26, −0.50 to −0.01) compared with active controls ( fig 4 ). These estimates are comparable to those of reviews that focused directly on psychotherapy (g=−0.67, −0.79 to −0.56) 7 or pharmacotherapy (g=−0.30, –0.34 to −0.26). 25 However, our review was not designed to find all studies of these treatments, so these estimates should not usurp these directly focused systematic reviews.

Despite the large number of studies in the network, confidence in the effects were low ( fig 5 ). This was largely due to the high within study bias described in the risk of bias summary plot. Reporting bias was also difficult to robustly assess because direct comparison with active control was often only provided in fewer than 10 studies. Many studies focused on one sex only, older adults, or those with comorbidities, so most arms had some concerns about indirect comparisons. Credible intervals were seldom wide enough to change decision making, so concerns about imprecision were few. Heterogeneity did plausibly change some conclusions around clinical significance. Few studies showed problematic incoherence, meaning direct and indirect evidence usually agreed. Overall, walking or jogging had low confidence, with other modalities being very low.

Fig 5

Summary table for credibility assessment using confidence in network meta-analysis (CINeMA). SSRI=selective serotonin reuptake inhibitor

Moderation by participant characteristics

The optimal modality appeared to be moderated by age and sex. Compared with models that only included exercise modality (R 2 =0.65), R 2 was higher for models that included interactions with sex (R 2 =0.71) and age (R 2 =0.69). R 2 showed no substantial increase for models including baseline depression (R 2 =0.67) or comorbidities (R 2 =0.66; see supplementary file, section S9).

Effects appeared larger for women than men for strength training and cycling ( fig 6 ). Effects appeared to be larger for men than women when prescribing yoga, tai chi, and aerobic exercise alongside psychotherapy. Yoga and aerobic exercise alongside psychotherapy appeared more effective for older participants than younger people ( fig 7 ). Strength training appeared more effective when prescribed to younger participants than older participants. Some estimates were associated with substantial uncertainty because some modalities were not well studied in some groups (eg, tai chi for younger adults), and mean age of the sample was only available for 71% of the studies.

Fig 6

Effects of interventions versus active control on depression (lower is better) by sex. Shading represents 95% credible intervals

Fig 7

Effects of interventions versus active control on depression (lower is better) by age. Shading represents 95% credible intervals

Moderation by intervention and design characteristics

Across modalities, a clear dose-response curve was observed for intensity of exercise prescribed ( fig 8 ). Although light physical activity (eg, walking, hatha yoga) still provided clinically meaningful effects (g=−0.58, −0.82 to −0.33), expected effects were stronger for vigorous exercise (eg, running, interval training; g=−0.74, −1.10 to −0.38). This finding did not appear to be due to increased weekly energy expenditure: credible intervals were wide, which meant that the dose-response curve for METs/min prescribed per week was unclear (see supplementary file, section S10). Weak evidence suggested that shorter interventions (eg, 10 weeks: g=−0.53, −0.71 to −0.35) worked somewhat better than longer ones (eg, 30 weeks: g=−0.37, −0.79 to 0.03), with wide credible intervals again indicating high uncertainty (see supplementary file, section S11). We also moderated for the lag between the end of treatment and the measurement of the outcome. We found no indication that participants were likely to relapse within the measurement period (see supplementary file, section S12); effects remained steady when measured either directly after the intervention (g=−0.59, −0.80 to −0.39) or up to six months later (g=−0.63, −0.87 to −0.40).

Fig 8

Dose-response curve for intensity (METs) across exercise modalities compared with active control. METs=metabolic equivalents of task

Supplementary file section S13 provides coding for the behaviour change techniques and autonomy for each exercise arm. None of the behaviour change techniques significantly moderated overall effects. Contrary to expectations, studies describing a level of participant autonomy (ie, choice over frequency, intensity, type, or time) tended to show weaker effects (g=−0.28, −0.78 to 0.23) than those that did not (g=−0.75, −1.17 to −0.33; see supplementary file, section S14). This effect was consistent whether or not we included studies that used physical activity counselling (usually high autonomy).

Use of group exercise appeared to moderate the effects: although the overall effects were similar for individual (g=−1.10, −1.57 to −0.64) and group exercise (g=−1.16, −1.61 to −0.73), some interventions were better delivered in groups (yoga) and some were better delivered individually (strength training, mixed aerobic exercise; see supplementary file, section S15).

As preregistered, we tested whether study funding moderated effects. Models that included whether a study was funded did explain more variance (R 2 =0.70) compared with models that included treatment alone (R 2 =0.65). Funded studies showed stronger effects (g=−1.01, −1.19 to −0.82) than unfunded studies (g=−0.77, −1.09 to −0.46). We also moderated for the type of measure (self-report v clinician report). This did not explain a substantial amount of variance in the outcome (R 2 =0.66).

Sensitivity analyses

Evidence of publication bias was found for overall estimates of exercise on depression compared with active controls, although not enough to nullify effects. The multilevel Egger’s test showed significance (F 1,98 =23.93, P<0.001). Funnel plots showed asymmetry, but the result of pooled effects remained statistically significant when only including non-significant studies (see supplementary file, section S16). No amount of publication bias would be sufficient to shrink effects to zero (s value=not possible). To reduce effects below clinical significance thresholds, studies with statistically significant results would need to be reported 58 times more frequently than studies with non-significant results.

Qualitative synthesis of mediation effects

Only a few of the studies used explicit mediation analyses to test hypothesised mechanisms of action. 54 55 56 57 58 59 One study found that both aerobic exercise and yoga led to decreased depression because participants ruminated less. 54 The study found that the effects of aerobic exercise (but not yoga) were mediated by increased acceptance. 54 “Perceived hassles” and awareness were not statistically significant mediators. 54 Another study found that the effects of yoga were mediated by increased self-compassion, but not rumination, self-criticism, tolerance of uncertainty, body awareness, body trust, mindfulness, and attentional biases. 55 One study found that the effects from an aerobic exercise intervention were not mediated by long term physical activity, but instead were mediated by exercise specific affect regulation (eg, self-control for exercise). 57 Another study found that neither exercise self-efficacy nor depression coping self-efficacy mediated effects of aerobic exercise. 56 Effects of aerobic exercise were not mediated by the N2 amplitude from electroencephalography, hypothesised as a neuro-correlate of cognitive control deficits. 58 Increased physical activity did not appear to mediate the effects of physical activity counselling on depression. 59 It is difficult to infer strong conclusions about mechanisms on the basis of this small number of studies with low power.

Summary of evidence

In this systematic review and meta-analysis of randomised controlled trials, exercise showed moderate effects on depression compared with active controls, either alone or in combination with other established treatments such as cognitive behaviour therapy. In isolation, the most effective exercise modalities were walking or jogging, yoga, strength training, and dancing. Although walking or jogging were effective for both men and women, strength training was more effective for women, and yoga or qigong was more effective for men. Yoga was somewhat more effective among older adults, and strength training was more effective among younger people. The benefits from exercise tended to be proportional to the intensity prescribed, with vigorous activity being better. Benefits were equally effective for different weekly doses, for people with different comorbidities, or for different baseline levels of depression. Although confidence in many of the results was low, treatment guidelines may be overly conservative by conditionally recommending exercise as complementary or alternative treatment for patients in whom psychotherapy or pharmacotherapy is either ineffective or unacceptable. 60 Instead, guidelines for depression ought to include prescriptions for exercise and consider adapting the modality to participants’ characteristics and recommending more vigorous intensity exercises.

Our review did not uncover clear causal mechanisms, but the trends in the data are useful for generating hypotheses. It is unlikely that any single causal mechanism explains all the findings in the review. Instead, we hypothesise that a combination of social interaction, 61 mindfulness or experiential acceptance, 62 increased self-efficacy, 33 immersion in green spaces, 63 neurobiological mechanisms, 64 and acute positive affect 65 combine to generate outcomes. Meta-analyses have found each of these factors to be associated with decreases in depressive symptoms, but no single treatment covers all mechanisms. Some may more directly promote mindfulness (eg, yoga), be more social (eg, group exercise), be conducted in green spaces (eg, walking), provide a more positive affect (eg, “runner’s high”’), or be more conducive to acute adaptations that may increase self-efficacy (eg, strength). 66 Exercise modalities such as running may satisfy many of the mechanisms, but they are unlikely to directly promote the mindful self-awareness provided by yoga and qigong. Both these forms of exercise are often practised in groups with explicit mindfulness but seldom have fast and objective feedback loops that improve self-efficacy. Adequately powered studies testing multiple mediators may help to focus more on understanding why exercise helps depression and less on whether exercise helps. We argue that understanding these mechanisms of action is important for personalising prescriptions and better understanding effective treatments.

Our review included more studies than many existing reviews on exercise for depression. 13 22 27 28 As a result, we were able to combine the strengths of various approaches to exercise and to make more nuanced and precise conclusions. For example, even taking conservative estimates (ie, the least favourable end of the credible interval), practitioners can expect patients to experience clinically significant effects from walking, running, yoga, qigong, strength training, and mixed aerobic exercise. Because we simultaneously assessed more than 200 studies, credible intervals were narrower than those in most existing meta-analyses. 13 We were also able to explore non-linear relationships between outcomes and moderators, such as frequency, intensity, and time. These analyses supported some existing findings—for example, our study and the study by Heissel et al 22 found that shorter interventions had stronger effects, at least for six months; our study and the study by Singh et al 13 both found that effects were stronger with vigorous intensity exercise compared with light and moderate exercise. However, most existing reviews found various treatment modalities to be equally effective. 13 27 In our review, some types of exercise had stronger effect sizes than others. We attribute this to the study level data available in a network meta-analysis compared with an overview of reviews 24 and higher power compared with meta-analyses with smaller numbers of included studies. 22 28 Overviews of reviews have the ability to more easily cover a wider range of participants, interventions, and outcomes, but also risk double counting randomised trials that are included in separate meta-analyses. They often include heterogeneous studies without having as much control over moderation analyses (eg, Singh et al included studies covering both prevention and treatment 13 ). Some of those reviews grouped interventions such as yoga with heterogeneous interventions such as stretching and qigong. 13 This practise of combining different interventions makes it harder to interpret meta-analytical estimates. We used methods that enabled us to separately analyse the effects of these treatment modalities. In so doing, we found that these interventions do have different effects, with yoga being an intervention with strong effects and stretching being better described as an active control condition. Network meta-analyses revealed the same phenomenon with psychotherapy: researchers once concluded there was a dodo bird verdict, whereby “everybody has won, and all must have prizes,” 67 until network meta-analyses showed some interventions were robustly more effective than others. 6 26

Predictors of acceptability and outcomes

We found evidence to suggest good acceptability of yoga and strength training; although the measurement of study drop-out is an imperfect proxy of adherence. Participants may complete the study without doing any exercise or may continue exercising and drop out of the study for other reasons. Nevertheless, these are useful data when considering adherence.

Behaviour change techniques, which are designed to increase adherence, did not meaningfully moderate the effect sizes from exercise. This may be due to several factors. It may be that the modality explains most of the variance between effects, such that behaviour change techniques (eg, presence or absence of feedback) did not provide a meaningful contribution. Many forms of exercise potentially contain therapeutic benefits beyond just energy expenditure. These characteristics of a modality may be more influential than coexisting behaviour change techniques. Alternatively, researchers may have used behaviour change techniques such as feedback or goal setting without explicitly reporting them in the study methods. Given the inherent challenges of behaviour change among people with depression, 29 and the difficulty in forecasting which strategies are likely to be effective, 68 we see the identification of effective techniques as important.

We did find that autonomy, as provided in the methods of included studies, predicted effects, but in the opposite direction to our hypotheses: more autonomy was associated with weaker effects. Physical activity counselling, which usually provides a great deal of patient autonomy, was among the lowest effect sizes in our meta-analysis. Higher autonomy judgements were associated with weaker outcomes regardless of whether physical activity counselling was included in the model. One explanation for these data is that people with depression benefit from the clear direction and accountability of a standardised prescription. When provided with more freedom, the low self-efficacy that is symptomatic of depression may stop patients from setting an appropriate level of challenge (eg, they may be less likely to choose vigorous exercise). Alternatively, participants were likely autonomous when self-selecting into trials with exercise modalities they enjoyed, or those that fit their social circumstances. After choosing something value aligned, autonomy within the trial may not have helpful. Either way, data should be interpreted with caution. Our judgement of the autonomy provided in the methods may not reflect how much autonomy support patients actually felt. The patient’s perceived autonomy is likely determined by a range of factors not described in the methods (eg, the social environment created by those delivering the programme, or their social identity), so other studies that rely on patient reports of the motivational climate are likely to be more reliable. 33 Our findings reiterate the importance of considering these patient reports in future research of exercise for depression.

Our findings suggest that practitioners could advocate for most patients to engage in exercise. Those patients may benefit from guidance on intensity (ie, vigorous) and types of exercise that appear to work well (eg, walking, running, mixed aerobic exercise, strength training, yoga, tai chi, qigong) and be well tolerated (eg, strength training and yoga). If social determinants permit, 66 engaging in group exercise or structured programmes could provide support and guidance to achieve better outcomes. Health services may consider offering these programmes as an alternative or adjuvant treatment for major depression. Specifically, although the confidence in the evidence for exercise is less strong than for cognitive behavioural therapy, the effect sizes seem comparable, so it may be an alternative for patients who prefer not to engage in psychotherapy. Previous reviews on those with mild-moderate depression have found similar effects for exercise or SSRIs, or the two combined. 13 14 In contrast, we found some forms of exercise to have stronger effects than SSRIs alone. Our findings are likely related to the larger power in our review (n=14 170) compared with previous reviews (eg, n=2551), 14 and our ability to better account for heterogeneity in exercise prescriptions. Exercise may therefore be considered a viable alternative to drug treatment. We also found evidence that exercise increases the effects of SSRIs, so offering exercise may act as an adjuvant for those already taking drugs. We agree with consensus statements that professionals should still account for patients’ values, preferences, and constraints, ensuring there is shared decision making around what best suits the patient. 66 Our review provides data to help inform that decision.

Strengths, limitations, and future directions

Based on our findings, dance appears to be a promising treatment for depression, with large effects found compared with other interventions in our review. But the small number of studies, low number of participants, and biases in the study designs prohibits us from recommending dance more strongly. Given most research for the intervention has been in young women (88% female participants, mean age 31 years), it is also important for future research to assess the generalisability of the effects to different populations, using robust experimental designs.

The studies we found may be subject to a range of experimental biases. In particular, researchers seldom blinded participants or staff delivering the intervention to the study’s hypotheses. Blinding for exercise interventions may be harder than for drugs 23 ; however, future studies could attempt to blind participants and staff to the study’s hypotheses to avoid expectancy effects. 69 Some of our ratings are for studies published before the proliferation of reporting checklists, so the ratings might be too critical. 23 For example, before CONSORT, few authors explicitly described how they generated a random sequence. 23 Therefore, our risk of bias judgements may be too conservative. Similarly, we planned to use the Cochrane risk of bias (RoB) 1 tool 40 so we could use the most recent Cochrane review of exercise and depression 12 to calibrate our raters, and because RoB 2 had not yet been published. 70 Although assessments of bias between the two tools are generally comparable, 71 the RoB 1 tool can be more conservative when assessing open label studies with subjective assessments (eg, unblinded studies with self-reported measures for depression). 71 As a result, future reviews should consider using the latest risk of bias tool, which may lead to different assessments of bias in included studies.

Most of the main findings in this review appear robust to risks from publication bias. Specifically, pooled effect sizes decreased when accounting for risk of publication bias, but no degree of publication bias could nullify effects. We did not exclude grey literature, but our search strategy was not designed to systematically search grey literature or trial registries. Doing so can detect additional eligible studies 72 and reveal the numbers of completed studies that remain unpublished. 73 Future reviews should consider more systematic searches for this kind of literature to better quantify and mitigate risk of publication bias.

Similarly, our review was able to integrate evidence that directly compared exercise with other treatment modalities such as SSRIs or psychotherapy, while also informing estimates using indirect evidence (eg, comparing the relative effects of strength training and SSRIs when tested against a waitlist control). Our review did not, however, include all possible sources of indirect evidence. Network meta-analyses exist that directly focus on psychotherapy 7 and pharmacotherapy, 25 and these combined for treating depression. 6 Those reviews include more than 500 studies comparing psychological or drug interventions with controls. Harmonising the findings of those reviews with ours would provide stronger data on indirect effects.

Our review found some interesting moderators by age and sex, but these were at the study level rather than individual level—that is, rather than being able to determine whether women engaging in a strength intervention benefit more than men, we could only conclude that studies with more women showed larger effects than studies with fewer women. These studies may have been tailored towards women, so effects may be subject to confounding, as both sex and intervention may have changed. The same finding applied to age, where studies on older adults were likely adapted specifically to this age group. These between study differences may explain the heterogeneity in the effects of interventions, and confounding means our moderators for age and sex should be interpreted cautiously. Future reviews should consider individual patient meta-analyses to allow for more detailed assessments of participant level moderators.

Finally, for many modalities, the evidence is derived from small trials (eg, the median number of walking or jogging arms was 17). In addition to reducing risks from bias, primary research may benefit from deconstruction designs or from larger, head-to-head analyses of exercise modalities to better identify what works best for each candidate.

Clinical and policy implications

Our findings support the inclusion of exercise as part of clinical practice guidelines for depression, particularly vigorous intensity exercise. Doing so may help bridge the gap in treatment coverage by increasing the range of first line options for patients and health systems. 9 Globally there has been an attempt to reduce stigma associated with seeking treatment for depression. 74 Exercise may support this effort by providing patients with treatment options that carry less stigma. In low resource or funding constrained settings, group exercise interventions may provide relatively low cost alternatives for patients with depression and for health systems. When possible, ideal treatment may involve individualised care with a multidisciplinary team, where exercise professionals could take responsibility for ensuring the prescription is safe, personalised, challenging, and supported. In addition, those delivering psychotherapy may want to direct some time towards tackling cognitive and behavioural barriers to exercise. Exercise professionals might need to be trained in the management of depression (eg, managing risk) and to be mindful of the scope of their practice while providing support to deal with this major cause of disability.

Conclusions

Depression imposes a considerable global burden. Many exercise modalities appear to be effective treatments, particularly walking or jogging, strength training, and yoga, but confidence in many of the findings was low. We found preliminary data that may help practitioners tailor interventions to individuals (eg, yoga for older men, strength training for younger women). The World Health Organization recommends physical activity for everyone, including those with chronic conditions and disabilities, 75 but not everyone can access treatment easily. Many patients may have physical, psychological, or social barriers to participation. Still, some interventions with few costs, side effects, or pragmatic barriers, such as walking and jogging, are effective across people with different personal characteristics, severity of depression, and comorbidities. Those who are able may want to choose more intense exercise in a structured environment to further decrease depression symptoms. Health systems may want to provide these treatments as alternatives or adjuvants to other established interventions (cognitive behaviour therapy, SSRIs), while also attenuating risks to physical health associated with depression. 3 Therefore, effective exercise modalities could be considered alongside those intervention as core treatments for depression.

What is already known on this topic

Depression is a leading cause of disability, and exercise is often recommended alongside first line treatments such as pharmacotherapy and psychotherapy

Treatment guidelines and previous reviews disagree on how to prescribe exercise to best treat depression

What this study adds

Various exercise modalities are effective (walking, jogging, mixed aerobic exercise, strength training, yoga, tai chi, qigong) and well tolerated (especially strength training and yoga)

Effects appeared proportional to the intensity of exercise prescribed and were stronger for group exercise and interventions with clear prescriptions

Preliminary evidence suggests interactions between types of exercise and patients’ personal characteristics

Ethics statements

Ethical approval.

Not required.

Acknowledgments

We thank Lachlan McKee for his assistance with data extraction. We also thank Juliette Grosvenor and another librarian (anonymous) for their review of our search strategy.

Contributors: MN led the project, drafted the manuscript, and is the guarantor. MN, TS, PT, MM, BdPC, PP, SB, and CL drafted the initial study protocol. MN, TS, PT, BdPC, DvdH, JS, MM, RP, LP, RV, HA, and BV conducted screening, extraction, and risk of bias assessment. MN, JS, and JM coded methods for behaviour change techniques. MN and DGG conducted statistical analyses. PP, SB, and CL provided supervision and mentorship. All authors reviewed and approved the final manuscript. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: None received.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Data sharing Data and code for reproducing analyses are available on the Open Science Framework ( https://osf.io/nzw6u/ ).

The lead author (MN) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

Dissemination to participants and related patient and public communities: We plan to disseminate the findings of this study to lay audiences through mainstream and social media.

Provenance and peer review: Not commissioned; externally peer reviewed.

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

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literature reviews systematic reviews and meta analyses

Association of nonpharmacological interventions for cognitive function in older adults with mild cognitive impairment: a systematic review and network meta-analysis

  • Published: 06 January 2023
  • Volume 35 , pages 463–478, ( 2023 )

Cite this article

  • Xueyan Liu   ORCID: orcid.org/0000-0001-6228-2822 1 ,
  • Guangpeng Wang   ORCID: orcid.org/0000-0003-1442-0789 2 &
  • Yingjuan Cao   ORCID: orcid.org/0000-0002-3063-304X 3  

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Understanding the effectiveness of nonpharmacological interventions to improve cognitive function in older adults with MCI and identifying the best intervention may help inform ideas for future RCT studies and clinical decision-making.

The main focus of this study was to assess the comparative effectiveness of nonpharmacological interventions on cognitive function in older adults with MCI and to rank the interventions.

RCT studies until September 2022 were searched from six databases, including PubMed, the Cochrane Library, Embase, Web of Science, PsycINFO and CINAHL. The risk of bias in eligible trials was evaluated using the Cochrane Risk of Bias tool. Both pairwise and network meta-analyses were used, and pooled effect sizes were reported using SMD and the corresponding 95% confidence intervals.

A total of 28 RCT studies were included in this study, pooling 18 categories of nonpharmacological interventions. MBE (mind–body exercise) (SMD (standard mean difference): 0.24, 95% CI: 0.08–0.41, P  = 0.004), DTE (dual-task exercise) (SMD: 0.61, 95% CI: 0.09–1.13, P  = 0.02), PE (physical exercise) (SMD: 0.58, 95% CI: 0.04–1.12, P  = 0.03) may be effective in improving cognitive function in older adults with MCI. Acupressure + CT (cognitive training) was the top-ranked intervention among all interventions. No greater benefits of MA (mindful awareness) on cognitive function were found.

Conclusions

Overall, nonpharmacological interventions significantly improved cognitive function in older adults with MCI. Acupressure + CT(cognitive training) was the most effective intervention for managing cognitive impairment. Future studies with high quality and large sample size RCT studies are needed to confirm our results.

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Liu, X., Wang, G. & Cao, Y. Association of nonpharmacological interventions for cognitive function in older adults with mild cognitive impairment: a systematic review and network meta-analysis. Aging Clin Exp Res 35 , 463–478 (2023). https://doi.org/10.1007/s40520-022-02333-3

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Injectable hydrogel-based combination therapy for myocardial infarction: a systematic review and Meta-analysis of preclinical trials

  • Han Gao 1 ,
  • Song Liu 1 ,
  • Shanshan Qin 1 ,
  • Jiali Yang 2 ,
  • Tian Yue 2 ,
  • Bengui Ye 3 ,
  • Yue Tang 4 ,
  • Jie Feng 5 ,
  • Jun Hou 6 &
  • Dunzhu Danzeng 1  

BMC Cardiovascular Disorders volume  24 , Article number:  119 ( 2024 ) Cite this article

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Introduction

This study evaluates the effectiveness of a combined regimen involving injectable hydrogels for the treatment of experimental myocardial infarction.

Patient concerns

Myocardial infarction is an acute illness that negatively affects quality of life and increases mortality rates. Experimental models of myocardial infarction can aid in disease research by allowing for the development of therapies that effectively manage disease progression and promote tissue repair.

Experimental animal models of myocardial infarction were established using the ligation method on the anterior descending branch of the left coronary artery (LAD).

Interventions

The efficacy of intracardiac injection of hydrogels, combined with cells, drugs, cytokines, extracellular vesicles, or nucleic acid therapies, was evaluated to assess the functional and morphological improvements in the post-infarction heart achieved through the combined hydrogel regimen.

A literature review was conducted using PubMed, Web of Science, Scopus, and Cochrane databases. A total of 83 papers, including studies on 1332 experimental animals (rats, mice, rabbits, sheep, and pigs), were included in the meta-analysis based on the inclusion and exclusion criteria.

The overall effect size observed in the group receiving combined hydrogel therapy, compared to the group receiving hydrogel treatment alone, resulted in an ejection fraction (EF) improvement of 8.87% [95% confidence interval (CI): 7.53, 10.21] and a fractional shortening (FS) improvement of 6.31% [95% CI: 5.94, 6.67] in rat models, while in mice models, the improvements were 16.45% [95% CI: 11.29, 21.61] for EF and 5.68% [95% CI: 5.15, 6.22] for FS.

The most significant improvements in EF (rats: MD = 9.63% [95% CI: 4.02, 15.23]; mice: MD = 23.93% [95% CI: 17.52, 30.84]) and FS (rats: MD = 8.55% [95% CI: 2.54, 14.56]; mice: MD = 5.68% [95% CI: 5.15, 6.22]) were observed when extracellular vesicle therapy was used. Although there have been significant results in large animal experiments, the number of studies conducted in this area is limited.

The present study demonstrates that combining hydrogel with other therapies effectively improves heart function and morphology. Further preclinical research using large animal models is necessary for additional study and validation.

Graphical abstract

literature reviews systematic reviews and meta analyses

Peer Review reports

Myocardial infarction, resulting from sudden ischemia and cell damage in the myocardial tissue, leads to irreversible cardiac impairment [ 1 ]. The recovery phase after injury involves both acute and chronic inflammation, which, coupled with increased cardiac load due to diminished heart function, exacerbates heart tissue damage. This detrimental cycle, known as “injury - increased cardiac load - heightened injury,” ultimately progresses to heart failure [ 2 ]. Although treatments for myocardial infarction include drug therapy, surgical device implantation, and organ transplantation, drug therapy is the most accessible option. Its goal is to decelerate the progression of cardiac injury by reducing the cardiac load. However, its effectiveness is limited and often accompanied by systemic toxicity and suboptimal drug utilization, which undermine the potential benefits of many clinical agents. Furthermore, myocardial infarction remains a significant cause of global morbidity and mortality [ 3 ].

Bioactive scaffolds, combined with bioactive drugs or cells to facilitate cellular attachments, have gained attention for their potential to promote tissue repair following myocardial infarction and reverse heart damage [ 4 ]. Currently, bioactive scaffolds take the form of hydrogels, patches, and nanoparticles [ 5 ]. Hydrogels, which are hydrophilic polymeric three-dimensional networks [ 6 ], possess suitable mechanical properties, moisturizing capabilities, biocompatibility, biodegradability, and biomimetic characteristics, all of which are crucial for sustained drug delivery and tissue regeneration [ 7 ]. Despite these advantages, hydrogels as biomaterials have a relative deficiency in bioactivity [ 8 ]. However, by incorporating various bioactive drugs, cells, and cellular appendages, hydrogels can exhibit anti-inflammatory, anti-apoptotic, and tissue repair capabilities. Targeted injections into the area of myocardial infarction can ensure the prolonged release of therapeutic agents, stabilizing therapeutic outcomes and improving prognosis [ 9 ].

Injectable hydrogel combination therapies for myocardial infarction are extensively investigated in preclinical studies. The surveyed literature includes investigations on cellular therapies, cytokine therapies, pharmacotherapies, extracellular vesicular therapies, and nucleic acid therapies. Additionally, there is an exploration of the combined use of these therapies in a multitherapy approach.

Although there have been numerous preclinical studies, clinical investigations on hydrogel-based treatments for myocardial infarction remain scarce [ 10 , 11 ]. However, there has been a particular focus on hydrogel combined with stem cell therapies. Building upon previous systematic review and meta-analysis literature, our study delves deeper into hydrogel-based therapeutic approaches [ 12 ]. We aimed to analyze the effects of combining hydrogel with various therapies on cardiac function and morphology following myocardial infarction. This analysis provides valuable insights for future research and supports the clinical application of hydrogel combination therapy.

Materials and methods

Protocols and registration.

This meta-analysis adhered to the Preferred Reporting Items for Systematic Evaluation and Meta-Analysis (PRISMA) guidelines (Supplementary Table I ). The review protocol was registered on PROSPERO ( CRD42023401702 ).

Search strategy and data sources

For this meta-analysis, relevant research literature was sourced from PubMed (National Library of Medicine, 2021/03/01), Web of Science (via Clarivate Analytics), Scopus (via Elsevier 1788–2021/03/01), and Cochrane Central Register of Controlled Trials (via The Cochrane Library, 2021/03/01). The search strategy for PubMed is presented in Supplementary Table II .

Study eligibility

Two independent evaluators (H.G. and T.Y.) initially assessed the titles and abstracts of the literature against the inclusion and exclusion criteria (Supplementary Table III ). Afterward, both evaluators conducted a comprehensive full-text review. This review focused on the outcomes of incorporating injectable hydrogels with various therapies (cellular therapy, pharmacotherapy, cytokine therapy, extracellular vesicular therapy, nucleic acid therapy, and polypharmacy) in animal models of myocardial infarction induced by LAD ligation, with the goal of evaluating improvements in cardiac function and morphology following treatment. To ensure the consistency of study protocols, we required a minimum follow-up duration exceeding 1 week in the included studies [ 12 , 13 ]. The infarct model was precisely defined as an animal model established using left anterior descending branch ligation, providing reliable and consistent results. Studies reporting immunogenic reactions or solely involving hydrogel injection without other therapies were excluded. There were no language or publication date restrictions in the literature inclusion criteria.

The primary outcome indicators in this study include left ventricular ejection fraction and fractional shortening. To be included in the literature review, the studies must present at least one of these primary outcome measures. Additionally, the secondary outcome indicators encompass left ventricular end-systolic volume (ESV), end-diastolic volume (EDV), end-systolic diameter (ESD), end-diastolic diameter (EDD), infarct size, and anterior wall thickness, covering both cardiac functional and morphological parameters. In cases where the necessary data were missing in the literature but evidence suggested that the primary outcome measures were collected, we contacted the respective authors via email. They were given a two-week period from the date of the email to provide the required information.

Data extraction

The relevant data for this analysis were extracted using a standardized approach. This included gathering information on the sample size of the experimental animals and measuring the following parameters: baseline, hydrogel group, and combined protocol group for ejection fraction; baseline, hydrogel alone, and combined protocol group for fractional shortening; hydrogel alone and combined protocol group for left ventricular end-systolic diameter, left ventricular end-diastolic diameter, left ventricular end-systolic volume, left ventricular end-diastolic volume, infarct area, and anterior ventricular wall thickness. When data appeared only in graphical format, manual extraction was performed using Image J software. To ensure data precision, both SS. Q and JL. Y independently conducted the extraction. In cases where discrepancies arose in the extraction outcomes, a separate re-measurement was performed to maintain data accuracy.

The literature data were extracted in the format of mean and standard deviation. In cases where the mean standard deviation was not provided, conversion was performed using standard errors and confidence intervals, following the guidelines of the Cochrane Collaboration Network.

The quality of articles was evaluated using the Heyland Methodological Quality Score (MQS) [ 14 ]. This score, which could reach a maximum of 18 points, was distributed among criteria such as randomization, analysis, blinding, selection, group comparability, degree of follow-up, treatment regimen, combined interventions, and outcome reporting, with each criterion receiving 2 points.

The risk of bias was assessed using SYRCLE’s Risk of Bias in Animal Testing tool [ 15 ]. The assessed elements included sequence generation, implementation, detection, attrition, and reporting bias. If no data were available, an “unclear” designation was assigned. A “high risk” designation was given when the methodology potentially compromised the accuracy of the results, and a “low risk” designation was assigned when the methodology was deemed not to influence the outcomes.

Statistical analysis

The analysis focused on changes in baseline values for the hydrogel injection and hydrogel combination treatment groups following myocardial infarction, particularly investigating left ventricular functional and morphological outcomes. The data were presented as mean ± standard deviation (SD). In cases where only mean and standard error were provided, we converted the standard error to standard deviation using the sample size. If a study included multiple intervention or control groups, we combined relevant outcome indicator groups, following established literature methodologies to minimize analysis errors [ 16 ]. The pooled analysis was conducted using the inverse variance method and a random effects model in the data software. A 95% confidence interval was adopted, with significance set at P  < 0.05.

The forest plots presented the relative treatment effects and their 95% confidence intervals (CIs) for each outcome indicator across individual studies, different combination therapy types, and the overall random-effects meta-analysis for each parameter investigated. To account for study heterogeneity, the analyses were stratified based on animal size. The initial data analysis was performed using Review Manager (RevMan) 5.3 (Nordic Cochrane Centre in collaboration with the Cochrane Collaboration in Copenhagen, Denmark).

In the priori subgroup analysis, we examined various variables, including combination therapy (encompassing multitherapy or monotherapy), subtype of hydrogel source, sex of the animals, intervals post-MI for both follow-up and treatment, Methodological Quality Score (MQS), general subtype of the animals, and specifically murine small animal subtype. For continuous variables such as cell dose, duration, and MQS, dichotomous subgroup analyses were conducted using the median value obtained from all studies included in the meta-analysis. Meta-regression analyses, employing STATA MP software v17 (StataCorp in College Station), were carried out when the study count reached or exceeded three, with a significance threshold of P  < 0.05, to determine the impact of subgroup variations.

The heterogeneity among the included studies was evaluated using the Cochran Q statistic, with statistical significance determined at P  < 0.10. The interpretation of the I 2 values was as follows: I 2  < 50%, indicating moderate heterogeneity; 50% ≤ I 2  ≤ 75%, indicating substantial heterogeneity; I 2  > 75%, indicating considerable heterogeneity. Further sensitivity analyses were performed to investigate potential sources of heterogeneity by systematically excluding individual trials and utilizing different effect models (STATA MP v17).

Publication bias was assessed through a combination of visual examination of funnel plot results and statistical tests, including Begg’s and Egger’s tests, with P  < 0.05 considered as evidence of a small study effect. To meet standard literature requirements, at least 9 studies were included in the assessment of publication bias [ 17 ].

Search results

The PRISMA review flowchart is depicted in Fig.  1 . Initially, the search of PubMed, Web of Science, Scopus, and Cochrane databases resulted in 5230 relevant articles. After screening the titles, 3345 articles were deemed irrelevant and discarded. Duplicates were eliminated in the remaining 1885 articles that underwent title and abstract review, leaving 352 articles. After evaluating the full text of these 352 articles, 269 were excluded as they did not meet the inclusion and exclusion criteria. As a result, 83 articles were deemed suitable for analysis.

figure 1

Flowchart of the review process for the meta-analysis

Study characteristic

Table  1 displays the characteristics of the included studies. The meta-analysis primarily focused on murine small animal models ( N  = 73; 88%), with rats ( N  = 54; 65.1%) and mice ( N  = 19; 22.9%) being the most prevalent. Other animal models consisted of rabbits ( N  = 3; 3.6%), sheep ( N  = 2; 2.4%), and pigs ( N  = 6; 7.2%). Notably, one study utilized both rat and sheep models. Among the selected studies, hydrogels fell into two categories: those of natural origin ( N  = 44; 53%) and chemically synthesized ones ( N  = 39; 47%). Hydrogels derived from natural material backbones were classified as natural origin. Combination therapies were predominantly represented by monotherapy ( N  = 62; 74.7%) and polytherapy ( N  = 21; 25.3%), each further categorized based on variations in therapeutic effects. Monotherapy included cell therapy ( N  = 32; 38.6%), cytokine therapy ( N  = 14; 16.9%), drug therapy ( N  = 10; 12%), extracellular vesicle therapy ( N  = 4; 4.8%), and nucleic acid therapy (N = 2; 2.4%). Most studies utilized male animal models ( N  = 68; 81.9%), while 12 studies (14.5%) incorporated female models. All animal models underwent the left coronary artery ligation method to induce myocardial infarction, ensuring consistent and reliable results. The majority of the animal studies had a 4-week follow-up period after intracardiac injection of the therapeutic hydrogel, followed by autopsy ( N  = 65; 78.3%). In larger animals such as sheep and pigs, the typical follow-up period was extended to 8 weeks, with the longest study having a follow-up period of 52 weeks. In 73 studies (88%), the hydrogel injection occurred immediately after myocardial infarction modeling. The funding sources varied, with 58 studies (69.9%) receiving joint funding from institutions and companies, 19 studies (22.9%) solely funded by institutions, and 6 studies (7.2%) solely funded by companies. One study (1.1%) did not report its funding source. Geographically, the majority of the studies were based in China (46) and the United States (17). Other contributions included Canada (4), Taiwan, China (3), Iran (2), Japan (2), Korea (2), and Singapore (2), with Denmark, France, Germany, and Italy each having contributed one study.

Quality and risk of Bias assessment

In assessing the quality of the literature included, a score of ≥11 was considered as indicative of high quality, as determined by the MQS analysis (Supplement Table 4 ). Out of the literature evaluated, 66 articles (69.5%) met the criteria for high quality. Additionally, only 25 articles (25.8%) explicitly stated the adoption of a blinded analysis when assessing outcome indicators.

The analysis of the risk of bias plot (Supplement Figure 1 ) revealed a high risk of bias among the literature included. Only 30 trials (36.1%) maintained blinding throughout the outcome measurement process. Most trials did not provide details of a blinding protocol or implement blinding in relation to the animal housing environment and group allocation, indicating a significant risk of bias. None of the trials were excluded from the primary analysis due to concerns regarding quality or bias.

Effect of injectable hydrogel combination therapy on cardiac function

Effects in small animal models.

The use of injectable hydrogel combination therapy resulted in significant improvements in EF (Fig.  2 a, b). For rats, the mean difference (MD) was 8.87% [95% confidence interval (CI): 7.53, 10.21], and for mice, the MD was 16.45% [95% CI: 11.29, 21.61]. Similarly, FS (Fig. 2 c, d) also showed improvement with the use of injectable hydrogel combination therapy. For rats, the MD was 6.31% [95% CI: 5.94, 6.67], and for mice, the MD was 5.68% [95% CI: 5.15, 6.22]. These improvements were significantly greater than those observed with hydrogels alone. Among the various therapies, cell therapy had the most trials and demonstrated significant enhancements in both EF and FS. For rats, the MD was 8.02% [95% CI: 5.28, 10.77] for EF and 7.99% [95% CI: 7.47, 8.50] for FS. For mice, the MD was 16.09% [95% CI: 9.35, 22.82] for EF and 5.42% [95% CI: 4.87, 5.96] for FS. Extracellular vesicle therapy also showed significant improvements in EF and FS. For rats, the MD was 9.63% [95% CI: 4.02, 15.23] for EF and 8.55% [95% CI: 2.54, 14.56] for FS. For mice, the MD was 23.93% [95% CI: 17.52, 30.84] for EF and 5.68% [95% CI: 5.15, 6.22] for FS. Similar improvements in cardiac function were observed for cytokine therapy and drug therapy. For EF, the MD for rats was 9.03% [95% CI: 7.18, 10.87], and for mice was 20.30% [95% CI: 15.78, 24.82]. For FS, the MD for rats was 5.26% [95% CI: 4.29, 6.23], and for mice was 5.13% [95% CI: 4.43, 5.82]. Only a single study using nucleic acids therapy measured FS as an endpoint. Substantial heterogeneity was observed between studies for both EF (rats: I 2  = 75%, p  < 0.0001; mice: I 2  = 96%, p  < 0.0001) and FS (rats: I 2  = 96%, p  < 0.0001; mice: I 2  = 97%, p  < 0.0001). Systematic removal of individual studies did not significantly alter the heterogeneity for either EF or FS. (Supplementary Figure 4 a, b).

figure 2

Forest plots of all trials investigating the effect of injectable hydrogel combination therapy on ejection fraction and fractional shortening outcomes in myocardial infarction treatment outcome studies ( a . Rats EF, b . Mice EF, c . Rats FS, d . Mice FS). Data are expressed as weighted mean differences with 95% CIs, using generic inverse-variance random-effects models. Between-studies heterogeneity was tested by using the Cochran Q statistic (chi-square) at a significance level of P  < 0.05. Reference numbers for each study can be found in Table 1 and list of references

Regarding the secondary outcomes, the analysis showed significant improvements in ESV for rats (MD = − 0.03 mL [95% CI: − 0.05, − 0.02]) and mice (MD = − 0.09 mL [95% CI: − 0.21, 0.03]). EDV also improved for rats (MD = − 0.03 mL [95% CI: − 0.04, − 0.02]). ESD exhibited improvements for rats (MD = − 0.84 mm [95% CI: − 1.16, − 0.53]) and mice (MD = − 1.23 mm [95% CI: − 2.14, − 0.32]). Similarly, EDD demonstrated improvements for rats (MD = − 0.66 mm [95% CI: − 0.82, − 0.51]) and mice (MD = − 1.13 mm [95% CI: − 3.04, 0.79]). The infarct size also showed positive outcomes with hydrogel combination therapy for rats (MD = − 9.90% [95% CI: − 11.84, − 7.95]) and mice (MD = − 7.64% [95% CI: − 13.67, − 1.62]). Furthermore, wall thickness increased for rats (MD = 0.27 mm [95% CI: 0.12, 0.42]) and mice (MD = 0.07 mm [95% CI: 0.01, 0.12]). These consistent findings indicate the superior treatment outcomes of hydrogel combination therapy compared to sole hydrogel injection (Supplementary Figure 2 ). Sensitivity analysis of secondary outcome measures also produced relatively robust results. (Supplementary Figure 4 c-h).

In addition, multitherapy yielded significant improvements in EF for rats (MD = 12.53% [95% CI: 7.85, 17.21]) and mice (MD = 10.59% [95% CI: 4.32, 16.86]). FS also showed notable improvements for rats (MD = 7.87% [95% CI: 7.00, 8.74]) and mice (MD = 5.88% [95% CI: 4.90, 6.86]). ESD demonstrated reductions for rats (MD = − 1.47 mm [95% CI: − 2.14, − 0.80]) and mice (MD = − 0.18 mm [95% CI: − 0.66, − 0.30]). Similarly, EDD exhibited reductions for rats (MD = − 1.26 mm [95% CI: − 2.51, 0.00]) and mice (MD = − 0.26 mm [95% CI: − 0.46, − 0.07]). Although EDV showed minimal change for rats (MD = − 0.07 mL [95% CI: − 0.18, 0.03]), ESV demonstrated a slight decrease (MD = − 0.07 mL [95% CI: − 0.11, − 0.03]). Infarct size also decreased significantly for rats (MD = − 13.59% [95% CI: − 19.82, − 7.36]) and mice (MD = − 13.44% [95% CI: − 21.66, − 5.22]). Lastly, wall thickness increased for rats (MD = 0.63 mm [95% CI: 0.38, 0.87]) (Supplementary Figure 3 ).

Effects in non- small animal models

In non-murine studies, the classification and analysis of animal types showed a significant improvement in EF, with an MD of 8.49% [95% CI: 7.46, 9.53]. Among the animal models, the pig model, which had a large sample size, demonstrated the most substantial effect, with an MD of 9.09% [95% CI: 7.89, 10.29]. The sheep (MD = 6.36% [95% CI: 3.19, 9.53]) and rabbit (MD = 7.07% [95% CI: 4.40, 9.74]) models also exhibited significant improvements (Fig. 3 ). However, secondary outcomes such as FS, ESV, EDV, ESD, EDD, infarct area, and ventricular wall thickness were either not reported or poorly represented, preventing correlation analysis (Tab. 1 ).

figure 3

Forest plot to study the effect of injectable hydrogel combination therapy on EF outcomes in a non-murine animal model in the myocardial infarction treatment outcome study. Data are expressed as weighted mean differences with 95% CIs, using generic inverse-variance random-effects models. Between-studies heterogeneity was tested by using the Cochran Q statistic (chi-square) at a significance level of P < 0.05. Reference numbers for each study can be found in Table 1 and list of references

Subgroup analysis

This subgroup analysis focused primarily on rat and mouse animal models. Subgroup analysis of combination therapy revealed that extracellular vesicular therapy had the most prominent therapeutic effect, But the larger confidence intervals require more experiments to further validate the actual effect. The second is multitherapy, because it involves many variables, the results are difficult to explain, so it is not included in the main analysis, but it still provides a larger sample size and robust treatment effect. Analyzing follow-up durations highlighted that a 4-week span ( P  < 0.005) yielded the most optimal overall impact, underscoring the significance of follow-up time on outcome indicators, no effect modifications were seen for sex, MQS, animal size, or hydrogels subtype for EF (Fig. 4 ).

figure 4

A meta-regression analysis of variables of interest affecting changes in left ventricular ejection fraction. A dichotomous a priori subgroup analysis was performed in a trial examining the effect of hydrogel combination therapy on ejection fraction. Point estimates at each subgroup level are pooled effect estimates for ejection fraction in the hydrogel combination therapy group compared with the hydrogel-only therapy group. a . Hydrogel type, b . Combination therapy, c . Sex, d . Small animal model, e . Time of treatment, f . Durations, g . MQS and h . Animal model were subjected to subgroup analysis. MQS = Hyland Methodological Quality

Continuous and subgroup meta-regression analyses demonstrated a significant effect for longer follow-up duration and time of treatment on reducing EF and FS (Fig. 4 , Supplement Table 5 a-b). For secondary outcomes, continuous meta-regression analyses demonstrated no effect of dose on either ESV, EDV, ESD, EDD, infarct size, or wall thickness. (Supplement Table 5 c-h).

In subgroup meta-regression analyses comparing rats and mice, we found that the rat correlation studies (56 articles 65%) had more stable confidence intervals than the mouse correlation studies (17 articles 20%). For secondary outcomes, subgroup meta-regression analyses demonstrated no significant effect of sex, MQS, hydrogel type, Animal model on either ESV, EDV, ESD, EDD, infarct size, or wall thickness (Fig. 4 , Supplementary Figure 6–11 ).

Publication Bias

Funnel plot analyses conducted on primary outcomes in a murine small animal model revealed the presence of significant publication bias. The funnel plots depicting EF and FS exhibited an asymmetric distribution. Both Begg’s and Egger’s tests confirmed the presence of publication bias in EF ( P  = 0.001). Additionally, Egger’s test identified bias in FS ( P  = 0.007). Given the discrepancies in the FS results (Begg’s test P  = 0.575, Egger’s test P  = 0.007), we rely on Egger’s test due to its slightly higher efficacy in testing (Fig.  5 ).

figure 5

Funnel plots for the effect of Injectable hydrogel-based combination therapy on (A) ejection fraction and (B) fractional shortening in small animal studies

Furthermore, the funnel plots for other secondary indicators displayed publication bias in all metrics, except for End Diastolic Volume, which showed no evidence of publication bias (Supplementary Figure 10 ).

In the case of trials involving large animals, the funnel plot for EF did not portray any noticeable asymmetry (Supplementary Figure 11 ). Both Egger’s and Begg’s tests yielded non-significant results for publication bias in EF, with reported values of P  = 0.39 and P  = 1.000, respectively. Unfortunately, the available data provided insufficient evidence to evaluate publication bias for FS and other secondary metrics in these trials.

Limited systematic evaluations and meta-analyses have been conducted on the therapeutic effectiveness of injectable hydrogels for infarcted myocardium. However, a previous comprehensive review encompassing different biological scaffolds (including injectable hydrogels, microspheres, and patches) combined with stem cell delivery to the infarcted myocardium revealed injectable hydrogels to be superior to other scaffold types [ 97 ]. Therefore, our study aimed to further investigate injectable hydrogels. We conducted an analysis of 83 relevant publications, specifically focusing on cardiac morphological and functional measurements that were assessed at the conclusion of the follow-up period in animal models with myocardial infarction induced through left coronary artery ligation. These evaluations encompassed combinations of chemically synthesized hydrogels or naturally derived hydrogels with various therapies, using a control group receiving only hydrogel injections. Our findings demonstrated that the combination of injectable hydrogel and therapy significantly improved primary outcomes, including Ejection Fraction and left ventricular short-axis shortening rate, in comparison to hydrogel injection alone. Additionally, secondary outcomes such as ESD, EDD, ESV, EDV, wall thickness, and infarct size exhibited substantial enhancements. Subgroup analyses indicated a limited body of literature on extracellular vesicle therapy, which poses challenges in drawing definitive conclusions. Cellular therapies, particularly those involving stem cells, consistently demonstrated positive effects. Although the classification of polypharmacy is complex due to the combination of various therapies, it is evident that the combined effect surpasses that of cellular therapy alone. Moreover, the implementation of targeted therapies at each stage of myocardial infarction holds promise as a comprehensive approach, deserving further investigation.

Monotherapy

Cellular therapy.

Cell therapy, particularly focusing on stem cell therapy, remains a central area of investigation in combination therapy research [ 98 , 99 ]. The literature predominantly emphasizes mesenchymal stem cells (MSCs) [ 62 ], monocytes [ 37 ], embryonic stem cells [ 45 ], and human-induced pluripotent stem cells [ 23 ]. The integration of stem cell therapy with hydrogel protocols finds applications in the repair of spinal cord injuries [ 100 , 101 ], osteoarthritis treatment [ 102 ], chronic diabetic wound healing [ 103 ], cardiovascular disease treatment [ 104 , 105 ], and hind limb ischemia treatment [ 106 ]. MSCs [ 107 ] emerge as a promising option due to their ease of isolation, robust proliferative capacity, immunomodulatory ability, and diverse differentiation potential [ 108 ]. Many studies encapsulate MSCs from various sources (e.g., bone marrow, adipose tissue, umbilical cord blood) within hydrogels. The enhanced paracrine secretion by MSCs plays a crucial role in the effective repair of cardiac tissue [ 109 ]. However, certain research suggests that encapsulation can impact stem cell proliferation and paracrine capability, likely due to limited intercellular interactions within hydrogels, resulting in reduced cytokine secretion [ 110 ]. MSCs are often subjected to pre-treatment using physicochemical environments (hypoxia [ 111 ], hyperoxia [ 112 ], hydrogen sulfide [ 113 ]), pharmacological modifications (trimetazidine [ 114 ], lipopolysaccharide [ 115 ]), and genetic modifications (CXCR4 [ 116 ], SDF-1 [ 117 ], and HGF [ 118 ]) to enhance the paracrine mechanism of MSCs. Yuanning Lyu et al. [ 119 ] utilized a combination of human E-cadherin fusion protein (hE-cad-Fc)-encapsulated poly (lactic-co-glycolic acid) (PLGA) particles (hE-cad-PLGA) along with human mesenchymal stem cells (hMSCs) to form 3D cell aggregates, which were then incorporated into hyaluronic acid (HA)-based hydrogels. Incorporating hepatocyte growth factor (HGF)-modified MSCs onto small molecule hydrogels increased Bcl-2 levels, while decreasing Bax and cystein-3 levels, promoting MSC growth and proliferation, and inhibiting apoptosis of cardiomyocytes in the lesioned areas. The pretreatment of MSCs proved more effective than the study without pretreatment. In conclusion, the combination of cell therapy and hydrogel treatment for heart attacks has displayed significant therapeutic effects. This approach offers advantages in promoting tissue regeneration and facilitating healing in areas affected by myocardial infarction through the use of various stem cells or immune cells. To address potential concerns with cell therapy, related studies have explored alternative approaches such as extracellular vesicle therapy or cytokine therapy, which can help mitigate immunogenicity concerns [ 120 ].

Cytokine therapy

Cytokines (CK) are soluble, low-molecular-weight proteins secreted by various cells and are involved in immune regulation, cell growth, and tissue repair [ 121 ]. They encompass different categories, including interleukins, interferons, tumor necrosis factor superfamily, colony-stimulating factors, chemokines, and growth factors. Cytokines play a central role in both the innate and adaptive immune systems, facilitating cell proliferation, activation, and maintaining physiological functions [ 122 ]. Jeffrey E. Cohen et al. [ 22 ] demonstrated improved ventricular function under ischemic conditions by incorporating epidermal growth factor neuromodulatory protein (NRG) into gelatin hydrogels, which stimulated cardiomyocyte mitogenic activity, reduced apoptosis, and enhanced ischemic ventricular function. Other treatment regimens primarily involve combinations of growth factors such as VEGF, bFGF, and HGF. Considering the complex post-ischemic myocardial environment, cytokine therapy alone may not provide comprehensive repair. Forest plot data indicate that cytokine therapy falls behind other treatments in terms of morphological outcomes following myocardial infarction. As a result, combination therapies or the integration of diverse approaches are often preferred, with further exploration discussed in the subsequent Multitherapy section.

Extracellular vesicle therapy

Extracellular vesicles, nanoscale vesicles that result from paracellular secretion, are abundant in the extracellular fluids of animals [ 123 ]. Furthermore, it has been demonstrated in related studies that beneficial exosomes can be isolated from plants [ 124 ]. These vesicles contain diverse biologically active components and possess properties such as immunomodulation, low antigenicity, and tissue protection [ 125 ]. Specifically, exosomes, a subset of these vesicles, carry biologically active biomolecules, including proteins, nucleic acids, lipids, and sugars, granting them a range of biological functions [ 126 ]. Their ability to serve as nanocarriers facilitates cell-mediated drug delivery, thereby maximizing therapeutic efficacy. Notably, certain exosomal proteins exhibit selective homing abilities, enhancing the efficiency of delivery [ 127 ]. The yield of exosomes is influenced by the type of cells involved, with immune cells often producing consistent and therapeutically potent yields. Clinical trials have successfully utilized exosomes in the diagnosis and treatment of various diseases [ 128 , 129 , 130 ].

In the setting of myocardial infarction, it is important to acknowledge that directly injected exosomes may be rapidly cleared due to the myocardial environment. As a result, there has been a growing interest in injectable hydrogel scaffolds to enhance the retention of extracellular vesicles. In a study conducted by Carol W. Chen et al. [ 35 ], it was demonstrated that extracellular vesicles, isolated from endothelial progenitor cells and anchored to shear-thinning hydrogels, promote angiogenesis, support functional recovery, and mitigate adverse ventricular remodeling after an infarction. Current research suggests that the therapeutic effects of MSCs are likely due to their paracrine release of cytokines, growth factors, and exosomes, rather than their direct cellular effects [ 131 , 132 ]. Renae Waters et al. [ 25 ] utilized lipid-derived MSCs on methacrylate-based gelatin nanocomposite scaffolds, achieving sustained release of important therapeutic growth factors that stimulate angiogenesis, reduce scarring, and protect the heart. Youming Zhang et al. [ 89 ] employed dendritic cell-derived exosomes on alginate hydrogels, revealing enhanced upregulation of Treg cells, polarization of M2 macrophages, reduction of inflammation, and cardiac protection following a myocardial infarction. In summary, extracellular vesicle therapy, which harnesses the paracrine/autocrine mechanisms of MSCs primarily mediated by exosomes, plays a crucial role in mitigating apoptosis, reducing inflammation, promoting angiogenesis, inhibiting fibrosis, and augmenting tissue repair. This meta-analysis highlights the superiority of experiments involving extracellular vesicles compared to other methods in terms of myocardial functional recovery. However, morphological recovery remains limited, and further studies are needed due to the scarcity of literature in this area. Several challenges persist in the development of extracellular vesicles, including the intricate isolation procedures and suboptimal yields [ 133 ].

Drug therapy

A wide range of medications used in combination with hydrogel for the treatment of myocardial infarction includes natural bioactive drugs such as tanshin and colchicine [ 90 ], curcumin [ 134 ], compounds (NO [ 135 ], Se [ 136 ]), and various synthetic products. Bioactive drugs, including curcumin and quercetin, possess strong anti-inflammatory, anti-apoptotic, and tissue repair properties. However, their limited solubility in water hinders efficient delivery through oral or traditional methods. In a study conducted by Cui Yang et al. [ 136 ], Se-containing PEG-PPG hydrogels were utilized to reduce pro-inflammatory cytokine secretion, improve myocardial fibrosis, and enhance left ventricular remodeling.

The common characteristic observed among the drugs explored in this section is their demonstrated effectiveness in treating cardiovascular diseases [ 134 , 137 ]. Nevertheless, their long-term efficacy is often compromised by difficulties in delivery. Hydrogels enable the sustained release of drugs [ 9 ], enhancing the local pharmacological benefits while minimizing systemic side effects. This approach is more effective in addressing the prolonged and complex pathological environment [ 138 , 139 , 140 ].

Nucleic acid therapy

Nucleic acids, such as deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) [ 141 ], are vital biomolecules present in living organisms. They are composed of a polymerization of numerous nucleotide monomers. Nucleic acid therapy has been established as a safe and effective approach for treatment. This therapeutic method has shown significant potential in gene regulation, leading to its rapid advancement in cancer treatment as well as the prevention and management of infectious diseases. In particular, mRNA vaccines developed for COVID-19 have played a pivotal role in combating the ongoing viral pandemic [ 142 ]. However, despite the promising prospects of nucleic acid therapy, challenges persist in manufacturing, delivery strategies, and targeted site retention.

Nucleic acid therapies, which involve targeting genetic information within the body, hold substantial potential for disease treatment. Unlike conventional therapies with limited effectiveness, nucleic acid approaches have the ability to produce long-lasting effects by modulating genes through suppression, addition, replacement, or editing [ 97 ]. However, when applied to cardiovascular diseases, nucleic acid delivery alone is not sufficient due to challenges such as enzymatic degradation, short serum half-life, and low cell transfection efficiency [ 143 ]. From a clinical perspective, ensuring effective delivery and retention of nucleic acids at the intended target sites is considered crucial for the success of nucleic acid therapy [ 9 ].

Hydrogels serve as promising platforms for nucleic acid therapies, but they require specific chemical modifications to ensure prolonged retention and stability of nucleic acids during treatment, as well as targeted tissue localization and efficient cell delivery. In a rat model, Wei-Guo Wan et al. [ 79 ] reported cardioprotective effects by combining a hydrogel with short-hairpin RNA (shRNA). Yan Li et al. [ 85 ] developed an injectable hydrogel system for microRNA-21-5p, which showed significant improvements in key indicators and reaffirmed the therapeutic potential of gene/nucleic acid therapy for myocardial infarction.

The microenvironment of the myocardium post-myocardial infarction undergoes a prolonged and complex immune response. Although preclinical studies have provided limited in-depth exploration, drawing definitive conclusions from the small number of existing studies remains challenging [ 144 ]. However, these limited findings do suggest the potential of nucleic acid therapy in reducing nucleic acid clearance through hydrogel combinations and effectively restoring damaged myocardial tissue through continuous and substantial gene regulation.

Multitherapy

Over the past decade, clinical insights and preliminary studies have revealed that a singular approach to treatment falls short of achieving optimal therapeutic outcomes due to the multifaceted nature and physiological intricacies of the disease [ 145 ]. As a result, with advancements in drug delivery techniques, the exploration of combination or multitherapy has emerged as a promising avenue of research [ 145 ].

Adam J. Rocker et al. [ 20 ] adopted a sequential delivery method for three cytokines: vascular endothelial growth factor (VEGF), interleukin-10 (IL-10), and platelet-derived growth factor (PDGF). Initially, VEGF induced angiogenesis and suppressed cardiomyocyte necrosis, followed by the modulation of excessive inflammation by IL-10. The final delivery of PDGF aimed to stabilize the myocardial microenvironment and rejuvenate substantial hemodialysis. This multicytokine approach tailored interventions to the therapeutic demands of various pathological phases. However, while these findings are promising, analysis suggests that the role of PDGF may be limited, indicating the need for further refinement of the regimen. Combining cell therapy with drug therapy has also demonstrated significant therapeutic potential. Enhancing the paracrine impact of MSCs through biomaterial integration can greatly boost therapeutic efficiency, as the full potential of the paracrine function of diverse stem cells is often not realized. Yang Liu et al. [ 86 ] incorporated stem cells with puerarin, a natural scavenger of ROS, to mitigate cardiomyocyte damage. Concurrently, in combination with puerarin, bone-derived mesenchymal stem cells increased the secretion of paracrine factors. A similar approach was employed by Shilan Shafei et al. [ 72 ], further highlighting the synergistic potential of such combinations.

In summary, strategic combinations of therapies can yield synergistic effects where the combined outcome surpasses the sum of individual contributions [ 145 ]. The advantages of combining multiple therapeutic agents outweigh the drawbacks of individual therapies, leading to significant therapeutic benefits [ 146 ]. However, it is crucial to ensure effective treatment while also considering biosafety [ 147 ]. The future direction of development lies in establishing efficient and safe approaches for combination therapy that undergo repeated research validation and clinical testing.

Hydrogel source

Injectable hydrogels have been found to be superior to other biological scaffold materials for drug delivery and cardiac implantation [ 148 ].

Various experimental results have shown that hydrogel injections can effectively impart specific physical, chemical, and electrical characteristics to the post-infarct myocardial area. This paper categorizes injectable hydrogels into two types: those of natural origin and those that are chemically synthesized. Natural-origin hydrogels, including collagen, fibrin, decellularized materials, chitosan, and alginate, display commendable biochemical properties, bioactivity, and biocompatibility, making them well-suited for in vivo implantation [ 149 ]. However, these naturally-derived hydrogels face challenges such as inadequate mechanical properties, consistent degradation rates, antioxidant capacities, and the necessary electrical conductivity for implantation [ 150 ]. In a clinical trial involving alginate injectable hydrogels, a higher mortality rate was observed in patients with advanced heart failure who received hydrogel implants compared to those without injections, highlighting significant limitations in the clinical application of natural hydrogels [ 151 ]. On the other hand, chemically synthesized hydrogels [ 152 ] (such as PNIPAAm-based hydrogels, Aniline-Based Materials, and PEG-based hydrogels) offer superior mechanical properties and stability compared to natural origin hydrogels [ 153 ], but often compromise biocompatibility [ 154 ]. Subgroup analyses have demonstrated superior functional recovery with natural hydrogels, while chemically synthesized hydrogels excel in morphological recovery. Therefore, the fusion of both categories in the form of hybrid hydrogels emerges as a promising avenue for future research [ 155 ].

Hybrid hydrogels provide versatile design options and adaptability to different functions, making them effective in various tissues. Given the distinctive vascular structure, electrical conduction signal function, high metabolism, and high compliance characteristics of myocardial tissue, it is crucial to construct injectable complexes using hybrid hydrogels specifically tailored for myocardial tissue [ 155 ]. The findings of this systematic review demonstrate that hybrid hydrogels designed based on the cardiac tissue structure can optimize M2 macrophage polarization, promote angiogenesis, enhance repair response (as indicated by the cardiomyocyte survival rate), thereby reducing infarct size, improving wall thickness, and enhancing cardiac contractility.

Publication Bias and quality assessment

Consistent with previous research, this analysis identified significant publication bias for the primary outcomes of Ejection Fraction and Fractional Shortening. The bias persisted even after conducting a sensitivity analysis. It is crucial to address this publication bias in order to facilitate genuine clinical trials utilizing injectable hydrogels for myocardial infarction treatment. Evaluation of the SYRCLE risk of bias tool revealed pronounced selection and implementation biases in many studies. Further refinement of research methodologies for myocardial infarction animal models, particularly in interdisciplinary settings, is necessary. To ensure reliable and replicable experimental results, it is imperative to employ blinded protocols for establishing animal models, treatment allocation, and outcome measurement.

Within the reviewed literature, the MQS analysis identified 66 (69.5%) high-quality articles. However, a significant portion of these studies either omitted details in the randomization protocol or did not utilize blinding methods for their experiments. During data collection, studies lacking primary outcome indicators were excluded, resulting in the omission of relevant experimental studies. Future research should prioritize the reporting of echocardiographic parameters and morphological assessments. Comprehensive reporting will not only ascertain the efficacy of experimental protocols but also provide dependable results for subsequent literature reviews and inform future research endeavors. Similar to the challenges observed with nucleic acid therapies discussed earlier, the lack of data compromised the depth of the literature analysis.

Strengths and limitations

The meta-analysis included 83 papers and provided valuable insights into current research trends. However, there are certain limitations that need to be acknowledged. Firstly, the study primarily focused on murine small animal models due to modeling challenges, and there was limited exploration of large animal models. Therefore, conducting further large animal experiments is necessary to validate the findings. Secondly, it is important to standardize the experimental data in order to facilitate future analyses. Thirdly, the current study faces heterogeneity due to variations in the targeted drug delivery method applied to the heart and the limited number of animal studies available at this stage. This heterogeneity poses a significant barrier to further clinical translation. To address this, standardized large-scale animal experiments are required for validation. Lastly, publication bias was identified in the main outcome indicators, which merits attention.

Clinical transformation status

With the rapid advancement of hydrogel technology, the clinical use of hydrogel-based combination therapy for various diseases is increasing. While preclinical studies have extensively investigated hydrogel combination therapy for targeted drug delivery and tissue defect repair, there are significant challenges in translating these findings into clinical practice. Hydrogel wound dressings have gained popularity in clinical settings due to their ease of implementation [ 156 , 157 ]. However, when it comes to diseases that require interventional therapy, conducting effective clinical trials presents substantial difficulties. Therefore, addressing the safety concerns associated with delivery methods is a prerequisite for the progress of injectable hydrogel combination therapy [ 158 ].

Clinical trials involving hydrogels in the context of cardiac applications remain limited. The unique structural characteristics of the human heart contribute to the relatively slow progress in developing clinical trials and exploring indications and contraindications. In a randomized controlled trial conducted in 2020, the injection of collagen hydrogel encapsulating mesenchymal stem cells via coronary artery bypass grafting was evaluated [ 159 ]. The trial results showed no adverse reactions. Evaluation of the left ventricular ejection fraction at three follow-up time points (3, 6, and 12 months) indicated percentages of 9.14, 9.84, and 9.35% in the hydrogel combined with stem cell treatment group, while the control group exhibited percentages of 4.17, 4.40, and 3.62%. Analysis of cardiac morphological indicators demonstrated no significant changes in myocardial scar tissue in the hydrogel combined with the stem cell group after the 12-month follow-up period. In contrast, both the stem cell treatment group and the control group showed a significant increase in scar tissue. These clinical trial results suggest that the hydrogel combined with stem cell treatment exhibits long-term therapeutic effects, improving cardiac function and morphology.

In conclusion, achieving comprehensive clinical transformation in hydrogel-based combination therapy for myocardial infarction depends on further optimizing the therapeutic approach and enhancing the safety and efficiency of the delivery method.

This article focuses on evaluating the therapeutic efficacy of injectable hydrogels compared to other types of bio-delivery scaffolds, as determined through a systematic review and meta-analysis. Additionally, this study examines the therapeutic effectiveness of combining injectable hydrogels with different therapies in animal models of myocardial infarction. The findings demonstrate that the combination of injectable hydrogels with other therapies significantly enhances therapeutic outcomes in the ischemic myocardial region, which is crucial for restoring myocardial function and preserving cardiac morphology. The analysis reveals that various combination therapy regimens effectively restore myocardial function and maintain cardiac morphology. Specifically, cellular therapy consistently proves to be therapeutically effective. Moreover, through careful design of functional adaptation and action staging, the utilization of a Multitherapy approach exhibits a synergistic effect, resulting in better outcomes compared to individual therapies alone.

Analyses have demonstrated the close interrelation between the recovery of myocardial function and morphology. However, given the complexity of the recovery process following myocardial ischemia, individual therapies often fall short in achieving efficient restoration of both functional and morphological aspects. Sole reliance on drugs or cellular therapies is inadequate to fully recover damaged myocardium. Therefore, future research should focus on exploring the potential of combined therapies. Furthermore, as the study of combination therapies progresses, it becomes increasingly important to systematically evaluate and conduct meta-analyses of protocols involving injectable hydrogels, which present challenges in subdivision.

In conclusion, hydrogel-based combination therapy demonstrates significant therapeutic effects for myocardial infarction. Based on our analysis of multiple literature sources, we strongly recommend comprehensive monitoring of the therapeutic process and outcome measures in small animal models. Subsequently, large-scale animal experiments should be conducted to validate these effects. Such an approach will provide reliable references for clinical translation and enhance our understanding of hydrogel-based combination therapy. Through a meta-analysis of a wide range of preclinical studies, combined with the findings from conducted clinical trials, it has been demonstrated that hydrogel-based combination therapy yields positive outcomes for the treatment of myocardial infarction.

Availability of data and materials

All data generated or analysed during this study are included in this published article [and its supplementary information files].

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This work is supported by the Natural Science Foundation of Tibet Autonomous Region Grant number (XZ202201ZR0036g), Tibetan University Master’s Degree “High-Level Talent Cultivation Program” Project (2021-GSP-S051), and Tibet Autonomous Region Science and Technology Department central guidance for local science and technology development funds project (XZ202301YD0016C).

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Han Gao, Song Liu, Shanshan Qin & Dunzhu Danzeng

School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, Sichuan, China

Jiali Yang & Tian Yue

West China School of Pharmacy, Sichuan University, Chengdu, Sichuan, China

School of Pharmacy, North Sichuan Medical College, Nanchong, Sichuan, China

School of Medicine, Southwest Jiaotong University, Chengdu, Sichuan, China

Department of Cardiology, Chengdu Third People’s Hospital, Chengdu, Sichuan, China

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COI Form Author 1 (First Author):Han Gao Conceptualization, Methodology, Software, Investigation, Formal Analysis, Writing - Original Draft; Author 2(Co- first Author): Song Liu Data Curation, Writing - Original Draft; Author 3: Shanshan Qin  Data Curation,Visualization, Investigation; Author 4: Jiali Yang  Data Curation,Resources, Supervision; Author 5: Tian Yue  Data Curation,Software, Validation. Author 6: Bengui Ye  Software, Validation Author 7: Yue Tang Visualization Author 8: Jie Fen  Visualization. Author8:(Co-corresponding Author): Jun Hou  Conceptualization,Writing - Review & Editing. Author 9:(Corresponding Author): Dunzhu Danzeng  Conceptualization, Funding Acquisition, Resources, Supervision, Writing - Review & Editing. The co-creators promise that this article has not been plagiarized and is not copyright free.

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Supplementary Information

Additional file 1: supplement table 1..

PRISMA Checklist. PRISMA Checklist From: Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med 6(6): e1000097. doi:10.1371/journal.pmed1000097. Supplement Table 2. Detailed search strategy. Supplement Table 3. Study inclusion and exclusion criteria. Supplement Table 4. Study quality assessment using the Heyland methodological quality. score. Supplement Table 5 . A. Ejection Fraction. B. Fractional Shortening. C. End Systolic Diameter. D. End Diastolic Diameter. E. End Diastolic Volume. F. End Systolic Volume. G. Infact size. H. Wall thickness. Continuous a priori subgroup analyses on (A) Ejection Fraction and (B) Fractional Shortening, (C) End Systolic Diameter, (D) End Diastolic Diameter, (E) End Systolic Volume, (F) End Diastolic Volume, (G) Infarct Size, and (H) Wall Thickness in the included studies. β is the slope derived from meta-regression analyses and represents the treatment effect of stem cell embedded scaffolds compared to independent injections of cells for primary and secondary outcomes in the included studies. The residual I 2 value indicates heterogeneity unexplained by the subgroup and is reported as a percent value, where I 2 ≤ 50% indicated “moderate” heterogeneity, I²≥ 50% indicated “substantial” heterogeneity, and ≥ 75% indicated “considerable” heterogeneity. P-value significance for heterogeneity was set as P < 0.10. Supplement Figure 1. Cochrane risk of bias tool to asses Selection Bias, Performance Bias, Detection Bias, Attrition Bias, and Reporting Bias in studies investigating the effects of stem cell-embedded scaffolds on cardiac repair. Authors’ judgments concerning each risk of bias item are presented as percentages across all included studies. Supplement Figure 2. A. End Systolic Diameter. B. End Diastolic Diameter. C. End Systolic Volume. D. End Diastolic Volume. E. Infarct Size. F. Wall Thickness. Forest plots of all trials investigating the effect of hydrogel combination therapy on left ventricular (A) End Systolic Diameter, (B) End Diastolic Diameter, (C) End Systolic Volume, (D) End Diastolic Volume, (E) Infarct Size, and (F) Wall Thickness in the included studies. Pooled effect estimates (diamonds) are shown: one each for trials using hydrogels, patches, microspheres/beads, and their combination (total). Data are expressed as weighted mean differences with 95% CIs, using generic inverse-variance random-effects models. Between-studies heterogeneity was tested by using the Cochran Q statistic (chi-square) at a significance level of P < 0.05. Reference numbers for each study can be found in Table 1 and list of references. Supplement Figure 3. A. EF. B. FS. C. End Systolic Diameter. D. End Diastolic Diameter. E. End Systolic Volume. F. Infarct Size. G. Wall Thickness. Forest plots of all trials investigating the effect of hydrogel combination multitherapy on left ventricular (A) EF, (B) FS, (C) End Systolic Diameter, (D) End Diastolic Diameter, (E) End Systolic Volume, (F) Infarct Size, and (G) Wall Thickness in the included studies. Pooled effect estimates (diamonds) are shown: one each for trials using hydrogels, patches, microspheres/beads, and their combination (total). Data are expressed as weighted mean differences with 95% CIs, using generic inverse-variance random-effects models. Between-studies heterogeneity was tested by using the Cochran Q statistic (chi-square) at a significance level of P < 0.05. Reference numbers for each study can be found in Table 1 and list of references. Supplement Figure 4. A.EF . B.FS. C. End Systolic Diameter. D. End Diastolic Diameter. E. End Systolic Volume. F. Infarct Size. G. Wall Thickness. Sensitivity analysis. A.EF,B.FS,C. End Systolic Diameter, D. End Diastolic Diameter, E. End Systolic Volume, F. Infarct Size and G. Wall Thickness. Supplement Figure 5. Meta-regression analysis of variables that may impact changes in Fractional Shortening. Dichotomous a priori subgroup analysis was performed in a trial investigating the effect of injectable hydrogel combination therapy on infarct size. Mean differences in end systolic diameter in the combination regimen treatment group compared to the injectable hydrogel-only treatment group were grouped by A hydrogel type, B combination therapy, C sex, D small animal model, E time of treatment, F Duration, G MQS. Supplement Figure 6. Meta-regression analysis of variables that may impact changes in LV End Systolic Diameter. Dichotomous a priori subgroup analysis was performed in a trial investigating the effect of injectable hydrogel combination therapy on infarct size. Mean differences in end systolic diameter in the combination regimen treatment group compared to the injectable hydrogel-only treatment group were grouped by A hydrogel type, B combination therapy, C sex, D small animal model, E time of treatment. Supplement Figure 7. Meta-regression analysis of variables that may impact changes in LV End Diastolic Diameter. Dichotomous a priori subgroup analysis was performed in a trial investigating the effect of injectable hydrogel combination therapy on infarct size. Mean differences in end diastolic diameter in the combination regimen treatment group compared to the injectable hydrogel-only treatment group were grouped by A hydrogel type, B combination therapy, C sex, D small animal model, E time of treatment. Supplement Figure 8. Meta-regression analysis of variables that may impact changes in LV End Systolic Volume. Dichotomous a priori subgroup analysis was performed in a trial investigating the effect of injectable hydrogel combination therapy on infarct size. Mean differences in end systolic volume in the combination regimen treatment group compared to the injectable hydrogel-only treatment group were grouped by A hydrogel type, B combination therapy, C sex, D small animal model, E time of treatment, F duration. Supplement Figure 9. Meta-regression analysis of variables that may impact changes in LV End Diastolic Volume. Dichotomous a priori subgroup analysis was performed in a trial investigating the effect of injectable hydrogel combination therapy on infarct size. Mean differences in end diastolic volume in the combination regimen treatment group compared to the injectable hydrogel-only treatment group were grouped by A hydrogel type, B combination therapy, C sex, D time of treatment, E duration. Supplement Figure 10. Meta-regression analysis of variables that may impact changes in Infarct Size. Dichotomous a priori subgroup analysis was performed in a trial investigating the effect of injectable hydrogel combination therapy on infarct size. Mean differences in wall thickness in the combination regimen treatment group compared to the injectable hydrogel-only treatment group were grouped by A hydrogel type, B combination therapy, C sex, D small animal model, E time of treatment, F duration, G MQS, H animal model. Supplement Figure 11. Meta-regression analysis of variables that may impact changes in Wall Thickness. Dichotomous a priori subgroup analysis was performed in a trial investigating the effect of injectable hydrogel combination therapy on wall thickness. Mean differences in wall thickness in the combination regimen treatment group compared to the injectable hydrogel-only treatment group were grouped by A hydrogel type, B combination therapy, C sex, D small animal model, E time of treatment, F duration, G animal model. Supplement Figure 12. Funnel plot for the effect of Injectable hydrogel combination therapy on (A) End Systolic Diameter, (B) End Diastolic Diameter, (C) End Systolic Volume, (D) End Diastolic Volume, (E) Infarct Size, and (F) Wall Thickness. Supplement Figure 13. Funnel plot for the effect of Injectable hydrogel combination therapy on Ejection Fraction in Non-mouse small animal models.

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Gao, H., Liu, S., Qin, S. et al. Injectable hydrogel-based combination therapy for myocardial infarction: a systematic review and Meta-analysis of preclinical trials. BMC Cardiovasc Disord 24 , 119 (2024). https://doi.org/10.1186/s12872-024-03742-0

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    Meta-analysis is a specialised type of systematic review which is quantitative and rigorous, often comparing data and results across multiple similar studies. This is a common approach in medical research where several papers might report the results of trials of a particular treatment, for instance.

  11. Systematic Reviews and Meta-Analysis: A Guide for Beginners

    Meta-analysis is a statistical tool that provides pooled estimates of effect from the data extracted from individual studies in the systematic review. The graphical output of meta-analysis is a forest plot which provides information on individual studies and the pooled effect.

  12. Systematic Reviews and Meta Analysis

    The Preferred Reporting Items for Systematic Reviews and Meta-Analyses is an evidence-based minimum set of items for reporting in systematic reviews and meta-analyses. A 27-item checklist, PRISMA focuses on randomized trials but can also be used as a basis for reporting systematic reviews of other types of research, particularly evaluations of ...

  13. Systematic reviews vs meta-analysis: what's the difference?

    In the levels of evidence pyramid, systematic reviews are only surpassed by meta-analyses. To conduct a systematic review, you will need, among other things: A specific research question, usually in the form of a PICO question. Pre-specified eligibility criteria, to decide which articles will be included or discarded from the review.

  14. Systematic Review

    A meta-analysis is a statistical analysis, not a type of review. A meta-analysis is a technique to synthesize results from multiple studies. It's a statistical analysis that combines the results of two or more studies, usually to estimate an effect size. Systematic review vs. literature review

  15. How to conduct a meta-analysis in eight steps: a practical guide

    Fisch C, Block J (2018) Six tips for your (systematic) literature review in business and management research. Manag Rev Quart 68:103-106. ... (2020) Literature searches in systematic reviews and meta-analyses: A review, evaluation, and recommendations. J Vocat Behav 118:103377. Article Google Scholar

  16. What is a Systematic Review?

    A meta-analysis is a type of systematic review that uses statistical methods to summarize the results of studies that meet the criteria for inclusion within the review. For more information on systematic reviews and meta-analyses, visit Systematic reviews and meta-analyses: a step-by-step guide by Kate McAllister.

  17. Method for conducting systematic literature review and meta-analysis

    This paper presents a method to conduct a systematic literature review (SLR) and meta-analysis studies on environmental science. SLR is a process that allowed to collect relevant evidence on the given topic that fits the pre-specified eligibility criteria and to have an answer for the formulated research questions.

  18. Conducting systematic literature reviews and bibliometric analyses

    R provides packages for various areas of interest, including systematic literature review or the related field of meta-analysis. 2 These include Bibliometrix (Aria and Cuccurullo, 2017), Revtools (Westgate, 2018) and Litsearchr (Grames et al, 2019) of the Metaverse project, 3 as well as Adjutant (Crisan et al., 2018) and Metagear (Lajeunesse ...

  19. A Practical Guide to Perform a Systematic Literature Review and Meta

    Nowadays, systematic literature reviews/meta-analyses of clinical trials are considered the best evidence in clinical research; thus, if performed appropriately, they can save resources...

  20. Pain assessment tools in adults with communication disorders

    Verbal communication is the "gold standard" for assessing pain. Consequently, individuals with communication disorders are particularly vulnerable to incomplete pain management. This review aims at identifying the current pain assessment instruments for adult patients with communication disorders. A systematic review with meta-analysis was conducted on PubMed, PEDRO, EBSCOhost, VHL and ...

  21. 'It depends': what 86 systematic reviews tell us about what strategies

    This review updates and extends our previous review of systematic reviews of interventions designed to implement research evidence into clinical practice. To identify potentially relevant peer-reviewed research papers, we developed a comprehensive systematic literature search strategy based on the terms used in the Grimshaw et al. [ 9 ] and ...

  22. Systematic Reviews and Meta-Analysis: A Guide for Beginners

    Meta-analysis is a statistical tool that provides pooled estimates of effect from the data extracted from individual studies in the systematic review. The graphical output of meta-analysis is a forest plot which provides information on individual studies and the pooled effect.

  23. Effect of exercise for depression: systematic review and network meta

    Objective To identify the optimal dose and modality of exercise for treating major depressive disorder, compared with psychotherapy, antidepressants, and control conditions. Design Systematic review and network meta-analysis. Methods Screening, data extraction, coding, and risk of bias assessment were performed independently and in duplicate. Bayesian arm based, multilevel network meta ...

  24. Association of nonpharmacological interventions for cognitive function

    This review followed the literature screening process. After removing duplicate literature, researchers (XY L, GP W) independently read the title and abstract according to the inclusion and exclusion criteria and then reviewed the remaining literature. ... Juni P (2014) Systematic reviews and meta-analyses of randomized trials: principles and ...

  25. Systematic Review & Evidence Synthesis

    Systematic review; Literature (narrative) review; Scoping review; Rapid review; Meta-analysis; Literature Reviews Explained (LITR-EX) Types of Evidence Synthesis - Evidence Synthesis Methods Interest Group ... Best practice in systematic reviews: the importance of protocols and registration. PLoS Med. 2011;8(2):e1001009.

  26. Effort-reward imbalance at work and risk of depressive disorders. A

    Methods: We conducted a systematic review and meta-analysis of published prospective cohort studies examining the association of ERI at baseline with onset of depressive disorders at follow-up. The work was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement and a detailed study ...

  27. PDF Methodological and statistical characteristics of meta-analyses on

    regional pain syndrome: systematic review and meta-analysis of randomized controlled trials. Pain Physician 2022;25:521-30. 24 Duarte RV, Nevitt S, Copley S, et al. Systematic review and network meta-analysis of ... systematic literature review and meta- analysis. Pain Ther 2021;10:849-74. 27 Karri J, Orhurhu V, Wahezi S, et al. Comparison ...

  28. Injectable hydrogel-based combination therapy for myocardial infarction

    A literature review was conducted using PubMed, Web of Science, Scopus, and Cochrane databases. A total of 83 papers, including studies on 1332 experimental animals (rats, mice, rabbits, sheep, and pigs), were included in the meta-analysis based on the inclusion and exclusion criteria. ... Building upon previous systematic review and meta ...