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• 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.

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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Systematic reviews: the good, the bad, and the ugly

Affiliation.

• 1 Division of Gastroenterology, Department of Medicine, McMaster University Health Science Centre, Hamilton, Ontario, Canada.
• PMID: 19417748
• DOI: 10.1038/ajg.2009.118

Systematic reviews systematically evaluate and summarize current knowledge and have many advantages over narrative reviews. Meta-analyses provide a more reliable and enhanced precision of effect estimate than do individual studies. Systematic reviews are invaluable for defining the methods used in subsequent studies, but, as retrospective research projects, they are subject to bias. Rigorous research methods are essential, and the quality depends on the extent to which scientific review methods are used. Systematic reviews can be misleading, unhelpful, or even harmful when data are inappropriately handled; meta-analyses can be misused when the difference between a patient seen in the clinic and those included in the meta-analysis is not considered. Furthermore, systematic reviews cannot answer all clinically relevant questions, and their conclusions may be difficult to incorporate into practice. They should be reviewed on an ongoing basis. As clinicians, we need proper methodological training to perform good systematic reviews and must ask the appropriate questions before we can properly interpret such a review and apply its conclusions to our patients. This paper aims to assist in the reading of a systematic review.

Publication types

• Comparative Study
• Systematic Review
• Evidence-Based Medicine / standards*
• Evidence-Based Medicine / trends
• Gastroenterology*
• Meta-Analysis as Topic*
• Randomized Controlled Trials as Topic
• Reproducibility of Results
• Research Design
• Review Literature as Topic*
• Sensitivity and Specificity

Strengths and Weaknesses of Systematic Reviews

Automate every stage of your literature review to produce evidence-based research faster and more accurately.

Systematic reviews are considered credible sources since they are comprehensive, reproducible, and precise in stating the outcomes. The type of review system used and the approach taken depend on the goals and objectives of the research. To choose the best-suited review system, researchers must be aware of the strengths and weaknesses of each one.

Let us now look at the strengths and limitations of systematic reviews.

Strengths Of Systematic Reviews

Systematic reviews have become increasingly popular owing to their transparency, accuracy, replicability, and reduced risk of bias. Some of the main benefits of systematic reviews are;

Specificity

Researchers can answer specific research questions of high importance. For example, the efficacy of a particular drug in the treatment of an illness.

Explicit Methodology

A systematic review requires rigorous planning. Each stage of the review is predefined to the last detail. The research question is formulated using the PICO (population, intervention, comparison, and outcome) approach. A strict eligibility criteria is then established for inclusion and exclusion criteria for selecting the primary studies for the review. Every stage of the systematic review methodology is pre-specified to the last detail and made publicly available, even before starting the review process. This makes all the stages in the methodology transparent and reproducible.

Reliable And Accurate Results

The results of a systematic review are either analyzed qualitatively and presented as a textual narrative or quantitatively using statistical methods such as meta-analyses and numeric effect estimates. The quality of evidence or the confidence in effect estimates is calculated using the standardized GRADE approach.

Comprehensive And Exhaustive

A systematic review involves a thorough search of all the available data on a certain topic. It is exhaustive and considers every bit of evidence in synthesizing the outcome. Primary sources for the review are collected from databases and multiple sources, such as blogs from pharmaceutical companies, unpublished research directly from researchers, government reports, and conference proceedings. These are referred to as grey literature. The search criteria and keywords used in sourcing are specific and predefined.

Reproducible

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Weaknesses Of Systematic Reviews

Although systematic reviews are robust tools in scientific research they are not immune to errors. They can be misleading, or even harmful if the data is inappropriately handled or if they are biased. Some of the limitations of systematic reviews include:

Mass Production

Due to the popularity systematic reviews have gained, they tend to be used more than required. The growth rate of systematic reviews has outpaced the growth rate of studies overall. This results in redundancy. For example, a survey published in the BMJ[1], included 73 randomly selected meta-analyses published in 2010 found that for two-thirds of these studies, there was at least one, and sometimes as many as 13, additional meta-analyses published on the same topic by early 2013.

Risk of Bias

Although systematic reviews have many advantages, they are also more susceptible to certain types of biases. A bias is a systematic or methodological error that causes misrepresentation of the study outcomes. As bias can appear at any stage, authors should be aware of the specific risks at each stage of the review process. Most of the known errors in systematic reviews arise in the selection and publication stages. The eligibility criterion in a systematic review helps to avoid selection bias. Poor study design and execution can also result in a biased outcome. It’s important to learn about the types of bias in systematic reviews .

Expressing Strong Opinions by Stealth

Selective outcome reporting is a major threat to a systematic review. The author or reviewer may decide to only report a selection of the statistically significant outcomes that suit his interest. The possibility of unfair or misleading interpretation of evidence outcomes in a systematic review can have serious implications.

Like any review system, systematic reviews have their advantages and disadvantages. Understanding them is essential to making a choice of which review system to use.

Overlapping meta-analyses on the same topic: survey of published studies. BMJ 2013; 347:f4501

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• Published: 08 April 2024

A systematic review and multivariate meta-analysis of the physical and mental health benefits of touch interventions

• Julian Packheiser   ORCID: orcid.org/0000-0001-9805-6755 2   na1   nAff1 ,
• Helena Hartmann 2 , 3 , 4   na1 ,
• Kelly Fredriksen 2 ,
• Valeria Gazzola   ORCID: orcid.org/0000-0003-0324-0619 2 ,
• Christian Keysers   ORCID: orcid.org/0000-0002-2845-5467 2 &
• Frédéric Michon   ORCID: orcid.org/0000-0003-1289-2133 2  

Nature Human Behaviour ( 2024 ) Cite this article

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• Human behaviour
• Paediatric research
• Randomized controlled trials

Receiving touch is of critical importance, as many studies have shown that touch promotes mental and physical well-being. We conducted a pre-registered (PROSPERO: CRD42022304281) systematic review and multilevel meta-analysis encompassing 137 studies in the meta-analysis and 75 additional studies in the systematic review ( n  = 12,966 individuals, search via Google Scholar, PubMed and Web of Science until 1 October 2022) to identify critical factors moderating touch intervention efficacy. Included studies always featured a touch versus no touch control intervention with diverse health outcomes as dependent variables. Risk of bias was assessed via small study, randomization, sequencing, performance and attrition bias. Touch interventions were especially effective in regulating cortisol levels (Hedges’ g  = 0.78, 95% confidence interval (CI) 0.24 to 1.31) and increasing weight (0.65, 95% CI 0.37 to 0.94) in newborns as well as in reducing pain (0.69, 95% CI 0.48 to 0.89), feelings of depression (0.59, 95% CI 0.40 to 0.78) and state (0.64, 95% CI 0.44 to 0.84) or trait anxiety (0.59, 95% CI 0.40 to 0.77) for adults. Comparing touch interventions involving objects or robots resulted in similar physical (0.56, 95% CI 0.24 to 0.88 versus 0.51, 95% CI 0.38 to 0.64) but lower mental health benefits (0.34, 95% CI 0.19 to 0.49 versus 0.58, 95% CI 0.43 to 0.73). Adult clinical cohorts profited more strongly in mental health domains compared with healthy individuals (0.63, 95% CI 0.46 to 0.80 versus 0.37, 95% CI 0.20 to 0.55). We found no difference in health benefits in adults when comparing touch applied by a familiar person or a health care professional (0.51, 95% CI 0.29 to 0.73 versus 0.50, 95% CI 0.38 to 0.61), but parental touch was more beneficial in newborns (0.69, 95% CI 0.50 to 0.88 versus 0.39, 95% CI 0.18 to 0.61). Small but significant small study bias and the impossibility to blind experimental conditions need to be considered. Leveraging factors that influence touch intervention efficacy will help maximize the benefits of future interventions and focus research in this field.

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The sense of touch has immense importance for many aspects of our life. It is the first of all the senses to develop in newborns 1 and the most direct experience of contact with our physical and social environment 2 . Complementing our own touch experience, we also regularly receive touch from others around us, for example, through consensual hugs, kisses or massages 3 .

The recent coronavirus pandemic has raised awareness regarding the need to better understand the effects that touch—and its reduction during social distancing—can have on our mental and physical well-being. The most common touch interventions, for example, massage for adults or kangaroo care for newborns, have been shown to have a wide range of both mental and physical health benefits, from facilitating growth and development to buffering against anxiety and stress, over the lifespan of humans and animals alike 4 . Despite the substantial weight this literature gives to support the benefits of touch, it is also characterized by a large variability in, for example, studied cohorts (adults, children, newborns and animals), type and duration of applied touch (for example, one-time hug versus repeated 60-min massages), measured health outcomes (ranging from physical health outcomes such as sleep and blood pressure to mental health outcomes such as depression or mood) and who actually applies the touch (for example, partner versus stranger).

A meaningful tool to make sense of this vast amount of research is through meta-analysis. While previous meta-analyses on this topic exist, they were limited in scope, focusing only on particular types of touch, cohorts or specific health outcomes (for example, refs. 5 , 6 ). Furthermore, despite best efforts, meaningful variables that moderate the efficacy of touch interventions could not yet be identified. However, understanding these variables is critical to tailor touch interventions and guide future research to navigate this diverse field with the ultimate aim of promoting well-being in the population.

In this Article, we describe a pre-registered, large-scale systematic review and multilevel, multivariate meta-analysis to address this need with quantitative evidence for (1) the effect of touch interventions on physical and mental health and (2) which moderators influence the efficacy of the intervention. In particular, we ask whether and how strongly health outcomes depend on the dynamics of the touching dyad (for example, humans or robots/objects, familiarity and touch directionality), demographics (for example, clinical status, age or sex), delivery means (for example, type of touch intervention or touched body part) and procedure (for example, duration or number of sessions). We did so separately for newborns and for children and adults, as the health outcomes in newborns differed substantially from those in the other age groups. Despite the focus of the analysis being on humans, it is widely known that many animal species benefit from touch interactions and that engaging in touch promotes their well-being as well 7 . Since animal models are essential for the investigation of the mechanisms underlying biological processes and for the development of therapeutic approaches, we accordingly included health benefits of touch interventions in non-human animals as part of our systematic review. However, this search yielded only a small number of studies, suggesting a lack of research in this domain, and as such, was insufficient to be included in the meta-analysis. We evaluate the identified animal studies and their findings in the discussion.

Touch interventions have a medium-sized effect

The pre-registration can be found at ref. 8 . The flowchart for data collection and extraction is depicted in Fig. 1 .

Animal outcomes refer to outcomes measured in non-human species that were solely considered as part of a systematic review. Included languages were French, Dutch, German and English, but our search did not identify any articles in French, Dutch or German. MA, meta-analysis.

For adults, a total of n  = 2,841 and n  = 2,556 individuals in the touch and control groups, respectively, across 85 studies and 103 cohorts were included. The effect of touch overall was medium-sized ( t (102) = 9.74, P  < 0.001, Hedges’ g  = 0.52, 95% confidence interval (CI) 0.42 to 0.63; Fig. 2a ). For newborns, we could include 63 cohorts across 52 studies comprising a total of n  = 2,134 and n  = 2,086 newborns in the touch and control groups, respectively, with an overall effect almost identical to the older age group ( t (62) = 7.53, P  < 0.001, Hedges’ g  = 0.56, 95% CI 0.41 to 0.71; Fig. 2b ), suggesting that, despite distinct health outcomes, touch interventions show comparable effects across newborns and adults. Using these overall effect estimates, we conducted a power sensitivity analysis of all the included primary studies to investigate whether such effects could be reliably detected 9 . Sufficient power to detect such effect sizes was rare in individual studies, as investigated by firepower plots 10 (Supplementary Figs. 1 and 2 ). No individual effect size from either meta-analysis was overly influential (Cook’s D  < 0.06). The benefits were similar for mental and physical outcomes (mental versus physical; adults: t (101) = 0.79, P  = 0.432, Hedges’ g difference of −0.05, 95% CI −0.16 to 0.07, Fig. 2c ; newborns: t (61) = 1.08, P  = 0.284, Hedges’ g difference of −0.19, 95% CI −0.53 to 0.16, Fig. 2d ).

a , Orchard plot illustrating the overall benefits across all health outcomes for adults/children across 469 in part dependent effect sizes from 85 studies and 103 cohorts. b , The same as a but for newborns across 174 in part dependent effect sizes from 52 studies and 63 cohorts. c , The same as a but separating the results for physical versus mental health benefits across 469 in part dependent effect sizes from 85 studies and 103 cohorts. d , The same as b but separating the results for physical versus mental health benefits across 172 in part dependent effect sizes from 52 studies and 63 cohorts. Each dot reflects a measured effect, and the number of effects ( k ) included in the analysis is depicted in the bottom left. Mean effects and 95% CIs are presented in the bottom right and are indicated by the central black dot (mean effect) and its error bars (95% CI). The heterogeneity Q statistic is presented in the top left. Overall effects of moderator impact were assessed via an F test, and post hoc comparisons were done using t tests (two-sided test). Note that the P values above the mean effects indicate whether an effect differed significantly from a zero effect. P values were not corrected for multiple comparisons. The dot size reflects the precision of each individual effect (larger indicates higher precision). Small-study bias for the overall effect was significant ( F test, two-sided test) in the adult meta-analysis ( F (1, 101) = 21.24, P  < 0.001; Supplementary Fig. 3 ) as well as in the newborn meta-analysis ( F (1, 61) = 5.25, P  = 0.025; Supplementary Fig. 4 ).

Source data

On the basis of the overall effect of both meta-analyses as well as their median sample sizes, the minimum number of studies necessary for subgroup analyses to achieve 80% power was k  = 9 effects for adults and k  = 8 effects for newborns (Supplementary Figs. 5 and 6 ). Assessing specific health outcomes with sufficient power in more detail in adults (Fig. 3a ) revealed smaller benefits to sleep and heart rate parameters, moderate benefits to positive and negative affect, diastolic blood and systolic blood pressure, mobility and reductions of the stress hormone cortisol and larger benefits to trait and state anxiety, depression, fatigue and pain. Post hoc tests revealed stronger benefits for pain, state anxiety, depression and trait anxiety compared with respiratory, sleep and heart rate parameters (see Fig. 3 for all post hoc comparisons). Reductions in pain and state anxiety were increased compared with reductions in negative affect ( t (83) = 2.54, P  = 0.013, Hedges’ g difference of 0.31, 95% CI 0.07 to 0.55; t (83) = 2.31, P  = 0.024, Hedges’ g difference of 0.27, 95% CI 0.03 to 0.51). Benefits to pain symptoms were higher compared with benefits to positive affect ( t (83) = 2.22, P  = 0.030, Hedges’ g difference of 0.29, 95% CI 0.04 to 0.54). Finally, touch resulted in larger benefits to cortisol release compared with heart rate parameters ( t (83) = 2.30, P  = 0.024, Hedges’ g difference of 0.26, 95% CI 0.04–0.48).

a , b , Health outcomes in adults analysed across 405 in part dependent effect sizes from 79 studies and 97 cohorts ( a ) and in newborns analysed across 105 in part dependent effect sizes from 46 studies and 56 cohorts ( b ). The type of health outcomes measured differed between adults and newborns and were thus analysed separately. Numbers on the right represent the mean effect with its 95% CI in square brackets and the significance level estimating the likelihood that the effect is equal to zero. Overall effects of moderator impact were assessed via an F test, and post hoc comparisons were done using t tests (two-sided test). The F value in the top right represents a test of the hypothesis that all effects within the subpanel are equal. The Q statistic represents the heterogeneity. P values of post hoc tests are depicted whenever significant. P values above the horizontal whiskers indicate whether an effect differed significantly from a zero effect. Vertical lines indicate significant post hoc tests between moderator levels. P values were not corrected for multiple comparisons. Physical outcomes are marked in red. Mental outcomes are marked in blue.

In newborns, only physical health effects offered sufficient data for further analysis. We found no benefits for digestion and heart rate parameters. All other health outcomes (cortisol, liver enzymes, respiration, temperature regulation and weight gain) showed medium to large effects (Fig. 3b ). We found no significant differences among any specific health outcomes.

Non-human touch and skin-to-skin contact

In some situations, a fellow human is not readily available to provide affective touch, raising the question of the efficacy of touch delivered by objects and robots 11 . Overall, we found humans engaging in touch with other humans or objects to have medium-sized health benefits in adults, without significant differences ( t (99) = 1.05, P  = 0.295, Hedges’ g difference of 0.12, 95% CI −0.11 to 0.35; Fig. 4a ). However, differentiating physical versus mental health benefits revealed similar benefits for human and object touch on physical health outcomes, but larger benefits on mental outcomes when humans were touched by humans ( t (97) = 2.32, P  = 0.022, Hedges’ g difference of 0.24, 95% CI 0.04 to 0.44; Fig. 4b ). It must be noted that touching with an object still showed a significant effect (see Supplementary Fig. 7 for the corresponding orchard plot).

a , Forest plot comparing humans versus objects touching a human on health outcomes overall across 467 in part dependent effect sizes from 85 studies and 101 cohorts. b , The same as a but separately for mental versus physical health outcomes across 467 in part dependent effect sizes from 85 studies and 101 cohorts. c , Results with the removal of all object studies, leaving 406 in part dependent effect sizes from 71 studies and 88 cohorts to identify whether missing skin-to-skin contact is the relevant mediator of higher mental health effects in human–human interactions. Numbers on the right represent the mean effect with its 95% CI in square brackets and the significance level estimating the likelihood that the effect is equal to zero. Overall effects of moderator impact were assessed via an F test, and post hoc comparisons were done using t tests (two-sided test). The F value in the top right represents a test of the hypothesis that all effects within the subpanel are equal. The Q statistic represents the heterogeneity. P values of post hoc tests are depicted whenever significant. P values above the horizontal whiskers indicate whether an effect differed significantly from a zero effect. Vertical lines indicate significant post hoc tests between moderator levels. P values were not corrected for multiple comparisons. Physical outcomes are marked in red. Mental outcomes are marked in blue.

We considered the possibility that this effect was due to missing skin-to-skin contact in human–object interactions. Thus, we investigated human–human interactions with and without skin-to-skin contact (Fig. 4c ). In line with the hypothesis that skin-to-skin contact is highly relevant, we again found stronger mental health benefits in the presence of skin-to-skin contact that however did not achieve nominal significance ( t (69) = 1.95, P  = 0.055, Hedges’ g difference of 0.41, 95% CI −0.00 to 0.82), possibly because skin-to-skin contact was rarely absent in human–human interactions, leading to a decrease in power of this analysis. Results for skin-to-skin contact as an overall moderator can be found in Supplementary Fig. 8 .

Influences of type of touch

The large majority of touch interventions comprised massage therapy in adults and kangaroo care in newborns (see Supplementary Table 1 for a complete list of interventions across studies). However, comparing the different types of touch explored across studies did not reveal significant differences in effect sizes based on touch type, be it on overall health benefits (adults: t (101) = 0.11, P  = 0.916, Hedges’ g difference of 0.02, 95% CI −0.32 to 0.29; Fig. 5a ) or comparing different forms of touch separately for physical (massage therapy versus other forms: t (99) = 0.99, P  = 0.325, Hedges’ g difference 0.16, 95% CI −0.15 to 0.47) or for mental health benefits (massage therapy versus other forms: t (99) = 0.75, P  = 0.458, Hedges’ g difference of 0.13, 95% CI −0.22 to 0.48) in adults (Fig. 5c ; see Supplementary Fig. 9 for the corresponding orchard plot). A similar picture emerged for physical health effects in newborns (massage therapy versus kangaroo care: t (58) = 0.94, P  = 0.353, Hedges’ g difference of 0.15, 95% CI −0.17 to 0.47; massage therapy versus other forms: t (58) = 0.56, P  = 0.577, Hedges’ g difference of 0.13, 95% CI −0.34 to 0.60; kangaroo care versus other forms: t (58) = 0.07, P  = 0.947, Hedges’ g difference of 0.02, 95% CI −0.46 to 0.50; Fig. 5d ; see also Supplementary Fig. 10 for the corresponding orchard plot). This suggests that touch types may be flexibly adapted to the setting of every touch intervention.

a , Forest plot of health benefits comparing massage therapy versus other forms of touch in adult cohorts across 469 in part dependent effect sizes from 85 studies and 103 cohorts. b , Forest plot of health benefits comparing massage therapy, kangaroo care and other forms of touch for newborns across 174 in part dependent effect sizes from 52 studies and 63 cohorts. c , The same as a but separating mental and physical health benefits across 469 in part dependent effect sizes from 85 studies and 103 cohorts. d , The same as b but separating mental and physical health outcomes where possible across 164 in part dependent effect sizes from 51 studies and 62 cohorts. Note that an insufficient number of studies assessed mental health benefits of massage therapy or other forms of touch to be included. Numbers on the right represent the mean effect with its 95% CI in square brackets and the significance level estimating the likelihood that the effect is equal to zero. Overall effects of moderator impact were assessed via an F test, and post hoc comparisons were done using t tests (two-sided test). The F value in the top right represents a test of the hypothesis that all effects within the subpanel are equal. The Q statistic represents heterogeneity. P values of post hoc tests are depicted whenever significant. P values above the horizontal whiskers indicate whether an effect differed significantly from a zero effect. Vertical lines indicate significant post hoc tests between moderator levels. P values were not corrected for multiple comparisons. Physical outcomes are marked in red. Mental outcomes are marked in blue.

The role of clinical status

Most research on touch interventions has focused on clinical samples, but are benefits restricted to clinical cohorts? We found health benefits to be significant in clinical and healthy populations (Fig. 6 ), whether all outcomes are considered (Fig. 6a,b ) or physical and mental health outcomes are separated (Fig. 6c,d , see Supplementary Figs. 11 and 12 for the corresponding orchard plots). In adults, however, we found higher mental health benefits for clinical populations compared with healthy ones (Fig. 6c ; t (99) = 2.11, P  = 0.037, Hedges’ g difference of 0.25, 95% CI 0.01 to 0.49).

a , Health benefits for clinical cohorts of adults versus healthy cohorts of adults across 469 in part dependent effect sizes from 85 studies and 103 cohorts. b , The same as a but for newborn cohorts across 174 in part dependent effect sizes from 52 studies and 63 cohorts. c , The same as a but separating mental versus physical health benefits across 469 in part dependent effect sizes from 85 studies and 103 cohorts. d , The same as b but separating mental versus physical health benefits across 172 in part dependent effect sizes from 52 studies and 63 cohorts. Numbers on the right represent the mean effect with its 95% CI in square brackets and the significance level estimating the likelihood that the effect is equal to zero. Overall effects of moderator impact were assessed via an F test, and post hoc comparisons were done using t tests (two-sided test).The F value in the top right represents a test of the hypothesis that all effects within the subpanel are equal. The Q statistic represents the heterogeneity. P values of post hoc tests are depicted whenever significant. P values above the horizontal whiskers indicate whether an effect differed significantly from a zero effect. Vertical lines indicate significant post hoc tests between moderator levels. P values were not corrected for multiple comparisons. Physical outcomes are marked in red. Mental outcomes are marked in blue.

A more detailed analysis of specific clinical conditions in adults revealed positive mental and physical health benefits for almost all assessed clinical disorders. Differences between disorders were not found, with the exception of increased effectiveness of touch interventions in neurological disorders (Supplementary Fig. 13 ).

Familiarity in the touching dyad and intervention location

Touch interventions can be performed either by familiar touchers (partners, family members or friends) or by unfamiliar touchers (health care professionals). In adults, we did not find an impact of familiarity of the toucher ( t (99) = 0.12, P  = 0.905, Hedges’ g difference of 0.02, 95% CI −0.27 to 0.24; Fig. 7a ; see Supplementary Fig. 14 for the corresponding orchard plot). Similarly, investigating the impact on mental and physical health benefits specifically, no significant differences could be detected, suggesting that familiarity is irrelevant in adults. In contrast, touch applied to newborns by their parents (almost all studies only included touch by the mother) was significantly more beneficial compared with unfamiliar touch ( t (60) = 2.09, P  = 0.041, Hedges’ g difference of 0.30, 95% CI 0.01 to 0.59) (Fig. 7b ; see Supplementary Fig. 15 for the corresponding orchard plot). Investigating mental and physical health benefits specifically revealed no significant differences. Familiarity with the location in which the touch was applied (familiar being, for example, the participants’ home) did not influence the efficacy of touch interventions (Supplementary Fig. 16 ).

a , Health benefits for being touched by a familiar (for example, partner, family member or friend) versus unfamiliar toucher (health care professional) across 463 in part dependent effect sizes from 83 studies and 101 cohorts. b , The same as a but for newborn cohorts across 171 in part dependent effect sizes from 51 studies and 62 cohorts. c , The same as a but separating mental versus physical health benefits across 463 in part dependent effect sizes from 83 studies and 101 cohorts. d , The same as b but separating mental versus physical health benefits across 169 in part dependent effect sizes from 51 studies and 62 cohorts. Numbers on the right represent the mean effect with its 95% CI in square brackets and the significance level estimating the likelihood that the effect is equal to zero. Overall effects of moderator impact were assessed via an F test, and post hoc comparisons were done using t tests (two-sided test). The F value in the top right represents a test of the hypothesis that all effects within the subpanel are equal. The Q statistic represents the heterogeneity. P values of post hoc tests are depicted whenever significant. P values above the horizontal whiskers indicate whether an effect differed significantly from a zero effect. Vertical lines indicate significant post hoc tests between moderator levels. P values were not corrected for multiple comparisons. Physical outcomes are marked in red. Mental outcomes are marked in blue.

Frequency and duration of touch interventions

How often and for how long should touch be delivered? For adults, the median touch duration across studies was 20 min and the median number of touch interventions was four sessions with an average time interval of 2.3 days between each session. For newborns, the median touch duration across studies was 17.5 min and the median number of touch interventions was seven sessions with an average time interval of 1.3 days between each session.

Delivering more touch sessions increased benefits in adults, whether overall ( t (101) = 4.90, P  < 0.001, Hedges’ g  = 0.02, 95% CI 0.01 to 0.03), physical ( t (81) = 3.07, P  = 0.003, Hedges’ g  = 0.02, 95% CI 0.01–0.03) or mental benefits ( t (72) = 5.43, P  < 0.001, Hedges’ g  = 0.02, 95% CI 0.01–0.03) were measured (Fig. 8a ). A closer look at specific outcomes for which sufficient data were available revealed that positive associations between the number of sessions and outcomes were found for trait anxiety ( t (12) = 7.90, P  < 0.001, Hedges’ g  = 0.03, 95% CI 0.02–0.04), depression ( t (20) = 10.69, P  < 0.001, Hedges’ g  = 0.03, 95% CI 0.03–0.04) and pain ( t (37) = 3.65, P  < 0.001, Hedges’ g  = 0.03, 95% CI 0.02–0.05), indicating a need for repeated sessions to improve these adverse health outcomes. Neither increasing the number of sessions for newborns nor increasing the duration of touch per session in adults or newborns increased health benefits, be they physical or mental (Fig. 8b–d ). For continuous moderators in adults, we also looked at specific health outcomes as sufficient data were generally available for further analysis. Surprisingly, we found significant negative associations between touch duration and reductions of cortisol ( t (24) = 2.71, P  = 0.012, Hedges’ g  = −0.01, 95% CI −0.01 to −0.00) and heart rate parameters ( t (21) = 2.35, P  = 0.029, Hedges’ g  = −0.01, 95% CI −0.02 to −0.00).

a , Meta-regression analysis examining the association between the number of sessions applied and the effect size in adults, either on overall health benefits (left, 469 in part dependent effect sizes from 85 studies and 103 cohorts) or for physical (middle, 245 in part dependent effect sizes from 69 studies and 83 cohorts) or mental benefits (right, 224 in part dependent effect sizes from 60 studies and 74 cohorts) separately. b , The same as a for newborns (overall: 150 in part dependent effect sizes from 46 studies and 53 cohorts; physical health: 127 in part dependent effect sizes from 44 studies and 51 cohorts; mental health: 21 in part dependent effect sizes from 11 studies and 12 cohorts). c , d the same as a ( c ) and b ( d ) but for the duration of the individual sessions. For adults, 449 in part dependent effect sizes across 80 studies and 96 cohorts were included in the overall analysis. The analysis of physical health benefits included 240 in part dependent effect sizes across 67 studies and 80 cohorts, and the analysis of mental health benefits included 209 in part dependent effect sizes from 56 studies and 69 cohorts. For newborns, 145 in part dependent effect sizes across 45 studies and 52 cohorts were included in the overall analysis. The analysis of physical health benefits included 122 in part dependent effect sizes across 43 studies and 50 cohorts, and the analysis of mental health benefits included 21 in part dependent effect sizes from 11 studies and 12 cohorts. Each dot represents an effect size. Its size indicates the precision of the study (larger indicates better). Overall effects of moderator impact were assessed via an F test (two-sided test). The P values in each panel represent the result of a regression analysis testing the hypothesis that the slope of the relationship is equal to zero. P values are not corrected for multiple testing. The shaded area around the regression line represents the 95% CI.

Demographic influences of sex and age

We used the ratio between women and men in the single-study samples as a proxy for sex-specific effects. Sex ratios were heavily skewed towards larger numbers of women in each cohort (median 83% women), and we could not find significant associations between sex ratio and overall ( t (62) = 0.08, P  = 0.935, Hedges’ g  = 0.00, 95% CI −0.00 to 0.01), mental ( t (43) = 0.55, P  = 0.588, Hedges’ g  = 0.00, 95% CI −0.00 to 0.01) or physical health benefits ( t (51) = 0.15, P  = 0.882, Hedges’ g  = −0.00, 95% CI −0.01 to 0.01). For specific outcomes that could be further analysed, we found a significant positive association of sex ratio with reductions in cortisol secretion ( t (18) = 2.31, P  = 0.033, Hedges’ g  = 0.01, 95% CI 0.00 to 0.01) suggesting stronger benefits in women. In contrast to adults, sex ratios were balanced in samples of newborns (median 53% girls). For newborns, there was no significant association with overall ( t (36) = 0.77, P  = 0.447, Hedges’ g  = −0.01, 95% CI −0.02 to 0.01) and physical health benefits of touch ( t (35) = 0.93, P  = 0.359, Hedges’ g  = −0.01, 95% CI −0.02 to 0.01). Mental health benefits did not provide sufficient data for further analysis.

The median age in the adult meta-analysis was 42.6 years (s.d. 21.16 years, range 4.5–88.4 years). There was no association between age and the overall ( t (73) = 0.35, P  = 0.727, Hedges’ g = 0.00, 95% CI −0.01 to 0.01), mental ( t (53) = 0.94, P  = 0.353, Hedges’ g  = 0.01, 95% CI −0.01 to 0.02) and physical health benefits of touch ( t (60) = 0.16, P  = 0.870, Hedges’ g  = 0.00, 95% CI −0.01 to 0.01). Looking at specific health outcomes, we found significant positive associations between mean age and improved positive affect ( t (10) = 2.54, P  = 0.030, Hedges’ g  = 0.01, 95% CI 0.00 to 0.02) as well as systolic blood pressure ( t (11) = 2.39, P  = 0.036, Hedges’ g  = 0.02, 95% CI 0.00 to 0.04).

A list of touched body parts can be found in Supplementary Table 1 . For the touched body part, we found significantly higher health benefits for head touch compared with arm touch ( t (40) = 2.14, P  = 0.039, Hedges’ g difference of 0.78, 95% CI 0.07 to 1.49) and torso touch ( t (40) = 2.23, P  = 0.031; Hedges’ g difference of 0.84, 95% CI 0.10 to 1.58; Supplementary Fig. 17 ). Touching the arm resulted in lower mental health compared with physical health benefits ( t (37) = 2.29, P  = 0.028, Hedges’ g difference of −0.35, 95% CI −0.65 to −0.05). Furthermore, we found a significantly increased physical health benefit when the head was touched as opposed to the torso ( t (37) = 2.10, P  = 0.043, Hedges’ g difference of 0.96, 95% CI 0.06 to 1.86). Thus, head touch such as a face or scalp massage could be especially beneficial.

Directionality

In adults, we tested whether a uni- or bidirectional application of touch mattered. The large majority of touch was applied unidirectionally ( k  = 442 of 469 effects). Unidirectional touch had higher health benefits ( t (101) = 2.17, P  = 0.032, Hedges’ g difference of 0.30, 95% CI 0.03 to 0.58) than bidirectional touch. Specifically, mental health benefits were higher in unidirectional touch ( t (99) = 2.33, P  = 0.022, Hedges’ g difference of 0.46, 95% CI 0.06 to 0.66).

Study location

For adults, we found significantly stronger health benefits of touch in South American compared with North American cohorts ( t (95) = 2.03, P  = 0.046, Hedges’ g difference of 0.37, 95% CI 0.01 to 0.73) and European cohorts ( t (95) = 2.22, P  = 0.029, Hedges’ g difference of 0.36, 95% CI 0.04 to 0.68). For newborns, we found weaker effects in North American cohorts compared to Asian ( t (55) = 2.28, P  = 0.026, Hedges’ g difference of −0.37, 95% CI −0.69 to −0.05) and European cohorts ( t (55) = 2.36, P  = 0.022, Hedges’ g difference of −0.40, 95% CI −0.74 to −0.06). Investigating the interaction with mental and physical health benefits did not reveal any effects of study location in both meta-analyses (Supplementary Fig. 18 ).

Systematic review of studies without effect sizes

All studies where effect size data could not be obtained or that did not meet the meta-analysis inclusion criteria can be found on the OSF project 12 in the file ‘Study_lists_final_revised.xlsx’ (sheet ‘Studies_without_effect_sizes’). Specific reasons for exclusion are furthermore documented in Supplementary Table 2 . For human health outcomes assessed across 56 studies and n  = 2,438 individuals, interventions mostly comprised massage therapy ( k  = 86 health outcomes) and kangaroo care ( k  = 33 health outcomes). For datasets where no effect size could be computed, 90.0% of mental health and 84.3% of physical health parameters were positively impacted by touch. Positive impact of touch did not differ between types of touch interventions. These results match well with the observations of the meta-analysis of a highly positive benefit of touch overall, irrespective of whether a massage or any other intervention is applied.

We also assessed health outcomes in animals across 19 studies and n  = 911 subjects. Most research was conducted in rodents. Animals that received touch were rats (ten studies, k  = 16 health outcomes), mice (four studies, k  = 7 health outcomes), macaques (two studies, k  = 3 health outcomes), cats (one study, k  = 3 health outcomes), lambs (one study, k  = 2 health outcomes) and coral reef fish (one study, k  = 1 health outcome). Touch interventions mostly comprised stroking ( k  = 13 health outcomes) and tickling ( k  = 10 health outcomes). For animal studies, 71.4% of effects showed benefits to mental health-like parameters and 81.8% showed positive physical health effects. We thus found strong evidence that touch interventions, which were mostly conducted by humans (16 studies with human touch versus 3 studies with object touch), had positive health effects in animal species as well.

The key aim of the present study was twofold: (1) to provide an estimate of the effect size of touch interventions and (2) to disambiguate moderating factors to potentially tailor future interventions more precisely. Overall, touch interventions were beneficial for both physical and mental health, with a medium effect size. Our work illustrates that touch interventions are best suited for reducing pain, depression and anxiety in adults and children as well as for increasing weight gain in newborns. These findings are in line with previous meta-analyses on this topic, supporting their conclusions and their robustness to the addition of more datasets. One limitation of previous meta-analyses is that they focused on specific health outcomes or populations, despite primary studies often reporting effects on multiple health parameters simultaneously (for example, ref. 13 focusing on neck and shoulder pain and ref. 14 focusing on massage therapy in preterms). To our knowledge, only ref. 5 provides a multivariate picture for a large number of dependent variables. However, this study analysed their data in separate random effects models that did not account for multivariate reporting nor for the multilevel structure of the data, as such approaches have only become available recently. Thus, in addition to adding a substantial amount of new data, our statistical approach provides a more accurate depiction of effect size estimates. Additionally, our study investigated a variety of moderating effects that did not reach significance (for example, sex ratio, mean age or intervention duration) or were not considered (for example, the benefits of robot or object touch) in previous meta-analyses in relation to touch intervention efficacy 5 , probably because of the small number of studies with information on these moderators in the past. Owing to our large-scale approach, we reached high statistical power for many moderator analyses. Finally, previous meta-analyses on this topic exclusively focused on massage therapy in adults or kangaroo care in newborns 15 , leaving out a large number of interventions that are being carried out in research as well as in everyday life to improve well-being. Incorporating these studies into our study, we found that, in general, both massages and other types of touch, such as gentle touch, stroking or kangaroo care, showed similar health benefits.

While it seems to be less critical which touch intervention is applied, the frequency of interventions seems to matter. More sessions were positively associated with the improvement of trait outcomes such as depression and anxiety but also pain reductions in adults. In contrast to session number, increasing the duration of individual sessions did not improve health effects. In fact, we found some indications of negative relationships in adults for cortisol and blood pressure. This could be due to habituating effects of touch on the sympathetic nervous system and hypothalamic–pituitary–adrenal axis, ultimately resulting in diminished effects with longer exposure, or decreased pleasantness ratings of affective touch with increasing duration 16 . For newborns, we could not support previous notions that the duration of the touch intervention is linked to benefits in weight gain 17 . Thus, an ideal intervention protocol does not seem to have to be excessively long. It should be noted that very few interventions lasted less than 5 min, and it therefore remains unclear whether very short interventions have the same effect.

A critical issue highlighted in the pandemic was the lack of touch due to social restrictions 18 . To accommodate the need for touch in individuals with small social networks (for example, institutionalized or isolated individuals), touch interventions using objects/robots have been explored in the past (for a review, see ref. 11 ). We show here that touch interactions outside of the human–human domain are beneficial for mental and physical health outcomes. Importantly, object/robot touch was not as effective in improving mental health as human-applied touch. A sub-analysis of missing skin-to-skin contact among humans indicated that mental health effects of touch might be mediated by the presence of skin-to-skin contact. Thus, it seems profitable to include skin-to-skin contact in future touch interventions, in line with previous findings in newborns 19 . In robots, recent advancements in synthetic skin 20 should be investigated further in this regard. It should be noted that, although we did not observe significant differences in physical health benefits between human–human and human–object touch, the variability of effect sizes was higher in human–object touch. The conditions enabling object or robot interactions to improve well-being should therefore be explored in more detail in the future.

Touch was beneficial for both healthy and clinical cohorts. These data are critical as most previous meta-analytic research has focused on individuals diagnosed with clinical disorders (for example, ref. 6 ). For mental health outcomes, we found larger effects in clinical cohorts. A possible reason could relate to increased touch wanting 21 in patients. For example, loneliness often co-occurs with chronic illnesses 22 , which are linked to depressed mood and feelings of anxiety 23 . Touch can be used to counteract this negative development 24 , 25 . In adults and children, knowing the toucher did not influence health benefits. In contrast, familiarity affected overall health benefits in newborns, with parental touch being more beneficial than touch applied by medical staff. Previous studies have suggested that early skin-to-skin contact and exposure to maternal odour is critical for a newborn’s ability to adapt to a new environment 26 , supporting the notion that parental care is difficult to substitute in this time period.

With respect to age-related effects, our data further suggest that increasing age was associated with a higher benefit through touch for systolic blood pressure. These findings could potentially be attributed to higher basal blood pressure 27 with increasing age, allowing for a stronger modulation of this parameter. For sex differences, our study provides some evidence that there are differences between women and men with respect to health benefits of touch. Overall, research on sex differences in touch processing is relatively sparse (but see refs. 28 , 29 ). Our results suggest that buffering effects against physiological stress are stronger in women. This is in line with increased buffering effects of hugs in women compared with men 30 . The female-biased primary research in adults, however, begs for more research in men or non-binary individuals. Unfortunately, our study could not dive deeper into this topic as health benefits broken down by sex or gender were almost never provided. Recent research has demonstrated that sensory pleasantness is affected by sex and that this also interacts with the familiarity of the other person in the touching dyad 29 , 31 . In general, contextual factors such as sex and gender or the relationship of the touching dyad, differences in cultural background or internal states such as stress have been demonstrated to be highly influential in the perception of affective touch and are thus relevant to maximizing the pleasantness and ultimately the health benefits of touch interactions 32 , 33 , 34 . As a positive personal relationship within the touching dyad is paramount to induce positive health effects, future research applying robot touch to promote well-being should therefore not only explore synthetic skin options but also focus on improving robots as social agents that form a close relationship with the person receiving the touch 35 .

As part of the systematic review, we also assessed the effects of touch interventions in non-human animals. Mimicking the results of the meta-analysis in humans, beneficial effects of touch in animals were comparably strong for mental health-like and physical health outcomes. This may inform interventions to promote animal welfare in the context of animal experiments 36 , farming 37 and pets 38 . While most studies investigated effects in rodents, which are mostly used as laboratory animals, these results probably transfer to livestock and common pets as well. Indeed, touch was beneficial in lambs, fish and cats 39 , 40 , 41 . The positive impact of human touch in rodents also allows for future mechanistic studies in animal models to investigate how interventions such as tickling or stroking modulate hormonal and neuronal responses to touch in the brain. Furthermore, the commonly proposed oxytocin hypothesis can be causally investigated in these animal models through, for example, optogenetic or chemogenetic techniques 42 . We believe that such translational approaches will further help in optimizing future interventions in humans by uncovering the underlying mechanisms and brain circuits involved in touch.

Our results offer many promising avenues to improve future touch interventions, but they also need to be discussed in light of their limitations. While the majority of findings showed robust health benefits of touch interventions across moderators when compared with a null effect, post hoc tests of, for example, familiarity effects in newborns or mental health benefit differences between human and object touch only barely reached significance. Since we computed a large number of statistical tests in the present study, there is a risk that these results are false positives. We hope that researchers in this field are stimulated by these intriguing results and target these questions by primary research through controlled experimental designs within a well-powered study. Furthermore, the presence of small-study bias in both meta-analyses is indicative that the effect size estimates presented here might be overestimated as null results are often unpublished. We want to stress however that this bias is probably reduced by the multivariate reporting of primary studies. Most studies that reported on multiple health outcomes only showed significant findings for one or two among many. Thus, the multivariate nature of primary research in this field allowed us to include many non-significant findings in the present study. Another limitation pertains to the fact that we only included articles in languages mostly spoken in Western countries. As a large body of evidence comes from Asian countries, it could be that primary research was published in languages other than specified in the inclusion criteria. Thus, despite the large and inclusive nature of our study, some studies could have been missed regardless. Another factor that could not be accounted for in our meta-analysis was that an important prerequisite for touch to be beneficial is its perceived pleasantness. The level of pleasantness associated with being touched is modulated by several parameters 34 including cultural acceptability 43 , perceived humanness 44 or a need for touch 45 , which could explain the observed differences for certain moderators, such as human–human versus robot–human interaction. Moreover, the fact that secondary categorical moderators could not be investigated with respect to specific health outcomes, owing to the lack of data points, limits the specificity of our conclusions in this regard. It thus remains unclear whether, for example, a decreased mental health benefit in the absence of skin-to-skin contact is linked mostly to decreased anxiolytic effects, changes in positive/negative affect or something else. Since these health outcomes are however highly correlated 46 , it is likely that such effects are driven by multiple health outcomes. Similarly, it is important to note that our conclusions mainly refer to outcomes measured close to the touch intervention as we did not include long-term outcomes. Finally, it needs to be noted that blinding towards the experimental condition is essentially impossible in touch interventions. Although we compared the touch intervention with other interventions, such as relaxation therapy, as control whenever possible, contributions of placebo effects cannot be ruled out.

In conclusion, we show clear evidence that touch interventions are beneficial across a large number of both physical and mental health outcomes, for both healthy and clinical cohorts, and for all ages. These benefits, while influenced in their magnitude by study cohorts and intervention characteristics, were robustly present, promoting the conclusion that touch interventions can be systematically employed across the population to preserve and improve our health.

Open science practices

All data and code are accessible in the corresponding OSF project 12 . The systematic review was registered on PROSPERO (CRD42022304281) before the start of data collection. We deviated from the pre-registered plan as follows:

Deviation 1: During our initial screening for the systematic review, we were confronted with a large number of potential health outcomes to look at. This observation of multivariate outcomes led us to register an amendment during data collection (but before any effect size or moderator screening). In doing so, we aimed to additionally extract meta-analytic effects for a more quantitative assessment of our review question that can account for multivariate data reporting and dependencies of effects within the same study. Furthermore, as we noted a severe lack of studies with respect to health outcomes for animals during the inclusion assessment for the systematic review, we decided that the meta-analysis would only focus on outcomes that could be meaningfully analysed on the meta-analytic level and therefore only included health outcomes of human participants.

Deviation 2: In the pre-registration, we did not explicitly exclude non-randomized trials. Since an explicit use of non-randomization for group allocation significantly increases the risk of bias, we decided to exclude them a posteriori from data analysis.

Deviation 3: In the pre-registration, we outlined a tertiary moderator level, namely benefits of touch application versus touch reception. This level was ignored since no included study specifically investigated the benefits of touch application by itself.

Deviation 4: In the pre-registration, we suggested using the RoBMA function 47 to provide a Bayesian framework that allows for a more accurate assessment of publication bias beyond small-study bias. Unfortunately, neither multilevel nor multivariate data structures are supported by the RoBMA function, to our knowledge. For this reason, we did not further pursue this analysis, as the hierarchical nature of the data would not be accounted for.

Deviation 5: Beyond the pre-registered inclusion and exclusion criteria, we also excluded dissertations owing to their lack of peer review.

Deviation 6: In the pre-registration, we stated to investigate the impact of sex of the person applying the touch. This moderator was not further analysed, as this information was rarely given and the individuals applying the touch were almost exclusively women (7 males, 24 mixed and 85 females in studies on adults/children; 3 males, 17 mixed and 80 females in studied on newborns).

Deviation 7: The time span of the touch intervention as assessed by subtracting the final day of the intervention from the first day was not investigated further owing to its very high correlation with the number of sessions ( r (461) = 0.81 in the adult meta-analysis, r (145) = 0.84 in the newborn meta-analysis).

Inclusion and exclusion criteria

To be included in the systematic review, studies had to investigate the relationship between at least one health outcome (physical and/or mental) in humans or animals and a touch intervention, include explicit physical touch by another human, animal or object as part of an intervention and include an experimental and control condition/group that are differentiated by touch alone. Of note, as a result of this selection process, no animal-to-animal touch intervention study was included, as they never featured a proper no-touch control. Human touch was always explicit touch by a human (that is, no brushes or other tools), either with or without skin-to-skin contact. Regarding the included health outcomes, we aimed to be as broad as possible but excluded parameters such as neurophysiological responses or pleasantness ratings after touch application as they do not reflect health outcomes. All included studies in the meta-analysis and systematic review 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 , 158 , 159 , 160 , 161 , 162 , 163 , 164 , 165 , 166 , 167 , 168 , 169 , 170 , 171 , 172 , 173 , 174 , 175 , 176 , 177 , 178 , 179 , 180 , 181 , 182 , 183 , 184 , 185 , 186 , 187 , 188 , 189 , 190 , 191 , 192 , 193 , 194 , 195 , 196 , 197 , 198 , 199 , 200 , 201 , 202 , 203 , 204 , 205 , 206 , 207 , 208 , 209 , 210 , 211 , 212 , 213 , 214 , 215 , 216 , 217 , 218 , 219 , 220 , 221 , 222 , 223 , 224 , 225 , 226 , 227 , 228 , 229 , 230 , 231 , 232 , 233 , 234 , 235 , 236 , 237 , 238 , 239 , 240 , 241 , 242 , 243 , 244 , 245 , 246 , 247 , 248 , 249 , 250 , 251 , 252 , 253 , 254 , 255 , 256 , 257 , 258 , 259 , 260 , 261 , 262 , 263 are listed in Supplementary Table 2 . All excluded studies are listed in Supplementary Table 3 , together with a reason for exclusion. We then applied a two-step process: First, we identified all potential health outcomes and extracted qualitative information on those outcomes (for example, direction of effect). Second, we extracted quantitative information from all possible outcomes (for example, effect sizes). The meta-analysis additionally required a between-subjects design (to clearly distinguish touch from no-touch effects and owing to missing information about the correlation between repeated measurements 264 ). Studies that explicitly did not apply a randomized protocol were excluded before further analysis to reduce risk of bias. The full study lists for excluded and included studies can be found in the OSF project 12 in the file ‘Study_lists_final_revised.xlsx’. In terms of the time frame, we conducted an open-start search of studies until 2022 and identified studies conducted between 1965 and 2022.

Data collection

We used Google Scholar, PubMed and Web of Science for our literature search, with no limitations regarding the publication date and using pre-specified search queries (see Supplementary Information for the exact keywords used). All procedures were in accordance with the updated Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines 265 . Articles were assessed in French, Dutch, German or English. The above databases were searched from 2 December 2021 until 1 October 2022. Two independent coders evaluated each paper against the inclusion and exclusion criteria. Inconsistencies between coders were checked and resolved by J.P. and H.H. Studies excluded/included for the review and meta-analysis can be found on the OSF project.

Search queries

We used the following keywords to search the chosen databases. Agents (human versus animal versus object versus robot) and touch outcome (physical versus mental) were searched separately together with keywords searching for touch.

TOUCH: Touch OR Social OR Affective OR Contact OR Tactile interaction OR Hug OR Massage OR Embrace OR Kiss OR Cradling OR Stroking OR Haptic interaction OR tickling

AGENT: Object OR Robot OR human OR animal OR rodent OR primate

MENTAL OUTCOME: Health OR mood OR Depression OR Loneliness OR happiness OR life satisfaction OR Mental Disorder OR well-being OR welfare OR dementia OR psychological OR psychiatric OR anxiety OR Distress

PHYSICAL OUTCOME: Health OR Stress OR Pain OR cardiovascular health OR infection risk OR immune response OR blood pressure OR heart rate

Data extraction and preparation

Data extraction began on 10 October 2022 and was concluded on 25 February 2023. J.P. and H.H. oversaw the data collection process, and checked and resolved all inconsistencies between coders.

Health benefits of touch were always coded by positive summary effects, whereas adverse health effects of touch were represented by negative summary effects. If multiple time points were measured for the same outcome on the same day after a single touch intervention, we extracted the peak effect size (in either the positive or negative direction). If the touch intervention occurred multiple times and health outcomes were assessed for each time point, we extracted data points separately. However, we only extracted immediate effects, as long-term effects not controlled through the experimental conditions could be due to influences other than the initial touch intervention. Measurements assessing long-term effects without explicit touch sessions in the breaks were excluded for the same reason. Common control groups for touch interventions comprised active (for example, relaxation therapy) as well as passive control groups (for example, standard medical care). In the case of multiple control groups, we always contrasted the touch group to the group that most closely matched the touch condition (for example, relaxation therapy was preferred over standard medical care). We extracted information from all moderators listed in the pre-registration (Supplementary Table 4 ). A list of included and excluded health outcomes is presented in Supplementary Table 5 . Authors of studies with possible effects but missing information to calculate those effects were contacted via email and asked to provide the missing data (response rate 35.7%).

After finalizing the list of included studies for the systematic review, we added columns for moderators and the coding schema for our meta-analysis per our updated registration. Then, each study was assessed for its eligibility in the meta-analysis by two independent coders (J.P., H.H., K.F. or F.M.). To this end, all coders followed an a priori specified procedure: First, the PDF was skimmed for possible effects to extract, and the study was excluded if no PDF was available or the study was in a language different from the ones specified in ‘ Data collection ’. Effects from studies that met the inclusion criteria were extracted from all studies listing descriptive values or statistical parameters to calculate effect sizes. A website 266 was used to convert descriptive and statistical values available in the included studies (means and standard deviations/standard errors/confidence intervals, sample sizes, F values, t values, t test P values or frequencies) into Cohen’s d , which were then converted in Hedges’ g . If only P value thresholds were reported (for example, P  < 0.01), we used this, most conservative, value as the P value to calculate the effect size (for example, P  = 0.01). If only the total sample size was given but that number was even and the participants were randomly assigned to each group, we assumed equal sample sizes for each group. If delta change scores (for example, pre- to post-touch intervention) were reported, we used those over post-touch only scores. In case frequencies were 0 when frequency tables were used to determine effect sizes, we used a value of 0.5 as a substitute to calculate the effect (the default setting in the ‘metafor’ function 267 ). From these data, Hedges’ g and its variance could be derived. Effect sizes were always computed between the experimental and the control group.

Statistical analysis and risk of bias assessment

Owing to the lack of identified studies, health benefits to animals were not included as part of the statistical analysis. One meta-analysis was performed for adults, adolescents and children, as outcomes were highly comparable. We refer to this meta-analysis as the adult meta-analysis, as children/adolescent cohorts were only targeted in a minority of studies. A separate meta-analysis was performed for newborns, as their health outcomes differed substantially from any other age group.

Data were analysed using R (version 4.2.2) with the ‘rma.mv’ function from the ‘metafor’ package 267 in a multistep, multivariate and multilevel fashion.

We calculated an overall effect of touch interventions across all studies, cohorts and health outcomes. To account for the hierarchical structure of the data, we used a multilevel structure with random effects at the study, cohort and effects level. Furthermore, we calculated the variance–covariance matrix of all data points to account for the dependencies of measured effects within each individual cohort and study. The variance–covariance matrix was calculated by default with an assumed correlation of effect sizes within each cohort of ρ  = 0.6. As ρ needed to be assumed, sensitivity analyses for all computed effect estimates were conducted using correlations between effects of 0, 0.2, 0.4 and 0.8. The results of these sensitivity analyses can be found in ref. 12 . No conclusion drawn in the present manuscript was altered by changing the level of ρ . The sensitivity analyses, however, showed that higher assumed correlations lead to more conservative effect size estimates (see Supplementary Figs. 19 and 20 for the adult and newborn meta-analyses, respectively), reducing the type I error risk in general 268 . In addition to these procedures, we used robust variance estimation with cluster-robust inference at the cohort level. This step is recommended to more accurately determine the confidence intervals in complex multivariate models 269 . The data distribution was assumed to be normal, but this was not formally tested.

To determine whether individual effects had a strong influence on our results, we calculated Cook’s distance D . Here, a threshold of D  > 0.5 was used to qualify a study as influential 270 . Heterogeneity in the present study was assessed using Cochran’s Q , which determines whether the extracted effect sizes estimate a common population effect size. Although the Q statistic in the ‘rma.mv’ function accounts for the hierarchical nature of the data, we also quantified the heterogeneity estimator σ ² for each random-effects level to provide a comprehensive overview of heterogeneity indicators. These indicators for all models can be found on the OSF project 12 in the Table ‘Model estimates’. To assess small study bias, we visually inspected the funnel plot and used the standard error as a moderator in the overarching meta-analyses.

Before any sub-group analysis, the overall effect size was used as input for power calculations. While such post hoc power calculations might be limited, we believe that a minimum number of effects to be included in subgroup analyses was necessary to allow for meaningful conclusions. Such medium effect sizes would also probably be the minimum effect sizes of interest for researchers as well as clinical practitioners. Power calculation for random-effects models further requires a sample size for each individual effect as well as an approximation of the expected heterogeneity between studies. For the sample size input, we used the median sample size in each of our studies. For heterogeneity, we assumed a value between medium and high levels of heterogeneity ( I ² = 62.5% 271 ), as moderator analyses typically aim at reducing heterogeneity overall. Subgroups were only further investigated if the number of observed effects achieved ~80% power under these circumstances, to allow for a more robust interpretation of the observed effects (see Supplementary Figs. 5 and 6 for the adult and newborn meta-analysis, respectively). In a next step, we investigated all pre-registered moderators for which sufficient power was detected. We first looked at our primary moderators (mental versus physical health) and how the effect sizes systematically varied as a function of our secondary moderators (for example, human–human or human–object touch, duration, skin-to-skin presence, etc.). We always included random slopes to allow for our moderators to vary with the random effects at our clustering variable, which is recommended in multilevel models to reduce false positives 272 . All statistical tests were performed two-sided. Significance of moderators was determined using omnibus F tests. Effect size differences between moderator levels and their confidence intervals were assessed via t tests.

Post hoc t tests were performed comparing mental and physical health benefits within each interacting moderator (for example, mental versus physical health benefits in cancer patients) and mental or physical health benefits across levels of the interacting moderator (for example, mental health benefits in cancer versus pain patients). The post hoc tests were not pre-registered. Data were visualized using forest plots and orchard plots 273 for categorical moderators and scatter plots for continuous moderators.

For a broad overview of prior work and their biases, risk of bias was assessed for all studies included in both meta-analyses and the systematic review. We assessed the risk of bias for the following parameters:

Bias from randomization, including whether a randomization procedure was performed, whether it was a between- or within-participant design and whether there were any baseline differences for demographic or dependent variables.

Sequence bias resulting from a lack of counterbalancing in within-subject designs.

Performance bias resulting from the participants or experiments not being blinded to the experimental conditions.

Attrition bias resulting from different dropout rates between experimental groups.

Note that four studies in the adult meta-analysis did not explicitly mention randomization as part of their protocol. However, since these studies never showed any baseline differences in all relevant variables (see ‘Risk of Bias’ table on the OSF project ) , we assumed that randomization was performed but not mentioned. Sequence bias was of no concern for studies for the meta-analysis since cross-over designs were excluded. It was, however, assessed for studies within the scope of the systematic review. Importantly, performance bias was always high in the adult/children meta-analysis, as blinding of the participants and experimenters to the experimental conditions was not possible owing to the nature of the intervention (touch versus no touch). For studies with newborns and animals, we assessed the performance bias as medium since neither newborns or animals are likely to be aware of being part of an experiment or specific group. An overview of the results is presented in Supplementary Fig. 21 , and the precise assessment for each study can be found on the OSF project 12 in the ‘Risk of Bias’ table.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

All data are available via Open Science Framework at https://doi.org/10.17605/OSF.IO/C8RVW (ref. 12 ). Source data are provided with this paper.

Code availability

All code is available via Open Science Framework at https://doi.org/10.17605/OSF.IO/C8RVW (ref. 12 ).

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Acknowledgements

We thank A. Frick and E. Chris for supporting the initial literature search and coding. We also thank A. Dreisoerner, T. Field, S. Koole, C. Kuhn, M. Henricson, L. Frey Law, J. Fraser, M. Cumella Reddan, and J. Stringer, who kindly responded to our data requests and provided additional information or data with respect to single studies. J.P. was supported by the German National Academy of Sciences Leopoldina (LPDS 2021-05). H.H. was supported by the Marietta-Blau scholarship of the Austrian Agency for Education and Internationalisation (OeAD) and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, project ID 422744262 – TRR 289). C.K. received funding from OCENW.XL21.XL21.069 and V.G. from the European Research Council (ERC) under European Union’s Horizon 2020 research and innovation programme, grant ‘HelpUS’ (758703) and from the Dutch Research Council (NWO) grant OCENW.XL21.XL21.069. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Julian Packheiser

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These authors contributed equally: Julian Packheiser, Helena Hartmann.

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Julian Packheiser, Helena Hartmann, Kelly Fredriksen, Valeria Gazzola, Christian Keysers & Frédéric Michon

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J.P. contributed to conceptualization, methodology, formal analysis, investigation, data curation, writing the original draft, review and editing, visualization, supervision and project administration. HH contributed to conceptualization, methodology, formal analysis, investigation, data curation, writing the original draft, review and editing, visualization, supervision and project administration. K.F. contributed to investigation, data curation, and review and editing. C.K. and V.G. contributed to conceptualization, and review and editing. F.M. contributed to conceptualization, methodology, formal analysis, investigation, writing the original draft, and review and editing.

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List of studies included in and excluded from the meta-analyses/review.

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Source Data Fig. 2

Effect size/error (columns ‘Hedges_g’ and ‘variance’) information for each study/cohort/effect included in the analysis. Source Data Fig. 3 Effect size/error (columns ‘Hedges_g’ and ‘variance’) together with moderator data (column ‘Outcome’) for each study/cohort/effect included in the analysis. Source Data Fig. 4 Effect size/error (columns ‘Hedges_g’ and ‘variance’) together with moderator data (columns ‘dyad_type’ and ‘skin_to_skin’) for each study/cohort/effect included in the analysis. Source Data Fig. 5 Effect size/error (columns ‘Hedges_g’ and ‘variance’) together with moderator data (column ‘touch_type’) for each study/cohort/effect included in the analysis. Source Data Fig. 6 Effect size/error (columns ‘Hedges_g’ and ‘variance’) together with moderator data (column ‘clin_sample’) for each study/cohort/effect included in the analysis. Source Data Fig. 7 Effect size/error (columns ‘Hedges_g’ and ‘variance’) together with moderator data (column ‘familiarity’) for each study/cohort/effect included in the analysis. Source Data Fig. 7 Effect size/error (columns ‘Hedges_g’ and ‘variance’) together with moderator data (columns ‘touch_duration’ and ‘sessions’) for each study/cohort/effect included in the analysis.

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Empowering learners with ChatGPT: insights from a systematic literature exploration

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• Published: 16 April 2024
• Volume 3 , article number  36 , ( 2024 )

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• Laila Mohebi   ORCID: orcid.org/0000-0003-2640-4532 1  

With the rapid emergence of artificial intelligence (AI) tools in the academic realm, understanding their implications, advantages, and challenges becomes crucial. ChatGPT, a leading AI conversational model, has gained significant traction in educational settings, warranting a comprehensive investigation into its academic impact. This systematic review aimed to elucidate the current state of research regarding implementing ChatGPT in academic cultures, focusing on its applications, challenges, and potential in reshaping contemporary pedagogies. An exhaustive review of 32 peer-reviewed articles from 2023 encompassed categorizing diverse research fields, journals, and studies. The research then delved into the challenges, factors affecting its use, and the myriad opportunities ChatGPT offers within academic settings. An overwhelming 75% of the studies emphasized the relevance of ChatGPT and generative AI tools within higher education, underscoring its importance. Significant challenges identified included pedagogical integration (31.25%) and student engagement (15.63%). However, ChatGPT's potentially inefficient content creation (25.00%) and enhanced personalized learning (21.88%) presented promising avenues for reshaping educational experiences. Furthermore, the tool's adaptability in catering to diverse student needs and fostering collaborative environments was notable. ChatGPT emerges as a transformative force in academia, with vast potential to revolutionize pedagogical practices. Yet, academic institutions must address inherent challenges to harness their full capabilities. Future directions point towards a symbiotic integration, with AI complementing human educators to promote inclusive, dynamic learning.

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1 Introduction

There has been a significant uptick in interest in artificial intelligence (AI) applications and implications in recent years, attributed to AI's increasingly widespread use in education. Particularly noteworthy is the fact that ChatGPT, a generative conversational AI, has been at the forefront of this discourse, as multiple studies have demonstrated. Early proponents such as Adiguzel et al. [ 1 ] and Mogavi et al. [ 2 ] acknowledged the transformative potential of ChatGPT in revolutionizing education. Crawford et al. [ 3 ] emphasized the ethical implications of ChatGPT. Additionally, ChatGPT's involvement in distinct areas of learning, such as programming [ 4 ] and teaching foreign languages [ 5 ], has expanded its role in the pedagogical sphere. These areas include teaching foreign languages and teaching programming. Despite this, the incorporation of ChatGPT into the academic culture has not been without its share of difficulties and apprehensions, as is typical for introducing new technological developments.

Despite the extensive discourse on incorporating GPT into academic settings, there still needs to be consolidated knowledge about the challenges institutions and educators face in the implementation. Studies such as Lawan et al. [ 6 ] discuss strategies to mitigate the adverse effects of generative AI in education,however, there needs to be more systematic research into specific obstacles. Even though it is widely acknowledged that ChatGPT has the potential to improve many facets of education, such as self-study experiences [ 7 ] and distance learning [ 8 ], there is still a noticeable knowledge gap regarding the factors that make the incorporation of ChatGPT essential in the contemporary educational setting.

The academic world is currently at a pivotal juncture in which it must weigh the undeniable potential of ChatGPT against the ambiguities and challenges associated with its incorporation. Many educators and academic institutions need help with the complexities of effectively integrating it into academic culture even though they recognize its transformative power. Because of these obstacles and the urgent requirement for its incorporation, there is a compelling need for an in-depth investigation to be conducted in order to simplify ChatGPT's incorporation and maximize the benefits it provides for educational environments.

A systematic review of ChatGPT in education literature reveals critical research gaps. First, there are few empirical studies on ChatGPT's long-term effects on education. Adiguzel et al. [ 1 ] and Mogavi et al. [ 2 ] have begun to explore ChatGPT's transformative potential in education and user perspectives, respectively, but there needs to be long-term research on how it affects student-learning outcomes. This includes understanding how ChatGPT technology interacts differently in K-12, higher education, and vocational training. Kasneci et al. [ 9 ] and Lawan et al. [ 6 ] have begun to address the opportunities and challenges of large language models in education, but there needs to be more research on the best pedagogical strategies to use with ChatGPT to improve learning.

The ethical and practical implications of using ChatGPT in educational assessments and assignments are another significant gap. Kolade et al. [ 10 ] have touched on new learning and assessment frontiers, but more research is needed on how ChatGPT is used responsibly and effectively without compromising academic integrity. Baskara [ 11 , 12 ] and Azaria et al. [ 13 ] also note that ChatGPT's potential in language learning has yet to be fully explored. This includes learning how ChatGPT facilitates different learning styles and is integrated into curriculum design to improve critical thinking and problem-solving. Finally, Panagopoulou et al. [ 14 ] found little research on ChatGPT's legal and ethical implications in education. Data privacy, student data usage, and AI-generated content ethics in education should be considered. Therefore, the study conducted a systematic literature review to answer the research questions:

What are the major challenges and obstacles educators and institutions face when implementing ChatGPT in academic settings?

What key factors drive the need to integrate ChatGPT into academic culture and curricula? and

What are the potential opportunities and benefits the implementation of ChatGPT could bring to academic culture and learning environments?

The primary objective of this research is to investigate the multifaceted challenges that educators, academic institutions, and students face when attempting to implement ChatGPT within the context of the academic culture. Drawing on the findings of studies such as Spennemann [ 15 ] and Farrokhnia et al. [ 16 ], the research will dissect the complexities and potential pitfalls associated with such an integration. The goal study aims to determine the underlying factors highlighting the importance and urgency of incorporating ChatGPT into academic frameworks. The purpose of this study is to investigate the myriad possibilities and opportunities that the implementation of ChatGPT heralds for the academic community and to determine the driving forces that underscore the indispensability of ChatGPT in modern academic settings through an examination of works such as Karakose et al. [ 17 ] and Rasul et al. [ 18 ]. This will be accomplished by exploring works such as Karakose et al. The research will outline the potential avenues that ChatGPT can open for enriching the educational landscape by utilizing the insights from Yilmaz and Yilmaz [ 4 ], Lawan et al. [ 6 ], and Santos [ 19 ]. Finally, the study offers the research objectives:

To explore the challenges in the implementation of ChatGPT in academic culture.

To identify the factors which make the use of ChatGPT necessary in academic culture.

To see the possibilities and opportunities in the implementation of ChatGPT in academic culture.

2 Review of the literature

2.1 chatgpt and academic institutions.

Incorporating ChatGPT into educational institutions has emerged as a transformative move, redefining conventional pedagogical practices and the role of the student in the learning process. According to Adiguzel et al. [ 1 ] and Mogavi et al. [ 2 ], generative conversational AI enhances learning experiences by providing students with a dynamic and responsive platform for inquiries, discussion, and knowledge acquisition. This is discussed as a tool that can enhance learning experiences. In more specialized fields, ChatGPT has been used to supplement specific learning areas as a learning supplement. For example, Yilmaz and Yilmaz [ 4 ] emphasized its potential in programming education and suggested that students could use the platform for instant feedback, code-related inquiries, and problem-solving assistance. This was based on the idea that students could leverage the platform for these purposes. Similarly, Huang and Li investigated ChatGPT's potential role in the instruction of foreign languages. They conceived ChatGPT as a "virtual language partner" that could guide students through acquiring a command of linguistic nuance through real-time conversational drills.

In addition, educational institutions are investigating the use of the tool in contexts other than the traditional classroom setting. Klnc [ 8 ] highlighted its significance in distance science education, pointing out that it can overcome geographical barriers and offer consistent educational experiences. The researchers Larsson and Eriksson [ 7 ] emphasized the positive impact that ChatGPT has on students' self-study experiences while enrolled in higher education. ChatGPT acts as an on-demand tutor, guiding students through complex topics and clearing up any confusion that they may have. ChatGPT is not only improving the landscape of academic learning through its myriad applications but also reshaping the dynamics of student–teacher interactions and self-directed learning within institutional settings.

2.2 Challenges in the implementation of ChatGPT in academic culture

While ChatGPT's integration into academic institutions holds significant transformative potential, various studies underscore its challenges within the literary culture. Dwivedi et al. [ 20 ] and Kolade et al. [ 10 ] explore the nuanced ethical concerns of relying on generative conversational AI for research and educational assessment. There needs to be more clarity about ensuring academic integrity and the risk of students depending on AI-generated content, possibly undermining the critical thinking and originality that form the foundation of scholarly pursuits. Similarly, the work by Lawan et al. [ 6 ] on the modified flipped learning approach reflects concerns about the adverse effects of generative AI on education, emphasizing the risk of students becoming overly reliant on AI-driven tools, thus potentially diminishing the development of independent cognitive skills.

In addition, Panagopoulou et al. [ 14 ] dive deep into the legal and ethical implications surrounding the use of tools like ChatGPT in the educational domain, bringing forth the issues of data privacy, potential biases in AI responses, and the challenges in ensuring equal access to such technologies. Spennemann's [ 15 ] exploration of ChatGPT's interpretation of cultural heritage values raises questions about the model's understanding and potential misrepresentation of cultural nuances, highlighting the necessity for a critical approach in utilizing AI in fields that demand profound cultural sensitivity. These studies collectively paint a picture of caution, urging academic institutions to navigate the integration of ChatGPT thoughtfully and responsibly, considering the broader implications on student learning, cultural understanding, and the overall educational ethos.

2.3 Factors which make the use of ChatGPT necessary in academic culture

It is impossible to deny that the artificial intelligence (AI) era has begun in educational institutions, and ChatGPT has emerged as a crucial instrument in revolutionizing various aspects of education. According to Adiguzel et al. [ 1 ], the incorporation of AI, in particular ChatGPT, can significantly improve personalized learning experiences by providing real-time responses tailored to individual students' specific requirements. This is especially useful in accommodating various learning tempos and approaches, making the educational experience more welcoming to a broader range of students. In addition, the research conducted by Yilmaz and Yilmaz [ 4 ] on the application of augmented intelligence in the learning of programming indicates that ChatGPT has the potential to serve as a helpful mentor, assisting students in comprehending complex programming concepts and potentially filling gaps where human mentors may be unavailable or limited.

In addition, Santos's [ 19 ] comparative case study on the enhancement of chemistry learning reveals that tools like ChatGPT can act as powerful cognitive agents, further emphasizing the necessity of ChatGPT in academic settings. Not only do they assist in transmitting knowledge, but they also stimulate critical thinking by presenting students with various perspectives and methods of approaching problem-solving. In addition, Bentley et al. [ 21 ] argue for the necessity of AI education and suggest that integrating tools such as ChatGPT into classrooms provides students with the knowledge and abilities necessary to navigate a future increasingly digital. In an era characterized by rapid technological advancements and global connectivity, ensuring that students are AI-literate is crucial. This makes incorporating platforms like ChatGPT beneficial and necessary for a forward-looking academic culture.

2.4 Possibilities and opportunities in the implementation of ChatGPT in academic culture

The incorporation of ChatGPT into academic institutions paves the way for a wide variety of possibilities that have the potential to be transformative. According to Adiguzel et al. [ 1 ], the utilization of ChatGPT has the potential to redefine personalized learning by optimizing the educational trajectory for each student based on the specific requirements and learning patterns unique to that student. This not only promises more individualized educational experiences but also increases the amount of engagement and retention that takes place. Research conducted by Yilmaz and Yilmaz [ 4 ] on the application of augmented intelligence in programming demonstrates, once again, the potential utility of ChatGPT as a platform for teamwork. Students can interact with an AI in this space to have their questions answered, gain coding experience, or even investigate more advanced topics, broadening the scope of their knowledge beyond the confines of traditional educational curricula.

On the other hand, the research conducted by Santos [ 19 ] demonstrates how ChatGPT can be utilized as an advanced cognitive agent in fields such as chemistry, thereby encouraging inquiry-based learning and motivating students to question established dogma. Thanks to ChatGPT's role as a dynamic educational assistant, learners can develop deeper comprehension and analytical skills. ChatGPT can pose challenging questions, simulate real-world scenarios, and provide instant feedback. In addition, the research conducted by Baskara [ 11 , 12 ] in the field of English language education highlights the numerous opportunities presented by using ChatGPT in simulations spanning multiple disciplines. These applications can be of tremendous use to language students because they enable students to engage in conversational practice, understand cultural nuances, and improve their language skills. When taken as a whole, these studies highlight the plethora of opportunities ChatGPT offers, thereby holding out hope for a revitalized, innovative, and interactive academic culture in the years to come.

3 Research methodology

This study followed the principles of a systematic literature review so PRISMA diagram in Fig.  1 shows the procedure of conducting the study.

PRISMA diagram

3.1 Search strategy

The study identifies the main keywords and terms related to the research question or topic. For the study on ChatGPT in academic culture, this includes terms such as "ChatGPT," "academic institutions," "AI in education," and "generative AI." The terms identified from literature studies such as those from Adiguzel et al. [ 1 ] and Mogavi et al. [ 2 ], were used as the key principles to conduct the SLR. The study searched those articles that were published after 2020 and focused on ChatGPT, AI and Generative AI tools used in educational contexts.

3.2 Database selection

After applying the search strategy, the study identifies an appropriate academic and professional database that contain relevant literature. Databases like PubMed, IEEE Xplore, ERIC, Google Scholar, and arXiv (considering preprints like [ 2 ] and [ 19 ] were used for this review. Total of 988 articles were found based on the keywords, however, the irrelevant articles other than from educational contexts, were removed from the sample. In order to finalize the sample, the study used only the papers that were totally based on ChatGPT, AI and Generative AI tools in educational contexts.

3.3 Inclusion and exclusion criteria

The study used the clear criteria for which studies to include or exclude based on relevance, publication date, type of publication, etc. For instance, only articles after 2020 were considered to ensure the latest perspectives on ChatGPT in academic settings. This often involves a two-step process:

Title and abstract screening: Eliminate irrelevant articles from their title and abstract.

Full-text screening: Read the full texts of the shortlisted articles to ensure they are pertinent to the research question.

3.4 Data extraction

Once the final list of literature is decided, extract relevant data from each study. This could be the methods they used, the main findings, the study context, etc. The depth of insight from articles like those of Dwivedi et al. [ 20 ] and Kasneci et al. [ 9 ] will provide comprehensive views on ChatGPT's roles and implications. Evaluate the quality of each study, considering the methodology used, sample size, potential biases, and so forth. Some studies, significantly peer-reviewed articles from journals like "Contemporary Educational Technology" or "International Journal of Information Management," may naturally carry more weight due to rigorous review processes.

3.5 Synthesis of findings

Group findings under thematic areas has been followed using six steps of Braun and Clarke [ 22 ]. For instance, the opportunities of ChatGPT in academia might be one theme, while challenges could be another. The diversity of topics, ranging from the perception of ChatGPT, as seen in Mogavi et al. [ 2 ], to its role in foreign language teaching as in Huang and Li [ 5 ], can provide a rich tapestry of insights. A systematic literature review based on the 27 literature references would require a structured, rigorous, and transparent approach to ensure the findings are comprehensive, unbiased, and informative.

4.1 Descriptive statistics

Table 1 provides an overview of academic research conducted in 2023 concerning the role and implications of ChatGPT and generative artificial intelligence in education and beyond. The predominant trend suggests that ChatGPT, a significant representative of advanced AI, has garnered considerable attention in the academic sector. Numerous studies have explored its multifaceted applications in areas ranging from revolutionizing general education to its potential in specialized domains like chemistry learning and language acquisition. The broad array of journal names reflects that the research is interdisciplinary, encompassing educational technology, information management, individual learning differences, and cultural heritage interpretation. Additionally, the predominance of research in education-focused journals indicates a growing consensus on the transformative potential of AI tools in reshaping pedagogical approaches and learning environments.

Furthermore, while most of the studies discuss the benefits, opportunities, and applications of ChatGPT in learning, there is also a concern about its challenges and implications. Articles like those by Lawan et al. [ 6 ] and Panagopoulou et al. [ 14 ] focus on the adverse effects and legal considerations of such AI tools in education. This suggests a balanced academic discourse, recognizing the advancements AI promises and the cautions required in its application. Notably, there is an ongoing interest in the more specialized roles of ChatGPT, with studies on topics such as digital leadership, technology integration, and the development of critical thinking skills highlighting the tool's expansive reach and potential. In sum, 2023 has been a year of substantial academic reflection on ChatGPT, acknowledging its multifaceted impact on the educational landscape.

4.2 Studies categorization

In the provided systematic literature review, an overwhelming majority of studies, precisely 24 out of 32, are concentrated on the context of higher education (Fig.  2 ). This accounts for 75% of the research, highlighting the substantial focus and perceived importance of ChatGPT and generative AI tools within universities and colleges. On the other hand, the school level is notably underrepresented, with just one study constituting a mere 3.125% of the total research. This suggests that primary and secondary education sectors still need to be a significant focal point for such investigations. Additionally, "Other Contexts" accounts for 7 studies or 21.875% of the total, indicating a broader interest in ChatGPT's application beyond mainstream educational settings. The data underscores the pressing relevance of AI in higher education while also pointing to the potential areas that might need further exploration in the future.

Number of studies and categorization

4.3 Challenges in the implementation of ChatGPT in academic culture

Table 2 offers an overview of the prevailing concerns and obstacles associated with incorporating ChatGPT in academic settings. At the forefront of these challenges is the "Pedagogical Integration" issue, with a significant 31.25% of the studies highlighting the difficulties of seamlessly embedding ChatGPT into existing educational frameworks. Such a concern underscores the importance of aligning technological advancements with teaching methods to ensure effective learning outcomes. Additionally, both "Student Engagement and Motivation" and "Educator Preparedness and Training" occupy an equal share of the concerns at 15.63%, highlighting a symbiotic relationship: educators’ need proper training to exploit the capabilities of ChatGPT, while students require motivation to utilize this tool effectively.

On the other hand, "Technological Infrastructure" and "Collaborative Learning" are on the lower end of the spectrum, with only 3.13% of studies highlighting these challenges. It might suggest that while the fundamental technical infrastructure is becoming commonplace in academic institutions, the real challenge lies in how the tool is integrated and used rather than mere access to it. Moreover, "Ethical Considerations and Bias" and "Language Proficiency and Communication" are not overlooked, considering the inherent biases AI models might have and the necessity for clear communication in educational settings. In conclusion, while ChatGPT presents promising educational potential, addressing these challenges is paramount to realizing its full benefits in fostering an enriched academic culture.

4.4 Factors affecting the use of ChatGPT in academic culture

Table 3 , titled "Factors affecting the use of ChatGPT in academic culture," offers insights into the potential advantages and drivers pushing for adopting ChatGPT in academic environments. Dominating the list is "Efficient Content Creation and Delivery" at 25.00%, suggesting that educators and institutions see a significant value in leveraging ChatGPT for efficiently curating and disseminating academic content. This is closely followed by "Enhanced Personalized Learning" at 21.88%, reflecting a growing demand in educational settings for tailored experiences that cater to individual learning paces and styles. "Accelerated Research and Writing," also at 21.88%, echoes the advantages of AI in streamlining the research process, pointing towards the tool's potential in simplifying academic writing tasks and enhancing research capabilities.

Moreover, "Support for Diverse Learning Needs" and "Collaborative Learning," both standing at 15.63%, underline the versatility of ChatGPT in catering to a variety of learners and promoting group-based learning, respectively. "Language Support and Translation Assistance" at 12.50% emphasizes the global nature of education today, where language barriers are continuously being broken down. Surprisingly, "Academic Support and Tutoring" only accounts for 6.25% of the studies, suggesting that while ChatGPT is a supplementary tool for academic help, it might not be the primary go-to solution for many. In conclusion, the multitude of factors advocating for adopting ChatGPT in academia underscores its versatility and potential to enhance various facets of the learning process, making it a significant asset in the ever-evolving educational landscape.

4.5 Possibilities and opportunities in the implementation of ChatGPT in academic culture

Table 4 sheds light on the myriad of possibilities and opportunities that arise from implementing ChatGPT in the academic realm. The leading chance, as seen by "Efficient Content Creation and Delivery," garnering 25.00%, indicates an educational shift where educators recognize the advantages of using ChatGPT to facilitate a more streamlined and timely dissemination of content. Following closely is the "Enhanced Personalized Learning" factor with 21.88%, underlining the capability of ChatGPT to provide tailored educational experiences, catering to unique learner demands and augmenting individual learning trajectories. Another notable element is "Accelerated Research and Writing," which stands at 21.88%, suggesting that the academic community acknowledges ChatGPT's potential in bolstering research endeavors and simplifying writing processes.

Additionally, aspects such as "Support for Diverse Learning Needs" and "Collaborative Learning," both at 15.63%, highlight ChatGPT's adaptability in addressing a wide range of student profiles and fostering collaborative learning environments. "Enhanced Student Engagement and Motivation" and "Development of Critical Thinking Skills," with percentages of 18.75%, each emphasize the tool's capability to invigorate student enthusiasm and instill higher-order cognitive skills. The lesser focus on "Academic Support and Tutoring" at 6.25% hints that while ChatGPT is appreciated for its supplementary educational roles, it might not be the mainstay for intensive academic assistance. Table 4 encapsulates a promising future for ChatGPT in the academic sector, where its multifaceted advantages can be harnessed to revolutionize contemporary educational practices and pedagogies.

5 Discussion

The findings of the systematic literature review make it clear that ChatGPT has become increasingly popular and has been incorporated into the academic sphere as a direct result. The findings from Adiguzel et al. [ 1 ] and Kasneci et al. [ 9 ], which looked at the transformative potential of ChatGPT and the opportunities and challenges presented by large language models in education, are consistent with the current research, which emphasizes the pedagogical integration of ChatGPT as a dominant theme. The current analysis reveals a high incidence of studies focusing on pedagogical integration (31.25%), which supports the previously identified imperative that educational institutions modify their teaching strategies to utilize AI's potential fully. Lawan et al.'s [ 6 ] investigation of modified flipped learning to reduce the possible adverse effects of artificial intelligence illustrates this, indicating that educators are actively looking for ways to integrate ChatGPT into pre-existing educational frameworks.

Furthermore, as 21.88% and 25.00% of studies have indicated, ChatGPT's contribution to personalized learning and effective content delivery significantly drives its necessity in academic culture. These features align with the opinions expressed by users regarding ChatGPT's educational applications, as highlighted by Mogavi et al. [ 2 ]. The focus on individualized learning aligns with the findings of Yilmaz and Yilmaz's [ 4 ] study on using augmented intelligence in programming instruction, which shows that ChatGPT's flexibility can accommodate different learning preferences and requirements.

The current study identifies ethical issues, educator readiness, and student involvement as significant concerns regarding the difficulties in implementing ChatGPT. These findings are consistent with Crawford et al. [ 3 ] on ethical leadership with AI and Kohnke et al. [ 24 ] on university instructors' readiness for AI. According to Karakose et al. [ 17 ], in their discussion of digital leadership, 15.63% of studies that concentrated on educator training and readiness highlight the need for professional development to give educators the tools they need to use ChatGPT in an academic setting.

Last, the current findings and earlier research highlight the potential benefits of ChatGPT's application in academic cultures, such as collaborative learning and developing critical thinking abilities. Mejia and Sargent's [ 28 ] claims regarding using technology to develop critical thinking skills are supported by the 18.75% of studies in the current analysis that focus on this skill's development. Similar to how 15.63% of studies report that ChatGPT facilitates collaborative learning, Azaria et al. [ 13 ] provide evidence of ChatGPT's effectiveness as an expert tool, which may promote higher-order collaboration and knowledge construction within academic communities.

5.1 Policy implications

The study offers policy implications for academic institutions. The significant focus on "Efficient Content Creation and Delivery" suggests that ChatGPT and similar AI tools can be integrated into curriculum design. Educational institutions should consider leveraging AI in formulating and updating course content. Educators can create a more dynamic, up-to-date, and relevant curriculum. Policies should thus promote training and workshops that introduce educators to the potentials and methodologies of such integration. Given the concerns related to "Pedagogical Integration" and "Educator Preparedness and Training," institutions should prioritize continuous professional development for educators in AI. This includes technical training and methodologies to incorporate AI tools into their teaching strategies seamlessly. A holistic understanding will enable educators to maximize the benefits of AI, thus enhancing student engagement and learning outcomes.

Institutions should develop comprehensive ethical guidelines for AI use, with "Ethical Considerations and Bias" being a recognized challenge. This would address potential biases and data privacy issues and ensure that the AI tools align with the institution's values and larger educational goals. It's imperative that while embracing AI, institutions also maintain the trust of students, educators, and stakeholders. Although "Technological Infrastructure" was less concerned, institutions must ensure a robust technological backbone to support widespread AI integration. This goes beyond mere access to the technology but also includes providing technical support, periodic maintenance, and regular updates. The potential for "Enhanced Personalized Learning" should be harnessed by creating policies that encourage the development of personalized learning pathways. These pathways, powered by AI, can adapt to students' needs in real-time, ensuring that every learner is catered to individually, optimizing their learning journey. ChatGPT's "Collaborative Learning" capability suggests that institutions should foster environments that promote group-based activities and collaborative projects, with AI tools as facilitators. Such policies will harness the potential of AI to enhance team dynamics and student cooperative efforts.

Incorporating these policy implications will not only harness the potential benefits of AI in academia but also address the challenges, ensuring a comprehensive and effective integration of tools like ChatGPT into the academic culture.

5.2 Limitations and future directions

Introducing artificial intelligence (AI) tools like ChatGPT into educational institutions has limitations. The currently available AI models have the potential to unintentionally perpetuate biases that are already present in their training data, which raises concerns about the neutrality and fairness of the content that is generated. A dependency on technological infrastructure also exists, which may prevent organizations with limited resources from taking advantage of these advancements. In addition, putting less stock in artificial intelligence can obscure essential human components of education, such as mentorship, experiential learning, and empathy. When looking to the future, it is necessary to make investments in AI systems that are objective and open to scrutiny, in addition to providing extensive training for educators. The way forward should center on a symbiotic integration in which artificial intelligence serves as a supplement to human educators rather than a replacement for them, and it should promote an inclusive, dynamic, and comprehensive learning environment.

Availability of data and materials

I confirm that all data and materials used in this study are readily available upon request to interested researchers.

Abbreviations

Artificial Intelligence

Chat generative pre-trained transformer

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Mohebi, L. Empowering learners with ChatGPT: insights from a systematic literature exploration. Discov Educ 3 , 36 (2024). https://doi.org/10.1007/s44217-024-00120-y

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• Volume 14, Issue 4
• Effect of metacognitive therapy on depression in patients with chronic disease: a protocol for a systematic review and meta-analysis
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• http://orcid.org/0009-0001-2964-8906 Zirui Zhang ,
• Peng Wang ,
• Jinjin Gu ,
• Qiang Zhang ,
• Changqing Sun ,
• Panpan Wang
• School of Nursing and Health , Zhengzhou University , Zhengzhou , Henan , China
• Correspondence to Dr Panpan Wang; wangpanpan{at}zzu.edu.cn

Background Chronic diseases have a high prevalence worldwide, and patients with chronic diseases often suffer from depression, leading to a poor prognosis and a low quality of life. Metacognitive therapy is a transdiagnostic psychotherapy intervention focused on thinking patterns, with the advantages of reliable implementation effect, short intervention period and low cost. It can help patients change negative metacognition, alleviate depression symptoms, and has a higher implementation value compared with other cognitive interventions. Therefore, metacognitive therapy may be an effective way to improve the mental health of patients with chronic diseases.

Methods and analysis CNKI, Wanfang Database, VIP Database for Chinese Technical Periodicals, Sinomed, PubMed, SCOPUS, Embase, The Cochrane Library, Web of Science and PsycINFO will be used to select the eligible studies. As a supplement, websites (eg, the Chinese Clinical Registry, ClinicalTrials.gov) will be searched and grey literature will be included. The heterogeneity and methodological quality of the eligible studies will be independently screened and extracted by two experienced reviewers. All the data synthesis and analysis (drawing forest plots, subgroup analysis and sensitive analysis) will be conducted using RevMan 5.4.1.

Ethics and dissemination This article is a literature review that does not include patients’ identifiable information. Therefore, ethical approval is not required in this protocol. The findings of this systematic review and meta-analysis will be published in a peer-reviewed journal as well as presentations at relevant conferences.

PROSPERO registration number CRD42023411105.

• Chronic Disease
• Meta-Analysis
• Protocols & guidelines
• Depression & mood disorders

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, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/ .

https://doi.org/10.1136/bmjopen-2023-075959

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STRENGTHS AND LIMITATIONS OF THIS STUDY

This systematic review and meta-analysis will evaluate the feasibility and effectiveness of metacognitive therapy (MCT) in reducing depression in patients with chronic diseases by collecting comprehensive evidence.

If heterogeneity is detected, differences in the effectiveness and durability of different types of MCTs will be determined in subgroup analysis.

Grey literature and clinical registration information will be searched manually to enrich evidence sources.

There may be a limited number of studies on the application of MCT to patients with chronic diseases, so the number of included studies may be small.

Only English and Chinese literature will be included.

Introduction

Chronic diseases, also called non-communicable diseases (NCDs), refer to diseases that do not result from infection but rather from damage by long-term accumulation. 1 Data show that billions of people suffer from chronic diseases, which has become an important public health issue. 2 3 Also, the United Nations prioritised the management of chronic diseases in the sustainable development goals. 4

Depression is one of the most common comorbidities among many chronic diseases. People with one chronic disease may have an increased risk of developing depressive symptoms by at least 20%. 5 6 Meanwhile, depression is associated with poor prognosis and increased medical costs in individuals with chronic diseases. 7 8 A literature review focused on patients with heart failure (HF) found that depression doubled the all-cause mortality in patients with HF, suggesting a possible association between depression and all-cause mortality. 9 The Lancet reported that data from 1990 to 2019 showed a high disability rate due to depression, and depression is considered one of the leading cause of burden worldwide. 10 Therefore, alleviating depression is crucial for maintaining the mental health of patients with chronic diseases. However, a systematic review comparing psychological and pharmacological interventions in patients with coronary artery disease found that neither strategy achieved the effect of alleviating depressive symptoms at the end of treatment. 11

Metacognitive therapy (MCT) was initially developed by Wells 12 and is an emerging theoretical based transdiagnostic psychotherapy that has been proven effective in alleviating depression. 13 According to Self-Regulation Executive Function model (S-REF), 14 depression will occur, persist and reoccur due to the development of unmanageable, repeated negative thinking pattern. 15 This thinking strategy called cognitive attentional syndrome (CAS), which focuses on the inner (attention, thinking and physical sensations), reflects on the past and worries about the future, accompanied by avoidance and maladaptation behaviours. The S-REF model assumes that CAS is influenced by positive or negative metacognitive beliefs. Negative metacognitive beliefs manifest as uncontrollable beliefs about contemplation and worry. Patients may express ‘My contemplation is uncontrollable’. While positive metacognitive beliefs manifest as useful beliefs about contemplation and worry, such as ‘My contemplation will help me find a solution’. 16 To alleviate depression symptoms, MCT helps patients in reducing CAS and developing healthy metacognitive beliefs. This enables patients to understand the negative effects and adverse consequences of CAS without denying the negative thinking content. 17 It includes several specific skills such as attention training technique (ATT), spatial attention control exercise, situational attention refocusing, detached mindfulness, etc. In the treatment of MCT, the ruminative thinking mode is blocked in the early stage. Patients are required to pay attention to external sounds or recognise different sound sources, helping them realise the independence of attention control from any internal and external events. 18 MCT establishes adaptive conditioned emotional response by mobilising the positive mental state of the patients, blocking the connection between conditional stimuli and negative emotions. 19

A systematic review and meta-analysis have indicated that MCT is an effective method for treating a range of psychological complaints. 13 Several clinical trials have examined the efficacy of MCT in patients with depression and chronic diseases. However, there are currently no meta-analysis related to MCT. Therefore, this protocol aims to conduct a systematic review and meta-analysis to assess the effectiveness of MCT in treating depression in patients with chronic disease.

Methods and analysis

Study registration.

This protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO) database on 4 April 2023. This protocol was reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses Statement. 20

Inclusion criteria

Types of studies.

Randomised controlled trials (RCTs) using MCT for treatment of patients with chronic disease will be included in the study. However, if <3 RCT studies will be included, we will also consider including quasi-experimental studies.

Types of participants

This study will include patients aged ≥18 years with any type of chronic disease. The NCDs considered in this study include, but are not limited to, cancer, stroke, coronary heart disease, HF, hypertension, diabetes and chronic obstructive pulmonary disease. The NCDs were diagnosed by 11th edition of the International Classification of Diseases (ICD-11). 21 There are no restrictions on gender, economic background, nationality, educational status or disease period.

Experimental and control interventions

Studies will be included if they assessed the effect of MCT or specific MCT techniques (eg, ATT) with appropriate qualified professionals are responsible for intervention delivery. According to Wells’ educational programme, 14 interventions can be lectures, self-help manuals, telephone support calls, group discussions, role plays and homework. Sessions should focus on deriving a case formulation and socialisation, practicing techniques to regulate worry and rumination, challenging metacognitive beliefs that maintain maladaptive patterns of thinking and developing a ‘helpful behaviours’ plan. The form of intervention can be adjusted according to the research objective. There is no limitation on the intervention period or intervention time. Interventions will be excluded if studies combine MCT with other psychotherapies (eg, mindfulness therapy).

In the included studies, the control group was defined as other interventions without MCT such as cognitive behavioural therapy, pharmacological treatment, wait-list control, usual care, clinical management and no interventions.

Outcome measures

The primary outcome was symptom of depression, which was evaluated using standardised and validated depressive symptom scale scores, such as The Hospital Anxiety and Depression Scale (HADS), the Beck Depression Inventory I or II, the Hamilton Depression Rating Scale and so on. We will include studies where depression is assessed as a primary or secondary outcome. If the reliability and validity of the scale used in the study are relatively low, the decision on include this study will be made through group discussion.

To comprehensively assess the effect of MCT on patients with chronic disease, our study also included anxiety, metacognitive beliefs, adverse events and traumatic stress symptoms as the secondary outcomes.

Exclusion criteria

RCTs with <10 participants.

Studies published in non-English and non-Chinese languages.

Studies that recruited ≥50% of patients with dementia or schizophrenia and it was not possible to distinguish between two groups of patients.

Studies that combine MCT techniques with other types of treatment (eg, cognitive behavioural therapy).

Studies that report similar results without further analysis or discussion.

Search methods

We will select literatures from the following four Chinese databases (CNKI, Wanfang Database, VIP Database for Chinese Technical Periodicals and Sinomed) and six English databases (PubMed, SCOPUS, Embase, The Cochrane Library, Web of Science and PsycINFO). The search time will be set from the beginning to January 2024, and the languages are limited to both Chinese and English. The Clinicaltrials.gov and Chinese Clinical Trial Registry will also be searched to obtain unpublished or ongoing trial data. The keywords of our study will be medical subject headings (MESH) terms and free-text terms corresponding to the subject heading for (1) MCT (eg, metacognitive therapy, metacognitive intervention); (2) depression (eg, depressive disorder, emotional distress, mood disorder) and (3) clinical trial. Specific searching strategy in PubMed is shown in table 1 . Appropriate modifications will be made in actual searching according to the searching methods of those databases. In addition, reference lists of the included studies will be examined to identify potentially eligible studies.

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Search strategies in PubMed

Searching other sources

Other websites will be searched as a supplement including: the Chinese Clinical Registry, the WHO International Clinical Trials Registry Platform and ClinicalTrials.gov.

Data collection and analysis

Selection of studies.

All search results will be imported into EndNote V.20 to select eligible studies, and duplicate studies will be deleted. The initial selection will be based on titles and abstracts, with two reviewers (ZZ and JG) working on separately. Those unrelated literature will be excluded. Next, full text of the remaining studies will be screened for further assessment according to the inclusion criteria. Any disagreements will be resolved through reviewers’ discussion. If an agreement cannot be reached, a third reviewer (PPW) will be consulted. The study selection flow chart is shown in figure 1 .

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Flow chart of the literature screening process and results.

Data extraction and management

The data extraction will be completed by two researchers (ZZ and JG) independently with a predesigned Microsoft Excel. Discrepancies in the data extraction will be resolved by consensus. If consensus cannot be reached, a third reviewer (PPW) will be consulted. The predefined items for extraction are the following: publication details (title, the first author’s name, publication year), characteristics of the research participants (sample size, gender, age, nationality, types of chronic disease, baseline data, diagnostic criteria for depression), interventions (type of MCT, number of sessions, duration of each lesson, intervention frequency), control condition (details of the treatment, including the name, dosage, frequency and course) and outcomes (outcome at each time point, adverse events in each group and numbers of dropouts). If the data are unclear or missing in our included studies, the corresponding author will be contacted through email to obtain complete data. If the data are still unattainable, only current data will be analysed, and the potential influence will be discussed.

Assessment of risk of bias

Cochrane Collaboration’s tool will be used to assess the risk of bias of included RCT studies by two authors (ZZ and JG) independently. This tool identifies bias in the following domains: random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, incomplete outcome data, selective reporting and other bias. 22 According to the criteria, the included RCTs will be classified as low, high or unclear risk of bias. Disagreement will be resolved by discussion and the third reviewer (PW) will be consulted when necessary. The quasi-experimental studies were evaluated by the JBI Critical Appraisal Checklist for Quasi-Experimental Studies. 23 If more than 10 trials are included, a funnel plot 24 will be used to detect publication bias, and the Egger test 25 will be carried out to analyse the asymmetry in the funnel plot.

Data synthesis

Quantitative data synthesis.

RevMan V.5.4.1 will be used to conduct statistical analysis. For categorical variables, we will use risk ratio and 95% CIs as analysis indicators. For continuous variables, mean difference (MD) will be calculated. Data of same indicators measured by different scales will be converted to standardised MD and calculated as Hedges’ g with 95% CI. When heterogeneity is not obvious (p>0.1 or I 2 <50%), the fixed effect model will be used for analysis, or we will choose the random effect model (when p≤0.1 or I 2 ≥50%). If quantitative synthesis is not appropriate, we will explain the reasons and make a qualitative analysis of the research results in the Discussion section.

Assessment of heterogeneity

Following the guideline in the Cochrane Handbook, χ 2 and I 2 statistics will be chosen to evaluate the heterogeneity. High, moderate and low heterogeneity correspond to I 2 of 25%, 50% and 75%, respectively. 26 If I 2 >50%, subgroup analysis will be performed to detect the reasons of heterogeneity. If there is no reason be found, we will provide a narrative summary without conducting data synthesis.

Subgroup analysis

If significant heterogeneity is detected, we will conduct subgroup analysis according to the characteristics of researches or participants including types of MCT, intervention time, frequency of intervention, age of participants, type of chronic diseases, nationality, etc.

Sensitivity analysis

Sensitive analysis will be performed to test the stability of the results. We will remove one study at a time to identify its effect on heterogeneity and effect size. Small change of heterogeneity and effect size after each removal shows reliable stability.

Strength of recommendations and the quality of evidence

To identify the quality of included studies, two researchers (ZZ and JG) will use the Grading of Recommendations Assessment, Development and Evaluation to evaluate the strength of evidence. 27 Disagreements will be solved by discussion or consultation with a third reviewer (PPW). Confidence in the results will be graded into high, moderate, low and very low. All eligible studies will be included in the final analysis irrespective of their quality score. Correspondingly, we will analyse the impact of different quality scores in our discussion and recommendations will be drawn cautiously.

Patient and public involvement

No patients or public will be involved in the design, conduct, reporting or dissemination of this research.

Study period

Our review had started in April 2023 and will be conducted until the end of April 2024.

Due to the high prevalence rates and the impact of depression on both physical and psychosocial outcomes, there is a need for effective depression interventions in chronic disease. However, the current depression treatment takes a long time and high costs, which brings a huge burden to patients with chronic diseases. Recent studies present that MCT is a theory-based, structured treatment and is suited to addressing the psychological needs of patients with chronic disease. Therefore, we intend to perform this systematic review and meta-analysis.

In our systematic review, we hold a keen interest in delving into the efficacy of MCT for patients with chronic diseases for several reasons: (1) specific intervention strategies of MCT will regulate repetitive negative thinking cycles and other unproductive behaviours that maintain depression, helping patients realise that worry and contemplation have no advantages and can be alleviated; (2) in MCT, patients will practice new reaction methods to enhance their attention control ability to get rid of worries and contemplation 28 ; (3) contrast to other therapies, MCT does not require in-depth analysis and challenging the patients’ concerns and (4) MCT has the advantages of short intervention period, convenient implementation methods, reliable implementation effects and low cost. 16

Some studies have been published related to the application of MCT on patients with chronic diseases. Wells et al recruited 799 eligible cardiac rehabilitation patients for MCT treatment, but approximately 58% patients refused to participate. 29 Fisher et al conducted a short-term MCT treatment on cancer survivors and depression that was evaluated by the HADS. 30 The study showed an excellent effect of MCT intervention, but only 75% of patients completed the entire treatment process. 30 Zahedian et al conducted MCT intervention on 24 patients and found MCT can significantly improve depression, but the sample size was limited. 31 In summary, inconsistent measurement tools, intervention types and participant characteristics make it difficult to obtain effective clinical evidence.

To the best of our knowledge, this study is the first comprehensive evidence for the application of MCT in chronic disease patients, and plays a crucial role in clinical intervention of depression. The anticipated benefits of this research encompass: (1) exploring the clinical efficacy of MCT intervention on depression and anxiety; (2) fostering a deeper comprehension of the psychological mechanism of MCT by using metacognitive beliefs as a secondary outcome and (3) discerning if the efficacy varies in specific MCT and the reasons for the differences.

There were several limitations in this study. First, due to differences in outcome measurements, intervention intensities and types of scales, there may exist a hi g h degree of clinical and statistical heterogeneity. If so, we will conduct subgroup analysis to identify heterogeneity sources. Second, although many clinical trials of MCT have been conducted, as an emerging psychological therapy, there may be many unpublished studies, and the lack of these data may have an impact on the research results. Therefore, we will also include clinical trial data to reduce this impact. Third, only English and Chinese literature was included in the analysis, which may have an impact on the results. We will discuss the impact in the discussion section.

Ethics and dissemination

Ethical approval is not required in this study because the patients’ personal information is not involved. The findings of the systematic review and meta-analysis will be published in a peer-reviewed journal and presented at conferences. Any changes in this protocol will be updated in PROSPERO and explanations of these modifications will be stated in the paper of this review.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

Acknowledgments.

The authors would like to thank all participants who contributed to this study.

• Alvarez P ,
• Brown J , et al
• Jiang C-H ,
• Bussotti M ,
• Sommaruga M
• Wierenga KL ,
• Cani D , et al
• ↵ Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990-2019: a systematic analysis for the global burden of disease study 2019 . The Lancet Psychiatry 2022 ; 9 : 137 – 50 . doi:10.1016/S2215-0366(21)00395-3 OpenUrl
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• Zahedian E ,
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• Ghasemi N , et al

ZZ and PW are joint first authors.

Contributors PPW, CS and QZ were responsible for the formulation of the article framework. ZZ, JG and PPW were responsible for the data collection and meta-analysis process. QZ was responsible for the content supplement. PW, PPW and CS were responsible for the feasibility analysis and improvement of the article. All authors read and approved the final manuscript.

Funding This study was supported by the funding from Science and Technology Department of Henan Province in China (232102311023), Health Commission of Henan Province (LHGJ20210496) and Zhengzhou University (XKLMJX202212).

Competing interests None declared.

Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Provenance and peer review Not commissioned; externally peer reviewed.

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• v.8(11); 2016 Nov

How to Conduct a Systematic Review: A Narrative Literature Review

Nusrat jahan.

1 Psychiatry, Mount Sinai Chicago

Sadiq Naveed

2 Psychiatry, KVC Prairie Ridge Hospital

Muhammad Zeshan

3 Department of Psychiatry, Bronx Lebanon Hospital Icahn School of Medicine at Mount Sinai, Bronx, NY

Muhammad A Tahir

4 Psychiatry, Suny Upstate Medical University, Syracuse, NY

Systematic reviews are ranked very high in research and are considered the most valid form of medical evidence. They provide a complete summary of the current literature relevant to a research question and can be of immense use to medical professionals. Our goal with this paper is to conduct a narrative review of the literature about systematic reviews and outline the essential elements of a systematic review along with the limitations of such a review.

Introduction and background

A literature review provides an important insight into a particular scholarly topic. It compiles published research on a topic, surveys different sources of research, and critically examines these sources [ 1 ]. A literature review may be argumentative, integrative, historical, methodological, systematic, or theoretical, and these approaches may be adopted depending upon the types of analysis in a particular study [ 2 ].

Our topic of interest in this article is to understand the different steps of conducting a systematic review. Systematic reviews, according to Wright, et al., are defined as a “review of the evidence on a clearly formulated question that uses systematic and explicit methods to identify, select and critically appraise relevant primary research, and to extract and analyze data from the studies that are included in the review” [ 3 ]. A systematic review provides an unbiased assessment of these studies [ 4 ]. Such reviews emerged in the 1970s in the field of social sciences. Systematic reviews, as well as the meta-analyses of the appropriate studies, can be the best form of evidence available to clinicians [ 3 ]. The unsystematic narrative review is more likely to include only research selected by the authors, which introduces bias and, therefore, frequently lags behind and contradicts the available evidence [ 5 ].

Epidemiologist Archie Cochrane played a vital role in formulating the methodology of the systematic review [ 6 ]. Dr. Cochrane loved to study patterns of disease and how these related to the environment. In the early 1970s, he found that many decisions in health care were made without reliable, up-to-date evidence about the treatments used [ 6 ].

A systematic review may or may not include meta-analysis, depending on whether results from different studies can be combined to provide a meaningful conclusion. David Sackett defined meta-analysis as a “specific statistical strategy for assembling the results of several studies into a single estimate” [ 7 - 8 ].

While the systematic review has several advantages, it has several limitations which can affect the conclusion. Inadequate literature searches and heterogeneous studies can lead to false conclusions. Similarly, the quality of assessment is an important step in systematic reviews, and it can lead to adverse consequences if not done properly.

The purpose of this article is to understand the important steps involved in conducting a systematic review of all kinds of clinical studies. We conducted a narrative review of the literature about systematic reviews with a special focus on articles that discuss conducting reviews of randomized controlled trials. We discuss key guidelines and important terminologies and present the advantages and limitations of systematic reviews.

Narrative reviews are a discussion of important topics on a theoretical point of view, and they are considered an important educational tool in continuing medical education [ 9 ]. Narrative reviews take a less formal approach than systematic reviews in that narrative reviews do not require the presentation of the more rigorous aspects characteristic of a systematic review such as reporting methodology, search terms, databases used, and inclusion and exclusion criteria [ 9 ]. With this in mind, our narrative review will give a detailed explanation of the important steps of a systematic review.

Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) checklist

Systematic reviews are conducted based on predefined criteria and protocol. The PRISMA-P checklist, developed by Moher, et al., contains 17 items (26 including sub-items) comprising the important steps of a systematic review, including information about authors, co-authors, their mailing and email addresses, affiliations, and any new or updated version of a previous systematic review [ 9 ]. It also identifies a plan for documenting important protocol amendments, registry names, registration numbers, financial disclosures, and other support services [ 10 ]. Moher, et al. also state that methods of systematic reviews involve developing eligibility criteria and describing information sources, search strategies, study selection processes, outcomes, assessment of bias in individual studies, and data synthesis [ 10 ].

Research question

Writing a research question is the first step in conducting a systematic review and is of paramount importance as it outlines both the need and validity of systematic reviews (Nguyen, et al., unpublished data). It also increases the efficiency of the review by limiting the time and cost of identifying and obtaining relevant literature [ 11 ]. The research question should summarize the main objective of a systematic review.

An example research question might read, “How does attention-deficit/hyperactivity disorder (ADHD) affect the academic performance of middle school children in North America?” The question focuses on the type of data, analysis, and topic to be discussed (i.e., ADHD among North American middle school students). Try to avoid research questions that are too narrow or broad—they can lead to the selection of only a few studies and the ability to generalize results to any other populations may be limited. An example of a research question that is too narrow would be, “What is the prevalence of ADHD in children and adolescents in Chicago, IL?” Alternately, if the research question is too broad, it can be difficult to reach a conclusion due to poor methodology. An example of a research question that is too broad in scope would be, “What are the effects of ADHD on the functioning of children and adolescents in North America?”

Different tools that can be used to help devise a research question, depending on the type of question, are: population, intervention, comparator, and outcomes (PICO); sample, phenomenon of interest, design, evaluation, and research type (SPIDER); setting, perspective, intervention, comparison, and evaluation (SPICE); and expectation, client group, location, impact, professionals, and service (ECLIPSE).

The PICO approach is mostly used to compare different interventions with each other. It helps to formulate a research question related to prognosis, diagnosis, and therapies [ 12 ].

Scenario: A 50-year-old white woman visited her psychiatrist with a diagnosis of major depressive disorder. She was prescribed fluoxetine, which she feels has been helpful. However, she experienced some unpleasant side effects of nausea and abdominal discomfort. She has recently been told by a friend about the use of St. John’s wort in treating depression and would like to try this in treating her current depression. (Formulating research questions, unpublished data).

In the above-mentioned scenario, the sample population is a 50-year-old female with major depressive disorder; the intervention is St. John’s wort; the comparison is fluoxetine; and the outcome would be efficacy and safety. In order to see the outcome of both efficacy and safety, we will compare the efficacy and safety of both St. John’s wort and fluoxetine in a sample population for treating depression. This scenario represents an example where we can apply the PICO approach to compare two interventions.

In contrast, the SPIDER approach is focused more on study design and samples rather than populations [ 13 ]. The SPIDER approach can be used in this research question: “What is the experience of psychiatry residents attending a transgender education?” The sample is psychiatry residents; the phenomenon of interest is transgender education; the design is a survey; the evaluation looks at the experience; and the research type is qualitative. 

The SPICE approach can be used to evaluate the outcome of a service, intervention, or project [ 14 ]. The SPICE approach applies to the following research question: “In psychiatry clinics, does the combined use of selective serotonin reuptake inhibitor (SSRI) and psychotherapy reduce depression in an outpatient clinic versus SSRI therapy alone?” The setting is the psychiatry clinic; the perspective/population is the outpatient; the intervention is combined psychotherapy and SSRI; the comparison is SSRI alone; and the evaluation is reduced depression. 

The ECLIPSE approach is useful for evaluating the outcome of a policy or service (Nguyen, et al., unpublished data). ECLIPSE can apply in the following research question: “How can a resident get access to medical records of patients admitted to inpatient from other hospitals?” The expectation is: “What are you looking to improve/change to increase access to medical records for patients admitted to inpatient?” The client group is the residents; the location is the inpatient setting; the impact would be the residents having easy access to medical records from other hospitals; and the professionals in this scenario would be those involved in improving the service experiences such as hospital administrators and IT staff.

Inclusion and exclusion criteria

Establishing inclusion and exclusion criteria come after formulating research questions. The concept of inclusion and exclusion of data in a systematic review provides a basis on which the reviewer draws valid and reliable conclusions regarding the effect of the intervention for the disorder under consideration [ 11 ]. Inclusions and exclusion are based on preset criteria for specific systematic review. It should be done before starting the literature search in order to minimize the possibility of bias.

Eligibility criteria provide the boundaries of the systematic review [ 15 ]. Participants, interventions, and comparison of a research question provide the basis for eligibility criteria [ 15 ]. The inclusion criteria should be able to identify the studies of interest and, if the inclusion criteria are too broad or too narrow, it can lead to an ineffective screening process.

Protocol registration

Developing and registering research protocol is another important step of conducting a systematic review. The research protocol ensures that a systematic review is carefully planned and explicitly documented before the review starts, thus promoting consistency in conduct for the review team and supporting the accountability, research integrity, and transparency of the eventually completed review [ 10 ]. PROSPERO and the Cochrane Database of Systematic Reviews are utilized for registering research protocols and research questions, and they check for prior existing duplicate protocols or research questions. PROSPERO is an international database of prospectively registered systematic reviews related to health care and social sciences (PRISMA, 2016). It is funded by the National Institute for Health Research. The Cochrane Collaboration concentrates on producing systematic reviews of interventions and diagnostic test accuracy but does not currently produce reviews on questions of prognosis or etiology [ 16 ].

A detailed and extensive search strategy is important for the systematic review since it minimizes bias in the review process [ 17 ].

Selecting and searching appropriate electronic databases is determined by the topic of interest. Important databases are: MEDLARS Online (MEDLINE), which is the online counterpart to the Medical Literature Analysis and Retrieval System (MEDLARS); Excerpta Medica Database (EMBASE); and Google Scholar. There are multiple electronic databases available based on the area of interest. Other important databases include: PsycINFO for psychology and psychiatry; Allied and Complementary Medicine Database (AMED) for complementary medicine; Manual, Alternative, and Natural Therapy Index System (MANTIS) for alternative medical literature; and Cumulative Index to Nursing and Allied Health Literature (CINAHL) for nursing and allied health [ 15 ].

Additional studies relevant for the review may be found by looking at the references of studies identified by different databases [ 15 ]. Non-indexed articles may be found by searching the content of journals, conferences proceedings, and abstracts. It will also help with letters and commentaries which may not get indexed [ 15 ]. Reviewing clinical trial registries can provide information about any ongoing trials or unpublished research [ 15 ]. A gray literature search can access unpublished papers, reports, and conference reports, and it generally covers studies that are published in an informal fashion, rather than in an indexed journal [ 15 ]. Further search can be performed by selecting important key articles and going through in-text citations [ 15 ].

Using Boolean operators, truncation, and wildcards

Boolean operators use the relationship between different search words to help with the search strategy. These are simple words (i.e., AND, OR, and NOT) which can help with more focused and productive results (poster, Jahan, et al.: How to conduct a systematic review. APPNA 39th Summer Convention. Washington, DC. 2016). The Boolean operator AND finds articles with all the search words. The use of OR broadens the focus of the search, and it will include articles with at least one search term. The researchers can also ignore certain results from the records by using NOT in the search strategy.

An example of AND would be using “depression” AND “children” in the search strategy with the goal of studying depression in children. This search strategy will include all the articles about both depression and children. The researchers may use OR if the emphasis of the study is mood disorders or affective disorders in adolescents. In that case, the search strategy will be “mood disorders” OR “affective disorders” AND “adolescents.” This search will find all the articles about mood disorders or affective disorders in adolescents. The researchers can use NOT if they only want to study depression in children and want to ignore bipolar disorder from the search. An example search in this scenario would be “depression” NOT “bipolar disorder” AND “children.” This will help ignore studies related to bipolar disorder in children.

Truncation and wildcards are other tools to make search strategy more comprehensive and focused. While the researchers search a database for certain articles, they frequently face terminologies that have the same initial root of a word but different endings. An example would be "autism," "autistic," and "autism spectrum disorder." These words have a similar initial root derived from “autis” but they end differently in each case. The truncation symbol (*) retrieves articles that contain words beginning with “autis” plus any additional characters. Wildcards are used for words with the same meanings but different spellings due to various reasons. For the words with spelling variations of a single letter, wildcard symbols can be used. When the researcher inputs “M+N” in the search bar, this returns results containing both “man” or “men” as the wildcard accounts for the spelling variations between the letters M and N.

Study selection

Study selection should be performed in a systematic manner, so reviewers deal with fewer errors and a lower risk of bias (online course, Li T, Dickersin K: Introduction to systematic review and meta-analysis. 2016. https://www.coursera.org/learn/systematic-review #). Study selection should involve two independent reviewers who select studies using inclusion and exclusion criteria. Any disagreements during this process should be resolved by discussion or by a third reviewer [ 10 ]. Specific study types can be selected depending on the research question. For example, questions on incidence and prevalence can be answered by surveys and cohort studies. Clinical trials can provide answers to questions related to therapy and screening. Queries regarding diagnostic accuracy can be answered by clinical trials and cross-sectional studies (online course, Li T, Dickersin K: Introduction to systematic review and meta-analysis. 2016. https://www.coursera.org/learn/systematic-review #). Prognosis and harm-related questions should use cohort studies and clinical trials, and etiology questions should use case-control and cohort studies (online course, Li T, Dickersin K: Introduction to systematic review and meta-analysis. 2016. https://www.coursera.org/learn/systematic-review #).

Data screening and data extractions are two of the major steps in conducting a systematic review [ 18 ]. Data screening involves searching for relevant articles in different databases using keywords. The next step of data screening is manuscript selection by reviewing each manuscript in the search results to compare that manuscript against the inclusion criteria [ 18 ]. The researchers should also review the references of the papers selected before selecting the final paper, which is the last step of data screening [ 18 ].

The next stage is extracting and appraising the data of the included articles [ 18 ]. A data extraction form should be used to help reduce the number of errors, and more than one person should record the data [ 17 ]. Data should be collected on specific points like population type, study authors, agency, study design, humanitarian crisis, target age groups, research strengths from the literature, setting, study country, type(s) of public health intervention, and health outcome(s) addressed by the public health intervention. All this information should then be put into an electronic database [ 18 ].

Assessing bias

Bias is a systematic error (or deviation from the truth) in results or inferences. Biases can change the results of any study and lead to an underestimation or overestimation of the true intervention effect [ 19 ]. Biases can impact any aspect of a review, including selecting studies, collecting and extracting data, and making a conclusion. Biases can vary in magnitude; some are small, with negligible effect, but some are substantial to a degree where an apparent finding may be entirely due to bias [ 19 ]. There are different types of bias, including, but not limited to, selection, detection, attrition, reporting, and performance.

Selection bias occurs when a sample selected is not representative of the whole general population. If randomization of the sample is done correctly, then chances of selection bias can be minimized [ 20 ].

Detection bias refers to systematic differences between groups in how outcomes are determined. This type of bias is based on knowledge of the intervention provided and its outcome [ 19 ].

Attrition bias refers to systematic differences between groups in withdrawals from a study [ 19 ]. The data will be considered incomplete if some subjects are withdrawn or have irregular visits during data collection.

Reporting bias refers to systematic differences between reported and unreported findings, and it is commonly seen during article reviews. Reporting bias is based on reviewer judgment about the outcome of selected articles [ 20 ].

Performance bias develops due to the knowledge of the allocated interventions by participants and personnel during the study [ 20 ]. Using a double-blind study design helps prevent performance bias, where neither the experimenter nor the subjects know which group contains controls and which group contains the test article [ 14 ].

Last step of systematic review: discussion

The discussion of a systematic review is where a summary of the available evidence for different outcomes is written and discussed [ 10 ]. The limitations of a systematic review are also discussed in detail. Finally, a conclusion is drawn after evaluating the results and considering limitations [ 10 ].

Discussion of the current article

Systematic reviews with or without a meta-analysis are currently ranked to be the best available evidence in the hierarchy of evidence-based practice [ 21 ]. We have discussed the methodology of a systematic review. A systematic review is classified in the category of filtered information because it appraises the quality of the study and its application in the field of medicine [ 21 ]. However, there are some limitations of the systematic review, as we mentioned earlier in our article. A large randomized controlled trial may provide a better conclusion than a systematic review of many smaller trials due to their larger sample sizes [ 22 ], which help the researchers generalize their conclusions for a bigger population. Other important factors to consider include higher dropout rates in large studies, co-interventions, and heterogeneity among studies included in the review.

As we discussed the limitations of the systematic review and its effect on quality of evidence, there are several tools to rate the evidence, such as the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system [ 22 ]. GRADE provides a structured approach to evaluating the risk of bias, serious inconsistency between studies, indirectness, imprecision of the results, and publication bias [ 22 ]. Another approach used to rate the quality of evidence is a measurement tool to assess systematic reviews (AMSTAR) [ 23 ]. It is also available in several languages [ 23 ].

Conclusions

Despite its limitations, a systematic review can add to the knowledge of the scientific community especially when there are gaps in the existing knowledge. However, conducting a systematic review requires different steps that involve different tools and strategies. It can be difficult at times to access and utilize these resources. A researcher can understand and strategize a systematic review following the different steps outlined in this literature review. However, conducting a systematic review requires a thorough understanding of all the concepts and tools involved, which is an extensive endeavor to be summed up in one article.

The Cochrane Handbook for Systematic Reviews of Interventions and the Center for Reviews and Dissemination (CRD) provide excellent guidance through their insightful and detailed guidelines. We recommend consulting these resources for further guidance.

Given that our article is a narrative review of the scholarly literature, it contains the same limitations as noted for any narrative review. We hope that our review of the means and methods for conducting a systematic review will be helpful in providing basic knowledge to utilize the resources available to the scientific community.

The content published in Cureus is the result of clinical experience and/or research by independent individuals or organizations. Cureus is not responsible for the scientific accuracy or reliability of data or conclusions published herein. All content published within Cureus is intended only for educational, research and reference purposes. Additionally, articles published within Cureus should not be deemed a suitable substitute for the advice of a qualified health care professional. Do not disregard or avoid professional medical advice due to content published within Cureus.

The authors have declared that no competing interests exist.