advantages of a systematic literature review

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Why systematic reviews matter

July 23, 2019 | 7 min read

By Tina Poklepović Peričić, Sarah Tanveer

A brief history, overview and practical guide for authors

This article was written as part of the Cochrane International Mobility Programme.

Introduction

The vast scale of scholarly literature occasions various problems. One is how to comprehensively record and assess the state of knowledge on a particular topic. A potent means of doing so is that of systematic reviews. The continuous growth of research, coupled with the demand to systematically summarize the available evidence to inform decisions from consumers and stakeholders, led to the formal development of systematic reviews (SRs) in the late 20th century 1-3 .

Systematic reviews search, appraise and collate all relevant empirical evidence in order to provide a complete interpretation of research results. Although conventional SRs are typically used in clinical research and social sciences, they have found application in various other subject areas for example in advertising, education, international development, public policy, ecology, environmental sciences, engineering and basic science research 4-7

A brief history of systematic reviews

The first example of a systematic review was conducted in 1753 by James Lind, who published a paper that aimed to provide a concise and unbiased summary of evidence on scurvy 8 9 . However, it was not until the 1970’s and 1980’s when more attention was paid to the growing need to improve the state of evidence synthesis.

In 1972, Archie Cochrane published a textbook titled “ Effectiveness and Efficiency: Random Reflections on Health Service ” 10 . Cochrane drew attention to the vital importance of randomized control trials in determining the effectiveness of health treatments. This led to a greater international emphasis on the need to improve research synthesis by policy makers, academics, and clinicians 3 . Gradually, topic areas outside of healthcare also adopted SRs as a way of comprehensively and systemically summarizing existing research.

How to conduct a systematic review

If you are considering embarking on a systematic review, there are several issues you need to contemplate if you wish to conduct one. In healthcare, for example, the first step would be to define an explicit research question by using the PICOTS (Population, Intervention, Comparator, Outcome, Timing, Setting) framework 11 , and also register the protocol for the review on PROSPERO ( https://www.crd.york.ac.uk/prospero/ opens in new tab/window ), the international database of prospectively registered systematic reviews. Protocols provide a complete detailed description of the process by which the review will be conducted. Registering the protocol reduces research bias, duplication of effort, resource waste, and provides greater transparency 12 . Outside of medical sciences, protocols can be uploaded to Open Science Framework ( https://osf.io opens in new tab/window ).

You must adopt a comprehensive, objective and reproducible search strategy to capture all relevant sources of evidence. In doing so, you can be confident of having incorporated all the appropriate material for the topic at hand. A thorough search strategy should involve multiple databases, registries, sources of grey literature ( https://onlinelibrary.london.ac.uk/resources/databases/opengrey opens in new tab/window ) 13 , conference proceedings and abstracts. Following the predefined eligibility criteria, you then need to analyze the screened search results to extract data from those publications that meet the inclusion criteria.

Don’t forget to assess the risk of bias when applicable (i.e., in clinical research). Ideally, these methodological steps should preferably be performed by two authors independently, one of which is a methodologist and the other a content area expert. Summarizing the results of the included studies and interpreting their findings in the light of certainty of evidence and their applicability are the final steps of completing a systematic review. You can also include a meta-analysis if applicable.

In order to assess the methodological quality of systematic reviews in biomedical sciences, checklists like the AMSTAR – “A MeaSurement Tool to Assess systematic Reviews" 14 (https://amstar.ca) can be utilized. Lastly, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 16 (https://prisma-statement.org/) checklist, is a minimum set of items for reporting in systematic reviews: include one with your full article. The flow diagram can also be adopted to use for non-medical research (https://prisma-statement.org/PRISMAStatement/FlowDiagram).

Why are systematic reviews important?

Systematic reviews offer a number of benefits. For starters, they deliver a clear and comprehensive overview of available evidence on a given topic. Moreover, SRs also help identify research gaps in our current understanding of a field. They can highlight methodological concerns in research studies that can be used to improve future work in the topic area 17 . Lastly, they can be used to identify questions for which the available evidence provide clear answers and thus for which further research is not necessary 18 .

The process of conducting systematic reviews, especially for new authors, will prove to be a worthwhile endeavour. Authors refine their knowledge on the subject area of interest, develop new research ideas, and gain critical skills in synthesising existing literature.

We hope that you have found this introduction to systematic reviews helpful. Additional information about SRs can be found on the Cochrane website opens in new tab/window . If you have any questions or observations, please feel free to comment below.

1. Meerpohl JJ, Herrle F, Reinders S, et al. Scientific value of systematic reviews: survey of editors of core clinical journals. PLoS One 2012;7(5):e35732. doi: 10.1371/journal.pone.0035732 [published Online First: 2012/05/09]

2. Higgins JPT, Green S, Cochrane Collaboration. Cochrane handbook for systematic reviews of interventions Version 5.1.0 [updated March 2011]. London: The Cochrane Collaboration,, 2011:1 online resource. doi: 10.1002/9780470712184

3. Chalmers I, Hedges LV, Cooper H. A brief history of research synthesis. Eval Health Prof 2002;25(1):12-37. doi: 10.1177/0163278702025001003 [published Online First: 2002/03/01]

4. O’Hagan EC, Matalon S, Riesenberg LA. Systematic reviews of the literature: a better way of addressing basic science controversies: American Physiological Society Bethesda, MD, 2018. doi: 10.1152/ajplung.00544.2017

5. Gilbody S, Wilson P, Watt I. Benefits and harms of direct to consumer advertising: a systematic review. BMJ Quality & Safety 2005;14(4):246-50. doi: 10.1136/qshc.2004.012781

6. Pullin AS, Stewart GB. Guidelines for systematic review in conservation and environmental management. Conservation biology 2006;20(6):1647-56. doi: 10.1111/j.1523-1739.2006.00485.x

7. Petticrew M. Systematic reviews from astronomy to zoology: myths and misconceptions. Bmj 2001;322(7278):98-101. doi: 10.1136/bmj.322.7278.98

8. Lind J. A treatise on the scurvy. In three parts. Containing an inquiry into the nature, causes, and cure, of that disease. London,: A. Millar 1753. doi:10.1136/bmj.330.7482.92-a

9. Clarke M, Chalmers I. Reflections on the history of systematic reviews. BMJ Evid Based Med 2018;23(4):121-22. doi: 10.1136/bmjebm-2018-110968 [published Online First: 2018/06/21]

10. Cochrane AL. Effectiveness and efficiency: random reflections on health services. London: Nuffield Provincial Hospitals Trust 1972. doi: 10.1017/cbo9781107256644

11. Santos CMdC, Pimenta CAdM, Nobre MRC. The PICO strategy for the research question construction and evidence search. Revista latino-americana de enfermagem 2007;15(3):508-11. doi: 10.1590/s0104-11692007000300023

12. Stewart L, Moher D, Shekelle P. Why prospective registration of systematic reviews makes sense. Syst Rev 2012;1:7. doi: 10.1186/2046-4053-1-7 [published Online First: 2012/05/17]

13. Mahood Q, Van Eerd D, Irvin E. Searching for grey literature for systematic reviews: challenges and benefits. Research synthesis methods 2014;5(3):221-34. doi: 10.1002/jrsm.1106

14. Shea BJ, Reeves BC, Wells G, et al. AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. Bmj 2017;358:j4008. doi: 10.1136/bmj.j4008

15. Chandler J, Churchill R, Higgins J, et al. Methodological standards for the conduct of new Cochrane Intervention Reviews. The Cochrane Library 2013 doi: 10.4073/cpg.2016.3

16. Moher D, Liberati A, Tetzlaff J, et al. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Annals of internal medicine 2009;151(4):264-69. doi: 10.7326/0003-4819-151-4-200908180-00135

17. Eagly AH, Wood W. Using research syntheses to plan future research. The Handbook of Research Synthesis: Russell Sage Foundation 1994:485-500. doi: 10.1002/(SICI)1097-0258(19970330)16:6<713::AID-SIM430>3.0.CO;2-4

18. Chalmers I, Glasziou P. Avoidable waste in the production and reporting of research evidence. Obstet Gynecol 2009;114(6):1341-5. doi: 10.1097/AOG.0b013e3181c3020d [published Online First: 2009/11/26]

Contributors

Tina Poklepović Peričić

Medical School

Sarah Tanveer

Authors' update - keeping journal authors in touch with industry developments, support and training.

advantages of a systematic literature review

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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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.

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Was it really an "ugly" meta-analysis? Fuccio L, Eusebi LH, Bazzoli F. Fuccio L, et al. Am J Gastroenterol. 2009 Nov;104(11):2853; author reply 2853-4. doi: 10.1038/ajg.2009.458. Am J Gastroenterol. 2009. PMID: 19888243 No abstract available.

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Carrying out systematic literature reviews: an introduction

Alan Davies

Lecturer in Health Data Science, School of Health Sciences, University of Manchester, Manchester

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Systematic reviews provide a synthesis of evidence for a specific topic of interest, summarising the results of multiple studies to aid in clinical decisions and resource allocation. They remain among the best forms of evidence, and reduce the bias inherent in other methods. A solid understanding of the systematic review process can be of benefit to nurses that carry out such reviews, and for those who make decisions based on them. An overview of the main steps involved in carrying out a systematic review is presented, including some of the common tools and frameworks utilised in this area. This should provide a good starting point for those that are considering embarking on such work, and to aid readers of such reviews in their understanding of the main review components, in order to appraise the quality of a review that may be used to inform subsequent clinical decision making.

Since their inception in the late 1970s, systematic reviews have gained influence in the health professions ( Hanley and Cutts, 2013 ). Systematic reviews and meta-analyses are considered to be the most credible and authoritative sources of evidence available ( Cognetti et al, 2015 ) and are regarded as the pinnacle of evidence in the various ‘hierarchies of evidence’. Reviews published in the Cochrane Library ( https://www.cochranelibrary.com) are widely considered to be the ‘gold’ standard. Since Guyatt et al (1995) presented a users' guide to medical literature for the Evidence-Based Medicine Working Group, various hierarchies of evidence have been proposed. Figure 1 illustrates an example.

Systematic reviews can be qualitative or quantitative. One of the criticisms levelled at hierarchies such as these is that qualitative research is often positioned towards or even is at the bottom of the pyramid, thus implying that it is of little evidential value. This may be because of traditional issues concerning the quality of some qualitative work, although it is now widely recognised that both quantitative and qualitative research methodologies have a valuable part to play in answering research questions, which is reflected by the National Institute for Health and Care Excellence (NICE) information concerning methods for developing public health guidance. The NICE (2012) guidance highlights how both qualitative and quantitative study designs can be used to answer different research questions. In a revised version of the hierarchy-of-evidence pyramid, the systematic review is considered as the lens through which the evidence is viewed, rather than being at the top of the pyramid ( Murad et al, 2016 ).

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Methodology

Systematic Review | Definition, Example, & Guide

Systematic Review | Definition, Example & Guide

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

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

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

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

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 Literature Reviews: An Introduction

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A systematic literature review of interpersonal communication strategies for optimizing government employee performance in the digital era

Siti Hatijah Umpain Roles: Funding Acquisition, Writing – Original Draft Preparation Nuri Herachwati Roles: Project Administration, Supervision, Writing – Review & Editing Yusuf Setiadi Roles: Methodology, Validation Anastasia Enike Hanorsian Roles: Writing – Review & Editing

This article is included in the Research Synergy Foundation gateway.

This research shows that ongoing digital changes have a significant impact, particularly in the government sector. In addition to ease and benefits, such as operational efficiency and faster public services, new challenges, such as problems related to system integration, human resources (HR) limitations in the use of technology, and public access to digital technology, are also present. Therefore, this study aims to explore the role of interpersonal communication in enhancing employee performance in the context of HR development in the digital era by employing the systematic literature review (SLR) method and using data from electronic databases, specifically Google Scholar using the Harsing’s Publish or Perish (Windows GUI Edition) 8.2.3944.8118, the application can be accessed by downloading (https://publish-or-perish.en.softonic.com/), and Scopus (www.scopus.com) with a focus on journals published between 2017 and 2024. By identifying research gaps, research questions were developed to understand the influence of interpersonal communication on improving employee performance in the context of public services, necessary adaptations, and interpersonal communication strategies. This research provides comprehensive insights and practical solutions for the government sector to address the dynamics of the current and future digital era, based on data analysis from reliable database sources. The risk of bias in this study was addressed through the selection criteria and data extraction method. Thematic analysis and quantitative summarization were used for data presentation and synthesis. The results of the meta-analysis indicate that interpersonal communication has a significant positive influence on employee performance. The effect size estimates for each intervention and the corresponding confidence intervals are presented. To ensure honesty and accuracy in the interpretation of the results, potential evidence limitations and biases should be acknowledged transparently.

Keywords: Interpersonal Communication, Government Employee Performance, HR Development, Digital Era

Introduction

Today’s digital changes make it easier for anyone to perform tasks in various areas of life, including government jobs. Some of the benefits obtained include support for administrative and operational processes, faster and easier public services, transparency, assistance in improving security, ease of risk management, and better decision-making by government organizations through data analysis ( Daraba et al., 2023 ). However, the rapid development of the digital era poses a challenge for the public sector in providing public services and managing human resources. Technological developments have changed the way people work, changed communication patterns and cooperation, and influenced the demand for human resources who have the necessary skills and knowledge for the task.

The role of communication has become crucial for building and improving government performance in the digital era in view of today’s challenges. Its importance lies in the ability to establish good working relations between government employees, including between superiors and subordinates, colleagues, and within government units. This agrees with organizational theory, which states that interpersonal communication is a straight line between impersonal and bureaucratic communication in organizations. Interpersonal communication is personal, involves social interaction, and is not limited to strict formal rules. Interpersonal communication involves both impersonal and bureaucratic aspects in the organizational environment ( Hardjana, Andre, 2019 ).

Interpersonal communication is also crucial for governments facing innovation and technological changes. This refers to Everett Rogers’ theory of innovation diffusion in 1993. In summary, diffusion refers to the spreading of information within a social system. This may involve new ideas, technologies, and cultures. According to the theory of innovation diffusion, interpersonal communication is essential for organizations in informing about changes to be accepted, especially regarding the impact of the digital era. This will contribute to creating and balancing individuals in adapting to changes and can mitigate resistance due to the inability to accept changes.

Interpersonal communication is also crucial, as seen in the existence, relatedness, and growth (ERG) theory, developed by Clayton Paul Alderfer as an adaptation of Maslow’s theory. In summary, this indicates that humans fundamentally desire to be valued and recognized (existence), invited and involved, and to establish relationships and socialize with others as a way to increase connections ( Usman, Husaini, 2006 ). Therefore, involvement and interpersonal relationships are important to create bonds among employees, enhance engagement, and enable more productive work and adaptation to changes.

With regard to productivity, the need for the best skills of employees is clear. In creating government employees capable of achieving good results, interpersonal communication plays a crucial role in forming an effective communication network and building an open and responsive organizational culture. It serves as a means of disseminating knowledge and skills and encouraging government employees to share information, best practices, and develop their abilities. Effective communication between individuals can greatly influence the performance of employees, as both positive and negative communication can make a difference ( Humaidi, 2020 ).

In the context of human resource development in the government, interpersonal communication can be considered an instrument used to motivate government employees in facing changes, ensuring that they remain receptive, adaptable, and continue working within the organization to achieve organizational goals with competitiveness, productivity, and innovation. Considering the relationship between interpersonal communication interconnections and employee performance in this study, this research becomes essential for government organizations in preparing for the current digital era.

On the basis of the findings and implications derived from the review of articles published between 2017 and 2024, several research gaps have been identified. Despite the acknowledged importance of interpersonal communication strategies, in-depth research has been conducted on how interpersonal communication specifically affects public service performance in the digital era. Further research on how government employees can integrate digital skills into interpersonal communication strategies is required. In addition, there is a gap in understanding how interpersonal communication strategies can specifically create an environment that supports psychological safety and collaboration within a high-performance work system ( Yasa et al., 2021 ).

1. Does Interpersonal Communication Influence Public Service Performance in the Digital Age?

2. Adaptation of interpersonal communication strategies to technological changes and the need for digital skills?

3. Can interpersonal communication strategies in high-performance work systems create a work environment that supports psychological safety and collaboration and encourages employee voice, thus motivating innovation?

A systematic literature review involves collecting and analyzing existing research on a specific topic with the objective of responding to established research questions ( Kitchenham & Charters, 2007 ). The literature review adheres to the systematic five-stage process outlined by Tranfield et al. (2003) , as demonstrated in Figure 1 . The following explain the stages.

Figure 1. Systematic literature review stages.

Planning the review.

An expert panel is formed at this stage to provide their specialized knowledge. Specifically crafted review questions will guide research. A search strategy, inclusion and exclusion criteria, and analytical methods were outlined in the review protocol.

This Systematic Literature Review employs Mean Difference, Risk Ratio, and Odds Ratio as effect measures to assess the influence of marketing, communication, and interpersonal communication skills training interventions, respectively.

Identifying and evaluating studies

Research is carried out utilizing databases such as Google Scholar and Scopus during this stage. Data is gathered from studies that adhere to established protocol criteria for ensuring data accuracy and significance.

Inclusion and exclusion criteria

The data search or information source strategy is the first stage of this research. Study selection was performed by assessing quality, adhering to eligibility criteria, using quality assessment instruments, data synthesis, and data extraction. In the data search, “interpersonal communication” “performance” “human resource development” “digital era” and “government” were the keywords used by the author.

Data search sources for the review came from Google Scholar using the Publish or Perish application and Scopus based on journals published between 2017 and 2024. The articles cover crucial themes in the public sector, including interpersonal communication, performance improvement, human resource development, and the digital transformation. To ensure data accuracy, sourcing was limited to Google Scholar and Scopus. The research methods must be both reliable and valid for comprehensive analysis. The chosen articles should meaningfully add to the study goals by substantially answering the research queries. Irrelevant studies were discarded. We analyzed publications only from 2017 to 2024 for data relevance. Ambiguous studies were omitted for precise assessment and combination. The review incorporated only studies of acknowledged quality standards. The study selection in the review was based on its alignment with the research objectives ( Figure 2 ).

Figure 2. Inclusion and exclusion criteria.

Extraching and synthesizing data.

Studies are evaluated based on pre-established criteria. To ensure reliability and trustworthiness. To prepare the data for presentation or synthesis, several careful steps are taken. Firstly, missing summary statistics are addressed using imputation methods, such as employing the mean or median of available data. Second, data from various sources are converted into a uniform format, which includes unit measurement conversion, normalization, and logarithmic transformation if necessary. Thirdly, the data are validated to ensure accuracy and consistency, with verification through comparison with the original sources. Finally, data from various studies are combined using meta-analysis techniques to provide stronger and more comprehensive conclusions ( Figure 3 ).

Figure 3. Extracting and synthesizing process.

Eligibility criteria from the Google Scholar database operated through the Publish or Perish application were chosen because of ease of data access and availability of the required data, which includes clear information such as title, author names, citations, and journal names. From Google Scholar, we found 223 articles that were then screened to obtain 29 relevant articles suitable for review. The Scopus database, which is an internationally indexed database, has been used to supplement references because the quality of the Scopus data is highly respected. The search resulted in 88 articles from the keywords, which were then screened using the Scopus CSV export file, yielding 3 articles for SLR information. Articles were selected on the basis of the relevance of the writing topic, the use of trusted and valid methods, reliable sources, sufficient data availability for analysis needs, and alignment with the writing objectives. The Process at Figure 1 . Review identification.

Results: Interpersonal communication positively and significantly influences government employee performance in the digital era. These results indicate that positive interpersonal communication can improve public services. Therefore, government organizations implement adapted interpersonal communication strategies to create a work environment that supports employee psychological safety, collaboration, and innovation. In addition, understanding team coordination patterns and factors that influence team performance, implementing technology-based policies, and improving human resource management are important. Therefore, in the digital era, government organizations need to pay attention to the role of interpersonal communication in developing human resources and improving employee performance.

The extraction and synthesis process in evaluating whether a paper is about interpersonal communication involves key steps to identify the paper’s main contributions to performance, the digital era, and human resources. First, the paper is assessed for its insights into enhancing individual or organizational performance, its relevance to challenges and opportunities in the digital era, and its contribution to human resource strategies and management. Next, the paper’s scope relevance is examined to ensure the topics covered meet research needs, and the relevance of findings is analyzed to determine if the research results can be effectively applied in broader contexts or specific situations. Finally, the paper is selected based on these evaluations to ensure that only those with substantial and relevant contributions are used in further research or academic writing.

This evident in prisma by Hatijah Umpain and Herachwati (2024b) . 32 papers were finally included from a pool of 311 after the stages of identification, screening, eligibility, and inclusion were completed. The researchers set the standards for selecting relevant and high-quality papers. 223 articles were identified using the Google Scholar database via the Publish or Perish application. 29 articles passed the screening process. 88 articles were obtained by searching the internationally recognized Scopus database using the keywords “interpersonal communication,” “performance,” and “human resource” on February 11, 2024. 3 more articles, chosen for SLR, following Scopus CSV file screening. Articles were chosen based on relavance to research objectives, trusted methods usage, reliable source credibility, data availability for analysis, and alignment with writing objectives. The studies included in the systematic review had to meet the predetermined eligibility criteria, ensuring its credibility and validity ( Figure 4 ).

Figure 4. Prisma diagram.

Data extraction and synthesis.

The data from the studies were extracted using standardized forms. Meta-analysis or qualitative synthesis methods are used to integrate findings from multiple studies.

Reporting and dissemination

In the final stage, the report is formatted for ease of understanding and application of the review results. The study’s findings aim to shape communication strategies and inform government decisions in the digital realm.

In the following sections, stages 4 and 5 of the Tranfield process are explored.

Results and Discussion

The analysis of articles sourced from the Scopus database totaled three out of 88 articles that went through a screening process, whereas the analysis from Google Scholar articles totaled 29 articles with a total initial source of 223 articles that went through the data screening process. Data were collected from both databases from 2017 to 2024. Five articles are from India, Nigeria (Brazil, Chile, and Portugal), the United States, and China; the remaining 27 articles are from Indonesia. Table 1. Full Data (extended data) by Hatijah Umpain and Herachwati (2024a) . The data analysis relates to interpersonal communication and the performance of government employees in the current digital era. There are several important points from the results of the review, namely regarding the research method used in the article being analyzed, namely using qualitative, quantitative, and SLR research methods. With 13 qualitative articles, the approach used is descriptive, post-positive, and case study, with the method used being participatory. The analysis technique uses cluster analysis, entropy, and triangulation techniques. There were 11 quantitative articles reviewed, the analysis of which used chi-square analysis, Pearson correlation, and linear regression. Data collection techniques that, when summarized, consist of observation, questionnaires, interviews, documentation, and literature. The sampling technique uses both full sampling and saturated sampling.

Influence of public service communication on employee performance in the digital era

In today’s digital era, communication, such as writing, is an important factor that can influence employee performance in public services ( Asri, 2018 ). It cannot be denied that technology can change the way individuals interact; however, these human relationships are central to organizational performance. The ability to communicate effectively in society can play a key role in the success of services Putri and Chatamallah (2023) and Rohida (2018) . Interpersonal communication, as per Thomassawa (2016) significantly impacts workplace relationships, thereby affecting service delivery and performance. In today’s digital era, employees’ ability to accommodate technological changes while maintaining personal relationships with the public is based on human communication. Evaluation of some author’s articles yielded the following data.

Table 2 . Influence of Public Service Communication on Employee Performance in the Digital Era. These studies indicate that communication among people can influence communication among government employees. Communication among employees fosters good relationships, resulting in openness and trust ( Saragih, 2020 ). Higher levels of communication correlate with better performance ( Tusakinah & Fadel, 2021 ). Attention should be paid to the quality of communication among individuals in the working environment.

Table 2. Influence of public service communication on employee performance in the digital era.

(Author, Year)	Method	Contribution
(Author, Year)	Method	Human resource	Interpersonal communication	Performance	Digital era
( )	Quantitative Method	✓	✓	✓	✓
( )	Quantitative Method	✓	✓	✓	✓
( )	Quantitative Method	✓	✓	✓	✓
( )	Quantitative Method	✓	✓	✓	✓
( )	Quantitative Method	✓	✓	✓	✓
( )	Qualitative method with descriptive approach	✓	✓	✓	✓
( )	Quantitative Method		✓
( )	Quantitative Descriptive Analysis	✓	✓	✓	✓
( )	Qualitative method	✓	✓		✓
( )	Quantitative Method a cross-sectional	✓	✓	✓	✓
( )	Qualitative method	✓	✓	✓	✓
( )	Qualitative method	✓	✓	✓	✓

Other research findings ( Pamungkas & Khotimah, 2022 ) suggest that civil servant performance can be enhanced through communication among employees. Openness and trust can be achieved through effective communication. Similar results have been shown in other studies ( Larasanti et al., 2024 ; Fatmasari & Adha, 2022 ) that examined openness, empathy, positive support, and equality, which have partial and simultaneous positive effects. Similar dimensions such as empathy, positive attitudes, openness, and benevolent attitudes positively influence work quality, quantity, and timeliness ( Arfiany et al., 2021 ).

Saputra (2023) evaluated the impact of people communication on employee performance and indicated that training and skill development can improve employee performance. Organizations are advised to pay attention to people’s communication to enhance employee performance. As described by Kristiyaningsih et al. (2017) , skilled employees tend to have more intensive interactions with visitors and show more empathy, making visitors feel valued and appreciated, ultimately driving a positive evaluation. Therefore, employees, especially librarians, can be more productive if they possess good working abilities.

When providing good public service, communication with the community is essential to ensure their satisfaction, given that the government’s role is to serve citizens. However, communication among colleagues is also crucial for aligning perceptions in achieving organizational goals, fostering collaboration, and promoting good collaboration. As stated in the article ( Sazali & Siregar, 2021 ), communication between employees plays a crucial role in facilitating internal work processes and can enhance employee performance. The greater the interactive relationships among employees, the higher the performance ( Muhammad et al., 2018 ).

In the digital age, the author deems crucial attention to interpersonal communication, which enhances both performance through technology and skill development while tackling technical and non-technical barriers including communication issues. Combines ideas from Asri (2018) , Meta Savari and Edi Prihantoro (2020) , Meydita Asima Megarani Simbolon (2023) , Taufik (2023), Putri Chatamallah (2023) and Rohida (2018) .

In today’s digital age, high-quality interpersonal communication, characterized by openness, empathy, and positive support, remains essential for improving employee performance in public services. Enhancing communication skills among employees not only increases internal interactions but also elevates public service satisfaction by improving external communications. Effective employee performance and high-quality public services can be achieved through investment in the improvement of interpersonal communication in the digital age.

Human communication strategies for implementing technological changes and digital skills requirements

In the digital era, organizations are confronted with a complexity of challenges and opportunities. Communication strategies have become a key aspect in designing and implementing necessary transformations in the midst of technological changes and the evolving skills landscape ( Dini Fajriyani et al., 2023 ). To effectively communicate the urgency and benefits of change, clear and persuasive communication that makes all parties feel heard can motivate employees and the public to adopt new technologies and realize their consequences. A supporting article is presented below for further insight

Table 3 . Human Communication Strategies for Implementing Technological. The test is based on the view that digital changes are beneficial in various aspects of government, including administrative processes, operations, public services, transparency, security, risk mitigation, and better decision-making through data evaluation. Ongoing technological developments have changed work patterns, communication, and combinations, and human resources should have the necessary skills and knowledge.

Table 3. Human communication strategies for implementing technological changes and digital skills requirements.

(Author, Year)	Method	Contribution
(Author, Year)	Method	Human resource	Interpersonal communication	Performance	Digital era
( )	Systematic literature Review	✓		✓	✓
( )	Evaluatif Research	✓	✓	✓
( )	Qualitative method	✓	✓
( )	Qualitative Method	✓		✓	✓
( )	Literature Study	✓	✓	✓	✓
( )	Quantitative Method	✓	✓	✓	✓
( )	Quantitative Method	✓	✓	✓	✓
( )	Literature Review	✓	✓	✓	✓

The article “The Role of People’s Communication Skills in Human Resource and Management” Ansari (2021) provides deep insights into the role of communication skills in human resource management. This study’s comprehensive understanding of how interactive communication can influence employee performance and team combination is also supported by Khairunnisa et al. (2023) , agile communication is emphasized as crucial for enhancing team collaboration and achieving organizational goals. This study highlights the importance of communication in human resource management during the digital era. Communication skills are the key to building effective relationships in the workplace. About adapting to technology, good interpersonal skills can help individuals interact effectively, emphasizing the need for persuasive skills in digital platforms. However, mastering these skills also requires digital literacy aligned with employees’ communication skills.

Another article discussing the “Human Resource Development Strategy in Facing the Era of Industrial Revolution 4.0 Towards the Era of Society 5.0” ( Tahar et al., 2022 ) reveals that people’s communication can impact the performance of government employees in the context of public services in the current digital era. The research findings indicate that effective human communication can enhance employee performance and teamwork. On the other hand, poor relational communication can hurt employee’s performance and result in low-quality public services. Therefore, during the digital era, it is crucial for government organizations to focus on people communication in human resource development. This study emphasizes the importance of developing communication skills that are aligned with evolving technological and digital needs.

Faced with the transition from the fourth industrial revolution to society 5.0, people communication strategies can be adapted to ensure alignment with technological advances and digital requirements. This includes the use of digital platforms for communication, the creation of strong relationships, and above all, effective participation.

Government organizations must adapt to changes to ensure sustainability. In this regard, the perception of change needs to be set in such a way that it can be accepted and realized as important to the progress of the government. Therefore, when human resources embrace the digital era, they will be willing to oppose and demonstrate their generous stance toward development.

Communication among people is crucial for building good working relationships between government employees, both within departments and within government units. From a diffusion theory perspective, effective human communication is vital for informing individuals about changes, especially regarding their impact in the digital era, and can assist individuals in adapting to these changes. Effective communication among people is believed to influence the performance of government employees in the digital era of public services. Good relations between employees and the public can improve efficiency, collaboration, and satisfaction in public services.

Government employees must successfully adopt people communication strategies to cope with technological changes and digital skill requirements. Effective communication in a digital environment strengthens collaboration, innovation, and responsiveness to current digital demands, as confirmed by Onsardi et al. (2019) , who emphasizes that people communication is key to understanding cultural diversity and building mutually beneficial relationships in a diverse work environment.

Improving the performance of government employees in the digital era through communication includes ensuring adequate digital literacy skills for employees. Strengthening can be achieved by involving employees in relevant technological training and learning activities related to their tasks. Government organizations should use current digital communication tools or platforms, such as Microsoft Teams or Google Workspace, to facilitate internal communication. Providing socialization is beneficial when facing governmental changes in the digital era, helping employees realize the benefits and positive impacts of change. The research title emphasizes the importance of enhancing people’s communication skills through training government employees, especially in the digital context, by engaging them in becoming accustomed to presenting skills online, managing and using time management in online communication, and instilling ethical digital communication practices.

Adapting people’s communication strategies to technological changes and digital skills can be applied by analyzing team communication patterns, using the entropy method to identify effective communication patterns, strengthening these patterns despite technological changes and digital skill requirements, and adapting communication strategies to be more dynamic, responsive, and in line with the demands of technological developments and digital skills ( Engome Tchupo & Macht, 2023 ). According to de Sousa Figueira et al. (2022) , steps for technological changes and employee skill requirements can be taken through open and clear communication with all employees to reduce concerns, involving employees in change by listening to their ideas to make them feel valued, providing training and skills development for employees to be better prepared, building good concurrence between leaders and employees with empathy, listening, and supporting employees, and encouraging participation in teamwork to strengthen collective skills and foster innovative idea exchange.

While technology shapes modern communication in the digital age, effective interpersonal skills significantly impact public sector employee productivity. Effective communication enhances relationships among employees and the public while also promoting the acceptance of new technologies and digital skills. Investing in communication training and digital literacy enhances employee performance, team collaboration, and public service quality. Effective communication is essential for successful transition during technological advancements.

Interpersonal communication strategies can create a workplace environment that supports psychological safety along with employee voices in a high-performance system, which ultimately drives innovation

Table 4 . Interpersonal communication strategies , Effective interpersonal communication strategies are the key to creating a work environment that promotes psychological safety and participation of employees in a high-performance work system. Open and benevolent communication motivates employees to innovate and contribute maximally to organizational goals. Larasanti et al. (2024) research explains the relationship between interpersonal communication and the creation of an innovative work environment. This study demonstrates that a high-performance work system has a positive and significant impact on employees’ innovative behavior. Employee voices, whether promoting ideas or expressing dissent, partially mediate the relationship between a high-performance work system and innovative behavior. Communicating with people can create a work environment that supports psychological safety and fosters positive relationships among colleagues through trust and mutual support. This type of work environment provides employees with a sense of security to share ideas, express opinions, and take initiative without fear of criticism or punishment.

Table 4. Interpersonal communication strategies.

(Author, Year)	Method	Implication
(Author, Year)	Method	Human resource	Interpersonal communication	Performance	Digital era
( )	Quantitative Method		✓	✓
( )	Quantitative Method	✓	✓	✓
( )	Quantitative Method	✓	✓	✓	✓

Rentao Miao et al. (2020) explained that psychological safety moderates the role of promoting voices or single reactions to others’ ideas, proposals, or actions. It emphasizes that effective interpersonal communication directly influences the relationship between a high-performance work system and innovation. Therefore, training, communication skills, creating a work culture, and ensuring employees’ psychological safety are crucial. Rentao’s research also found that low psychological safety weakens the impact of a high-performance work system on employees’ innovative behavior through their voices in providing ideas. Thus, to influence positive attitudes toward innovation, it is crucial to balance focus and control in management.

Good interpersonal communication facilitates effective participation among employees, promotes idea sharing and joint problem-solving, and creates innovative solutions through good collaboration, mutual support, and strengthening each other to achieve common goals. Ultimately, this supports the effective participation of employees in discussions and providing input or expressing their opinions. This demonstrates the importance of creating a culture within the organization that values employee voices, which is crucial for decision-making. When a work environment is directed by effective interpersonal communication, guaranteed psychological safety, good collaboration, and appreciation for employee voices, it can drive innovation. Employees who feel assisted and valued are more likely to be creative and bold when presenting new ideas or suggestions and contribute to organizational innovation efforts.

Government organizations need to train and develop employees in interpersonal communication skills. It helps them understand the importance of interpersonal communication and motivates them to improve their skills in this area. Government organizations should also build a work environment that supports positive interpersonal relationships, starting with creating a more open and transparent work culture and fostering mutual respect among employees, superiors, and government units. Implementing reward systems and recognition for employees who have performed well and successfully built positive interpersonal relationships can motivate them to improve their performance. Providing opportunities for employees to be involved in decision-making by allowing discussions or giving ideas is also crucial. Additionally, implementing technology that supports interpersonal communication, such as online cooperation platforms, websites, or chat applications, can connect everyone and facilitate communication in the current digital era, demanding quick and satisfying services. This will improve communication between employees and superiors.

Understanding team communication patterns through entropy analysis, as explained by Engome Tchupo and Macht (2023) , should not be forgotten. It helps identify patterns in an ever-changing environment. Understanding how teams communicate and coordinate includes (a) identifying effective communication patterns to strengthen cooperation and team performance, (b) adapting interpersonal communication strategies based on proven patterns that successfully achieve team goals, and (c) effectively responding to a rapidly changing work environment through communication strategies that align with team dynamics.

Utilizing effective communication strategies fosters a work environment promoting psychological safety and employee involvement. Open and supportive communication and a work culture that values employee voices have been shown to both improve employee performance and encourage innovative behavior. Effective communication training and digital skills development are vital in today’s digital age for successfully implementing new technologies and enhancing public services. Effective innovation and organizational transformation can be achieved through investments in interpersonal communication and digital literacy.

In conclusion, interpersonal communication plays a crucial role in enhancing the performance of government employees in the digital era. Effective interpersonal communication positively impacts employee performance in the evolving digital age. With good interpersonal communication, government organizations can create a work environment that supports optimal performance, including psychological safety, collaboration, and encouraging employees to voice their opinions, thereby triggering innovation. However, understanding team coordination patterns and the factors influencing team performance is important. Effective interpersonal communication facilitates the implementation of government systems based on technology and enhances workforce competence. Therefore, governments need to create effective communication strategies, focus on developing communication skills, and implement training programs aimed at improving communication skills and collaborative activities to foster a positive work environment.

Ethics and consent

No Ethics and Consent required.

Data availability

Underlying data.

No data associated with this article.

Underlying data The article lacks accompanying data.

Extended data

Figshare: Tabel 1. Full Data https://doi.org/10.6084/m9.figshare.26310283.v2 ( Hatijah Umpain & Herachwati, 2024a ).

Data is available under CC0 license

Figshare: Checklist Prisma 2020 https://doi.org/10.6084/m9.figshare.26342569.v2 ( Hatijah Umpain & Herachwati, 2024b ).

Figshare: Data Attachment From Scopus https://doi.org/10.6084/m9.figshare.26404420.v2 ( Hatijah Umpain & Herachwati, 2024c ).

Figshare: Data Attachment From Google Scholar https://doi.org/10.6084/m9.figshare.26404507.v3 ( Hatijah Umpain & Herachwati, 2024d ).

Figshare: Appendix https://doi.org/10.6084/m9.figshare.26404603.v1 ( Hatijah Umpain & Herachwati, 2024e ).

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Supply Chain Management Practice and Competitive Advantage: Systematic Literature Review

2023, Logistic and Operation Management Research (LOMR)

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J Otolaryngol Head Neck Surg

Systematic and other reviews: criteria and complexities

Robert t. sataloff.

1 Editor-in-Chief, Journal of Voice, Philadephia, USA

2 Editor Emeritus, Ear, Nose and Throat Journal, Philadephia, USA

Matthew L. Bush

3 Assistant Editor, Otology & Neurotology, Lexington, USA

Rakesh Chandra

4 Editor-in-Chief, Ear, Ear, Nose and Throat Journal, Nashville, USA

Douglas Chepeha

5 Editor-in-Chief, Journal of Otolaryngology – Head & Neck Surgery, Toronto, Canada

Brian Rotenberg

6 Editor-in-Chief, Journal of Otolaryngology – Head & Neck Surgery, London, Canada

Edward W. Fisher

7 Senior Editor, Journal of Laryngology and Otology, Birmingham, UK

David Goldenberg

8 Editor-in-Chief, Operative Techniques in Otolaryngology – Head and Neck Surgery, Hershey, USA

Ehab Y. Hanna

9 Editor-in-Chief, Head & Neck, Houston, USA

Joseph E. Kerschner

10 Editor-in-Chief, International Journal of Pediatric Otorhinolaryngology, Milwaukee, USA

Dennis H. Kraus

11 Co-Editor-in-Chief, Journal of Neurological Surgery Part B: Skull Base, New York, USA

John H. Krouse

12 Editor-in-Chief, Otolaryngology – Head and Neck Surgery, Philadelphia, USA

13 Editor-in-Chief, OTO-Open, Philadelphia, USA

14 Editor-in-Chief, Journal for Oto-Rhino-Laryngology, Head and Neck Surgery, Philadelphia, USA

15 Editor-in-Chief, World Journal of Otorhinolaryngology – Head and Neck Surgery, Philadelphia, USA

Michael Link

16 Co-Editor-in-Chief, Journal of Neurological Surgery Part B: Skull Base, Rochester, USA

Lawrence R. Lustig

17 Editor-in-Chief, Otology & Neurotology, New York, USA

Samuel H. Selesnick

18 Editor-in-Chief, The Laryngoscope, New York, USA

Raj Sindwani

19 Editor-in-Chief, American Journal of Rhinology & Allergy, Cleveland, USA

Richard J. Smith

20 Editor-in-Chief, Annals of Otology, Rhinology & Laryngology, Iowa City, USA

James Tysome

21 Editor-in-Chief, Clinical Otolaryngology, Cambridge, UK

Peter C. Weber

22 Editor-in-Chief, American Journal of Otolaryngology, Boston, USA

D. Bradley Welling

23 Editor-in-Chief, Laryngoscope Investigative Otolaryngology, Boston, USA

Review articles can be extremely valuable. They synthesize information for readers, often provide clarity and valuable insights into a topic; and good review articles tend to be cited frequently. Review articles do not require Institutional Review Board (IRB) approval if the data reviewed are public (including private and government databases) and if the articles reviewed have received IRB approval previously. However, some institutions require IRB review and exemption for review articles. So, authors should be familiar with their institution’s policy. In assessing and interpreting review articles, it is important to understand the article’s methodology, scholarly purpose and credibility. Many readers, and some journal reviewers, are not aware that there are different kinds of review articles with different definitions, criteria and academic impact [ 1 ]. In order to understand the importance and potential application of a review article, it is valuable for readers and reviewers to be able to classify review articles correctly.

Systematic reviews

Authors often submit articles that include the term “systematic” in the title without realizing that that term requires strict adherence to specific criteria. A systematic review follows explicit methodology to answer a well-defined research question by searching the literature comprehensively, evaluating the quantity and quality of research evidence rigorously, and analyzing the evidence to synthesize an answer to the research question. The evidence gathered in systematic reviews can be qualitative or quantitative. However, if adequate and comparable quantitative data are available then a meta-analysis can be performed to assess the weighted and summarized effect size of the studies included. Depending on the research question and the data collected, systematic reviews may or may not include quantitative meta-analyses; however, meta-analyses should be performed in the setting of a systematic review to ensure that all of the appropriate data were accessed. The components of a systematic review can be found in an important article by Moher et al. published in 2009 that defined requirements for systematic reviews and meta-analyses [ 2 ].

In order to optimize reporting of meta-analyses, an international group developed the Quality of Reporting of Meta-Analyses (QUOROM) statement at a meeting in 1996 that led to publication of the QUOROM statement in 1999 [ 3 ]. Moher et al. revised that document and re-named the guidelines the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The PRISMA statement included both meta-analyses and systematic reviews, and the authors incorporated definitions established by the Cochrane Collaboration [ 4 ]. The PRISMA statement established the current standard for systematic reviews. To qualify as a systematic review, the methods section should acknowledge use of the PRISMA guidelines, and all PRISMA components should be incorporated strictly in all facets of the paper from the research question to the discussion. The PRISMA statement includes a checklist of 27 items that must be included when reporting a systematic review or meta-analysis [ 2 ]. A downloadable version of this checklist can be used by authors, reviewers, and journal editorial staff to ensure compliance with recommended components [ 5 ]. All 27 will not be listed in this brief editorial (although authors and reviewers are encouraged to consult the article by Moher et al. and familiarize themselves with all items), but a few will be highlighted.

The research question, as reflected in the title, should be a hypothesis-based specific research inquiry. The introduction must describe the rationale for the review and provide a specific goal or set of goals to be addressed. The type of systematic review, according to the Cochrane Collaboration, is based on the research question being asked and may assess diagnostic test accuracy, review prognostic studies evidence, evaluate intervention effect, scrutinize research methodology, or summarize qualitative evidence [ 6 ].

In the methods section, the participants, interventions, comparisons, outcomes and study design (PICOS) must be put forward. In addition to mentioning compliance with PRISMA, the methods section should state whether a review protocol exists and, if so, where it can be accessed (including a registration number). Systematic reviews are eligible for registration in the International Prospective Register of Systematic Reviews (PROSPERO) as established at the University of York (York, UK). When PROSPERO is used (it is available but not required for systematic reviews), registration should occur at the initial protocol stage of the review, and the final paper should direct to the information in the register. The methods section also must include specific study characteristics including databases used, years considered, languages of articles included, specific inclusion and exclusion criteria for studies; and rationale for each criterion must be included. Which individuals specifically performed searches should be noted. Electronic search strategy (with a full description of at least one electronic search strategy sufficient to allow replication of the search), process for article selection, data variables sought, assumptions and simplifications, methods for assessing bias risk of each individual study (such as selective reporting in individual studies) and utilization of this information in data synthesis, principal summary measures (risk ratio, hazard ratio, difference in means, etc.), methods of data management and combining study results, outcome level assessment, and other information should be reported.

The results section should include the number of studies identified, screened, evaluated for eligibility (including rationale for exclusion), and those included in the final synthesis. A PRISMA flow diagram should be included to provide this information succinctly [ 7 ]. The results also should include the study characteristics, study results, risk of bias within and across studies, and a qualitative or quantitative synthesis of the results of the included studies. This level of rigor in acquiring and evaluating the evidence of each individual study is one of the criteria that distinguishes systematic reviews from other categories. If the systematic review involves studies with paired samples and quantitative data, a summary of data should be provided for each intervention group along with effect estimates and confidence intervals for all outcomes of each study. If a meta-analysis is performed, then synthesized effect size should be reported with confidence intervals and measures of consistency (i.e. – data heterogeneity such as I 2 ) for each meta-analysis, and assessment of bias risk across studies. A forest plot, which provides a graphical presentation of the meta-analysis results, should be included.

The discussion section should summarize the main findings commenting on the strength of evidence for each outcome, as well as relevance to healthcare providers, policymakers and other key stake-holders; limitations of the study and outcomes; and conclusions highlighting the interpretation of results in the context of other research, and implications for future research.

Without adhering to of all of these criteria and the others listed in the PRISMA statement and checklist, the review does not qualify to be classified as “systematic”.

Meta-analyses

Meta-analyses, when feasible based on available and comparable quantitative data, supplement a systematic review evaluation, by adding a secondary statistical analysis of the pooled weighted outcomes of similar studies. This adds a level of objectivity in the synthesis of the review’s findings. Meta-analyses are appropriate when at least 2 individual studies contain paired samples (experimental group and control group) and provide quantitative outcome data and sample size. Studies that lack a control group may over-estimate the effect size of the experimental intervention or condition being studied and are not ideal for meta-analyses [ 8 ]. It also should be remembered that the conclusions of a meta-analysis are only as valid as the data on which the analysis is based. If the articles included are flawed, then the conclusions of the meta-analysis also may be flawed. Systematic reviews and meta-analyses are the most rigorous categories of review.

Other types of reviews

Mixed methods reviews.

Systematic reviews typically contain a single type of data, either qualitative or quantitative; however, mixed methods reviews bring together a combination of data types or study types. This approach may be utilized when quantitative data, in the setting of an intervention study, only provide a narrow perspective of the efficacy or effectiveness of the intervention. The addition of qualitative data or qualitative studies may provide a more complete picture of the knowledge, attitudes, and behaviors of clinicians, patients or researchers regarding that intervention. This type of review could involve collecting either the quantitative or the qualitative data using systematic review methodology, but often the qualitative data are gathered using a convenience sampling. Many qualitative studies provide useful insights into clinical management and/or implementation of research interventions; and incorporating them into a mixed methods review may provide valuable perspective on a wide range of literature. Mixed methods reviews are not necessarily systematic in nature; however, authors conducting mixed methods reviews should follow systematic review methodology, when possible.

Literature and narrative reviews

Literature reviews include peer-reviewed original research, systematic reviews, and meta-analyses, but also may include conference abstracts, books, graduate degree theses, and other non-peer reviewed publications. The methods used to identify and evaluate studies should be specified, but they are less rigorous and comprehensive than those required for systematic reviews. Literature reviews can evaluate a broad topic but do not specifically articulate a specific question, nor do they synthesize the results of included studies rigorously. Like mixed method reviews, they provide an overview of published information on the topic, although they may be less comprehensive than integrative reviews; and, unlike systematic reviews, they do not need to support evidence-based clinical or research practices, or highlight high-quality evidence for the reader. Narrative reviews are similar to literature reviews and evaluate the same scope of literature. The terms sometimes are used interchangeably, and author bias in article selection and data interpretation is a potential concern in literature and narrative reviews.

Umbrella reviews

An umbrella review integrates previously published, high-quality reviews such as systematic reviews and meta-analyses. Its purpose is to synthesize information in previously published systematic reviews and meta-analyses into one convenient paper.

Rapid review

A rapid review uses systematic review methodology to evaluate existing research. It provides a quick synthesis of evidence and is used most commonly to assist in emergent decision-making such as that required to determine whether COVID-19 vaccines should receive emergent approval.

Scoping, mapping, and systematized reviews

If literature has not been reviewed comprehensively in a specific subject that is varied and complex, a mapping review (also called scoping review) may be useful to organize initial understanding of the topic and its available literature. While mapping reviews may be helpful in crystallizing research findings and may be published, they are particularly useful in helping to determine whether a topic is amenable to systematic review, and to help organize and direct the approach of the systematic review or other reviews of the subject. Systematized reviews are used most commonly by students. The systematized review provides initial assessment of a topic that is potentially appropriate for a systematic review, but a systematized review does not meet the rigorous criteria of a systematic review and has substantially more limited value. Additional types of reviews exist including critical review, state-of-the-art review, and others.

Reviews can be invaluable; but they also can be misleading. Systematic reviews and meta-analyses provide readers with the greatest confidence that rigorous efforts have attempted to eliminate bias and ensure validity, but even they have limitations based upon the strengths and weaknesses of the literature that they have assessed (and the skill and objectivity with which the authors have executed the review). Risks of bias, incomplete information and misinformation increase as the rigor of review methodology decreases. While review articles may summarize research related to a topic for readers, non-systematic reviews lack the rigor to answer adequately hypothesis-driven research questions that can influence evidence-based practice. Journal authors, reviewers, editorial staff, and should be cognizant of the strengths and weaknesses of review methodology and should consider them carefully as they assess the value of published review articles, particularly as they determine whether the information presented should alter their patient care.

Authors’ contributions

The author(s) read and approved the final manuscript.

Declarations

The authors declare no competing interests.

This article is co-published in the following journals: Journal of Voice, Otology & Neurotology, Ear, Nose and Throat Journal, Journal of Laryngology and Otology, Operative Techniques in Otolaryngology – Head and Neck Surgery, Head & Neck, International Journal of Pediatric Otorhinolaryngology, Journal of Neurological Surgery Part B: Skull Base, Otolaryngology – Head and Neck Surgery, World Journal of Otorhinolaryngology – Head and Neck Surgery, The Laryngoscope, American Journal of Rhinology & Allergy, Annals of Otology, Rhinology & Laryngology, Clinical Otolaryngology, American Journal of Otolaryngology, Laryngoscope Investigative Otolaryngology.

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Targeted prostate biopsy: how, when, and why a systematic review.

1. Introduction

2. materials and methods, 2.1. data collection and study selection, 2.2. screening and eligibility criteria, 2.3. data extraction and synthesis, 2.4. quality assessment, 2.5. potential bias and limitations, clinical trial, 4. discussion, 4.1. economic considerations and accessibility, 4.2. potential of artificial intelligence, 4.3. advancements in active surveillance and prostate biopsy strategies, 5. conclusions and recommendations, recommendations, author contributions, institutional review board statement, data availability statement, conflicts of interest.

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Click here to enlarge figure

Year	Reference	Number of Patients	Prior Negative Biopsy	Transrectal or Transperineal Biopsy	Median Age	Median PSA (ng/mL)	Median Prostate Volume (cc)	Cores per Target (MRI)	Comparator (Cores)	Definition of Clinically Significant Prostate Cancer	Detection Rate	Complications
2016	(Baco et al., 2016) [ ]	175	Biopsy naive	TR	65	7.3	42	2	If a palpable and/or TRUS suspicious lesion was found, two targeted biopsies from the lesion were performed, followed by 12-core	MCCL > 5 mm for Gleason 6 or any MCCL for Gleason 7 disease	38%
2016	(Pepe et al., 2017) [ ]	200	saturation TP prostate biopsy	TR and TP	61	8.6		Targeted mpMRI/TRUS TR fusion guided biopsies (4 cores) and TP cognitive biopsies (4 cores)	//	Gleason score 6 and/or >2 positive cores	30%	None.
2017	(Porpiglia et al., 2017) [ ]	107	Biopsy naive	TR	64	5.9	46.2	3–6	12	biopsy GS > 7 or MCCL > 5 mm	47%
2018	(Kasivisvanathan et al., 2018) [ ]	252	Biopsy naive	TR or TP route, according to local expertise	64.4	6.75	NR	4	12	>Gleason7 (3 + 4)	38%	Blood in the urine (30% vs. 63%), blood in the semen (32% vs. 60%), pain at the site of the procedure (13% vs. 23%), rectal bleeding (14% vs. 22%), and erectile dysfunction (11% vs. 16%). Two percent had serious adverse events.

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Share and Cite

Rebez, G.; Barbiero, M.; Simonato, F.A.; Claps, F.; Siracusano, S.; Giaimo, R.; Tulone, G.; Vianello, F.; Simonato, A.; Pavan, N. Targeted Prostate Biopsy: How, When, and Why? A Systematic Review. Diagnostics 2024 , 14 , 1864. https://doi.org/10.3390/diagnostics14171864

Rebez G, Barbiero M, Simonato FA, Claps F, Siracusano S, Giaimo R, Tulone G, Vianello F, Simonato A, Pavan N. Targeted Prostate Biopsy: How, When, and Why? A Systematic Review. Diagnostics . 2024; 14(17):1864. https://doi.org/10.3390/diagnostics14171864

Rebez, Giacomo, Maria Barbiero, Franco Alchiede Simonato, Francesco Claps, Salvatore Siracusano, Rosa Giaimo, Gabriele Tulone, Fabio Vianello, Alchiede Simonato, and Nicola Pavan. 2024. "Targeted Prostate Biopsy: How, When, and Why? A Systematic Review" Diagnostics 14, no. 17: 1864. https://doi.org/10.3390/diagnostics14171864

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Published: 29 August 2024

A meta-analysis of performance advantages on athletes in multiple object tracking tasks

Hui Juan Liu 1 ,
Qi Zhang 1 ,
Sen Chen 1 ,
Yu Zhang 1 &

Scientific Reports volume 14 , Article number: 20086 ( 2024 ) Cite this article

Metrics details

Neuroscience

This study compared the multiple object tracking (MOT) performance of athletes vs. non-athletes and expert athletes vs. novice athletes by systematically reviewing and meta-analyzing the literature. A systematic literature search was conducted using five databases for articles published until July 2024. Healthy people were included, specifically classified as athletes and non-athletes, or experts and novices. Potential sources of heterogeneity were selected using a random-effects model. Moderator analyses were also performed. A total of 23 studies were included in this review. Regarding the overall effect, athletes were significantly better at MOT tasks than non-athletes, and experts performed better than novices. Subgroup analyses showed that expert athletes had a significantly larger effect than novices, and that the type of sport significantly moderated the difference in MOT performance between the two groups. Meta-regression revealed that the number of targets and duration of tracking moderated the differences in performance between experts and novices, but did not affect the differences between athletes and non-athletes. This meta-analysis provides evidence of performance advantages for athletes compared with nonathletes, and experts compared with novices in MOT tasks. Moreover, the two effects were moderated by different factors; therefore, future studies should classify participants more specifically according to sports levels.

Little associations exist between the three commonly used functional screening tests in collegiate athletes

Three-Dimensional Motion Capture Data of a Movement Screen from 183 Athletes

The use of wearable resistance and weighted vest for sprint performance and kinematics: a systematic review and meta-analysis

Introduction.

Visual attention plays a crucial role in all tasks involving perception and action, particularly in sports. In highly dynamic and constantly changing scenarios, players need to flexibly adjust their visual attention while simultaneously performing various activities to act successfully, requiring continuous attention throughout the process 1 , 2 . Nakayama and Mackeben 3 first linked perceptual research to attention by dividing it into instantaneous and continuous attention. This study focused on continuous dynamic attention, involving multiple moving objects simultaneously over a period of a few seconds. Continuous attention may be static or dynamic, as the stimulus may remain stationary, or motion may occur during sustained attention to the target. The process of multiple object tracking (MOT) involves continuous attention. The core aspects of attention include selectivity, capacity limitations, and subjective effort 4 ; MOT serves as a visual illustration of these three components of attention 5 .

The MOT paradigm is a cognitive task originally developed to study visual attention 4 ; the paradigm was later used by researchers to evaluate and enhance the ability to track targets within a dynamic environment where all objects are in constant motion 6 , 7 . Performance on MOT tasks is defined as being able to successfully track several moving circles within a specified arena 8 . Typically, tasks require participants to track multiple targets. The general procedure is as follows: first, objects with all the same characteristics appear in the visual field (usually 6 to 10 objects); then, several of these objects are designated as targets (usually 2 to 5 objects), and the participant tracks the objects during the following time period. At the end of the object movement, the tracking performance for one or all targets is tested. Researchers can manipulate the number of targets 9 and distractors 10 , speed of movement 11 , and tracking duration 12 to explore and compare the dynamic visual attention of various populations. Differences in MOT performance among experts and novices in various fields have also been studied, such as drivers 13 , video game players 14 , and athletes 12 , 15 . Especially in sports, many studies have focused on the MOT performance advantages of expert athletes.

However, previous study on the performance advantages of athletes in MOT are controversial. Some studies found that individuals with sports expertise can perform better on MOT tasks, including tracking accuracy 16 , reaction time 17 , and tracking speed thresholds 6 . A previous study found that volleyball athletes had faster reaction times than nonathletes when detecting changes in targets in MOT 18 . Recent studies have also found that basketball and volleyball athletes have a performance advantage over nonathletes in MOT tasks 10 , 18 , 19 , 20 , 21 , 22 . Professional athletes in soccer, ice hockey, rugby, and other sports also outperformed high-level amateur athletes and nonathletes in MOT tasks, and showed higher learning efficiency 23 . These studies investigated players from team ball games (e.g., basketball 19 , 24 ) because dynamic visual attention plays a key role in these types of sports. Players need to simultaneously pay attention not only to the spatial position of the ball and to the court but also to the movement and position of teammates and opponents 25 .

Nevertheless, not all empirical evidence is consistent with this conclusion; some studies have found no statistical difference between athletes and non-athletes or experts and novices. Memmert et al. 26 showed that team sports experts did not perform better than novices on visual attention tasks. Li et al. 27 also found that there was no significant difference in tracking accuracy between expert athletes and novices when the number of tracking targets was small. These findings demonstrated that there was no discernible difference in the performance of MOT tasks between athletes and nonathlete college students or athletes of different levels. In addition, one study found that basketball players had lower MOT scores than nonathletes 28 . This demonstrates that the MOT performance advantages of athletes are controversial.

Given the inconsistent findings in prior studies concerning the MOT, no meta-analyses have examined MOT performance advantages in athletes. A meta-analysis on whether athletes show performance advantages in MOT is worth considering. Following the cognitive skill transfer hypothesis, training in a cognitive task may enhance performance on related, but untrained, cognitive tasks 29 , 30 . The so-called broad transfer hypothesis argues that long-term experience in team sports leads to adaptations in basic cognitive abilities causing performance differences between experts and novices even on tasks independent to experts’ domain 31 , 32 . In this regard, the specific cognitive demands of open-skill sports like basketball, or soccer were argued to cause superior cognitive abilities in elite athletes 33 .

The cognitive advantages in other areas for expert athletes have been explored in many meta-analyses 31 , 34 . A previous meta-analysis 31 that examined whether professional athletes remained ‘experts’ in the cognitive lab found that expert athletes performed better on measures of processing speed and a category of varied attentional paradigms (e.g., the Paced Auditory Serial Addition Task (PASAT) and the Eriksen arrow flankers task). They proposed further research with higher-level cognitive tasks, such as tasks of executive function and complex tasks involving attention (e.g., MOT) 31 . Previous systematic reviews have also shown that sporting experts are more successful than novices when reacting to an upcoming event, while recruiting fewer attentional resources and devoting more attention to subsequent targets analysis in unexpected situations 35 . For MOT tasks, attentional resource theory 36 would hold that there is a pool of resources required for tracking objects, and that the limit on tracking depends on the resource demands required to track each object. For example, if the tracking task were so difficult that tracking one target consumed all available tracking resources, then only a single item could be tracked. However, if each item only required 1/4th of the total available resources, then four objects could be tracked. Experts may require less attentional resource for each item due to their superior cognitive abilities 37 . Therefore, it is reasonable to speculate that this expert advantage may be revealed in MOT tasks.

Furthermore, the reasons for differences in the results of MOT tasks performed by athletes with different expertise levels in previous studies may vary; they may include task parameters, task presentation, and the type and experience of participants. Therefore, the moderating factors affecting the differential performance in MOT need to be examined. The difficulty level of the MOT task can be varied parametrically (e.g., number of targets 38 ; number of distractors 39 ; speed of the target 40 ; and duration of tracking 41 ), which may result in group differences (i.e., expertise effects 20 , 42 ). For example, Qiu et al. 10 conducted MOT tests with graded levels of the number of targets (two, three, or four); compared with nonathletes, athletes performed better in the three- and four-target conditions. Zhang et al. 43 found that as the speed of the movement increased, athletes showed a stronger tracking advantage than non-athletes; moreover, the higher the skill level of the athletes, the more obvious the effect. As different settings of MOT parameters affect the MOT performance, the number of targets and distractors, speed of the targets, and duration of tracking should be used as moderating variables in meta-analysis.

In recent years, MOT has not been limited to 2D frames; with the development of virtual reality technology, 3D-MOT in sports has attracted the attention of researchers. Compared to 2D-MOT, 3D-MOT can bring advantages to motion performance, such as creating and controlling virtual motion scenes 44 , presenting stereoscopic vision 7 and immersing the visual scene; that is, the athletes can experience the sports scene in person instead of watching the video from a third-person perspective 45 . Based on these advantages, some studies indicated that virtual reality technology can more effectively measure perception and motor performance in the field of motion than traditional methods 46 . Cooke et al. 47 found that the tracking accuracy of 3D-MOT tasks was better than that of 2D-MOT tasks; when the object distance increased, individuals could track objects at a faster speed in 3D-MOT task. This suggests that an increase in depth information enhances tracking performance by increasing object differentiation because objects that are confused in a two-dimensional plane are likely to be distinguished in a three-dimensional space. However, the effect of 3D-MOT on the final score remains unclear. The nature of this difference is to display whether it is 3D or 2D, which we studied as a moderating variable called the display type.

Moreover, athletes’ sports level and sport type may influence MOT performance. A meta-analysis of cognitive function in expert and elite athletes showed that high-performance-level athletes have superior cognitive function compared to low-performance-level athletes 48 . This indicates that the competitive level plays a role in athletes’ MOT performance. However, the cognitive functions in Scharfen and Memmert’s 48 study only included executive functions, visual perceptual ability, and motor inhibition and did not focus on MOT. In addition, a previous meta-analysis found a significant moderating effect of sport type on the relationship between expertise level and perceptual cognitive skills, suggesting that the difference in performance between experts and novices may differ in various sports 34 . This suggests that the type of sport may have an effect on perceptual cognition; therefore, the effect of competitive level and sport type on MOT was also explored in this study.

Additionally, previous studies on the advantage of expert athletes included different group comparisons; some compared experts with novices 35 , while others compared athletes with non-athletes 16 , 49 . This difference in classification is mainly due to differences in the control group; that is, the classification and characteristics of novices and nonathletes are inconsistent. However, there is no unified concept for novice athletes. For example, one study considered novice athletes to be players with no more than two years of formal experience in any sport 26 , while others defined novice athletes as players with less than a few years of practice in particular sports, such novice athletes in martial arts had less than one year of practice 50 and those in badminton had less than 2–3 years of practice 51 ; some studies referred to novice athletes as people with no sports experience 52 , that is, confusing novice athletes with non-athletes.

Thus far, similar meta-analyses on differences in athletes’ advantages have compared experts with novices in areas such as cognitive function 48 , visual search 53 , and quiet eyes 54 . However, it is important to note that these studies differentiated only between experts and novices and mixed different athletic levels (non-athletes were not distinguished from novices), which may not be the clearest comparison, and the results may be different if further differentiation is made. Vague definitions of novices or experts directly affect performance 53 . One study 43 categorized participants into three groups: experts, novices, and controls or non-athletes, and found that the tracking accuracy of the expert group was significantly higher than that of the control group. Furthermore, the difference in tracking accuracy between the novice and control groups was marginally significant, whereas there was no significant difference between the expert and novice groups. This suggests that different categories produce differences in performances.

Based on previous studies, this meta-analysis addresses the differences between athletes and non-athletes. Athletes include both expert athletes and novice athletes, who have different level of sports experiences, while non-athletes refer to those who have no sports training experience. Therefore, we aimed to distinguish the performance advantage between athletes and nonathletes, as well as between expert and novice athletes. This approach is necessary to avoid potential confounders caused by unclear definitions.

In sum, the MOT performance advantage in athletes is controversial and is affected by various factors. However, no previous meta-analysis has reported the dominant performance of athletes in MOT tasks. Therefore, the present study aimed to conduct a systematic review and meta-analysis to obtain a clearer and stronger conclusion about the differences between athletes vs. nonathletes or experts vs. novices in dynamic visual tracking in sports. Potential moderators such as athletes’ competitive level, type of sport, parameters of the MOT task and type of display were considered. Based on the above discussion, we made the following hypotheses:

H1 (hypothesis related to overall effect sizes):

Athletes would have a significant MOT performance advantage over non-athletes (H1a), and likewise experts would have a MOT performance advantage over novices (H1b), although the extent of this advantage may vary.

H2 (hypothesis related to moderator analyses of athletes vs. non-athletes):

Type of sport (H2a), parameters of the MOT task (H2b), type of display (H2c) and athletes’ competitive level (H2d) would significantly modulate the differential performance of MOT task for comparisons athletes vs. non-athletes.

H3 (hypothesis related to moderator analyses of experts vs. novices):

Type of sport (H3a), parameters of the MOT task (H3b) and type of display (H3c) would significantly modulate the differential performance of MOT task for comparisons experts vs. novices.

H4 (hypothesis related to comparison of the two groups: athletes vs. non-athletes and experts vs. novices):

A comparison of effect sizes between athletes vs. nonathletes and experts vs. novices would reveal a significant difference.

Materials and methods

The review procedures followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement 55 . Following recent best practices to enhance transparency, replicability, and robustness of systematic reviews in sport and exercise psychology, this study was registered via the Open Science Framework. The systematic review, search strategy, and meta-analysis are detailed in a registration document available in the Open Science Framework, as are all supplemental files ( https://osf.io/ncq7v/ ).

Data search and selection criteria

The categories for comparison of MOT performance between athletes and other groups included athletes and non-athletes, as well as experts and novices. This review focused on published research that explored the differences in performance on MOT tasks between athletes and non-athletes or experts and novices.

Systematic database searches were performed up to July 2024 using the Web of Science, PubMed, SPORTdiscus, ProQuest and Scopus. As there are many related Chinese studies, the Chinese database-Chinese National Knowledge Infrastructure (CNKI) was also included in the search. For the searches, the terms ‘multiple object tracking*’ OR ‘MOT’ OR ‘2D-MOT’ OR ‘3D-MOT’ were combined (AND) with ‘sport*’ OR ‘athlete*’ OR ‘expert*’ OR ‘player*’ OR ‘elite*’ OR ‘novice*’ OR ‘nonathlete*’. Additionally, we conducted a manual search using Google Scholar. We searched references of eligible articles and used Google Scholar to identify articles that cited eligible articles. Two experienced researchers conducted the literature search with assistance of two other researchers.

After removing duplicates, an initial screening at the titles and/or abstracts was conducted using the following inclusion criteria to identify relevant research reports (articles, dissertations, and theses): (1) involving healthy athletes, (2) performed MOT tasks and performance on MOT tasks is reported. Following the initial screening, studies were included if they (1) included athletes and non-athletes or experts and novices, (2) reported a comparison of MOT task performances between experts and novices or athletes and non-athletes, (3) reported participation outcomes, and (4) had sufficient data to calculate effect sizes. If relevant research/data was not available online, the authors were contacted personally.

Exclusion criteria related to insufficient data reporting included lack of data on athletes or nonathletes, no specific sport mentioned, and no MOT exposure. Literature reviews were excluded.

Data extraction

Data were extracted independently by two authors, and the remaining authors were responsible for verification. The extracted data included study characteristics (authors, publication year, and sports), sample characteristics (team country, sample size, age, and so on.), MOT parameters (number of targets and distractors, targets’ movement speed, and duration of tracking), type of MOT display, and outcomes. The coding process was conducted by two authors separately according to a coding manual determined in advance and then cross-checked 56 . The disputed documents were discussed in groups and a consensus was reached to ensure the accuracy of the coding. The final consistency was 97.4% (ICC).

The basis for the comparative classification of the articles was as follows: first, according to the different definitions of nonathletes and novices, samples in previous difference comparison studies were divided into athlete and nonathlete comparison groups or expert and novice comparison groups. As the athletes included both experts and novices, they were divided into two groups (expert and novice athletes) for the moderating analysis. This is different from the comparison of experts and novices in the other group classification. This classification effectively distinguishes two different control groups: for the comparison of athletes vs. non-athletes, the subgroups were expert athletes vs. non-athletes and novice athletes vs. non-athletes, both using non-athletes as the control; for the comparison of experts vs. novices, the control group was novice athletes.

Different studies often used different classification criteria, and we attempted to match these criteria. The classification criteria for athletes and nonathletes, as well as experts and novices, were as follows: In previous studies, the highest level of competition among athletes and years of training experience were commonly used as criteria to distinguish expertise levels 57 . This study considered the highest level of competition and years of experience as criteria for evaluating the classification of athletes. Based on the division of years of training by Memmert et al. 26 , the study also divided experts and novice athletes by years of exercise (over 10 years and under 10 years). For the highest level of competition, those at the provincial level and above are classified as experts. In addition, in most Chinese articles included, the grades of the athletes were listed, and sports-level certification was the main criterion for evaluating Chinese athletes. Chinese athletes are typically classified into three technical grades: master sportsman, national first-level athletes, and national second-level athletes. Among these, master sportsman is deemed the highest title awarded by the State Sports Commission of China. Athletes with the master sportsman certificate are required to participate in international competitions and have achievements in international competitions such as the Olympic Games, World Championships, World Cup, Asian Games, and Asian Championships. Athletes with the national first-level athlete certificate are required to place in the top three non-team events or fourth to eighth places in team events in the national Championships. As the criteria for master sportsmen and national first-level athletes are comparable to the criteria for expert athletes in other countries, they were classified as expert athletes. Meanwhile, athletes with the national second-level athlete certificates are those who have participated in the National A-League, B-League, Cup League, and National Youth Games, or who have won first to fourth place in provincial, autonomous region, and municipality championships, and were classified as novice athletes 43 . In addition to certified athletes, some physical education students in sports colleges in China also receive systematic sports training, but their level is usually lower than the Chinese national second-level athletes; they were also classified as novice athletes. Finally, if a study included a mix of two categories in one, the category with the highest level was considered. Table 1 shows the criteria for defining experts, novices, and non-athletes in this study. Participants were classified as those who met one or more of these criteria. Table 2 shows the classification of the two comparison groups (athletes and non-athletes 10 , 12 , 16 , 17 , 19 , 20 , 21 , 22 , 24 , 28 , 43 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 ; experts and novices 10 , 21 , 26 , 27 , 43 , 51 , 60 , 66 ) of the studies included in this meta-analysis.

Assessment of study quality

As the studies were non-randomized by nature (i.e., experts were compared with novices) and the term ‘exposure’ was more appropriate than ‘intervention’, Cochrane’s RoBANS tool 67 was used to assess the risk of bias arising from (i) participant selection, (ii) confounding variables, (iii) measurement of exposure, (iv) blinding of outcome assessment, (v) incomplete outcome data, and (vi) selective outcome-reporting. Six categories each were assessed as ‘high risk’, ‘unclear risk’, or ‘low risk’. The risk of bias was considered similar for all primary outcomes, as the data were generated from the same MOT technology in each study. Therefore, only one risk of bias assessment was performed for each study. The RoBANS assessment was conducted independently by two authors (L and Z). Disagreements were resolved by consensus or consultation with a third assessor (C), when required.

Summary measures, synthesis of results, and publication bias

MOT data were analyzed using Comprehensive Meta-Analysis 3.3 (CMA 3.3) software (Biostat, Englewood, NJ, USA) for meta-analysis and meta-regression. The level of statistical significance was set at p < 0.05. The MOT outcomes were (1) MOT task accuracy (ACC), (2) MOT task reaction time (RT), and (3) tracking speed thresholds. The two groups were compared based on the above outcomes. Based on previous study about the methods for dealing with multiple outcomes in meta-analysis 56 , 68 , for different categories of outcomes MOT variable, we conducted each analysis followed by moderator analyses.

Hedges’ g (standardized mean difference effect size) between the two groups with the corresponding 95% confidence interval (95% CI) was calculated. A random-effects model was used to account for differences between studies 69 . The evaluation criteria for the effect size were as follows 70 : trivial: <0.2; small: 0.2–0.6; moderate: 0.6–1.2; large: 1.2–2.0; very large: 2.0–4.0; and extremely large: >4.0. The direction of Hedges’ g was manually adjusted to consider variable outcomes, with a positive effect size indicating that the athlete or expert performed better on the MOT task.

The heterogeneity test of variance of the effect sizes in this study was carried out using the Q statistic. Q statistic is a measure of the total observed dispersion of the estimated effect sizes. Total heterogeneity ( Q T ) is used to determine whether the effect sizes for all studies are homogeneous 71 . If a significant Q T value is observed ( p < 0.05), this indicates heterogeneity of results and may result in a search for potential moderating variables 72 . In the moderator analyses, a Q B and Q W statistic is computed to test respectively, for between- and within-group homogeneity. Q W signifies the degree of heterogeneity of studies within a moderator category, whereas the Q B statistic refers to a difference in the pooled effect sizes between moderator categories 71 . In addition to the Q statistics, the I 2 statistic was also utilized to analyze the heterogeneity test results. The I 2 statistic is a more thorough metric that shows the percentage of variability related to true heterogeneity. Heterogeneity was classified as follows: low, moderate, and high when the I 2 values were <25%, between 25 and 75%, and >75%, respectively 73 .

Publication bias for the studies pooled for the meta-analysis was assessed by visually inspecting funnel plots and by computing Egger’s test results 74 . Statistical significance was defined as 2-sided p < 0.05, which was taken as a reflection of the presence of publication bias. In the absence of publication bias, the funnel plots should resemble an inverted funnel shape, with studies scattered symmetrically around the pooled effect size estimate.

Moderator analyses

Using a random-effects model for analysis, potential sources of heterogeneity likely to influence MOT performance in athletes, non-athletes, experts, and novices were examined.

The potential moderating effects of participant and/or study characteristics (covariates) on the mean overall effect size were explored using meta-regression 75 and subgroup analysis. Continuous variables including (1) number of targets, (2) number of distractors, (3) speed of target movement, and (4) duration of tracking were analyzed using meta-regression. Categorical variables including (5) type of sport, (6) competitive level, and (7) type of display were used for subgroup analysis. No moderator analysis was conducted for gender because of an insufficient number of studies reported results for different sexes separately.

Coding of subgroup variables

For a subgroup (categorical) variable, each subgroup should have a minimum of 4 studies(k≥4) 76 .

Competitive level

Based on the classification criteria in our data extraction section, we categorized athletes into expert and novice athletes as shown in Table 1 . However, the competitive level as a moderating variable was only applicable for comparisons between athletes and non-athletes. Due to insufficient available literature, competitive level could not be used as a moderating variable for expert vs. novice comparisons.

Type of sport

The included studies span various sports, including basketball, ice-hockey, handball, soccer, volleyball, rugby, badminton, swimming, and mixed sports. Given the number of studies on non-ball sports (e.g., swimming) was small (k < 4) and the mixed sports contained both ball and non-ball sports, and they were both a single category that could not be easily unified into one category, we did not include non-ball and mixed sports in the subgroup analysis. Sports with more than four effect sizes, such as basketball, ice hockey, and handball, were grouped into individual categories. Ball sports with fewer than four effect sizes were combined into an “other ball sports” category.

Type of display

The classification was based on the display mode of MOT. 2D-MOT was conducted on a computer display, while 3D-MOT used a perceptual-cognitive training system based on a 3D virtual environment 77 . However, since only one study included 3D-MOT, this type of display was not incorporated in the meta-analysis.

Search results

The search process identified 1,571 studies (462 from Web of Science, 112 from PubMed, 160 from SPORTdiscus, 197 from ProQuest, 424 from Scopus, 211 from CNKI, and 5 additional records identified through other sources (Google Scholar)). A PRISMA flow diagram of the screening process and the number of retrieved and excluded articles is shown in Fig. 1 ref. 55 . Among these, 492 duplicate studies were excluded. A further 1031 studies were excluded after reading their titles and abstracts. After full-text screening, 9 additional studies were excluded because: (1) there was no difference comparison 78 , 79 , 80 ; (2) participants were grouped according to MOT scores rather than skill 81 ; (3) data were insufficient 82 ; (4) the participants were athletes with intellectual disabilities 83 ; (5) the number of targets was only one 84 ; (6) participants did not include non-athletes or novices 85 (soccer experience: ≥6.4 years); (7) participants were all non-athletes 11 (they had only been involved in sports activities). For one study with insufficient data, the original data were obtained from the author via email 12 .

PRISMA flowchart of article screening and selection.

After reviewing the full texts, we identified 27 articles that met the inclusion criteria. These articles included three outcome variables for MOT: accuracy (ACC), reaction time (RT), and speed thresholds. Ideally, a meta-analysis should be performed for each outcome variable. However, only one article used RT as the sole outcome variable, and three articles used speed threshold as the sole outcome variable. Additionally, although three papers included both RT and ACC, and one paper included both speed and ACC, the number of articles that included RT or speed, and could be classified into athlete vs. non-athlete or expert vs. novice groups, was fewer than three. Due to the limited number of articles reporting RT and speed variables, we decided to include only articles that reported ACC outcomes. Consequently, four articles that did not include ACC indicators were excluded 6 , 18 , 86 , 87 . Ultimately, we included 23 articles that utilized ACC as the outcome variable for meta-analysis and moderator analysis.

Accordingly, 23 studies were considered eligible for meta-analysis with 25 comparisons. Eighteen presented comparisons between athletes and non-athletes, and seven between expert and novice athletes. Studies by Qiu et al. 10 and Zhang et al. 43 included experts, novice players, and non-athletes; therefore, the 2 studies both included the comparison between athletes and non-athletes as well as between experts and novices. The classifications are listed in Table 2 .

Study characteristics

The sample characteristics and exposures used in the included studies are shown in the supplemental file (Table S1 ). The main content included is shown below. (1) Sample size: a total of 1453 participants participated in the included studies. The groups’ sample sizes ranged from 8 to 44. (2) Age: all studies reported the ages of the participants. The age range was between 16 and 29 years old with a mean age of 22.05 years old. (3) Competitive level: all studies reported the competitive level of the participants. Of the included studies, 14 reported on expert athletes (61%), 11 reported on novice athletes (48%), and 2 included both experts and novices. (4) Type of sport: among the 23 articles, there was 9 articles about basketball (39%), 3 articles about handball (13%), 3 articles about soccer (13%), 2 articles about ice-hockey (8%), one about rugby (5%),one about badminton (5%) and four about mix sport (including ball sports and non-ball sports)(17%), respectively. (5) Type of display: only one study reported 3D-MOT (4%) while other 22 study reported 2D-MOT (96%). (6) MOT parameters: 18 articles (78%) reported all parameters of MOT (number of targets, number of distractors, speed of target movement, and duration of tracking). Of the remaining five articles, two did not report duration of tracking and three did not report tracking speed.

Study quality

Regarding participant selection, a low risk of bias was identified in 96% of the studies. The remaining 4% were at high risk due to the vague definition of novice inclusion. A low risk of bias was reported in 61% of the studies when analyzing the issue of confounding variables, as almost half of the trials included a single-blind experiment. However, in some studies, this was not mentioned or the participants knew the purpose of the experiment, which may have influenced the results because of the Hawthorne effect. Most studies had a low risk of bias for exposure measurement (83%), as most trials provided familiarization with the testing procedures and reported the procedures of the MOT task in detail. Regarding the blinding outcome assessment, usually, testers were not blinded, but in some studies, a second, independent tester provided inter-rater reliability calculations. In cases where this did not occur, we judged the study to be at high risk for blinding outcome assessment. A low risk of bias was found in 91% of the studies. For incomplete outcome data, most studies had a low risk of bias (74%) and often not only reported the final sample, but also provided a clear indication of whether the participants were part of a larger sample of the initially recruited group. Finally, considering selective outcome reporting, almost half of the studies had a pre-registered or pre-published protocol with which to compare manuscripts. Therefore, it was unclear whether the reporting outcome was complete or selective in 39% of the studies. The complete quality scores for each study are presented in the supplemental file (Table S2 ).

Effect sizes

Athletes vs. non-athletes.

The results of this meta-analysis showed a significant small effect, with a better MOT performance for athletes compared to non-athletes (g = 0.56; 95% CI = 0.29 to 0.84; p < 0.001; Fig. 2 ). There was high heterogeneity in the overall effect ( Q T = 130.478, p < 0.001, I 2 = 80.37%). No significant bias was detected within Egger’s test ( p = 0.993). Visual inspection of the funnel plots also indicated low publication bias (supplemental file: Fig. S1 -A).

The effect sizes (Hedges’ g) of the MOT performance for athletes compared with non-athletes included in the meta-analysis. 95%CI = 95% confidence interval. The size of the plotted squares reflects the statistical weight of each study.

Expert athletes vs. novice athletes

The results of this meta-analysis showed a significant moderate effect, with a better MOT performance for expert athletes compared to novice athletes (g = 0.92; 95% CI = 0.45 to 1.39; p <.001; Fig. 3 ). There was high heterogeneity in the overall effect ( Q T = 60.25, p < 0.001, I 2 = 83.40%). No significant bias was detected within Egger’s test ( p = 0.088). Visual inspection of the funnel plots also indicated low publication bias (supplemental file: Fig. S1 -B).

The effect sizes (Hedges’ g) of the MOT performance for experts compared with novices included in the meta-analysis. 95%CI = 95% confidence interval. The size of the plotted squares reflects the statistical weight of each study.

Comparison of the two differences

A comparison of the effect sizes of the difference between athletes and nonathletes and between expert and novice athletes showed no significant difference in the effect sizes ( Q B = 2.14, p = 0.144).

Moderating effect

The competitive level, type of sport, and type of display were analyzed for the subgroups. Based on previous studies, the athletes included in this study were further categorized into expert and novice athletes. Seventeen effect sizes were reported for expert athletes and ten for novice athletes. The competitive level of the sample had a significant effect on the effect size, Q B (1) = 10.21, p = 0.001. The effect size for expert athletes vs. non-athletes in the MOT task (g = 0.84, p < 0.001; moderate effect; Q W (16) = 80.15, p = 0.001) was significantly higher than that for novice athletes vs. non-athletes (g = 0.07, p = 0.673; trivial effect; Q W (9) = 22.49, p = 0.007). Regarding the type of sport, we divided the types of sports into basketball, ice-hockey, handball, and other ball sports. Sports type had no significant effect on effect size Q B (4) = 4.94, p = 0.177. For the display type, only one effect size was reported for 3D display while 74 were reported for 2D displays; therefore, it was not included in the moderating analysis.

The MOT parameters were analyzed using meta-regression. The number of tracked targets ranged from 2 to 6, and had no effect on the effect size (coefficient: -0.01, p = 0.970). The number of distractors ranged from 2 to 12, and had no effect on the effect size (coefficient: 0.04, p = 0.228). The speed of the targets ranged from 3 to 25.5°/s, and had no effect on the effect size (coefficient: -0.01, p = 0.689). The tracking duration ranged from 4s to 12s, and had no effect on the effect size (coefficient: 0.05, p = 0.094). Results of the moderating analysis are summarized, with the full values presented in Table 3 .

The type of sport was analyzed for each subgroup. Regarding type of sport, we divided the types of sports into basketball, ice hockey, and other ball sports as moderating variables. Sports type had a significant effect on the effect size Q B (2) = 12.26, p = 0.002. The results indicated that sport type had a significant moderating effect on the difference in MOT performance between experts and novices. Further analysis showed that the effect sizes of the basketball (g = 1.46, p < 0.001; large effect; Q W (22) = 107.23, p < 0.001) was significant, whereas those of ice hockey (g = 0.41, p = 0.079; small effect; Q W (3) = 6.64, p = 0.084) and other ball sports (g = 0.92, p = 0.081; moderate effect; Q W (3) = 35.78, p < 0.001) subgroups were not. For the display type, no one effect size was reported for 3D display; therefore, it was not included in the moderating analysis.

The MOT parameters were analyzed using meta-regression. The number of tracked targets ranged from 2 to 8, and had a positive and significant effect on the effect size (coefficient: 0.23, p = 0.004). As the number of targets increased, the effect size also increased. The duration of tracking ranged from 1 to 8s, and had a significant negative effect on effect size (coefficient: -0.12, p = 0.040). The longer the tracking duration, the smaller the effect size. The number of distractors ranged from 4 to 8, and the speed of targets ranged from 3°/s to 10°/s. However, there was no significant effect size for either the number of distractors (coefficient: 0.18, p = 0.350) or speed of targets (coefficient: −0.01, p = 0.980). Results of the moderating analysis are summarized, with the full values presented in Table 4 .

This meta-analysis compared the performance of different populations (athletes, nonathletes, experts, and novices) on MOT tasks. The results highlighted performance differences in MOT tasks among different populations and, importantly, that the magnitudes of these effects depended on population characteristics. The effect size between nonathletes and athletes was not affected by the MOT parameters, whereas the effect size between experts and novices was affected. Although there was great heterogeneity in the methods of exposure stimulation, athletes, particularly experts, displayed superior performances on MOT tasks.

Overall difference analysis

Consistent with Hypothesis 1a, our study demonstrated athletes have a MOT performance advantage over non-athletes. Compared with nonathletes, athletes demonstrated a significant advantage in MOT task performance (g = 0.56), indicating that sports experience may transfer from a sport-specific task to MOT tasks. This finding is consistent with those of the majority of the studies included, which showed that athletes exhibit superior dynamic visual attention compared to non-athletic university students. Expertise in the sports domain, characterized by dynamically changing, high-paced, and unpredictable scenarios, may be transferred to a more general perceptual-cognitive domain (i.e., MOT) 23 . A previous review suggested that the possible reasons for the benefits of exercise on executive function are either the aerobic hypothesis or the athlete’s cognitive demand for exercise itself, both of which have been explored in different ways 88 . In team ball sports, players not only monitor the position and movement of the ball, but also track the positions of opponents and teammates in the field, which are comparable to the cognitive demands of the MOT paradigm 89 . Therefore, it is reasonable to suggest that professional players in ball sports may exhibit superior MOT performance owing to their high demand for wider attention 90 . Harris et al. 11 found that individuals who regularly engaged in object-tracking sports displayed improved tracking performance relative to those engaging in non-tracking sports but reported no differences in gaze strategy. Training on an adaptive MOT task led to improved working memory capacity, but no significant changes in gaze strategy. Consequently, MOT expertise is more closely linked to processing capacity limits than to perceptual-cognitive strategies 11 . Future studies should focus on whether expertise depends on overt visual attention or capacity limitations.

Some studies showed that basketball players do not exhibit superior MOT performance 10 , 28 . Nevertheless, Gong et al. 28 found that expert athletes had a wider field of vision, gazed longer at blank areas between the tracking targets, and predicted the changing locations of the targets. This indicates that players may be focused not only on the targets but also on the relevant area of their future direction. This tracking strategy may be consistent with real-world sports scenarios. Therefore, researchers should pay attention not only to the differences in task performance but also to the tracking strategies underlying the behavioral that underpin such differences 62 . However, the studies in this review mainly focused on strategic sports, and the moderating effect of sports type was significant. As only basketball played a significant role in this analysis, further research is required to determine whether the effect is significant in non-team ball sports.

Some studies have shown that superior tracking performance in MOT tasks is significantly related to players’ training experience 10 , 23 . Consistent with H1b, the results supported that experts performed better than novice in MOT tasks. Compared with novice athletes, there was a moderate effect for performance advantages of expert athletes (g = 0.92). Experts tend to adopt a chunking strategy during cognitive processing, which, coupled with long-term training, enables them to better adapt to the fast-changing dynamics of sports 27 . The adjustable view of the focus of attention holds posits that the scope of attention can be controlled to either narrow or expand, allowing the focus of attention to range from 1 to 3–5 information blocks 91 . When the number of targets exceeds the attention focus capacity, experts tend to use a chunking strategy, integrating several targets into one information unit to produce one information block, and integrating several other targets into another information unit to produce another information block. Consequently, experts demonstrated a better tracking performance under high-workload conditions. Another plausible explanation is that individuals skilled in tracking multiple objects are more easily drawn to ball sports pursuits and continue to play ball sports, and sports training strengthens their ability to track multiple objects 20 , 92 . For team sports, many of the required sport skills may be translated into general cognitive domains 93 . This may also account for MOT performance differences between team sports experts and novices. Overall, our results extend previous studies and confirm that expert athletes have better visual tracking abilities than novices. Consequently, H1a and H1b were fully supported.

A comparison of effect sizes between athletes vs. nonathletes and experts vs. novices revealed no significant differences. Thus, H4 was not supported. However, this should not be interpreted as rendering the categorical comparisons meaningless. Imprecision in the criteria used to define athletes as experts or elite threatens the validity of sports expertise research. Recently, a study 94 reinforced the importance of a clear definition of athletes’ level, highlighting that athletic success may be explained by different attributes, with athletic level influencing the intervention results. Therefore, it is crucial to consider the effects of comparing different classes of athletes, such as experts, novices, and non-athletes, on outcomes. One possible explanation for the lack of significant differences between the two categories in this study is that experts (i.e., all national first-level athletes or individuals with 10+ years of exercise experience) have a higher athletic level than athletes overall (comprising expert and novice athletes), and novices exhibit a higher athletic level than non-athletes, thereby resulting in similar disparities in athletic levels between these two groups. Thus, experts vs. novices produced an effect size similar to that of athletes vs. non-athletes. This adds to the evidence that competitive levels play an important role in MOT tasks. Interestingly, our results showed that the two classifications are affected by different moderating variables, suggesting that moderating variables may also influence the differences between different classifications. Taken together, future studies should aim to establish more consistent conditions to investigate nonathletes, along with expert and novice players.

For athletes vs. non-athletes, competitive level was considered a potential moderator in MOT task performance in the subgroup analysis. Our findings indicated a notable difference in effect sizes between studies involving expert athletes and non-athletes (g = 0.84; p < 0.001; moderate effect) compared to those involving novice athletes and non-athletes (g = 0.07; p = 0.673; trivial effect). This result supported H2d. Specifically, the higher the competitive level of the athletes, the more obvious the MOT difference between athletes and non-athletes. These results support previous research on the MOT that has consistently shown that expert athletes tend to perform better than intermediate or novice athletes 10 . Overall, our study provides further evidence for the differences in expertise observed between experts and novices.

The present study found a significant moderating effect of sport type (basketball, ice-hockey, and other ball sports) on the difference between expert and novice athletes results. Specifically, the effect size of the difference in MOT performance between basketball was significant (g = 1.46, p < 0.001; large effect). This suggests that the difference in MOT performance between basketball experts and novices was even more pronounced compared to other sports. For basketball players, MOT is an important indicator that separates novices from experts. Basketball is a fast-paced team sports game that requires the players to pay attention not only to the movement and position of teammates and opponents at the same time but also to the spatial position of the ball and the field 25 . Basketball players with good tracking abilities can predict and evaluate their athletic performance 15 . In addition, the actual motion scenario of basketball may be better suited for the MOT process than other sports. However, in our investigation of differences in MOT between athletes and nonathletes, the results were not entirely consistent with our hypothesis. H3a was supported, while H2a was not supported. We did not observe that sport type moderated these differences, which may also be associated with variations in competitive level (due to a mix of experts and novices). Nevertheless, further studies that focus on the surveillance of various sports are necessary to confirm these findings.

Regarding the MOT task parameters, first, compared with athletes and nonathletes, the difference in effect size between experts and novices was more subject to be moderated by the parameter settings of the MOT task (e.g., number of targets and duration of tracking). This is not exactly the same as our assumption that the parameters can significantly modulate both the two comparison groups. H3b was partially supported. Specifically, for the difference between experts and novices, we found that the larger the number of targets tracked, the greater the difference in effect size. This indicates that experts have a broader ability to focus on more targets than novices. Previous research has shown that the effects of expertise in team ball sports transferring to visual attention tracking tasks occur only in elite athletes with extensive training under higher attentional load 10 . According to the flexible resource theory 95 , tracking is mediated by a finite attentional resource distributed among the targets. The number of objects that could be tracked would be inversely related to the resource demands for each individual object 37 . When tracking more targets, professional athletes allocated less attentional resource to each target than novice athletes, thereby professional athletes could track more targets with a limited pool of resources. In contrast, when tracking fewer targets, both experts and novices had sufficient resources for each set of targets to process information precisely. In addition, we found that with an increase in the duration of tracking, the difference in the magnitude of the effect between expert and novice athletes was significantly reduced. Perhaps because the situation on the sports field changes frequently, the tracking time is usually short and phased. Expert athletes need to keep tracking in a particular situation and change the tracking target when the situation changes; therefore, their advantages are reflected more in relatively short tracking. However, the effects of the MOT parameters were significant only between experts and novices, but not between athletes and non-athletes. H2b was not supported. This could be because the difference in competitive level between athletes and nonathletes was not very large (as athletes included experts and novices), while novices and nonathletes had small differences in MOT performance (g = 0.07, p = 0.673; trivial effect), resulting in a small overall effect size, and it was not easy to observe the moderating effect.

Unfortunately, due to the insufficient number of articles on 3D-MOT included in our study, a subgroup analysis of the moderating variable display type was not conducted. Therefore, H2c and H3c were not tested. However, exploring this factor remains meaningful. A comparative study of 2D-MOT and 3D-MOT found that soccer players were more efficient at visual tracking in 3D virtual reality dynamic tracking tasks and that the longer their professional sports experience, the better their tracking performance in 3D dynamic tracking tasks 86 . This suggests that differences may also be influenced by sports levels. The movement of objects in a real three-dimensional space at different depth positions is continuous rather than confined to different depth planes. Moreover, current 3D-MOT tests typically use a staircase procedure 7 , where the speed increases if the participant successfully tracks all targets and decreases if at least one target is missed. Speed thresholds in this procedure are determined using a one-up one-down method 7 . In contrast 2D-MOT usually employs a fixed speed and uses ACC as an outcome measure. Future research could focus on more other factors as moderating factors, not only 3D display but also staircases and field of view in 3D-MOT. Future studies also should include 3D-MOT interventions in athletes to gain deeper insight. Romeas et al. 7 demonstrated that 3D-MOT training could improve the accuracy of soccer-passing decisions. However, one review questioned the effectiveness of 3D-MOT training 96 .

Recently, to make the 3D-MOT evaluation and training more effective, 3D-MOT has become more ecological in the field of virtual reality. Ehmann et al. 85 , 97 proposed a 360-degree alternative to the classical tracking task, with humanoid avatars running on a curved screen surrounding the observer. They validated the use of their tool to assess visuospatial performance in a visual tracking task 97 and discriminated the effect of expertise in young soccer players 85 . Vu et al. 63 used a multiple-soccer player tracking task in virtual reality to compare the visual tracking performance of soccer players and non-soccer players perceiving virtual players moving along real games or pseudo-random trajectories. Yet the results indicate that the use of soccer-specific trajectories may not be sufficient to replicate the representativeness of field conditions in a study on visual tracking performance. Future research should focus on the ecological application of 3D-MOT in various sports.

Limitations

Our results showed no support for a publication bias either when comparing the overall effect of athletes vs. nonathletes or experts vs. novices. Therefore, in the context of the existing statistical and methodological tools, it seems reasonable to assume that publication bias is not an issue. However, a more definite answer could be provided by recent initiatives for better scientific practices, including mandatory usage of open data repositories for all published studies 98 . Although the narrow inclusion criteria (e.g., the inclusion of studies involving athletes compared to nonathletes and experts compared to novices) helped reduce the risk of bias, it also produced certain limitations. Numerous studies on various team sports with large sample sizes and comprehensive data were excluded because they examined athletes exclusively or did not report sufficient data. This reduced the data in our meta-analysis, thereby weakening the evidence base, especially for team sports other than basketball. Among the 23 studies selected, 10 included basketball players as participants; therefore, basketball dominated the sample and results of our study. This may have impacted the moderating variable of sport type, which could potentially bias conclusions for team sports in general. This highlights the pressing need for further research in other sports to address this issue.

Another limitation was the heterogeneity of the estimates. The heterogeneity of the main effects in athletes vs. non-athletes ( I 2 = 80.37%) and experts vs. novices ( I 2 = 83.40%) both exceeded 75%, showing that there was a lot of dispersion in the results. Furthermore, although several moderators were tested for their potential influence on differences, there might have been other influencing factors not considered in our meta-regression because of a lack of information. For example, Ehmann et al. 85 demonstrated different MOT task performance levels among different age groups. Legault et al. 6 reported differences in MOT performance patterns between male and female athletes. Previous studies have found that visuospatial working memory can significantly predict MOT performance 99 , and 3D-MOT is also significantly correlated with visuospatial working memory 97 . Future studies should consider the moderating effects of age, gender, visuospatial working memory, etc. In addition, our study included few individual sports, and future studies should consider both individual and team sports.

This meta-analysis provides evidence of superior performance in athletes, especially in higher-level athletes, on MOT tasks. Our results support the superior perceptual cognitive ability of athletes vs. non-athletes and experts vs. novices. Moreover, the difference in effect size between non-athletes and athletes was moderated by the athletes’ competitive level. Similarly, the difference in the effect size of the MOT performance between experts and novices was influenced by the type of sport. Adjusting the parameters of the MOT task, such as the number of targets and the tracking duration, affected the difference in effect size between experts and novices, but did not influence the difference between athletes and non-athletes. Future studies should include a wider variety of sports; more clearly classify participants’ sports levels, such as experts, novices, and non-athletes; and compare MOT performance between different age groups (such as high school athletes and peers) and genders.

Data availability

All relevant data are within the manuscript and its supplemental files.

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National Natural Science Foundation of China (No. 32071087) and Fundamental Research Funds for the Central Universities (Beijing Sport University) (No. 2018PT016, 2017YB029, 2016RB025) supported this work through the corresponding authors (JL and YZ). The funders were not directly linked to this project and in no way influenced study design, collection, analysis, interpretation of data, writing of the report, or the decision to submit the paper for publication.

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H.J.L. did the study conception and design, data collection, statistics, and writing; Q.Z. helped the study data collection, statistics, and writing; JL and YZ participated in study conception and design and writing; S.C. helped with the data collection and re-check data. J.L. and Y.Z. contributed equally to this work and should be considered co-coresponding authors. All authors have read and approved the final version of the manuscript, and agree with the order of presentation of the authors.

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Impacts of long-term transit system disruptions and transitional periods on travelers: a systematic review

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Governments around the world are heavily investing in building new transit infrastructures and expanding existing ones. The construction of these projects does not happen overnight and can lead to extended long-term disruptions in the transit network, which can have undesirable impacts. Research regarding such disruptive periods, or transitional periods, seems to be thematically and geographically dispersed in the literature. Similarly, a consolidated understanding of the impacts of long-term transit service disruptions due to other causes, such as labor strikes and transit system failures, on travelers’ behavior seems missing from the literature. Using a systematic review method, this study aims at providing a comprehensive review of the academic literature that focused on analyzing the impacts of the aforementioned issues on transit users’ travel behavior and perceptions, while understanding the mitigation strategies applied to address these effects. Given the wide array of disruption types, durations, spatial coverage, and the modes affected, the review indicates a dearth of knowledge regarding their impacts along with a very limited understanding of the relative benefits of mitigation strategies. The most common impacts are mode changes. Some evidence, which is rather limited, shows that transit users did return to their previous travel behavior after the end of long-term service disruptions. The study offers a better understanding of the relative impacts of transit systems’ long-term disruptions and transitional periods, while highlighting important gaps in the current literature.

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1 Introduction

Governments around the world are heavily investing in building new transit infrastructure or upgrading or expanding existing ones [ 1 , 2 ]. This is in order to draw higher levels of transit ridership, while decreasing the attractiveness of non-sustainable modes of transport (such as private car use). Achieving these goals would help societies in reaching their emerging climate goals by reducing the emissions from the transport sector. The construction of these new transit projects does not happen overnight and can take from a few weeks to several months or even years, which can lead to extended long-term disruptions in the transit network. These long-term disruptions, or transitional periods, can have undesirable impacts on people’s travel behavior, and they could also increase traffic congestion as well as decrease air quality in the short and long terms. The impacts of these transitional periods, in general, seem thematically and geographically dispersed. Similarly, a consolidated understanding of the impacts of long-term disruptions due to other causes, such as labor strikes and transit system failures, on travelers’ behavior seems to be missing from the literature. Albeit different in their origins and characteristics, both transitional periods and long-term disruptions share several attributes, especially in terms of their effects on travel behavior as well as travelers’ needs and perceptions. They both impact travelers for an extended period, which can lead to them developing new habits and attitudes to adapt to such changes. Therefore, both transitional periods and long-term disruptions that resulted from transit system construction and repairs, infrastructure failure, and labor strikes were included in this study.

In this study, the term “transitional periods” refers to any planned changes in the transit system that alter the service’s structure and quality and require an extended period of disruption of regular service operations to implement, such as the construction of new transit infrastructure or the substantial upgrade of such infrastructure. After these periods, users expect to have improved transit service quality or options (Fig. 1 ), which may have an impact on their travel behavior and perception. It should be noted that not all infrastructure-related disruptions should be considered transitional periods, as some projects may not improve service afterwards. For example, maintenance-related projects can lead to similar levels of service.

Conceptual framework

The term “long-term disruptions” was used to refer to any long-term transit system disruptions in which transit returns to its initial service configuration after the disruption, with no to minor changes to the service. For both types of disruptions, transit agencies and cities implement a wide array of mitigation strategies. Therefore, this study aims at achieving the following three goals: comprehending the current state of knowledge in the academic literature regarding the impacts of long-term transit system disruptions and transitional periods on travel behavior, travelers’ perceptions and travel needs; understanding the applied mitigation strategies and technologies used to address any undesirable impacts of these disruptions; and synthesizing the findings to identify areas of overlap between studies and prominent gaps in the current state of knowledge. Exploring these issues together informs transit planners and practitioners of lessons learned across studies regarding similarities and differences in terms of impacts, thereby guiding their future practice.

2 Research context

A considerable number of academic studies explored the factors affecting travel behavior and ridership at the city, route, and stop level during regular operational periods of the transit service. Several studies provided a systematic literature review of these factors’ impacts on travel behavior and ridership [ 3 , 4 ]. Nevertheless, there are numerous types of disruptions that can affect the normal operations of the transit network. There are also various classifications for these disruptions. Some studies differentiated between them in terms of whether they are planned or unplanned [ 5 , 6 , 7 ], while another categorization can be in terms of duration (long-term or short-term disruption) as was discussed by Kattan, de Barros [ 8 ].

Disruptions can also be divided based on the transport mode or system they affect (like rail transit or road disruptions) or in terms of magnitude—whether these disruptions resulted in closures of the affected transit stations or only caused the redirection of the stations’ lines, for example [ 9 ]. Furthermore, Zhu and Levinson [ 10 ] categorized transport network disruptions based on their causes; this included transit strikes, bridge closures, earthquakes and special events. A considerable number of researchers investigated the impacts of short-term transit system disruptions that last from a few minutes to hours on travel behavior and transit users’ perceptions [ 11 ]. For example, Saxena, Hossein Rashidi [ 12 ] compared how travelers weigh trip attributes differently in the case of either canceled or delayed transit service when choosing a mode of transport. Other studies focused on understanding the impacts of long-term disruptions of the transport network [ 6 , 8 , 10 , 13 , 14 , 15 , 16 , 17 ].

In regard to review papers that focused on transport network disruptions, Shalaby, Li [ 18 ] conducted a systematic review to identify and analyze journal articles that focused on management strategies for short-term rail transit disruptions and modeling approaches. Zhang, Lo [ 19 ] provided a similar review of the academic literature concerning metro system disruption management and substitute bus service, whilst Zhu and Levinson [ 10 ] discussed theoretical and empirical studies that focus on traffic and behavioral impacts of transport network disruptions. To the best of the authors’ knowledge, none of the previous research efforts provided an in-depth systematic review of the contemporary academic literature concerning the impacts of long-term transit system disruptions and transitional periods on travel behavior, travelers’ perceptions and travel needs. To address this gap, this paper focuses on developing a comprehensive systematic review of the literature regarding the topic.

3 Methodology

A comprehensive systematic review of the academic literature was carried out. Systematic literature reviews are a powerful approach to identify and analyze all relevant research on a given topic within certain parameters. The research process followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for systematic reviews [ 20 ]. It was initiated by conducting a scan of a few relevant articles to establish the keyword search syntax that would then be used to conduct an article search on three notable research databases. These databases were the Transport Research International Documentation (TRID), the Web of Science Core Collection and Scopus. TRID is a comprehensive database that includes more than one million records of transport research [ 21 ]. Following this initial review, four different combinations of keyword search syntaxes were generated based, mainly, on the different categories of long-term transit system disruptions within the scope. More specifically, the four themes of the search queries were as follows: construction and maintenance, general disruptions and closures, labor disputes and strikes, and a more general search related to transport system upgrades or improvements (Table 1 ). It should be noted that the database search queries pertaining to the fourth keyword category were developed and carried out at a later stage to further expand the limited pool of relevant articles found.

Using EndNote, research records for all the documents, which included the abstracts, were organized and some duplicate records were removed. Afterwards, the entire list of records was uploaded to the Rayyan web application [ 22 ] where the remaining duplicates were removed and the record screening process was initialized. Rayyan provided a platform for a collaborative work environment for both authors to screen articles, add notes, label articles, and conduct keyword searches. The screening was mainly done by the first author, while checking the undecided articles was done by both authors. To establish clear guidelines to elucidate the process of determining which documents are relevant, inclusion and exclusion criteria were formulated and subsequently followed to filter out the out-of-scope documents (Table 2 ).

The criteria that guided the selection process included that all papers must be in English and must be full peer-reviewed journal articles published no earlier than in 2011. They must also be mainly concerned with the effects of long-term transit system disruptions. There is no clear distinction between short- and long-term disruptions in the literature. Some researchers generally classified short-term disruptions by lasting up to two or three days [ 23 ]. Therefore, and to increase the number of included studies in this literature review, we considered the threshold of 2 days or more for identifying long-term transit service disruption. Moreover, research articles investigating disruptions due to highway construction or road maintenance works or that study long-term disruptions of other transport modes, such as air travel and ferries, were beyond the scope of this review and, as a result, were excluded. Additionally, efforts that investigated the effects of natural disasters, epidemics, and pandemics such as the COVID-19 pandemic were removed from the analysis, as these types of events are not only impacting the transit system, but also impacting all transportation modes and people’s willingness to make trips to different destinations.

Based on the criteria presented, the number of records that had their titles and/or abstracts manually reviewed by the authors and were subsequently excluded was 6,588, while the number of records deemed preliminarily relevant and were then pursued for full text retrieval were 47 articles. The review of the articles was based on reviewing the papers’ titles first, if the paper title is not clear or suspected of inclusion, the abstract was then reviewed. Afterwards, a swift review of the full text was carried out for these 47 articles. As a result, 30 articles were excluded, and 17 were included (end of Phase 1). Phase 2, the final phase of this process, comprised scanning the reference lists of the 17 acquired articles for any new articles, while applying the same inclusion and exclusion criteria. This step led to the procurement of 5 additional articles that required a full-text review. Only 2 were deemed relevant and were included in the finalized list of articles for the systematic review (19 in total). This process is illustrated in Fig. 2 , which was adapted from Page, McKenzie [ 20 ]. Articles that made it to the finalized list were categorized based on the type of disruption that they each discuss. Subsequent to this categorization, a full review of the 19 articles was performed, and the relevant information regarding the different aspects of each paper, such as the issues addressed; the data used; the utilized research methods; and the key findings, was extracted and organized. This was followed by conducting a qualitative analysis of the papers by analyzing the information on individual, categorical, and general scales as well as synthesizing and comparing the findings and other significant features of the articles.

Flow diagram of the systematic review process conducted

In total, 19 articles were found to focus on long-term transit system disruptions and transitional periods. Even though this may seem like an inconsiderable number of articles for a systematic review, other systematic reviews in the field had similar numbers [ 24 , 25 , 26 ]. Of these 19, three focused generally on long-term disruptions. Seven papers discussed construction-related transit disruptions, and nine papers explored the effects of transit labor disputes and strikes. Appendices 1, 2, and 3 depict the results of this systematic review. In the appendices, studies that have modeled the impact of a mitigation strategy or controlled for it in the model, or have critically discussed the effects of a mitigation strategy were highlighted.

4.1 General disruptions studies

As can be seen in Appendix 1, only three articles focused on general long-term transit service disruptions or included sections that focused on them. One paper used semi-structured interviews to understand the factors influencing the mode shift to car among transit users in the case of a transit service disruption. In this study, long-term disruptions referred to the hypothetical absence of transit for 10 years [ 27 ]. Pnevmatikou, Karlaftis [ 17 ] investigated the impact of a 5-month partial closure of a metro line in Athens, Greece. Lastly, Yap, Nijënstein [ 28 ] analyzed the impacts of four tram line disruptions in The Hague, the Netherlands. These analyzed disruptions lasted 5 and 20 days.

4.1.1 Methods and data

Nguyen-Phuoc, Currie [ 27 ] used a discourse analysis approach to analyze data collected from semi-structured interview responses of 30 transit users in Melbourne. Most of the participants were from an academic institution (i.e., Monash University). Interviews were coupled with brief questionnaires to collect participants’ socioeconomic information (age, income, occupation, car ownership and driving license) and current travel behavior (i.e., last transit trip). In contrast, Pnevmatikou, Karlaftis [ 17 ] adopted nested logit (NL) and multinomial logit (MNL) models to analyze revealed preference (RP) and stated preference (SP) data. The RP survey (1038 responses) examined the impact of a 5-month metro line closure on transport mode choice, which was conducted immediately after the line’s reopening. The SP survey of transit users was web-based and was collected to understand stated preferences towards travel patterns during hypothetical metro closures. In contrast to the previous two studies, Yap, Nijënstein [ 28 ] utilized smart card data obtained from an automated fare collection (AFC) system during disruptions to compare between predicted and realized transit ridership.

4.1.2 Studies key findings

Nguyen-Phuoc, Currie [ 27 ] found that in the long term, only context-specific factors (travel distance, travel time, travel cost, trip destination and flexibility of alternative mode) have an influence on transport mode shift. The postulated reasons were that participants did not perceive any alteration to the individual-specific factors in the future and that the authors focused on the unavailability of transit for an extremely long period (i.e., next ten years). The study indicated that the prolonged absence of transit services is expected to have an impact on land use and individuals in terms of changing their residential or workplace locations, or both. Participants did not consider trip cancellations during the long-term unavailability of the transit service.

Pnevmatikou, Karlaftis [ 17 ] showed that a metro user’s income was an important element in their decision-making process regarding whether to shift to buses or cars during metro service disruptions. Low-income metro users, regardless of car ownership, prefer using buses during metro disruptions. Additionally, during the disruptions, using a car for travel was negatively correlated with having a flexible work schedule. Yap, Nijënstein [ 28 ] indicated that in-vehicle time in the shuttle bus service (i.e., rail replacement buses) was perceived about 1.1 times more negatively compared to the in-vehicle time in the initial tram line, while waiting times for the shuttle bus service were perceived as approximately 1.3 times higher compared to the waiting times for the regular bus and tram services. The paper also indicated that if the prediction model does not account for vehicle capacity, integrating the positive effect of higher bus frequency would only overestimate the level of service provided by the shuttle buses during disruptions.

4.1.3 Section summary

Very few studies focused on long-term transit service disruptions or included sections that centered around long-term disruptions. One of them used a qualitative approach to develop a conceptual model of mode shift to car among transit users. The other two articles used specific case studies of partial closures of the metro and tram system in Athens and The Hague, respectively. There were no studies exploring system-wide (or a large portion of the system’s) long-term disruptions, nor were there studies that explored long-term disruptions within the North American context. All three studies focused primarily on current transit users’ travel behaviors. Nevertheless, other travelers may respond differently to additional costs imposed by increased traffic congestion. Additionally, since these studies explored only two cases of disruptions, future work is needed to include a wider array of disruptions because transit users’ responses will depend on: available travel options; duration, type, and degree of disruptions; and the used mitigation strategies’ effectiveness. The two quantitative studies investigated the impacts of providing shuttle buses and using an existing parallel transit service to mitigate the impacts of tram and metro closures, respectively. However, the effectiveness of other mitigation strategies is yet to be explored.

4.2 Construction studies

As seen in Appendix 2, seven studies explored the impacts of construction-related disruptions. Most of them focused on the impacts of heavy rail systems’ (e.g., metro and rail systems) construction and maintenance, while only two discussed light rail transit (LRT) systems [ 8 , 9 ]. Construction studies are related to the idea of transitional periods, in which people are expected to have improved transit service quality (or options) after these periods. Of the seven papers, 3 focused on the impacts of construction-related transit disruptions on bike-sharing systems usage. The four others investigated the impacts of construction on: travel behavior changes and travelers’ responses to enroute real-time information disseminated through variable message signs (VMS) [ 8 , 13 ], local air quality [ 29 ], and bus performance [ 14 ]. Additionally, only five articles critically discussed or analyzed the impacts of mitigation strategies.

4.2.1 Methods and data

Of the seven studies, five utilized statistical models to investigate construction-related transit disruptions’ effects on travel behavior and air quality. Bike-share system studies used ridership data from fixed docking stations [ 30 ] or from free-floating bike-sharing systems [ 9 ]. Based on bike-share data, these studies explored the impact of metro and LRT closures that lasted from 7 to 25 days in Washington, D.C., USA and Cologne, Germany. They used autoregressive Poisson log-level time series modeling [ 30 ], negative binomial regression [ 9 ] and linear regressions [ 31 ] to analyze ride-share data for periods before, during, and after disruptions, while controlling for a set of variables (weather conditions, season, day, time of day, etc.). One study [ 31 ] did not directly model changes in ridership due to disruptions, but rather used sensitivity analysis to explore the effects of implementing a new $2 single-trip fare (STF) on ridership, which was introduced concurrently with the SafeTrack program’s operations in Washington, D.C. Another study modeled bike-sharing usage from geographical and temporal perspectives [ 9 ].

On the other hand, other papers used participant survey data to understand changes in travel behavior using summary statistics. For example, Kattan, de Barros [ 8 ] used a revealed preference survey (430 responses) collected one year after the West LRT line’s construction in Calgary, Alberta, Canada, had started but before it ended (the construction project’s duration was ~ 3 years). Nevertheless, it did not model travel mode changes but focused on multinomial logit modeling to study travelers’ behavioral responses to VMS information. Zhu, Masud [ 13 ] used descriptive statistics to analyze panel survey data before (318 and 420 responses) and after (74 and 64 responses) two reductions and closures of service. Another study used fixed-effect modeling to understand the impacts of rail transit construction on the air quality index using air quality data from 28 cities in China [ 29 ]. Lastly, Shiqi, Zhengfeng [ 14 ] used fuzzy aggregation and summary statistics to evaluate the bus layout adjustment scheme from passenger and car driver perspectives and investigated passenger volumes at stops.

4.2.2 Studies key findings

Usage of bike-sharing systems generally increased during construction-related transit disruptions but at different levels. For example, Younes, Nasri [ 30 ] reported ridership increases on weekdays during disruption. Once the affected metro stations reopened, bike-share ridership returned to its pre-surge levels, suggesting a limited lasting effect of the studied disruptions. They also suggested the likelihood of travelers using bike-share as a first- and last-mile solution rather than as an alternative to transit. Similarly, Schimohr and Scheiner [ 9 ] reported a reversion to the original bike-share ridership levels once the disruption had ceased. On another note, Kaviti, Venigalla [ 31 ] indicated that implementing a new $2 single-trip fare increased the number of first-time bike-share riders by as much as 79% immediately after its introduction. The introduction of this new fare co-existed with metro service closures. Additionally, there was also a statistically significant increase in the daily ridership of registered members and casual users at docks near metro stations impacted by the metro service closures.

In regard to the other four studies, Kattan, de Barros [ 8 ] stated that the total demand for travel in areas affected by the construction did not decrease, neither were trip departure times rescheduled. Travel behavior changes were mainly route switching, followed by mode shifting, and then by destination changing. Zhu, Masud [ 13 ] indicated that transit users changed modes or destinations instead of departure time during the complete closure of metro stations. They also reported that ~ 20% of respondents did not return to using the metro even after the service’s full restoration. However, it is not known whether these mode changes are temporary or permanent. Additionally, they observed that wealthier riders are more likely to drive or switch to for-hire options (e.g., Uber and Lyft). On another note, Sun, Zhang [ 29 ] found that rail construction has a greater impact on improving air quality than urban road reconstruction, while Shiqi, Zhengfeng [ 14 ] suggested that factors attributed to transit service and traffic were degraded when bus routing schemes were implemented during disruptions.

4.2.3 Section summary

Few studies in the literature focus on long-term disruptions caused by transit construction or maintenance projects. Three of the seven studies focused exclusively on understanding the disruptions’ impacts on bike-share ridership rather than their effects on using active transportation modes and cycling behavior. Only two articles employed traveler surveys to gain a better picture of people’s travel behavior instead of their tendency to use one specific mode (bike-share) during such disruptions. Moreover, none modeled transport mode changes due to transit system construction projects, but they rather used descriptive statistics to provide information regarding, for example, route and mode changes, while indicating limited trip cancellations or changes in trip departure times.

Additionally, very few studies looked into long-term changes in travel behavior or home and work location decisions influenced by long-term disruptions as Nguyen-Phuoc, Currie [ 27 ] discussed. Most studies focused on immediate impacts and accounted for disruptions within limited time frames. Only five studies explored or modeled the impacts of mitigation strategies on travelers’ travel behaviors or perceptions (Appendix 2). Similarly, it was rare to find articles utilizing detailed mitigation strategy data (e.g., shuttle buses and travel information communications) and travelers’ data in combination with disruption data to explore the mitigation strategies’ effectiveness during transit construction projects. Furthermore, none of the studies were concerned with the relative impact of bus rapid transit (BRT) system construction projects; most of them focused on LRT and metro service construction.

4.3 Labor dispute and strike studies

Only nine papers focused on labor disputes and strikes (Appendix 3). Four of them discussed the impacts of transit operators’ strikes on air quality [ 32 , 33 , 34 , 35 ]. Three focused on transit service strikes’ effects on traffic conditions, while one article investigated the strikes’ impacts on the usage of bike-sharing systems. The remaining paper explored a strike’s effect on undergraduate students’ travel choices.

4.3.1 Methods and data

Of the nine articles, six utilized statistical models to investigate transit labor disputes’ effects on people’s travel behavior and on air quality. Regarding air quality studies, they used pollutant levels as proxies to investigate people’s shifting from transit to using private vehicles. Using air quality monitoring stations data, most of them used case studies that lasted from 3 days [ 32 ] to 51 days [ 33 , 34 ]. Additionally, two used a 2013 strike that occurred in Ottawa as their case study, and only one used a sample of more than one long-term strike for investigating their impacts on air quality [ 35 ]. The most commonly used data analysis methods included regression, difference-in-difference models [ 34 , 35 ], and summary statistics [ 33 ].

On the other hand, three studies explored transit strikes’ effects on traffic conditions using freeways and highways’ loop detector data [ 36 , 37 , 38 ]. These studies used different indicators to understand changes in traffic conditions such as changes in average delay, traffic flow, mean speed, and travel time. The used methodological approaches include summary statistics and developing regression and generalized linear models [ 36 , 37 ]. Only one study [ 39 ] used statistical modeling to isolate strikes’ impacts on bike-sharing system usage; it analyzed the impact of one strike that lasted 7 days in Philadelphia, Pennsylvania, USA, using interrupted time series models. Additionally, only one study [ 40 ] explored strikes’ impacts on transit users and non-users’ travel behavior using surveys and mobile phone app (called iEpi) data. Using four weeks of data, comprising two weeks during the strike and two weeks of normal operation after the strike ended, they developed descriptive statistics to understand changes in walking distance, trips frequency, and visited locations.

4.3.2 Studies key findings

Most of the air quality studies showed substantial increases in fine particulate matter (PM) concentrations during strikes, particularly in busy urban areas. Moreover, some studies indicated large increases in O 3 and CO concentrations. These impacts were mainly attributed to travel behavioral changes; transit users shifted to using private cars. In contrast, two articles reported reductions in NO concentrations during strikes. NO is a gas that is mainly produced by diesel engines, which can be found in public transit buses. Interestingly, one article suggested that PM and O 3 concentrations significantly decreased in the strike’s final 3 weeks. This was attributed to travel behavioral changes; travelers started using more environmentally friendly transport modes as they adapted to the transit service’s absence.

Regarding traffic studies, Anderson [ 37 ] reported an about 47% increase in highway delays during a 35-day strike in Los Angeles, California, USA. More delays were observed along freeways with parallel transit lines that are characterized by heavy ridership. Other researchers made the same spatial observation [ 38 ]. Furthermore, Spyropoulou [ 36 ], using a case study from Athens, Greece, of several strikes lasting between 1 and 5 days, reported similar results. Spyropoulou [ 36 ] stated that strikes increased congestions by increasing traffic flow (up to 30%), reducing mean speed (up to 27%) and increasing travel times (up to 25%).

Strikes also had significant impacts on using active transportation modes. Fuller, Luan [ 39 ] reported a 57% increase in bike-sharing system usage by members and non-members during the 7-day transit operators’ strike. However, bike-share usage quickly returned to previous trends directly after the strike. Additionally, some results suggest that non-members might have used bike-sharing for a slightly longer period. In contrast to previous studies that analyzed the usage of one mode during the strikes (i.e., car or bike-share system), Stanley, Bell [ 40 ] focused on the overall changes in the travel behavior of transit and non-transit users during a longer strike that lasted for more than 30 days. They indicated that transit users visited fewer places and walked more during the 30-day strike.

4.3.3 Section summary

Traffic studies showed that the impacts on the transport network were not equal; this is usually not captured by studies that used aggregate air quality data from fixed monitoring stations. Most of the studies examined changes in using one mode, namely cars or bike-sharing systems. However, only very few studies explored and modeled overall alterations in travel behavior. Moreover, most of the articles used passive data sources from traffic loop detectors, air quality stations, or a bike-sharing system at the aggregate level; this does not offer insights into individuals’ behavioral changes in terms of route choice, departure time, travel perception and overall experience. In fact, none of the studies relied on using social surveys to investigate the short- and long-term impacts of transit operators’ strikes on users. In other words, none of them explicitly focused on understanding changes in transit users’ perceptions and needs. Instead, studies tried to draw conclusions regarding transit users’ and non-transit users’ travel behavior.

Only one study utilized mobile-phone data to explore changes in travelers’ behaviors in more detail. This calls for more investigation into possibly using such tools in obtaining larger and more representative samples that can be combined with surveys to better understand different aspects of travelers’ decision-making behaviors during transit operators’ strikes. Furthermore, none of the reviewed studies explored the impacts of service strikes and information availability on travelers’ perceptions using data collected from social media platforms such as Twitter, for example. Table 3 shows an aggregated summary of the analyzed papers from the three categories (general causes, construction-related, and labor-related disruptions) and their different aspects including users’ perceptions and travel behaviors that were investigated.

4.4 Mitigation strategies

Several mitigation strategies were discussed in the reviewed literature. They generally fall under three broad categories: backup transport services, policy-based measures, and impact assessments (Table 4 ). In the table, articles are also sorted by the disruption type that they are associated with. Of the 19 papers chosen for this review, 10 articles discussed disruption mitigation strategies in some form.

Backup transport services are mitigative actions where some alternative form(s) of transport is provided during disruptions to regular transit services. They can be considered to be a form of policy-based measures; however, a distinction has been made between the two since not all of the policy-based measures discussed in the articles were backup transport services. Several articles discussed the level of backup transport services and their impact. For example, Kattan, de Barros [ 8 ] discussed the benefits of implementing a temporary BRT service, which followed an alignment similar to that of the LRT under construction, as a proactive mitigation measure that encouraged travelers to shift to using transit. Yap, Nijënstein [ 28 ] found that people overestimated the in-vehicle and waiting times associated with using bridging buses compared to their in-vehicle and waiting times using the initial tram service. On another note, Schimohr and Scheiner [ 9 ] indicated that travelers’ proximity to stations with substitute or redirected lines was associated with a decrease in the number of bike-sharing trips, suggesting that people used transit at these locations more than they used the bike-share system during service disruption. Nevertheless, most of the studies that discuss this point agree that bus bridging involves a higher level of inconvenience for users, thereby encouraging them to change modes or destinations.

The second category is policy-based measures. This strategy includes providing and improving communications and the dissemination of information to travelers through delivering consistent updates regarding the disrupted transit services, traffic conditions, available alternative modes of transport, etc. It could also include reduced fares, for using transit or a form of active transportation for example, to promote using sustainable transport modes during the disruption to alleviate the hike in traffic congestion. The reviewed articles discussed or analyzed several policy-related amendments or measures. They include introducing a single-trip fare option to encourage riders to use the bike-sharing system and using improved communications methods to disseminate information; those approaches were studied by Kaviti, Venigalla [ 31 ] and Kattan, de Barros [ 8 ], respectively. In addition, Anderson [ 37 ] reported that an additional transit service was contracted (i.e., the Red Line Special bus service) to duplicate part of a closed metro route; this could also be considered a backup transport service. Similarly, Moylan, Foti [ 38 ] indicated that during the Bay Area Rapid Transit (BART) service shutdown, a local bus agency (i.e., AC Transit) increased frequencies on Transbay bus service routes. However, the benefits of policies like contracting new services or increasing transit services offered by other agencies were not explicitly measured in previous efforts. Regarding the third category, only one paper [ 14 ] focused on evaluating a metro construction project’s disruptive effects on bus performance in the city of Ningbo, China.

5 Discussions and policy implications

The results of this study demonstrate that there is generally a lack of academic research concerning long-term transit service disruptions and transitional periods. Nevertheless, the majority of the identified academic papers are relatively recent and were published during the past five years. This may suggest that this key topic has been gaining more traction in recent years. This showcases this topic’s relevance and the possibility of having more efforts in this area soon. This is in alignment with the increase in worldwide governmental funding opportunities to develop new transit infrastructure to foster economic growth and face climate change. For example, Canada’s federal government revealed a new sizeable funding of $14.9 B for new public transit infrastructure in February 2020 [ 41 ]. Similar efforts dedicated to providing more funding for building and upgrading public transport can be found in Europe, the US and China [ 44 , 45 , 46 ]

According to the number of identified documents, there is a wide agreement and overlap in the reviewed literature regarding the negative impacts such disruptions and transitional periods have on travelers and also regarding the importance of using a range of mitigation strategies. The most common impacts are mode changes. Very few studies indicated other types of changes such as route changes, trip departure time changes, and destination changes. Some evidence, which is rather limited, shows that transit users did return to their previous travel behavior after the end of long-term service disruptions. Nevertheless, it is not clear if these changes are temporary or permanent. Other studies indicated that bike-share systems ridership increased during disruptions. However, these ridership levels returned to their pre-disruption levels after the reopening of the transit service, suggesting a limited lasting effect of long-term disruptions on people’s mode choice to continue using the bike-sharing systems. Providing new backup transit services and rerouting and enhancing parallel services were the most common mitigation approaches widely discussed in the literature to deal with long-term transit disruptions. Previous efforts showed good use of passive data sources from air quality monitoring stations, highway loop detectors, bike-share system counters, and automated fare collection systems to establish evidence of the negative effects of such periods.

Despite these efforts in the literature, travelers’ perceptions and needs during these periods are minimally addressed or analyzed. Additionally, it was rare to find studies that explicitly incorporated or controlled for the expected impacts of the transit projects after their completion. In other words, transitional periods may have more positive outcomes on transit users’ perceptions and travel behaviors after the project is finalized, due to enhanced service quality for instance, compared to other long-term disruptions. Such effects were underexplored in the literature.

The academic literature on long-term disruptions and transitional periods is currently quite divorced from the practice. For example, there is a dearth of studies that seek to derive lessons from past and current practices to help advance the practice of using effective mitigation strategies in different contexts and for different purposes. Additionally, a considerable number of the articles focused solely on understanding the impacts of long-term service disruptions on the usage of one transport mode, such as bike-sharing system usage, or one element, such as air quality or traffic conditions, rather than drawing a complete picture of people’s decision-making process and changes in their travel behaviors and needs. It was also rare to find studies that used a statistical model to better understand travelers’ behaviors during and/or following different types of long-term disruptions and transitional periods.

Most of the studies focused on measuring the short-term impacts of transit service disruptions. This may be related to the fact that most of the analyzed disruptions in the academic literature lasted for a few days or weeks. Nevertheless, articles exploring both short- and long-term impacts of longer transit service disruptions and transitional periods that last for a few months or even years were very limited. In fact, only two studies focused on exploring the impacts of longer disruptions that lasted more than a few months. Using surveys, one study regarding disruptions in Athens explored the immediate impact of a 5-month disruption on travel behaviors, while another study from Calgary explored the 1-year impact of an LRT system construction project, which lasted for about 3 years. Therefore, it is challenging, based on the limited research available, to understand the changes in travel behavior during these prolonged periods and to understand whether these long-term disruptions have an extended or permanent impact on travelers’ behaviors. Potential changes that may not be considered in shorter disruptions include relocation and/or reductions in travel demand because of moving closer to work or school, changing jobs, joining a ride-sharing program, and increasing telecommuting. Some of the key policy recommendations of this research are listed below.

With the need for academic studies that focus on the short- and long-term impacts of different long-term disruptions and transitional periods, cities and transit agencies are encouraged to work with the academic community to test different sets of mitigation strategies in different contexts, scenarios, and at different scales. These studies should also include information about changes in travelers’ behaviors, perceptions, and well-being in order to evaluate the used mitigation strategies and their relative impacts, which would inform future policy making.

With the emergence of more academic studies, as well as non-academic reports, in this area, lessons from the literature and practice should be organized and used in more systematic ways to assist in developing a policy guide to help in managing these disruptions. This will aid in guiding future practices that should aim to maintain higher levels of the transit services’ attractiveness during such periods for both transit and non-transit users. The prospect of providing adequate active transport alternatives that would encourage people to shift to using active transportation modes should also be explored. This could potentially reduce the stress on the public transportation and road networks.

Using agreements with private bus operators, ride-hailing services, bike shops, advocacy groups, and bike-sharing and scooter-sharing companies, cities can help reduce the impact of such periods on travelers. As seen in the literature, offering bike-sharing services and making them cheaper or more accessible by offering more payment options during long-term transit service disruptions can work as a mitigation strategy. This could be coupled with looking beyond the physical availability of alternative modes by testing different pricing scenarios to provide transport alternative(s).

Research suggests that using greener transport options may be adopted more widely by travelers if adequate policies were in place during such disruptions. This would capitalize on the increased flexibility of travelers to try out new transport modes during such periods. This might help in increasing cities’ shares of active transportation if such mode changes could be sustained after the disruption and adopted permanently by travelers. Nevertheless, currently, there is limited evidence that this is the case.

People will not benefit or suffer increases in their monetary and non-monetary costs equally because of any long-term transit system disruptions or transitional periods. Therefore, transit agencies should assess the equity impacts of such extended time periods on different groups of travelers. This will be context-specific and will help in articulating more sensitive policies that match different groups of users’ needs.

Finally, it was reported in the literature that the provision of alternative public transport options with a high transit service level coupled with the efficient dissemination of pre-trip and enroute, real-time travel information (e.g., updates on traffic and on areas affected by the disruption) resulted in an increase in transit use during the disruption. Moreover, evidence of the importance of using social media, graphics, and short videos in communicating information during the COVID-19 pandemic was discussed in the literature [ 47 ]. Learning from these lessons, more understanding of the importance of efficient and timely dissemination of information plans using social media or other platforms is essential. This is to help cities in informing people about expected impacts and options, which can help in alleviating stress on transit segments.

6 Conclusions

This study aimed to explore the current state of knowledge concerning transit systems’ transitional periods and long-term disruptions and to understand the actively used disruption mitigation strategies and technologies that are implemented to address or alleviate any of their undesirable impacts on travelers. To achieve these goals, a comprehensive systematic review of the academic literature was conducted. In total, 19 peer-reviewed journal articles were identified and analyzed. This systematic review helps identify the major knowledge gaps in the literature. The results of this study demonstrate that there is generally a lack of academic research works concerning long-term transit service disruptions and transitional periods. In fact, travelers’ perceptions, travel behaviors and changing needs during these disruptive periods were minimally addressed or analyzed. Key conclusions and recommendations are discussed below.

Given the range of different types of long-term transit system disruptions (e.g., construction, labor disputes and service failures); various disruption time frames (from a few days to several years); varying spatial coverage (from one line to system level); and different disrupted modes (e.g., bus, metro and tram), much more work can be done to explore the effects of such disruptions on people’s travel behavior and perceptions.

The possible impact of long-term disruptions on travelers in terms of changing modes, routes, trip departure times, frequency of trips, destinations, and work and home locations in addition to increasing telecommuting and trip sharing are widely recognized in the literature. However, studies rarely explored explicitly the factors affecting changes in travel behavior like shifting to different routes or changing the frequency of trips or the factors influencing relocation for transit and non-transit users using statistical models. This can be an important area for future work.

The relative importance and impact of disruption mitigation measures, while understanding how these measures could work together, within the context of long-term disruptions are rarely investigated in the literature. In fact, the current academic literature provides transit agencies with very limited information to assist them. Such knowledge can inform the processes of planning for long-term disruptions to implement more efficient and effective strategies.

Most of the studies were quantitative in nature and provided some relevant findings; however, qualitative studies can provide in-depth insights into the intersection between how, why, who, and what questions that are related to different types of disruptions. For example, it can help in understanding the importance of using different mitigation strategies for different groups of the population and their relation to different types of disruptions. Therefore, future research can focus on using qualitative approaches to elicit information not only from transit and non-transit users, but also from transit agencies and operators to understand their perspectives.

The reviewed literature generally used data from two main sources: from traveler surveys and from passive data sources that are obtained from air quality monitoring stations, bike-share systems, and highway loop detectors. Therefore, using emerging data sources such as cellular phone data and mobile app data can be explored for future studies to give a better understanding of the different long-term disruptions’ impacts. Social media data, farebox system data, and web surveys can be also incorporated in these studies.

Several studies used summary statistics, difference-in-difference approaches, or a dummy variable to isolate the impacts of long-term disruptions on different aspects (e.g., air quality, traffic conditions, and bike-share usage). However, these studies ignore that a long-term disruption entails an extended period of time, which can see different movement patterns within this period as indicated by Chandler and Shymko [ 34 ]. They stated that relying only on short-term results to draw conclusions regarding long-term impacts of long-term disruptions can lead to an overestimation of the negative effects. Therefore, future research could look into different patterns within such time frames.

Similarly, temporal changes in travel behavior after long-term disruptions or transitional periods end are rarely explored in the literature, particularly for longer disruptions that last for more than a few weeks. Some authors indicated that transit ridership can take several weeks to reach pre-disruption levels [ 37 ]. Therefore, exploring changes occurring over time to travelers and, more specifically, transit users’ travel behavior could be a viable future research endeavor.

Previous studies explored the impacts of long-term disruptions and transitional periods on bike-share usage; however, the academic literature is lacking in studies that investigate their impacts on using other active transportation modes, such as walking and cycling. Since these trips, particularly walking trips, are usually underreported in travel surveys, using sensors data from mobile phone apps can be beneficial to understand changes before, during, and after long-term disruptions for different groups of travelers.

It was found that very few articles explored changes in travelers’ perceptions, satisfaction, and needs due to transitional periods in comparison with long-term transit system disruptions triggered by other causes. Transitional periods, which usually lead to different outcomes in terms of improved service quality after ending compared to other long-term disruption periods, were not explicitly explored in the literature. Transitional periods’ ultimate positive outcomes on users’ perceptions and travel behaviors could be a viable focal point for future research efforts.

Incorporating social issues, such as equity concerns, and seeking to derive lessons to help understand the equity impacts of long-term disruptions and transitional periods on different groups of populations are yet to be accomplished. These groups can include Indigenous populations, visible minorities, and people with systemic barriers to using other transportation modes. Additionally, there is a lack of research to both understand and address the sociopolitical, institutional and community capacity dimensions, which is an important aspect during such periods.

Finally, this study aimed to derive lessons from the current academic literature on the effects of transit systems’ long-term disruptions and transitional periods. Exploration of this rather diverse research area will not only inform professionals but will also highlight important gaps in the current literature for researchers. Future research can expand the presented efforts and focus on reviewing transit agency reports and studies and conducting surveys and interviews with transit planners to record their experience and to better understand their perspective of the effects of transit systems’ long-term disruptions and transitional periods. This is to help cities and transit agencies to better anticipate and manage “change”. This will, in turn, help to facilitate and secure the development of their public transport networks while planning and being better prepared for long-term transit system disruptions, thereby aiding them in achieving their overarching sustainability goals.

Data availability

No datasets were generated or analysed during the current study.

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We would like to thank the Social Sciences and Humanities Research Council (SSHRC) of Canada and Infrastructure Canada (INFC) for partially funding this research.

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Mohamed G. Noureldin & Ehab Diab

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1.1 General disruptions studies

Paper	Disruption & duration	Issues addressed	Data/methods	Main investigated factors in model(s)	Key summary statistics used	Key findings
[ ]	Hypothetical absence of transit due to long-term disruption. 10 years	To understand the factors influencing the mode shift to car among transit users in the case of a transit disruption	Semi-structured interview responses of 30 transit users from different age groups and areas of Melbourne		Conceptual model of the factors affecting transit user mode shift in the short and long terms	In the long term, only context-specific factors (travel distance, time, cost, trip destination, and flexibility of alternative mode) have an influence on transport mode shift
[ ]	Tram lines closures (4 lines). 5 and 20 days	The impacts of 4 planned transit system disruptions on transit ridership to adjust a parameter set in order to provide more accurate estimation of ridership during planned disruptions	•AFC system data collected during the disruptions were utilized to compare between predicted and realized transit ridership Transit ridership prediction modeling of different time periods	Factors considered in the modeling include generalized costs on origin–destination (OD) pair, in-vehicle travel time, walking time, waiting time, # of transfers, value of time, distance travelled, and demand on OD pair, elasticity, and frequency	Prediction accuracy for predefined scenarios Comparison between the default and proposed parameter sets’ values Comparison between the new parameter set and the default set on predicted ridership reduction, the average generalized travel costs per passenger, and the prediction accuracy for all 4 disruptions investigated	In-vehicle time and waiting time for the shuttle bus service were perceived about 1.1 and 1.3 times more negatively in comparison to the perception of in-vehicle and waiting time of the initial services Passengers do not perceive the benefit behind the higher frequency of shuttle bus services in comparison to that of the replaced tram line’s frequency For different case studies, the new parameter set improves prediction accuracy by 3% to 13% in comparison to predictions based on the default parameter set
[ ]	Metro service disruptions. 5 months	Investigates changes in travel patterns due to long-term metro service disruption	RP survey (1038 responses) and SP survey (1944 records) data MNL model for RP data, MNL model for SP data, Joint RP-SP MNL, and Joint RP-SP NL	Y = Mode alternatives X = Modes, level of service (door-to-door time, in-vehicle time, out-of-vehicle time, transfer inconvenience, cost), socio-demographics (income, age, gender), trip purpose, and flexible work schedule		The odds of shifting to using buses or cars during metro disruptions are mainly related to income Using a car was negatively correlated with the possibility of having a flexible work schedule during metro disruptions The Joint RP-SP NL model generally performed better than the joint RP-SP MNL model

Paper

Disruption & duration

Issues addressed

Data/methods

Main investigated factors in model(s)

Key summary statistics used

Key findings

[ ]

Hypothetical absence of transit due to long-term disruption. 10 years

To understand the factors influencing the mode shift to car among transit users in the case of a transit disruption

Semi-structured interview responses of 30 transit users from different age groups and areas of Melbourne

Conceptual model of the factors affecting transit user mode shift in the short and long terms

In the long term, only context-specific factors (travel distance, time, cost, trip destination, and flexibility of alternative mode) have an influence on transport mode shift

[ ]

Tram lines closures (4 lines). 5 and 20 days

The impacts of 4 planned transit system disruptions on transit ridership to adjust a parameter set in order to provide more accurate estimation of ridership during planned disruptions

•AFC system data collected during the disruptions were utilized to compare between predicted and realized transit ridership

Transit ridership prediction modeling of different time periods

Factors considered in the modeling include generalized costs on origin–destination (OD) pair, in-vehicle travel time, walking time, waiting time, # of transfers, value of time, distance travelled, and demand on OD pair, elasticity, and frequency

Prediction accuracy for predefined scenarios

Comparison between the default and proposed parameter sets’ values

Comparison between the new parameter set and the default set on predicted ridership reduction, the average generalized travel costs per passenger, and the prediction accuracy for all 4 disruptions investigated

In-vehicle time and waiting time for the shuttle bus service were perceived about 1.1 and 1.3 times more negatively in comparison to the perception of in-vehicle and waiting time of the initial services

Passengers do not perceive the benefit behind the higher frequency of shuttle bus services in comparison to that of the replaced tram line’s frequency

For different case studies, the new parameter set improves prediction accuracy by 3% to 13% in comparison to predictions based on the default parameter set

[ ]

Metro service disruptions. 5 months

Investigates changes in travel patterns due to long-term metro service disruption

RP survey (1038 responses) and SP survey (1944 records) data

MNL model for RP data, MNL model for SP data, Joint RP-SP MNL, and Joint RP-SP NL

Y = Mode alternatives

X = Modes, level of service (door-to-door time, in-vehicle time, out-of-vehicle time, transfer inconvenience, cost), socio-demographics (income, age, gender), trip purpose, and flexible work schedule

The odds of shifting to using buses or cars during metro disruptions are mainly related to income

Using a car was negatively correlated with the possibility of having a flexible work schedule during metro disruptions

The Joint RP-SP NL

model generally performed better than the joint RP-SP MNL model

Rows 2 and 3 include a study that has modeled a mitigation strategy’s impact. Automated fare collection (AFC). Stated preference (SP). Revealed preference (RP). Multinomial Logit Model (MNL). Nested logit (NL). “Y” refers to the model’s dependent variable(s). “X” refers to the model’s independent variables

2.1 Construction studies

Paper	Disruption & duration	Issues addressed	Data/methods	Main investigated factors in model(s)	Key summary statistics used	Key findings
[ ]	Construction of LRT line. ~ 3 years	LRT construction impacts on travel behavior, and travelers’ preferences about different sources of traffic information and responses to enroute VMS	RP survey. 430 responses 1 year after construction started and before it ended Summary statistics Multinomial logit model	Y = Route choice in reaction to VMS X = Profession, driving experience, frequency of using affected roads, travel time, trip purpose, time of trip, type of trip information, and delays	Perceived travel times on the affected roads and travelers’ preferred modes before and during construction Preference of use and response to different sources of traffic information	Only 1.5% of trips were cancelled or rescheduled, many travelers shifted to transit 43% of people took routes of their choice, while 27% followed VMS Travel behavioral reactions to VMS are highly influenced by the traveled path, trip travel time, departure time and trip purpose
[ ]	Construction of rail infrastructure and urban roads. Unspecified long-term periods	To compare the effect of rail construction with the improvement effect of road reconstruction on air quality	Quarterly panel data of 28 cities in China with rail transit from 2013–16 Operational rail length, length of rail built in season, and air quality data Fixed effect models (364 records)	Y = Air quality index, SO , NO , PM and PM X = Rail length in operations, rail length built in season, road area, GDP, and a set of control variables	Correlation between the operational rail length and air quality index Correlation between the length of rail built in season and air quality index	Rail construction has greater impact on improving air quality than urban road reconstruction Rail transit reduces air pollution in the long run. However, it has a negative short-term effect Improvement effect on PM , PM and SO is apparent, but improvement effect on NO is relatively weak
[ ]	Large LRT construction ~ 14 days	Changes in free-floating bike-sharing system usage during disruptions caused by a light rail construction project	76,859 trips for 1,110 bikes for periods before, during and after the disruption (35 days) Summary statistics Two negative binomial regression models (spatial and temporal models)	Y = # of trips starting within a grid cell, # of trips within one hour X = Land uses, # of points of interest, distances to selected destinations, socio-demographics, weather variables, weekday, time of day, and periods (semester and construction)	The aggregated number of trips that started during each hour per weekday during the study period The number of trips per day during different weather conditions The number of trips per day to destinations within 300 m of stations affected by the construction	The disruptions and subsequent changes in the transit network that were caused by the construction project had only minor impacts on bike-sharing usage patterns in the short term The average number of total daily trips increased during construction but decreased afterwards to, roughly, its original level
[ ]	Metro closures. 12 and 16 days in 2016	Travel behavior changes due to Metro SafeTrack maintenance projects (or surges)	Panel survey data before and after Surge 1 and 2 318 responses before Surge 1, 420 before Surge 2 74 follow-up responses for Surge 1 and 68 for Surge 2 Summary statistics		Stated changes in travel behavior due to metro closure before disruption Comparison between the stated preferences and the actual behavior % of respondents who explored alternative modes of transport during SafeTrack Surge 1 and 2	Transit users changed modes or destinations instead of changing departure time with complete metro station shutdown Wealthier riders are more likely to drive or switch to for-hire options Many did not choose the option they reported in survey before the disruption. Also, 20% of people did not return to using metro after service was fully restored
[ ]	Metro closures. From 7 to 25 days in 2016	Changes in bike-share ridership due to metro closures as a result of the SafeTrack projects	Bike-share trip data included periods before, during, and after closures (Jan 1st, 2015, to Dec 31st, 2017) Autoregressive Poisson log-level time series model	Y = Daily bike-share activity for stations within 0.8 km of affected stations for Surges 2, 4, and 10 X = Weather, summer, weekends/holidays, 3 lag terms, and surge presence	Daily number of trips during study period Description of ridership for each surge location for periods before, during, and after each surge Kernel density estimation visualization of the top 80% of increases in ridership during surges	Disruptions increased bike-share ridership at the local level, mainly during weekdays. Peak-hour usage largely increased for Surge 10 but not for the other 2 surges (Surge 10 lasted nearly a month and spanned a busier and wider area) After metro reopened, bike-share ridership returned to its original levels
[ ]	Metro construction. Unspecified period	The impact of metro construction on bus performance, transit users, and car drivers	Quantitative data regarding subway construction, covering 64 bus lines in vicinity of metro line Fuzzy weighted average technique		Transit service level: non-linear coefficient, line length, transfer distance and average waiting time Traffic impact: saturation degree on work-zone section, at intersection, and volumes	Factors attributed to transit service level and traffic impact degree were degraded when bus routing scheme was implemented during construction Bus routing scheme may have increased traffic on some roads but did not hinder daily travel of residents
[ ]	Metro closures Several durations	The effects of implementing a new $2 single-trip fare (STF) for bike-share trips on the ridership and revenue, which included studying the influence of metro closures on ridership	Revenue and bike-share ridership data before and after the implementation of STF for a period more than 2 years Summary statistics ANOVA tests Regression analysis	Y = Daily bike-share ridership X = Weather conditions (temperature and precipitation), day of the week, STF, and season	Daily ridership of registered and casual users for one week before closure, during closure, and one week after closure for bike-sharing stations within 0.25 and 0.5-mile (0.4 and 0.8 km) radii of affected metro stations	There was a statistically significant increase in the daily ridership of registered members and casual users at docks located near metro stations that were impacted by metro service closures The concurrency of STF introduction with the SafeTrack maintenance projects might have influenced this increase

Rows 1, 3, and 7 include a study that has modeled the impact of a mitigation strategy. Rows 4 and 6 include a study that has discussed or investigated the impact of a mitigation strategy using summary statistics. “Y” refers to the model’s dependent variable(s). “X” refers to the model’s independent variables. Stated preference (SP). Revealed preference (RP). Sulfur dioxide (SO 2 ). Nitrogen dioxide (NO 2 ). Fine particulate matter smaller than or equal to 10 µm in diameter (PM 10 ). Fine particulate matter smaller than or equal to 2.5 µm in diameter (PM 2.5 ). Gross domestic product (GDP). Analysis of variance (ANOVA). Variable message signs (VMS). Light rail transit (LRT)

3.1 Labor dispute and strike studies

Paper	Disruption & its duration	Issues addressed	Data/methods	Main investigated factors in model(s)	Key summary statistics used	Key findings
[ ]	Bus strike. 3 days	Biomonitoring of air genotoxicity during and after a transit strike	Two monitoring sites’ data during and after the strike Summary statistics Regression model	Y = Frequency of micronuclei X = PM concentration, # of buses, weather conditions	Frequency of buses, PM levels, micronuclei levels, and weather conditions	The frequency of micronuclei was significantly higher in the city centre compared to that of the control site, and the highest levels recorded were during the transit strike
[ ]	Strike affecting all transit services (city bus, subway and trolley services). 7 days	Impact of a public transit strike on bicycle-share usage	Bike-share systems data, January to December of 2016 Interrupted time series and Bayesian structural time series models	Y = Daily bicycle share X = Time period (pre-strike, during strike period, post-strike), weather conditions, other cities’ bicycle-share use	Total number of daily bicycle-share trips (pre-strike, strike, post-strike) in the cities of Philadelphia, Boston, Chicago, and Washington Observed vs predicted data regarding number of bike-share trips	The strike resulted in short-term increased bike-share use for members and non-members. However, usage quickly returned to previous trends after the strike Increase in ridership during strike was about 57%. Results suggest that non-members might have used bike-share for a slightly longer period
[ ]	Strike affecting bus and train services. 51 days	Changes in the chemical composition and the mass concentration levels of airborne pollutants	Downtown air monitoring station data during and after the strike Summary statistics and condition probability function		CPF plots for the total particle number levels and geometric mean diameter during and after strikes Particle number size distributions, and average mass concentrations of PM	During the strike, ambient particles were dominated by ultrafine particles with diameters as minute as 15 nm throughout the day There was more than a 100% increase in mass concentrations of the particulate matter, elemental carbon, and organic carbon
[ ]	Strike affecting bus, bus rapid transit (BRT) and light rail transit (LRT) operations. 51 days	The environmental impact of public transit on 4 air pollutants: PM , sulphur dioxide (SO ), ozone (O ) and nitrogen oxide (NO)	19 air monitoring stations’ data for weekdays in Ontario for four years Summary statistics Difference-in-difference models	Y = Hourly levels of PM , SO , O and NO X = Pre-strike, strike, pre-strike * Ottawa, strike * Ottawa, weather condition, and fixed-effect control variables X = Pre-strike, strike phase 1, strike phase 2, strike phase 3, pre-strike * Ottawa, strike phases interactions with Ottawa, and fixed-effect control variables	Distribution of the pollutants Kernel Density Function charts for the distribution of the concentration of pollutants Average daily concentrations in Ottawa and the other Ontario-based stations	During the strike, there was a significant increase in PM and O levels, along with a decrease in NO levels, which is a gas produced by diesel engines found in transit buses The PM and O levels significantly decreased in the final 3 weeks of the strike, suggesting that travelers started using environmentally friendly means of transport. Thus, previous studies that only investigated short-term strikes may overestimate the long-term environmental effects of transit
[ ]	Strike. 35 days	The impacts of the Metropolitan Transportation Authority’s (MTA) strike on traffic congestion	Freeways loop detector data for all major freeways Mathematical model development RD (Regression Discontinuity) model	Y = Average delay in min per mile for detector, share of time detector is occupied and hourly lane traffic flow X = Date, strike, and date & strike interactions	No strike vs. during strike traffic flow and delay Weekly peak hour average delay and changes in hourly traffic flow	Average highway delay increased by 47% during the strike. This increase continues through the strike More delays were observed along freeways with parallel transit lines with heavy ridership
[ ]	Strike. 4 and 5 days	2013 Bay Area Rapid Transit (BART) strike impacts on freeway traffic conditions	Freeways loop detectors data for one year Summary statistics Non-parametric modeling to compare travel-time distributions		Traffic conditions (travel times & volume-weighted travel times) during typical conditions and strike days Differences between the observed median baseline and strike traffic volumes as well as travel rates	Insignificant changes to network conditions, but segments parallel to the BART lines saw large delays like those of the worst day of a week For one bottleneck, strikes showed a significant impact on travel times and volumes nearly doubling the median values on the worst day
[ ]	Strike. Various durations	Identified the short-term effect of public transit on air pollution	Air monitoring stations’ data and transit strikes data Econometric models	Y = Daily levels of NO, CO and PM X = Strike dummy, weather, and fixed-effect control variables	Changes in pollutant levels due to transit strike	Transit strikes lead to a large and statistically significant decrease in NO concentrations and an increase in CO; however, they have mixed effects on PM concentrations
[ ]	Strike. + 30 days	The benefit of using mobile phone-based sensor monitoring for analyzing longitudinal behavior	Mobile phone app (iEpi) data of 28 participants, month-long monitoring and demographic surveys Summary statistics		Behavior of participants (dwell time, trip length, walking steps and visit frequency) for transit and non-transit users during and after the strike	The paper demonstrated the benefits of automated data for understanding travel behavior and the impact of strikes Transit users visited fewer places during the strike and walked more
[ ]	Mostly metro service strikes. 1, 2, 3, and 5 days	The effect of public transport strikes on traffic conditions	Loop detector data from 8 major arterials Generalized Linear Models (GLMs)	Y = Traffic flow, mean speed, and travel time X = Site, strike type (metro, bus, all), time, day, direction, toll, traffic light, transit, area, road type and saturation level	Changes in hourly traffic flow per lane, mean traffic flow per lane, and mean speed Traffic flow and mean speed changes for two selected sites	Strikes increased congestion by increasing traffic flow, reducing mean speed, and increasing travel times Strike coverage was a major factor in congestion Other related factors were and

Row 9 includes a study that has modeled the impact of a mitigation strategy. Rows 5 and 6 include a study that has discussed or investigated the impact of a mitigation strategy using summary statistics. “Y” refers to the model’s dependent variable(s). “X” refers to the model’s independent variables. Fine particulate matter smaller than or equal to 10 μm in diameter (PM 10 ). Fine particulate matter smaller than or equal to 2.5 μm in diameter (PM 2.5 ). Sulphur dioxide (SO 2 ). Ozone (O 3 ). Nitrogen oxide (NO or NO X ). Carbon monoxide (CO). Condition probability function (CPF). Bay Area Rapid Transit (BART). Los Angeles County Metropolitan Transportation Authority (MTA)

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Noureldin, M.G., Diab, E. Impacts of long-term transit system disruptions and transitional periods on travelers: a systematic review. Discov Cities 1 , 15 (2024). https://doi.org/10.1007/s44327-024-00015-5

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Published : 27 August 2024

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Tahayaseen, Ali, et al. "A Systematic Review of Mobile Health Adoption Factors for Iraqi Healthcare Institutions." International journal of health sciences , vol. 6, no. S1, 2022, pp. 6693-6709, doi: 10.53730/ijhs.v6nS1.6424 .

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A Systematic Review of Mobile Health Adoption Factors for Iraqi Healthcare Institutions Image

The advancement of new technologies, particularly information technology (IT), has a significant influence on healthcare as well as the quality of life. Mobile health is becoming increasingly important in healthcare. However, previous research on this area has been primarily anecdotal, scattered, and speculative. This study includes a comprehensive evaluation of mobile health implementations worldwide, as well as reporting on results such as difficulties, variables and advantages connected with mobile health adoption. However, as described in the literature, the adoption of this sophisticated innovation is a challenging undertaking; hence, careful thought and preparation to all critical elements that impact the adoption process through stakeholders is essential. The purpose of this research is to assess the factors that impact the adoption of mobile health frameworks in healthcare organizations. The study employed a non-experimental research exploratory research design. This exploratory study includes an important secondary data inquiry. The creation of an investigation and the modeling using secondary data in order to emphasize the research's ultimate conclusions. Through a review of the literature on existing frameworks for mobile health adoption, it was discovered that healthcare institutions in Iraq require ongoing attention in order to obtain government support.

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A systematic literature review of interpersonal communication strategies for optimizing government employee performance in the digital era

Introduction

Figure 1. Systematic literature review stages.

Identifying and evaluating studies

Figure 2. Inclusion and exclusion criteria.

Figure 3. Extracting and synthesizing process.

Figure 4. Prisma diagram.

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Influence of public service communication on employee performance in the digital era

Table 2. Influence of public service communication on employee performance in the digital era.

Human communication strategies for implementing technological changes and digital skills requirements

Table 3. Human communication strategies for implementing technological changes and digital skills requirements.

Interpersonal communication strategies can create a workplace environment that supports psychological safety along with employee voices in a high-performance system, which ultimately drives innovation

Table 4. Interpersonal communication strategies.

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