journal article review psychology

The Portable Mentor pp 163–173 Cite as

How to Write an Effective Journal Article Review

• Dennis Drotar PhD 2 ,
• Yelena P. Wu PhD 3 &
• Jennifer M. Rohan MA 4  
• First Online: 01 January 2012

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The experience of reviewing manuscripts for scientific journals is an important one in professional development. Reviewing articles gives trainees familiarity with the peer review process in ways that facilitate their writing. For example, reviewing manuscripts can help students and early career psychologists understand what reviewers and editors look for in a peer-reviewed article and ways to critique and enhance a manuscript based on peer review. Experiences in review can facilitate early career faculty with early entry into and experience being a reviewer for a professional journal. The experience of journal reviews also gives students a broader connection to the field of science in areas of their primary professional interest. At the same time reviewing articles for scientific journals poses a number of difficult challenges (see Hyman, 1995; Drotar, 2000a, 2009a, 2009b, 2009c, 2009d, 2010, 2011; Lovejoy, Revenson, & France, 2011). The purpose of this chapter is to provide an introduction to the review process and give step by step guidance in conducting reviews for scientific journals. Interested readers might wish to read Lovejoy et al.’s (2011) primer for manuscript review, which contains annotated examples of reviews and an editor’s decision letter.

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Dennis Drotar PhD

Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, OH, 45229-3039, USA

Yelena P. Wu PhD

Division of Behavioral Medicine and Clinical Psychology, Department of Psychology, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, OH, 45229-3039, USA

Jennifer M. Rohan MA

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Mitchell J. Prinstein

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Drotar, D., Wu, Y.P., Rohan, J.M. (2013). How to Write an Effective Journal Article Review. In: Prinstein, M. (eds) The Portable Mentor. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3994-3_11

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Writing Research Papers

• Writing a Literature Review

When writing a research paper on a specific topic, you will often need to include an overview of any prior research that has been conducted on that topic.  For example, if your research paper is describing an experiment on fear conditioning, then you will probably need to provide an overview of prior research on fear conditioning.  That overview is typically known as a literature review.  

Please note that a full-length literature review article may be suitable for fulfilling the requirements for the Psychology B.S. Degree Research Paper .  For further details, please check with your faculty advisor.

Different Types of Literature Reviews

Literature reviews come in many forms.  They can be part of a research paper, for example as part of the Introduction section.  They can be one chapter of a doctoral dissertation.  Literature reviews can also “stand alone” as separate articles by themselves.  For instance, some journals such as Annual Review of Psychology , Psychological Bulletin , and others typically publish full-length review articles.  Similarly, in courses at UCSD, you may be asked to write a research paper that is itself a literature review (such as, with an instructor’s permission, in fulfillment of the B.S. Degree Research Paper requirement). Alternatively, you may be expected to include a literature review as part of a larger research paper (such as part of an Honors Thesis). 

Literature reviews can be written using a variety of different styles.  These may differ in the way prior research is reviewed as well as the way in which the literature review is organized.  Examples of stylistic variations in literature reviews include: 

• Summarization of prior work vs. critical evaluation. In some cases, prior research is simply described and summarized; in other cases, the writer compares, contrasts, and may even critique prior research (for example, discusses their strengths and weaknesses).
• Chronological vs. categorical and other types of organization. In some cases, the literature review begins with the oldest research and advances until it concludes with the latest research.  In other cases, research is discussed by category (such as in groupings of closely related studies) without regard for chronological order.  In yet other cases, research is discussed in terms of opposing views (such as when different research studies or researchers disagree with one another).

Overall, all literature reviews, whether they are written as a part of a larger work or as separate articles unto themselves, have a common feature: they do not present new research; rather, they provide an overview of prior research on a specific topic . 

How to Write a Literature Review

When writing a literature review, it can be helpful to rely on the following steps.  Please note that these procedures are not necessarily only for writing a literature review that becomes part of a larger article; they can also be used for writing a full-length article that is itself a literature review (although such reviews are typically more detailed and exhaustive; for more information please refer to the Further Resources section of this page).

Steps for Writing a Literature Review

1. Identify and define the topic that you will be reviewing.

The topic, which is commonly a research question (or problem) of some kind, needs to be identified and defined as clearly as possible.  You need to have an idea of what you will be reviewing in order to effectively search for references and to write a coherent summary of the research on it.  At this stage it can be helpful to write down a description of the research question, area, or topic that you will be reviewing, as well as to identify any keywords that you will be using to search for relevant research.

2. Conduct a literature search.

Use a range of keywords to search databases such as PsycINFO and any others that may contain relevant articles.  You should focus on peer-reviewed, scholarly articles.  Published books may also be helpful, but keep in mind that peer-reviewed articles are widely considered to be the “gold standard” of scientific research.  Read through titles and abstracts, select and obtain articles (that is, download, copy, or print them out), and save your searches as needed.  For more information about this step, please see the Using Databases and Finding Scholarly References section of this website.

3. Read through the research that you have found and take notes.

Absorb as much information as you can.  Read through the articles and books that you have found, and as you do, take notes.  The notes should include anything that will be helpful in advancing your own thinking about the topic and in helping you write the literature review (such as key points, ideas, or even page numbers that index key information).  Some references may turn out to be more helpful than others; you may notice patterns or striking contrasts between different sources ; and some sources may refer to yet other sources of potential interest.  This is often the most time-consuming part of the review process.  However, it is also where you get to learn about the topic in great detail.  For more details about taking notes, please see the “Reading Sources and Taking Notes” section of the Finding Scholarly References page of this website.

4. Organize your notes and thoughts; create an outline.

At this stage, you are close to writing the review itself.  However, it is often helpful to first reflect on all the reading that you have done.  What patterns stand out?  Do the different sources converge on a consensus?  Or not?  What unresolved questions still remain?  You should look over your notes (it may also be helpful to reorganize them), and as you do, to think about how you will present this research in your literature review.  Are you going to summarize or critically evaluate?  Are you going to use a chronological or other type of organizational structure?  It can also be helpful to create an outline of how your literature review will be structured.

5. Write the literature review itself and edit and revise as needed.

The final stage involves writing.  When writing, keep in mind that literature reviews are generally characterized by a summary style in which prior research is described sufficiently to explain critical findings but does not include a high level of detail (if readers want to learn about all the specific details of a study, then they can look up the references that you cite and read the original articles themselves).  However, the degree of emphasis that is given to individual studies may vary (more or less detail may be warranted depending on how critical or unique a given study was).   After you have written a first draft, you should read it carefully and then edit and revise as needed.  You may need to repeat this process more than once.  It may be helpful to have another person read through your draft(s) and provide feedback.

6. Incorporate the literature review into your research paper draft.

After the literature review is complete, you should incorporate it into your research paper (if you are writing the review as one component of a larger paper).  Depending on the stage at which your paper is at, this may involve merging your literature review into a partially complete Introduction section, writing the rest of the paper around the literature review, or other processes.

Further Tips for Writing a Literature Review

Full-length literature reviews

• Many full-length literature review articles use a three-part structure: Introduction (where the topic is identified and any trends or major problems in the literature are introduced), Body (where the studies that comprise the literature on that topic are discussed), and Discussion or Conclusion (where major patterns and points are discussed and the general state of what is known about the topic is summarized)

Literature reviews as part of a larger paper

• An “express method” of writing a literature review for a research paper is as follows: first, write a one paragraph description of each article that you read. Second, choose how you will order all the paragraphs and combine them in one document.  Third, add transitions between the paragraphs, as well as an introductory and concluding paragraph. 1
• A literature review that is part of a larger research paper typically does not have to be exhaustive. Rather, it should contain most or all of the significant studies about a research topic but not tangential or loosely related ones. 2   Generally, literature reviews should be sufficient for the reader to understand the major issues and key findings about a research topic.  You may however need to confer with your instructor or editor to determine how comprehensive you need to be.

Benefits of Literature Reviews

By summarizing prior research on a topic, literature reviews have multiple benefits.  These include:

• Literature reviews help readers understand what is known about a topic without having to find and read through multiple sources.
• Literature reviews help “set the stage” for later reading about new research on a given topic (such as if they are placed in the Introduction of a larger research paper). In other words, they provide helpful background and context.
• Literature reviews can also help the writer learn about a given topic while in the process of preparing the review itself. In the act of research and writing the literature review, the writer gains expertise on the topic .

Downloadable Resources

• How to Write APA Style Research Papers (a comprehensive guide) [ PDF ]
• Tips for Writing APA Style Research Papers (a brief summary) [ PDF ]
• Example APA Style Research Paper (for B.S. Degree – literature review) [ PDF ]

Further Resources

How-To Videos     

• Writing Research Paper Videos
• UCSD Library Psychology Research Guide: Literature Reviews

External Resources

• Developing and Writing a Literature Review from N Carolina A&T State University
• Example of a Short Literature Review from York College CUNY
• How to Write a Review of Literature from UW-Madison
• Writing a Literature Review from UC Santa Cruz  
• Pautasso, M. (2013). Ten Simple Rules for Writing a Literature Review. PLoS Computational Biology, 9 (7), e1003149. doi : 1371/journal.pcbi.1003149

1 Ashton, W. Writing a short literature review . [PDF]     

2 carver, l. (2014).  writing the research paper [workshop]. , prepared by s. c. pan for ucsd psychology.

Back to top

• Research Paper Structure
• Formatting Research Papers
• Using Databases and Finding References
• What Types of References Are Appropriate?
• Evaluating References and Taking Notes
• Citing References
• Writing Process and Revising
• Improving Scientific Writing
• Academic Integrity and Avoiding Plagiarism
• Writing Research Papers Videos

Psychology Resources: Peer-Reviewed Journal Articles

• Getting Started
• Reference Books
• Electronic Books
• Peer-Reviewed Journal Articles

What Is a Peer-Reviewed Article?

Peer Review is a process that journals use to ensure the articles they publish represent the best scholarship currently available. When an article is submitted to a peer reviewed journal, the editors send it out to other scholars in the same field (the author's peers) to get their opinion on the quality of the scholarship, its relevance to the field, its appropriateness for the journal, etc.

Publications that don't use peer review (Time, Cosmo, Salon) just rely on the judgement of the editors whether an article is up to snuff or not. That's why you can't count on them for solid, scientific scholarship. --University of Texas at Austin

The databases listed in this Research Guide are available only to Truckee Meadows Community College students, faculty and staff. You will need your TMCC credentials (Username and Password) to access them off-campus.

Examples of a magazine article and a peer-reviewed article (about the ridiculously broad topic "child development"):

• Magazine articles  (and web pages) tend to have lots of pictures and colors -  Giving Back...and Forward
• Peer-reviewed or empirical articles  report original research and have dense text and tables (and have lots of authors and more pages) - Associations Between Publicly Funded Preschool and Low-Income Children's Kindergarten Readiness: The Moderating Role of Child Temperament

Notice the difference?  The Giving Back article  features pictures and newsy content, while  Associations  has a longer title, charts and lots of dense text.

How to Read a Peer-Reviewed Journal Article

Tips for Reading a Research Article

Read the Abstract . It consists of a brief summary of the research questions and methods. It may also state the findings. Because it is short and often written in dense psychological language, you may need to read it a couple of times. Try to restate the abstract in your own nontechnical language. And just skim the Methods section. It is assumed that the audience is familiar with these methods, and it is often filled with highly technical jargon and statistical terminology.

• Read the Introduction . This is the beginning of the article, appearing first after the Abstract. This contains information about the authors' interest in the research, why they chose the topic, their hypothesis , and methods. This part also sets out the operational definitions of variables.
• Skim the Methods section. This section assumes you know what the authors are talking about, and you probably don't. This is graduate-level information.
• Skip the Results section. The language will be too technical and confusing.
• Read the Discussion section. The Discussion section will explain the main findings in great detail and discuss any methodological problems or flaws that the researchers discovered.
• Read the Conclusion/Discussion section. It's written in "mostly" plain English. It's the last section of the report (before any appendices) summarizes the findings, but, more important for social research, it sets out what the researchers think is the value of their research for real-life application and for public policy. This section often contains suggestions for future research, including issues that the researchers became aware of in the course of the study.
• Following the conclusions are appendices, usually tables of findings, presentations of questions and statements used in self-reports and questionnaires, and examples of forms used (such as forms for behavioral assessments).

Modified from Net Lab

See also the Evaluating Sources Guide , especially the How to Read and Evaluate Articles page .

Peer Review

Databases with Peer-Reviewed Journals on Psychology

Databases with peer-reviewed psychology journal content . 

In most of these databases, you must check Scholarly (Peer Reviewed) Journals, usually before you click Search, or modify the search after you have received your results.  Check with the Reference Librarian to determine if a journal article is peer reviewed.

How to Find a Peer-Reviewed Psychology Article Using TMCC Resources

The following collections include only psychology-related journals. Gale OneFile Psychology has a few psychology magazines as well, but defaults to peer-reviewed results.

• APA PsycArticles (EBSCO)
• Gale OneFile Psychology
• Psychology and Behavioral Science Collection  (EBSCO)

Search the library catalog ( a "discovery service") to search these collections and many more resources in a single search, including open-access resources and eBooks.

• << Previous: Electronic Books
• Next: Web Sites >>
• Last Updated: Dec 6, 2022 10:25 AM
• URL: https://libguides.tmcc.edu/psychology

SYSTEMATIC REVIEW article

Exercise promotes brain health: a systematic review of fnirs studies.

• College of P. E. and Sports, Beijing Normal University, Beijing, China

Exercise can induce brain plasticity. Functional near-infrared spectroscopy (fNIRS) is a functional neuroimaging technique that exploits cerebral hemodynamics and has been widely used in the field of sports psychology to reveal the neural mechanisms underlying the effects of exercise. However, most existing fNIRS studies are cross-sectional and do not include exercise interventions. In addition, attributed to differences in experimental designs, the causal relationship between exercise and brain functions remains elusive. Hence, this systematic review aimed to determine the effects of exercise interventions on alterations in brain functional activity in healthy individuals using fNIRS and to determine the applicability of fNIRS in the research design of the effects of various exercise interventions on brain function. Scopus, Web of Science, PubMed, CNKI, Wanfang, and Weipu databases were searched for studies published up to June 15, 2021. This study was performed in accordance with the PRISMA guidelines. Two investigators independently selected articles and extracted relevant information. Disagreements were resolved by discussion with another author. Quality was assessed using the Cochrane risk-of-bias method. Data were pooled using random-effects models. A total of 29 studies were included in the analysis. Our results indicated that exercise interventions alter oxygenated hemoglobin levels in the prefrontal cortex and motor cortex, which are associated with improvements in higher cognitive functions (e.g., inhibitory control and working memory). The frontal cortex and motor cortex may be key regions for exercise-induced promotion of brain health. Future research is warranted on fluctuations in cerebral blood flow during exercise to elucidate the neural mechanism underlying the effects of exercise. Moreover, given that fNIRS is insensitive to motion, this technique is ideally suited for research during exercise interventions. Important factors include the study design, fNIRS device parameters, and exercise protocol. The examination of cerebral blood flow during exercise intervention is a future research direction that has the potential to identify cortical hemodynamic changes and elucidate the relationship between exercise and cognition. Future studies can combine multiple study designs to measure blood flow prior to and after exercise and during exercise in a more in-depth and comprehensive manner.

1 Introduction

Exercise intervention is a convenient and adaptive approach to effectively enhance the cognitive function and emotion of individuals ( Verburgh et al., 2014 ; Kawagoe et al., 2017 ). Indeed, an increasing number of studies have demonstrated its beneficial effects on the healthy development of brain function ( Mandolesi et al., 2018 ; Chen, 2020 ). Recent studies have predominantly focused on the variations in cognitive function and brain functional activity, such as cerebral blood flow, before and after exercise intervention ( Fujihara et al., 2021 ; Kim et al., 2021 ; Zhang et al., 2021 ). Exploring real-time alterations in cerebral blood flow during exercise interventions can reveal hemodynamic changes ( Endo et al., 2013 ; Eggenberger et al., 2016 ; Carius et al., 2020 ) and execution ( Chen et al., 2017 ; Coetsee and Terblanche, 2017 ; Yang et al., 2020 ) and enhance our understanding of the mechanism underlying the effects of exercise on the brain.

The development of functional near-infrared spectroscopy (fNIRS) has enabled the exploration of hemodynamic changes in cerebral blood flow during exercise interventions. Specifically, it allows non-invasive monitoring of brain tissue oxygenation and hemodynamics ( Hoshi, 2005 ) and possesses distinct advantages over other neuroimaging modalities, such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). In addition, it balances both temporal resolution and spatial resolution and is comparatively less sensitive to motion ( Leff et al., 2011 ; Scarapicchia et al., 2017 ). Previous exercise intervention studies using fNIRS devices largely focused on exercise interventions such as walking ( Hamacher et al., 2015 ), posture, and walking ( Herold et al., 2017 ), which are practical within the laboratory setting. Given the diversity in experimental designs, the effects of exercise on the brain exhibit substantial variability.

The application of fNIRS in the field of sport and exercise psychology is heterogeneous due to variations in the utilization of fNIRS and experimental design. Therefore, to improve uniformity across different studies investigating the influence of exercise on brain functional activity, this review aimed to examine studies that employed near-infrared spectroscopy to detect changes in brain hemodynamics before, during, and after exercise. The purpose of this review was as follows: (1) offer recommendations regarding study designs and research related to fNIRS technology in exercise intervention studies; (2) analyze the designs of various exercise protocols and compare the results obtained after or during exercise; and (3) evaluate the characteristics of changes in cerebral blood flow after and during exercise. Overall, the objective of this review was to investigate the effects of various exercise interventions on alterations in brain functional activity from different perspectives (before and after exercise vs. during exercise).

This systematic review was performed and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines ( Page et al., 2021 ) and the Cochrane Collaboration Handbook ( Higgins et al., 2019 ).

2.1 Search strategy

Two reviewers (J.M.H. and T.X.) conducted an independent literature search to screen related studies. The third reviewer, Q.Q.S., resolved disagreements by arbitration.

Scopus, Web of Science, PubMed, CNKI, Wanfang, and Weipu databases were searched from inception to June 15, 2021. The keywords were ( Verburgh et al., 2014 ) exercise (physical activity, exercise, fitness, and sport) and (2) fNIRS (functional near-infrared spectroscopy). These terms were consistently applied across each database, serving as the main topic and free-text words in the title.

2.2 Eligibility criteria

Studies were considered eligible if they fulfilled the following criteria: (1) the subjects were healthy; (2) the articles were published in the English language or Chinese language in peer-reviewed journals; (3) exercise-related intervention studies utilizing large muscle groups of the whole body; and (4) at least one cerebral cortical blood flow change was assessed using fNIRS.

Our review focused on the effect of exercise interventions on common healthy participants. The exclusion criteria were as follows: unclear exercise protocols, exercise protocols not designed to improve brain or cognitive health (e.g., exercise test to exhaustion), and studies involving combined interventions (e.g., nutrition and cognition). To ensure generalizability, research utilizing clinical samples (e.g., overweight/obese) and those examining special groups (athletes or people with long-term exercise habits) were excluded.

2.3 Data extraction

Duplicated studies screened from the database search and reference lists were initially excluded. Next, the titles and abstracts were individually evaluated by two authors (J.M.H. and T.X.) to further exclude articles based on the eligibility criteria. Afterward, the two authors independently evaluated the articles. Disagreements were resolved by discussion and consensus among the three authors (Q.Q.S., J.M.H., and T.X.).

The two authors independently extracted the following data from eligible studies: (1) basic information, including the year of publication, participant characteristics, and study design; (2) study design, including study group or condition design, fNIRS state (resting-state or task-design), physiological outcome index, and behavioral outcome index; (3) fNIRS device parameters, including types of fNIRS devices, fNIRS sampling frequency, number of light emitting diodes, laser diodes, channels, fNIRS instrument location and area of interest, and position/arrangement and placement of the light source and detector; (4) the exercise intervention design, covering exercise type, exercise intervention period, frequency of exercise, exercise intensity, and single intervention duration; and (5) the primary endpoints of the studies.

2.4 Risk of bias assessment

The risk of bias in selected studies was independently assessed by two authors (J.M.H. and T.X.) using the Cochrane Collaboration Risk-of-Bias tool ( Higgins et al., 2011 , 2019 ). Disagreements were resolved by discussion with another author (Q.Q.S.) to achieve consensus (see Table 1 and Figure 1 ).

Table 1 . Quality of included studies.

Figure 1 . (A) Risk of bias ratings. (B) Risk of bias graph: percentage of trials with low, unclear, or high risk of bias ratings for each domain (Yanagisawa, 2010 ; Hyodo, 2012 ; Kurz et al., 2012 ; Endo et al., 2013 ; Byun et al., 2014 ; Wen et al., 2015a , b ; Auger et al., 2016 ; Eggenberger et al., 2016 ; Jiang and Wang, 2016 ; Kriel et al., 2016 ; Lambrick et al., 2016 ; Monroe et al., 2016 ; Chen et al., 2017 ; Coetsee and Terblanche, 2017 ; Kenville et al., 2017 ; Hashimoto et al., 2018 ; Kujach, 2018 ; Herold et al., 2019 ; Ji et al., 2019 ; Xu et al., 2019 ; Carius et al., 2020 ; Lai et al., 2020 ; Stute et al., 2020 ; Yang et al., 2020 ; Kim et al., 2021 ; Miyashiro et al., 2021 ; Zhang et al., 2021 ).

3.1 Study selection and characteristics

The search process is detailed in a flow chart illustrated in Figure 2 . The search strategy yielded 6,220 studies from the pre-defined databases. After excluding duplicates and reviewing the full text, 69 studies met the criteria based on the consensus reached by the reviewers. From these, 22 eligible articles were included in the first category (cerebral hemodynamics were measured before and after exercise) and 8 in the second category (cerebral hemodynamics were measured during exercise). Among them, one study was simultaneously in both categories.

Figure 2 . Flow chart.

Overall, 29 studies were included in the systematic review. Regarding the study region, 18 studies were conducted in Asia (Yanagisawa, 2010 ; Hyodo, 2012 ; Endo et al., 2013 ; Byun et al., 2014 ; Wen et al., 2015a , b ; Jiang and Wang, 2016 ; Chen et al., 2017 ; Coetsee and Terblanche, 2017 ; Hashimoto et al., 2018 ; Kujach, 2018 ; Ji et al., 2019 ; Xu et al., 2019 ; Lai et al., 2020 ; Yang et al., 2020 ; Kim et al., 2021 ; Miyashiro et al., 2021 ; Zhang et al., 2021 ), 5 in Europe ( Eggenberger et al., 2016 ; Kenville et al., 2017 ; Herold et al., 2019 ; Carius et al., 2020 ; Stute et al., 2020 ), 2 in Oceania ( Kriel et al., 2016 ; Lambrick et al., 2016 ), 3 in North America ( Kurz et al., 2012 ; Auger et al., 2016 ; Monroe et al., 2016 ), and 1 in Africa ( Coetsee and Terblanche, 2017 ). A total of 664 participants were examined, with sample sizes ranging from 10 to 67. The age of patients across studies spanned from 72.3 months to 75 years.

3.2 Quality of included studies

The details on the quality of the included studies in bias risk assessment are summarized in the supporting material. Of note, 28 studies did not provide details on selective reporting, 27 studies reported no other biases, 23 studies reported complete outcome data, 15 studies reported random sequence generation, 1 study reported allocation concealment, 1 study reported blinding of participants and personnel, and 1 study reported blinding of outcome assessment.

3.3 Study design

Twenty-two studies measured cerebral hemodynamics before and after exercise interventions, eight studies (including only adults) documented cerebral hemodynamics during the exercise intervention, and one study recorded cerebral hemodynamics before, during, and after the exercise intervention.

3.3.1 Study design encompassing measurements before and after exercise intervention

In this category (see Table 2 ), 22 studies provided information on cerebral hemodynamics and activity before and after exercise in the exercise group compared to levels measured before exercise in this group (14 studies) (Yanagisawa, 2010 ; Hyodo, 2012 ; Endo et al., 2013 ; Kujach, 2018 ; Miyashiro et al., 2021 ; Byun et al., 2014 ; Wen et al., 2015a , b ; Jiang and Wang, 2016 ; Lambrick et al., 2016 ; Hashimoto et al., 2018 ; Ji et al., 2019 ; Xu et al., 2019 ; Kim et al., 2021 ) or cerebral hemodynamics levels measured before and after exercise in another group (8 studies) ( Eggenberger et al., 2016 ; Chen et al., 2017 ; Coetsee and Terblanche, 2017 ; Lai et al., 2020 ; Stute et al., 2020 ; Yang et al., 2020 ; Fujihara et al., 2021 ; Zhang et al., 2021 ).

Table 2 . Study design of measurement before and after exercise intervention.

Only one study measured hemodynamic changes and activity in the resting state. In this particular study, baseline brain activity was assessed in the seated position for 5 min ( Endo et al., 2013 ).

All studies evaluated cortical hemodynamic activation using different task designs: 14 studies assessed inhibitory control (Flanker or Stroop task) (Yanagisawa, 2010 ; Hyodo, 2012 ; Endo et al., 2013 ; Byun et al., 2014 ; Wen et al., 2015a , b ; Jiang and Wang, 2016 ; Lambrick et al., 2016 ; Chen et al., 2017 ; Coetsee and Terblanche, 2017 ; Kujach, 2018 ; Ji et al., 2019 ; Yang et al., 2020 ; Fujihara et al., 2021 ), 3 studies examined working memory (N-back task) ( Lai et al., 2020 ; Stute et al., 2020 ; Kim et al., 2021 ), 1 study investigated attention (paced auditory serial addition test) ( Hashimoto et al., 2018 ), 1 study assessed cognitive reappraisal (implicit cognitive reappraisal task) ( Zhang et al., 2021 ), 1 study investigated the mirror neuron system (table-setting task) ( Xu et al., 2019 ), one study applied a concentration task (2-back task) ( Miyashiro et al., 2021 ), and one study assessed an exercise task (walking) ( Eggenberger et al., 2016 ). Interestingly, the majority of designs were block designs used in 12 studies ( Wen et al., 2015a , b ; Eggenberger et al., 2016 ; Jiang and Wang, 2016 ; Chen et al., 2017 ; Hashimoto et al., 2018 ; Ji et al., 2019 ; Xu et al., 2019 ; Lai et al., 2020 ; Yang et al., 2020 ; Zhang et al., 2021 ), whereas an event-related design was applied in four studies (Yanagisawa, 2010 ; Hyodo, 2012 ; Byun et al., 2014 ; Kujach, 2018 ). The remaining studies did not report the study design.

Nine studies carried out physiological measurements after exercise, among which seven studies measured heart rate (HR) (Hyodo, 2012 ; Endo et al., 2013 ; Wen et al., 2015a , b ; Lambrick et al., 2016 ; Kujach, 2018 ; Stute et al., 2020 ). Other physiological indicators, namely, heart rate reverse (HRR) ( Fujihara et al., 2021 ), mean arterial blood pressure (MAP) ( Endo et al., 2013 ), walking endurance ( Coetsee and Terblanche, 2017 ), oxygen intake (VO 2 ), minute ventilation (V E ), respiratory exchange ratio (RER), and energy expenditure, were exclusively analyzed in one particular study ( Lambrick et al., 2016 ).

Twelve studies investigated other behavioral indexes without examining cortical hemodynamic activation, among which eight studies measured Rating of Perceived Exertion (RPE) (Hyodo, 2012 ; Endo et al., 2013 ; Byun et al., 2014 ; Wen et al., 2015a , b ; Kujach, 2018 ; Stute et al., 2020 ; Fujihara et al., 2021 ), and two studies measured Two-Dimensional Mood Scale (TDMS) ( Byun et al., 2014 ; Kujach, 2018 ). Other behavioral indices, namely the Profile of Mood States (POMS) (short version) ( Chen et al., 2017 ), physical fitness ( Lai et al., 2020 ), Eston-Parfitt Scale ( Lambrick et al., 2016 ), Trail Making Test Part A (TMT-A), Trail Making Test Part B (TMT-B), Stroop Word-Color Interference task, Executive Control task, Montreal Cognitive Assessment (MoCA), Short Physical Performance Battery (SPPB), Falls Efficacy Scale International (FES-I), and geriatric depression scale (GDS), were solely analyzed in one study ( Eggenberger et al., 2016 ).

3.3.2 Study design involving measurements during exercise interventions

In this category (see Table 3 ), eight studies presented data on cerebral hemodynamic activation during the exercise intervention. Of note, all studies used exercise tasks to investigate the task design. Most studies either used block designs or did not specify the design, whilst few studies provided a detailed description of the design of exercise tasks. As anticipated, studies adopting a block design employed relatively short durations for each block, similar to the cognitive task, ranging from 20 to 40 seconds.

Table 3 . Study design of measurement during exercise interventions.

These exercise tasks, such as walking ( Kurz et al., 2012 ; Herold et al., 2019 ), cycling ( Endo et al., 2013 ; Auger et al., 2016 ; Kriel et al., 2016 ; Monroe et al., 2016 ), basketball slalom dribbling ( Carius et al., 2020 ), and barbell squats ( Kenville et al., 2017 ) were easy to perform in laboratory settings.

Furthermore, six studies conducted physiological measurements, of which five studies measured HR ( Endo et al., 2013 ; Kriel et al., 2016 ; Monroe et al., 2016 ; Herold et al., 2019 ; Carius et al., 2020 ). Some physiological indicators, namely MAP ( Endo et al., 2013 ), VO 2 ( Kriel et al., 2016 ), peak power output (PPO) ( Auger et al., 2016 ), power output ( Kriel et al., 2016 ), peak power ( Monroe et al., 2016 ), oxygen uptake ( Monroe et al., 2016 ), and LF/HF ratio ( Herold et al., 2019 ), were only analyzed in one specific study.

Five studies investigated the effects of exercise on behavioral indices that only appeared in one particular study, namely stride time interval ( Kurz et al., 2012 ), PRE ( Endo et al., 2013 ; Monroe et al., 2016 ), Profile of Mood States-Brie (POMS-B) ( Monroe et al., 2016 ), walking speed ( Herold et al., 2019 ), and visual analog scale (VAS) ( Carius et al., 2020 ).

3.4 fNIRS devices

3.4.1 measurements before and after exercise interventions.

Most included studies conducted fNIRS tests before and after a single, long-term exercise intervention using eleven different fNIRS devices. The device sampling frequency ranged from 1 to 50 Hz, with the majority of devices utilizing 16 emitting diodes and 16 laser diodes. The number of channels ranged from 2 to 48.

Four studies focused on multiple cortical areas ( Hashimoto et al., 2018 ; Xu et al., 2019 ; Stute et al., 2020 ). Among them, one study focused on motor areas, such as the premotor cortex (PMC) ( Xu et al., 2019 ; Stute et al., 2020 ), whilst two studies reported findings on the parietal cortex ( Xu et al., 2019 ; Stute et al., 2020 ), such as the inferior parietal cortex (IPC) and superior parietal lobule (SPL). Besides, two studies reported data on the prefrontal cortex ( Stute et al., 2020 ; Yang et al., 2020 ), one study investigated temporal areas ( Hashimoto et al., 2018 ), and one study assessed the inferior frontal gyrus (IFG). Nineteen studies exclusively assessed activation of the PFC (Yanagisawa, 2010 ; Hyodo, 2012 ; Endo et al., 2013 ; Byun et al., 2014 ; Wen et al., 2015a , b ; Eggenberger et al., 2016 ; Jiang and Wang, 2016 ; Lambrick et al., 2016 ; Chen et al., 2017 ; Coetsee and Terblanche, 2017 ; Kujach, 2018 ; Ji et al., 2019 ; Lai et al., 2020 ; Yang et al., 2020 ; Fujihara et al., 2021 ; Kim et al., 2021 ; Miyashiro et al., 2021 ; Zhang et al., 2021 ). Details are listed in Table 4 .

Table 4 . The fNIRS devices used in the study design for measurement before and after exercise intervention.

3.4.2 Measurements during exercise interventions

fNIRS was conducted during acute exercise interventions (see Table 5 ) using four distinct fNIRS devices were used. The device sampling frequency ranged from 1 to 1,000 Hz, and 8 emitting diodes and 8 laser diodes were employed in the majority of the studies. The number of channels ranged from 4 to 24.

Table 5 . The fNIRS devices used in the study design for measurement during exercise intervention.

Four studies focused on multiple cortical areas ( Kurz et al., 2012 ; Kenville et al., 2017 ; Herold et al., 2019 ; Carius et al., 2020 ). All studies focused on motor areas, such as the PMC, primary motor cortex (M1), supplementary motor area (SMA), and precentral gyrus (PCG) ( Kurz et al., 2012 ; Kenville et al., 2017 ; Herold et al., 2019 ; Carius et al., 2020 ). Among them, three studies reported findings on the parietal cortex ( Kurz et al., 2012 ; Kenville et al., 2017 ; Carius et al., 2020 ), such as the IPC and SPL, one study reported data on the PFC ( Herold et al., 2019 ), one study assessed brain areas related to auditory, frontal and visual functions ( Kurz et al., 2012 ; Kenville et al., 2017 ), including the primary somatosensory cortex (SSC) and, postcentral gyrus (POCG). Lastly, four studies reported data on PFC activation ( Endo et al., 2013 ; Auger et al., 2016 ; Kriel et al., 2016 ; Monroe et al., 2016 ).

3.5 Exercise intervention

All exercise interventions were categorized into three types according to their frequency and duration, regardless of study design. In other words, they were measured before and after long-term exercise interventions ( n = 5), measured before and after one-time exercise interventions ( n = 17), and measured during one-time exercise interventions ( n = 8). Among them, merely one study presented data before, during, and after acute exercise interventions. Major confounding factors adjusted for across these studies included exercise type, duration, intensity, frequency, and duration of activity.

3.5.1 Design of measurements before and after exercise interventions

Five studies investigated hemodynamic changes before and after long-term exercise interventions. Since before-after tests were used, the influence of exercise on fNIRS imaging results was not considered. A broad range of exercise interventions was implemented in these studies, including walking ( Coetsee and Terblanche, 2017 ), Tai Chi Chuan (TCC) ( Yang et al., 2020 ), Baduanjin mind-body (BMB) ( Chen et al., 2017 ), tennis ( Lai et al., 2020 ) or interactive cognitive-motor video game dancing (DANCE), and balance and stretching training (BALANCE) ( Eggenberger et al., 2016 ). The exercise intervention period lasted 8 weeks in most studies ( Eggenberger et al., 2016 ; Chen et al., 2017 ; Lai et al., 2020 ; Yang et al., 2020 ), with only one study extending to 16 weeks ( Coetsee and Terblanche, 2017 ). The frequency of exercise ranged from 2 to 5 times a week. Exercise intensity was classified into three categories: low, moderate, and high. Most studies employed moderate exercise intensity, except for one study that did not report data on intensity ( Chen et al., 2017 ) and one that used moderate-vigorous ( Coetsee and Terblanche, 2017 ) intensity. The duration of a single intervention ranged from 30 min to 90 min. Details are listed in Table 6 .

Table 6 . Exercise protocol used in the study design for measurement before and after long-term exercise intervention.

Seventeen studies measured cerebral blood flow before and after acute exercise interventions (see Table 7 ). Exercise types involved cycling and running in the majority of studies, with the exception of one study that incorporated push-ups ( Miyashiro et al., 2021 ). The duration of a single intervention varied from 10 min to 30 min, with seven studies employing a 10-min duration (Yanagisawa, 2010 ; Hyodo, 2012 ; Byun et al., 2014 ; Wen et al., 2015a , b ; Kujach, 2018 ; Kim et al., 2021 ), five studies opting for 15 min ( Endo et al., 2013 ; Hashimoto et al., 2018 ; Ji et al., 2019 ; Stute et al., 2020 ; Fujihara et al., 2021 ), two studies using a 20-min duration ( Jiang and Wang, 2016 ; Miyashiro et al., 2021 ), one study implementing a duration of 25 min ( Xu et al., 2019 ), and two studies extending to 30 min ( Lambrick et al., 2016 ; Zhang et al., 2021 ). Lastly, exercise intensity was mostly moderate.

Table 7 . Exercise protocol used in the study design for measurement before and after acute exercise intervention.

3.5.2 Design of measurements during acute exercise intervention

In the one-time exercise interventions, eight studies measured fNIRS during the exercise intervention (see Table 8 ). These studies mainly selected exercise interventions involving minimal head movement, such as cycling ( Endo et al., 2013 ; Auger et al., 2016 ; Kriel et al., 2016 ; Monroe et al., 2016 ), basketball slalom dribbling ( Carius et al., 2020 ), barbell squats ( Kenville et al., 2017 ), and walking ( Kurz et al., 2012 ; Herold et al., 2019 ). Moreover, most studies implemented cycling and walking interventions, while four studies used cycling ( Endo et al., 2013 ; Auger et al., 2016 ; Kriel et al., 2016 ; Monroe et al., 2016 ), and two studies used walking ( Kurz et al., 2012 ; Herold et al., 2019 ). The duration of the intervention ranged from 10 min to 25 min. While moderate intensity was used in most of the eight studies, some studies did not report exercise intensity and instead reported data on the exercise load.

Table 8 . Exercise protocol used in the study design measurement during acute exercise intervention.

3.6 Main results

A total of 29 studies investigated oxyhemoglobin (oxy-Hb), deoxyhemoglobin (deoxy-Hb), and total hemoglobin (total Hb) levels following exercise interventions. Specifically, three studies measured oxy-Hb, deoxy-Hb, and total Hb levels ( Auger et al., 2016 ; Lambrick et al., 2016 ; Coetsee and Terblanche, 2017 ), eight studies measured oxy-Hb and deoxy-Hb levels (Hyodo, 2012 ; Kurz et al., 2012 ; Endo et al., 2013 ; Byun et al., 2014 ; Monroe et al., 2016 ; Kenville et al., 2017 ; Herold et al., 2019 ; Carius et al., 2020 ), 16 studies measured oxy-Hb levels (Yanagisawa, 2010 ; Wen et al., 2015a , b ; Eggenberger et al., 2016 ; Jiang and Wang, 2016 ; Chen et al., 2017 ; Hashimoto et al., 2018 ; Kujach, 2018 ; Ji et al., 2019 ; Xu et al., 2019 ; Lai et al., 2020 ; Yang et al., 2020 ; Fujihara et al., 2021 ; Kim et al., 2021 ; Miyashiro et al., 2021 ; Zhang et al., 2021 ), one study measured deoxy-Hb levels ( Kriel et al., 2016 ), and one study computed the HBdiff (oxy-Hb minus deoxy-Hb) ( Stute et al., 2020 ).

3.6.1 Changes in brain functional activity before and after exercise interventions

Five studies investigated cerebral blood flow after long-term exercise interventions (see Table 9 ). One study measured oxy-Hb, deoxy-Hb, and total Hb levels ( Coetsee and Terblanche, 2017 ), whilst the remaining four studies measured oxy-Hb levels ( Eggenberger et al., 2016 ; Chen et al., 2017 ; Lai et al., 2020 ; Yang et al., 2020 ). After the long-term intervention, oxy-Hb levels were increased in the left PFC during the flanker and N-back tasks. Likewise, deoxy-Hb levels were increased in the left PFC during the Stroop task across almost all studies. One study used a walking task and described that oxy-Hb levels were higher in the left PFC and right PFC during walking.

Table 9 . Changes of brain functional activity before and after long-term exercise interventions.

Seventeen studies investigated brain function before and after the acute exercise intervention (see Table 10 ). One study measured oxy-Hb, deoxy-Hb, and total Hb levels ( Lambrick et al., 2016 ), three studies analyzed oxy-Hb and deoxy-Hb levels (Yanagisawa, 2010 ; Endo et al., 2013 ; Miyashiro et al., 2021 ), 12 studies measured oxy-Hb levels (Yanagisawa, 2010 ; Wen et al., 2015a , b ; Jiang and Wang, 2016 ; Hashimoto et al., 2018 ; Kujach, 2018 ; Ji et al., 2019 ; Xu et al., 2019 ; Fujihara et al., 2021 ; Kim et al., 2021 ; Miyashiro et al., 2021 ; Zhang et al., 2021 ), and one study calculated the HBdiff (oxy-Hb minus deoxy-Hb) ( Herold et al., 2019 ). After the acute intervention, eight articles explored changes in the Stroop task (Yanagisawa, 2010 ; Hyodo, 2012 ; Endo et al., 2013 ; Byun et al., 2014 ; Lambrick et al., 2016 ; Kujach, 2018 ; Ji et al., 2019 ; Fujihara et al., 2021 ) and observed an increase in oxy-Hb levels in the left dorsolateral prefrontal cortex (DLPFC) (Yanagisawa, 2010 ; Hyodo, 2012 ; Byun et al., 2014 ; Kujach, 2018 ; Ji et al., 2019 ), bilateral PFC ( Endo et al., 2013 ), right frontopolar area (FPA) (Hyodo, 2012 ), left FPA ( Byun et al., 2014 ), middle PFC ( Fujihara et al., 2021 ), right ventrolateral prefrontal cortex (VLPFC) ( Ji et al., 2019 ), and supraorbital ridge of the dominant side ( Lambrick et al., 2016 ). Meanwhile, three articles explored changes in flanker task performance and observed that exercise resulted in an increase in oxy-Hb levels in the bilateral DLPFC ( Wen et al., 2015b ), right DLPFC, right FPA ( Wen et al., 2015a ), and left FPA ( Wen et al., 2015b ). Similarly, three articles explored fluctuations in performance on the n-back task and noted a rise in oxy-Hb levels in the bilateral orbitofrontal cortex (OFC) ( Miyashiro et al., 2021 ) and left DLPFC ( Kim et al., 2021 ) and a concomitant decrease in oxy-Hb levels in the right DLPFC during moderate-intensity exercise interventions ( Kim et al., 2021 ), whilst HBdiff was decreased in both regions (frontal and parietal) and hemispheres (left and right) at almost all time points ( Stute et al., 2020 ). One study applied the table-setting task ( Xu et al., 2019 ) and found elevated oxy-Hb levels in the PMC, SPL, inferior frontal gyrus (IFG), and rostral inferior parietal lobule (IPL). Another study used the Paced Auditory Serial Addition Test ( Hashimoto et al., 2018 ) and revealed that oxy-Hb levels in the left PFC increased with different exercise intensities. Finally, one study used the implicit cognitive reappraisal task but did not identify specific regions of interest (ROIs) with changes in activity after exercise and reported elevated oxy-Hb levels in channels 11, 16, 21, 23, and 27 ( Zhang et al., 2021 ).

Table 10 . Changes of brain functional activity before and after acute exercise interventions.

3.6.2 Changes in brain functional activity during exercise interventions

Eight studies investigated hemodynamic changes during exercise interventions (see Table 11 ). Studies using a one-time exercise intervention measured cerebral blood flow during exercise ( Endo et al., 2013 ). Six studies analyzed both oxy-Hb and deoxy-Hb levels ( Kurz et al., 2012 ; Endo et al., 2013 ; Monroe et al., 2016 ; Kenville et al., 2017 ; Herold et al., 2019 ; Carius et al., 2020 ), one study exclusively analyzed oxy-Hb, deoxy-Hb, and total Hb levels ( Auger et al., 2016 ), and one study detected deoxy-Hb levels ( Kriel et al., 2016 ).

Table 11 . Changes of brain functional activity during acute exercise interventions.

During the acute intervention, eight studies explored changes in various exercise conditions. Three studies explored changes in different cycling intensities and noted that oxy-Hb levels were increased in bilateral PFC during exercise at the intensity of 60% EX max ( Endo et al., 2013 ) and that under all three conditions of rest, 40%, and 80% intensity levels. The subjects at rest exhibited significantly lower extracerebral and cerebral deoxy-Hb levels compared to values measured at the 80% intensity level of exercise. Furthermore, another study detected significant alterations in the hemodynamic response in almost all channels, with increased oxy-Hb in the bilateral SPL and left PMC after short-distance channel regression ( Kenville et al., 2017 ). At the same time, two studies explored the effects of different levels of exercise difficulties on cerebral hemodynamics. Four studies varied exercise intensity and found an increase in oxy-Hb levels in the bilateral PFC, bilateral M1, SSC, SMA, SPL, left IPL, and right PMC and a concurrent decrease in deoxy-Hb levels in the PFC. One study compared forward walking with backward walking and documented that oxy-Hb levels were higher in the SMA, PCG, and SPL, whereas deoxy-Hb levels were decreased in the SMA. Another study compared brain function in overground and treadmill conditions and observed an increase in oxy-Hb levels in the L-PFC, R-PFC, L-PMC, R-PMC, and B-SMA. One study explored the effect of active recovery on brain function and detected an increase in PFC activity. An earlier study employed BSDT to explore brain function under different basketball dribbling conditions and found that IL-M1 (deoxy-Hb levels) and contralateral PMC-SMA (deoxy-Hb levels) activities were decreased. One study investigated the effect of active recovery on brain function and evinced a significant interaction between condition and bout (every 30 seconds of high-intensity exercise is termed a bout) for mean changes in Δ[HHb] across bouts. Within conditions, significant increases in mean Δ[HHb] were observed across bouts, with values progressively increasing over time under HIITPASS and HIITACT conditions.

4 Discussion

4.1 study design.

Most task-related fNIRS studies aimed to detect activation before and after exercise. Notably, relatively few studies have been conducted on resting-state hemodynamics in this field; only one study examined changes in resting-state hemodynamics before and after exercise ( Endo et al., 2013 ) and did not identify significant changes. Studies performing measurements before and after exercise interventions provide evidence of the effects of exercise on an individual's neural mechanisms.

A minority of the included studies explored cerebral blood flow during exercise interventions ( Kurz et al., 2012 ; Endo et al., 2013 ; Auger et al., 2016 ; Kriel et al., 2016 ; Monroe et al., 2016 ; Kenville et al., 2017 ; Herold et al., 2019 ; Carius et al., 2020 ), all of which were task-designed and used exercise type as an exercise task. Nevertheless, it is worth mentioning that there are limited available exercise-type options, and they are relatively fixed. Most exercise tasks in those studies adopted the same design as cognitive tasks and controlled the duration of each trial to approximately 30 to 40 seconds ( Kurz et al., 2012 ; Kenville et al., 2017 ; Carius et al., 2020 ). However, from a practical perspective, exercise tasks lasting <30 seconds are challenging to implement. Thus, fNIRS should be implemented to develop an exercise task that combines the characteristics of the movement with the feasibility of fNIRS, and suitable methods should be selected to analyze the fNIRS data. Notably, although fNIRS is not as sensitive to motion artifacts as functional MRI and EEG, the rapid motion of any vibrating fiber may lead to substantial changes in the hemoglobin signal. Therefore, when designing exercise tasks, frequent and intense head movements should be avoided.

In addition, numerous studies have explored the effects of exercise interventions on behavioral and physiological indicators before, during, and after exercise. Most of the behavioral indicators are related to cognition (Yanagisawa, 2010 ; Hyodo, 2012 ; Endo et al., 2013 ; Byun et al., 2014 ; Wen et al., 2015a , b ; Jiang and Wang, 2016 ; Lambrick et al., 2016 ; Chen et al., 2017 ; Coetsee and Terblanche, 2017 ; Hashimoto et al., 2018 ; Kujach, 2018 ; Ji et al., 2019 ; Xu et al., 2019 ; Lai et al., 2020 ; Yang et al., 2020 ; Fujihara et al., 2021 ; Kim et al., 2021 ; Miyashiro et al., 2021 ) and emotion ( Zhang et al., 2021 ), while the primary physiological indicator is heart rate, which is closely related to exercise. However, studies examining the relationship between functional brain activity and the three behavioral and physiological domains were scarce.

Future studies can combine real-time changes in cerebral blood flow before, during, and after exercise in a more in-depth and comprehensive manner to establish the relationship between exercise and brain plasticity. In addition, the benefits and mechanisms of exercise interventions on the health of individuals should be explored from multiple perspectives, combining behavioral, physiological, and cerebral assessments.

4.2 fNIRS equipment and parameter settings

The included studies measured cerebral hemodynamics before and after exercise employing a relatively large number of channels. In studies designed to measure cerebral hemodynamics during exercise, the number of channels was relatively low, with the maximum number of channels in the included studies being 24 ( Kurz et al., 2012 ). Attributed to its task specificity, studies that measured cerebral hemodynamics during exercise intervention all used portable devices.

Besides, the selection of ROIs also varied with the study design. Studies designed to measure cerebral hemodynamic changes before and after exercise activity explored the effects of exercise on cognition, with the prefrontal lobe being a key region. In contrast, studies designed to measure cerebral hemodynamics during exercise intervention targeted more locations in the sensorimotor areas ( Endo et al., 2013 ; Auger et al., 2016 ; Kriel et al., 2016 ; Monroe et al., 2016 ; Kenville et al., 2017 ; Herold et al., 2019 ; Carius et al., 2020 ) or other brain areas.

Several exercise processes require a combination of physical activity and cognitive engagement. Hence, it is critical not only to place channels in the motor cortex but also to consider its impact on the prefrontal cortex. Advances in technology have led to an increase in the number of channels for portable devices, thus enabling the measurement of cerebral hemodynamics in multiple ROIs. Given the potential for signal quality issues for measurement during exercise, it is recommended to establish a short-distance channel and instruct participants to minimize head movement during the testing procedure.

4.3 Exercise intervention

Among studies designed to measure fluctuations in cerebral hemodynamics before and after exercise interventions, both long-term exercise interventions and short-term exercise interventions were available. Conversely, few long-term interventions were investigated, most likely due to challenges in conducting the assessment over extended periods. Exercise protocols for long-term interventions were flexible, featuring a diverse range of exercise protocols without overlap between studies and a uniform exercise frequency of 2 to 3 sessions per week ( Eggenberger et al., 2016 ; Coetsee and Terblanche, 2017 ; Lai et al., 2020 ; Yang et al., 2020 ). Only one study applied an exercise frequency of 5 times per week ( Chen et al., 2017 ). Similarly, exercise intensity and duration were relatively consistent, with all being of moderate intensity ( Eggenberger et al., 2016 ; Chen et al., 2017 ; Coetsee and Terblanche, 2017 ; Lai et al., 2020 ; Yang et al., 2020 ) and each session lasting over 30 min ( Chen et al., 2017 ; Lai et al., 2020 ; Yang et al., 2020 ).

At present, studies focusing on the effects of long-term exercise interventions on brain functional activity using fNIRS predominantly aim to detect changes in cognitive-task-related brain functional activity before and after exercise interventions but do not involve the detection of brain functional activity during the exercise task. In studies designed to measure changes in cerebral hemodynamics during acute exercise intervention, all exercise types were relatively easy to implement in the laboratory, requiring minimal activity space and offering flexibility, such as cycling and running. These factors may account for the fact that only three exercise programs were used. In the future, it might be possible to further explore changes in cerebral blood flow during other commonly practiced exercises that involve minimal head movement, such as Tai Chi Chuan and yoga.

The length of the single exercise session, on the other hand, did not exceed 30 min (Yanagisawa, 2010 ; Hyodo, 2012 ; Endo et al., 2013 ; Wen et al., 2015b ; Lambrick et al., 2016 ; Coetsee and Terblanche, 2017 ; Hashimoto et al., 2018 ; Kujach, 2018 ; Ji et al., 2019 ; Xu et al., 2019 ; Stute et al., 2020 ; Fujihara et al., 2021 ; Miyashiro et al., 2021 ; Zhang et al., 2021 ), with the majority lasting for only 10 min (Yanagisawa, 2010 ; Hyodo, 2012 ; Byun et al., 2014 ; Wen et al., 2015a , b ; Kujach, 2018 ; Kim et al., 2021 ). Consequently, changes in cerebral hemodynamics during prolonged exercise sessions remain enigmatic.

To explore hemodynamic changes during exercise, future studies may extend the duration of exercise sessions to 30 min to determine the effect of long exercise session durations on cerebral cortical blood flow.

4.4 Effect of exercise on cerebral cortical blood flow changes

In the exploration of task designs, the activation of hemoglobin was frequently discussed, with most studies using oxygenated hemoglobin as an indicator to evaluate brain activation (Yanagisawa, 2010 ; Wen et al., 2015a , b ; Eggenberger et al., 2016 ; Jiang and Wang, 2016 ; Chen et al., 2017 ; Hashimoto et al., 2018 ; Kujach, 2018 ; Ji et al., 2019 ; Xu et al., 2019 ; Lai et al., 2020 ; Yang et al., 2020 ; Fujihara et al., 2021 ; Kim et al., 2021 ; Miyashiro et al., 2021 ; Zhang et al., 2021 ).

Achieving consistent results with long-term exercise interventions was challenging due to the diversity of the tasks performed. Interestingly, although the adopted tasks were different, most studies identified changes in the left PFC during inhibitory control tasks ( Chen et al., 2017 ; Coetsee and Terblanche, 2017 ; Yang et al., 2020 ). In general, higher hemoglobin activity was observed in the cortical region after the cessation of one-time exercise compared with baseline cortical activity, similar to changes in cortical activation during cognitive tasks. Besides, alterations in the prefrontal cortex and motor cortex were observed during one-time exercise sessions.

Considering that fNIRS signals are significantly impacted by physiological artifacts of the system ( Caldwell et al., 2016 ), the influence of exercise on the cerebral cortex is assumed to mainly arise from physiological confounders after exercise. According to the findings of a methodological investigation, fNIRS signals are affected by systemic physiological artifacts for up to approximately 8 min after stopping cycling for 10 min ( Byun et al., 2014 ). Therefore, the results of studies that performed fNIRS tests after exercise (<~8 min) should consider the effect of physiological confounds. However, a reasonable hypothesis suggests that some fNIRS signals observed post-exercise originated from neuronal activity. The entire prefrontal cortex should be impacted by systemic physiological changes if the greater cortical activity observed after exercise is primarily a result of systemic physiological artifacts. The fact that greater cortical activity was observed only in specific regions of the prefrontal cortex rather than the entire prefrontal cortex is significant because it supports the hypothesis that the fNIRS signals were at least partially derived from neuronal activity. The positive neurobehavioural correlation between cognitive ability and cortical activity in various regions of the prefrontal cortex further supports this notion.

The review indicated that exercise interventions alter oxygenated hemoglobin levels in the prefrontal cortex and motor cortex, which are associated with improvements in higher cognitive functions such as inhibitory control and working memory. These findings further support the hypothesis that exercise promotes changes in the prefrontal and motor cortex, which may be key regions for exercise to promote brain health. To deepen our understanding of the fundamental processes by which exercise modifies the brain, future studies should concentrate on alterations in cerebral blood flow during physical exertion.

4.5 Limitations

Nevertheless, this review has limitations that merit acknowledgment. To begin, this review only explored studies involving exercise durations over 10 min, but future studies should expand their scope to explore durations of exercise that contribute to brain health. The primary purpose of this review was to conduct subgroup analysis based on different research designs (measuring fNIRS before, during, and after exercise intervention). Future analyses should aim to stratify group populations or exercise types into different subgroups. The systematic search, dual-author screening, eligibility assessment, and quality appraisal were employed to minimize biased selection of studies, whilst dual-author auditors ensured a thorough search. However, this review was not preregistered and not available for inspection by other researchers, and future reviews can be registered with PROSPERO in advance. A meta-analysis was not conducted due to the high degree of heterogeneity across studies and the low number of studies examining each outcome.

5 Conclusions and future perspectives

Overall, fNIRS is a promising neuroimaging tool that provides insights into changes in exercise-induced hemodynamics before, during, and after exercise interventions.

Studies using fNIRS to measure cerebral blood flow before and after exercise interventions have predominantly incorporated relatively few long-term interventions and predominantly featured short-term interventions. These studies have mainly focused on the effects of exercise on brain activity during cognitive tasks such as inhibitory control. However, the study equipment and study intervention protocols were less stringent and relatively more flexible. All studies on this topic have focused on changes in oxygenation in the prefrontal area of interest.

Few of the included studies measured hemodynamic changes during exercise, with all of them employing short-term interventions, relatively fixed exercise intervention protocols, minimal head motion, and short exercise durations. Of note, the exercises were easy to execute in the laboratory setting, and the fNIRS systems had a limited number of channels.

Exercise protocols for long-term interventions encompassed a wide range of exercise protocols and an exercise frequency ranging from 2 to 3 sessions per week. The exercise intensity was moderate, and the duration of a single session exceeded 30 min. Exercise types for long-term interventions were those that necessitated minimal space and were flexible, such as cycling and running. The duration of a single exercise session did not exceed 30 min, with most sessions lasting for 10 min. Finally, exercise intensities were maintained at a moderate level.

The results of this review signaled that exercise interventions alter oxygenated hemoglobin levels in the prefrontal cortex and motor cortex, which are associated with improvements in higher cognitive functions such as inhibitory control and working memory. The frontal cortex and motor cortex may be key regions for exercise to promote brain health. Future research could further focus on changes in cerebral blood flow during exercise to better understand the underlying mechanisms by which exercise influences brain function.

Moreover, due to its insensitivity to motion, the fNIRS is ideally suited for research during exercise interventions. It is paramount to thoroughly scrutinize the study design, fNIRS device parameters, and exercise protocols. Cerebral blood flow during exercise intervention represents a future research direction that has the potential both to identify cortical hemodynamic changes and elucidate the relationship between exercise and cognition.

Future studies can combine two study designs that measure blood flow before, during, and after exercise in a more in-depth and comprehensive manner. Additionally, they can utilize multiple indicators, such as functional connectivity, to accurately reflect the effects of exercise on brain functional networks.

Data availability statement

The original contributions presented in the study are included in the article/ Supplementary material , further inquiries can be directed to the corresponding authors.

Author contributions

Q-QS: Writing – original draft, Writing – review & editing. J-MH: Writing – review & editing. TX: Writing – review & editing. J-YZ: Writing – review & editing. D-LW: Writing – review & editing. YY: Writing – review & editing. RL: Writing – review & editing. Z-LX: Writing – review & editing. H-cY: Supervision, Writing – review & editing, Funding acquisition, Project administration. LC: Supervision, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Key Program of the National Social Science Foundation (Grant No. 19ATY010).

Acknowledgments

We extend our gratitude to Professor Shaojun Luy from the College of P. E. and Sports, Beijing Normal University, China, for his invaluable assistance with the Tai Chi (Bafa Wubu) intervention.

Conflict of interest

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

Publisher's note

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2024.1327822/full#supplementary-material

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Keywords: exercise, physical activity, brain plasticity, functional near-infrared spectroscopy (fNIRS), review

Citation: Shen Q-Q, Hou J-M, Xia T, Zhang J-Y, Wang D-L, Yang Y, Luo R, Xin Z-L, Yin H-c and Cui L (2024) Exercise promotes brain health: a systematic review of fNIRS studies. Front. Psychol. 15:1327822. doi: 10.3389/fpsyg.2024.1327822

Received: 25 October 2023; Accepted: 19 March 2024; Published: 10 April 2024.

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Copyright © 2024 Shen, Hou, Xia, Zhang, Wang, Yang, Luo, Xin, Yin and Cui. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Heng-chan Yin, yinhengchan@bnu.edu.cn ; Lei Cui, cuilei@bnu.edu.cn

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

The Impact of Peer Review on Writing in a Psychology Course: Lessons Learned

• Naureen Bhullar Indian Institute of Management Bangalore (IIMB) Author
• Karen C. Rose Widener University Author
• Janine M. Utell Widener University Author
• Kathryn N. Healey Widener University Author

The authors assessed the impact of peer review on student writing in four sections of an undergraduate Developmental Psychology course. They hypothesized that peer review would result in better writing in the peer review group compared to the group with no peer review. Writing was rated independently by two instructors who were blind to the conditions. The results showed a pattern opposite to this hypothesis, with the no-peer review group performing significantly better than the peer review group. This enhancement in the performance of the no-peer review group was reminiscent of the Hawthorne effect. Surveys also were obtained from both groups regarding their perceptions about the process and use of different tools (for example, informal peer review and writing centers). Implications for the future use of peer review in writing among undergraduates are discussed.

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Recovery from gaming addiction: A thematic synthesis

Vol.18, no.2 (2024).

https://doi.org/10.5817/CP2024-2-5

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• Author biographies
• Additional information

In recent years the field of gaming addiction has experienced an upsurge in empirical studies on various treatment approaches. Despite the advances in our understanding of how gaming addiction can be treated, the wider concept of recovery continues to be under-researched. The purpose of this review was to explore how individuals addicted to video games experience the process of recovery. Seven databases were systematically searched for qualitative studies. Eight studies representing the views of 225 participants were included in the review. Study findings were exported into NVivo software and analysed using Thematic Synthesis. Six themes were constructed: “developing awareness”, “deciding to change”, “the process of quitting”, “the challenges of quitting”, “recovery never stops” and “treatment for gaming addiction”. Except for the last, themes represent processes that most participants had gone through during recovery, though significant variation was found in how each process was experienced. In addition to overcoming addiction symptoms, recovery involved management of concomitant problems and various negative consequences of excessive gaming. Regarding practice implications, current findings suggest that treatment programs should adopt a multidimensional approach, providing evidence-based treatments, help for co-occurring problems, as well as pre- and post-treatment support to accommodate individuals at different stages of recovery. Further research is needed to expand our understanding of recovery, for instance, the impact of gender differences or how recovery experiences change based on different recovery goals (i.e., abstinence or reduced play time).

Ksenija Vasiljeva

Department of psychology, teesside university.

Ksenija Vasiljeva is a doctoral student on the program of Doctorate in Counselling Psychology at Teesside University. Her research interests include gaming addiction, depressive disorders, and cultural studies.

Alex Kyriakopoulos

Alexandros Kyriakopoulos is a Senior Lecturer in Counselling Psychology at Teesside University. He is interested in research regarding applied cyberpsychology; e-parenting; e-wellbeing; and using e-interventions to improve mental health and wellbeing.

Christopher Wilson

Department of psychology, school of social sciences, humanities and law, teesside university, middlesbrough, united kingdom.

Christopher Wilson is currently an Associate Professor in Psychology at Teesside University. He obtained his PhD from Maynooth University in 2010 where he was awarded a scholarship for doctoral research by the Irish Research Council (IRCHSS). His research interests include: Risk-taking and decision-making; attention and perception; Behaviour and cognition in virtual and online environments.

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Coyne, S. M., Stockdale, L. A., Warburton, W., Gentile, D. A., Yang, C., & Merrill, B. M. (2020). Pathological video game symptoms from adolescence to emerging adulthood: A 6-year longitudinal study of trajectories, predictors, and outcomes. Developmental Psychology , 56 (7), 1385–1396. https://doi.org/10.1037/dev0000939

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Authors’ Contribution

Ksenija Vasiljeva: conceptualization, methodology, investigation, formal analysis, writing—original draft. Alexandros Kyriakopoulos: conceptualization, writing—review & editing, supervision. Christopher Wilson: supervision.

Editorial Record

First submission received: October 19, 2023

Revisions received: March 3, 2024

Accepted for publication: March 11, 2024

Editor in charge: Lenka Dedkova

Introduction

According to Newzoo report, there were 3.38 billion video game players globally in 2023, and this number is forecast to rise to 3.79 billion by 2026 (Newzoo, 2023). In the UK, 56% of adults and 91% of children play video games (Ofcom, 2023). Similarly, the statistics for the US are 62% and 76% respectively (Entertainment Software Association, 2023). Although gaming is an important recreational activity for many people, some individuals may play excessively to the detriment of their health, wellbeing, interpersonal relationships, and occupational opportunities (Mihara & Higuchi, 2017; Sugaya et al., 2019). A longitudinal study by Coyne et al. (2020) found that participants who played games excessively had elevated levels of anxiety, depression, aggression, shyness, and cell phone use. Problematic gaming behaviour has been linked to adverse health outcomes, such as poorer sleep quality, reduced physical activity and hand and wrist pain (Männikkö et al., 2020). Socially, excessive gaming can result in deteriorating personal relationships, occupational problems, and overall worse quality of life (Carmona & Whiting, 2021).

Mounting evidence of the negative impact of excessive gaming led the World Health Organisation to include a diagnosis of “Gaming disorder” (GD) in the latest edition of International Classification of Diseases (ICD-11; WHO, 2018, 2023). According to ICD-11, three criteria must be met over a period of 12 months for a diagnosis of GD: impaired control over gaming behaviour (e.g., duration, termination), gaming is given priority over other life interests and activities, and gaming is continued or escalates despite the occurrence of negative consequences (WHO, 2023). Stevens and colleagues (2021) estimated that the global prevalence of GD is 3.05%, which was reduced to 1.96% when including studies that met strict sampling criteria. A meta-analysis by H. S. Kim et al. (2022) similarly found that the global prevalence of GD was 3.3%. Both studies found that young age and male gender were associated with higher prevalence.

Recovery is a “process of behavior change characterized by improvements in biopsychosocial functioning and purpose in life” (Witkiewitz et al., 2020, p. 9). It is a multidimensional and complex phenomenon. Leamy and colleagues (2011), for instance, argue that recovery consists of the following five dimensions: connectedness (relationships, community participation), hope and optimism about the future (motivation to change, aspirations), identity (rebuilding identity, overcoming stigma), meaning in life (spirituality, quality of life) and empowerment (personal responsibility, control over life). This theoretical conceptualisation is supported by the experiences of individuals in recovery. For instance, in Dekkers et al. (2021) study participants’ recovery encompassed developing a new sense of self, feeling supported by others, finding safe places of growth, and working towards a better future. In a study of participants in residential treatment, recovery was conceived as abstinence coupled with positive gains on at least one of the following psychosocial dimensions: psychological, physical health, spiritual, occupational, social relations, or daily life functioning (Costello et al., 2020).

Our evolving understanding of recovery has directly shaped the development of treatment approaches. Historically, addiction was conceptualised as a physical disease that deprived people of free will; hence, it was believed that recovery was only achievable through medical interventions (Yates & Malloch, 2010). Treatment often took the form of acute care (i.e., crisis support and inpatient treatment), a person was considered “cured” from addiction at the end of treatment (White & Kelly, 2011). Although the disease model is still prevalent, the emphasis has shifted to viewing addiction as a chronic condition (Best & Lubman, 2012). This re-conceptualisation brought with it the idea that addiction should be treated similarly to other chronic conditions by offering ongoing long-term support (Scott & Dennis, 2011). Recovery-oriented treatments focus on early detection and intervention, provision of community services, peer support groups and residential programs (Kelly & White, 2011). Although acute care is still used at crisis points, preference is given to administering treatment in the community and helping individuals achieve their personal recovery goals. This aligns with service users’ experience of recovery as a long-term process that is more about personal growth and psychosocial improvement than achieving a definitive “cure” (Costello et al., 2020; Neale et al., 2015; Pickering et al., 2020). Therefore, the way we treat addiction has changed as a result of re-conceptualising recovery as a chronic condition.

Within the field of gaming addiction, the notion of recovery is only starting to take shape. Gavriel-Fried et al. (2023) conducted a scoping review, investigating how GD treatment studies ( n = 47) conceptualised recovery. Most studies ( n = 42) used terms “decrease/reduction” and/or “increase/improvement” of symptoms to describe changes in participants’ GD before and after interventions. Although the term “recovery” was mentioned by 18 studies, only 5 of them discussed it in relation to study aims, hypotheses, sample characteristics or findings. These studies conceptualised recovery as reduction in GD scores and/or no longer meeting the criteria for a GD diagnosis. Despite the limited use of the term “recovery”, all 47 studies included at least one psychological ( n = 32), neurobiological ( n = 20) and/or social ( n = 7) measure to evaluate the impact of treatment. It was found that certain personality traits (impulsivity, higher aggression, harm avoidance) and comorbid disorders (ADHD, depression) hinder patient recovery, whereas treatment (psychotherapy, medication) and social factors (family support) promote recovery. Although the review showed that the efficacy of GD treatments was evaluated against various psychosocial and neurobiological dimensions, there was no fundamental agreement on what “recovery” is beyond a person no longer meeting the diagnostic criteria for GD. Furthermore, only 13 studies in this review measured positive improvement (e.g., wellbeing, family cohesion, quality of life). This contrasts with the notions of recovery discussed earlier that advocate for focusing less on pathology and amelioration of disease in favour of helping individuals develop strength and resilience to live the life of greatest value to them (Witkiewitz et al., 2020).

Cognitive-Behavioural Therapy (CBT) is currently considered to be the gold standard of GD treatment (Stevens et al., 2019). Therapy covers topics such as stimulus control, cognitive restructuring, behavioural activation, relapse prevention and others (Hofstedt et al., 2023). Treatment usually lasts between 8 to 12 weeks, with follow up at 2 to 6 months (Stevens et al., 2019). Although CBT is an effective treatment, the way it is often delivered as a standalone intervention with no further support provided aligns more closely with the acute, rather than the chronic care provision pathway. J. Kim et al. (2022) evaluated different psychological interventions for GD and concluded that CBT+mindfulness, CBT+family therapy, and mindfulness were superior treatments to standalone CBT. Addiction recovery research emphasises that the best treatment outcomes are achieved when adopting a holistic approach that combines personal, relational, and contextual aspects (Dekkers et al., 2021) which may explain why combined treatments were more effective than standalone CBT in J. Kim et al. (2022) study.

It has been argued that Internet-based addictions (gaming, gambling, pornography, shopping, social media) share common aetiology, developing through behavioural conditioning, with predisposing neurobiological, individual and social factors (James & Turney, 2017; Kotyuk et al., 2020; Laier & Brand, 2014; Sun & Zhang, 2021). CBT for Internet Addiction (includes behaviour modification, cognitive restructuring, harm reduction) has been recommended as the treatment of choice for all Internet-based addictions, irrespective of the type (Goslar et al., 2020; Sherer & Levounis, 2022). However, even if Internet-based addictions develop through similar mechanisms, they are qualitatively and experientially different. Gaming is unique in that it offers highly rewarding, immersive and interactive environments, creating a more appealing reality than individuals’ day-to-day experiences (King & Delfabbro, 2018). Some individuals may use in-game achievements to compensate for their perceived offline “failures”, becoming overly invested in gaming to satisfy psychological needs (Snodgrass et al., 2013). Being part of an online gaming community is important for the development of gaming-related identity (Sirola et al., 2021). However, it was also shown that online communities play an important role in informing GD (Gandolfi et al., 2023), and community engagement can motivate escalation of gaming and in-game purchasing behaviours (Sirola et al., 2021). Furthermore, unlike many other addictive behaviours, gaming is accessible to very young children. Earlier onset of habitual weekly gaming (5 years old and younger) increases the risk of problematic gaming in adolescence (Nakayama et al., 2020). From these examples it can be seen that despite sharing aetiology with other addictions, there are key differences that may affect treatment prospects and recovery.

To reiterate, having a robust definition of recovery is fundamental for developing better treatments. However, at this point in time, there is no shared understanding of what recovery from GD looks like, and whether it is different to other Internet-based addictions. Our current conceptualisation of recovery is based on quantitative evidence which is limited to investigating changes in psychosocial and neurobiological symptoms (Gavriel-Fried et al., 2023). However, as discussed above, recovery may encompass processes not easily captured by psychometric measures, such as identity change, meaning in life and empowerment (Leamy et al., 2011). To the best of the author’s knowledge this is the first review sought to develop a conceptualisation of GD recovery by employing qualitative methodology to investigate the topic of recovery inductively, drawing on lived experiences of individuals affected by GD. The guiding review question was:

RQ1: How do individuals addicted to playing video games experience recovery?

Search Strategy

The literature review protocol was registered with PROSPERO. The review adopted a systematic search methodology in accordance with PRISMA guidelines (Page et al., 2021). The following databases were searched: PubMED, PsycINFO, PsycArticles, Scopus, Cinahl, Web of Science and MedLine. Search terms were chosen according to selection criteria using SPIDER framework (Cooke et al., 2012). Key terms included a combination of synonyms for “gaming”, “addiction”, “interview”, “experience” and “qualitative” (see Table A1 for a full list of terms). The searches were run on 1 st November 2022. References were exported into Mendeley. Articles were sifted by title and abstract, articles that clearly did not meet the selection criteria were excluded. Full-text articles were retrieved for the remaining records and assessed for eligibility. Additional articles were identified through reference lists of included studies and Google Scholar. The search and sifting were carried out by the first author.

Selection Criteria

Studies were included if they represented original qualitative research and had information relevant to recovery from gaming addiction. Studies could use any method of data collection or analysis. Importantly, studies had to represent opinions, insights and experiences of people who are or have been addicted to video games. Sample could include individuals of any age and could be clinical (diagnosis of GD) or non-clinical (self-reported addiction to video games). Since this review was primarily interested in people’s experiences of gaming addiction, rather than homogeneity of results, a diagnosis of GD was not considered essential. No restrictions were set based on the year of publication. Only peer-reviewed articles in English were included. Studies were excluded, if they: 1. had quantitative design, 2. focused on individuals without gaming addiction (e.g., healthy gamers, parents and spouses of people with GD, health professionals), 3. had no information relevant to recovery from gaming addiction, 4. thesis or grey literature.

Data Extraction

Descriptive data was entered into an Excel spreadsheet. The following information was extracted: Title, Authors, Year, Country, Aims/Questions, Theoretical framework, Context, Method of recruitment, Participants ( N; age: M, range; N : female), Method of data collection, Method of analysis, Notes. See Table A3 for the full data extraction table. To prepare qualitative data for analysis, findings of included studies were copied into separate Word documents and uploaded to Nvivo 12 . In this study, “findings” were considered to be everything in the “Results” section, including text, tables, participant quotes and authors’ interpretations. However, since the review is interested in the recovery process from the perspective of individuals addicted to gaming, accounts of parents and health professionals were not included in the analysis, even if they were present in the “Results” section.

Data Analysis

Data was analysed using thematic synthesis (Thomas & Harden, 2008). During the planning stages of the review the first author scoped the literature to determine whether there was sufficient material for a review. The extent to which recovery was discussed differed greatly across studies. Some studies had a separate theme dedicated to it, while others mentioned it in passing, meaning that findings would need to be re-analysed to identify processes relevant to recovery. Therefore, chosen analytical strategy had to be interpretative, rather than descriptive or aggregative. Thematic synthesis is a flexible interpretative approach, suitable for drawing together common elements from heterogenous studies (Lucas et al., 2007). Furthermore, it aims to produce new, higher-order constructs that go beyond individual findings of primary studies (Xiao & Watson, 2019). Given that the aim of this review was to develop a conceptualisation of GD recovery, thematic synthesis was considered to be a suitable method of analysis.

Analysis was conducted in three stages: free line-by-line coding, organisation of free codes into descriptive themes and creation of analytical themes. Line-by-line coding is inductive analysis of each sentence to consider its meaning and content. This process ensures that all data is carefully considered, even if not all sentences get coded (Thomas & Harden, 2008). More than one sentence could make up a code, as well as several codes could be applied to one sentence. For instance, a quote “I’m really torn though, I lead a guild and have [a lot] of friends in-game, but I’d like to quit and live my life” (Carmona & Whiting, 2021) was coded as “Deciding to quit is difficult” and “Social pressure to continue playing”. Each transcript was analysed in turn. Although codes could be used across transcripts, the emphasis during this stage of analysis was on capturing the richness and diversity of data in codes. Line-by-line coding yielded 268 codes. After this all transcripts were reviewed again to a) check the validity of codes, and b) determine if any additional coding was needed. This process resulted in multiple codes being added, removed or amended, bring the final number of codes to 290.

Codes were grouped together based on similarities in meaning. A new label was given to each group, making them into descriptive themes. For instance, “Deciding to quit is difficult”, “Weighing up advantages and disadvantages” and “Decreasing gaming time as a conscious choice” were grouped into “Deciding to quit” descriptive theme. There were 17 descriptive themes. In order to “go beyond” the data to generate new insights and concepts (Thomas & Harden, 2008) the first (KV) and the second (AK) authors, guided by the research question, reviewed descriptive themes in terms of their relevance to the process of recovery from GD. Several themes representing recovery were identified, such as “Process of realization”, “Deciding to quit”, “Social support” and “Quit or reduce playtime”. Through this process it was noted that the unifying characteristic of most studies was that participants went through similar stages on their journey through gaming addiction. Therefore, the analytic themes were developed around these stages, trying to capture key (sometimes different or conflicting) processes of each stage.

Included Studies

The search returned 9,262 records. After sifting by title and abstract, 31 records remained. Excluded records focused on other behavioural addictions, sports, serious games, gaming as a therapeutic tool, exergaming, and quantitative studies on GD. After assessing full-text records, 5 records met the inclusion criteria. Additional 3 records were identified through reference lists of included studies and Google Scholar. In total, 8 studies were included in the review. These studies were marked with an (*) in the list of references. See Figure 1 for PRISMA diagram.

Figure 1. PRISMA Diagram.

Study characteristics can be found in Table A3. Studies were published in the period between 2006 and 2022. Research was conducted in seven different countries: Spain, Chile, The Netherlands, Finland, New Zealand, Singapore, and Germany. In total, there were 225 participants. The sample was predominantly male (92%). Reported mean age ranged from M = 15.3 (Sim et al., 2021) to M = 30.4 (Karhulahti et al., 2022). Most studies recruited participants who were either undergoing or completed treatment. Participants were recruited through various institutions (hospitals, treatment programs, addiction centres) and in the community (adverts, flyers, posters, snowballing). Two studies obtained data from online forums, so no participant characteristics were obtained (Carmona & Whiting, 2021; Chappell et al., 2006).

All studies employed qualitative methodology. Data was collected through semi-structured interviews or forum posts. Data was analysed using Grounded Theory, Interpretative Phenomenological Analysis, Thematic Analysis or Qualitative Content Analysis. The primary aim of five studies was to understand gaming addiction through a qualitative lens from the perspective of individuals affected by it (Beranuy et al., 2013; Carmona & Whiting, 2021; Chappell et al., 2006; Haagsma et al., 2013; Karhulahti et al., 2022). Two studies evaluated GD treatment programs for adolescents (Sim et al., 2021; Wendt et al., 2021). One study sought to identify optimal components of a health care system for early intervention and treatment of gaming addiction (Park et al., 2021).

Quality Appraisal

Quality appraisal was conducted using the Critical Appraisal Skills Program checklist for qualitative studies (CASP, 2018). The purpose of quality appraisal is to evaluate the rigour of studies included in the review. CASP checklist consists of 10 questions evaluating studies’ methodology, ethical issues and application of findings. Questions are answered either with “yes”, “can’t tell” or “no”. If a question was answered with “yes”, then the criterion was met. If a question was answered with “can’t tell” or “no”, the criterion was not met. CASP guidelines do not provide a scoring system, therefore, studies’ quality was judged by the authors based on the number of met criteria (CASP, 2018). No studies were excluded based on their quality.

Three studies met between 9–10 criteria and were considered of excellent quality (Carmona & Whiting, 2021; Karhulahti et al., 2022; Park et al., 2021). Two studies met between 7–8 criteria and were considered of good quality (Sim et al., 2021; Wendt et al., 2021). Finally, three studies met 6 criteria and were considered of medium quality (Beranuy et al., 2013; Chappell et al., 2006; Haagsma et al., 2013). The best reported elements were whether qualitative methodology was appropriate to answer the research question(s), whether data was collected in a way to answer the research question(s), and whether there was a clear statement of findings; all studies met these criteria. The least reported element was whether the relationship between the researchers and participants was considered; only two studies mentioned it (Carmona & Whiting, 2021; Karhulahti et al., 2022). See Table A2 for full details.

Thematic Synthesis

Articles included limited data on the process of recovery. Only one study had a theme dedicated to recovery (Carmona & Whiting, 2021), and another one had a theme on decreasing gaming time (Haagsma et al., 2013). Other studies discussed recovery in the context of participants’ wider stories of addiction (Beranuy et al., 2013; Chappell et al., 2006; Karhulahti et al., 2022; Park et al., 2021) or in terms of treatment impact (Sim et al., 2021; Wendt et al., 2021). Recovery processes were captured in six themes: “Developing awareness”, “Deciding to change”, “The process of quitting”, “The challenges of quitting”, “Recovery never stops” and “Treatment for gaming addiction”. An overview of themes and subthemes with contributions of each study can be found in Table A4.

Developing Awareness

Developing an understanding that gaming was a problem often appeared as the first step to recovery. For a small number of people, the realization came instantly, like a lightbulb moment: “Then it hit me I had been obsessing over this game in the same fashion for a long time” (Haagsma, 2013). However, the majority described a process of dawning realization, whereby they developed an awareness of their problematic behaviour over time. This was often driven by the negative consequences of excessive gaming, such as loss of relationships, failing at school/university, losing work, deteriorating physical and mental health.

Awareness could also be developed through changing sense of identity or life goals. Individuals realised that their personality has changed because of gaming: “I went from a person who gained energy, pleasure and release from being sociable … to someone who finds it taxing to spend many hours around family and friends” (Carmona & Whiting, 2021). Recognising the destructive impact of gaming on identity drove individuals to re-evaluate their engagement with it: “One day I just started to feel sick, I had to leave and go find myself again … I’ve only now come to understand what [those years] cost me” (Karhulahti et al., 2022). Furthermore, individuals realised that gaming stood in the way of their life aspirations, they would be unable to achieve their goals if they continued playing excessively.

In some cases, awareness was developed when individuals’ relationship with gaming changed. Players realised that despite enjoying gaming initially, at some point it stopped being fun and satisfying:

“I eventually did get burned out and had thoughts of quitting the game entirely... I hadn’t even realized I had been playing in such an obsessive fashion up to that point.” (Haagsma, 2013)

This was supported by a realisation that achievements and skills obtained through gaming were meaningless outside of it. No matter how well individuals performed in gaming, most of the skills and knowledge could not be transferred to real life.

Deciding to Change

Becoming aware of the detrimental impact of gaming led individuals to a shift in perspective to the importance of gaming in their lives, and a subsequent decision to change their behaviour. People used different ways to facilitate this process, e.g., weighing up advantages and disadvantages of playing or re-evaluating their current priorities in life. But in the end, they realised that gaming was an obstacle to whatever they wanted to achieve in real life. Individuals developed a sense that they were missing out on life, realising that other activities were more valuable than gaming: “I can do other things, which is more meaningful than playing computer for the whole day, like reading a book, doing my homework, and having family time” (Sim et al., 2021). Individuals compared virtual and real lives, and the former started losing its attractiveness, consolidating participants’ decision to change their behaviour:

“I have begun to realise how much of a time sink EQ [ EverQuest ] is. I often think about how having a meaningful romantic relationship just doesn’t fit with EQ dominating my life. For the last few months I have let EQ be my social life, and it’s just recently begun to dawn on me how pathetic it’s become.” (Chappell et al., 2006)

Although many participants did want to fully quit gaming, others wanted to achieve a reduction in gaming time. Individuals who wanted to quit gaming felt that it stopped being a meaningful activity for them, and if they continued, it would only be taking away from their life. They have already lost many valuable things, like time, relationships, or health, so they no longer wanted gaming influencing their lives any further: “[I am] trying to quit, why, [because] this game [has] made me lonely, so god damn lonely that it hurts and I break down crying sometimes” (Carmona & Whiting, 2021). On the other hand, some individuals wanted to continue playing, but change how they engaged with gaming. In Sim et al. (2021) study five out of ten participants chose to reduce gaming time rather than abstain, and in Wendt et al. (2021) study a treatment goal was to teach participants how to control their gaming behaviour, rather than to promote abstinence.

The Process of Quitting

Some individuals were able to quit nearly instantly by crossing gaming out of their lives (Carmona & Whiting et al., 2021; Chappell et al., 2006). There seemed to be very little time passing between an individual deciding to quit and doing it: “I went back to the dorm, said goodbye to my EverQuest friends, and logged off for the last time” (Chappell et al., 2006). Others, by contrast, found it very difficult to cut all ties with gaming, instead going through a process of slowly withdrawing from it: “I have been slowly weaning away but thinking about anything but the game has been hard” (Carmona & Whiting, 2021).

Change was achieved via different ways. Help was sought from hospitals, counselling, addiction recovery centres and specialised programs. Practical strategies for quitting were finding activities to replace gaming, getting rid of gaming accounts and hardware, installing applications blocking access to gaming content, quitting with another person, restrictions set by parents, and taking on real-life responsibilities (Carmona & Whiting, 2021; Haagsma et al., 2013; Karhulahti et al., 2022). Social support was mentioned as an important factor that helped individuals follow through with quitting (Carmona & Whiting, 2021; Karhulahti et al., 2022). Several individuals attributed their successful recovery efforts to receiving support from family, partners or friends: “I am so lucky that my sister loved me enough to save me from myself” (Chappell et al., 2006).

The Challenges of Quitting

Social ties.

One of the key elements that made quitting difficult was the social pull of the game. As many individuals became high-ranking players, they felt that their teammates were relying on them to successfully complete in-game challenges. Hence, there was a deep sense of responsibility, not wanting to let other people down: “I’m really torn though, I lead a guild and have [a lot] of friends in-game, but I’d like to quit and live my life” (Carmona & Whiting, 2021).

Furthermore, individuals forged ties of friendship with other players. When trying to quit, therefore, they often faced pressure from their online friends to come back to the game and play again: “The other day one of my clan told me to connect and play with them. I told them that not yet, maybe later” (Beranuy et al., 2013). One study noted that gaming culture discouraged help-seeking and normalised playing long hours, making individuals feel ashamed of coming out to their online friends about having a “gaming problem” (Park et al., 2021).

However, the forging of strong in-game social ties came at the expense of negative real-life consequences. Gaming provided access to online social communities that were friendly and welcoming, a haven from the outside world. On the flip side, becoming heavily involved with online communities often meant that players withdrew from real-life social interactions. Through the process of quitting some individuals had to face up to their loneliness and a lack of social support in real life. Although some tried to keep in touch with their online friends outside of gaming, this often proved difficult. Gaming was their only common interest, and speaking to these friends rekindled the desire to play:

“A friend just asked me to come and play again, as there was an event that gives a free mount [virtual game item] … I’ve already decided to never play again let alone collect any mounts, and then I hear that I might lose a mount that I’d never ever want, and I will instantly start wanting it and feel disappointed.” (Karhulahti et al., 2022)

An Exciting Activity

Even though it was mentioned in “Developing awareness” that over time gaming stopped being fun, this was not the case for all individuals. Though they spent years playing, some still found gaming to be the most engaging and stimulating activity in their lives. Despite understanding the negative consequences it brought them, there was sadness to having to give up gaming: “When I play, it’s like all the cells in my body scream ‘this is what we were made for!’ It is such a pleasurable, activating and stimulating feeling … and that’s very sad” (Karhulahti et al., 2022). These people recognised that they could not play casually without getting too invested and spiralling into addiction. This made the process of quitting even more difficult, as these individuals felt that they may never encounter an activity that would be as fun and fulfilling as gaming.

An Ingrained Habit

Over the months and years of playing, gaming has become an integral part of peoples’ lives, and their main way of managing and occupying free time. Individuals often resorted to gaming when feeling bored or lonely: “One day, in which you’re home alone, have nothing to do and nobody calls you … I’ve just moved for my basic necessities, take food and that´s all, just play” (Beranuy et al., 2013). Thus, an important element of recovery was to engage in new activities and develop new hobbies to fill the free time left after quitting. However, doing so was often challenging, as individuals found they had reduced motivation and focus, felt controlled by gaming, or no longer had real-life friends to spend time with.

A Way of Coping

People who started playing as a way of escaping the troubles of real life continued to rely on gaming as their main way of dealing with life’s difficulties. Although gaming helped individuals cope, it came at the cost of spiralling into addiction again: “[But] I didn’t even get any interviews, so then one day I went to see a friend with my laptop, started playing again and … that’s a blur for next half a year again” (Karhulahti et al., 2022). Thus, it was difficult to stop gaming in the absence of more adaptive coping mechanisms.

Recovery Never Stops

Coming to terms with loss.

In some accounts there was a sense that the action of giving up gaming was, in some ways, only the first step to recovery (Carmona & Whiting, 2021; Chappell et al., 2006; Karhulahti et al., 2022). What followed was a long process of rebuilding one’s life anew. Quitting gave individuals space and time to fully acknowledge how gaming has impacted their lives. Often this meant recognising and coming to terms with the negative consequences. Many regretted picking up gaming in the first place, because the impact of addiction was damaging and long-lasting. As an example, a participant in Chappell et al. (2006) study lost his job and family because of the addiction, but even after quitting he could not put things right, as his wife met somebody else, and he rarely saw his children.

Abstinence Is the Only Option

Although some players chose a reduction of gaming time as their recovery goal (Sim et al., 2021; Wendt et al., 2021), others were adamant that complete abstinence from gaming was their only choice (Chappell et al., 2006; Karhulahti et al., 2022). Individuals often framed their decision in biological terms, such as having an “addictive personality” or something being wrong with their brain: “I’ve been trying to tackle other elements around addiction such as alcohol and marijuana, but the truth is I have an addictive personality that extends to many areas of my life” (Park et al., 2021). Such language demonstrates individuals’ conviction that gaming addiction is (at least partly) biologically determined, so they would be unable to control their gaming behaviour if they tried playing casually.

Desire to Continue Playing

Despite recognising the destructive impact of gaming, the desire to play never fully went away. Staying clean from gaming was seen as a constant battle, a difficult choice that individuals had to make every day, prioritising their wellbeing over the thrill of gaming:

“It is so difficult, maybe because I am weak, but even though I consider my life to be successful and happy, there is always EQ , sitting there at the back of my mind, and the desire to play is strong, almost like the pull of the ring in LOTR [ Lord of the Rings ].” (Chappell et al., 2006)

In addition to these cravings, some people experienced withdrawal-like symptoms. One player mentioned that not having the ability to play made them feel nervous and anxious. When not playing, individuals were preoccupied with thoughts about gaming. The urge to play again could be triggered by talking to friends who still play, watching videos about gaming, visiting gaming-related websites, thinking about how good gaming felt and others.

Relapse was mentioned briefly by four studies (Bearnuy et al., 2013; Carmona & Whiting. 2021; Haagsma et al., 2013; Karhulahti et al., 2022). Individuals relapsed after not playing for weeks or even months. Some felt that they could play casually but quickly spiralled into addiction again. Others came back for their social communities, or after being convinced to play again by friends. Relapse could also be triggered by the same factors that started the addiction, e.g., to escape stress or conflict. Several individuals described having an on-off relationship with gaming, stopping for a short while, then coming back to it and playing even more intensely to make up for the progress lost while not playing. Several players mentioned that they came back after a new expansion or update was released.

Being Free From Gaming

One study described the positive impact of recovery (Carmona & Whiting, 2021). The recovery journey was long and difficult for many, so upon finally quitting they felt a great sense of relief and achievement. Individuals described a sense of liberation: they escaped the clutches of gaming and were now free to spend their time on more meaningful activities. People felt excited and hopeful, looking forward to a future without the influence of gaming.

Treatment for Gaming Addiction

Barriers to treatment-seeking.

Various psychological, social and physical barriers have been cited as reasons for not seeking help for problematic gaming (Park et al., 2021). The addiction itself could dominate a person’s life to such an extent that seeking help became difficult:

“The fact that I did not have the willpower to break away made me feel weak and made me even more depressed! I was on the verge of going to my doctor and asking for some pills, or therapy, or something… but that would break into my WoW-time!” (Carmona & Whiting, 2021)

Individuals reported feeling a deep sense of shame and embarrassment at admitting that they had a “gaming problem”. They felt that gaming was a “soft addiction”, not as severe as substance misuse or other mental health issues. This view was reinforced by perceived negative social attitudes and stigma towards problematic gaming.

Many practical barriers were reported, such as a lack of knowledge/availability of services; the cost of paying for services; services perceived to be geared towards treating adolescents, rather than adults, and others. Some individuals who tried accessing support felt that services did not understand what GD was and how to treat it. Lack of services reinforced the idea that problematic gaming was not a problem worthy of treatment.

Helpful Treatment Components

Two studies (Sim et al., 2021; Wendt et al., 2021) explored adolescents’ experiences with group treatment for gaming addiction. Participants thought that it was important for therapists to be understanding, non-judgemental and trustworthy. In Sim et al. (2021) study therapists acted as role models, for instance, showing how to play games in a healthy way by regulating gaming time during therapeutic group gaming sessions. Regarding treatment elements, participants found the following useful: psychoeducation, understanding the roots of problematic gaming, exploring individual problems, time management, developing a daily plan, and having individual therapy sessions. Most participants were in favour of the group setting of therapy, as it encouraged participants to build real-life social connections and support each other on their recovery journey. Furthermore, when someone successfully achieved a goal, it motivated others to work harder. Participants engaged with treatment better if they were allowed to have fun in sessions, play together and engage in other activities, such as outdoors games.

Unhelpful Treatment Components

The unhelpful treatment components primarily concerned group composition and dynamics (Sim et al., 2021; Wendt et al., 2021). Treatment group in Wendt et al. (2021) study had mixed group composition (in-patient and community) which participants found difficult to adjust to. Some individuals found it challenging to be open in front of other group members due to being shy or having social anxiety. There was also a concern that therapists did not equally allocate time to every person, instead focusing on two or three individuals each session. Some participants were dissatisfied if modules covered in treatment were not personally relevant.

Positive Impact of Treatment

Individuals reported that treatment was helpful in a range of ways, for instance, it improved their insight and made them feel more mature (Sim et al., 2021; Wendt et al., 2021). Treatment helped participants to develop skills for controlling their gaming behaviour (such as time management), and, as a result, reduced gaming time. It improved family relations and academic performance. Group composition gave participants an opportunity to create real-life friendships and learn new skills through activities.

The purpose of this review was to understand how individuals addicted to video games experience recovery. Of eight studies included in the review, only two discussed recovery in a separate subtheme (Carmona & Whiting, 2021; Haagsma et al., 2013), showing that this concept has been under-explored in qualitative research. The core finding of the review is that individuals go through five processes on the road to recovery from gaming addiction: develop awareness of the problem, make a decision to change their behaviour, employ change strategies, address setbacks and difficulties, and work on sustaining recovery.

Even though these processes could be found across studies, there was significant variation in how participants experienced them. Awareness of gaming being a problem could come like a lightbulb moment or develop gradually over time. A decision to change could be motivated by positive aspirations (wanting to get a new relationship, complete a degree) or negative consequences (failing at school, relationship breakdown). Individuals differed based on whether they wanted to quit gaming and abstain from it in the future or reduce play time and maintain a healthy level of engagement. Finally, when it came to quitting, some individuals were able to quit instantly (i.e., decided to quit and did it straight away), whereas for others it was a long and drawn-out process. Thus, though unified by the same processes, there was significant diversity to how recovery from gaming addiction was experienced.

Reiterating the definition given in the introduction, recovery is a “process of behavior change characterized by improvements in biopsychosocial functioning and purpose in life” (Witkiewitz et al., 2020, p. 9). This definition can be applied to the concept of recovery developed in this review. Individuals sought to change their gaming behaviour by using various strategies (e.g., seeking treatment, selling gaming accounts/hardware), and their life improved as a result. The benefits of overcoming gaming addiction were improved relationships, better academic performance, improved self-awareness, and others. Although the definition implies that a shift in individuals’ purpose in life accompanies behaviour change, current findings suggest that it happens prior to change occurring. After acknowledging that gaming has had a negative impact on their lives, participants re-evaluated their life priorities and aspirations which often led to a realisation that gaming was an obstacle to achieving meaningful things in life, and this became motivation for behaviour change.

Recovery processes identified in this review appear to align with the stages of change proposed by the Transtheoretical Model (TTM; Prochaska et al., 1992). The model has been used extensively to study recovery in other addiction fields and to inform practice (Ferreira et al., 2015; Kushnir et al., 2016; Tseng et al., 2022), meaning that this conceptual framework can also be adapted to gaming addiction and to the present findings. The model holds that every individual goes through five stages while trying to change their behaviour: precontemplation, contemplation, preparation, action, and maintenance (see Table 1). Progression between stages is facilitated by 10 change processes that represent a shift in individuals’ thinking, behaviour or affect. For instance, moving from “precontemplation” to “contemplation” stage is facilitated by a process of consciousness-raising as individuals become aware of the causes, consequences, and cures of their problems (Prochaska & DiClemente, 2005, p. 150).

“Precontemplation” stage was not identified in this study, as most studies included participants who were in recovery or undergoing treatment, indicating that they were already at later stages of change. Theme “Developing awareness” represents the “contemplation” stage when individuals realised that their gaming was problematic. “Deciding to change” was also assigned to “contemplation” stage, as it encompasses individuals thinking about overcoming their addiction, weighing up advantages and disadvantages of doing so. Unfortunately, with the available data, it was not possible to determine whether a decision to change may have also represented an active intention to change behaviour, otherwise this theme would have been assigned to “preparation” stage. Themes “The process of quitting” and “The challenges of quitting” represent the “action” stage of the model. Individuals actively attempted to change their behaviour to overcome the addiction, and encountered various difficulties while doing so, such as struggling to let go of online friendships, finding activities to replace gaming, or developing new coping strategies. Finally, “Recovery never stops” is about “maintenance”, continuing to engage in new behaviours, coming to terms with loss, battling old habits, and managing the desire to play and relapse.

Table 1. Stages of Change and Current Themes.

TTM conceptualises change as a spiral, arguing that achieving long-lasting “maintenance” may require multiple attempts and relapses, it is an iterative process (Prochaska et al., 1992). The present findings support this notion with participants describing episodes of relapse, as well as encountering various challenges that hindered their progression through the stages. Even though studies in the current review tended to mention relapse briefly, research shows that relapse is a core element of addiction recovery, it allows individuals to develop greater awareness of their problematic behaviour and learn better coping strategies (Kougiali et al., 2017).

However, present findings also differ from the TTM conceptualisation of change. Beyond the spiral of relapse, progression through stages is sequential and linear within the model (Prochaska et al., 1992). However, in line with the model’s criticisms (Sussman et al., 2022), current findings indicate that individuals may not necessarily progress through all five stages on their road to recovery. For instance, it could be argued that individuals who quit gaming instantly upon realising it was problematic have gone straight from “contemplation” to “action” stage, missing the “preparation” stage. Similarly, although TTM argues that developing awareness of one’s problematic behaviour is crucial for recovery (Prochaska et al., 1992), other researchers have argued that insight may not be necessary for change to occur (Sussman et al., 2022; White, 2007). Supporting this notion, treatment evaluation studies in this review (Sim et al., 2021; Wendt et al., 2021) contributed limited data to “Developing awareness” theme, indicating that insight may not be necessary to engage in gaming addiction treatment.

While TTM focuses on internal motivation for change, this review found that individuals’ social environments could have a significant impact on the course of recovery. For instance, being part of an online social community and/or not having real-life friends could make it difficult to quit. Previous research has established that removing distractions and creating a secure environment (i.e., going into residential treatment or taking time off work) was important for stability during the initial stages of recovery (Martinelli et al., 2023). Another study found that supportive relationships and mutual aid communities are key to successful recovery (Goshorn et al., 2023). Many individuals in this review felt that social support received from family and friends was imperative for successful recovery.

In line with previous research (Watkins et al., 2021), current review found that identity transformation played an important role in the process of recovery. Addiction “spoils” identities, and recovery involves “identity reverting” (re-engaging in roles and responsibilities individuals had prior to addiction), as well as establishing new roles (developing relationships, gaining employment and education; Reith & Dobbie, 2012). Changing identity from a substance “user” to “non-user” not only allows individuals to think and feel differently about themselves, but also gives a qualitatively different way of relating to others (Martinelli et al., 2023). Present findings similarly indicate that recovery from gaming addiction involves transformation of identity. For instance, over the course of recovery some individuals developed a belief that they were biologically susceptible to addiction. This had implications for self-perception (i.e., their sense of agency, control over gaming behaviour), how individuals made sense of their experiences, and recovery behaviours (i.e., choosing abstinence over reduced play time to stay well). Although the process of identity transformation is considered to be occurring organically in the course of recovery (Watkins et al., 2021), current findings suggest that participants may also consciously engage in it. At the stage of “Developing awareness” individuals actively considered the negative impact of excessive gaming on their identity compared to how they were like before becoming addicted (e.g., becoming reclusive, losing social skills). This realisation caused a dissatisfaction with the present state of self which contributed to individuals’ desire to change their behaviour.

The process of recovery from gaming addiction is comparable to recovery from gambling addiction. Recognition that gambling is problematic comes over time as the negative consequences become more severe (Stefani, 2023), and developing insight is a prerequisite for recovery (Pickering et al., 2020). Identity transformation plays a central role in gambling recovery, with many participants talking about having an “authentic” self and a “gambling” self that are in conflict with each other (Pickering et al., 2020; Reith & Dobbie, 2012; Stefani, 2023). The behaviour of the “gambling” self is perceived as incongruous with people’s values and aspirations which facilitates the process of change (Pickering et al., 2020; Reith & Dobbie, 2012; Stefani, 2023). Similarly to the current study, participants in recovery from gambling underline the significance of having supportive social networks (Nilsson et al., 2023; Pickering et al., 2020).

The question of whether gaming should be studied separately to other Internet-based addictions was raised earlier in the paper. While there is a lot of overlap in how gambling and gaming addictions are experienced, there are also clear differences. This review found that in-game social connections played an important role in the maintenance of gaming addiction. By contrast, engaging with gambling online communities appears to be a protective factor against excessive play (Sirola et al., 2021). Also notably, money plays a crucial role in gambling addiction, but it has not been identified as an issue in the current study. Among participants with gambling addiction many recovery goals are tied to gaining financial stability, employment, and paying off loans (Oakes et al., 2019; Pickering et al., 2020; Reith & Dobbie, 2012). Therefore, it can be seen that the process of recovery from gaming addiction shares similarities with recovery from gambling, but there also unique features.

There were several recovery processes mentioned by the gambling studies that have not been identified by the current study. Participants in Pickering et al. (2020) study spoke about the daily struggles of recovery, such as managing gambling triggers, urges and cravings. Relapse also plays a much bigger role, being a source of significant anxiety, it instilled feelings of hopelessness around the possibility of change (Nilsson et al., 2023; Pickering et al., 2020). Erroneous cognitions and thinking biases were shown to interfere with gambling recovery (Oakes et al., 2019; Pickering et al., 2020). These experiences may be relevant to gaming addiction too, but further research is needed in this area.

Practice Implications

A unique finding of this review was that that individuals’ recovery efforts could be compromised by online friendships, social isolation, a feeling of boredom, a lack of daily structure and other activities/hobbies, and poor coping mechanisms. To address these complexities, treatment interventions must adopt a multidimensional approach. A comprehensive service should offer individuals access to reliable information on GD, validated screening tools, evidence-based treatments, peer support networks, and assistance for co-occurring issues (Park et al., 2021).

Recognizing boredom as a significant barrier to recovery, it is advisable to establish non-gaming activity groups for patients. Furthermore, given that boredom is an obstacle to recovery, it would be useful to organise non-gaming activity groups for patients. There groups would give individuals opportunities to expand their real-life social networks and develop hobbies outside of gaming. In addition, it may also be helpful to introduce pre- and post-treatment support. The review found that some individuals may struggle with insight into their problematic behaviour and may feel ambivalent about change. Research shows that having strong internal, but not external, motivation to change increases the likelihood of being further along the TTM change continuum and feeling more ready to change (Kushnir et al., 2016). Thus, a pre-treatment intervention could provide psychoeducation and screening to raise clients’ self-awareness and foster their internal motivation to engage in treatment. Post-treatment support could provide help around adjustment to life without the addiction, managing cravings to play and relapse. A multi-stage treatment model like this one could address the needs of clients at different points in their recovery journey.

Theme “Recovery never stops” demonstrates that even after individuals quit or improve their gaming behaviour, their recovery continues in the form of coming to terms with loss, managing withdrawal symptoms, cravings to play and relapse. A gambling addiction follow-up study showed that 43.7% of patients relapsed at least once, the average time of a relapse occurring was 1.39 years after first observed recovery (Grall-Bronnec et al., 2021). However, a recent review of the efficacy of cognitive-behavioural therapy for gaming addiction found that follow-ups were conducted 8 weeks to 6 months after treatment (Stevens et al., 2019) which in light of the previous findings may be insufficient to detect relapse and long-term impact of treatment. Therefore, it is suggested that treatments ought to incorporate relapse prevention strategies, and follow-ups should be conducted at longer time-periods.

Limitations and Future Directions

There were several limitations to the current study. The first limitation was that this review was carried out and written primarily by the first author which could have introduced bias into the review process. The second author was involved as the supervisor of the project and provided support around refining superordinate themes and preparing the manuscript. To minimise risk of bias, the first author kept a reflective diary throughout the study, and both authors held regular meetings to discuss how the review process was going.

The second limitation was that the majority (92%) of participants were male. A meta-analysis by Stevens et al. (2021) established that GD male to female prevalence ratio is 2.5:1. None of the studies in this review met this participant ratio, and three studies had male-only samples (Beranuy et al., 2013; Haagsma et al., 2013; Sim et al., 2021). There are sex differences in how GD is experienced, for instance, male gamers score higher on impulsivity and aggression scales, whereas female gamers score higher on depression and social phobia scales (Marraudino et al., 2022). Therefore, it has been suggested that GD treatment should target executive functioning in men and emotional distress in women (Dong & Potenza, 2022). However, further research is needed to test these assumptions and develop better understanding of how recovery processes may vary for men and women.

The third limitation was that the majority of included studies did not discuss recovery directly. Instead, information relevant to recovery had to be inferred from participants’ narratives of addiction. The implications of this for current findings are that, firstly, potentially relevant recovery processes or experiences may not had been mentioned, and secondly, given limited amount of available data, the importance of certain elements could have been over- or under- emphasised. To illustrate, many participants in included studies chose to quit gaming which could give the impression that abstinence is the preferred recovery goal. However, GD treatment studies measure recovery in terms of symptom reduction (Gavriel-Fried et al., 2023) which aligns more with reduced play time recovery goal, rather than abstinence. A gambling addiction treatment follow-up study found that out of 70% of participants who no longer met the criteria for a gambling disorder, 40% abstained and 30% continued to gamble without adverse consequences (Müller et al., 2017). Therefore, it would be useful to explore how the experiences of people who chose reduced play time versus quitting recovery goals may be different. For instance, it is unclear whether experiences identified in themes “The process of quitting” and “The challenges of quitting” would still apply to those who chose reduced play time recovery route.

Although all studies included in the review recruited participants who had some form of addiction treatment, it is unclear what role treatment plays in recovery. “Treatment for gaming addiction” theme outlines the benefits of intervention programs for gaming addiction. However, it is well-known in other addiction fields that people can recover naturally, without ever seeking professional support (el-Guebaly, 2012). Understanding the role of treatment in recovery would allow us to develop more effective ways of supporting individuals battling gaming addiction.

Conclusions

This review offered unique insights into the recovery process from gaming addiction by highlighting that individuals with gaming addiction go through similar processes on their recovery journey: develop awareness of the problem, decide to change their behaviour, employ change strategies, address challenges and setbacks, and work on maintaining recovery after getting better. However, it was also discovered that there is significant variation in how each process is experienced, for instance, whether an individual decides to quit or reduce their play time. Recovery processes identified in this review support previous findings on the role of TTM, social support and identity change in addiction recovery. Nevertheless, further research would be needed to expand our understanding of recovery from gaming addiction, such as about the role of gender differences or the impact of treatment in recovery. Current findings have implications for treatment of gaming addiction, highlighting the importance of a multidimensional treatment approach to address comorbid conditions and co-occurring problems, as well the potential benefits of introducing pre- and post-treatment support.

Conflict of Interest

The authors have no conflict of interests to declare.

Acknowledgement

The literature review protocol was registered with PROSPERO (CRD42022302202).

Table A1. SPIDER Framework.

Table A2. Quality Appraisal—CASP Qualitative Checklist .

Table A3. Data Extraction .

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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License .

Copyright © 2024 Ksenija Vasiljeva, Alex Kyriakopoulos, Christopher Wilson

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