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• How to Write a Literature Review | Guide, Examples, & Templates

How to Write a Literature Review | Guide, Examples, & Templates

Published on January 2, 2023 by Shona McCombes . Revised on September 11, 2023.

What is a literature review? A literature review is a survey of scholarly sources on a specific topic. It provides an overview of current knowledge, allowing you to identify relevant theories, methods, and gaps in the existing research that you can later apply to your paper, thesis, or dissertation topic .

There are five key steps to writing a literature review:

• Search for relevant literature
• Evaluate sources
• Identify themes, debates, and gaps
• Outline the structure
• Write your literature review

A good literature review doesn’t just summarize sources—it analyzes, synthesizes , and critically evaluates to give a clear picture of the state of knowledge on the subject.

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Table of contents

What is the purpose of a literature review, examples of literature reviews, step 1 – search for relevant literature, step 2 – evaluate and select sources, step 3 – identify themes, debates, and gaps, step 4 – outline your literature review’s structure, step 5 – write your literature review, free lecture slides, other interesting articles, frequently asked questions, introduction.

• Quick Run-through
• Step 1 & 2

When you write a thesis , dissertation , or research paper , you will likely have to conduct a literature review to situate your research within existing knowledge. The literature review gives you a chance to:

• Demonstrate your familiarity with the topic and its scholarly context
• Develop a theoretical framework and methodology for your research
• Position your work in relation to other researchers and theorists
• Show how your research addresses a gap or contributes to a debate
• Evaluate the current state of research and demonstrate your knowledge of the scholarly debates around your topic.

Writing literature reviews is a particularly important skill if you want to apply for graduate school or pursue a career in research. We’ve written a step-by-step guide that you can follow below.

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See an example

Writing literature reviews can be quite challenging! A good starting point could be to look at some examples, depending on what kind of literature review you’d like to write.

• Example literature review #1: “Why Do People Migrate? A Review of the Theoretical Literature” ( Theoretical literature review about the development of economic migration theory from the 1950s to today.)
• Example literature review #2: “Literature review as a research methodology: An overview and guidelines” ( Methodological literature review about interdisciplinary knowledge acquisition and production.)
• Example literature review #3: “The Use of Technology in English Language Learning: A Literature Review” ( Thematic literature review about the effects of technology on language acquisition.)
• Example literature review #4: “Learners’ Listening Comprehension Difficulties in English Language Learning: A Literature Review” ( Chronological literature review about how the concept of listening skills has changed over time.)

You can also check out our templates with literature review examples and sample outlines at the links below.

Download Word doc Download Google doc

Before you begin searching for literature, you need a clearly defined topic .

If you are writing the literature review section of a dissertation or research paper, you will search for literature related to your research problem and questions .

Make a list of keywords

Start by creating a list of keywords related to your research question. Include each of the key concepts or variables you’re interested in, and list any synonyms and related terms. You can add to this list as you discover new keywords in the process of your literature search.

• Social media, Facebook, Instagram, Twitter, Snapchat, TikTok
• Body image, self-perception, self-esteem, mental health
• Generation Z, teenagers, adolescents, youth

Search for relevant sources

Use your keywords to begin searching for sources. Some useful databases to search for journals and articles include:

• Your university’s library catalogue
• Google Scholar
• Project Muse (humanities and social sciences)
• Medline (life sciences and biomedicine)
• EconLit (economics)
• Inspec (physics, engineering and computer science)

You can also use boolean operators to help narrow down your search.

Make sure to read the abstract to find out whether an article is relevant to your question. When you find a useful book or article, you can check the bibliography to find other relevant sources.

You likely won’t be able to read absolutely everything that has been written on your topic, so it will be necessary to evaluate which sources are most relevant to your research question.

For each publication, ask yourself:

• What question or problem is the author addressing?
• What are the key concepts and how are they defined?
• What are the key theories, models, and methods?
• Does the research use established frameworks or take an innovative approach?
• What are the results and conclusions of the study?
• How does the publication relate to other literature in the field? Does it confirm, add to, or challenge established knowledge?
• What are the strengths and weaknesses of the research?

Make sure the sources you use are credible , and make sure you read any landmark studies and major theories in your field of research.

You can use our template to summarize and evaluate sources you’re thinking about using. Click on either button below to download.

Take notes and cite your sources

As you read, you should also begin the writing process. Take notes that you can later incorporate into the text of your literature review.

It is important to keep track of your sources with citations to avoid plagiarism . It can be helpful to make an annotated bibliography , where you compile full citation information and write a paragraph of summary and analysis for each source. This helps you remember what you read and saves time later in the process.

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To begin organizing your literature review’s argument and structure, be sure you understand the connections and relationships between the sources you’ve read. Based on your reading and notes, you can look for:

• Trends and patterns (in theory, method or results): do certain approaches become more or less popular over time?
• Themes: what questions or concepts recur across the literature?
• Debates, conflicts and contradictions: where do sources disagree?
• Pivotal publications: are there any influential theories or studies that changed the direction of the field?
• Gaps: what is missing from the literature? Are there weaknesses that need to be addressed?

This step will help you work out the structure of your literature review and (if applicable) show how your own research will contribute to existing knowledge.

• Most research has focused on young women.
• There is an increasing interest in the visual aspects of social media.
• But there is still a lack of robust research on highly visual platforms like Instagram and Snapchat—this is a gap that you could address in your own research.

There are various approaches to organizing the body of a literature review. Depending on the length of your literature review, you can combine several of these strategies (for example, your overall structure might be thematic, but each theme is discussed chronologically).

Chronological

The simplest approach is to trace the development of the topic over time. However, if you choose this strategy, be careful to avoid simply listing and summarizing sources in order.

Try to analyze patterns, turning points and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred.

If you have found some recurring central themes, you can organize your literature review into subsections that address different aspects of the topic.

For example, if you are reviewing literature about inequalities in migrant health outcomes, key themes might include healthcare policy, language barriers, cultural attitudes, legal status, and economic access.

Methodological

If you draw your sources from different disciplines or fields that use a variety of research methods , you might want to compare the results and conclusions that emerge from different approaches. For example:

• Look at what results have emerged in qualitative versus quantitative research
• Discuss how the topic has been approached by empirical versus theoretical scholarship
• Divide the literature into sociological, historical, and cultural sources

Theoretical

A literature review is often the foundation for a theoretical framework . You can use it to discuss various theories, models, and definitions of key concepts.

You might argue for the relevance of a specific theoretical approach, or combine various theoretical concepts to create a framework for your research.

Like any other academic text , your literature review should have an introduction , a main body, and a conclusion . What you include in each depends on the objective of your literature review.

The introduction should clearly establish the focus and purpose of the literature review.

Depending on the length of your literature review, you might want to divide the body into subsections. You can use a subheading for each theme, time period, or methodological approach.

As you write, you can follow these tips:

• Summarize and synthesize: give an overview of the main points of each source and combine them into a coherent whole
• Analyze and interpret: don’t just paraphrase other researchers — add your own interpretations where possible, discussing the significance of findings in relation to the literature as a whole
• Critically evaluate: mention the strengths and weaknesses of your sources
• Write in well-structured paragraphs: use transition words and topic sentences to draw connections, comparisons and contrasts

In the conclusion, you should summarize the key findings you have taken from the literature and emphasize their significance.

When you’ve finished writing and revising your literature review, don’t forget to proofread thoroughly before submitting. Not a language expert? Check out Scribbr’s professional proofreading services !

This article has been adapted into lecture slides that you can use to teach your students about writing a literature review.

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

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

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

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

There are several reasons to conduct a literature review at the beginning of a research project:

• To familiarize yourself with the current state of knowledge on your topic
• To ensure that you’re not just repeating what others have already done
• To identify gaps in knowledge and unresolved problems that your research can address
• To develop your theoretical framework and methodology
• To provide an overview of the key findings and debates on the topic

Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.

The literature review usually comes near the beginning of your thesis or dissertation . After the introduction , it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology .

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

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

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• Open access
• Published: 20 April 2021

Getting into a “Flow” state: a systematic review of flow experience in neurological diseases

• Beatrice Ottiger   ORCID: orcid.org/0000-0002-0242-2632 1 ,
• Erwin Van Wegen 2 , 3 ,
• Katja Keller 1 ,
• Tobias Nef 4 ,
• Thomas Nyffeler 1 , 4 ,
• Gert Kwakkel 2 , 3 , 5 &
• Tim Vanbellingen 1 , 4  

Journal of NeuroEngineering and Rehabilitation volume  18 , Article number:  65 ( 2021 ) Cite this article

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Flow is a subjective psychological state that people report when they are fully involved in an activity to the point of forgetting time and their surrounding except the activity itself. Being in flow during physical/cognitive rehabilitation may have a considerable impact on functional outcome, especially when patients with neurological diseases engage in exercises using robotics, virtual/augmented reality, or serious games on tablets/computer. When developing new therapy games, measuring flow experience can indicate whether the game motivates one to train. The purpose of this study was to identify and systematically review current literature on flow experience assessed in patients with stroke, traumatic brain injury, multiple sclerosis and Parkinson’s disease. Additionally, we critically appraised, compared and summarized the measurement properties of self-reported flow questionnaires used in neurorehabilitation setting.

A systematic review using PRISMA and COSMIN guidelines.

MEDLINE Ovid, EMBASE Ovid, CINAHL EBSCO, SCOPUS were searched. Inclusion criteria were (1) peer-reviewed studies that (2) focused on the investigation of flow experience in (3) patients with neurological diseases (i.e., stroke, traumatic brain injury, multiple sclerosis and/or Parkinson’s disease). A qualitative data synthesis was performed to present the measurement properties of the used flow questionnaires.

Ten studies out of 911 records met the inclusion criteria. Seven studies measured flow in the context of serious games in patients with stroke, traumatic brain injury, multiple sclerosis and Parkinson’s disease. Three studies assessed flow in other activities than gaming (song-writing intervention and activities of daily living). Six different flow questionnaires were used, all of which were originally validated in healthy people. None of the studies presented psychometric data in their respective research population.

The present review indicates that flow experience is increasingly measured in the physical/cognitive rehabilitation setting in patients with neurological diseases. However, psychometric properties of used flow questionnaires are lacking. For exergame developers working in the field of physical/cognitive rehabilitation in patients with neurological diseases, a valid flow questionnaire can help to further optimize the content of the games so that optimal engagement can occur during the gameplay. Whether flow experiences can ultimately have positive effects on physical/cognitive parameters needs further study.

Flow experience is a subjective psychological state that people report when they are completely involved in something to the point of forgetting time and their surrounding except the activity itself [ 1 , 2 ]. During flow, subjective perception of time may change: Time can pass faster or slower and the environment is hardly or no longer perceived. Attention is fully invested in the task at hand, and the person functions at his or her fullest capacity. The flow state was first described by Csikszentmihalyi (1975) as the “optimal experience”. He began his research on flow experiences with the simple question of why people are often highly committed to activities without obvious external rewards. Csikszentmihalyi’s first studies involved interviews with people from different backgrounds such as athletes, chess masters, rock climbers, dancers, composers of music and many more [ 3 ]. Csikszentmihalyi and his colleagues developed the “Flow-theory” with general attributes of an optimal experience and its proximal conditions. The Flow-theory proposes nine key characteristics: challenge-skill balance (balance between the challenge of the activity and personal skills), action-awareness merging (involvement in the task; actions become automatic), clear goals (clear idea of what needs to be accomplished), unambiguous feedback (clear and immediate feedback), concentration on task at hand (complete focused on the task), sense of control (clear feeling of control), loss of self-consciousness (no concerns with appearance, focused only the activity), transformation of time (altered perception of time; either speeding up or down), and autotelic experience (the activity is intrinsically rewarding) [ 2 , 4 ]. Many researchers tried to adapt the Flow-theory [ 5 ] and explored predictors and consequences of flow, but its definition and key characteristics as shortly described above, remained largely the same. In fact, a recent paper about flow clearly advocates Csikszentmihalyi’s Flow-theory as the only valid and default conceptualization so far [ 5 ].

Because flow experience is associated with elements such as motivation, peak performance, peak experience and enjoyment, the Flow-theory was further explored in various research fields, such as sports, educational science, work and software engineering for gaming [ 6 , 7 , 8 , 9 ]. Positive associations were found between athletes’ flow experience and their performance measures, indicating that positive psychological flow states are related to increased levels of performance. In addition, significant prediction of the athletes’ performance could be made based on the level of flow experience during the competition [ 10 ].

Attempts to systematically measure flow experience started in the 1990’s. Self-reported flow questionnaires were used to measure flow during specific activities, such as computer interactions among students and accountants [ 11 ], and among athletes practicing various sports such as basketball, athletics, hiking, jogging and other types of sports [ 4 ]. In the past 30 years, different flow questionnaires were developed [ 12 , 13 ]. They focussed either on the dispositional or core flow experience (tendency to experience flow in general) [ 14 ] or on the state flow experience (flow experience in a specific activity) [ 4 ]. This lead to some disagreement in literature about how flow actually should be measured, and as well as to the context and task in which a flow questionnaire should be applied [ 5 ].

Interestingly, over the last decade, several computer or tablet-based serious games, and virtual/augmented reality therapeutic training applications have been developed that integrate many of the key flow characteristics mentioned above. Furthermore, various studies evaluated the player’s flow experience with questionnaires when applying these newer technologies [ 15 , 16 , 17 ]. Serious games are intentionally programmed so that the goals are presented very clearly (i.e., visually through nice icons), and that the requirements of the exercises are adaptable according to the level of player performance. Also, the exercises should be both exciting and attractive enough to maintain the player’s attention. In this way, the player obtains a certain automatic feeling of flow while having full control over his or her actions. These games are sometimes so well designed that one loses track of time. Serious games, robotics, virtual/augmented reality, have found their way into neurorehabilitation [ 18 , 19 , 20 , 21 ], and theory of flow experience emerged in recent neurorehabilitation studies [ 22 , 23 ]. Indeed, serious exergames may have an explicit educational and/or therapeutic purpose and are often designed in such a way that they may also improve cognitive or physical capabilities [ 22 , 24 ]. Interestingly, exergame developers began to look at new games from the perspective of flow experience in order to adapt the game conditions of the players, and used flow questions to assess the users’ engagement for the new therapy form [ 23 , 25 ]. To assess flow experience during a therapeutic session with a patient, valid questionnaires are needed which may guide a clinician in adapting the level of difficulty, attractiveness, amount of feedback of an exercise, possibly further attributing to an optimal flow experience. Such optimization of the motor learning environment may enhance therapeutic efficacy during an individual training session.

However, to date, there is no consensus on how flow experience should be measured in neurologically impaired patients. Furthermore, no systematic overview exists so far, about current existing flow questionnaires and their psychometric properties. Therefore, the first aim of the present study was to identify and systematically review current literature on flow experience assessed in patients with acquired neurological diseases such as stroke, traumatic brain injury (TBI), multiple sclerosis (MS) and Parkinson’s disease (PD). The second aim was to critically appraise, compare and summarize the measurement properties of self-reported flow questionnaires used in a neurorehabilitation setting. Since flow experience has been assessed already in neurological rehabilitation and measurement tools exist, we expected these tools to be well validated.

This systematic review followed the guideline from the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Statement (PRISMA) [ 26 ]. The Consensus-based standards for the selection of health measurement instruments (COSMIN guidelines) were applied for the evaluation of the measurement properties of the flow questionnaires [ 27 ]. A flow questionnaire is a research instrument consisting of a series of questions for the purpose of gathering information from respondents about their flow experience when performing an activity.

Protocol and registration

The protocol was registered with the International prospective register of systematic review (PROSPERO) https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020187510 on July 5, 2020 [ 28 ].

Electronic search strategy

Databases were searched up from date of inception (1975) to June 2020 (MEDLINE Ovid, EMBASE Ovid, CINAHL-EBSCO, SCOPUS). Text words and MeSH (Medical Subject Headings) terms for flow experience, flow questionnaire, flow theory, positive psychology, neurorehabilitation, neurological disease, stroke, traumatic brain injury, multiple sclerosis and Parkinson’s disease to identify intervention studies which used flow as outcome parameter. References of the included studies were screened for additional articles. The search strategy was created by one author (KK) and peer reviewed by another author (BO).

The PubMed search strategy was as follows: (flow exp*) NOT (cereb* flow OR dyn* flow OR exp* flow OR blood flow OR venous flow)) AND (stroke OR Parkinson OR traumatic brain injury OR multiple sclerosis). The search string was adapted appropriately for each database (Additional file 1 ).

Eligibility criteria

According to PRISMA guidelines [ 26 ], the Population-Intervention-Comparison-Outcome-Study Design (PICOS) approach was applied to systematically define the eligibility criteria. Inclusion and exclusion criteria are presented in Table 1 .

Selection of studies

Two reviewers (BO, KK) independently screened all titles and abstracts for the eligibility criteria. The full text papers of relevant studies were obtained if both reviewers agreed for inclusion. Otherwise, a third reviewer (TV) made the final decision. The search results were imported into Mendeley Reference Manager ( https://www.mendeley.com ) to further check for duplicates. In addition, we obtained the original validation papers of each flow questionnaire. These validation papers were used to critically appraise the validity, reliability, and responsiveness of the flow questionnaires.

The Electronic search strategy identified 911 records, of which 22 were retrieved in full text for further assessment. This resulted in the exclusion of another twelve studies (Fig.  1 ). Ten studies were included in the review.

Flow diagram for study selection

Data extraction and assessment of methodological quality

The general characteristics of the included studies were extracted as following: population (diagnosis, sample size, age, gender), study design, intervention (therapeutic activity in a rehabilitation setting), main outcomes parameters, flow measurement and key findings regarding flow experience. The results are presented in Table 2 . The characteristics of the flow questionnaires used, such as the flow construct, mode of administration/instruction, subscales (items) and response option were extracted and are listed in Table 3 . Furthermore, we evaluated the measurement properties of the flow questionnaires by assessing the content validity (including relevance, comprehensiveness and comprehensibility of the construct, population and context of use in order to apply the flow questionnaires in a neurorehabilitation setting), construct validity (including structural validity, hypotheses testing, and cross-cultural validity), reliability (containing the measurement properties internal consistency and measurement error and test–retest) and responsiveness (the ability of the flow questionnaires to detect change over time in the flow experience) following the COSMIN guidelines [ 27 ]. We verified whether the content of the questionnaires was an adequate reflection of the flow construct. For this purpose, we recorded if the target population was asked about the relevance, comprehensiveness, and comprehensibility of the flow questionnaire (content validity). Regarding construct validity, we examined if the scores of the flow questionnaire were an adequate reflection of the dimensionality of the flow construct (structural validity). We also investigated if the scores of the questionnaires were consistent with hypotheses based on the assumption that the questionnaires validly measure the flow construct (hypotheses testing). Additionally, we investigated if the performance of the items on a translated or culturally adapted questionnaire were an adequate reflection of the performance of the items of the original version of the questionnaire (cross-cultural validity). The domain reliability refers to the degree to which the measurement is free from measurement error. For this reason, we reviewed the degree of the interrelatedness among the items (internal consistency) and the proportion of the total variance in the measurements which was due to true differences between patients (reliability). The results and the psychometric properties’ rating criteria of the flow questionnaires are presented in the Additional file 2 . The Summary of Findings (SoF) per measurement property, its overall rating and the grading of the quality of evidence are presented in Table 4 . The COMSIN guidelines [ 27 ] were applied for the rating of the SoF.

Different flow questionnaires and their use in neurological diseases

The Flow State Scale (FSS) was used in patients with PD [ 43 ] and in patients with MS [ 44 ]. Baker et al. (2015) applied the Short Flow Scale (SFS) and the Core Flow Scale (CFS) [ 40 ] in patients with TBI. Van der Kuil et al. (2018) used a self-developed overall appreciation questionnaire in patients with stroke, TBI and spinal cord injury. Six items in this questionnaire were adapted from the FSS and three items were further added. The Flow State Scale for Occupational Tasks questionnaires (FSSOT) was used by Yoshida Kazuki, et al. (2014; 2018) in patients with TBI and was also used by Yoshida Ippei, et al. (2018) in patients with stroke and spinal cord injury. In contrast to these previous studies, which used known questionnaires, Shin and colleagues (2014) used six different flow questions [ 45 ] in patients with stroke, which were slightly adapted from another study done in TBI [ 46 ].

The different flow questionnaires were mainly used to get an overall impression of the flow psychological state of neurologically impaired patients when they were engaged in different training modes, such as upper limb or lower limb training in patients with stroke [ 45 ] 45 , balance training in patients with MS [ 44 ] and PD [ 43 ], cognitive training in patients with TBI [ 47 , 48 ], and stroke [ 42 ]. In seven out of the ten studies, as presented in Table 2 , serious games were used as therapeutic intervention. The designs of the studies were either pilot and explorative in nature, testing the usability of a new serious game [ 42 , 43 , 45 , 47 ] or pilot Randomized Controlled Trials (RCT) evaluating the preliminary efficacy of new games [ 44 , 46 , 48 ].

Four usability studies measured flow in order to quantify the level of immersion into the gameplay [ 42 , 43 , 45 , 47 ]. Shin et al. (2014) developed a task-specific interactive, game-based virtual reality rehabilitation system (RehabMaster) for the rehabilitation of the upper extremities after a stroke. During the development phase 20 stroke patients completed a six-item questionnaire adopted by [ 11 ] to test if they were engaged and if the training was a positive experience, so that they were motivated to continue. For all statements, the participants gave lower scores for the negative questions (e.g., “Using RehabMaster was boring for me”) and higher scores for the positive questions (e.g., “RehabMaster was fun for me to use”) on a 5-point Likert Scale [ 45 ]. The participants indicated that the RehabMaster-based training and games maintained their attention, were enjoyable and without eliciting any negative feelings [ 45 ]. Galna et al. (2014) developed a computer game to rehabilitate dynamic postural control for patients with PD using the Microsoft Kinect. Also, during the pilot phase, flow experience was recorded from nine participants with PD by means of the FSS questionnaire. The FSS was rated on a 5-point Likert Scale. The flow subscales “concentration” showed the highest mean value across the participants (Mean 4.56), followed by high scores of the subscales “loss of self-consciousness” (Mean 4.14), clear goals (Mean 4.22) and enjoyment (Mean 4.03). Lower flow scores were found in the subscale “transience” (Mean 2.67) and action-awareness (Mean 3.11). Van der Kuil et al. (2018) designed a cognitive rehabilitation therapy for patients with acquired brain injuries in form of a serious game. The aim of the serious game was to aid patients in the development of compensatory navigation strategies by providing exercises in 3D virtual environments on their home computers. During the testing of the software application, questions about the general appreciation were asked at the beginning and at the end of the experimental phase. Van der Kuil et al. (2018) constructed an “overall appreciation questionnaire” of nine items rated on a 5-point Likert scale. Six items were adapted from the FSS and three items were constructed in the context of a usability test. The highest scores were found in the “attention” (Mean 4.79) and “concentration” items (Mean 4.54). The item “control” presented the lowest score (Mean 3.29). Yoshida K. et al. (2014) conducted an exploratory case study with two patients with attention-deficit disorder after TBI. Two types of video game tasks for attention training were created. The first type of video game was balancing levels of skill and challenge and gave quick feedback about the score. In the second type of video game, the level of the difficulty of the task was constant and the participant received no information about the goal or a score feedback. Patient A performed the first type of video game for 14 days after receiving general occupational therapy for 11 days. Patient B performed the first type of video game for 15 days after performing the second type of video game for 10 days. The FSSOT was administered to identify the patient’s flow state. The results for Patient A suggested that the first type of video game was more effective than general occupational therapy for improving attention deficits. The results for Patient B suggested that the first type of video game was more effective than the second type of video game.

Five RCTs measured flow in intervention groups and in control groups. Three RCTs used video games and actually compared levels of flow between the intervention and control group (Wii Fit™ vs. traditional balance training in patients with MS [ 44 ]; or Mobile Game—Neuromuscular Electrical Stimulation (NMES) vs. Conventional NMES in patients with stroke [ 46 ] and Yoshida K. et al. (2018) compared flow in an attention gameplay intervention in patients with traumatic brain injuries. In Robinson et al. (2015) the intervention group that trained balance with Wii Fit™ showed significantly higher flow scores in the flow subscales clear goals (p = 0.05), concentration on the task (p = 0.03), unambiguous feedback (p = 0.04), action awareness merging (p = 0.03) and transformation of time (p = 0.001) than the control group [ 44 ]. Likewise, the hand-wrist and foot–ankle training with serious games presented significantly higher scores in attention (p < 0.05), curiosity (p < 0.05) and intrinsic interest (p < 0.05) compared to the control group which was not playing serious games [ 46 ]. Both previous RCT’s focused on videogames based on physical training, whereas the third RCT by Yoshida K. et al. (2018) investigated flow during cognitive training. They examined whether the intervention group during a serious game for attentional training by adapting the challenge to the patient’s skill, gave clear goals and prompt feedback about the score. The level of the difficulty of the task was constant in the control group and they received no information about the goal or score feedback. The study population in this RCT had a traumatic brain injury at least 6 months ago. The researchers stated that the FSSOT score was significantly higher in the intervention group than in the control group. Both groups showed a positive association between the increase in the composite score of the attention tests [Trail Making Test (TMT), Symbol Digit Modalities Test (SDMT), Paced Auditory Serial Addition Test (PASAT)] and the FSSOT score. Although the correlation coefficients presented a large effect, the correlations were not significant (Flow: r = 0.456, p = 0.21; Control r = 0.554, p = 0.9). The total of the Moss attention rating scale (MARS) demonstrated no association with the FSSOT score, except one subitem that obtained a significant negative correlation (sustained/consistent attention, r = 0.51, p < 0.05). Two RCT’s by Yoshida I. et al. (2018; 2019) did not use videogame-based training but consciously adapted the challenge to the abilities during occupational therapy (OT) in patients with cerebral, spinal disease [ 49 ] and older adults with various neurological disease [ 50 ]. Attention was paid to an optimal challenge-skill balance when performing activities of daily living (ADLs) such as eating, laundry, cooking, shopping, etc. The training was adapted so that in the interventions group the participants and the therapists quantified and shared the task performance based on a scale of challenges and skills and adjusted the requirements for the task accordingly. On the other hand, in the control group the challenge-skill of the trained ADLs was not adjusted over the training sessions. In the 2018 paper there were 10 sessions, once a week and training focused on just one activity, evaluated and selected after filling out the Canadian Occupational Performance Measure (COPM) [ 51 ]. The COPM is a personalized, client-centred instrument designed to identify the occupational performance problems experienced by the client. Using a semi-structured interview, the therapist initiates the COPM process by engaging the client in identifying daily occupations of importance that they either want to do, need to do, or are expected to do but are unable to accomplish [ 51 ]. In the 2019 study, the participants selected not one, but several ADLs based on the outcome of the COPM as treatment goals. Treatments in each group comprised sessions lasting 40–60 min, conducted six times per week. In both RCT’s flow experience was measured pre- and post-treatments with the FSSOT. In the first RCT [ 50 ] there was a highly significant interaction effect for flow (p = 0.008, d = 0.82), in favour of the adjusted challenge-skill OT, as compared with the control group. This interaction was not confirmed in their follow-up study (p > 0.05, d = 0.31) [ 49 ].

Similar to Yoshida I. (2018, 2019), Baker et al. (2015) also did not use videogame based training but explored if song writing interventions for patients with TBI and spinal cord injuries in the early phase of neurorehabilitation would support a change in self-concept and well-being [ 52 ]. By means of a non-randomized repeated measures design, they found that flow scores were very high after the intervention. However, these scores did not significantly correlate with self-concept Head Injury Semantic Differential Scale (HISDS) (State Flow Scale r = − 0.10; p > 0.05; Core Flow Scale r = 0.02; p > 0.05) nor with 7 different well-being measures evaluating sense of flourishing, life satisfaction, coping, affect, depression, and anxiety (State Flow Scale r = between − 0.40 and 0.43; p > 0.05; Core Flow Scale r = between − 0.24 and 0.32; p > 0.05).

Psychometric properties of flow questionnaires

The Summary of Findings (SoF) per measurement property, its overall rating and the grading of the quality of evidence are presented in Table 4 . The COMSIN guidelines [ 27 ] were applied for the rating of the SoF and were as following: [Overall Rating: sufficient (+), insufficient (−), undetermined (?); Quality of Evidence high (h), moderate (m), low (l), very low (lw)]. If a measurement property was not analysed or not reported, the rating box remains empty. The rating criteria for good measurement properties and for the quality of evidence are presented in the Additional file 2 .

Content validity

Content validity including relevance, comprehensiveness and comprehensibility was assessed for the FSS and for FSSOT. Jackson et al. conducted two qualitative studies with elite athletes [ 58 , 59 ] prior to the development of the FSS. The SFS and CFS were also developed by the Jackson Group with the intention of creating a short version of the FSS and DFS, respectively. Yoshida K. et al. (2013) tested the FSSOT in the development phase by experts on flow theory. Both Jackson et al. (1996) and Yoshida K. et al. (2013) conducted pilot-testing before the validation procedure.

Structural validity

Structural validity, by means of confirmatory and internal consistency was determined in all flow questionnaires. All studies presented good internal consistency (Cronbach alpha above 0.70). Confirmatory factory analysis was performed in all flow questionnaires. Taking the strict COSMIN guidelines [ 27 ] into account the CFS questionnaire fulfilled the parameters requested by the COSMIN guidelines (CFI or TLI > 0.95 OR RMSEA < 0.06 OR SRMR < 0.08), the SFS, FSS and FSSOT had parameters approaching closely these cut-offs, so validating high quality of evidence. The questionnaire by Webster et al. (1993) showed considerably lower scores, pinpointing to moderate quality of evidence.

Cross-cultural validity

The FSS was cross-culturally validated in Greek [ 55 , 56 ] and in Spanish [ 57 ]. They all followed standard back and forward translation procedures. Stavrou and Zervas (2004) tested a second FSS-Greek version, since the first one done by Doganis et al. (2002) indicated rather a moderately fit to the data, whereas the internal consistency (Cronbach alpha) was below 0.70 for some of the FSS subscales (action-awareness merging = 0.34, concentration on task at hand = 0.64, transformation of time = 0.67). The FSS-Greek version by Stavrou and Zervas (2004) presented an internal structure validity ranging from Cronbach alpha of 0.75 to 0.92 (mean = 0.82) and a closely fit to the cut-off’s parameters requested by the COSMIN guidelines. The Spanish version of the FSS presented a good internal consistency (Cronbach alpha above 0.70) and the structural validity was tested with a confirmatory factory analysis, demonstrating a close fit to the cut-offs parameters [ 57 ].

Construct validity

Construct validity, by means of convergent validity, was assessed for the FSSOT total scores, showing significant negative correlations with the total score of State-Trait Anxiety Inventory (STAI) (r = − 0.537, p < 0.01) [ 41 ]. Jackson et al. (1998) examined psychological correlates of state flow in a separate study than the original validation paper [ 4 ]. Significant associations were found between the variables FSS total and perceived athletic ability (PSA) (r = 0.33, p < 0.01); total anxiety (A-SUM) (r = − 0.34, p < 0.01) and intrinsic motivation to experience stimulation (IMSTIM) (r = 0.25, p < 0.01). A series of external validity analyses was conducted for the SFS and CFS by Martin et al. (2008) for each subdomain “work”, “sport” and “music” in SFS and “general school”, “mathematics” and “extracurricular” in CFS with the Motivation and Engagement Scale (MES), which includes the following key correlates: participation (SFS: mean r 0.74–0.90; CFS: mean r 0.25–0.56), enjoyment (SFS: mean r 0.73–0.89); CFS mean r 0.13–0.71), buoyancy (SFS: mean r 0.68–0.81; CFS: mean r 0.15–0.42), aspirations (SFS: mean r 0.71–0.81; CFS: mean r 0.12–0.68), adaptive cognitions (SFS: mean r 0.72–0.82; CFS: mean r 0.23–0.74), adaptive behaviours (SFS: mean r 0.59–0.70; CFS: mean r 0.18–0.83), impeding/maladaptive cognitions (SFS: mean r − 0.37 to − 0.59; CFS: mean r − 0.10 to − 0.23), and maladaptive behaviours (SFS: mean r − 0.47 to − 70; CFS: mean r − 0.15 to − 0.79). The SFS presents higher correlations with the MES than the CFS. Significance of the correlations was not reported.

Reliability

None of the identified studies investigated reliability (test–retest), measurement error, criterion validity or responsiveness of the flow questionnaires. As far as we know, none of the flow questionnaires have been tested for their psychometric properties in neurologically impaired people.

Interpretability and feasibility of the included flow questionnaires

Floor and ceiling effects, completion time and costs of instrument and contact information of used outcomes measuring flow are listed in Table 5 .

The aim of the present study was to identify and systematically review current literature on flow experience assessed in patients with neurological diseases such as stroke, TBI, MS and PD. In addition, we critically appraised, compared and summarized the measurement properties of self-reported flow questionnaires used in a neurorehabilitation setting.

Flow experience in patients with neurological disorders has so far been measured in only a few studies, some of them very pilot in nature, being usability studies, other were RCTs, and mostly related to serious gaming [ 42 , 43 , 44 , 45 , 47 , 48 ]. One aim of such interventions is to achieve an optimal flow state of the patient, possibly creating an optimal learning environment to improve either physical and/or cognitive functions (being for example improving balance, or attention). Flow questionnaires are one way to capture this flow state, since the patient is, immediately after the intervention, asked for his or her experiences. In this way, the clinician gets an overall impression whether the patient was in an optimal psychological state of flow or not. Our systematic review demonstrated that six flow questionnaires were used so far.

However, psychometric properties of these questionnaires were established only in athletes and other healthy populations so far, and not in neurologically impaired patients. Latter population often suffer from cognitive problems (disturbed vigilance, working memory deficits, language comprehension difficulties) which may impact the assessment of flow.

The FSS and FSSOT appear to be good candidate questionnaires, based on their good psychometric validity properties in healthy subjects. The FSSOT, compared to the FSS, requires less administration time so probably being more feasible for neurologically impaired patients, taking mild cognitive deficits into account. Besides proper validation, reliability measures such as test–retest, measurement errors will have to be established as well because these reliability measures give an overall impression about the stability of item responses. A final aspect will be to evaluate the internal (the ability to measure change over time) and external responsiveness (the extent to which changes in a measure relate to corresponding changes in a reference measure) of these flow questionnaires. Only when these psychometric properties are well defined the outcome of flow questionnaires can be better interpreted in either usability studies or RCT’s.

The investigation of flow experience in neurological patients started at about the same time as the development of serious games for rehabilitation therapy. The integration of motivational strategies in the form of “gamification” is one of the benefits of the new therapy options [ 19 , 60 ]. The expectation of such therapy programs is that they will strengthen compliance with repetitive high-dose functional training programs [ 19 , 60 ]. The game developer's aim is to bring the patient into a flow state that leads to an optimal gaming experience [ 61 ]. They expect to foster the engagement through the gamification of the therapeutic exercises and at the same time give the therapist the possibility to control and customize the levels of complexity of the rehabilitation training. Seven of the ten included studies measured flow experience in the context of serious games in patients with stroke, PD, MS and/or TBI [ 42 , 43 , 44 , 45 , 46 , 47 , 48 ]. Flow experience was mainly assessed in the context of usability studies in newly developed serious game therapy programs for rehabilitation purposes [ 42 , 43 , 45 , 47 ]. Our review showed that total flow mean scores between 3.76 and 4.33 points on a 5-point Likert scale were achieved in all studies when serious games were used as physical-therapeutic exercises [ 42 , 43 , 44 , 45 , 46 ] compared to control groups without serious games, these flow mean points reached 3.65–3.76 [ 44 , 46 ]. It turns out that therapeutic interventions with a game-like character stimulate concentration and enjoyment. This assumption was substantiated as flow experience was higher in game therapy versus conventional therapy, shown in two intervention studies investigating balance with Wii FIT™ [ 44 ] and hand wrist, foot ankle training with serious games [ 46 ] (Table 1 ). An advantage of rehabilitation therapy with a game character is that the goals and the rules of the task of the game are clearly defined. In addition, players receive immediate feedback of performance as to whether the task was performed correctly or not, a key element of the motor learning theory [ 57 ]. This, in turn, allows the movements to be deliberately adjusted in line with performance. If these components are appropriate, this also has a positive effect on concentration. In the principles of motor learning, feedback, but also the ability to concentrate on a task, and the motivation to perform an exercise, are essential for learning new motor skills [ 62 , 63 ]. Therefore, we assume that positive flow experiences during physical exercises support motor learning. From this perspective, it makes sense to measure flow experience in the development and testing phase of new therapy games. In this way it is possible to determine which adjustments should be made, e.g., to define the goal or the rules of the application more precisely.

Whether flow experiences ultimately had a positive effect on the physical outcome parameters was not investigated in these studies. Three studies from Japan explored in TBI patients and older adults with various neurological diseases whether flow experience had an effect on attention [ 48 ] and health related quality of life [ 49 , 50 ]. In a small RCT (n = 20), Yoshida K. et al. (2018) created two types of attention demanding serious games exercises, the flow task and the control task. The control task maintained a constant level of task difficulty regardless of the patient’s skill and did not give any goal and feedback about the score. Both tasks had identical content, except that the flow task was designed to induce flow by increasing task difficulty according to patients’ skill and giving clear goals and quick feedback about the score. Yoshida and colleagues (2018) referred to the Flow Theory of Nakamura and Csikszentmihalyi (2009), suggesting that three key characteristics of the flow theory (challenge-skill balance, clear goals and feedback) are essential to generate flow experience and that these characteristics are externally controllable. They found significantly (p-value not reported in the paper) higher flow total values in the intervention group (flow task) compared to the control group (control task) [ 48 ], suggesting that the way a serious game is designed, with regard to its task difficulty, can positively affect the flow state of a patient. Both groups showed a positive, but non-significant association between the increase in the composite score of cognitive attention tests (TMT, SDMT, PASAT) and the FSSOT total score (Flow: r = 0.456, p = 0.21; Control r = 0.554, p = 0.9) [ 48 ]. The lack of significant correlation, between attention and flow test scores may be explained by the pilot nature and small sample size of this RCT. Regardless, the fact that the flow psychological state was amenable to task difficulty gave a first indication that the state of flow may facilitate training, being worthwhile to investigate in further studies.

In two larger RCT’s, both conducted by Yoshida I. (2018, 2019), the outcomes of both RCT’s differed regarding the effect of the training on flow. While in their first RCT significant effects on flow, in favour of the experimental OT were found, this was not the case in their follow-up RCT. The reason for this discrepancy may be twofold. Firstly, in their first RCT the focus was on one activity and not on multiple ADLs, as in their second RCT. Presumably, in a rehabilitation setting, the focus is on improving the skills of one activity at a time rather than several at once. Therefore, it may be easier for participants to experience flow. For achievement of performance competence is a process that takes time, practice, and thorough skill development until the optimal performance of the skill (referred to as mastery) is characterized by an obvious ease and grace [ 2 ]. According to Flow-theory, to attain this state, an optimal balance between challenge and skill during training is crucial [ 36 , 49 ]. This is because anxiety is experienced when challenge exceeds ability, and boredom is experienced when ability exceeds challenge. Thus, it can be said that the better the challenge is matched to the ability and the expertise in performing is increasing, the easier it is to experience flow, as shown in other studies [ 6 , 7 , 64 ]. The second reason may lie in the much higher baseline flow levels the patients had in the second RCT, as compared with the flow levels of the patients in the first RCT, therefore leaving almost no room for further improvement. Irrespective of the discrepancy of results between both RCTs, the fact that patients could improve their flow by means of an adjusted challenge-skill OT training, by focusing on one specific ADL task is promising. One could explore, in future studies, for example the effects of improved flow on upper limb skills by doing challenge-skill ADL training, and this in different contexts, so the patient gets into high levels of flow.

Six different flow questionnaires were applied in these studies, leaving the question open which one to be taken for future validation in neurologically impaired patients. Based on their good psychometric properties in healthy subjects, both FSS and the FSSOT seem to be good candidates. The flow questions in the FSS are strongly related to concepts in the field of sport, and its administration time is rather long, (36 items). Therefore, feasibility might be questionable, especially if one considers the rather busy schedules of clinicians working in neurorehabilitation facilities. Subsequent shorter versions of the FSS were developed, being the SFS and CFS [ 40 ]. Still, the authors do recommend combining these measures when evaluating flow, which may be impractical. Furthermore, the flow questions are still very much related to the context of sport psychology, and less for neurorehabilitation purposes. This might also explain why, for example, Van der Kuil et al. (2018), for their study in patients with acquired brain injury, used 6 items of the FSS and then adapted them content wise, to make it more comprehensible and applicable for these patients’ group.

With regard to the FSSOT, its 14-item length seems more feasible as compared to the longer FSS. Furthermore, having been used already in two RCT’s to assess flow experience after challenge-skills based ADL training [ 49 , 50 ] and in one RCT to assess flow experience in attentional training in patients with neurological impairments [ 48 ], this questionnaire seems to be best candidate, and worthwhile to be properly validated in these patient groups. Depending on other contexts, such as upper limb virtual reality or robotic-assisted training, the questions of the FSSOT can be further adapted in the light of different cultural backgrounds.

A possible limitation of this review was that we could not present a quality assessment of study design, since both exploratory, non-randomized as well as randomized trials were included. Another limitation is that we included studies in patients with various neurological disorders that affect overall study population homogeneity. Hence, one has to be careful in comparing the results of these studies directly. Finally, publication bias may be present, as well as a language bias, given that we considered only flow questionnaires described in predefined databases and restricted our search to English language publications.

To sum up, the present review indicates that flow experience is increasingly measured in the physical/cognitive rehabilitation setting in patients with neurological disease such as stroke, TBI, MS and PD. Flow experience was mainly measured immediately after a therapeutic intervention that aimed to improve physical or cognitive functions with serious exergaming. In seven out of ten studies in which new games for therapy were developed, patients flow experience was measured to find out to what extent they were engaged to the new games [ 42 , 43 , 44 , 45 , 46 , 47 , 48 ]. The other three studies assessed flow during occupational therapy when practicing ADL’s [ 49 , 50 ] and during music therapy [ 52 ]. Six different flow questionnaires were applied in these studies. None were specifically validated in patients with neurological diseases. Therefore, the psychometric properties of used tests for measuring flow experience are lacking and will have to be evaluated in future studies. For exergame developers working in the field of physical/cognitive rehabilitation in patients with neurological diseases, a valid flow questionnaire can help to further optimize the content of the games so that optimal engagement can occur during the gameplay.

Availability of data and materials

All data generated or analysed during this study are included in the published article.

Abbreviations

Activity of daily living

Total anxiety

Comparative fit index

Core Flow Scale

Canadian occupational performance measure

Consensus-based standards for the selection of health measurement instrument

Fugl-Meyer assessment

Flow State Scale

Flow State Scale occupational task

Grading of recommendations assessment, development, and evaluation

Head injury semantic differential scale

Intrinsic motivation to experience stimulation

Modified Barthel Index

Motivation and engagement scale

Medical subject headings

Multiple sclerosis

Neuromuscular electrical stimulation

Not reported

Occupational therapy

Paced auditory serial addition test

Parkinson’s disease

Participants, intervention, comparison, outcome, study design framework

Preferred reporting items for systematic reviews and meta-analyses statement

Patients (or participants)-reported outcome measures

International prospective register of systematic review

Perceived athletic ability

Ray’s auditory verbal learning test

Randomized controlled trial

Root mean square error of approximation

Standard deviation

Symbol digit modalities test

Short-form health survey for general health

Short Flow Scale

Summary of findings

Standardized root mean square residual

State-Trait anxiety inventory

Traumatic brain injury

Tucker-Lewis Index

Trail-making test

Upper extremity

Unified theory of acceptance and use of technology

Virtual reality

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Methodology quality and results of flow questionnaires per measurement properties and the rating criteria for good measurement properties.

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Ottiger, B., Van Wegen, E., Keller, K. et al. Getting into a “Flow” state: a systematic review of flow experience in neurological diseases. J NeuroEngineering Rehabil 18 , 65 (2021). https://doi.org/10.1186/s12984-021-00864-w

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A Scoping Review of Flow Research

Affiliations.

• 1 Department of Psychology, University of Lübeck, Lübeck, Germany.
• 2 Faculty of Psychology, Ruhr-Universität Bochum, Bochum, Germany.
• 3 Department of Psychology, Linnaeus University, Växjö, Sweden.
• 4 ULR 4354 - CIREL - Centre Interuniversitaire de Recherche en Education de Lille, Université de Lille, Lille, France.
• 5 Department of Psychology, Goldsmiths University of London, London, United Kingdom.
• 6 School of Psychology, University of Minho, Braga, Portugal.
• 7 Faculty of Human and Social Sciences, University Fernando Pessoa, Porto, Portugal.
• 8 Department of Education, University of Aarhus, Aarhus, Denmark.
• 9 Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, Eindhoven, Netherlands.
• 10 Institute of Psychology Henri Pieron, Université Paris 5 René Descartes, Paris, France.
• 11 Department of Cultural Heritage and Environment, University of Milan, Milan, Italy.
• 12 IESE Business School, University of Navarra, Barcelona, Spain.
• 13 Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy.
• PMID: 35465560
• PMCID: PMC9022035
• DOI: 10.3389/fpsyg.2022.815665

Flow is a gratifying state of deep involvement and absorption that individuals report when facing a challenging activity and they perceive adequate abilities to cope with it (EFRN, 2014). The flow concept was introduced by Csikszentmihalyi in 1975, and interest in flow research is growing. However, to our best knowledge, no scoping review exists that takes a systematic look at studies on flow which were published between the years 2000 and 2016. Overall, 252 studies have been included in this review. Our review (1) provides a framework to cluster flow research, (2) gives a systematic overview about existing studies and their findings, and (3) provides an overview about implications for future research. The provided framework consists of three levels of flow research. In the first "Individual" level are the categories for personality, motivation, physiology, emotion, cognition, and behavior. The second "Contextual" level contains the categories for contextual and interindividual factors and the third "Cultural" level contains cultural factors that relate to flow. Using our framework, we systematically present the findings for each category. While flow research has made progress in understanding flow, in the future, more experimental and longitudinal studies are needed to gain deeper insights into the causal structure of flow and its antecedents and consequences.

Keywords: contextual level; cultural level; flow; individual level; scoping review.

Copyright © 2022 Peifer, Wolters, Harmat, Heutte, Tan, Freire, Tavares, Fonte, Andersen, van den Hout, Šimleša, Pola, Ceja and Triberti.

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Conflict of interest statement

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.

Categorization of flow research 2000–2016.

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• DOI: 10.1177/1029864919877564
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REVIEW article

A scoping review of flow research.

• 1 Department of Psychology, University of Lübeck, Lübeck, Germany
• 2 Faculty of Psychology, Ruhr-Universität Bochum, Bochum, Germany
• 3 Department of Psychology, Linnaeus University, Växjö, Sweden
• 4 ULR 4354 - CIREL - Centre Interuniversitaire de Recherche en Education de Lille, Université de Lille, Lille, France
• 5 Department of Psychology, Goldsmiths University of London, London, United Kingdom
• 6 School of Psychology, University of Minho, Braga, Portugal
• 7 Faculty of Human and Social Sciences, University Fernando Pessoa, Porto, Portugal
• 8 Department of Education, University of Aarhus, Aarhus, Denmark
• 9 Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, Eindhoven, Netherlands
• 10 Institute of Psychology Henri Pieron, Université Paris 5 René Descartes, Paris, France
• 11 Department of Cultural Heritage and Environment, University of Milan, Milan, Italy
• 12 IESE Business School, University of Navarra, Barcelona, Spain
• 13 Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy

Flow is a gratifying state of deep involvement and absorption that individuals report when facing a challenging activity and they perceive adequate abilities to cope with it ( EFRN, 2014 ). The flow concept was introduced by Csikszentmihalyi in 1975, and interest in flow research is growing. However, to our best knowledge, no scoping review exists that takes a systematic look at studies on flow which were published between the years 2000 and 2016. Overall, 252 studies have been included in this review. Our review (1) provides a framework to cluster flow research, (2) gives a systematic overview about existing studies and their findings, and (3) provides an overview about implications for future research. The provided framework consists of three levels of flow research. In the first “Individual” level are the categories for personality, motivation, physiology, emotion, cognition, and behavior. The second “Contextual” level contains the categories for contextual and interindividual factors and the third “Cultural” level contains cultural factors that relate to flow. Using our framework, we systematically present the findings for each category. While flow research has made progress in understanding flow, in the future, more experimental and longitudinal studies are needed to gain deeper insights into the causal structure of flow and its antecedents and consequences.

Introduction

Flow “is a gratifying state of deep involvement and absorption that individuals report when facing a challenging activity and they perceive adequate abilities to cope with it” ( EFRN, 2014 ). The phenomenon was described by Csikszentmihalyi (1975) in order to explain why people perform activities for no reason but for the activity itself, without extrinsic rewards. During flow, people are deeply motivated to persist in their activities and to perform such activities again ( Csikszentmihalyi, 1975 ; EFRN, 2014 ). Csikszentmihalyi (1975 , 1990) distinguished up to nine characteristics of the flow experience: (1) challenge-skill-balance, (2) merging of action and awareness, (3) clear goals, (4) unambiguous feedback, (5) concentration on the task, (6) sense of control, (7) loss of self-consciousness, (8) time transformation, and (9) autotelic experience.

The first of these characteristics—the challenge-skill balance—gained much attention in flow research. In his Flow Channel Model, Csikszentmihalyi (1975) operationalized flow in the context of skills and challenges: if the individual’s skills meet the situational challenges, the individual is in the so-called flow channel and flow occurs. In later modifications of this model, as in the Experience Fluctuation Model (EFM), flow was said to occur if both challenges and skills are high and in balance (e.g., Massimini et al., 1987 ; Carli et al., 1988 ; Csikszentmihalyi, 1997 ). This assumption gained empirical support: for example, Inkinen et al. (2014) showed that if challenges and skills are high and in balance, this combination is characterized by an active and pleasant emotional experience, as described in the EFM. Also, a recent meta-analytical study confirmed the stability of challenge-skill balance as a condition of flow ( Fong et al., 2015 ), together with clear goals and sense of control.

Later, Nakamura and Csikszentmihalyi (2002) and Landhäußer and Keller (2012) sorted Csikszentmihalyi’s (1990) characteristics of flow experience into preconditions and components of flow. They also defined the balance between task demands and skills as a central precondition of flow, together with clear goals and clear feedback. They defined components of flow as concentration, merging of action and awareness, sense of control, autotelic experience, reduced self-consciousness, and transformation of time. Further conceptualizations of flow exist (e.g., Bakker, 2005 ; Engeser and Rheinberg, 2008 ; Abuhamdeh, 2021 ; Barthelmäs and Keller, 2021 ; for an overview see Engeser et al., 2021 ; Peifer and Engeser, 2021 ). Recently, Peifer and Engeser (2021) have critically discussed the existing components of flow and proposed an integration of those into the three meta-components absorption , perceived demand-skill balance , and enjoyment .

Since the introduction of the flow concept, there has been much research investigating the concept itself, its preconditions, and its consequences. The research shows that “flow experiences can have far-reaching implications in supporting individuals’ growth, by contributing both to personal wellbeing and full functioning in everyday life” ( EFRN, 2014 ). Potentially due to its positive consequences, flow research is further growing and there is a wealth of empirical articles dedicated to this phenomenon. However, due to the large amount of studies, there is a lack of a broad and systematic overview on flow research. Accordingly, this review aims to provide such a structured overview of flow research and to provide directions for future flow research.

Since 2012, the European Flow-Researchers’ Network (EFRN) has met on a yearly basis to foster scientific progress in flow research and application. Following this aim and having identified the described lack of agreement within flow research, the network decided in their meeting in November 2015 to unite their expertise and provide a scoping review on studies addressing flow experience published as of the year 2000. The advantage of a scoping review is that it collects, evaluates and presents the available research with a more systematic approach than is used in traditional review articles ( Arksey and O’Malley, 2005 ). Compared to meta-analyses or systematic reviews, a scoping review regards not just a specific, narrow research question, but a broad scope of research with respect to a certain concept ( Arksey and O’Malley, 2005 ), in our case, flow experience. Accordingly, a scoping review aims to identify and structure existing research in order to provide a framework and to build a basis for future research.

The scoping review follows three steps: first, we present a framework to structure flow research. Second, we review empirical flow research that has been published between 2000 and 2016. Third, based on our results, we discuss implications for future research.

Framework to Structure Flow Research

In order to structure and review the empirical research regarding flow experiences, the authors developed a framework (see Figure 1 ). The framework consists of three circles lying within each other and containing categories of flow research. The inner circle represents individual factors. On this individual level, we distinguish between the categories of personality, motivation, physiology, emotion, cognition, and behavior. The middle circle—the contextual level—represents the categories contextual and interindividual factors and the outer circle represents the cultural category. Within our framework, all categories contain preconditions or consequences of flow, and all categories can influence each other.

Figure 1. Categorization of flow research 2000–2016.

As proposed by Arksey and O’Malley (2005) , our scoping review was developed using the following 6 steps.

Identification of the Research Question

The importance of providing a scoping review on flow experience was identified during the 4th meeting of the European Flow Researchers’ Network (EFRN) in Braga (Portugal), 2015. To fulfill this aim, the network searched for a systematic overview of the existing flow research as a basis for future research. The finding of that literature search was that the number of publications on flow experience is growing, but that a systematic overview was not available. Accordingly, the EFRN decided to unite their expertise to develop such a systematic overview, i.e., a scoping review. To start, during the 4th EFRN meeting in Braga (Portugal), EFRN members worked on a preliminary framework to categorize flow research.

Literature Research

For the literature search, we consulted the platforms PsycInfo, PubMed, PubPsych, Web of Science and Scopus . We searched for empirical studies using the terms “ flow/optimal experience/challenge-skill balance” in order to cover different terms for flow which are typically used in the literature. Also, we excluded “cerebral blood flow” and “work-flow centrality,” as these terms produces many false hits. Further, we decided to add the term “ Csikszentmihalyi ” to the search, as we considered that reputable articles on flow would cite Csikszentmihalyi and, at the same time, many articles which are not related to flow experience would be excluded. We only included empirical studies that were published between 2000 and 2016. The resulting search string was (for PsycInfo):

(((“flow” or “optimal experience” or “challenge-skill balance”) and “Csikszentmihalyi”) not “cerebral blood flow” not “work-flow centrality”).af. and (“2000” or “2001” or “2002” or “2003” or “2004” or “2005” or “2006” or “2007” or “2008” or “2009” or “2010” or “2011” or “2012” or “2013” or “2014” or “2015” or “2016”).yr.

We did not include conference abstracts or articles that were not in the English language. Also, within this first step, we excluded publications that clearly did not deal with the topic of flow experience. The literature search was conducted in 2016 and updated in 2017 to cover also the full year of 2016.

Selection of Relevant Studies

Overall, we found 257 publications that were then rated by the authors with respect to their relevance for our scoping review. In the next step, publications were excluded if they did not contain original data on flow experience. Accordingly, twelve empirical studies were excluded because although the concept of flow was discussed, their data did not investigate flow experience. Forty-six articles were excluded because they were theoretical articles, reviews, meta-analyses or book chapters without original data. From the 257 publications, 199 empirical studies were included in the review ( Table 1 ).

Table 1. Overview of the studies included in this review ( N = 252).

Charting the Information

During the 5th EFRN meeting in Milan (Italy), in November 2016, the preliminary framework of flow research as agreed during the 4th EFRN meeting was adapted. Based on the identified articles within our literature research, categories were added if necessary to adequately describe the literature. The final framework that was used in this Scoping Review is depicted in Figure 1 .

During the meeting in Milan, experts from the EFRN were assigned to each category, and were responsible for that category in the following process. All experts are active flow researchers and members of the EFRN, who have published peer-reviewed papers in the field of their respective category. These experts are the team of authors of this Scoping Review.

In order to ensure a common understanding of the categories, the experts provided a clear description of their category. These were gathered, shared, and discussed between the authors. The outcome of step 4 was a final document which contained the agreed list of categories and their respective descriptions. This document forms the basis of the categorization of articles in the following step 5.

All articles were then distributed among the authors for them to rate their relevance for each category (see Figure 1 ) based on the abstracts. It was therefore possible that one article would be rated as being relevant for more than one category. Every article was independently reviewed by two authors. Empirical studies that were rated as relevant to the category by both authors were immediately included in the review of the category. Empirical studies that were only rated as relevant to the category by one author were again rated by the responsible expert(s). If he or she rated this article as relevant, it was also included in the review of the category. Otherwise, it was excluded.

Collating, Summarizing and Reporting of Study Results

A large table listing all articles with their respective categories as rated by the authors was sent to the experts (i.e., the authors for a specific category) in order to start the process of summarizing the study results. In addition to the articles in the table, experts could include further empirical articles which had not been found in the initial search that they considered relevant for their respective category. That way, we aimed at providing a broad picture of flow research, as required in a Scoping Review. Forty-one additional empirical studies were included in the review by our experts and twelve articles from the EFRN publication list. Table 1 presents all included empirical studies. Next, experts extracted all relevant articles for their category from the large table and created a table of articles of their category. The final tables of included articles for each category can be found in the Results section for the respective categories.

Based on this extraction, and on the description of the category, experts summarized the results of articles placed in their assigned category, thereby ignoring findings reported in an article that did not belong to that category: 93 of the articles are represented in more than one category, each time with a different focus (see Table 1 ). To achieve a coherent manuscript without too many redundancies, the content of each category was revised during an internal review process.

Discussion of the Results and Implications for Future Research

In addition to the summaries of the categories in the result section, experts collected points for discussion. These points were picked up and integrated into our general discussion of flow research, which built step 6 of our Scoping Review. During the 6th EFRN meeting in Tilburg (Netherlands, 2017), these points were discussed within the network and further elaborated. At this point, and in line with the aims of the EFRN, implications for future research which would foster scientific progress in flow research were identified.

The following section provides the expert summaries of each category. Table 2 provides an overview of all categories, the number of integrated articles and the operationalization of the respective category.

Table 2. Overview of categories.

Personality

The category Personality and Flow included studies that investigated personality traits and motives as stable individual factors. Studies that dealt with heritability or genes of flow proneness and individual differences were also included. Expert ratings revealed that 31 articles have met these inclusion criteria. Seven additional articles were included by our experts and two articles from the EFRN publication list. The final list of articles that were integrated into this section is depicted in Table 3 .

Table 3. Personality.

The personality studies on flow can be divided into four categories: (1) studies dealing with autotelic personality, (2) dispositional proneness to experience flow and its relation to Big Five personality traits, (3) the relationship of flow with other personality traits or motives and (4) flow and motive-fitting situations.

Studies Dealing With Autotelic Personality

Autotelic personality is the ability to enter a flow state relatively easily ( Csikszentmihalyi and Csikszentmihalyi, 1988 ) which was investigated in an interview-study from Sugiyama and Inomata (2005) . Moneta (2004) and Abuhamdeh and Csikszentmihalyi (2009) state that intrinsic motivation is associated with autotelic personality, but little is known about its exact components. Existing studies suggest that these components of autotelic personality are personal innovativeness, self-efficacy, control, focused attention ( Tan and Chou, 2011 ), and the achievement motive ( Baumann and Scheffer, 2011 ; Busch et al., 2013 ).

Dispositional Proneness to Experience Flow and Its Relation to Big Five Personality Traits

Flow proneness is a dispositional tendency to experience flow and there are large individual differences in the frequency and intensity of flow experiences. Several self-report questionnaires have been developed to measure the variation between individuals in flow proneness e.g., Jackson and Eklund’s Dispositional Flow Scale-2 ( Jackson and Eklund, 2002 ; Jackson et al., 2008 ; e.g., applied by Sinnamon et al., 2012 , Johnson et al., 2014 ); and the Swedish Flow Proneness Questionnaire (SFPQ, Ullén et al., 2012 ). Existing studies suggest that flow proneness is related to well-established personality traits and that this association has a biological basis: Ullén et al. (2012) found that flow proneness is correlated with the Big Five personality traits emotional stability (i.e., low neuroticism) and conscientiousness. In addition, trait flow is related to extraversion, openness to experience, and agreeableness ( Ullén et al., 2016 ). Other studies found that dispositional flow is associated with high extraversion and low neuroticism, and trait emotional intelligence in musicians ( Marin and Bhattacharya, 2013 ; Heller et al., 2015 ). In addition, openness and music-specific flow were found to be the strongest predictors of music practice ( Butkovic et al., 2015 ). In line with this, further studies suggest that extraversion and openness to experience are positively related to flow, while high neuroticism and introversion related to less flow experience ( Vittersø, 2003 ; Baumann and Scheffer, 2010 ; Mesurado and Richaud de Minzi, 2013 ; Bassi et al., 2014b ; Heller et al., 2015 ).

The Relationship of Flow With Other Personality Traits or Motives

Other personality traits also seem to be associated with flow experience: Bailis (2001) found that athletes’ trait self-handicapping score was positively related to optimal experience in competition. High mental toughness, i.e., a personal capacity supporting the process of high performance ( Jackman et al., 2016 ), perceived motivational climates, and individuals’ goal orientations ( Moreno Murcia et al., 2008 ) could account for differences in dispositional flow in athletes. Further, Kuhnle et al. (2012) found that self-control predicted flow experiences in eighth graders. Keller and Blomann (2008) found that a strong internal locus of control fosters flow under a skill-demand fit. Furthermore, studies suggest that action orientation fosters flow under skill-demand fit ( Keller and Bless, 2008 ) and even under suboptimal (no skill-demand fit) conditions ( Baumann et al., 2016 ). Beard and Hoy (2010 ; using state flow) and Vealey and Perritt (2015 ; using dispositional flow) found that optimism was positively related to flow whereas another study with Japanese students found that shyness predicted the frequency of flow experience ( Hirao et al., 2012b ). However, while empirical studies show that personality factors foster flow experiences, situational factors seem to have a bigger effect on flow ( Fullagar and Kelloway, 2009 ; Ullén et al., 2016 ).

Using the SFPQ, Mosing et al. (2012) measured genetic influences on flow proneness in a cohort of adult twins and multivariate twin modeling indicated a moderate heritability of flow proneness. De Manzano et al. (2013) used positron emission tomography (PET) and found a positive relation between flow proneness and D2 receptor availability in the striatum. Their results suggested that the differences in the dopamine system could reflect personality differences.

Flow and Motive-Fitting Situations

Studies indicate that motives foster flow experiences in motive-fitting situations ( Schattke, 2011 ; Oertig et al., 2014 ; Schüler et al., 2016 ). For example, Schüler et al. (2016) found that people scoring high on the autonomy motive experience flow in situations that satisfied participant’s autonomy-motive. Furthermore, Mills and Fullagar (2008) found that the need for autonomy moderated the relationship between flow and intrinsic motivation. Oertig et al. (2014) found that a high avoidance motive results in greater flow when performance-avoidance goals were induced. Schüler et al. (2010) found that the feeling of competence resulted in higher flow of participants who had a high achievement motive in sports [see also Schüler and Brandstätter (2013) ]. Furthermore, high achievement motive and high hope of success were positively correlated with flow experience of wall climbers’ and students ( Peterson and Miller, 2004 ; Schüler, 2007 ; Schattke, 2011 ; Schattke et al., 2014 ).

The category Motivation and Flow included studies that dealt with intrinsic or extrinsic motivation, interest, and volition. Also included were studies that dealt with motivational concepts such as self-determination, self-efficacy, self-regulation, and locus of control. Expert ratings revealed that 44 articles have met these inclusion criteria. Another eight articles were included by our experts and two articles from the EFRN publication list. The final list of articles that were integrated into this section is depicted in Table 4 .

Table 4. Motivation.

The motivation studies on flow can be divided into four categories: studies dealing with flow and (1) motivational indicators (volition, engagement, goal orientation, achievement motive, interest, intrinsic motivation), (2) self-determination (3) self-efficacy, and (4) social motivation.

Motivational Indicators

If “motivation” can be simplistically defined as “move to action,” for its part, “volition” can be simplistically defined as “will to persist in action.” Thus, if motivation promotes an intention to act, then volition protects it ( Corno, 2001 ). It was found that volition is positively linked to flow (e.g., Schattke, 2011 ). Another motivational indicator associated with flow is engagement, which “reflects an employee’s intention to throw their full self—heads, hands, and heart—into their work” ( Plester and Hutchison, 2016 , p. 4). Many studies investigated the association between the two concepts (e.g., Karageorghis et al., 2000 ; Shernoff et al., 2003 ; Montgomery et al., 2004 ; Rha et al., 2005 ; Steele and Fullagar, 2009 ; Belchior et al., 2012 ; Ulrich et al., 2014 ; Valenzuela and Codina, 2014 ; Pocnet et al., 2015 ; Mesurado et al., 2016 ; Plester and Hutchison, 2016 ). Goal orientation was also found to be linked to flow (e.g., Delle Fave and Massimini, 2005 ; Moreno Murcia et al., 2008 ; Schüler et al., 2010 ; Oertig et al., 2013 , 2014 ; Bonaiuto et al., 2016 ; Jackman et al., 2016 ; Ozkara et al., 2016 ), as well as the achievement motive (e.g., Engeser and Rheinberg, 2008 ; Baumann and Scheffer, 2011 ; Busch et al., 2013 ; Schüler and Brandstätter, 2013 ; Schattke et al., 2014 ; see Personality and Flow). Furthermore, interest, which can be described as a motivational state resulting from attraction to a certain domain or activity ( Reeve, 2008 ), was found to be related to flow (e.g., Eisenberger et al., 2005 ; Bressler and Bodzin, 2013 ; Bachen et al., 2016 ; Bricteux et al., 2017 ). Intrinsic motivation was investigated particularly often in its relation to flow, with evidence for a positive link found in various settings, such as education ( Schüler et al., 2010 ; Keller et al., 2011b ; Valenzuela and Codina, 2014 ; Meyer et al., 2016 ), Information and Communication Technologies (ICT) use ( Voiskounsky and Smyslova, 2003 ; Montgomery et al., 2004 ; Keller and Bless, 2008 ; Yan and Davison, 2013 ; Kim et al., 2014 ; Chen and Lu, 2016 ); daily activities ( Gaggioli et al., 2013 ) and physiological aspects ( Keller et al., 2011a ; Ulrich et al., 2014 ).

Self-Determination

Self-determination theory (SDT) “is an empirically derived theory of human motivation and personality in social contexts that differentiates motivation in terms of being autonomous and controlled” ( Deci and Ryan, 2012 , p. 416). Autonomous motivation combines forms of intrinsic motivation with those forms of extrinsic motivation, which go along with a sense of identification with the activity and its values; accordingly, it goes along with increased volition and self-endorsement ( Deci and Ryan, 2008 ). In contrast, controlled motivation is associated with experiencing the “pressure to think, feel, or behave in particular ways” ( Deci and Ryan, 2008 , p. 182). Many authors (e.g., Schüler et al., 2010 ; Schattke, 2011 ; Bassi and Delle Fave, 2012a , b ; Fulmer and Tulis, 2016 ) consider that flow experience is linked to autonomous motivation. Studies which examine flow in the context of self-determination theory showed for example that work-related flow is associated with both autonomous regulation and controlled regulation ( Bassi and Delle Fave, 2012a ). Furthermore, raising children in a way that promotes self-determination will help them to engage in activities which will enhance their flow experience ( Schattke, 2011 ). In another study, it was found that flow enhanced learning motivation in computer-based learning systems if participants experienced self-control ( Kim et al., 2014 ). Goal-directed activities with clear instructions are supported in environments where the individual feels autonomous and self-determined (e.g., providing choices). These activities are motivating as well as flow-inducing ( Novak et al., 2003 ). Conceptually, and on the approach-avoidance spectrum, the approach aspect of goals is likely to promote intrinsic motivation because it facilitates challenge appraisals and task absorption, whereas the avoidance aspect of goals is likely to undermine intrinsic motivation because it evokes threat appraisals, anxiety, and self-concern ( Elliot, 2005 ).

Self-Efficacy

This category of studies within this section reviews studies dealing with flow and self-efficacy, i.e. the “people’s judgments of how well they can organize and execute, constituent cognitive, social, and behavioral skills in dealing with prospective situations” ( Bandura, 1983 , p. 467). The degree of self-efficacy affects the initiation, persistence and effort in activities ( Bandura, 1977 ), and is, thus, an influential motivational theory. Results of empirical studies confirm that self-efficacy is linked with flow frequency and higher levels of challenge and skills showing that self-efficacy predicts flow over time ( Rodríguez-Sánchez et al., 2011a ; Heutte et al., 2016 ). Collective efficacy beliefs predict collective flow over time ( Salanova et al., 2014 , see sections Interindividual Factors and Flow and Cognition and Flow ). High levels of efficacy beliefs have a positive impact on flow experiences in academic settings ( Salanova et al., 2006 ; Bassi et al., 2007 ; Heutte et al., 2016 ). Various aspects of Bandura’s (1986) self-regulation learning model were shown to exert a significant and positive effect on flow ( Lee and LaRose, 2007 ; Rodríguez-Sánchez et al., 2011a ; Chen and Sun, 2016 ).

Social Motivation

Some first studies highlight the social motivational conditions of flow ( Sawyer, 2003 ; Armstrong, 2008 ; Walker, 2010 ; Heutte et al., 2016 ). Although this requires further investigation, it seems that the quality of interpersonal relationships, supporting in particular basic psychological needs (autonomy, competence, and relatedness), will support a motivational climate favorable to the emergence of flow within a group.

The category Physiology and Flow included studies that used physiological and/or neuropsychological methods (e.g., ECG, EEG, EMG, fMRI, eye-tracking, saliva sampling, etc.) to measure the relationship of physiological parameters with flow. Expert ratings revealed that nine articles meet these inclusion criteria. Another twelve articles were included by the experts. The final list of articles integrated into this section is set out in Table 5 .

Table 5. Physiology.

Subtopics identified in the literature include flow’s relationship with (1) physiological arousal as represented by sympathetic (SA) and parasympathetic activation (PA), and cortisol, (2) facial muscle activation (FMA) and (3) neural activity.

Physiological Arousal

Flow was found to relate negatively to cardiac output and systolic blood pressure, and positively to diastolic blood pressure and heart rate ( de Manzano et al., 2010 ; Gaggioli et al., 2013 ; Harris et al., 2017 ). Furthermore, mixed associations of flow with SA were found, with some studies showing positive associations ( Nacke and Lindley, 2008 ; de Manzano et al., 2010 ; Gaggioli et al., 2013 ; Ulrich et al., 2016b ), other studies showing negative associations ( Harmat et al., 2015 ; Tozman et al., 2015 ; Harris et al., 2017 ) and—under stress—the relationship was found to be inverted u-shaped ( Peifer et al., 2014 ; Tozman et al., 2015 ). Two studies found no association between flow and SA ( Kivikangas, 2006 ; Hirao et al., 2012a ). Similarly, PA has been negatively associated with flow ( de Manzano et al., 2010 ; Keller et al., 2011a ), but under stress, studies identified a positive relationship ( Peifer et al., 2014 ) and an inverted u-shaped relationship ( Tozman et al., 2015 ). Respiratory depth, related to PA, increased during flow ( de Manzano et al., 2010 ). Regarding flow and cortisol, studies have found a positive association ( Keller et al., 2011a ), no association ( Brom et al., 2014 ), a negative effect of high cortisol on flow ( Peifer et al., 2015 ) and an inverted u-shaped relationship between cortisol and flow in stress-relevant conditions ( Peifer et al., 2014 ; Tozman et al., 2015 ).

Facial Muscle Activation

Studies examining FMA found associations with flow for the Zygomaticus Major ( de Manzano et al., 2010 ; Nacke et al., 2011 ), Orbicularis Oculi ( Nacke et al., 2011 ), and Corrugator Supercilii ( Kivikangas, 2006 ). In this sub-category, findings were also inconsistent.

Neural Activity

Neuroscientific research showed that flow was characterized by greater activation of the “multiple-demand system,” which is involved in task-relevant cognitive functions, and reduced activation of the default mode network ( via a relative increase in the dorsal raphe nucleus), which is linked to self-referential processing ( Ulrich et al., 2014 , 2016a , 2016b ). Computer gamers reporting flow showed increased activity in the neocerebellum, somatosensory cortex, and motor areas, possibly indicating a synchronization between reward-related brain structures and task-relevant cortical and cerebellar areas during flow ( Klasen et al., 2012 ). Larger stimulus-preceding negativities (SPNs) were found during flow, indicating increased motivation and anticipatory attention ( Meng et al., 2016 ). Experts experiencing more flow had greater right temporal cortical activity when imagining the activity, possibly reflecting the automaticity of a highly trained skill ( Wolf et al., 2015 ).

Of particular interest is frontal activity during flow, inspired by the Hypofrontality Hypothesis suggested by Dietrich (2004) . The Hypofrontality Hypothesis states that analytical and meta-conscious capacities are temporarily suppressed during flow, physiologically indicated by a downregulation of prefrontal activity. Respective findings support no association of flow with frontal activity ( Harmat et al., 2015 ), or a greater activation of the ventrolateral prefrontal cortex ( Yoshida et al., 2014 ). Findings regarding EEG activity were similarly mixed: Nacke et al. (2011) found no relationship, while Berta et al. (2013) found that alpha and lower- and mid-beta power predicted flow.

The category Emotion and Flow included studies that dealt with a wide range of concepts associated with different components of the emotional experience, which tends to be generally associated with a certain subjective degree of pleasure and displeasure, or positive and negative experiences, such as affect, mood, wellbeing, enjoyment, activation, or excitement. Although a unique and clear definition of emotion does not exist in these articles, the relation of emotion with flow experience seems to follow a clear understanding of the kind of emotional components that can be relevant when studying this relationship. Although the concept of emotion, in its broad sense, can integrate cognitive, affective, and behavioral or even physiological aspects, this section tried to avoid overlapping with others that are specifically devoted to one of these components in its relation with flow experience (e.g., cognition and flow). Expert ratings revealed that 40 articles have met these inclusion criteria. Four additional articles were included by our experts and five articles from the EFRN publication list. The final list of articles that were integrated into this section is depicted in Table 6 .

Table 6. Emotion.

The identified studies show four main subtopics, i.e., (1) affect, (2) wellbeing, (3) enjoyment, and (4) emotional contagion. Studies investigated relationships of the emotional concepts with several components of flow, in particular with challenge-skill balance ( Delespaul et al., 2004 ; Delle Fave and Massimini, 2005 ; Sugiyama and Inomata, 2005 ; Schweinle et al., 2008 ; Tramonte and Willms, 2010 ; Robinson et al., 2012 ; Panadero et al., 2014 ; Sartori and Delle Fave, 2014 ). In general, these studies showed that high challenge-skill balance is associated with higher positive emotional states (e.g., activation, excitement, positive affect).

Regarding the first subtopic, several studies suggest a positive relationship between flow and positive affect. Of relevance is the study by Baumann and Scheffer (2010) showing that achievement flow is supported by dynamic changes in positive affect, highlighting the role of reduced and restored positive affect. Some other findings show that flow predicts positive mood or positive affect ( Eisenberger et al., 2005 ; Schüler, 2007 ; Collins et al., 2009 ; Fullagar and Kelloway, 2009 ; Baumann and Scheffer, 2010 ; Tobert and Moneta, 2013 ; Inkinen et al., 2014 ; Bachen et al., 2016 ; Ozkara et al., 2016 ). The reverse relationship also exists, with studies demonstrating that both positive and negative affect are significant predictors of flow experience (e.g., Collins et al., 2009 ; Kopačević et al., 2011 ; Hirao and Kobayashi, 2013 ; Tobert and Moneta, 2013 ). Cseh et al. (2015) demonstrated that flow is purported to have positive consequences on affect and performance. Some other studies looked at the relationship between flow and affect in different groups of participants ( Rogatko, 2009 ; Fullagar et al., 2013 ; Bassi et al., 2014a ; Fink and Drake, 2016 ; Tyagi et al., 2016 ), different activities or domains ( Pinquart and Silbereisen, 2010 ; Engeser and Baumann, 2016 ; Silverman et al., 2016 ), or in relation to specific variables, for example, the quality of a relationship or experiential wisdom ( Karageorghis et al., 2000 ; Graham, 2008 ; Rathunde, 2010 ), and trait emotional intelligence ( Marin and Bhattacharya, 2013 ; see Personality and Flow ).

In studies considering wellbeing, flow experience tends to be positively associated with the concept of emotional wellbeing ( Wanner et al., 2006 ), and psychological wellbeing ( Bassi et al., 2014a , b ), with others showing that flow experience can predict psychological wellbeing ( Steele and Fullagar, 2009 ; Bassi et al., 2014b ), life satisfaction ( Collins et al., 2009 ; Chen et al., 2010 ; Bassi et al., 2014b ), happiness ( Csikszentmihalyi and Hunter, 2003 ), job satisfaction ( Maeran and Cangiano, 2013 ), course satisfaction ( Shin, 2006 ), and e-satisfaction and e-loyalty ( Hsu et al., 2013 ).

Regarding enjoyment, studies showed that it is positively associated with flow, with authors trying to understand which flow dimensions are related to the perception of enjoyment and under what circumstances ( Wright et al., 2007 ; Wissmath et al., 2009 ; Thin et al., 2011 ; Diaz and Silveira, 2013 ; Inkinen et al., 2014 ; Schmierbach et al., 2014 ). In a diary study which aimed at examining the relationship between flow experiences and energy both during work and non-work, results indicated that the flow-characteristics absorption and enjoyment were associated with energy only after work, accompanied by feeling more vigorous and less exhausted ( Demerouti et al., 2012 ).

Emotional Contagion

Two studies brought the topic of flow to collective and group contexts. It was shown that positive collective gatherings could stimulate shared flow experiences, promoting personal wellbeing and social cohesion ( Zumeta et al., 2016 ). In the group context of a classroom, it was shown that Students’ perceptions of their classmates’ flow as well as their teachers’ flow were related to their own reported flow experience ( Culbertson et al., 2015 ). Authors concluded that their finding can be explained by contagion effects of flow within the group, in line with emotional contagion theory ( Hatfield et al., 1994 ).

The category Cognition and Flow included studies that dealt with perception, attention, decision-making, and cognitive control. Also, brain studies referring to cognitive processes during flow experiences and effortless attention were reviewed in this section. Studies dealing with embodied cognition (e.g., body image, agency, intentions) and effects of flow experiences on cognitive processes (e.g., memory and reasoning) were reviewed. Expert ratings revealed that 26 articles met these inclusion criteria. Two additional articles were included by our experts and one article from the EFRN publication list. The final list of articles that were integrated into this section is presented in Table 7 .

Table 7. Cognition.

Cognition studies on flow can be divided into two main areas: (1) those that studied its relationships with cognitive processes, and (2) those that analyzed cognitive aspects of flow-related processes while considering flow in specific applied contexts.

First of all, flow itself can be considered a state of consciousness in which an individual is fully concentrated on, paying attention to and engaged in a certain activity ( Delle Fave and Massimini, 2005 ); at the same time, flow can be considered as a process or a dynamic mental activity characterized by clear goals, a match between capacity and challenge, absence of disturbances, experience of mastery, etc. ( Pearce et al., 2005 ; Kawabata and Mallett, 2011 ). There is not a discrepancy between state and process—rather they can be seen as related and interdependent; a flow state typically occurs when an individual engages in a process with the formerly mentioned characteristics.

Relationships With Cognitive Processes

Flow is related to attentional processes. For example, as demonstrated by Harris et al. (2017) , sustained attention toward the task is needed as a component of flow. Indeed, from a cognitive point of view, when attention is hindered by other processes or stimuli, flow experience is reduced or blocked. For instance, in the experiment by Guizzo and Cadinu (2016) , feeling objectified by men’s gaze draws women’s attention away from the rewarding activity and decreases flow. However, studies on flow proneness highlight no relation or very weak relation with intelligence in two large samples ( Ullén et al., 2012 ), showing that although flow is related to cognitive processes, it is only weakly associated with cognitive ability. In general, cognitive studies tend to confirm the skill-demands compatibility hypothesis in the generation of flow ( Payne et al., 2011 ; Schiefele and Raabe, 2011 ; Harris et al., 2017 ). Moreover, flow has been found to be positively related to an intuitive approach to decision making ( Kuhnle and Sinclair, 2011 ). Consistently, flow seems to be disassociated from sense of agency or the impression of being the author of one’s own actions ( Vuorre and Metcalfe, 2016 ). Indeed, sense of agency is partially influenced by metacognitive, complex judgments of authorship over the action ( Synofzik et al., 2008 ), which are more influenced by overall evaluation of one’s own control over the task, while flow appears to be associated with positive assessment and enjoyment of the overall experience. In other words, the reporting of having experienced an optimal experience is not related to feel more or less to be the author of one’s own actions. Neuropsychological data also showed that flow is associated with sense of control ( Ulrich et al., 2014 , see Physiology and Flow ). Further, it was found that cognitive flexibility ( Moore, 2013 ) and mindfulness predicted flow ( Kee and John Wang, 2008 ; Moore, 2013 ). Studies on flow involving creative activities highlighted that flow was not affected by cognitive load ( Cseh et al., 2016 ). Rather, flow experience could help banish or reduce unwanted cognitive processes (e.g., intrusive thoughts, rumination), for example in cancer patients ( Reynolds and Prior, 2006 ).

Cognitive Aspects of Flow-Related Processes in Specific Contexts

The most popular field of research with regards to flow and cognitive processes are studies related to learning. Ortner et al. (2014) analyzed the effects of computerized adaptive testing (CAT) vs. computerized fixed item testing (FIT) on Students’ motivation and flow using a matrices non-verbal computer-based test assessing reasoning on the basis of figural items. The CAT version adapts to the learner’s online performance selecting items on the basis of the learner’s previous response, while the FIT version features fixed items increasing in difficulty. Contrary to hypotheses, fixed item testing obtained superior ratings of motivation and no differences between the conditions were found for flow. In a study by Konradt and Sulz (2001) , most of the participants entered flow while using a hypermedia learning system, independently of task condition (scanning or browsing the contents); importantly, however, flow was not associated with improved learning. Diaz and Silveira (2013) analyzed flow experiences in high school music students attending a summer music camp; the highest ranked flow-inducing activities showed strong associations between attention and enjoyment. Another study ( Winberg and Hedman, 2008 ) compared guiding/open instructions during a learning task and considered their effects on flow components. Guiding instructions correlated with high levels of “challenge,” “enjoyment,” and “concentration” and low levels of “perception of control,” while the opposite happened for the other condition. However, Pearce et al. (2005) found that a “process” (rather than a state) model of flow more adequately explains students’ outcomes, in that skills may change over time during learning (e.g., growing). In this sense, flow should probably be measured more times than just once after or during the learning process. Schweinle et al. (2008) employed experience sampling methods to analyze flow following 12 class lessons. They found that individual affect was influenced by the interaction of challenge and skill while social affect and efficacy were more impacted by perceived skill than by challenge (see Emotion and Flow ). This is consistent with studies attempting to integrate flow with social-cognitive theory, namely, the idea of behavior resulting from cognitive processes and external/environmental influences ( Lee and LaRose, 2007 ; Rodríguez-Sánchez et al., 2011a ). These studies found that high self-efficacy, or the belief about one’s own abilities to perform a given action, may be a predictor of optimal experience (see Motivation and Flow ).

Another important field of flow research is sports. For example, Swann et al. (2017) employed interviews to explore the characteristics of clutch performances (i.e., performance under pressure) in professional athletes. They found that clutch performances are different from flow, in that they are characterized by heightened awareness, deliberate concentration and intense effort. Also, an “inductive” qualitative research study on golfers ( Swann et al., 2015a ), or in other words, a methodology that did not intend to confirm flow characteristics as described by traditional theory but instead intended to capture the experience of the participants as described by them, suggested that flow was self-aware, observable and characterized by altered cognitive and kinesthetic perceptions.

Finally, flow has been found to be positively related to transportation and spatial presence while watching movies ( Wissmath et al., 2009 ). Transportation has been defined as the “process where all mental systems and capacities become focused on events in the narrative” ( Green and Brock, 2000 , p. 701), with high involvement and absorption of the user in the movie he or she is watching, while sense of presence consists in the sensation of “being” inside a real or virtual environment, related to the impression of being able to enact one’s own intentions ( Triberti and Riva, 2015 ).

The category Behavior and Flow included studies that dealt with flow and different forms of behavior such as performance (e.g., in-role/extra-role performance, physical, athletic, creative, or cognitive performance), risk taking, consumption behavior, online behavior, and addiction, as well as variables that are closely related to performance and motivate high performance such as engagement, commitment, and persistence. Expert ratings revealed that 46 articles have met these inclusion criteria. Another six articles were included by our experts and one from the ERFN publication list, although they were not found in the literature search. The final list of articles that were integrated into this section is set out in Table 8 .

Table 8. Behavior.

Within this category, the following subtopics could be identified: (1) The relationship between flow and different kinds of performance in different contexts, (2) variables that are related to high performance such as engagement and commitment, and (3) other forms of behavior such as risk taking, consumption behavior, online behavior, and addiction.

Performance

Most studies dealing with flow and behavior address the topic of performance, and they show a positive relationship between flow and performance in most cases (e.g., Demerouti, 2006 ; Engeser and Rheinberg, 2008 ; Min et al., 2015 : productivity in design process). For work-related performance, it was found that flow at work is positively related with in-role ( Demerouti, 2006 ) and extra-role performance ( Eisenberger et al., 2005 ; Demerouti, 2006 ). Baumann and Scheffer (2011) additionally found that the flow achievement motive is positively associated with work efficiency according to multisource feedback. The positive effects of flow on performance could also be shown at the team-level ( Aubé et al., 2014 ). Likewise, Kuo and Ho (2010) found that flow has positive effects on employee-reliability and paying attention to customers’ needs.

Besides work-related performance, several other studies deal with the topic of flow and athletic and physical performance (e.g., Bailis, 2001 ; Jackson et al., 2001 ): Most studies find a positive relation between flow and physical performance ( Schüler and Brunner, 2009 ; Bakker et al., 2011 ), including performance under pressure ( Swann et al., 2017 ). Similarly, training and preparation appear to have a positive effect on flow and mediate effects on performance ( Schüler and Brunner, 2009 ; Swann et al., 2015b ). Swann et al. (2015a) also find that flow is related to changes in the behavior of golfers (such as playing faster, staying calm, and showing a confident body language).

In terms of performance at school and/or cognitive performance in general, flow was found to be positively related to exam performance ( Schüler, 2007 ), cognitive performance ( Engeser and Rheinberg, 2008 ; Harris et al., 2017 ) and goal progress ( Schüler et al., 2010 ). The achievement flow motive also predicts academic success ( Busch et al., 2013 ). Guizzo and Cadinu (2016) find that low levels of flow are associated with decreased cognitive performance in an attention to response task. Furthermore, studies suggest that practice and learning in general are positively related to flow experience ( Brinthaupt and Shin, 2001 ; Pearce et al., 2005 ; Marin and Bhattacharya, 2013 ; Valenzuela and Codina, 2014 ; Heller et al., 2015 ; Bressler and Bodzin, 2016 ) and that flow is positively associated with reengagement in a task ( Keller et al., 2011b ; Pratt et al., 2016 ). Another study found that flow and learning retention in gaming were also positively associated ( Hong et al., 2013 ). Flow also presented positive effects on performance in online games ( Thornton and Gilbert, 2011 ). Overall, there seems to be a positive relation between flow and enhanced performance (for an overview see Landhäußer and Keller, 2012 ). However, two studies did not find a positive association between flow and performance ( Konradt and Sulz, 2001 ; Culbertson et al., 2015 ). The former authors, however, suggest that the students in their investigation experienced flow and therefore felt self-confident and were not open to learn for a following quiz (for more explanations, see Culbertson et al., 2015 ). Several studies find a positive relationship between flow experiences and enhanced creativity or engagement in creative tasks ( Byrne et al., 2003 ; Griffiths, 2008 ; Cseh et al., 2015 ; Dawoud et al., 2015 ; Zubair and Kamal, 2015a , b ), especially in the field of music ( MacDonald et al., 2006 ; Wrigley and Emmerson, 2013 ).

Variables That Are Related to High Performance

With respect to variables that are related to high performance, flow seems to be positively related with student engagement in the classroom ( Shernoff et al., 2003 ; Mesurado et al., 2016 ) and with learning engagement ( Bassi et al., 2007 ). Furthermore, several studies have found a positive relation between the fact of “being active” and flow ( Bassi et al., 2012 : engagement in meaningful rehabilitation activities; Drengner et al., 2008 ; Graham, 2008 ; Dawoud et al., 2015 ). Another study by Seddon et al. (2008) finds while investigating a 6-year online collaboration (working together in an online setting) that flow and engagement in that collaboration were positively related.

Other Forms of Behavior

With respect to other forms of behavior, Schüler and Nakamura (2013) found that risk behavior and flow were positively associated but only for inexperienced climbers; the relationship is mediated by self-efficacy beliefs. In line with that, Delle Fave et al. (2003) found that the opportunity to experience flow motivates climbers to take part in a risky expedition. Urmston and Hewison (2014) also find a positive relationship between flow and risk taking in learning. A study by Szymanski and Henning (2007) found that flow was negatively related to women’s self-objectification behavior. Further studies on self-objectification behavior were not found. Furthermore, Niu and Chang (2014) found that flow is positively associated with unplanned buying and that it moderates the positive relationship between internet addiction and consumer behavior. Liu and Shiue (2014) found that flow fosters purchase intention in online games. Another study found that experiencing flow was positively related with engagement in a human-animal-interaction game ( Cheok et al., 2011 ). Hsu et al. (2013) find that flow and e-loyalty are positively related.

Context Factors

The category Context Factors and Flow included studies that investigated different contexts and activities in which flow occurs (e.g., different kinds of work, study, sports etc.), as well as contextual characteristics/external circumstances that foster or hinder flow (e.g., differences in environmental characteristics, external demands and resources). Expert ratings revealed that 84 articles met these inclusion criteria. Another three articles were included by our experts and seven from the ERFN publication list, although they were not found in the literature search. The final list of articles that were integrated into this section is shown in Table 9 .

Table 9. Context factors.

In this category, the following subtopics were identified: (1) Flow in different contexts and activities and how they affect flow, (2) contextual factors and their relationships with flow, and (3) the fit of contextual factors with characteristics of the individual.

Flow in Different Contexts and Activities

Flow is always investigated during a certain activity in a certain context, and their variety in the identified studies is large: (a) work- or study-related activities such as work, learning ( Peterson and Miller, 2004 ; Rathunde and Csikszentmihalyi, 2005 ; Wright et al., 2007 ; Ceja and Navarro, 2011 ; Stephanou, 2011 ; Demerouti et al., 2012 ; Ryu and Parsons, 2012 ; Debus et al., 2014 ; Escartin Solanelles et al., 2014 ; Hernandez et al., 2014 ), and teaching ( Coleman, 2014 ), (b) leisure ( Rodríguez-Sánchez et al., 2011b ), (c) professional dancing ( Hefferon and Ollis, 2006 ; Panebianco-Warrens, 2014 ), (d) music festivals ( Jonson et al., 2015 ), (e) creative activities such as designing clothes ( Min et al., 2015 ) and visiting arts courses or making art ( Reynolds and Prior, 2006 ; Bass, 2007 ; Jones, 2013 ; van der Hoorn, 2015 ), (f) gaming (e.g., Ivory and Magee, 2009 ; Thin et al., 2011 ; Bressler and Bodzin, 2013 , 2016 ) and several online activities (e.g., Guo and Poole, 2009 ; Faiola et al., 2013 ; Hsu et al., 2013 ; Meyer and Jones, 2013 ; Wang et al., 2015 ), (g) research activities ( Hudock, 2015 ; Zha et al., 2015 ) and information technology use ( Pilke, 2004 ), (h) sports (e.g., Koehn and Morris, 2014 ; Deol and Singh, 2016 ; training vs. competition; Swann et al., 2012 , 2015a ), (i) translation activities ( Mirlohi et al., 2011 ), (j) psychological rehabilitation activities (e.g., Bassi et al., 2012 ; Nissen-Lie et al., 2015 ), (k) extreme contexts such as rituals ( Lee, 2013 ) and extreme weather during climbing ( Bassi and Delle Fave, 2010 ) and even (l) first-aid activities, whereby professionals experienced more flow than volunteers ( Sartori and Delle Fave, 2014 ). This large list shows that flow can occur in a large variety of activities and contexts ( Diaz and Silveira, 2013 ).

Are there differences between activities in their likelihood to produce flow? In general, it was found that flow is higher during working-activities compared to (active and passive) leisure activities ( Engeser and Baumann, 2016 ). For example, Bassi and Delle Fave (2012a) found that school teachers experienced more flow during work than during free-time (see: paradox of work; Csikszentmihalyi and LeFevre, 1989 ). The paradox of work states that although work is commonly associated as an unpleasant activity, individuals experience more flow—a pleasant state—during work than during free-time ( Csikszentmihalyi and LeFevre, 1989 ). In contrast, MacNeill and Cavanagh (2013) found that school leaders experienced more flow in non-school contexts. Rodríguez-Sánchez et al. (2011b) found that the flow component enjoyment was higher during non-working activities whereas absorption was higher during working activities. Magyaródi and Oláh (2015) found that work, sports and creative activities were the most typical solitary activities and work and sports were the most typical social activities that foster flow. Of course, flow has also been investigated in social contexts (e.g., Ryu and Parsons, 2012 ). For a better overview, the authors of this scoping review decided to define “ interindividual factors ” as a separate category (see below). At work, planning, problem solving, and evaluative activities especially seem to foster flow ( Nielsen and Cleal, 2010 ).

Contextual Factors and Their Relationships With Flow

The research explored in this scoping review shows that there are many contextual factors that are associated with flow at work. Maybe that is why Ceja and Navarro (2012) found in their study that there are many abrupt changes in experiencing flow at work; While flow is a self-reinforcing inner state of consciousness, contextual factors are external circumstances which cannot fully be controlled by an individual. A change of contextual factors can thus interrupt flow—and the more contextual factors exist that affect flow, the more likely are such sudden changes in flow. It was found that the motivating job characteristics of Hackman et al. (1975) are context factors that are positively associated with flow in the workplace ( Demerouti, 2006 ; Maeran and Cangiano, 2013 ). In line with this, it was found that subjective relevance ( Shernoff et al., 2003 ; Dawoud et al., 2015 ), importance ( Rha et al., 2005 ; Engeser and Rheinberg, 2008 ), and meaningfulness ( Banfield and Burgess, 2013 ; Hsu et al., 2013 ; Jonson et al., 2015 ; Bonaiuto et al., 2016 ) are positively associated with flow. All of these are concepts at the interface between person and context; if a context (e.g., a certain task or environment) aligns with the needs, values or motives of a person, it will become subjectively relevant, important and meaningful. Moreover, feedback and support are relevant precursors of flow ( Bakker, 2005 ; Guo and Poole, 2009 ; Steele and Fullagar, 2009 ; Panadero et al., 2014 ; Swann et al., 2015a ). Creative tasks (e.g., sketching: Cseh et al., 2016 ) or having the opportunity for creativity ( Moneta, 2012 ) seems also to be positively associated with flow. Having a clear goal ( Shin, 2006 ; Guo and Poole, 2009 ; van Schaik et al., 2012 ) and a clear role ( Steele and Fullagar, 2009 ; Panadero et al., 2014 ) as well as having control ( Shernoff et al., 2003 ) or autonomy ( Bakker, 2005 ) are positively associated with flow. Furthermore, it was found that being prepared ( Swann et al., 2012 ) and being recovered in the morning is positively associated with flow at work during the day ( Debus et al., 2014 ). Smith et al. (2012) found that organizational safety climate is associated with flow. In general, having enough resources is positively associated with flow at work ( Mäkikangas et al., 2010 ); a study by Emanuel et al. (2016) found that job resources (e.g., support from supervisor and autonomy) are positively associated with the flow experience of journalists. In addition, an internal locus of control was found to be positively associated with freelance journalists’ flow experience.

There are several factors of a game’s design that seem to facilitate flow. In general, interactivity and playfulness are positively associated with flow ( Rha et al., 2005 ; Voiskounsky et al., 2005 ; Cheok et al., 2011 ; Hong et al., 2013 ; Khan and Pearce, 2015 ) in gaming and in the working or learning context ( Dawoud et al., 2015 ; Meyer et al., 2016 ), while one study found that the content is more important for flow than the interaction ( Marston, 2013 ). Sharitt (2010) additionally found that a balance of difficulty was an important criterion for flow-associated game design. Lastly, instruction type is also relevant for flow: Winberg and Hedman (2008) found in an experimental design that guided instructions foster the flow components of enjoyment and concentration whereas free guiding instructions facilitate the flow component of control.

Fit of Contextual Factors With Characteristics of the Individual

Besides general context factors, the fit of the context to the individual (see also Personality and Flow ) seems to particularly matter: Moneta (2012) found evidence that a person-environment-fit fosters flow. In this respect, the best investigated flow condition is the fit between challenges of the activity and skills of the person, i.e., the challenge skill balance ( Gnoth et al., 2000 ; Eisenberger et al., 2005 ; Engeser and Rheinberg, 2008 ; Freer, 2009 ; Bassi et al., 2012 ; Belchior et al., 2012 ; Hsu et al., 2013 ; Harris et al., 2017 ; ease of use; Voiskounsky and Smyslova, 2003 ; Keller and Bless, 2008 ; Katuk et al., 2013 ; Llorens et al., 2013 ; Wrigley and Emmerson, 2013 ; Koehn and Morris, 2014 ; Sartori and Delle Fave, 2014 ; Sartori et al., 2014 ; Wang and Hsu, 2014 ). In line with this, Schmierbach et al. (2012) found that the possibility to personalize a game facilitates flow. A study from Baumann et al. (2016) found that a dynamic (i.e., varying demands) and not a static challenge-skill balance is best for flow. Similar results were found by Ceja and Navarro (2009) who state that flow experiences follow a complex dynamic. In general, and in association with the challenge-skill balance, having enough resources ( Delle Fave and Bassi, 2009 ; Bakker et al., 2011 ) and risk or uncertainty ( Urmston and Hewison, 2014 ) are associated with flow. Another example for a flow-promoting fit between the context and the individual was shown by Vittersø et al. (2001) , who found that a fit between individual’s preferred recreational mode and the recreational activity (e.g., being active or passive) was positively associated with flow.

Interindividual Factors

The category Interindividual Factors and Flow included studies that dealt with flow in social contexts, measured at the individual or collective level and as a social phenomenon (e.g., team flow, group flow, social flow etc.). Studies which looked at the effects of flow on more than one individual (e.g., small groups, social settings, networks, and other collectives) were also included. Expert ratings revealed that twelve articles met these inclusion criteria. Another article was included by our experts, although they were not found in the literature search. The final list of articles that were integrated into this section is shown in Table 10 .

Table 10. Interindividual factors.

Even though many human activities are done in social settings, the research on collective flow has not been vast, but the number of contributions is recently growing. As subtopics, we differentiate the experience of flow at the individual level , while being part of a social context (cf. Walker, 2010 ), from the experience of flow at the collective level , as if the collective has an experience of flow (cf. Sawyer, 2003 ).

Interpersonal Flow Studies at the Individual Level

Walker (2010) differentiates solitary flow experiences from social flow experiences, the latter varying on the degree of interdependence (ranging from co-active to highly interdependent). He found that participants in highly interdependent (sport) teams reported more joy than individuals performing less interdependently. Ryu and Parsons (2012) investigated social flow in the context of collaborative mobile learning and found that experiencing social flow is positively associated with the mobile learning experience. In addition, Bakker et al. (2011) studied team member flow experience among young soccer players. In short, the results indicate that social support and performance feedback from the coach are important facilitators of flow.

Magyaródi and Oláh (2015) found that for interpersonal flow experiences in social settings the level of perceived challenges should be high, as well as the level of cooperation, the immediateness/clarity of feedback, and the perceived level of skills. van Schaik et al. (2012) studied flow within an immersive virtual environment for collaborative learning. They found that the flow enablers challenge-skill match, goal clarity and feedback mediated the relationship between task constraints and learning experience. In the context of a group music composition task, MacDonald et al. (2006) found that the “no fear of failure” condition contributed to better flow. Moreover, they found that higher levels of flow related to a higher quality level of the output. In music teaching, Bakker (2005) found a crossover of the teacher’s experience of flow to students through contagion. In addition, Keeler et al. (2015) found that group singing reduces stress and fosters social flow at the individual level.

In the context of work, Smith et al. (2012) found that flow moderates the effect of leadership styles on job satisfaction and organizational commitment and partially mediates the effect on safety climate. Gute et al. (2008) found through the analysis of existing interview reports from highly creative persons that parents who foster both integration (e.g., providing emotional support) and its opposite, differentiation, (e.g., stimulation to work on personal goals) cultivate environments for creativity and flow. Using Csikszentmihalyi’s flow theory, Boyns and Appelrouth (2011) investigated the suspension of activity in public isolation and found that for most participants, “non-doing” leads to counterparts of the flow characteristics (e.g., boredom and anxiety).

Interpersonal Flow Studies at the Collective Level

Pioneering research in this perspective is the work of Keith Sawyer who defined group flow as a collective state that occurs when a group is performing at the peak of its abilities ( Sawyer, 2003 , p. 167). In this line, Salanova et al. (2014) found that collective efficacy beliefs predict collective flow over time, and that the two constructs are reciprocally related. Also, Zumeta et al. (2016) investigated shared flow during positive collective tambours/drummer (Tamborrada) gatherings. They found that positive collective gatherings stimulate shared flow experiences and in turn promote personal wellbeing and social cohesion.

Cultural Factors

Culture can be seen both as an antecedent and as a consequence of flow experience. On the one hand, culture directs the individual toward actions, behaviors and activities that can more or less favor the experience of flow activities ( Delle Fave et al., 2011 ); on the other hand, flow affects the actions of individuals, their decision-making processes, their focus of attention and their focus of behavior on certain purposes, which cause elements of culture ( Inghilleri et al., 2014 ). Considering this premise in the category Cultural Factors and Flow , studies were included that did cross-cultural investigations or dealt with individualism or collectivism, culture and the construction of the self, social identity, or special artifacts (e.g., Manga). Additionally, studies that addressed specific countries were also included here. Expert ratings revealed that 13 articles met these inclusion criteria. Another three articles were included by our experts, although they were not found in the literature search. The final list of articles that were integrated into this section is depicted in Table 11 .

Table 11. Cultural factors.

To understand the interaction between flow and culture, there are two main frameworks of research: the cross-cultural perspective, focusing on a comparison of flow experience between different cultures, and the cultural perspective, focusing on the role of flow in the diffusion or the maintenance of specific relevant cultural phenomena.

Cross-Cultural Perspective

Even if flow has been recognized as a universally valued subjective state ( Asakawa, 2010 ; Delle Fave et al., 2011 ; Csikszentmihalyi and Wong, 2014 ), several studies collect data about cross-cultural differences in the flow experience (e.g., Garces-Bacsal, 2016 ). Results in this field seem not to be proposing a unique view about which kind of culture gives more opportunity to its members to experience flow. Despite studies finding higher frequency and intensity of flow in Western societies compared to non-Western ones ( Asakawa, 2010 ; Liu et al., 2015 ; Mesurado et al., 2016 ), Western individuals seem to have a lower propensity to experience flow in meaningful social activities, related to future goals and linked to personal growth ( Coatsworth et al., 2005 ; Asakawa and Csikszentmihalyi, 2010 ; Montijo and Mouton, 2016 ). Group activities involved with flow are associated with higher reports of social identification in collectivistic societies than in individualistic ones ( Mao et al., 2016 ). Data shows that flow experience is more intense within the members of cultures characterized by a good balance between the values of both autonomy and relatedness ( Busch et al., 2013 ).

Cultural Perspective

Flow seems to be involved in the spread and the maintenance over time of several specific cultural phenomena. Flow experience represents a useful concept to reach a deep knowledge of youth behavioral trends ( Niu and Chang, 2014 ) and it seems to be involved in several leisure activities that are characteristic of different cultural environments ( Jonson et al., 2015 ; Tanaka and Ishida, 2015 ). Furthermore, flow correlates with extrinsic and intrinsic religious orientations ( Brown and Westman, 2008 ). An Italian study ( Guizzo and Cadinu, 2016 ) demonstrated that flow disruption can depend on the degree to which people rely on society beauty ideals typically promoted by Western media. Further, flow can play a key role exerting influence on the quality of the migration experience ( Delle Fave and Bassi, 2009 ; Lee, 2013 ). Despite the implication that flow can foster positive cultural and societal phenomena ( Delle Fave and Bassi, 2009 ; Lee, 2013 ; Jonson et al., 2015 ), its amoral character can also lead to dysfunctional ones (i.e., Niu and Chang, 2014 ). Evidence in this area of interest are still scarce and further research is needed to clarify and validate results.

General Discussion

With this Scoping Review, we aimed to (1) present a framework to structure flow research and (2) provide a systematic overview on empirical flow research of the years 2000–2016. In this general discussion, we summarize the results of this review, outline central points of discussion and describe strengths and weaknesses identified in the literature. Following this, we address our final aim: (3) to discuss the implications of our review for future research.

Firstly, we provided a framework to structure flow research. Secondly, this was then used to collate and summarize the existing literature in the field. Thirdly, based on the first and second, we are able to discuss implications for future research.

The framework distinguishes between individual, interindividual, contextual and cultural levels. Most research has been done on the individual level, with Personality (40 studies; Table 3 ), Motivation (54 studies; Table 4 ), Emotion (49 studies, Table 6 ), Cognition (26 studies; Table 7 ), and Behavior (53 studies; Table 8 ) being the largest categories. On the individual level, the Physiology of flow (21 studies; Table 5 ) is the least studied category; however, in recent years there is a growing trend of research being conducted in this area. There are 94 context level studies ( Table 9 ). In comparison, research on flow at the interindividual (13 studies; Table 10 ) and cultural level is underrepresented (16 studies; Table 11 ).

In a Nutshell: Discussion of Findings Within the Categories

The personality studies on flow were divided into four categories: autotelic personality, dispositional proneness to experience flow, flow and motive-fitting situations and other motives and personality traits. Several dimensions seem to characterize the concept of autotelic personality which are related to flow. However, there is still no widely agreed upon definition of the autotelic personality. Studies on individual differences in flow experiences depend on both situational variables, e.g., the environmental opportunities to engage in flow promoting activities, and personality traits (i.e., openness to experience, extraversion, and conscientiousness). Situational factors seem to have a stronger influence on flow experiences ( Fullagar and Kelloway, 2009 ; Ullén et al., 2016 ). However, more research is needed to specify the relationship of dispositional and situational factors to predict flow experiences.

Achievement motives and other motives and personality traits, (i.e., optimism, autonomy, self-handicapping, self-control) also seem to be associated with flow experience. However, the variety and even inconsistency (e.g., shyness and mental toughness) of personality traits and motives associated with flow, make it difficult to draw overall conclusions. Relating personality traits and motives to fitting situations seems to be a more promising way to investigate the effects of personality traits and motives on flow in the future.

Flow experience is historically linked to motivation (see e.g., Heutte et al., 2021 ). In line with this, results of this category showed that many motivational indicators, such as volition, engagement, goal orientation, achievement motive, interest, and intrinsic motivation are positively related to flow. Flow was also investigated in the context of self-determination, with results showing associations of flow with autonomous and controlled motivation. Results thus indicate that flow can be considered one of the major volitional theories. This is also in line with results of a meta-analysis by Fong et al. (2015) , that highlights the links between flow antecedents (e.g., concentration, merging of action and awareness, and feedback) and sense of autonomy, one of the central components of self-determination. Finally, self-efficacy was an often investigated motivational concept, with results confirming a relationship between self-efficacy and flow. While first studies in this category were largely correlational, more recent studies have started to investigate models that integrate various motivational concepts (often from Bandura or Deci and Ryan’s theories) as predictors of flow using structural equation models.

A new and promising challenge in the category Motivation concerns modeling research studies that investigate both collective motivational conditions and social dimensions of flow (see Salanova et al., 2014 ; Heutte et al., 2016 ). However, in order to fulfill this aim, this work requires construction and validation of multidimensional and short, specific measurement instruments for flow, which also include collective motivational dimensions of flow.

Studies on the physiology of flow are yet in their infancy and results are scarce and inconsistent. While the first studies in this category were mostly correlational, more recent studies have started to investigate flow using experimental designs. Some studies regard flow as a predictor of certain physiological states. Others regard physiological states as predictors of flow. A clear physiological pattern of flow has not yet been identified, but this seems to be the next major task for research on the physiology of flow. Presumably, the physiological pattern during flow will not be represented by a single physiological indicator, but rather by a combination of several different physiological indicators. Current developments of machine learning may help to identify such a pattern. Once a physiological pattern of flow is identified, this will help flow research to find a deeper understanding of the flow concept. Flow can then be measured continuously during an activity, without the need to interrupt people. Accordingly, the dynamics of flow over time can be assessed, as well as the variations of flow intensity. Still, it is unlikely that there will be just the one flow-characteristic pattern; rather the physiology of flow depends on the particular activity that one is doing, with people in flow showing the optimal physiological activation to meet task demands (see Peifer, 2012 ). Building upon this, the second future research question is how context conditions, such as characteristics of the task (e.g., difficulty) or conditions at the interface between context and person, (e.g., task relevance) moderate the typical physiology of flow.

Studies under the topic of emotion and flow cover a wide range of concepts and variables related to affect, wellbeing, or specific feelings like enjoyment. In general, results show a clear association between flow and positive emotional states. There is clearly a predominant focus on the study of positive affect, with only few studies analyzing the relationship between negative affect and flow, so more research is needed here. The majority of the studies investigated the role of flow as a predictor of different emotional aspects, showing that the reversed relationship is less studied. Flow and related emotional aspects have been studied mainly from an individual or subjective perspective, with social components of flow and emotion becoming an emergent research issue. Studies under this topic shed light on the importance of understanding the emotional functioning of flow experience to improve its positive outcomes in individuals’ lives. Results of the various studies show a large spectrum of practical implications in different areas, such as sports, educational contexts, the video game industry, organizational areas, general health, or quality of life.

Cognition studies on flow are extremely broad and touch on very different topics. Most of these look at flow in specific fields and include some cognitive variables but without a main focus on them and also without deeper discussion of the cognitive aspect of flow. “Attention” appears in several “cognition and flow” studies, but how flow and attention exactly are linked is not sufficiently explained. For example, some studies point to attention skills as a necessary precondition for obtaining flow, whilst other studies find that sometimes, people with poor attention skills can still find flow in, for example, activities where they have high levels of interest and engagement. More research is needed to understand the relation of flow with cognitive processes. This research could also help both deepening and widening some of the research questions that have emerged from the reviewed studies, relating, for example, to the disassociation between sense of control and sense of agency in flow experiences, or the understanding of the exact role of awareness in optimal experience.

Overall, many effects of flow on behavioral outcomes were shown. Most studies in this category dealt with performance-related outcomes and found positive association between the two. However, one has to be careful when interpreting direction of the effects: Most studies in this category are correlational only and therefore it is not possible to deduce the direction of effects. Landhäußer and Keller (2012) argue that flow, on the one hand, has a direct positive effect on performance, because individuals in flow are highly concentrated. On the other hand, individuals have a higher motivation to re-engage in a task when flow was experienced resulting in higher performance through practice ( Landhäußer and Keller, 2012 ). Accordingly, there is a clear need for longitudinal studies and for identifying moderators and mediators in the relationship between flow and performance in order to specify the direction of effects. Other studies looking at behavioral outcomes such as customer-oriented behavior and (online) consumption-behavior hold interesting implications for organizations, advertisement and therapy, though again more longitudinal and experimental research should be conducted to reach more solid conclusions and to start designing useful interventions to increase performance and wellbeing.

Contextual Factors

In summary, flow occurs in many different contexts and activities, and there are many contextual factors that promote flow. A fit between contextual factors (e.g., demands) and individual characteristics (e.g., skills; see also section on personality and flow) seems to play a particularly important role in the emergence of flow. However, this category contains many articles, as it includes all environmental factors which may affect flow experience. It is presumably the broadest category within this review. While we have at least distinguished the social environment as a sub-category within the contextual level, future frameworks could further distinguish different environmental factors, such as factors on the task level, the social/organizational level (for work settings) and factors at the interface of the individual with the task and the organization. A framework which has recently tried to implement such a structure is the three spheres framework of flow antecedents ( Peifer and Wolters, 2021 ). In addition, it could be useful to differentiate direct interaction from more indirect social influence such as organizational climate.

Overall, studies in this category were yet quite scarce, but we could see a growing tendency to measure, conceptualize and investigate interindividual factors of flow. This is evidenced by a growing number of studies published in more recent years within the timeframe of our review. Furthermore, within the EFRN, we see a growing number of conference contributions and EFRN members starting to investigate flow in social contexts. We conclude that there is increasing awareness of interaction effects among people in relation to flow experiences. When reviewing the existing literature, we found that the research on interpersonal flow lacks a broad conceptualization and is instead limited to individual flow experiences while being part of a collective (e.g., dyad, group). A clear challenge of future flow research is to differentiate individual flow in social contexts from social flow as a social phenomenon with potentially different qualities than individual flow. A recent suggestion to differentiate flow and team flow was made by Peifer et al. (2021) , suggesting that flow and team flow share the central components of individual flow, while team flow-specific components are added. In their studies, van den Hout et al. (2018) bridge individual experiences of flow with collective experiences of flow. In their conceptualization of team flow, they differentiate individual experiences of flow while being part of a team dynamic, with experiences of flow at the team level, where the team dynamic (or team process) itself, as a coherent unit, is flowing . When all members that are part of the team dynamic are experiencing flow while executing their personal tasks/roles for the team, and the collective itself is flowing a unique experience emerges, which they refer to as full team flow , that is originated by seven prerequisites and four experiential characteristics ( van den Hout et al., 2019 ; van den Hout and Davis, 2021 ).

Future directions include studying interindividual flow through the grounded theory approach (see Csikszentmihalyi, 1975 ), conceptual cross-fertilization with social and organizational psychology, and developing reliable self-reported and behavioral measures of the phenomenon, experimentation and longitudinal studies. Social flow and its emotional features appear as an emergent issue in flow studies. However, finding a measure for assessing interindividual flow as a group phenomenon without passing through aggregation of self-reported individual data is a major methodological challenge for future research of this topic.

Culture and Flow represents an important theoretical perspective and several theoretical and empirical contributions in this field have been collected recently in specific scientific books (i.e., Delle Fave et al., 2011 ; Csikszentmihalyi and Wong, 2014 ; Inghilleri et al., 2014 ). Despite this, we notice a general lack of published empirical articles dealing with flow in the cultural context, even if existing research shows its underlying relevance for investigating flow-fostering activities. Furthermore, flow has the potential to interact significantly with cultural phenomena of different nature, both positive and negative for human beings. Thus, we suggest that future research should put additional emphasis on the effects of culture on flow and vice versa.

Overarching Aspects for Future Research and Limitations of This Review

After having discussed the specific open research questions for each category, we would now like to outline general aspects for future research, which we could identify as overarching topics and as limitations of this review. In particular, these concern (1) definitional and operational issues, (2) methodological issues and the resulting problems of causal conclusions regarding antecedents and consequences of flow, as well as (3) the time frame of this scoping review.

Definitional and Operational Issues

Many studies worked with different definitions and operationalization of flow experience so one must be careful when comparing results. For example, some studies (e.g., Baumann and Scheffer, 2011 ; Oertig et al., 2014 ) used the Flow Short Scale ( Rheinberg et al., 2003 ). Others used the Practice Flow Inventory ( Heller et al., 2015 ), Jacksons’ and Eklunds’ (2002) Dispositional Flow Scale-2 ( Sinnamon et al., 2012 ) the Flow State Scale-2 ( Wrigley and Emmerson, 2013 ) or the EduFlow model ( Heutte et al., 2016 ). While beyond the scope of this review, for future research, there is a need to find a common definition and operationalization of the flow concept, including a common measure of flow which is used in future research to enhance the comparability of results. The EFRN has started to fulfill this aim by agreeing on a definition of flow (see section “Introduction”), and members of the EFRN have suggested models to aggregate components of flow and team flow (e.g., van den Hout et al., 2018 ; Heutte et al., 2021 ; Peifer and Engeser, 2021 ; Peifer et al., 2021 ). The next steps will be to discuss and agree on models and respective measurements.

Methodological Issues

In general, while conducting the review, the authors found many correlative studies, and causal interpretation of such data is not possible. Many of the reported studies suggest a causal interpretation of their results based on theoretical assumptions. However, this is problematic, as different theoretical assumptions also seem possible. In conclusion, antecedents and consequences of flow are not yet as clear as they should be, considering the immense amount of studies which have been conducted. While this is beyond the scope of this review, future reviews should focus on a systematic look at the methods behind the studies. Here, we want to emphasize that what is needed in the future is mainly longitudinal and experimental studies.

Another methodological aspect which we found as an overall topic is that most of the research was conducted with (young) adults; there is a lack of flow research on children as well as adolescent and elderly populations. In general, there is a need for studies testing more complex models to understand multiple relations between variables.

Time Frame and Inclusion Criteria of This Scoping Review

Our Scoping Review provides a systematic overview on flow research between the years 2000 and 2016. A task force of flow research from the EFRN united their expertise in order to provide a sound scientific summary and discussion of flow research in these years and implications for future research. The work on this scoping research started in November 2015, during the EFRN meeting in Braga, Portugal. The literature search was conducted in 2016 and updated in 2017 in order to cover all articles until the end of the year 2016. The process of writing and revising the article took a long time and another update of the literature search would have exceeded the word limit of a journal article, particularly as flow research has been further increasing in more recent years.

Furthermore, we set strong exclusion criteria by only allowing studies that mentioned “ Csikszentmihalyi” and that were listed in specific search platforms. We selected the most relevant platforms for our literature search, thereby excluding other platforms (e.g., CINAHL, ProQuest, SocIndex, and SocAbs). Therefore, it is entirely possible that not all relevant flow studies are included in our review. As experts were allowed to add additional studies they considered relevant, we hope that in the final analysis we have identified the majority of relevant studies. Furthermore, we only included studies that were published in the English language, and there are certainly interesting results published in other languages that are not covered here.

While the time frame as well as the strong exclusion criteria are clear limitations of this review, we still believe that the provided overview will help to stimulate and direct future flow research.

Flow research between 2000 and 2016 has made huge progress in understanding flow. Our review provides a framework to cluster flow research and gives a systematic overview about existing studies and their findings in the field. While much research has been done in the past, our review derives future lines of research to foster scientific progress in flow research.

Author Contributions

CP: project coordinator, introduction and discussion. GW: project coordinator, literature research, discussion, behavior, and context factors. ST: professional advice during the process. GW and LH: personality. JHe and GW: motivation. CP and JT: physiology. TF, DT, and CF: emotion. ST and FA: cognition. JHo and MŠ: interindividual factors. LP: cultural factors. LC: review of parts of the manuscript. All authors: categorization and selection of abstracts.

TF was funded by Psychology Research Centre (PSI/01662), School of Psychology, University of Minho, supported by the Foundation for Science and Technology (FCT) through the Portuguese State Budget (UIDB/PSI/01662/2020). JH was funded by I-SITE Université Lille Nord-Europe (ULNE), supported by the French state through the General Secretariat for Investment (SGPI) and the National Research Agency (I-SITE ULNE / ANR-16-IDEX-0004 ULNE).

Conflict of Interest

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

Publisher’s Note

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

Acknowledgments

We would like to thank Ruth T. Naylor for proofreading an early version of this manuscript.

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Schiefele, U., and Raabe, A. (2011). Skills-demands compatibility as a determinant of flow experience in an inductive reasoning task. Psychol. Rep. 109, 428–444. doi: 10.2466/04.22.PR0.109.5.428-444

Schmierbach, M., Chung, M. -Y., Wu, M., and Kim, K. (2014). No one likes to lose. The effect of game difficulty on competency, flow, and enjoyment. J. Med. Psychol. 26, 105–110. doi: 10.1027/1864-1105/a000120

Schmierbach, M., Limperos, A. M., and Woolley, J. K. (2012). Feeling the need for (personalized) speed: how natural controls and customization contribute to enjoyment of a racing game through enhanced immersion. Cyberpsychol. Behav. Soc. Netw. 15, 364–369. doi: 10.1089/cyber.2012.0025

Schüler, J. (2007). Arousal of flow experience in a learning setting and its effects on exam performance and affect. Zeitschrift Für Pädagogische Psychol. 21, 217–227. doi: 10.1024/1010-0652.21.3.217

Schüler, J., and Brandstätter, V. (2013). How basic need satisfaction and dispositional motives interact in predicting flow experience in sport. J. Appl. Soc. Psychol. 43, 687–705. doi: 10.1111/j.1559-1816.2013.01045.x

Schüler, J., and Brunner, S. (2009). The rewarding effect of flow experience on performance in a marathon race. Psychol. Sport Exerc. 10, 168–174. doi: 10.1016/j.psychsport.2008.07.001

Schüler, J., and Nakamura, J. (2013). Does flow experience lead to risk? How and for whom. Appl. Psychol. Health Well-Being 5, 311–331. doi: 10.1111/aphw.12012

Schüler, J., Sheldon, K. M., and Fröhlich, S. M. (2010). Implicit need for achievement moderates the relationship between competence need satisfaction and subsequent motivation. J. Res. Pers. 44, 1–12. doi: 10.1016/j.jrp.2009.09.002

Schüler, J., Sheldon, K. M., Prentice, M., and Halusic, M. (2016). Do some people need autonomy more than others? Implicit dispositions toward autonomy moderate the effects of felt autonomy on well-being. J. Pers. 84, 5–20. doi: 10.1111/jopy.12133

Schweinle, A., Turner, J. C., and Meyer, D. K. (2008). Understanding young Adolescents’ optimal experiences in academic settings. J. Exp. Educ. 77, 125–146. doi: 10.3200/JEXE.77.2.125-146

Seddon, K., Skinner, N. C., and Postlethwaite, K. C. (2008). Creating a model to examine motivation for sustained engagement in online communities. Educ. Inform. Technol. 13, 17–34. doi: 10.1007/s10639-007-9048-2

Sharitt, M. (2010). Designing game affordances to promote learning and engagement. Cogn. Technol. 14, 43–57.

Shernoff, D. J., Csikszentmihalyi, M., Shneider, B., and Shernoff, E. S. (2003). Student engagement in high school classrooms from the perspective of flow theory. Sch. Psychol. Q. 18, 158–176. doi: 10.1521/scpq.18.2.158.21860

Shin, N. (2006). Online learner’s ‘flow’ experience: an empirical study. Br. J. Educ. Technol. 37, 705–720. doi: 10.1111/j.1467-8535.2006.00641.x

Silverman, M. J., Baker, F. A., and MacDonald, R. A. R. (2016). Flow and meaningfulness as predictors of therapeutic outcome within songwriting interventions. Psychol. Music 44, 1331–1345. doi: 10.1177/0305735615627505

Sinnamon, S., Moran, A., and O’Connell, M. (2012). Flow among musicians: measuring peak experiences of student performers. J. Res. Music Educ. 60, 6–25. doi: 10.1177/0022429411434931

Smith, M. B., Koppes Bryan, L., and Vodanovich, S. J. (2012). The counter-intuitive effects of flow on positive leadership and employee attitudes: incorporating positive psychology into the management of organizations. Psychol.-Manager J. 15, 174–198. doi: 10.1080/10887156.2012.701129

Steele, J. P., and Fullagar, C. J. (2009). Facilitators and outcomes of student engagement in a college setting. J. Psychol. 143, 5–27. doi: 10.3200/JRLP.143.1.5-27

Stephanou, G. (2011). Students? classroom emotions: socio-cognitive antecedents and school performance. Electron. J. Res. Educ. Psychol. 8, 5–48. doi: 10.25115/ejrep.v9i23.1423

Sugiyama, T., and Inomata, K. (2005). Qualitative examination of flow experience among top Japanese athletes. Perceptual Motor Skills 100, 969–982. doi: 10.2466/pms.100.3c.969-982

Swann, C., Crust, L., Jackman, P., Vella, S. A., Allen, M. S., and Keegan, R. (2017). Performing under pressure: exploring the psychological state underlying clutch performance in sport. J. Sports Sci. 35, 2272–2280. doi: 10.1080/02640414.2016.1265661

Swann, C., Crust, L., Keegan, R., Piggott, D., and Hemmings, B. (2015a). An inductive exploration into the flow experiences of European Tour golfers. Qualitat. Res. Sport Exerc. Health 7, 210–234. doi: 10.1080/2159676X.2014.926969

Swann, C., Piggott, D., Crust, L., Keegan, R., and Hemmings, B. (2015b). Exploring the interactions underlying flow states: a connecting analysis of flow occurrence in European Tour golfers. Psychol. Sport Exerc. 16, 60–69. doi: 10.1016/j.psychsport.2014.09.007

Swann, C., Keegan, R., Piggott, D., Crust, L., and Smith, M. (2012). Exploring flow occurrence in elite golf. Online J. Sport Psychol. 4, 171–186.

Synofzik, M., Vosgerau, G., and Newen, A. (2008). Beyond the comparator model: a multifactorial two-step account of agency. Consciousness Cogn. 17, 219–239. doi: 10.1016/j.concog.2007.03.010

Szymanski, D. M., and Henning, S. L. (2007). The role of self-objectification in Women’s depression: a test of objectification theory. Sex Roles 56, 45–53. doi: 10.1007/s11199-006-9147-3

Tan, F. B., and Chou, J. P. (2011). Dimensions of autotelic personality and their effects on perceived playfulness in the context of mobile internet and entertainment services. Australasian J. Inform. Syst. 17, 5–22.

Tanaka, H., and Ishida, S. (2015). Enjoying manga as fujoshi: exploring its innovation and potential for social change from a gender perspective. Int. J. Behav. Sci. 10, 77–85. doi: 10.14456/IJBS.2015.5

Thin, A. G., Hansen, L., and McEachen, D. (2011). Flow experience and mood states while playing body movement-controlled video games. Games Culture 6, 414–428. doi: 10.1177/1555412011402677

Thornton, D., and Gilbert, J. (2011). Investigating player behavior and experience in speech-enabled multimodal video games. Int. J. Technol. Knowledge Soc. 7, 165–177.

Tobert, S., and Moneta, G. B. (2013). Flow as a function of affect and coping in the workplace. Individ. Differ. Res. 11, 102–113.

Tozman, T., Magdas, E. S., MacDougall, H. G., and Vollmeyer, R. (2015). Understanding the psychophysiology of flow: a driving simulator experiment to investigate the relationship between flow and heart rate variability. Comput. Hum. Behav. 52, 408–418. doi: 10.1016/j.chb.2015.06.023

Tramonte, L., and Willms, D. (2010). La prévalence de l’anxiété chez les élčves des écoles intermédiaires et secondaires au Canada [The prevalence of anxiety among middle and secondary school students in Canada]. Canadian J. Public Health 101, 20–S23. doi: 10.1007/BF03403977

Triberti, S., and Riva, G. (2015). Being present in action: a theoretical model about the “Interlocking” between intentions and environmental affordances. Front. Psychol. 6:2052. doi: 10.3389/fpsyg.2015.02052

Tyagi, A., Cohen, M., Reece, J., Telles, S., and Jones, L. (2016). Heart rate variability, flow, mood and mental stress during yoga practices in yoga practitioners, non-yoga practitioners and people with metabolic syndrome. Appl. Psychophysiol. Biofeedback 41, 381–393. doi: 10.1007/s10484-016-9340-2

Ullén, F., Harmat, L., Theorell, T., and Madison, G. (2016). “Flow and individual differences – a phenotypic analysis of data from more than 10,000 Twin individuals,” in Flow Experience: Empirical Research and Applications , 1st Edn, eds L. Harmat, F. Ørsted Andersen, F. Ullén, J. Wright, and G. Sadlo (Berlin: Springer International Publishing).

Ullén, F., Manzano, O., Almeida, R., Magnusson, P. K., Pedersen, N. L., Nakamura, J., et al. (2012). Proneness for psychological flow in everyday life: associations with personality and intelligence. Person. Individ. Differ. 52, 167–172. doi: 10.1016/j.paid.2011.10.003

Ulrich, M., Keller, J., and Grön, G. (2016b). Neural signatures of experimentally induced flow experiences identified in a typical fMRI block design with BOLD imaging. Soc. Cogn. Affect. Neurosci. 11, 496–507. doi: 10.1093/scan/nsv133

Ulrich, M., Keller, J., and Grön, G. (2016a). Dorsal raphe nucleus down-regulates medial prefrontal cortex during experience of flow. Front. Behav. Neurosci. 10:169. doi: 10.3389/fnbeh.2016.00169

Ulrich, M., Keller, J., Hoenig, K., Waller, C., and Grön, G. (2014). Neural correlates of experimentally induced flow experiences. NeuroImage 86, 194–202. doi: 10.1016/j.neuroimage.2013.08.019

Urmston, E., and Hewison, J. (2014). Risk and flow in contact improvisation: pleasure, play and presence. J. Dance Somatic Pract. 6, 219–232. doi: 10.1386/jdsp.6.2.219_1

Valenzuela, R., and Codina, N. (2014). Habitus and flow in primary school musical practice: relations between family musical cultural capital, optimal experience and music participation. Music Educ. Res. 16, 505–520. doi: 10.1080/14613808.2013.859660

van den Hout, J. J. J., and Davis, O. C. (2021). Promoting the emergence of team flow in organizations. Int. J. Appl. Posit. Psychol. 1–47. doi: 10.1007/s41042-021-00059-7

van den Hout, J. J. J., Davis, O. C., and Weggeman, M. C. D. P. (2018). The conceptualization of team flow. J. Psychol. 152, 388–423. doi: 10.1080/00223980.2018.1449729

van den Hout, J. J. J., Gevers, J. M., Davis, O. C., and Weggeman, M. C. (2019). Developing and Testing the Team Flow Monitor (TFM). Cogent Psychol. 6:1643962. doi: 10.1080/23311908.2019.1643962

van der Hoorn, B. (2015). Playing projects: identifying flow in the ‘lived experience’. Int. J. Project Manag. 33, 1008–1021. doi: 10.1016/j.ijproman.2015.01.009

van Schaik, P., Martin, S., and Vallance, M. (2012). Measuring flow experience in an immersive virtual environment for collaborative learning. J. Comput. Assisted Learn. 28, 350–365. doi: 10.1111/j.1365-2729.2011.00455.x

Vealey, R. S., and Perritt, N. C. (2015). Hardiness and optimism as predictors of the frequency of flow in collegiate athletes. J. Sports Behav. 38, 321–338.

Vittersø, J. (2003). Flow versus life satisfaction: a projective use of cartoons to illustrate the difference between the evaluation approach and the intrinsic motivation approach to subjective quality of life. J. Happiness Stud. 4, 141–167. doi: 10.1023/A:1024413112234

Vittersø, J., Vorkinn, M., and Vistad, O. I. (2001). Congruence between recreational mode and actual behavior - a prerequisite for optimal experiences? J. Leisure Res. 33, 137–159.

Voiskounsky, A. E., Mitina, O. V., and Avetisova, A. A. (2005). Communicative patterns and flow experience of MUD players. Int. J. Advanced Med. Commun. 1, 5–25.

Voiskounsky, A. E., and Smyslova, O. V. (2003). Flow-based model of computer Hackers’ motivation. CyberPsychol. Behav. 6, 171–180. doi: 10.1089/109493103321640365

Vuorre, M., and Metcalfe, J. (2016). The relation between the sense of agency and the experience of flow. Consciousness. Cogn. 43, 133–142. doi: 10.1016/j.concog.2016.06.001

Walker, C. J. (2010). Experiencing flow: is doing it together better than doing it alone? J. Positive Psychol. 5, 3–11. doi: 10.1080/17439760903271116

Wang, C. -C., Chen, C. -F., and Chen, C. -T. (2015). Exploring the different aspects of Internet leisure use by college students. Inform. Dev. 31, 5–12. doi: 10.1177/0266666913494909

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Wrigley, W. J., and Emmerson, S. B. (2013). The experience of the flow state in live music performance. Psychol. Music 41, 292–305. doi: 10.1177/0305735611425903

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Zubair, A., and Kamal, A. (2015b). Work related flow, psychological capital, and creativity among employees of software houses. Psychol. Stud. 60, 321–331. doi: 10.1007/s12646-015-0330-x

Zumeta, L., Basabe, N., Wlodarczyk, A., Bobowik, M., and Paez, D. (2016). Flujo compartido y reuniones colectivas positivas [Shared flow and positive collective gatherings]. Anal. Psicol. 32:717. doi: 10.6018/analesps.32.3.261651

Keywords : flow, scoping review, individual level, contextual level, cultural level

Citation: Peifer C, Wolters G, Harmat L, Heutte J, Tan J, Freire T, Tavares D, Fonte C, Andersen FO, van den Hout J, Šimleša M, Pola L, Ceja L and Triberti S (2022) A Scoping Review of Flow Research. Front. Psychol. 13:815665. doi: 10.3389/fpsyg.2022.815665

Received: 15 November 2021; Accepted: 17 January 2022; Published: 07 April 2022.

Reviewed by:

Copyright © 2022 Peifer, Wolters, Harmat, Heutte, Tan, Freire, Tavares, Fonte, Andersen, van den Hout, Šimleša, Pola, Ceja and Triberti. 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: Corinna Peifer, [email protected]

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.

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YSN Doctoral Programs: Steps in Conducting a Literature Review

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• Steps in Conducting a Literature Review

What is a literature review?

A literature review is an integrated analysis -- not just a summary-- of scholarly writings and other relevant evidence related directly to your research question.  That is, it represents a synthesis of the evidence that provides background information on your topic and shows a association between the evidence and your research question.

A literature review may be a stand alone work or the introduction to a larger research paper, depending on the assignment.  Rely heavily on the guidelines your instructor has given you.

Why is it important?

A literature review is important because it:

• Explains the background of research on a topic.
• Demonstrates why a topic is significant to a subject area.
• Discovers relationships between research studies/ideas.
• Identifies major themes, concepts, and researchers on a topic.
• Identifies critical gaps and points of disagreement.
• Discusses further research questions that logically come out of the previous studies.

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1. Choose a topic. Define your research question.

Your literature review should be guided by your central research question.  The literature represents background and research developments related to a specific research question, interpreted and analyzed by you in a synthesized way.

• Make sure your research question is not too broad or too narrow.  Is it manageable?
• Begin writing down terms that are related to your question. These will be useful for searches later.
• If you have the opportunity, discuss your topic with your professor and your class mates.

2. Decide on the scope of your review

How many studies do you need to look at? How comprehensive should it be? How many years should it cover? 

• This may depend on your assignment.  How many sources does the assignment require?

3. Select the databases you will use to conduct your searches.

Make a list of the databases you will search. 

Where to find databases:

• use the tabs on this guide
• Find other databases in the Nursing Information Resources web page
• More on the Medical Library web page
• ... and more on the Yale University Library web page

4. Conduct your searches to find the evidence. Keep track of your searches.

• Use the key words in your question, as well as synonyms for those words, as terms in your search. Use the database tutorials for help.
• Save the searches in the databases. This saves time when you want to redo, or modify, the searches. It is also helpful to use as a guide is the searches are not finding any useful results.
• Review the abstracts of research studies carefully. This will save you time.
• Use the bibliographies and references of research studies you find to locate others.
• Check with your professor, or a subject expert in the field, if you are missing any key works in the field.
• Ask your librarian for help at any time.
• Use a citation manager, such as EndNote as the repository for your citations. See the EndNote tutorials for help.

Review the literature

Some questions to help you analyze the research:

• What was the research question of the study you are reviewing? What were the authors trying to discover?
• Was the research funded by a source that could influence the findings?
• What were the research methodologies? Analyze its literature review, the samples and variables used, the results, and the conclusions.
• Does the research seem to be complete? Could it have been conducted more soundly? What further questions does it raise?
• If there are conflicting studies, why do you think that is?
• How are the authors viewed in the field? Has this study been cited? If so, how has it been analyzed?

Tips: 

• Review the abstracts carefully.  
• Keep careful notes so that you may track your thought processes during the research process.
• Create a matrix of the studies for easy analysis, and synthesis, across all of the studies.
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Flow Research in Music Contexts: A Systematic Literature Review

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Writing in the Health and Social Sciences: Literature Reviews and Synthesis Tools

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• What are Literature Reviews?
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Systematic Literature Reviews: Steps & Resources

These steps for conducting a systematic literature review are listed below . 

Also see subpages for more information about:

• The different types of literature reviews, including systematic reviews and other evidence synthesis methods
• Tools & Tutorials

Literature Review & Systematic Review Steps

• Develop a Focused Question
• Scope the Literature  (Initial Search)
• Refine & Expand the Search
• Limit the Results
• Download Citations
• Abstract & Analyze
• Create Flow Diagram
• Synthesize & Report Results

1. Develop a Focused   Question 

Consider the PICO Format: Population/Problem, Intervention, Comparison, Outcome

Focus on defining the Population or Problem and Intervention (don't narrow by Comparison or Outcome just yet!)

"What are the effects of the Pilates method for patients with low back pain?"

Tools & Additional Resources:

• PICO Question Help
• Stillwell, Susan B., DNP, RN, CNE; Fineout-Overholt, Ellen, PhD, RN, FNAP, FAAN; Melnyk, Bernadette Mazurek, PhD, RN, CPNP/PMHNP, FNAP, FAAN; Williamson, Kathleen M., PhD, RN Evidence-Based Practice, Step by Step: Asking the Clinical Question, AJN The American Journal of Nursing : March 2010 - Volume 110 - Issue 3 - p 58-61 doi: 10.1097/01.NAJ.0000368959.11129.79

2. Scope the Literature

A "scoping search" investigates the breadth and/or depth of the initial question or may identify a gap in the literature. 

Eligible studies may be located by searching in:

• Background sources (books, point-of-care tools)
• Article databases
• Trial registries
• Grey literature
• Cited references
• Reference lists

When searching, if possible, translate terms to controlled vocabulary of the database. Use text word searching when necessary.

Use Boolean operators to connect search terms:

• Combine separate concepts with AND  (resulting in a narrower search)
• Connecting synonyms with OR  (resulting in an expanded search)

Search:  pilates AND ("low back pain"  OR  backache )

Video Tutorials - Translating PICO Questions into Search Queries

• Translate Your PICO Into a Search in PubMed (YouTube, Carrie Price, 5:11) 
• Translate Your PICO Into a Search in CINAHL (YouTube, Carrie Price, 4:56)

3. Refine & Expand Your Search

Expand your search strategy with synonymous search terms harvested from:

• database thesauri
• reference lists
• relevant studies

Example: 

(pilates OR exercise movement techniques) AND ("low back pain" OR backache* OR sciatica OR lumbago OR spondylosis)

As you develop a final, reproducible strategy for each database, save your strategies in a:

• a personal database account (e.g., MyNCBI for PubMed)
• Log in with your NYU credentials
• Open and "Make a Copy" to create your own tracker for your literature search strategies

4. Limit Your Results

Use database filters to limit your results based on your defined inclusion/exclusion criteria.  In addition to relying on the databases' categorical filters, you may also need to manually screen results.  

• Limit to Article type, e.g.,:  "randomized controlled trial" OR multicenter study
• Limit by publication years, age groups, language, etc.

NOTE: Many databases allow you to filter to "Full Text Only".  This filter is  not recommended . It excludes articles if their full text is not available in that particular database (CINAHL, PubMed, etc), but if the article is relevant, it is important that you are able to read its title and abstract, regardless of 'full text' status. The full text is likely to be accessible through another source (a different database, or Interlibrary Loan).  

• Filters in PubMed
• CINAHL Advanced Searching Tutorial

5. Download Citations

Selected citations and/or entire sets of search results can be downloaded from the database into a citation management tool. If you are conducting a systematic review that will require reporting according to PRISMA standards, a citation manager can help you keep track of the number of articles that came from each database, as well as the number of duplicate records.

In Zotero, you can create a Collection for the combined results set, and sub-collections for the results from each database you search.  You can then use Zotero's 'Duplicate Items" function to find and merge duplicate records.

• Citation Managers - General Guide

6. Abstract and Analyze

• Migrate citations to data collection/extraction tool
• Screen Title/Abstracts for inclusion/exclusion
• Screen and appraise full text for relevance, methods, 
• Resolve disagreements by consensus

Covidence is a web-based tool that enables you to work with a team to screen titles/abstracts and full text for inclusion in your review, as well as extract data from the included studies.

• Covidence Support
• Critical Appraisal Tools
• Data Extraction Tools

7. Create Flow Diagram

The PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) flow diagram is a visual representation of the flow of records through different phases of a systematic review.  It depicts the number of records identified, included and excluded.  It is best used in conjunction with the PRISMA checklist .

Example from: Stotz, S. A., McNealy, K., Begay, R. L., DeSanto, K., Manson, S. M., & Moore, K. R. (2021). Multi-level diabetes prevention and treatment interventions for Native people in the USA and Canada: A scoping review. Current Diabetes Reports, 2 (11), 46. https://doi.org/10.1007/s11892-021-01414-3

• PRISMA Flow Diagram Generator (ShinyApp.io, Haddaway et al. )
• PRISMA Diagram Templates  (Word and PDF)
• Make a copy of the file to fill out the template
• Image can be downloaded as PDF, PNG, JPG, or SVG
• Covidence generates a PRISMA diagram that is automatically updated as records move through the review phases

8. Synthesize & Report Results

There are a number of reporting guideline available to guide the synthesis and reporting of results in systematic literature reviews.

It is common to organize findings in a matrix, also known as a Table of Evidence (ToE).

• Reporting Guidelines for Systematic Reviews
• Download a sample template of a health sciences review matrix  (GoogleSheets)

Steps modified from: 

Cook, D. A., & West, C. P. (2012). Conducting systematic reviews in medical education: a stepwise approach.   Medical Education , 46 (10), 943–952.

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Ten Simple Rules for Writing a Literature Review

Marco pautasso.

1 Centre for Functional and Evolutionary Ecology (CEFE), CNRS, Montpellier, France

2 Centre for Biodiversity Synthesis and Analysis (CESAB), FRB, Aix-en-Provence, France

Literature reviews are in great demand in most scientific fields. Their need stems from the ever-increasing output of scientific publications [1] . For example, compared to 1991, in 2008 three, eight, and forty times more papers were indexed in Web of Science on malaria, obesity, and biodiversity, respectively [2] . Given such mountains of papers, scientists cannot be expected to examine in detail every single new paper relevant to their interests [3] . Thus, it is both advantageous and necessary to rely on regular summaries of the recent literature. Although recognition for scientists mainly comes from primary research, timely literature reviews can lead to new synthetic insights and are often widely read [4] . For such summaries to be useful, however, they need to be compiled in a professional way [5] .

When starting from scratch, reviewing the literature can require a titanic amount of work. That is why researchers who have spent their career working on a certain research issue are in a perfect position to review that literature. Some graduate schools are now offering courses in reviewing the literature, given that most research students start their project by producing an overview of what has already been done on their research issue [6] . However, it is likely that most scientists have not thought in detail about how to approach and carry out a literature review.

Reviewing the literature requires the ability to juggle multiple tasks, from finding and evaluating relevant material to synthesising information from various sources, from critical thinking to paraphrasing, evaluating, and citation skills [7] . In this contribution, I share ten simple rules I learned working on about 25 literature reviews as a PhD and postdoctoral student. Ideas and insights also come from discussions with coauthors and colleagues, as well as feedback from reviewers and editors.

Rule 1: Define a Topic and Audience

How to choose which topic to review? There are so many issues in contemporary science that you could spend a lifetime of attending conferences and reading the literature just pondering what to review. On the one hand, if you take several years to choose, several other people may have had the same idea in the meantime. On the other hand, only a well-considered topic is likely to lead to a brilliant literature review [8] . The topic must at least be:

• interesting to you (ideally, you should have come across a series of recent papers related to your line of work that call for a critical summary),
• an important aspect of the field (so that many readers will be interested in the review and there will be enough material to write it), and
• a well-defined issue (otherwise you could potentially include thousands of publications, which would make the review unhelpful).

Ideas for potential reviews may come from papers providing lists of key research questions to be answered [9] , but also from serendipitous moments during desultory reading and discussions. In addition to choosing your topic, you should also select a target audience. In many cases, the topic (e.g., web services in computational biology) will automatically define an audience (e.g., computational biologists), but that same topic may also be of interest to neighbouring fields (e.g., computer science, biology, etc.).

Rule 2: Search and Re-search the Literature

After having chosen your topic and audience, start by checking the literature and downloading relevant papers. Five pieces of advice here:

• keep track of the search items you use (so that your search can be replicated [10] ),
• keep a list of papers whose pdfs you cannot access immediately (so as to retrieve them later with alternative strategies),
• use a paper management system (e.g., Mendeley, Papers, Qiqqa, Sente),
• define early in the process some criteria for exclusion of irrelevant papers (these criteria can then be described in the review to help define its scope), and
• do not just look for research papers in the area you wish to review, but also seek previous reviews.

The chances are high that someone will already have published a literature review ( Figure 1 ), if not exactly on the issue you are planning to tackle, at least on a related topic. If there are already a few or several reviews of the literature on your issue, my advice is not to give up, but to carry on with your own literature review,

The bottom-right situation (many literature reviews but few research papers) is not just a theoretical situation; it applies, for example, to the study of the impacts of climate change on plant diseases, where there appear to be more literature reviews than research studies [33] .

• discussing in your review the approaches, limitations, and conclusions of past reviews,
• trying to find a new angle that has not been covered adequately in the previous reviews, and
• incorporating new material that has inevitably accumulated since their appearance.

When searching the literature for pertinent papers and reviews, the usual rules apply:

• be thorough,
• use different keywords and database sources (e.g., DBLP, Google Scholar, ISI Proceedings, JSTOR Search, Medline, Scopus, Web of Science), and
• look at who has cited past relevant papers and book chapters.

Rule 3: Take Notes While Reading

If you read the papers first, and only afterwards start writing the review, you will need a very good memory to remember who wrote what, and what your impressions and associations were while reading each single paper. My advice is, while reading, to start writing down interesting pieces of information, insights about how to organize the review, and thoughts on what to write. This way, by the time you have read the literature you selected, you will already have a rough draft of the review.

Of course, this draft will still need much rewriting, restructuring, and rethinking to obtain a text with a coherent argument [11] , but you will have avoided the danger posed by staring at a blank document. Be careful when taking notes to use quotation marks if you are provisionally copying verbatim from the literature. It is advisable then to reformulate such quotes with your own words in the final draft. It is important to be careful in noting the references already at this stage, so as to avoid misattributions. Using referencing software from the very beginning of your endeavour will save you time.

Rule 4: Choose the Type of Review You Wish to Write

After having taken notes while reading the literature, you will have a rough idea of the amount of material available for the review. This is probably a good time to decide whether to go for a mini- or a full review. Some journals are now favouring the publication of rather short reviews focusing on the last few years, with a limit on the number of words and citations. A mini-review is not necessarily a minor review: it may well attract more attention from busy readers, although it will inevitably simplify some issues and leave out some relevant material due to space limitations. A full review will have the advantage of more freedom to cover in detail the complexities of a particular scientific development, but may then be left in the pile of the very important papers “to be read” by readers with little time to spare for major monographs.

There is probably a continuum between mini- and full reviews. The same point applies to the dichotomy of descriptive vs. integrative reviews. While descriptive reviews focus on the methodology, findings, and interpretation of each reviewed study, integrative reviews attempt to find common ideas and concepts from the reviewed material [12] . A similar distinction exists between narrative and systematic reviews: while narrative reviews are qualitative, systematic reviews attempt to test a hypothesis based on the published evidence, which is gathered using a predefined protocol to reduce bias [13] , [14] . When systematic reviews analyse quantitative results in a quantitative way, they become meta-analyses. The choice between different review types will have to be made on a case-by-case basis, depending not just on the nature of the material found and the preferences of the target journal(s), but also on the time available to write the review and the number of coauthors [15] .

Rule 5: Keep the Review Focused, but Make It of Broad Interest

Whether your plan is to write a mini- or a full review, it is good advice to keep it focused 16 , 17 . Including material just for the sake of it can easily lead to reviews that are trying to do too many things at once. The need to keep a review focused can be problematic for interdisciplinary reviews, where the aim is to bridge the gap between fields [18] . If you are writing a review on, for example, how epidemiological approaches are used in modelling the spread of ideas, you may be inclined to include material from both parent fields, epidemiology and the study of cultural diffusion. This may be necessary to some extent, but in this case a focused review would only deal in detail with those studies at the interface between epidemiology and the spread of ideas.

While focus is an important feature of a successful review, this requirement has to be balanced with the need to make the review relevant to a broad audience. This square may be circled by discussing the wider implications of the reviewed topic for other disciplines.

Rule 6: Be Critical and Consistent

Reviewing the literature is not stamp collecting. A good review does not just summarize the literature, but discusses it critically, identifies methodological problems, and points out research gaps [19] . After having read a review of the literature, a reader should have a rough idea of:

• the major achievements in the reviewed field,
• the main areas of debate, and
• the outstanding research questions.

It is challenging to achieve a successful review on all these fronts. A solution can be to involve a set of complementary coauthors: some people are excellent at mapping what has been achieved, some others are very good at identifying dark clouds on the horizon, and some have instead a knack at predicting where solutions are going to come from. If your journal club has exactly this sort of team, then you should definitely write a review of the literature! In addition to critical thinking, a literature review needs consistency, for example in the choice of passive vs. active voice and present vs. past tense.

Rule 7: Find a Logical Structure

Like a well-baked cake, a good review has a number of telling features: it is worth the reader's time, timely, systematic, well written, focused, and critical. It also needs a good structure. With reviews, the usual subdivision of research papers into introduction, methods, results, and discussion does not work or is rarely used. However, a general introduction of the context and, toward the end, a recapitulation of the main points covered and take-home messages make sense also in the case of reviews. For systematic reviews, there is a trend towards including information about how the literature was searched (database, keywords, time limits) [20] .

How can you organize the flow of the main body of the review so that the reader will be drawn into and guided through it? It is generally helpful to draw a conceptual scheme of the review, e.g., with mind-mapping techniques. Such diagrams can help recognize a logical way to order and link the various sections of a review [21] . This is the case not just at the writing stage, but also for readers if the diagram is included in the review as a figure. A careful selection of diagrams and figures relevant to the reviewed topic can be very helpful to structure the text too [22] .

Rule 8: Make Use of Feedback

Reviews of the literature are normally peer-reviewed in the same way as research papers, and rightly so [23] . As a rule, incorporating feedback from reviewers greatly helps improve a review draft. Having read the review with a fresh mind, reviewers may spot inaccuracies, inconsistencies, and ambiguities that had not been noticed by the writers due to rereading the typescript too many times. It is however advisable to reread the draft one more time before submission, as a last-minute correction of typos, leaps, and muddled sentences may enable the reviewers to focus on providing advice on the content rather than the form.

Feedback is vital to writing a good review, and should be sought from a variety of colleagues, so as to obtain a diversity of views on the draft. This may lead in some cases to conflicting views on the merits of the paper, and on how to improve it, but such a situation is better than the absence of feedback. A diversity of feedback perspectives on a literature review can help identify where the consensus view stands in the landscape of the current scientific understanding of an issue [24] .

Rule 9: Include Your Own Relevant Research, but Be Objective

In many cases, reviewers of the literature will have published studies relevant to the review they are writing. This could create a conflict of interest: how can reviewers report objectively on their own work [25] ? Some scientists may be overly enthusiastic about what they have published, and thus risk giving too much importance to their own findings in the review. However, bias could also occur in the other direction: some scientists may be unduly dismissive of their own achievements, so that they will tend to downplay their contribution (if any) to a field when reviewing it.

In general, a review of the literature should neither be a public relations brochure nor an exercise in competitive self-denial. If a reviewer is up to the job of producing a well-organized and methodical review, which flows well and provides a service to the readership, then it should be possible to be objective in reviewing one's own relevant findings. In reviews written by multiple authors, this may be achieved by assigning the review of the results of a coauthor to different coauthors.

Rule 10: Be Up-to-Date, but Do Not Forget Older Studies

Given the progressive acceleration in the publication of scientific papers, today's reviews of the literature need awareness not just of the overall direction and achievements of a field of inquiry, but also of the latest studies, so as not to become out-of-date before they have been published. Ideally, a literature review should not identify as a major research gap an issue that has just been addressed in a series of papers in press (the same applies, of course, to older, overlooked studies (“sleeping beauties” [26] )). This implies that literature reviewers would do well to keep an eye on electronic lists of papers in press, given that it can take months before these appear in scientific databases. Some reviews declare that they have scanned the literature up to a certain point in time, but given that peer review can be a rather lengthy process, a full search for newly appeared literature at the revision stage may be worthwhile. Assessing the contribution of papers that have just appeared is particularly challenging, because there is little perspective with which to gauge their significance and impact on further research and society.

Inevitably, new papers on the reviewed topic (including independently written literature reviews) will appear from all quarters after the review has been published, so that there may soon be the need for an updated review. But this is the nature of science [27] – [32] . I wish everybody good luck with writing a review of the literature.

Acknowledgments

Many thanks to M. Barbosa, K. Dehnen-Schmutz, T. Döring, D. Fontaneto, M. Garbelotto, O. Holdenrieder, M. Jeger, D. Lonsdale, A. MacLeod, P. Mills, M. Moslonka-Lefebvre, G. Stancanelli, P. Weisberg, and X. Xu for insights and discussions, and to P. Bourne, T. Matoni, and D. Smith for helpful comments on a previous draft.

Funding Statement

This work was funded by the French Foundation for Research on Biodiversity (FRB) through its Centre for Synthesis and Analysis of Biodiversity data (CESAB), as part of the NETSEED research project. The funders had no role in the preparation of the manuscript.

Princeton Correspondents on Undergraduate Research

Writing a Literature Review? Some Tips Before You Start

Writing the literature review section for a scientific research article can be a daunting task. This blog post is a summary of what I have personally found to best help when writing about scientific research. I hope some of these tips can help make the process an easier and more fulfilling experience!

1.      Create an outline for your paper

When I was learning how to write a research paper, I was taught that they must be written in the order of Introduction -> Literature Review -> Methodology -> Results -> Discussion -> Conclusion -> References. There exist many papers that stray from this arrangement, but no matter what style you choose, it should flow smoothly and tell a story. I like to think of my literature review as a means to situate my specific findings in the broader context of my research area.

2.      Have a running vocabulary list at hand

Two years ago, when I venture into the unfamiliar field of fluid mechanics, I came across several unfamiliar terms like “von Karman vortex street”. I kept a pad of paper at my desk and would write these words down. Then as I read the papers, I would take note of how different researchers explain the ideas differently. This would help me form my own understanding of the terms which turned out to be incredibly helpful when trying to define it myself when writing my literature review.

3.      Highlight and annotate the papers you are reading

There is a lot to process when reading research articles, so to maximize your learnings, try to be an active reader! Highlight and take notes while you read. This was even studied by Blanchard and Mikkelson to improve performance outcomes in retaining what you are studying! This journals your thought process and can be useful when explaining the topics yourself as you write your literature review.

4.      Summarize your notes/annotations in a master list

Especially if you are reading many papers, have a document or spreadsheet where you summarize the important parts. I also include my annotations and personal notes. That way, if I am writing my paper later and want to reference something but don’t remember where I read it, I have my master list to direct me back to the resource.

5.      Have a separate document recording the important results and conclusions from each resource you used

Summarizing just the results across papers gives you a better idea of how the body of knowledge in your field has developed recently. You can also use this when writing your Results and Discussion section when comparing your own results to other research. I do this by “filtering” my summary and notes from the previous step to only include the results of the research. I focus on general trends observed, parameters and other independent variables, and arbitrarily chosen constants in research experiments.  

6.      Browse through the book or paper’s references/bibliography, other writings by the author/s, and other publications from the lab.

Whenever I read a good research paper, I look through the references to see the research that they cite often or seems relevant to my work, and I look up the author/s of the research and their lab to see other similar work they have done on the same topic. For example, in my current work in the Computational Turbulent Reacting Flow Laboratory (CTRFL), I have been reading papers by my adviser and from his advisers and colleagues when he was in graduate school. Also, don’t be afraid to ask for more papers, books, or other resources that your adviser can recommend!

7.      Use a bibliography tool like Zotero

Preparing citations and bibliographies by hand can be a tedious and error-prone process. Using a tool speeds up the process of writing your bibliographies exponentially. However, do double check the auto-generated citations and bibliography to make sure that they are all correct. Here is a PCUR post that guides you through using automated bibliographies!

8.      Let other people read your literature review!

Try to get someone in your lab who is not writing the paper with you to read the literature review and give feedback. They will likely be the best at spotting any major technical errors and will also have the best advice on how to write a paper in your field. Also try to get someone in your broader field but not working in the same specific research area to read it. (Try a classmate from the same major!)

Finally, please take all these tips with a grain of salt. What works for me may not work for everybody. Do try out different techniques to find what works best for you and makes you the best researcher you can be!

— Agnes Robang, Engineering Correspondent

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Peer-reviewed

Research Article

Classification of white blood cells (leucocytes) from blood smear imagery using machine and deep learning models: A global scoping review

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Writing – original draft

Affiliation Spatiotemporal Environmental Epidemiology Research (STEER) Group, Technological University Dublin, Dublin, Ireland

Roles Conceptualization, Investigation, Methodology, Supervision, Writing – review & editing

Affiliation National University of Sciences and Technology (NUST), Islamabad, Pakistan

Roles Conceptualization, Investigation, Methodology, Project administration, Resources, Writing – review & editing

Roles Supervision, Writing – review & editing

* E-mail: [email protected]

• Rabia Asghar, 
• Sanjay Kumar, 
• Arslan Shaukat, 

• Published: June 17, 2024
• https://doi.org/10.1371/journal.pone.0292026
• Reader Comments

Machine learning (ML) and deep learning (DL) models are being increasingly employed for medical imagery analyses, with both approaches used to enhance the accuracy of classification/prediction in the diagnoses of various cancers, tumors and bloodborne diseases. To date however, no review of these techniques and their application(s) within the domain of white blood cell (WBC) classification in blood smear images has been undertaken, representing a notable knowledge gap with respect to model selection and comparison. Accordingly, the current study sought to comprehensively identify, explore and contrast ML and DL methods for classifying WBCs. Following development and implementation of a formalized review protocol, a cohort of 136 primary studies published between January 2006 and May 2023 were identified from the global literature, with the most widely used techniques and best-performing WBC classification methods subsequently ascertained. Studies derived from 26 countries, with highest numbers from high-income countries including the United States (n = 32) and The Netherlands (n = 26). While WBC classification was originally rooted in conventional ML, there has been a notable shift toward the use of DL, and particularly convolutional neural networks (CNN), with 54.4% of identified studies (n = 74) including the use of CNNs, and particularly in concurrence with larger datasets and bespoke features e.g., parallel data pre-processing, feature selection, and extraction. While some conventional ML models achieved up to 99% accuracy, accuracy was shown to decrease in concurrence with decreasing dataset size. Deep learning models exhibited improved performance for more extensive datasets and exhibited higher levels of accuracy in concurrence with increasingly large datasets. Availability of appropriate datasets remains a primary challenge, potentially resolvable using data augmentation techniques. Moreover, medical training of computer science researchers is recommended to improve current understanding of leucocyte structure and subsequent selection of appropriate classification models. Likewise, it is critical that future health professionals be made aware of the power, efficacy, precision and applicability of computer science, soft computing and artificial intelligence contributions to medicine, and particularly in areas like medical imaging.

Citation: Asghar R, Kumar S, Shaukat A, Hynds P (2024) Classification of white blood cells (leucocytes) from blood smear imagery using machine and deep learning models: A global scoping review. PLoS ONE 19(6): e0292026. https://doi.org/10.1371/journal.pone.0292026

Editor: Sivaramakrishnan Rajaraman, National Library of Medicine, UNITED STATES

Received: September 28, 2023; Accepted: May 13, 2024; Published: June 17, 2024

Copyright: © 2024 Asghar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Global review of previously published literature, with all extracted and harmonised data available as attached supplementary files . It is important to note that depending on reporting requirements/quality of primary publication, not all variables are uniform.

Funding: The author(s) received no specific funding for this work.

Competing interests: NO authors have competing interests.

1. Introduction

White blood cells (WBCs) play a vital role in the human immune system. They identify and neutralize pathogens including bacteria, viruses, and cancer cells. Classification of WBCs is therefore vital for accurate and early diagnosis and treatment of a range of diseases and medical conditions [ 1 ]. Machine learning techniques, both traditional and deep, have been widely adopted for myriad applications, including medical image analysis (MIA). MIA is critical in modern healthcare systems, aiding medical professionals in making well-informed decisions. It is currently used to diagnose brain tumors, lung cancer, anemia, leukemia, and malaria, via a range of image modalities including Magnetic Resonance Imaging (MRI), Computed Tomography (CT-Scans), Ultrasounds, Positron Emission Tomography (PET), Blood Smear images, and hybrid modalities [ 2 ]. Accordingly, MIA has attracted significant attention from computer vision experts, with traditional and deep machine learning techniques having been applied in leukocyte segmentation, cancer detection, classification, medical image annotation, and image retrieval in computer-aided diagnosis (CAD). The efficacy of these methods therefore directly influences clinical diagnosis and treatment strategies, highlighting the significance of technological advancements, such as high-speed computational resources and improved hardware and storage capabilities for CAD [ 3 – 5 ]. One of the primary application areas for CAD systems using traditional machine learning and deep learning is segmentation and classification of leukocytes (WBCs). Leukocytes provide valuable information to medical professionals (doctors, hematologists, pathologists, and radiologists), for diagnosing various blood-related issues, including Human Immunodeficiency Virus (HIV) and blood cancer (leukemia). Changes in the WBC count and/or morphological cell alterations, for instance variations in size, shape, and color observed in blood smear images, can provide valuable insights into various health disorders [ 6 – 9 ].

Blood cells are categorized into three major types: WBCs (leukocytes), red blood cells (erythrocytes), and platelets (thrombocytes). Leukocytes are subdivided into five types: monocytes, lymphocytes, neutrophils, basophils, and eosinophils ( Fig 1 ). Over the past two decades, significant advances have been made in traditional ML and DL methods for classification and segmentation of WBCs in microscopic blood smear images. Conventional methods depend on manually analyzing these images using microscopy, which is typically slow, laborious and error prone. Thus, development of automated and computer-aided systems has become crucial in accurate, systematic, unbiased and rapid clinical diagnosis and effective treatment. Automated analysis of WBCs in blood smear images can significantly reduce the workload of hematologists and provide fast, accurate, and efficient results to assist medical professionals in the diagnostic process [ 10 – 13 ]. There are two overarching methods typically used to achieve automated WBC classification in blood smear images: traditional machine learning (ML) and deep learning (DL) techniques. These techniques have the potential to make medical hematology more efficient. A generalized overview of machine learning and deep learning techniques used to classify WBCs is presented in Fig 2 . The traditional machine learning process involves interconnected steps such as segmenting the region of interest and extracting features, followed by optimal classification [ 14 , 15 ]. The feature extraction phase in traditional machine learning methods is challenging and directly impacts classification performance. More recently, deep learning approaches are increasingly used due to higher performance and decreasing complexity. Advanced deep learning methods with transfer learning have further improved implementation of automated systems for classification of WBCs. Notwithstanding the importance of ML and DL in medical image analysis (MIA), a gap remains in white blood cell classification via blood smear imagery; to date, no global review of these approaches is available in the published scientific literature. Accordingly, the present study sought to comprehensively identify and synthesize ML and DL methods, focusing on classifying five white blood cell types, and present this in concurrence with an overview of recommended future work, challenges and limitations associated with the identified approaches.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

White blood cell categories [From 17 ] (a) Neutrophils (b) Lymphocytes (c) Monocytes (d) Eosinophils (e) Basophils.

https://doi.org/10.1371/journal.pone.0292026.g001

Neutrophil classification in blood smear images via (a) Traditional machine learning model (b) Advanced deep learning model.

https://doi.org/10.1371/journal.pone.0292026.g002

2. Review protocol

A well-organized and formally structured review process is essential to identify, scan, include/exclude and synthesize targeted literature which satisfies preexisting search criteria and effectively employs existing resources [ 16 ]. In the current review, the authors sought to incorporate the most recent and relevant research articles based on manual and automatic searches to identify all significant content. The approach was initiated by identifying pertinent research questions. The two research questions (RQ) formulated in accordance with the PCC (Population, Concept, Context) search framework are as follows:

• How have systems been developed for classification of WBCs based on ML and DL ?
• What are the applications of traditional machine learning and deep learning methods for effectively classifying WBCs in blood smear images ?

Relevant studies were identified using specific keywords extracted from the research questions ( Table 1 ). These keywords covered various aspects, including segmentation, classification and detection of WBCs. The study explored machine learning techniques, involving both traditional and advanced deep learning methods. The research recognized the importance of big data and employed artificial intelligence (AI), as indicated by keywords like "Big data" and "Artificial Intelligence" respectively. This careful selection of keywords ensured a focused and comprehensive search across databases, resulting in retrieving relevant data for the study.

https://doi.org/10.1371/journal.pone.0292026.t001

The next review phase after RQ development was identification of relevant articles/studies via automated searching of electronic databases based on extracted keywords from RQs (Iterative combinations of ((A1 –A4) x (B1 –B4)) from Table 1 ). Articles published from 2006 to May 2023 were included for review. To align with the study’s emphasis on recent research trends and technological progress, articles prior to 2006 were omitted. Research articles were located from three repositories including Google Scholar, Scopus, and Web of Science. The inclusion and exclusion criteria are presented in Table 2 .

https://doi.org/10.1371/journal.pone.0292026.t002

Overall, a total of 3750 articles from Google Scholar, Web of Science, and Scopus were identified ( Fig 3 ). Following deduplication, this collection decreased to 2210 articles. Based on a thorough evaluation of article titles, abstracts and included data (from methodology section and Appendices), a further 2075 articles were excluded from further consideration. The final article cohort includes only articles published in English between January 1 st 2006 and May 31 st 2023, and independently adjudged (2 x groups of 2 authors) by the author team as being directly relevant to the topic ( Table 2 ). Quality assessment of included research papers, while not strictly considered necessary for scoping reviews, is critical in assessing literature consistency, validity, and overall credibility [ 18 ]. Accordingly, the authors employed a non-summative 5-point quality system adapted from Wylde et al (2017). Our tool consisted of five items used to assess 1. relevance to scoping review question (based on full paper review), 2. selection bias (i.e., input data sources provided), 3. transferability (open-source data usage, open code), 4. bias due to missing data and/or lack of clarity, and 5. consideration of analytical confounding, model overfitting and/or study limitations. Each item was rated as adequate, inadequate or not reported, with only articles attributed as being “adequate” across all five criteria adjudged to be acceptable quality for narrative inclusion and data synthesis.

https://doi.org/10.1371/journal.pone.0292026.g003

3. Review of identified relevant literature

3.1 study characteristics.

Overall, 136 relevant studies were identified between January 2006 and May 2023, with the research timeline, based on article number per annum, presented in Fig 4 . As shown, the annual number of publications remained relatively constant from 2006 to 2014, after which a marked increase was observed, reaching a peak in 2019 (n = 32). Subsequently, there was a significant decrease in publications after 2019, likely due to the COVID-19 pandemic and its impact on data accessibility, in addition to a shift in research focus among researchers in the realms of biomedical image analyses and classification algorithms.

https://doi.org/10.1371/journal.pone.0292026.g004

Overall, 26 countries were represented by identified studies, with the highest number of studies emanating from the United States (n = 32) and The Netherlands (n = 26) ( Fig 5 ). High-income countries were, perhaps unsurprisingly, well represented, likely due to the availability of large datasets for training and testing, in addition to increasingly mature/well-funded national healthcare systems. As shown ( Fig 6 ), 8 overarching model architectures and methods were employed for classification, including both traditional machine learning and deep learning models. Traditional machine learning models included Decision Trees (DT), K-means, Naive Bayes Classifier (NBC), Nearest Neighbor Classifier (NNC), Support Vector Machines (SVM), Artificial Neural Networks (ANN), and thresholding techniques. Within the deep learning domain, convolutional neural networks were the most frequently employed approach, likely due to their high performance and accuracy (compared and presented in Sections 3.2–3.6).

https://doi.org/10.1371/journal.pone.0292026.g005

Model architectures (dark blue) and methods (light blue) used for white blood cell classification. (Note–Several comparative studies compared >1 method and/or developed ensemble architectures).

https://doi.org/10.1371/journal.pone.0292026.g006

In total, 27 datasets were specifically referenced across the identified relevant studies ranging from 21 images [ 88 ] to 92,800 images [ 73 ] ( Table 3 ), including ALL-IDB, one Private Dataset [ 60 ], CellaVision, AA-IDB2, Hayatabad Medical Dataset, Isfahan Al-Zahra and Omid hospital, ALL-IDB2/Leishman stained peripheral blood smears, one Public Dataset [ 73 ], BCCD, Kaggle, LISC and BCCD, Jiangxi Tecom Science Corporation/CellaVision/Bsisc/LISC, KMC hospital Manipal India, Hybrid-Leukocyte database/e Hybrid-Slide database, Acquired from Sixth People’s Hospital of Shenzhen, SMC-IDB/IUMS-IDB/ALL-IDB, and SBILab. The full list of datasets along with the corresponding dataset size (number of images) is provided in Table 3 . Just two studies [ 6 ] specifically referred to the use of thin blood smear images, with the remaining studies either expressly referring to the use of thick blood smears or not reporting on smear type; this is notable, as thick features have inherent advantages over thin features in WBC classification outcomes.

https://doi.org/10.1371/journal.pone.0292026.t003

3.2 White blood cell classification using conventional machine learning

Various studies have explored conventional machine learning methods for WBC classification, which for the purpose of clarity, the authors have organized into pre-processing-based techniques (Section 3.3.1), feature extraction (Section 3.3.2), and classification (Section 3.3.3). A total of 39 studies were identified, with 13 papers (33.3%) focused on pre-processing techniques, 15 papers (38.5%) delving into feature extraction methods, and 11 papers (28.2%) emphasizing classification techniques for WBC classification. This distribution of approaches and objectives is evident in Tables 4 – 7 , highlighting diverse emphases on these sub-processes within the conventional machine learning domain for classifying WBCs.

https://doi.org/10.1371/journal.pone.0292026.t004

https://doi.org/10.1371/journal.pone.0292026.t005

https://doi.org/10.1371/journal.pone.0292026.t006

https://doi.org/10.1371/journal.pone.0292026.t007

3.2.1 Pre-processing-based ML techniques.

Pre-processing-based techniques include methods that manipulate and enhance raw data prior to further analysis. In the context of WBC classification, these techniques play a critical role in refining images to enable accurate categorization. Rosyadi et al. [ 19 ] used optical microscopy to generate blood samples images, with their method comprising four stages: image pre-processing, segmentation, feature extraction, and classification. In the first phase of image pre-processing, images were transformed from RGB to grayscale and binary images. Subsequently, in the second phase, resizing, cropping, and edge detection were applied to all images. Five geometrical features were considered in the feature extraction phase that represent important geometric characteristics of the segmented cells: normalized area, solidity, eccentricity, circularity, and normalized perimeter. These characteristics help differentiate various types of WBCs and enable accurate classification through K-means clustering. The study focus was analysis of each feature for accuracy. After experimentation, it was concluded that the circularity feature was most significant as it achieved the highest accuracy (67%), with the eccentricity feature having the lowest accuracy of 43%.

Gautam et al. [ 20 ] also presented a technique initiated via pre-processing of microscopic images. Pre-processing involved conversion of RGB (Red, Green, Blue) images to grayscale, contrast stretching, and histogram equalization. Subsequently, they applied segmentation through Otsu’s thresholding method, followed by geometrical feature extraction, including perimeter, area, eccentricity, and circularity. Finally, a Naïve Bayes classifier was used for classification with the maximum likelihood method, achieving 80.88% accuracy. Savkare et al. [ 21 ] presented an alternative method for blood cell segmentation; their pre-processing approach employed median and Laplacian filters to enhance image quality. After pre-processing, images were transformed from RGB to HSV (Hue, Saturation, Value) color space. Subsequently, K-mean clustering was applied for segmentation of blood cells. Furthermore, they used morphological operation and a watershed algorithm to refine cell separation. The proposed method through K-mean clustering acquired an accuracy of 95.5%.

3.2.2 Feature extraction-based ML techniques.

Typically, a differential counting method of WBCs is used to assess a patient’s immune system. This method involves using flow cytometry and fluorescent markers, which may disturb the cell due to repetitive sample preparation. Accordingly, label-free techniques that use imaging flow cytometry and ML algorithms to classify unstained WBCs are considered a more effective approach. Toh et al. [ 22 ] previously reported a mean F1-score of 97% across B and T subtypes, with each individual subtype achieving a distinct F1 score of 78%. Tsai et al. [ 23 ] proposed a multi-class support vector machine (SVM) approach to hierarchically identify and categorize blood cell images; segmentation was implemented on digital images to retrieve geometric features from each segment, enabling identification and classification of different blood cell types. The experimental outcomes were compared with manual results, revealing that the proposed method significantly outperformed manual classification with an accuracy of 95.3%. Likewise, Şengür et al. [ 24 ] presented a model combining image processing (IP) and ML techniques for WBC classification. Shape-based features and deep features were utilized to describe WBCs, with a long-short-term memory (LSTM) model applied to a dataset comprising 349 blood smear images with 10-fold cross-validation, from which 35 geometric and statistical features were extracted. More recently, Elen and Turan [ 25 ] compared six ML techniques (decision tree classifier, Random Forest, K-Nearest Neighbor, Multinomial Logistic Regression, Naïve Bayes, and SVM) for WBC categorization. Using shape-based features, an accuracy of 80% was achieved, while deep features achieved 82.9% accuracy. Overall, Multinomial Logistic Regression returned the highest precision rate of 95%, followed by Random Forest.

Huang et al. [ 26 ] presented a technique for WBC segmentation, delineating their approach into three phases: nucleus segmentation and recognition, feature extraction, and classification. A leukocyte (WBC) nucleus enhancer (LNE) was used to enhance the contrast of nucleus colors for segmentation, after which, multiple levels of Otsu’s thresholding were applied, effectively preserving only the WBCs and suppressing other cell types. During the feature extraction phase, a gray-level co-occurrence matrix was employed from which 80 texture features were extracted. Subsequently, they incorporated shape-based features, including compactness and roughness, after which Principal Component Analysis (PCA) was used to reduce feature dimensions. Classification was achieved using a genetic-based parameter selector (GBPS) with 50X cross-validation, resulting in 95% classification accuracy. Yampri et al. [ 27 ] also segmented out the WBCs via automatic thresholding (i.e., segregation of cell nucleus from cytoplasm) and feature extraction. Eigen cells were used to remove segments by applying the following approach: conversion of cell image to vector, computation of mean and covariance of vector, computation of eigen values and eigen vectors. Principle component analysis (PCA) was used to transform high dimensional eigen space to significantly lower dimensional space, with 92% classification accuracy achieved.

3.2.3 Classification-based (focused) ML techniques.

Tavakoli et al. [ 28 ] developed a three-phase ML method for improved WBC classification delineated as follows: nuclei/cytoplasm detection, extract features, and classification. A novel process was designed to segment the entire nucleus, while cytoplasm segmentation involves location detection inside the convex region. In the next phase, four unique colors and three shape features were extracted, and finally, in the last phase, SVM was used for WBC classification. Overall, 94.2% accuracy was achieved on the BCCD dataset, 92.2% with LISC dataset, and 94.65% with the Raabin-WBC datasets, however, hyperparameter issues were encountered.

An innovative "Computer-aided diagnostic system" method was proposed by Malkawu et al in 2020 with this process utilizing a hybrid approach, whereby CNN was employed as a feature extractor. The performance of several classifiers was measured, with Random Forest (RF) outperforming other classifiers based on a 98.7% accuracy [ 29 ]. A similar multi-approach (i.e., comparison of several ML algorithms) by Gupta et al. [ 30 ] presents an optimized form of the Binary Bat algorithm inspired by bat echolocation techniques. Using OBBA ( Table 3 ), dimensionality reduction was achieved by eliminating ≥11 similar features. Four classifiers (KNN, Logistic Regression, RF, and DT) were applied for WBC classification, demonstrating highest performance, with a mean accuracy of 97.3%, thereby surpassing other optimizers like the Optimized Crow Search Algorithm (OCSA), which attained an accuracy of 92.8% and the Optimized Cuttlefish Algorithm (OCFA), with an accuracy of 95.2%.

Lee [ 31 ] proposed an innovative approach to image segmentation based on grey-level thresholding, based on previous findings that cell-type specific reaction of the cells produces adequate evidence to allow precise classification. This method was tested on a dataset comprising 1149 WBCs from 13 altered, clinically significant categories. Cells were randomly selected from 20 blood smear images obtained from leukemia patients, with cell sorting based on quantitative volumes in the segmented images producing a classification accuracy of 82.6%.

3.3 White blood cell classification using deep learning techniques.

Wibawa et al. [ 32 ] proposed a DL model for classifying two WBC types, comparing the results with conventional machine learning methods (support vector machines), using nine features for classification. The authors report that deep learning significantly exceeded conventional ML methods, achieving highest accuracy of 95.5%. Toğaçar [ 33 ] introduced a WBC classification approach based on the coefficient and ridge feature selection method utilizing a CNN model with GoogleNet and ResNet50 for feature extraction. They achieved 97.95% accuracy for WBC classification and counting. Likewise, CNN was employed to identify and classify segmented WBC images as being “granular” or “non-granular”. Subsequently, granular cells were further categorized into eosinophils and neutrophils, while non-granular cells were classified as lymphocytes and monocytes [ 34 ]. To enhance dataset robustness, augmentation approaches were implemented, resulting in improved accuracy for both binary and multi-classification of blood cell subtypes, leading to 98.51% precision for binary WBC classification and 97.7% precision for subtype classification.

Lippeveld et al. [ 35 ] employed a relatively small dataset to examine human blood samples using image flow cytometry, with two models used to identify eight WBC types and eosinophils exclusively. ML models were applied to both datasets to classify human blood cells with 5-fold cross validation. Random Forest (RF) and Gradient Boosting (GB) were used for the first model, while deep learning CNN architecture (ResNet and DeepFlow (DF)) were employed for the second model. On the WBC dataset, results demonstrated a relatively balanced accuracy of 77.8% and 70%, while similarly for the eosinophil dataset, a balanced accuracy of 87.1% and 85.6% was achieved. DF outperformed the RN architecture on the WBC dataset, acquiring a classification accuracy of 70.3% compared to RN’s 64.9%.

Rawat et al. [ 36 ] introduced another deep learning method employing the DenseNet121 model for classification of several WBC types—The proposed model was estimated, with an accuracy of 98.84%. Results indicate that the DenseNet121 model with a batch size of 8 exhibited the highest overall performance. The dataset, consisting of 12,444 images, was obtained from Kaggle. Nazlibilek et al. [ 37 ] proposed a DL-based method that leveraged image variation operations and generative adversarial networks (GAN) for accurately classifying WBCs into five distinct types. Likewise, Sadeghian et al. [ 38 ] developed a two-stage model comprising an initial alteration using a pre-trained model, followed by the integration of a ML classifier. They employed the BCCD dataset, a downscaled blood cell detection dataset, and achieved a precision of 97.03%. Likewise, Sadeghian et al. [ 38 ] developed a two-stage model comprising an initial alteration using a pre-trained model, followed by the integration of a ML classifier. They reported 97.03% classification accuracy on the BCCD dataset, a downscaled blood cell detection dataset. Macawile et al. [ 39 ], utilized Convolutional Neural Networks (CNNs) to effectively classify and count WBCs in microscopic blood images. Among the proposed models AlexNet, GoogleNet, and ResNet-101. AlexNet performed better than the other two. It demonstrated an overall accuracy of 96.63%, albeit with a relatively lower sensitivity rate of 89.18%.

Liang et al. [ 40 ] introduced an innovative approach that merges convolutional neural networks (CNNs) with recurrent neural networks (RNNs). This fusion, termed the CNN-RNN framework, enhances understanding of image content and structured feature learning, enabling end-to-end training for comprehensive medical image data analysis. They applied transfer learning, adapting pre-trained weight parameters from the ImageNet dataset for the CNN segment. Additionally, a customized loss function was integrated to expedite training and achieve precise weight parameter convergence. Experimental results indicate a classification accuracy of 90.79%. More recently, Sharma et al. [ 41 ] presented yet another CNN-based classification methodology, achieving an impressive 96% accuracy for binary classification and 87% accuracy for multiclass classification.

Togacar et al. [ 42 ] employed a very different DL approach to WBC classification by using a computer-aided automated approach. Utilizing Regionally Based Helixal Neural Networks, their study effectively classified and differentiated WBCs, achieving an objectively high level of classification accuracy (99.52%). Toğaçar et al. [ 33 ] also introduced a method composed of three essential phases. In the initial stage, CNN models specifically AlexNet, GoogleNet, and ResNet-50 are utilized as feature extractors. Subsequently, the features extracted from these CNN model layers are fused. In the second phase, the technique incorporates feature selection methods, including MIC and Ridge Regression. In the third phase, these selected features are amalgamated. The overlapping features derived from the MIC and Ridge Regression techniques are then classified using the QDA method. This integrated approach achieves a remarkable overall success rate of 97.95% in classifying WBCs.

Mohamed [ 43 ] introduced an alternative method for the identification and classification of blood cells based on CNN. The study presented two distinct approaches for classifying WBCs. In the initial approach, CNN was employed with transfer learning, utilizing pre-trained weight parameters applied to the images. In contrast, the second approach utilized Support Vector Machines (SVM) for the classification process. The classification results demonstrated a remarkable 98.4% accuracy for CNN and 90.6% accuracy for SVM. The classification results of CNN are higher compared to SVM. Yao et al. [ 44 ] introduced a CNN-based approach for the classification of WBCs. In their method, CNN integrated an optimizer to adaptively adjust parameters such as the learning rate, leveraging the efficient net architecture. The utilization of the optimizer responded to changes in loss and accuracy. Their proposed model demonstrated exceptional performance, achieving an impressive accuracy of 90%.

Khosrosereshki et al. [ 45 ] developed an R-CNN-based model to identify neutrophils, eosinophils, monocytes, and lymphocytes, with two models employed, namely Faster RCNN and Yolov4. Faster RCNN obtained an accuracy of 96.25%, while Yolov4 was slightly lower at 95%. Likewise, Bouchet et al. [ 46 ] utilized the Inception Recurrent Residual Convolutional Neural Network (IRRCNN) model, an advanced hybrid architecture based on residual networks and RCNN principles. The proposed IRRCNN demonstrated exceptional accuracy in experiments, achieving a 100% accuracy rate for WBC classification.

Jha et al. [ 47 ] developed a leukemia detection module specifically designed for blood smear images with their multi-phase detection process comprising pre-processing, segmentation, feature extraction, and classification. The segmentation step utilizes a hybrid model based on Mutual Information (MI), which combines results from the active contour model and fuzzy C means algorithm. Subsequently, statistical and Local Directional Pattern (LDP) features are extracted from the segmented images. These features are then fed into a novel Deep CNN classifier based on the proposed Chronological Sine Cosine Algorithm (SCA) for classification purposes. Testing used blood smear images from the AA-IDB2 database, with simulation results indicating that the developed classifier achieved an accuracy of 98.7%.

Ullah et al. [ 48 ] introduced a 3D-CNN feature-based CBVR system that is highly efficient and effective for retrieving similar content from vast video data repositories. After an in-depth exploration of its effectiveness in representing sequential frames, they selected middle layer features of a 3D-CNN model. Leveraging a mechanism for selecting convolutional features, only the active feature maps from the CNN layer that correspond to the ongoing event in the frame sequence are chosen. To condense the size of the extracted high-dimensional features for streamlined retrieval and expedited storage, they introduced the concept of hashing. These high-dimensional features are represented in compact binary codes through PCA, ensuring efficient search and reduced storage requirements for WBCs classification. For the classification of WBCs, the achieved accuracy is 85%.

Imran et al. [ 49 ] conducted a study involving the utilization of a four-hidden-layer feed-forward DNN and CNN. The research also extensively examines the impact of Mel-Frequency Cepstral Coefficients (MFCC) and Filter Bank Energies (FBE)features trained with various context sizes on two deep learning models, evaluated under normal, slow, and fast speaking rates. Micro-level analysis of results was conducted, revealing that the four-hidden-layer CNN slightly outperforms the DNN in classifying WBCs. The CNN achieved an accuracy of 83% in classifying WBCs. Kastrati et al. [ 50 ] introduced a convolutional neural network with three hidden layers, each having 1024 neurons, showcasing excellent performance in white blood cell classification on the INFUSE dataset, achieving accuracy of 78.10%.

Ullah et al. [ 51 ] introduced an innovative conflux Long Short-Term Memory (LSTM) network for WBC classification. The framework involves four stages: 1) frame-level feature extraction, 2) feature propagation through the conflux LSTM network 3) pattern acquisition and correlation computation, and 4) action classification. The process begins with extracting deep features using a pre-trained VGG19 CNN model from frame sequences for each view. Extracted features then undergo conflux LSTM processing to learn unique view-specific patterns. Interview correlations are computed by utilizing pairwise dot products from LSTM outputs across views, thus acquiring interdependent patterns. The VGG19 CNN model achieved a classification accuracy of 88.9%. Meanwhile, Banik et al. [ 52 ] recently presented a CNN)-based WBC image classifier which merges features from both the initial and final convolutional layers, while utilizing input image propagation through a convolutional layer to enhance performance. A dropout layer is added to counter overfitting, resulting in a classification accuracy of 98.61%. Another CNN-based approach has been developed by Ku et al. [ 53 ] who propose an automated system for leucocyte classification using a dual-stage CNN. A dataset of 2,174 patch images was collected for training and testing purposes, with the dual-stage CNN used to classify images into 4 classes, achieving an overall accuracy of 97.06%.

Karthikeyan et al. [ 54 ] introduced the Leishman-stained function deep classification (LSM-TIDC) model for WBC classification. Interestingly, the LSM-TIDC method explores the potential of interpolation and Leishman-stained function without the need for explicit segmentation, which if successfully implemented, effectively eliminates false regions in multiple input images. Following image pre-processing, relevant features are extracted through multi-directional feature extraction, with a system then developed, utilizing a transformation invariant model to extract nuclei and subsequently employing convolutional and pooling characteristics for cell classification. Method testing was conducted on the Kaggle dataset, and classification accuracy of 94.42% was achieved.

Acevedo et al. [ 55 ] used a large dataset of 17,092 peripheral blood cell images across eight classes gathered using the CellaVision DM96 analyzer. Pathologist-verified ground truth data were used to train two CNN architectures: Vgg-16 and Inceptionv3. In the first setup, networks acted as feature extractors for an SVM, achieving test accuracies of 86% (Vgg-16) and 90% (Inceptionv3). In the second setup, fine-tuning resulted in “end-to-end” models, yielding 96% accuracy (Vgg-16) and 95% accuracy (Inceptionv3).

Upon comparing the identified 136 relevant studies, as a general observation, detection of WBCs through conventional methods (ML) tends to focus on cell segmentation after data pre-processing, with segmented data then typically employed for feature extraction in WBC classification. Accordingly, the traditional ML methods were associated with better results as accurate identification of WBCs is impractical in the absence of efficient segmentation, thus resulting in higher levels of classification accuracy (Tables 4 – 8 ). Research teams employed a range of methods for data segmentation and obtained a range of classification accuracies; while some conventional models achieved up to 99% accuracy, accuracy was shown to decrease in concurrence with decreasing dataset size (e.g., Lippeveld et al. [ 35 ]). Deep learning models exhibited improved performance for more extensive datasets and exhibited higher levels of accuracy in concurrence with increasingly large datasets ( Table 6 ). Several authors implemented a combination of different datasets, to probe the accuracy of their models on unknown datasets (i.e., blind testing). Deep learning models have represented a significant breakthrough in myriad domains and as shown in the identified literature, the use of traditional machine learning models within biomedical applications in general, and WBC classification in particular is undoubtedly shifting toward the use of deep learning models based on dataset size. However, deep learning algorithms (and associated research) are now in a significantly more advanced phase, with proven capacity to solve increasingly complex problems with higher performance. Notwithstanding, there is a clear gap in the use of the latest advances in deep learning, including the use of transfer knowledge and meta-learning processes.

https://doi.org/10.1371/journal.pone.0292026.t008

Comparative analysis of deep learning models applied to various large datasets revealed remarkably high levels of achieved accuracy across various studies ( Table 9 ). Baghel et al. [ 97 ] demonstrated a high level of efficacy associated with the use of CNNs, achieving an accuracy of 98.51%, while Riaz et al. [ 117 ] used a Convolutional Generative Adversarial Network (GAN) to obtain a classification accuracy of 99.9% on the Catholic University of Korea dataset. Mosabbir et al. [ 118 ] addressed the challenging National Institutes of Health (NIH) dataset using CNN, attaining an accuracy of 97.92%. Tusneem et al. [ 119 ] also used CNN and demonstrated its strength, with a 99.7% classification accuracy. Kakumani et al. [ 120 ] utilized a pre-trained InceptionV3 model on the Kaggle dataset and achieved 99.76% classification accuracy.

https://doi.org/10.1371/journal.pone.0292026.t009

4. Limitations of previous studies and future challenges

ML/DL researchers have made significant advances in increasingly accurate classification of WBCs in recent years. Among all techniques based on SVM, Sajjad et al. [ 6 ] achieved maximum accuracy, sensitivity, and specificity of 98.6%, 96.2%, and 98.5%. Using KNN, Abdeldaim et al. [ 68 ] achieved maximum accuracy of 98.6%. Similarly, using ANN, Hegde et al. [ 70 ] acquired accuracy, sensitivity, and specificity of 99%, 99.4%, and 99.18%, respectively. Using DL methods, Loey et al. achieved maximum precision and sensitivity of 100% each and specificity of 98.2%; However, while many researchers achieved close to maximum performance, several limitations and constraints have been associated with previous and current techniques. Accordingly, the research community faces several fundamental obstacles in the field of MIA that must be accepted and resolved. These include the lack of easily accessible, large, high-quality datasets, a shortage of dedicated medical professionals, and the complexity of Transfer Learning and Deep Learning methods. Several DML strategies, mathematical and theoretical foundations are also a source of several challenges [ 96 , 104 ], with unsupervised or semi-supervised systems needed to address these issues [ 105 ]. Moreover, TML and DL-based MIA applications and systems still have significant work to adopt “real-time application”.

4.1 Lack of publicly accessible datasets

The lack of publicly accessible datasets represents the primary issue affecting medical image analysis. Scientists need to inspire health organizations to address this problem, it would be beneficial if high-quality data were available to researchers. Initiatives promoting open data availability from various health organizations worldwide should also be encouraged. However, authorization should also be required (e.g., hospital data and conditional access to datasets). When data are readily available in large quantities, just like in other fields such as environmental science, weather forecasting, and bioinformatics, the issue becomes more relevant for research (e.g., video summarization [ 106 ], IoT [ 107 ], energy management [ 108 ], and so on). Acquiring very large, high-quality datasets with accurate labeling is crucial for MIA applications.

4.2 Generalization skills for trained predictors

Another very significant challenge associated with MIA and WBC identification and classification is the availability of appropriately trained predictors. A perfect learning method that balances computational efficiency with generalization capacity is required to solve this issue. To build a model with impressive generalization capabilities, a learning approach that incorporates true or random labels is necessary. This approach provides efficient training algorithms and practical tools to handle available datasets using accurate or arbitrary labels. Many MIA tasks, including identifying brain tumors, lung cancer, breast cancer, and leucocytes, have shown significant empirical success. Despite the inherent challenges posed by non-convex optimization, basic techniques such as stochastic gradient descent (SGD) can efficiently discover viable solutions, effectively minimizing training errors. More interestingly, the networks created in this manner have strong generalization capabilities [ 109 ], even when there are far more parameters than training data [ 110 ]. Only reducing the training error during model training is insufficient. The choice of global minima greatly impacts the generalization behavior of the predictor. It is crucial to select the appropriate algorithm to minimize training errors for better results. Different initialization, update, learning rate, and halting conditions for optimization algorithms will result in global minima with various degrees of generalizability.

4.3 Reliable methods for real-world scenarios

TML and DL approaches provide reliability to real-world health diagnosis systems [ 111 ]. However, MIA and leukocyte classification models requires expertise and technical skill. In the future, researchers should prioritize crafting accurate and trustworthy procedures applicable in real-world healthcare situations, eliminating the necessity for medical specialists. Real-world health diagnosis systems greatly gain from the dependability of Machine Learning (ML) and Deep Learning (DL) approaches [ 111 ]. Yet, constructing exact models for Medical Image Analysis (MIA) and leukocyte classification necessitates a high degree of expertise and technical proficiency. As research advances, it becomes crucial for researchers to tackle the task of developing reliable procedures that can smoothly integrate into real-world healthcare environments, reducing the reliance on specialized medical professionals. This involves tackling issues related to model generalization, data variability, interpretability, and ensuring consistent performance across diverse patient populations and clinical scenarios.

5. Future research directions

The biomedical engineering and research community should dedicate substantial effort to support MIA, particularly leukocyte examination in blood images, due to the significant challenges faced by the MIA community, as detailed in section V.

i . Data augmentation methods to complete the dataset deficit .

This work addresses the issue of limited dataset availability in MIA and leucocyte classification. We present data augmentation approach and leverage transfer learning algorithms to enhance the identification of WBCs.

ii . Technical skills and medical experience required .

TML and DL models have shown significant potential for computer-aided MIA-based diagnostic applications, and popular open-source frameworks like TensorFlow, Caffe, and Keras offer access to these advanced models [ 121 ]. Developing effective machine learning models for medical image analysis (MIA) requires careful consideration and expertise in the clinical and medical domains. It is essential to choose and train the suitable model to achieve accurate and reliable results in MIA applications.

iii . Resource-aware DL models for classifying leukocytes .

Medical Image Analysis (MIA) with the adoption of advanced DL models like GANs, R-CNN, Fast R-CNN, and faster R-CNN, along with the integration of TML and DL methods. These models have shown superior performance in tasks like brain tumor detection, leukocyte classification, breast cancer diagnosis, and various other MIA applications. However, their biggest concerns are the significant memory needs and computational costs. Therefore, it is necessary to investigate the computationally and environmentally friendly TML and DL models for leukocyte analysis in blood images.

iv . Models for the detection and classification of leukocytes

DNNs provide a superior alternative to conventional learning techniques. The end-to-end models, especially CNNs, stem from their efficient process and the capability to classify leucocytes into five classes. These models compete with complex MIA models built on DNN based on data-driven learning methodologies. WBC detection and categorization in images can also be accomplished using a variety of end-to-end designs [ 122 – 124 ].

v . TML AND DL universal evaluation in MIA

The MIA research community often relies on subjective evaluation methods, which can be challenging, inefficient, and prone to errors. Therefore, comprehensive evaluation techniques that can automatically assess the effectiveness of Traditional Machine Learning (TML) and Deep Learning (DL) models for MIA from various views.

vi . Vision Transformers and Vision Formation Models

While Vision Transformers (ViTs) were not included in the current review, they represent a likely cutting-edge approach for the future of white blood cell (WBC) classification (and other forms of imagery analyses), employing an advanced self-attention mechanism to extract crucial features from input images. Additionally, ViTs leverage transfer learning by incorporating pre-trained model weights, further boosting their performance. This dual approach meticulously captures subtle features, significantly enhancing the precision and accuracy of WBC classification—a major advancement in the realm of medical imaging. Likewise, vision foundation models are powerful generative deep learning models trained on large datasets for classification, segmentation, and detection, and will likely become a frequently employed approach for medical imaging in future.

6. Conclusion

We provide a comprehensive review of the TML and DL techniques applied to WBCs classification. We thoroughly explored and compared various methods for WBC categorization in this context. The data for this research is compiled from 136 primary papers published between 2006 and 2023. These papers encompass TML and DL methodologies for leukocyte classification and their applications in medical diagnosis. The comprehensive analysis of these studies reveals the significant contributions of TML and DL techniques to MIA. The main objective of this work is to identify and synthesize the myriad TML and DL applications in MIA, particularly in the domain of leucocyte classification in blood smear images. This research aims to provide valuable insights into the complex characteristics of TML and DL in MIA by thoroughly analyzing existing literature. Based on literature review outcomes, Deep Learning models like CNNs for image classification and GANs for data augmentation should be increasingly employed to negate the limitations (e.g., time) and human biases/inaccuracies associated with manual classification used. The study’s results emphasize the importance of conducting more research on using TML and DL methods effectively in MIA and classifying leucocytes in blood smear images. Besides leucocyte classification, this study explored applications for advanced DL models. Collecting all these data in this study will help the research industry by indicating where they should focus their future investigation of TML and DL models for MIA. These methods have the potential to lead to significant advancements in speech analysis, natural language processing (NLP), and medical imaging in the future. In addition to WBCs, TML and DL approaches are employed to identify and categorize various MIA domains, such as the analysis of MRI, CT, X-ray, and ultrasound images. Blood smear images are a growing field in MIA that has drawn attention from the research community over the past three decades. Additionally, we recognized the problems, instructions, and solutions for the developments of TML and DL models in MIA, notably for classifying WBCs in blood smear images. The potential of TML and DL approaches will be used to expand our research to include different MIA domains, including MRI, CT, Ultrasound, and X-ray images.

Supporting information

S1 checklist. prisma 2020 flow diagram for new systematic reviews which included searches of databases and registers only..

https://doi.org/10.1371/journal.pone.0292026.s001

S2 Checklist. Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist.

https://doi.org/10.1371/journal.pone.0292026.s002

S1 Dataset.

https://doi.org/10.1371/journal.pone.0292026.s003

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Indo-caribbean youth and suicidal behavior: a systematic review.

1. Introduction

2. materials and methods, 2.1. eligibility criteria, 2.2. information sources and search strategy, 2.3. study selection, 2.4. quality appraisal, 2.5. data extraction and synthesis, 3.1. summary of included literature, 3.2. guyana, 3.3. trinidad and tobago, 3.4. suriname, 4. discussion, 4.1. gender differences, 4.2. family structure and relationships, 4.3. social and societal pressures, 4.4. methods of suicide, 4.5. protective factors against suicide, 4.6. management of suicidal behavior, 5. limitations, 6. conclusions, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Share and Cite

Ruiz Camacho, R.; Sukhram, S.D. Indo-Caribbean Youth and Suicidal Behavior: A Systematic Review. Int. J. Environ. Res. Public Health 2024 , 21 , 801. https://doi.org/10.3390/ijerph21060801

Ruiz Camacho R, Sukhram SD. Indo-Caribbean Youth and Suicidal Behavior: A Systematic Review. International Journal of Environmental Research and Public Health . 2024; 21(6):801. https://doi.org/10.3390/ijerph21060801

Ruiz Camacho, Raul, and Shiryn D. Sukhram. 2024. "Indo-Caribbean Youth and Suicidal Behavior: A Systematic Review" International Journal of Environmental Research and Public Health 21, no. 6: 801. https://doi.org/10.3390/ijerph21060801

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