Control ( 20)
The training period was 4–24 weeks (mean = 11.49; S.D. = 6.88). One study by Lee et al. had two length periods and total hours because the study examined video game training of two types. The total training hours were 16–90 h (mean = 40.63; S.D. = 26.22), whereas the training intensity was 1.5–10.68 h/week (mean = 4.96; S.D. = 3.00). One study did not specify total training hours. Two studies did not specify the training intensity. The training periods and intensities are in Table 8 .
Periods and intensities of video gaming intervention.
Author | Year | Length (Week) | Total Hours | Average Intensity (h/Week) |
---|---|---|---|---|
Gleich et al. [ ] | 2017 | 8 | 49.5 | 6.2 |
Haier et al. [ ] | 2009 | 12 | 18 | 1.5 |
Kuhn et al. [ ] | 2014 | 8 | 46.88 | 5.86 |
Lorenz et al. [ ] | 2012 | 8 | 28 | 3.5 |
Lee et al. [ ] | 2015 | 8–11 * | 27 | n/a |
Martinez et al. [ ] | 2013 | 4 | 16 | 4 |
Roush [ ] | 2013 | 24 | ns | n/a |
West et al. [ ] | 2017 | 24 | 72 | 3 |
West et al. [ ] | 2018 | 8.4 | 90 | 10.68 |
The training length was converted into weeks (1 month = 4 weeks). ns, not specified; n/a, not available; * exact length is not available.
Of nine eligible studies, one study used resting-state MRI analysis, three studies (excluding that by Haier et al. [ 40 ]) used structural MRI analysis, and five studies used task-based MRI analysis. A study by Haier et al. used MRI analyses of two types [ 40 ]. A summary of MRI analyses is presented in Table 9 . The related resting-state, structural, and task-based MRI specifications are presented in Table 10 , Table 11 and Table 12 respectively.
MRI analysis details of eligible studies.
MRI Analysis | Author | Year | Contrast | Statistical Tool | Statistical Method | Value |
---|---|---|---|---|---|---|
Resting | Martinez et al. [ ] | 2013 | (post- > pre-training) > (post>pre-control) | MATLAB; SPM8 | TFCE uncorrected | <0.005 |
Structural | Haier et al. * [ ] | 2009 | (post>pre-training) > (post>pre-control) | MATLAB 7; SurfStat | FWE corrected | <0.005 |
Kuhn et al. [ ] | 2014 | (post>pre-training) > (post>pre-control) | VBM8; SPM8 | FWE corrected | <0.001 | |
West et al. [ ] | 2017 | (post>pre-training) > (post>pre-control) | Bpipe | Uncorrected | <0.0001 | |
West et al. [ ] | 2018 | (post>pre-training) > (post>pre-control) | Bpipe | Bonferroni corrected | <0.001 | |
Task | Gleich et al. [ ] | 2017 | (post>pre-training) > (post>pre-control) | SPM12 | Monte Carlo corrected | <0.05 |
Haier et al. * [ ] | 2009 | (post>pre-training) > (post>pre-control) | SPM7 | FDR corrected | <0.05 | |
Lee et al. [ ] | 2012 | (post>pre-training) > (post>pre-control) | FSL; FEAT | uncorrected | <0.01 | |
Lorenz et al. [ ] | 2015 | (post>pre-training) > (post>pre-control) | SPM8 | Monte Carlo corrected | <0.05 | |
Roush [ ] | 2013 | post>pre-training | MATLAB 7; SPM8 | uncorrected | =0.001 |
* Haier et al. conducted structural and task analyses. + Compared pre-training and post-training between groups without using contrast. TFCE, Threshold Free Cluster Enhancement; FEW, familywise error rate; FDR, false discovery rate.
Resting-State MRI specifications of eligible studies.
Author | Year | Resting State | Structural | ||||||
---|---|---|---|---|---|---|---|---|---|
Imaging | TR (s) | TE (ms) | Slice | Imaging | TR (s) | TE (ms) | Slice | ||
] | 2013 | gradient-echo planar image | 3 | 28.1 | 36 | T1-weighted | 0.92 | 4.2 | 158 |
Structural MRI specifications of eligible studies.
Author | Year | Imaging | TR (s) | TE (ms) |
---|---|---|---|---|
Kuhn et al. [ ] | 2014 | 3D T1 weighted MPRAGE | 2.5 | 4.77 |
West et al. [ ] | 2017 | 3D gradient echo MPRAGE | 2.3 | 2.91 |
West et al. [ ] | 2018 | 3D gradient echo MPRAGE | 2.3 | 2.91 |
Task-Based MRI specifications of eligible studies.
Author | Year | Task | BOLD | Structural | ||||||
---|---|---|---|---|---|---|---|---|---|---|
Imaging | TR (s) | TE (ms) | Slice | Imaging | TR (s) | TE (ms) | Slice | |||
Gleich et al. [ ] | 2017 | win–loss paradigm | T2 echo-planar image | 2 | 30 | 36 | T1-weighted | 2.5 | 4.77 | 176 |
Haier et al. [ ] | 2009 | Tetris | Functional echo planar | 2 | 29 | ns | 5-echo MPRAGE | 2.53 | 1.64; 3.5; 5.36; 7.22; 9.08 | ns |
Lee et al. [ ] | 2012 | game control | fast echo-planar image | 2 | 25 | ns | T1-weighted MPRAGE | 1.8 | 3.87 | 144 |
Lorenz et al. [ ] | 2015 | slot machine paradigm | T2 echo-planar image | 2 | 30 | 36 | T1-weighted MPRAGE | 2.5 | 4.77 | ns |
Roush [ ] | 2013 | digit symbol substitution | fast echo-planar image | 2 | 25 | 34 | diffusion weighted image | ns | ns | ns |
All analyses used 3 Tesla magnetic force; TR = repetition time; TE = echo time, ns = not specified.
This literature review evaluated the effect of noncognitive-based video game intervention on the cognitive function of healthy people. Comparison of studies is difficult because of the heterogeneities of participant ages, beneficial effects, and durations. Comparisons are limited to studies sharing factors.
Video gaming intervention affects all age categories except for the children category. The exception derives from a lack of intervention studies using children as participants. The underlying reason for this exception is that the brain is still developing until age 10–12 [ 52 , 53 ]. Among the eligible studies were a study investigating adolescents [ 40 ], six studies investigating young adults [ 41 , 42 , 43 , 47 , 49 , 51 ] and two studies investigating older adults [ 48 , 50 ].
Differences among study purposes underlie the differences in participant age categories. The study by Haier et al. was intended to study adolescents because the category shows the most potential brain changes. The human brain is more sensitive to synaptic reorganization during the adolescent period [ 54 ]. Generally, grey matter decreases whereas white matter increases during the adolescent period [ 55 , 56 ]. By contrast, the cortical surface of the brain increases despite reduction of grey matter [ 55 , 57 ]. Six studies were investigating young adults with the intention of studying brain changes after the brain reaches maturity. The human brain reaches maturity during the young adult period [ 58 ]. Two studies were investigating older adults with the intention of combating difficulties caused by aging. The human brain shrinks as age increases [ 56 , 59 ], which almost invariably leads to declining cognitive function [ 59 , 60 ].
Three beneficial outcomes were observed using MRI method: grey matter change [ 40 , 42 , 50 ], brain activity change [ 40 , 43 , 47 , 48 , 49 ], and functional connectivity change [ 41 ]. The affected brain area corresponds to how the respective games were played.
Four studies of 3D video gaming showed effects on the structure of hippocampus, dorsolateral prefrontal cortex (DLPFC), cerebellum [ 42 , 43 , 50 ], and DLPFC [ 43 ] and ventral striatum activity [ 49 ]. In this case, the hippocampus is used for memory [ 61 ] and scene recognition [ 62 ], whereas the DLPFC and cerebellum are used for working memory function for information manipulation and problem-solving processes [ 63 ]. The grey matter of the corresponding brain region has been shown to increase during training [ 20 , 64 ]. The increased grey matter of the hippocampus, DLPFC, and cerebellum are associated with better performance in reference and working memory [ 64 , 65 ].
The reduced activity of DLPFC found in the study by Gleich et al. corresponds to studies that showed reduced brain activity associated with brain training [ 66 , 67 , 68 , 69 ]. Decreased activity of the DLPFC after training is associated with efficiency in divergent thinking [ 70 ]. 3D video gaming also preserved reward systems by protecting the activity of the ventral striatum [ 71 ].
Two studies of puzzle gaming showed effects on the structure of the visual–spatial processing area, activity of the frontal area, and functional connectivity change. The increased grey matter of the visual–spatial area and decreased activity of the frontal area are similar to training-associated grey matter increase [ 20 , 64 ] and activity decrease [ 66 , 67 , 68 , 69 ]. In this case, visual–spatial processing and frontal area are used constantly for spatial prediction and problem-solving of Tetris. Functional connectivity of the multimodal integration and the higher-order executive system in the puzzle solving-based gaming of Professor Layton game corresponds to studies which demonstrated training-associated functional connectivity change [ 72 , 73 ]. Good functional connectivity implies better performance [ 73 ].
Strategy gaming affects the DLPFC activity, whereas rhythm gaming affects the activity of visuospatial working memory, emotional, and attention area. FPS gaming affects the structure of the hippocampus and amygdala. Decreased DLPFC activity is similar to training-associated activity decrease [ 66 , 67 , 68 , 69 ]. A study by Roush demonstrated increased activity of visuospatial working memory, emotion, and attention area, which might occur because of exercise and gaming in the Dance Revolution game. Results suggest that positive activations indicate altered functional areas by complex exercise [ 48 ]. The increased grey matter of the hippocampus and amygdala are similar to the training-associated grey matter increase [ 20 , 64 ]. The hippocampus is used for 3D navigation purposes in the FPS world [ 61 ], whereas the amygdala is used to stay alert during gaming [ 74 ].
Change of the brain structure and function was observed after 16 h of video gaming. The total durations of video gaming were 16–90 h. However, the gaming intensity must be noted because the gaming intensity varied: 1.5–10.68 h per week. The different intensities might affect the change of cognitive function. Cognitive intervention studies demonstrated intensity effects on the cortical thickness of the brain [ 75 , 76 ]. A similar effect might be observed in video gaming studies. More studies must be conducted to resolve how the intensity can be expected to affect cognitive function.
Almost all studies used inclusion criteria “little/no experience with video games.” The criterion was used to reduce the factor of gaming-related experience on the effects of video gaming. Some of the studies also used specific handedness and specific sex of participants to reduce the variation of brain effects. Expertise and sex are shown to affect brain activity and structure [ 77 , 78 , 79 , 80 ]. The exclusion criterion of “MRI contraindication” is used for participant safety for the MRI protocol, whereas exclusion criteria of “psychiatric/mental illness”, “neurological illness”, and “medical illness” are used to standardize the participants.
Some concern might be raised about the quality of methodology, assessed using Delphi criteria [ 45 ]. The quality was 3–9 (mean = 6.10; S.D. = 1.69). Low quality in most papers resulted from unspecified information corresponding to the criteria. Quality improvements for the studies must be performed related to the low quality of methodology. Allocation concealment, assessor blinding, care provider blinding, participant blinding, intention-to-treat analysis, and allocation method details must be improved in future studies.
Another concern is blinding and control. This type of study differs from medical studies in which patients can be blinded easily. In studies of these types, the participants were tasked to do either training as an active control group or to do nothing as a passive control group. The participants can expect something from the task. The expectation might affect the outcomes of the studies [ 81 , 82 , 83 ]. Additionally, the waiting-list control group might overestimate the outcome of training [ 84 ].
Considering the sample size, which was 20–75 (mean = 43.67; S.D. = 15.63), the studies must be upscaled to emphasize video gaming effects. There are four phases of clinical trials that start from the early stage and small-scale phase 1 to late stage and large-scale phase 3 and end in post-marketing observation phase 4. These four phases are used for drug clinical trials, according to the food and drug administration (FDA) [ 85 ]. Phase 1 has the purpose of revealing the safety of treatment with around 20–100 participants. Phase 2 has the purpose of elucidating the efficacy of the treatment with up to several hundred participants. Phase 3 has the purpose of revealing both efficacy and safety among 300–3000 participants. The final phase 4 has the purpose of finding unprecedented adverse effects of treatment after marketing. However, because medical studies and video gaming intervention studies differ in terms of experimental methods, slight modifications can be done for adaptation to video gaming studies.
Several unresolved issues persist in relation to video gaming intervention. First, no studies assessed chronic/long-term video gaming. The participants might lose their motivation to play the same game over a long time, which might affect the study outcomes [ 86 ]. Second, meta-analyses could not be done because the game genres are heterogeneous. To ensure homogeneity of the study, stricter criteria must be set. However, this step would engender a third limitation. Third, randomized controlled trial video gaming studies that use MRI analysis are few. More studies must be conducted to assess the effects of video gaming. Fourth, the eligible studies lacked cognitive tests to validate the cognitive change effects for training. Studies of video gaming intervention should also include a cognitive test to ascertain the relation between cognitive function and brain change.
The systematic review has several conclusions related to beneficial effects of noncognitive-based video games. First, noncognitive-based video gaming can be used in all age categories as a means to improve the brain. However, effects on children remain unclear. Second, noncognitive-based video gaming affects both structural and functional aspects of the brain. Third, video gaming effects were observed after a minimum of 16 h of training. Fourth, some methodology criteria must be improved for better methodological quality. In conclusion, acute video gaming of a minimum of 16 h is beneficial for brain function and structure. However, video gaming effects on the brain area vary depending on the video game type.
We would like to thank all our other colleagues in IDAC, Tohoku University for their support.
PRISMA Checklist of the literature review.
Section/Topic | # | Checklist Item | Reported on Page # |
---|---|---|---|
Title | 1 | Identify the report as a systematic review, meta-analysis, or both. | 1 |
Structured summary | 2 | Provide a structured summary including, as applicable: background; objectives; data sources; study eligibility criteria, participants, and interventions; study appraisal and synthesis methods; results; limitations; conclusions and implications of key findings; systematic review registration number. | 1 |
Rationale | 3 | Describe the rationale for the review in the context of what is already known. | 1, 2 |
Objectives | 4 | Provide an explicit statement of questions being addressed related to participants, interventions, comparisons, outcomes, and study design (PICOS). | 2 |
Protocol and registration | 5 | Indicate if a review protocol exists, if and where it is accessible (e.g., Web address), and if available, provide registration information including registration number. | 2 |
Eligibility criteria | 6 | Specify study characteristics (e.g., PICOS, length of follow-up) and report characteristics (e.g., years considered, language, publication status) used as criteria for eligibility, giving rationale. | 2 |
Information sources | 7 | Describe all information sources (e.g., databases with dates of coverage, contact with study authors to identify additional studies) in the search and date last searched. | 2 |
Search | 8 | Present full electronic search strategy for at least one database, including any limits used, such that it could be repeated. | 2 |
Study selection | 9 | State the process for selecting studies (i.e., screening, eligibility, included in systematic review, and if applicable, included in the meta-analysis). | 3 |
Data collection process | 10 | Describe method of data extraction from reports (e.g., piloted forms, independently, in duplicate) and any processes for obtaining and confirming data from investigators. | 3 |
Data items | 11 | List and define all variables for which data were sought (e.g., PICOS, funding sources) and any assumptions and simplifications made. | 3 |
Risk of bias in individual studies | 12 | Describe methods used for assessing risk of bias of individual studies (including specification of whether this was done at the study or outcome level), and how this information is to be used in any data synthesis. | 2 |
Summary measures | 13 | State the principal summary measures (e.g., risk ratio, difference in means). | - |
Synthesis of results | 14 | Describe the methods of handling data and combining results of studies, if done, including measures of consistency (e.g., I ) for each meta-analysis. | - |
Risk of bias across studies | 15 | Specify any assessment of risk of bias that might affect the cumulative evidence (e.g., publication bias, selective reporting within studies). | - |
Additional analyses | 16 | Describe methods of additional analyses (e.g., sensitivity or subgroup analyses, meta-regression), if done, indicating which were pre-specified. | - |
Study selection | 17 | Give numbers of studies screened, assessed for eligibility, and included in the review, with reasons for exclusions at each stage, ideally with a flow diagram. | 3,5 |
Study characteristics | 18 | For each study, present characteristics for which data were extracted (e.g., study size, PICOS, follow-up period) and provide the citations. | 5-11 |
Risk of bias within studies | 19 | Present data on risk of bias of each study, and if available, any outcome level assessment (see item 12). | 5,6 |
Results of individual studies | 20 | For all outcomes considered (benefits or harms), present, for each study: (a) simple summary data for each intervention group (b) effect estimates and confidence intervals, ideally with a forest plot. | 4 |
Synthesis of results | 21 | Present results of each meta-analysis done, including confidence intervals and measures of consistency. | - |
Risk of bias across studies | 22 | Present results of any assessment of risk of bias across studies (see Item 15). | - |
Additional analysis | 23 | Give results of additional analyses, if done (e.g., sensitivity or subgroup analyses, meta-regression [see Item 16]). | - |
Summary of evidence | 24 | Summarize the main findings including the strength of evidence for each main outcome; consider their relevance to key groups (e.g., healthcare providers, users, and policy makers). | 12,13 |
Limitations | 25 | Discuss limitations at study and outcome level (e.g., risk of bias), and at review-level (e.g., incomplete retrieval of identified research, reporting bias). | 13 |
Conclusions | 26 | Provide a general interpretation of the results in the context of other evidence, and implications for future research. | 14 |
Funding | 27 | Describe sources of funding for the systematic review and other support (e.g., supply of data); role of funders for the systematic review. | 14 |
For more information, visit: www.prisma-statement.org .
D.B.T., R.N., and R.K. designed the systematic review. D.B.T. and R.N. searched and selected the papers. D.B.T. and R.N. wrote the manuscript with R.K. All authors read and approved the final manuscript. D.B.T. and R.N. contributed equally to this work.
Study is supported by JSPS KAKENHI Grant Number 17H06046 (Grant-in-Aid for Scientific Research on Innovative Areas) and 16KT0002 (Grant-in-Aid for Scientific Research (B)).
None of the other authors has any conflict of interest to declare. Funding sources are not involved in the study design, collection, analysis, interpretation of data, or writing of the study report.
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Teenagers today often spend a great deal of time playing video games. These games are fun and engaging and young people often feel like playing games is a great thing to do in their spare time. However, you shouldn’t spend too much time gaming and there are a number of reasons for this: it’s unhealthy, you should spend more time studying, and you need to socialize more.
The primary reason to cut down on gaming time has to be that youths that spend a long time on computers and games consoles are often not getting enough exercise. With high obesity rates, it’s very important for young people to spend as much time being active as possible, especially since studies show that people who are overweight during childhood and adolescence are far more likely to become overweight adults than those of a healthy weight when they’re young. There is just as much, if not more, fun to be had in getting out and about and being active. This doesn’t have to mean going for long runs if that’s not your thing; team sports are both fun and great exercise. Dance or fitness classes are also a good option for those not into sports.
Secondly, getting a good education is that best thing that you can do for your future. If every teenager cut down on their gaming time by 30 minutes per day and used this time to study, the whole of that generation would achieve better results and be more likely to have the career that they want. With education, you get out what you put in, so it’s really up to you to put in the additional effort. If you want to go to a good college, extra study in your own time will be completely vital, but gaming can reduce your concentration span and make this more difficult.
The third main reason that playing video games too often is that it’s no good for your social skills. Some teenagers may argue that because they can now play their games online with friends, they are socializing in their own way. However, you can’t beat face-to-face contact, and if you want to succeed in interviews and build good relationships in later life, you need to develop some proper communication skills. This shouldn’t be a chore, or difficult, because hanging out with friends is far more fun than sitting indoors playing on games all day anyway!
In conclusion, there can be no doubt that spending too much time gaming isn’t good for you. You don’t have to stop all together, but it’s all about moderation. Cut down and allocate your additional free time to doing things that are good for you, and you definitely won’t regret it in later life.
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