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.
Cognitive behavioral therapy and mindfulness training are promising treatments for problem gaming
Vol. 55 No. 5 Print version: page 52
Dan’s interest in video games started as many do: at age 5, playing educational games. By age 8, his parents—a working couple in rural Switzerland—tried to cap his PlayStation use to an hour a day.
As a teen, though, Dan, a pseudonym used in a 2021 case study, was spending upwards of 12 hours daily playing mostly first-person shooter games. He rarely saw peers outside of school and lost an apprenticeship because of perpetual tardiness and fatigue ( Niedermoser, D. W., et al., International Journal of Environmental Research and Public Health , Vol. 18, No. 4, 2021 ).
It wasn’t until his next job stipulated psychotherapy that Dan and his parents began to view, and treat, his habit as an addiction. According to the study authors, who saw him at their private psychiatric practice, the approach worked: After 8 months of weekly cognitive behavioral therapy, Dan had reduced his gaming time to about 1.5 hours a day and, uniquely, hadn’t “shifted” his addiction to another vice, like pornography viewing or tobacco use, the authors say. Dan’s depression and insomnia—which were severe and moderately severe, respectively, at the start of therapy—receded, too.
Dan’s story—boy overcomes gaming disorder, a condition that the World Health Organization added to the International Classification of Diseases (ICD-11) in 2018—is presented in the researchers’ paper as a success. “In this case,” they wrote, “the patient could keep and probably successfully finish his apprenticeship. This is of major importance for his later prospects to live a self-determined and independent life.”
But not all mental health professionals would tell stories like Dan’s the same way. Some might point to Dan’s often absent parents as the root of the issue and so the first place to intervene, while others might see Dan’s case as a missed opportunity to nurture a young person’s passion and sense of competence. Until arriving in therapy, Dan thought of gaming not as a problem but as a path to wealth and fame. And some psychologists bristle at the term gaming “disorder” or “addiction,” which they see as more about politics than science. “A problematic diagnosis may promulgate policy efforts that restrict free speech and minors’ rights, without appreciable positive impacts,” a group of psychologists wrote in a 2018 APA Division 46 (Society for Media Psychology and Technology) statement expressing concern over the WHO’s classification.
Despite the concept’s controversy, some people’s gaming habits are significantly conflicting with multiple areas of their life, which calls for clinical attention. Many of them, however, are finding balance with psychologists’ interventions—or in some cases, simply time, said Zsolt Demetrovics, PhD , chair of the Centre of Excellence in Responsible Gaming at the University of Gibraltar.
“The nature of development of most addictive disorders is progressing to worse, and that’s not clearly the situation in the case of video games,” he said. While more longitudinal research is needed, “there are signs of a much higher proportion of spontaneous recovery or just normalization of gaming after a more problematic period of gaming than in the case of other disorders.”
Some data suggest 76% of under-18-year-olds and 67% of adults play video games in the United States . “Esports,” or competitive video gaming, is a fast-growing extracurricular activity at high schools and colleges across the United States.
But more people are gamers than they realize, including those who compete with their friends through Wordle or fire up Candy Crush on their phone while waiting in line, said Mitu Khandaker, PhD , a game designer and arts professor at New York University’s Game Center who served on a panel APA hosted at the Consumer Electronics Show in January.
“Games, and our desire to create them, have always existed,” said Khandaker, the founder and CEO of Glow Up Games, which builds games that feature and celebrate Black and Brown characters and storylines. “Games exist at this intersection between whatever our latest technological capability is and whatever it is that we want to express as a culture at the time.”
Still, the ubiquity and history of gaming doesn’t shield games from becoming problematic for some people. To the contrary, their increasing pervasiveness and advanced design is precisely what can make their use harder and harder to control. Researchers have shown how, for instance, even some of the simplest social media and game apps on phones use psychological theories, including the mere exposure effect (the more you see it, the more you like it), the Zeigarnik effect (the tendency to remember interrupted tasks better than completed ones), and social comparison to encourage prolonged usage ( Montag, C., et al., International Journal of Environmental Research and Public Health , Vol. 16, No. 4, 2019 ).
“The element that really is a game changer is the online element,” said Mark Griffiths, PhD, a distinguished professor of behavioral addiction at Nottingham Trent University in England. “You could technically play 24 hours a day, 365 days a year.”
But most people don’t. According to a 2016 study that looked at a random sample of 3,389 gamers in Norway, just 1.4% were “addicted gamers,” meaning they experienced the so-called four pillars of addiction—relapse, withdrawal, conflict, and problems—at least sometimes, and 7.3% of study participants were pegged as “problem gamers,” meaning they met two or three of the criteria sometimes. The rest of the sample was considered either “engaged” (3.9%) or “normal” (87.4%) ( Wittek, C. T., et al., International Journal of Mental Health and Addiction , Vol. 14, No. 5, 2016 ).
A 2022 meta-analysis of 61 studies across 29 countries found other estimates of pathological gaming range from 0.3% to 17.7% ( Kim, H. S., et al., Addictive Behaviors , Vol. 126, 2022 ).
“A small but significant minority of people, usually young people, have a genuine problem with their video game playing,” said Griffiths, who is the director of the International Gaming Research Unit. “Whether we call it a disorder, whether we call it addiction or a problem—to me, that’s irrelevant. We have a small group of people where video game playing is basically negatively affecting every other area of their life.”
That appears to be the case for some of the tens of thousands of members in a Reddit community called StopGaming .
“Playing video games makes me procrastinate from doing important work. Playing video games prevents me from connecting with others. Playing video games prevents me from making life decisions,” one self-described addict wrote. “I need help.”
[ Related: Developing games that build skills and promote well-being ]
According to the American Psychiatric Association—which added “internet gaming disorder” (IGD) to the research appendix of the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) in 2013—the condition is a “persistent and recurrent use of the Internet to engage in games, often with other players, leading to clinically significant impairment or distress.”
The WHO similarly says the condition is characterized by “impaired control over gaming, increasing priority given to gaming over other activities to the extent that gaming takes precedence over other interests and daily activities, and continuation or escalation of gaming despite the occurrence of negative consequences.”
A person is only typically diagnosed with IGD, the organization says, if their behavior patterns are severe enough to impair multiple key areas of their life, such as their employment and their personal relationships, and endure for at least 12 months.
“We’ve got lots of biological studies, lots of nationally representative, large-scale epidemiological studies. We’ve got a massive increase in the number of papers on treatment … and the number of dedicated gaming treatment clinics,” Griffiths said. “So for me, it is quite clearly a genuine disorder.”
But there’s little consensus when it comes to how best to categorize, and study, the concept. Vivien Wen Li Anthony, PhD , an associate professor at Rutgers University School of Social Work and scientific director for video gaming and esports at the university’s Center for Gambling Studies, and others view gaming disorder as a behavioral addiction, similar to a gambling, sex, or food addiction.
Anthony points to research showing how, like other behavioral addictions, video games activate the same brain regions associated with reward and reinforcement as psychoactive drugs, but gamers don’t experience the types of physical withdrawal symptoms seen in substance-based addictions ( Weinstein, A., & Lejoyeux, M., Dialogues in Clinical Neuroscience , Vol. 22, No. 2, 2020 ). Problem gamers also tend to experience a loss of control and significant impairment in other areas of their life, she said.
Still, gaming has some features that set it apart from other behavioral addictions—namely, its lack of natural guardrails. “If you’re a sex addict, you don’t tend to be having sex for 10 hours in a row, but with gambling and gaming, people have incredibly long playing sessions every single day,” Griffiths said.
Other potentially addictive substances and activities also aren’t introduced in toddlerhood, Demetrovics added. “When we talk about gaming, it’s technically all kids, all adults,” he said. “So whatever intervention or regulation we want to introduce, we have to think about the whole population.” Plus, for a minority of gamers, gaming is indeed a legitimate career trajectory .
Douglas Gentile, PhD , who runs Iowa State University’s Media Research Lab , prefers to conceptualize problem gaming as an impulse-control disorder. In one of his earlier studies, which followed more than 3,000 children in Singapore for 3 years, he and colleagues found that pathological gaming tendencies were persistent over time and, among other traits, predicted by impulsivity ( Pediatrics , Vol. 127, No. 2, 2011 ).
“If my belief about this is accurate, then the solution doesn’t need to be you have to quit cold turkey and never play again,” Gentile said. “The issue is one of balance.”
Other psychologists see excessive gaming not as a condition itself, but rather as a symptom of, or coping mechanism for, life circumstances or mental illness.
“Are there people who have allowed games to take up a bigger space in their life because of circumstances? Or because of not knowing positive gaming strategies? Absolutely,” said Ashley Elliott, PsyD , a psychologist in private practice in Arlington, Virginia, and a workshop consultant for Take This, a mental health nonprofit that serves the gaming community. “But for the majority of people who are experiencing these things, the game is not the culprit. Life is the culprit.”
A 2023 study in the Journal of Sleep Research (Liu, Y., et al., Vol. 32, No. 4), for one, found that adolescents with insomnia were more than twice as likely to develop IGD and substance use than those without the sleep disorder. One study that tracked hundreds of kids in South Korea over 4 years also suggests that issues like academic stress can lead to decreased self-control, which in turn raises the risk of pathological gaming ( Jeong, E. J., et al., Journal of Youth and Adolescence , Vol. 48, 2019 ).
“What tends to happen is you have someone who started with mental illness, and then they look for something fun that makes them feel important, or at least distracts them from their distress,” study coauthor Chris Ferguson, PhD, a psychology professor at Stetson University in DeLand, Florida, said. “And games are fun, so it’s like self-medication.”
In some cases, like Dan’s from the case study, though, the reverse pattern is plausible, too. Gentile said he was surprised to see his research suggest that psychiatric disorders including depression, social phobias, and anxiety seemed to follow problematic gaming.
“This demonstrates that these are likely comorbid problems, because if you just went in and treated the depression, that’s not going to fix it—the gaming seems to be an independent or interacting factor,” Gentile said. “A good clinician doesn’t say, ‘Well, which one came first? We’ll just treat that one.’ A good clinician has to look at the total picture.”
One thing most psychologists do agree on is that sheer time spent gaming isn’t enough to qualify someone’s gaming as pathological. It’s all about context.
“Anything you love to do, you’re probably sacrificing some other area of life for,” Gentile said. “If you love golfing, you might skip out of work early some days or refuse to do something with your partner on a weekend. Does that harm your work? Yes. Does that harm your relationship? I guess that depends on your partner. But that doesn’t make it an addiction.”
While researchers are still working to understand what sets the “addicted” gamers apart from the “normal” ones, some traits—including being male, young, high in impulsivity and neuroticism, and low in openness and conscientiousness—put people at higher risk for the problem.
One 2023 study in the journal Computers in Human Behavior (Fraser, R., et al., Vol. 144) also found that people who said they felt less meaning in life were more likely to experience greater gaming disorder symptoms. Other research shows “psychological needs frustration” and “obsession passion” is related to problem gaming ( Remedios, J. C., et al., Addiction Research & Theory , 2023 ).
People’s motives for gaming matter, too. If you play for fun and socialization, for example, your gaming is more likely to remain healthy. But, “if you play games in order to forget about your problems, and you want to overcome your negative feelings with gaming, that predicts a problem,” Demetrovics said, pointing to his work and to Griffiths’s work ( Comprehensive Psychiatry , Vol. 94, 2019).
Certain conditions, including depression, anxiety, attention-deficit/hyperactivity disorder (ADHD), and social phobias also tend to co-occur with problem gaming, research shows ( González-Bueso, V., et al., International Journal of Environmental Research and Public Health , Vol. 15, No. 4, 2018 ). The links make sense: Differences in the dopamine receptors among people with ADHD, for one, may help explain their need for highly stimulating activities, like gaming. Their tendency to hyperfocus, too, might make them especially susceptible to playing for long hours.
Brain differences don’t explain everything. “You can take five people who have a gaming disorder, and they’ll all have a different etiology explaining why they’re hooked on those games,” Griffiths said. “Some of them will be because of a predisposing psychological or physical or neurodevelopmental illness. For others, there may not be any comorbidities at all.”
But for all of them, there are potential solutions.
Cognitive behavioral therapy seems to be especially promising. A 2020 study in the journal Clinical Psychology and Psychotherapy (Han, J., et al., Vol. 27, No. 2), for example, found the modality significantly improved problem gamers’ symptoms of IGD, as well as anxiety, impulsivity, and social avoidance, as compared to problem gamers assigned to a “supportive therapy” treatment.
A range of medications, including antidepressants and stimulants typically used to treat ADHD, can also benefit problem gamers, a 2023 meta-analysis found ( Clorado de Sá, R. R., et al., Psychiatry Investigation , Vol. 20, No. 8, 2023 ). “Several addiction drugs seem to be helpful here, too, which is interesting because that kind of makes the point plain that this is a brain disease, and it’s not that different from [substance use disorders],” Gentile said.
Anthony’s work has also revealed how mindfulness can curb people’s problematic gaming habits. In a small, 2017 Stage 1 clinical trial, she and colleagues randomly assigned participants to a mindfulness-based intervention or a support group. After 8 weeks, the mindfulness group had significantly greater reductions in the number of DSM-5 criteria they met for IGD, as well as fewer cravings for video gaming and maladaptive cognitions associated with gaming. The benefits held at the 3-month follow-up ( Li, W., et al., Psychology of Addictive Behaviors , Vol. 31, No. 4, 2017 ).
“Mindfulness doesn’t just help to regulate the behavior, it also helps cope with any sudden urge or craving,” Anthony said. “And, for people who use gaming as their primary way to cope with negative moods, emotions, or interpersonal conflict, mindfulness teaches alternative coping skills.”
Policy interventions may also help combat problem gaming. One 2023 study found that a year after China implemented policies to curb problematic smartphone use, the amount of time kids spent on their phones significantly dropped ( Yang, Q., et al., BMC Psychiatry , Vol. 23, No. 1, 2023 ). The group that had met criteria for addiction also fell below, on average, that threshold a year out.
Household rules can make a difference, too. In one of Gentile’s favorite studies, he and colleagues followed about 1,300 kids in two states over the course of a school year. They found that if parents set limits on the time and content of their kids’ video games, the kids tended to get better sleep, gain less weight, get better grades, and display more prosocial behavior and less aggression, as rated by their teachers ( JAMA Pediatrics , Vol. 168, No. 5, 2014 ).
“One simple thing—setting limits on amount and content of your children’s media—influences all of that,” Gentile said.
Even simpler: Learn about your kids’ interest in gaming before condemning it, psychologists stress. “We always encourage parents to play together with their kids, to try to understand what they do and why they do it, because otherwise, it just looks like a stupid, useless activity,” Demetrovics said. “There might be rational reasons or seemingly irrational ones, but we have to go together on this path with them.”
Can you really be addicted to video games? Jabr, F., The New York Times Magazine , Oct. 22, 2019
Prevalence of gaming disorder: A meta-analysis Kim, H. S., et al., Addictive Behaviors , 2022
An official Division 46 statement on the WHO proposal to include gaming related disorders in ICD-11 Ferguson, C., et al., The Amplifier Magazine , 2018
Esports see explosive growth in U.S. high schools Flannery, M. E., neaToday , Sept. 16, 2021
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The development of molecular formulations that become drugs to treat or cure diseases is at the heart of the pharmaceutical industry. Development is so fundamental that pharma spends a full 15 percent of its sales on R&D—a huge sum that accounts for more than 20 percent of total R&D spending across all industries in the global economy. This investment goes hand in hand with innovation: constantly seeking to improve the R&D process, pharma companies have for decades been early adopters of computational chemistry’s digital tools, such as molecular dynamics (MD) simulations and density functional theory (DFT). More recently, pharma R&D has taken advantage of artificial intelligence (AI). The next digital frontier is quantum computing (QC).
In a recent article , we analyzed the impact of QC on the chemical industry, which, similarly to pharma, relies on the development and manufacture of molecules, and concluded that it will be one of the first industries to benefit. In this article, we explain the profound impact that QC could have on the pharma industry and present use cases for its application. We also provide a set of strategic questions to get clarity on the path forward for industry players.
Identifying and developing small molecules and macromolecules that might help cure illnesses and diseases is the core activity of pharmaceutical companies. Given its focus on molecular formations, pharma as an industry is a natural candidate for quantum computing. The molecules (including those that might be used for drugs) are actually quantum systems; that is, systems that are based on quantum physics. QC is expected to be able to predict and simulate the structure, properties, and behavior (or reactivity) of these molecules more effectively than conventional computing can. Exact methods are computationally intractable for standard computers, and approximate methods are often not sufficiently accurate when interactions on the atomic level are critical, as is the case for many compounds. Theoretically, quantum computers have the capacity to efficiently simulate the complete problem, including interactions on the atomic level. As these quantum computers become more powerful, tremendous value will be at stake.
A conventional computer, built on transistor-based classical bits operated by voltages, can be in only one of two states: 0 or 1. A quantum computer, instead, uses systems based on quantum physics, such as superconducting loops or ions hovering in electromagnetic fields (ion traps), which are operated by microwave radiation or lasers, respectively. As a result of the laws of quantum mechanics, such systems can be held in a special physical state, called a quantum superposition, in which quantum bits (qubits) exist in a probabilistic combination of the two states—0 and 1—simultaneously.
The implications of these effects for QC are dramatic. Qubits can process far more information than conventional computers can. Qubits use the characteristics of quantum-mechanical systems to solve complex equations in a probabilistic manner, so a computation solved with a quantum algorithm enables sampling from the probabilistic distribution of being correct. The combination of greater speed with probabilistic solutions means that quantum computing fits well with a certain subset of computing needs and applications, such as optimization, the simulation of chemicals, and AI.
While the technology behind quantum computing is rather difficult to understand intuitively (see sidebar, “The basics of quantum computing”), its impact is much easier to grasp: it will handle certain kinds of computational tasks exponentially faster than today’s conventional computers do. Thus, once fully developed, QC could add value across the entire drug value chain—from discovery through development to registration and postmarketing.
While QC may benefit the entire pharma value chain—from research across production through commercial and medical—its primary value lies in R&D (Exhibit 1).
Currently, pharma players process molecules with non-QC tools, such as MD and DFT, in a methodology called computer-assisted drug discovery (CADD). But the classical computers they rely on are sorely limited, and basic calculations predicting the behavior of medium-size drug molecules could take a lifetime to compute accurately. CADD on quantum computers could increase the scope of biological mechanisms amenable to CADD, shorten screening time, and reduce the number of times an empirically based development cycle must be run by eliminating some of the research-related “dead ends,” which add significant time and cost to the discovery phase. Exhibit 2 shows where QC-enhanced CADD would improve the development cycle.
QC could make current CADD tools more effective by helping to predict molecular properties with high accuracy. That can affect the development process in several ways, such as modeling how proteins fold and how drug candidates interact with biologically relevant proteins. Here, QC may allow researchers to screen computational libraries against multiple possible structures of the target in parallel. Current approaches usually restrict the structural flexibility of the target molecule due to a lack of computational power and a limited amount of time. These restrictions may reduce the chances of identifying the best drug candidates.
In the longer term, QC may improve generation and validation of hypotheses by using machine-learning (ML) algorithms to uncover new structure-property relationships. Once it has reached sufficient maturity, QC technology may be able to create new types of drug-candidate libraries that are no longer restricted to small molecules but also include peptides and antibodies. It could also enable a more automated approach to drug discovery, in which a large structural library of biologically relevant targets is automatically screened against drug-like molecules via high-throughput approaches.
One could even envision QC triggering a paradigm shift in pharmaceutical R&D, moving beyond today’s digitally enabled R&D toward simulation-based or in silico drug discoveries—a trend that has been seen in other industries as well.
The following QC use cases apply to different aspects of drug discovery and will emerge at different points over an extended timeline. All of them, however, may enable more accurate and efficient development of targeted compounds.
During target identification, QC can be leveraged to reliably predict the 3-D structures of proteins. Obtaining high-quality structural data is a lengthy process often leading to low-quality results. Despite all efforts, researchers have yet to crystallize many biologically important proteins—be it due to their size, solubility (for example, membrane proteins), or inability to express and purify in sufficient amount. Pharma companies sometimes develop drugs without even knowing the structure of a protein—accepting the risk of a trial-and-error approach in subsequent steps of drug development—because the business case for a given drug is potentially so strong.
AlphaFold, developed by Google’s DeepMind, was a breakthrough in AI-driven protein folding but has not resolved all of the challenges of classical computing-based simulation, including, for example, formation of protein complexes, protein-protein interactions, and protein-ligand interactions. It’s the interactions that are most difficult to classically solve and, thus, may benefit from QC, which allows for the explicit treatment of electrons. Additionally, QC may allow for strong computational efficiencies here given that Google’s AI model—which is trained on around 170,000 different structures of protein data—requires more than 120 high-end computers for several weeks.
QC’s ability to parallel process complex phenomena would be particularly valuable during hit generation and validation. With existing computers, pharma companies can only use CADD on small to medium-size drug candidates and largely in a sequential manner. Computing power is the bottleneck. With powerful enough QC, pharma companies would be able to expand all use cases to selected biologics as well, for instance, semi-synthesized biologics or fusion proteins, and perform in silico search and validation experiments in a more high-throughput fashion. This use case would go beyond the identification of the protein and eventually encompass almost the entire known biological world. With a robust enough hit-generation and validation approach, this step would already deliver potential lead molecules that are much easier and quicker to optimize.
During lead optimization, which is a top-three parameter to improve R&D productivity, 1 Steven M. Paul et al., “How to improve R&D productivity: The pharmaceutical industry’s grand challenge,” Nature Reviews Drug Discovery , March 2010, Volume 9, pp. 203–214, nature.com. QC may allow for enhanced absorption, distribution, metabolism, and excretion (ADME); more accurate activity and toxicity predictions for organ systems; dose and solubility optimization; and other safety issues.
The metalevel of R&D very much consists of linking appropriate data together—for instance, creating sensible connections between data points through effective (semantic) management. The more complex the biological information that can be processed, the more extensive the graphs that inform the drug discovery research process become. There is currently research on “topological data analysis” under way that aims to identify “holes” and “connections” across large data sets. 2 Silvano Garnerone, Seth Lloyd, and Paolo Zanardi, “Quantum algorithms for topological and geometric analysis of data,” Nature Communications , January 2016, Volume 7, Article 10138, nature.com. This may at some point enable R&D specialists to identify concrete cases and “industry verticals” where such algorithms are applicable, for example, in identifying connections across brain cells in response to a drug.
Moreover, QC could be used to “deepfake” missing data points throughout the research process, that is, generate a type of fake data by using ML algorithms. This could be particularly useful wherever there is a scarcity of data, such as in rare diseases, that can then be mitigated through artificial data sets. QC will set a new bar here regarding speed in training ML models, amount of initial data needed, and level of accuracy.
Clinical trials could be optimized through patient identification and stratification and population pharmacogenetic modeling. 3 Paul et al., 2010. In trial planning and execution, QC could optimize the selection of the trial sites. QC could also augment causality analyses for side effects to improve active safety surveillance.
While the potential value of QC in pharma R&D is immense, it will also likely play a role further down the value chain. In the production of active ingredients, QC may aid in the calculation of reaction rates, optimize catalytic processes, and, ultimately, create significant efficiencies in the development of new product formulations. In the business-related value pools, QC in pharma could include the optimization of logistics (for instance, the optimization of on-site flows of materials, heat, and waste in production facilities) and improvements in the supply chain. Finally, toward market access and commercial, QC may even enable automatic drug recommendations.
The development of quantum computers began nearly four decades ago, but it is the gains in QC technology realized over the past few years that paved the way for practical applications in pharma. We see the key, value-adding QC activities in pharma unfolding over two distinct eras as the technology further matures (Exhibit 3):
Exactly when a particular company begins to capture QC’s benefits will depend on its tech starting point (that is, its current level of R&D digitization) and its business focus: the number of small active pharmaceutical ingredients (APIs) in its portfolio. Pharma companies that have a strong footprint in CADD and focus their R&D on smaller molecules will be among the first to take advantage of emergent QC. Exhibit 4 maps key CADD methods along the drug-discovery continuum and offers an indication of the applicability of QC. It’s expected that QC will be mostly applicable in the discovery phase of hit generation, hit-to-lead, and also in lead optimization.
In the next five to ten years, we expect that the first QC tools pharma players deploy will rely on hybrid methodologies that use classical algorithms alongside QC subroutines when they can create additional value. The prominent examples are the imaginary time evolution (an algorithm to find the ground-state and excited-state energy of many-particle systems) and the variational quantum eigen-solver, or VQE (an algorithm to calculate the binding affinity between an API and a target receptor). The value that algorithms such as VQE will add depends on the size of the quantum hardware. Describing small-molecule drugs generally requires less-mature quantum computers, while biologicals will be tackled only as QC matures.
The pharma sector is well positioned to take full advantage of this opportunity. Its tech-ready culture already embraces a wide array of digital tools: CADD, AI, ML, and non-QC DFT- and MD-simulation tools already play a big role in the sector’s R&D. On top of this, pharma players are already working with quantum-chemical simulations, so the barrier to entry is quite low. Scientists will not have to change the way they develop drugs in any fundamental way—they will just be working with more capable tools.
That said, companies will make their own decisions regarding whether and how to move toward a QC-enabled business. Some pharma players may take a pass on deploying QC, others may wait and observe, while still others are going “all in,” ginning up early in-house development. Most pharma players, however, will likely undertake joint-development strategies with upstream players. No matter what, answering some key strategic questions will help companies make more informed decisions on their stance for QC.
Pharmaceutical companies should assess QC now and potentially lay the groundwork to reap the benefits of the technology later. QC may give many of them a huge opportunity, yet each pharma player needs to figure out how much exposure it has and the size of its QC opportunity in the context of its current pace of development. Thus, pharma players should consider three key strategic questions to determine their optimal QC strategy (Exhibit 5):
Subject to the above answers, moving early can help secure valuable intellectual property for the algorithms that drive QC and can also address a key issue: pharma won’t be the first industry sector to benefit from QC, so late-moving players could face a lack of suitable talent.
Some pharmaceutical players have already realized the need to join forces on the topic of QC and have started to collaborate and/or form partnerships. For example, QuPharm formed in late 2019 by major pharmaceutical players to pool ideas and expertise around QC use cases. QuPharm also collaborates with the Quantum Economic Development Consortium (QED-C), which was created in 2018 by the US government as part of the National Quantum Initiative Act and aims to enable commercial QC use-case efforts. Additionally, the Pistoia Alliance is a life sciences membership organization, which was organized to facilitate precompetitive collaboration and foster R&D innovation.
Partnering with pure quantum players taps into their existing expertise to test early use cases and facilitate development. At the moment, there are more than 100 QC-focused companies—both start-ups and established firms—around the world, focusing on software, hardware, or enabling services. Approximately 25 companies are targeting applications in the pharma industry. Less than 15 focus on algorithms or solutions for pharma players, and very few are focusing exclusively on the needs of pharma players.
Digital talent gaps are already a reality, and QC may only exacerbate them. Unlike other important digital tools, such as AI, quantum computing depends on niche know-how. Pharma companies already struggle to attract people with capabilities in the less specialized digital technologies, and hiring quantum-computing experts may prove to be even more of a challenge.
A pharma company’s “way of working” will also be central to its success in QC. The traditional walls that separate the work of the organization’s various functions and units—for example, research, tech, business—will have to fall away. Cross-functional collaboration in both spirit and action will characterize the pharma companies that are able to take full advantage of QC.
Quantum computing could be the key to exponentially more efficient discovery of pharmaceutical cures and therapeutics as well as to hundreds of billions of dollars in value for the pharma industry. Experts predict, for example, that today’s $200 billion market for protein-based drugs could grow by 50 to 100 percent in the medium term if better tools to develop them became available. Given QC’s vast potential, we expect global pharma spending on QC in R&D to be in the billions by 2030. Pharma companies would be well advised to assess the QC opportunity for themselves and begin laying the groundwork in securing their place in this new competitive and technological landscape.
Matthias Evers is a senior partner in McKinsey’s Hamburg office, and Anna Heid is a consultant in the Zurich office, where Ivan Ostojic is a partner.
The authors wish to thank Nicole Bellonzi, Matteo Biondi, Thomas Lehmann, Lorenzo Pautasso, Katarzyna Smietana, Matija Zesko, and the many industry/academia experts for their contributions to this article.
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