Category Archives: I.T.

Tubular hells: A brief look at ‘addiction’ to watching YouTube videos

 

A few days ago, I unexpectedly found my research on internet addiction being cited in a news article by Paula Gaita on compulsive viewing of YouTube videos (‘Does compulsive YouTube viewing qualify as addiction?‘). The article was actually reporting a case study from a different news article published by PBS NewsHour by science correspondent Lesley McClurg (‘After compulsively watching YouTube, teenage girl lands in rehab for digital addiction’). As Gaita reported:

“The story profiles a middle school student whose obsessive viewing of YouTube content led to extreme behavior changes and eventually, depression and a suicide attempt. The student finds support through therapy at an addiction recovery center…The student in question is a young girl named Olivia who felt at odds with the ‘popular’ kids at her Oakland area school. She began watching YouTube videos after hearing that it was a socially acceptable thing to do… Her viewing habits soon took the place of sleep, which impacted her energy and mood. Her grades began to falter, and external problems within her house – arguments between her parents and the death of her grandmother – led to depression and an admission of wanting to hang herself. Her parents took her to a psychiatric hospital, where she stayed for a week under suicide watch, but her self-harming compulsion continued after her release. She began viewing videos about how to commit suicide, which led to an attempt to overdose on Tylenol[Note: The name of the woman – Olivia – was a pseudonym].

McClurg interviewed Olivia’s mother for the PBS article and it was reported that Olivia went from being a “bubbly daughter…hanging out with a few close friends after school” to “isolating in her room for hours at a time”. Olivia’s mother also claimed that her daughter had always been kind of a nerd, a straight. A student who sang in a competitive choir. But she desperately wanted to be popular, and the cool kids talked a lot about their latest YouTube favorites”. According to news reports, all Olivia would do was to watch video after video for hours and hours on end and developed sleeping problems. Over time, the videos being watched focused on fighting girls and other videos featuring violence.

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The news story claimed that Olivia was “diagnosed with depression that led to compulsive internet use”. When Olivia went back home she was still feeling suicidal and then spent hours watching YouTube videos on how to commit suicide (and it’s where she got the idea for overdosing on Tylenol tablets).

After a couple of spells in hospital, Olivia’s parents took her to a Californian centre specialising in addiction recovery (called ‘Paradigm’ in San Rafael). The psychologist running the Paradigm clinic (Jeff Nalin) claimed Olivia’s problem was “not uncommon” among clients attending the clinic. Nalin believes (as I do and have pointed out in my own writings) that treating online addictions is not about abstinence but about getting the behaviour under control but developing skills to deal with the problematic behaviour. He was quoted as saying:

“I describe a lot of the kids that we see as having just stuck a cork in the volcano. Underneath there’s this rumbling going on, but it just rumbles and rumbles until it blows. And it blows with the emergence of a depression or it emerges with a suicide attempt…The best analogy is when you have something like an eating disorder. You cannot be clean and sober from food. So, you have to learn the skills to deal with it”.

The story by Gaita asked the question of whether compulsive use of watching YouTube could be called a genuine addiction (and that’s where my views based on my own research were used). I noted that addiction to the internet may be a symptom of another addiction, rather than an addiction unto itself. For instance, people addicted to online gambling are gambling addicts, not internet addicts. An individual addicted to online gaming or online shopping are addicted to gaming or shopping not to the internet.

An individual may be addicted to the activities one can do online and is not unlike saying that an alcoholic is not addicted to a bottle, but to what’s in it. I have gone on record many times saying that I believe anything can be addictive as long there are continuous rewards in place (i.e., constant reinforcement). Therefore, it’s not impossible for someone to become addicted to watching YouTube videos but the number of genuine cases of addiction are likely to be few and far between. Watching video after video is conceptually no different from binge watching specific television series or television addiction itself (topics that I have examined in previous blogs).

I ought to end by saying that some of my own research studies on internet addiction (particularly those co-written with Dr. Attila Szabo and Dr. Halley Pontes and published in the Journal of Behavioral Addictions and Addictive Behaviors Reports – see ‘Further reading’ below) have examined the preferred applications by those addicted to the internet, and that the watching of videos online is one of the activities that has a high association with internet addiction (along with such activities such as social networking and online gaming). Although we never asked participants to specify which channel they watched the videos, it’s fair to assume that many of our participants will have watched them on YouTube), and (as the Camelot lottery advert once said) maybe, just maybe, a few of those participants may have had an addiction to watching YouTube videos.

Dr Mark Griffiths, Professor of Gambling Studies, International Gaming Research Unit, Nottingham Trent University, Nottingham, UK

Further reading

Gaita, P. (2017). Does compulsive YouTube viewing qualify as addiction? The Fix, May 19. Located at: https://www.thefix.com/does-compulsive-youtube-viewing-qualify-addiction

Griffiths, M.D. (2000). Internet addiction – Time to be taken seriously? Addiction Research, 8, 413-418.

Griffiths, M.D., Kuss, D.J., Billieux J. & Pontes, H.M. (2016). The evolution of internet addiction: A global perspective. Addictive Behaviors, 53, 193–195.

Griffiths, M.D. & Pontes, H.M. (2014). Internet addiction disorder and internet gaming disorder are not the same. Journal of Addiction Research and Therapy, 5: e124. doi:10.4172/2155-6105.1000e124.

Griffiths M.D. & Szabo, A. (2014). Is excessive online usage a function of medium or activity? An empirical pilot study. Journal of Behavioral Addictions, 3, 74-77.

Kuss, D.J. & Griffiths, M.D. (2015). Internet Addiction in Psychotherapy. Basingstoke: Palgrave Macmillan.

Kuss, D.J., Griffiths, M.D. & Binder, J. (2013). Internet addiction in students: Prevalence and risk factors. Computers in Human Behavior, 29, 959-966.

Kuss, D.J., Griffiths, M.D., Karila, L. & Billieux, J. (2014). Internet addiction: A systematic review of epidemiological research for the last decade. Current Pharmaceutical Design, 20, 4026-4052.

Kuss, D.J., van Rooij, A.J., Shorter, G.W., Griffiths, M.D. & van de Mheen, D. (2013). Internet addiction in adolescents: Prevalence and risk factors. Computers in Human Behavior, 29, 1987-1996.

McClurg, L. (2017). After compulsively watching YouTube, teenage girl lands in rehab for ‘digital addiction’. PBS Newshour, May 16. Located at: http://www.pbs.org/newshour/rundown/compulsively-watching-youtube-teenage-girl-lands-rehab-digital-addiction/

Pontes, H.M., Kuss, D.J. & Griffiths, M.D. (2015). The clinical psychology of Internet addiction: A review of its conceptualization, prevalence, neuronal processes, and implications for treatment. Neuroscience and Neuroeconomics, 4, 11-23.

Pontes, H.M., Szabo, A. & Griffiths, M.D. (2015). The impact of Internet-based specific activities on the perceptions of Internet Addiction, Quality of Life, and excessive usage: A cross-sectional study. Addictive Behaviors Reports, 1, 19-25.

Widyanto, L. & Griffiths, M.D. (2006). Internet addiction: A critical review. International Journal of Mental Health and Addiction, 4, 31-51.

Search of the poisoned mind? A brief look at ‘internet search dependence’

Despite being a controversial topic, research into a wide variety of online addictions has grown substantially over the last decade. My own research into online addictions has been wide ranging and has included online social networking, online sex addiction, online gaming addiction, online shopping addiction, and online gambling addiction. As early as the late 1990s/early 2000s, I constantly argued that when it came to online addictions, most of those displaying problematic behaviour had addictions on the internet rather than addictions to the internet (i.e., they were not addicted to the medium of the internet but addicted to applications and activities that could be engaged in via the internet).

A recent 2016 paper by Dr. Yifan Wang and colleagues in the journal Frontiers in Public Health described the development of the Questionnaire of Internet Search Dependence (QISD), a tool developed to assess individuals who may be displaying a dependence on using online search engines (such as Google and Baidu). The notion of individuals being addicted to using search engines is not new and was one of five types of internet addiction outlined in a 1999 typology in a paper in the Student British Medical Journal by Dr. Kimberley Young (and what she termed ‘information overload’ and referred to compulsive database searching). Although I criticized the typology on the grounds that most of the types of online addict were not actually internet addicts but were individuals using the medium of the internet to fuel other addictive behaviours (e.g., gambling, gaming, day trading, etc.), I did implicitly acknowledge that activities such as internet database searching could theoretically exist, even if I did not think it was a type of internet addiction.

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As far as I am aware, the new scale developed by Wang et al. (2016) is the first to create and psychometrically evaluate an instrument to assess ‘internet search dependence’. As noted by the authors:

Subsequently, we compiled 16 items to represent psychological characteristics associated with Internet search dependence, based on the literature review and a follow-up interview with 50 randomly selected university students…We adopted the six criteria for behavioral addiction formulated by Griffiths (i.e., salience, mood modification, tolerance, withdrawal, conflict, and relapse) [Griffiths, 1999b]”.

Given the authors claimed they used an early version of my addiction components model (i.e., one from 1999 rather than my most recent 2005 formulation) to help inform item construction, I was obviously interested to see the scale’s formulated items. I have to admit that I had a lot of misgivings about the paper so I wrote a commentary on it that has just been published in the same journal (Frontiers in Public Health). More specifically, I noted in my paper that if an individual was genuinely addicted to searching online databases I would have expected to see all of my six criteria applied as follows:

  • Salience – This occurs when searching internet databases becomes the single most important activity in the person’s life and dominates their thinking (preoccupations and cognitive distortions), feelings (cravings) and behaviour (deterioration of socialized behaviour). For instance, even if the person is not actually searching the internet they will be constantly thinking about the next time that they will be (i.e., a total preoccupation with internet database searching).
  • Mood modification – This refers to the subjective experiences that people report as a consequence of internet database searching and can be seen as a coping strategy (i.e., they experience an arousing ‘buzz’ or a ‘high’ or paradoxically a tranquilizing feel of ‘escape’ or ‘numbing’ when searching internet databases).
  • Tolerance – This is the process whereby increasing amounts of time searching internet databases are required to achieve the former mood modifying effects. This basically means that for someone engaged in internet database searching, they gradually build up the amount of the time they spend searching internet databases every day.
  • Withdrawal symptoms – These are the unpleasant feeling states and/or physical effects (e.g., the shakes, moodiness, irritability, etc.), that occur when an individual is unable to search internet databases because they are ill, the internet is unavailable, or there is no Wi-Fi on holiday, etc.
  • Conflict – This refers to the conflicts between the person and those around them (interpersonal conflict), conflicts with other activities (social life, hobbies and interests) or from within the individual themselves (intra-psychic conflict and/or subjective feelings of loss of control) that are concerned with spending too much time searching internet databases.
  • Relapse – This is the tendency for repeated reversions to earlier patterns of excessive internet database searching to recur and for even the most extreme patterns typical of the height of excessive internet database searching to be quickly restored after periods of control.

Of the 12 QISD items constructed in the new scale, very few appeared to have anything to do with addiction and/or dependence but this is most likely due to the fact that the authors also used data collected from 50 participants to inform their items and not just the criteria in the addiction components model. However, relying heavily on input from their participants resulted in a number of key features in addiction/dependence not even being assessed (i.e., no assessment of salience, mood modification, conflict, relapse or tolerance). A couple of items may peripherally assess withdrawal symptoms (e.g., ‘I will be upset if I cannot find an answer to a complex question through Internet search’) but not in any way that is directly associated with addiction or dependence. This may be because the authors’ conceptualization of ‘dependence’ was more akin to ‘over-reliance’ rather than traditional definitions of dependence.

While the QISD may be psychometrically robust I argued that it appears to have little face validity and does not appear to assess problematic engagement in internet database searching (irrespective of how addiction or dependence is defined). Based on the addiction components model, I concluded my paper by creating my own scale to assess internet search dependence based directly on the addiction components model and which I argued would have much greater face validity than any item currently found in the QISD:

  • Internet database searching is the most important thing in my life.
  • Conflicts have arisen between me and my family and/or my partner about the amount of time I spend searching internet databases.
  • I engage in internet database searching as a way of changing my mood.
  • Over time I have increased the amount of internet database searching I do in a day.
  • If I am unable to engage in internet database searching I feel moody and irritable.
  • If I cut down the amount of internet database searching I do, and then start again, I always end up searching internet databases as often as I did before.

Dr. Mark Griffiths, Professor of Behavioural Addiction, International Gaming Research Unit, Nottingham Trent University, Nottingham, UK

Further reading

Andreassen, C.S., Griffiths, M.D., Pallesen, S., Bilder, R.M., Torsheim, T. Aboujaoude, E.N. (2015). The Bergen Shopping Addiction Scale: Reliability and validity of a brief screening test. Frontiers in Psychology, 6:1374. doi: 10.3389/fpsyg.2015.01374.

Andreassen, C.S., Pallesen, S., Griffiths, M.D. (2017). The relationship between excessive online social networking, narcissism, and self-esteem: Findings from a large national survey. Addictive Behaviors, 64, 287-293.

Canale, N., Griffiths, M.D., Vieno, A., Siciliano, V. & Molinaro, S. (2016). Impact of internet gambling on problem gambling among adolescents in Italy: Findings from a large-scale nationally representative survey. Computers in Human Behavior, 57, 99-106.

Griffiths, M.D. (1998). Internet addiction: Does it really exist? In J. Gackenbach (Ed.), Psychology and the Internet: Intrapersonal, Interpersonal and Transpersonal Applications (pp. 61-75). New York: Academic Press.

Griffiths, M.D. (1999a). Internet addiction: Internet fuels other addictions. Student British Medical Journal, 7, 428-429.

Griffiths, M.D. (1999b). Internet addiction: Fact or fiction? The Psychologist: Bulletin of the British Psychological Society, 12, 246-250.

Griffiths, M.D. (2000). Internet addiction – Time to be taken seriously? Addiction Research, 8, 413-418.

Griffiths, M.D.  (2005). A ‘components’ model of addiction within a biopsychosocial framework. Journal of Substance Use, 10, 191-197.

Griffiths, M.D. (2012). Internet sex addiction: A review of empirical research. Addiction Research and Theory, 20, 111-124.

Griffiths, M.D. (2017). Commentary: Development and validation of a self-reported Questionnaire for Measuring Internet Search Dependence. Frontiers in Public Health, in press.

Griffiths, M.D., Kuss, D.J., Billieux J. & Pontes, H.M. (2016). The evolution of internet addiction: A global perspective. Addictive Behaviors, 53, 193–195.

Kuss, D. J., Griffiths, M. D., Karila, L. & Billieux, J. (2014). Internet addiction: A systematic review of epidemiological research for the last decade. Current Pharmaceutical Design, 20, 4026-4052.

Pontes, H. & Griffiths, M.D. (2015). Measuring DSM-5 Internet Gaming Disorder: Development and validation of a short psychometric scale. Computers in Human Behavior, 45, 137-143.

Wang, Y., Wu, L., Zhou, H., Xu, J. & Dong, G. (2016). Development and validation of a self-reported Questionnaire for Measuring Internet Search Dependence. Frontiers in Public Health, 4, 274. doi: 10.3389/fpubh.2016.00274

Young, K. S. (1999). Internet addiction: evaluation and treatment. Student British Medical Journal, 7, 351-352.

A diction for addiction: A brief overview of our papers at the 2017 International Conference on Behavioral Addictions

This week I attended (and gave one of the keynote papers at) the fourth International Conference on Behavioral Addictions in Haifa (Israel). It was a great conference and I was accompanied by five of my colleagues from Nottingham Trent University all of who were also giving papers. All of the conference abstracts have just been published in the latest issue of the Journal of Behavioral Addictions (reprinted below in today’s blog) and if you would like copies of the presentations then do get in touch with me.

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Griffiths, M.D. (2017). Behavioural tracking in gambling: Implications for responsible gambling, player protection, and harm minimization. Journal of Behavioral Addictions, 6 (Supplement 1), 2.

  • Social responsibility, responsible gambling, player protection, and harm minimization in gambling have become major issues for both researchers in the gambling studies field and the gaming industry. This has been coupled with the rise of behavioural tracking technologies that allow companies to track every behavioural decision and action made by gamblers on online gambling sites, slot machines, and/or any type of gambling that utilizes player cards. This paper has a number of distinct but related aims including: (i) a brief overview of behavioural tracking technologies accompanied by a critique of both advantages and disadvantages of such technologies for both the gaming industry and researchers; (ii) results from a series of studies carried out using behavioural tracking (particularly in relation to data concerning the use of social responsibility initiatives such as limit setting, pop-up messaging, and behavioural feedback); and (c) a brief overview of the behavioural tracking tool mentor that provides detailed help and feedback to players based on their actual gambling behaviour.

Calado, F., Alexandre, J. & Griffiths, M.D. (2017). Youth problem gambling: A cross-cultural study between Portuguese and English youth. Journal of Behavioral Addictions, 6 (Supplement 1), 7.

  • Background and aims: In spite of age prohibitions, most re- search suggests that a large proportion of adolescents engage in gambling, with a rate of problem gambling significantly higher than adults. There is some evidence suggesting that there are some cultural variables that might explain the development of gambling behaviours among this age group. However, cross­cultural studies on this field are generally lacking. This study aimed to test a model in which individual and family variables are integrated into a single perspective as predictors of youth gambling behaviour, in two different contexts (i.e., Portugal and England). Methods: A total of 1,137 adolescents and young adults (552 Portuguese and 585 English) were surveyed on the measures of problem gambling, gambling frequency, sensation seeking, parental attachment, and cognitive distortions. Results: The results of this study revealed that in both Portuguese and English youth, the most played gambling activities were scratch cards, sports betting, and lotteries. With regard to problem gambling prevalence, English youth showed a higher prevalence of problem gambling. The findings of this study also revealed that sensation seeking was a common predictor in both samples. However, there were some differences on the other predictors be- tween the two samples. Conclusions: The findings of this study suggest that youth problem gambling and its risk factors appear to be influenced by the cultural context and highlights the need to conduct more cross-cultural studies on this field.

Demetrovics, Z., Richman, M., Hende, B., Blum, K., Griffiths,
M.D, Magi, A., Király, O., Barta, C. & Urbán, R. (2017). Reward Deficiency Syndrome Questionnaire (RDSQ):
A new tool to assess the psychological features of reward deficiency. Journal of Behavioral Addictions, 6 (Supplement 1), 11.

  • ‘Reward Deficiency Syndrome’ (RDS) is a theory assuming that specific individuals do not reach a satisfactory state of reward due to the functioning of their hypodopaminergic reward system. For this reason, these people search for further rewarding stimuli in order to stimulate their central reward system (i.e., extreme sports, hypersexuality, substance use and/or other addictive behaviors such as gambling, gaming, etc.). Beside the growing genetic and neurobiological evidence regarding the existence of RDS little re- search has been done over the past two decades on the psychological processes behind this phenomenon. The aim of the present paper is to provide a psychological description of RDS as well as to present the development of the Reward Deficiency Syndrome Questionnaire (developed using a sample of 1,726 participants), a new four-factor instrument assessing the different aspects of reward deficiency. The results indicate that four specific factors contribute to RDS comprise “lack of satisfaction”, “risk seeking behaviors”, “need for being in action”, and “search for overstimulation”. The paper also provides psychological evidence of the association between reward deficiency and addictive disorders. The findings demonstrate that the concept of RDS provides a meaningful and theoretical useful context to the understanding of behavioral addictions.

Demetrovics, Z., Bothe, B., Diaz, J.R., Rahimi­Movaghar, A., Lukavska, K., Hrabec, O., Miovsky, M., Billieux, J., Deleuze,
J., Nuyens, P. Karila, L., Nagygyörgy, K., Griffiths, M.D. & Király, O. (2017). Ten-Item Internet Gaming Disorder Test (IGDT-10): Psychometric properties across seven language-based samples. Journal of Behavioral Addictions, 6 (Supplement 1), 11.

  • Background and aims: The Ten-Item Internet Gaming Disorder Test (IGDT-10) is a brief instrument developed to assess Internet Gaming Disorder as proposed in the DSM­5. The first psychometric analyses carried out among a large sample of Hungarian online gamers demonstrated that the IGDT-10 is a valid and reliable instrument. The present study aimed to test the psychometric properties in a large cross-cultural sample. Methods: Data were collected among Hungarian (n = 5222), Iranian (n = 791), Norwegian (n = 195), Czech (n = 503), Peruvian (n = 804), French­speaking (n = 425) and English­ speaking (n = 769) online gamers through gaming­related websites and gaming-related social networking site groups. Results: Confirmatory factor analysis was applied to test the dimensionality of the IGDT-10. Results showed that the theoretically chosen one-factor structure yielded appropriate to the data in all language­based subsamples. In addition, results indicated measurement invariance across all language-based subgroups and across gen- der in the total sample. Reliability indicators (i.e., Cronbach’s alpha, Guttman’s Lambda-2, and composite reliability) were acceptable in all subgroups. The IGDT- 10 had a strong positive association with the Problematic Online Gaming Questionnaire and was positively and moderately related to psychopathological symptoms, impulsivity and weekly game time supporting the construct validity of the instrument. Conclusions: Due to its satisfactory psychometric characteristics, the IGDT-10 appears to be an adequate tool for the assessment of internet gam- ing disorder as proposed in the DSM-5.

Throuvala, M.A., Kuss, D.J., Rennoldson, M. & Griffiths, M.D. (2017). Delivering school-based prevention regarding digital use for adolescents: A systematic review in the UK. Journal of Behavioral Addictions, 6 (Supplement 1), 54.

  • Background: To date, the evidence base for school-delivered prevention programs for positive digital citizenship for adolescents is limited to internet safety programs. Despite the inclusion of Internet Gaming Disorder (IGD) as a pro- visional disorder in the DSM-5, with arguable worrying prevalence rates for problematic gaming across countries, and a growing societal concern over adolescents’ digital use, no scientifically designed digital citizenship programs have been delivered yet, addressing positive internet use among adolescents. Methods: A systematic database search of quantitative and qualitative research evidence followed by a search for governmental initiatives and policies, as well as, non­profit organizations’ websites and reports was conducted to evaluate if any systematic needs assessment and/or evidence-based, school delivered prevention or intervention programs have been conducted in the UK, targeting positive internet use in adolescent populations. Results: Limited evidence was found for school-based digital citizenship awareness programs and those that were identified mainly focused on the areas of internet safety and cyber bullying. To the authors’ knowledge, no systematic needs assessment has been conducted to assess the needs of relevant stakeholders (e.g., students, parents, schools), and no prevention program has taken place within UK school context to address mindful and positive digital consumption, with the exception of few nascent efforts by non­profit organizations that require systematic evaluation. Conclusions: There is a lack of systematic research in the design and delivery of school-delivered, evidence-based prevention and intervention programs in the UK that endorse more mindful, reflective attitudes that will aid adolescents in adopting healthier internet use habits across their lifetime. Research suggests that adolescence is the highest risk group for the development of internet addictions, with the highest internet usage rates of all age groups. Additionally, the inclusion of IGD in the DSM-5 as provisional disorder, the debatable alarming prevalence rates for problematic gaming and the growing societal focus on adolescents’ internet misuse, renders the review of relevant grey and published research timely, contributing to the development of digital citizenship programs that might effectively promote healthy internet use amongst adolescents.

Bányai, F., Zsila, A., Király, O., Maraz, A., Elekes, Z., Griffiths, M.D., Andreassen, C.S. & Demetrovics, Z. (2017). Problematic social networking sites use among adolescents: A national representative study. Journal of Behavioral Addictions, 6 (Supplement 1), 62.

  • Despite being one of the most popular activities among adolescents nowadays, robust measures of Social Media use and representative prevalence estimates are lacking in the field. N = 5961 adolescents (49.2% male; mean age 16.6 years) completed our survey. Results showed that the one-factor Bergen Social Media Addiction Scale (BSMAS) has appropriate psychometric properties. Based on latent pro le analysis, 4.5% of the adolescents belonged to the at-risk group, who reported low self-esteem, high level of depression and the elevated social media use (34+ hours a week). Conclusively, BSMAS is an adequate measure to identify those adolescents who are at risk of problematic Social Media use and should therefore be targeted by school-based prevention and intervention programs.

Bothe, B., Toth-Király, I. Zsila, A., Griffiths, M.D., Demetrovics, Z. & Orosz, G. (2017). The six-component problematic pornography consumption scale. Journal of Behavioral Addictions, 6 (Supplement 1), 62.

  • Background and aims: To our best knowledge, no scale ex- ists with strong psychometric properties assessing problematic pornography consumption which is based on an over- arching theoretical background. The goal of the present study was to develop a short scale (Problematic Pornography Consumption Scale; PPCS) on the basis of Griffiths` (2005) six-component addiction model that can assess problematic pornography consumption. Methods: The sample comprised 772 respondents (390 females; Mage = 22.56, SD = 4.98 years). Items creation was based on the definitions of the components of Griffiths’ model. Results: A confirmatory factor analysis was carried out leading to an 18­item second­order factor structure. The reliability of the PPCS was good and measurement invariance was established. Considering the sensitivity and specificity values, we identified an optimal cut­off to distinguish between problematic and non-problematic pornography users. In the present sample, 3.6% of the pornography consumers be- longed to the at-risk group. Discussion and Conclusion: The PPCS is a multidimensional scale of problematic pornography consumption with strong theoretical background that also has strong psychometric properties.

Dr Mark Griffiths, Professor of Behavioural Addiction, International Gaming Research Unit, Nottingham Trent University, Nottingham, UK

General selection: Is voluntary self-exclusion a good proxy measure for problem gambling?

A couple of months ago, Dr. Michael Auer and I published a short paper in the Journal of Addiction Medicine and Therapy (JAMT) critically addressing a recent approach by researchers that use voluntary self-exclusion (VSE) by gamblers as a proxy measure for problem gambling in their empirical studies. We argued that this approach is flawed and is unlikely to help in developing harm-minimization measures.

For those who don’t know, self-exclusion practices typically refer to the possibility for gamblers to voluntarily ban themselves from playing all (or a selection of) games over a predetermined period. The period of exclusion can typically be chosen by the gambler although some operators have non-negotiable self-exclusion periods. Self-exclusion in both online sites and offline venues has become an important responsible gambling practice that is widely used by socially responsible operators.

There are many reasons why players self-exclude. In a 2011 study in the Journal of Gambling Studies by Dr. Tobias Hayer and Dr. Gerhard Meyer, players frequently reported excluding as a preventive measure and annoyance with the gambling operator as reasons for VSE. Furthermore, only one-fifth of self-excluders reported to be problem gamblers (21.2%). A recent 2016 (conference) paper by Dr. Suzanne Lischer (2016) reported that in a study of three Swiss casinos, 29% of self-excluders were pathological gamblers, 33% were problem gamblers, and 38% were recreational gamblers. Given that many voluntary self-excluders do not exclude themselves for gambling-related problems, Dr. Lischer concluded that self-exclusion is not a good indicator of gambling-related problems. In line with these results, a 2015 study published in International Gambling Studies led by Simo Dragicevic compared self-excluders with other online players and reported no differences in the (i) mean number of gambling hours per month or (ii) minutes per gambling session. The study also reported that 25% of players self-excluded within one day of their registration with the online operator. This could also be due to the fact that online players can self-exclude with just a few mouse-clicks.

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Most studies to date report that the majority of voluntary self-excluders tend to be non-problem gamblers. Additionally, in 2010, the Australian Productivity Commission reported 15,000 active voluntary self-exclusions from 2002 to 2009 and that this represented only 10-20% of the population of problem gamblers. This means that in addition to most self-excluders being non-problem gamblers, that most problem gamblers are not self-excluders. This leads to the conclusion that there is little overlap between problem gambling and self-excluding.

Over the decade, analytical approaches to harm minimization have become popular. This has led to the development of various tracking tools such as PlayScan (developed by Svenska Spel), Observer (developed by 888.com), and mentor (developed by neccton and myself). Furthermore, regulators are increasingly recognizing the importance of early risk detection via behavioural tracking systems. VSE also plays an important role in this context. However, some systems use VSE as a proxy of at-risk or problem gambling.

Based on the findings from empirical research, self-exclusion is a poor proxy measure for categorizing at-risk or problem gamblers and VSE should not be used in early problem gambling detection systems. The reasons for this are evident:

  • There is no evidence of a direct relationship between self-exclusion and problem gambling. As argued above, self-excluders are not necessarily problem gamblers and thus cannot be used for early risk detection.
  • There are various reasons for self-exclusion that have nothing to do with problem gambling. Players exclude for different reasons and one of the most salient appears to be annoyance and frustration with the operator (i.e., VSE is used as a way of venting their unhappiness with the operator). In this case, an early detection model based on self-exclusion would basically identify unhappy players and be more useful to the marketing department than to those interested in harm minimization
  • Problem gamblers who self-exclude are already actively changing their behaviour. The trans-theoretical ‘stages of change’ model (developed by Dr. Carlo DiClemente and Dr. James Prochaska) argues that behavioural change follows stages from pre-contemplation to action and maintenance. One could argue that the segment of players who self-exclude because they believe their gambling to be problematic are the ones who already past the stages where assistance is usually helpful in triggering action to cease gambling. These players are making use of a harm-minimization tool. The ones actually in need of detection and intervention are the ones who have not yet reached this stage of change yet and are not thinking about changing their behaviour at all. This is one more argument for the inappropriateness of self-exclusion as a proxy for problem gambling.

But what could be done to prevent the development of gambling-related problems in the first place? For the reasons outlined above, we would argue that the attempt to identify problem gambling via playing patterns that are derived from self-excluders does not assist harm minimization. Firstly, this approach does not target problem gamblers, and secondly it does not provide any insights into the prevention of such problems.

It is evident that any gambling environment should strive to minimize gambling-related harm and reduce the amount of gambling among vulnerable groups. It is also known that information that is given to individuals to enable behavioural change should encourage reflection because research has shown that self-monitoring can enable behavioural change in the desired direction. Dr. Jim Orford has also stated that attempts to explain such disparate gambling types from a single theoretical perspective are essentially a fool’s errand. This also complements the notion that problem gambling is not a homogenous phenomenon and there is not a single type of problem gambler (as I argued in my first book on gambling back in 1995). This also goes in line with the belief of Dr. Auer and myself that gambling sites have to personalize communication and offer the right player the right assistance based on their individual playing history. Recent research that Dr. Auer and I have carried out supports this line of thinking.

Studies have also shown that dynamic feedback in the form of pop-up messages has a positive effect on gambling behaviour and gambling-related thoughts. For instance, research from Dr. Michael Wohl’s team in Canada have found that animation-based information enhanced the effectiveness of a pop-up message related to gambling time limits. Our own research has found that an enhanced pop-up message (that included self-appraisal and normative feedback) led to significantly greater number of players ending their session than a simple pop-up message. In a real-world study of online gamblers, we also found that personalized feedback had a significant effect in reducing the time and money spent gambling.

Personalized feedback is a player-centric approach and in addition to gambling-specific research, there is evidence from many other areas that shows the beneficial effects on behavioural change. For instance, personalized messages have shown to enable behavioural change in areas such as smoking cessation, diabetes management, and fitness activity. Contrary to the self-exclusion oriented detection approach, we concluded in our recent JAMT paper that personalized feedback aims to prevent and minimize harm in the first place and is a much better approach to the prevention of problem gambling than using data from those that self-exclude from gambling.

Dr. Mark Griffiths, Professor of Behavioural Addiction, International Gaming Research Unit, Nottingham Trent University, Nottingham, UK

 Further reading

Auer, M. & Griffiths, M. D. (2014). Personalised feedback in the promotion of responsible gambling: A brief overview. Responsible Gambling Review, 1, 27-36.

Auer, M. Griffiths, M.D. (2015). The use of personalized behavioral feedback for online gamblers: an empirical study. Frontiers in Psychology, 6, 1406.  doi:10.3389/fpsyg.2015.01406

Auer, M., Griffiths, M.D. (2015). Testing normative and self-appraisal feedback in an online slot-machine pop-up in a real-world setting. Frontiers in Psychology. 6, 339 doi: 10.3389/fpsyg.2015.00339

Auer, M., Littler, A., Griffiths, M. D. (2015). Legal Aspects of Responsible Gaming Pre-commitment and Personal Feedback Initiatives. Gaming Law Review and Economics. 19, 444-456.

DiClemente, C. C., Prochaska, J. O., Fairhurst, S. K., Velicer, W. F., Velasquez, M. M., & Rossi, J. S. (1991). The process of smoking cessation: an analysis of precontemplation, contemplation, and preparation stages of change. Journal of Consulting and Clinical Psychology, 59, 295-304.

Dragicevic, S., Percy, C., Kudic, A., Parke, J. (2015). A descriptive analysis of demographic and behavioral data from Internet gamblers and those who self-exclude from online gambling platforms. Journal of Gambling Studies. 31, 105-132.

Gainsbury, S. (2013). Review of self-exclusion from gambling venues as an intervention for problem gambling. Journal of Gambling Studies, 30, 229-251.

Griffiths, M. D. (1995). Adolescent gambling. London: Routledge.

Griffiths, M.D. & Auer, M. (2016). Should voluntary self-exclusion by gamblers be used as a proxy measure for problem gambling? Journal of Addiction Medicine and Therapy, 2(2), 00019.

Hayer, T., & Meyer, G. (2011). Self-exclusion as a harm-minimization strategy: Evidence for the casino sector from selected European countries. Journal of Gambling Studies, 27, 685-700

Kim, H. S., Wohl, M. J., Stewart, M. K., Sztainert, T., Gainsbury, S. M. (2014). Limit your time, gamble responsibly: setting a time limit (via pop-up message) on an electronic gaming machine reduces time on device. International Gambling Studies, 14, 266-278.

Lischer, S. (2016, June). Gambling-related problems of self-excluders in Swiss casinos. Paper presented at the 16th International Conference on Gambling & Risk Taking, Las Vegas, USA.

Suurvali, H., Hodgins, D. C., Cunningham, J. A. (2010). Motivators for resolving or seeking help for gambling problems: A review of the empirical literature. Journal of Gambling Studies, 26, 1-33

Don’t blame the game: Parents, videogame content, and age ratings

Back in March 2015, BBC News reported that parents of children in 16 Cheshire county schools had been sent a letter saying that head teachers would report them to the authorities if they allowed their children to play videogames that are rated for adults (i.e., games that have an ‘18’ rating). The teachers claimed that popular games like Grand Theft Auto and Call of Duty are too violent to be played by those under the age of 18 years. They also stated that such games increased sexualised behaviour and left children vulnerable to sexual grooming. The schools also threatened to report parents who let their children play such games because it was a form of parental neglect. The author of the letter, Mary Hennessy Jones, was quoted as saying that:

“We are trying to help parents to keep their children as safe as possible in this digital era. It is so easy for children to end up in the wrong place and parents find it helpful to have some very clear guidelines”.

I’m sure the letter to parents was written with the best of intentions but as a parent of three ‘screenagers’ and someone that has spent almost three decades researching the effects of video games on human behaviour, this appears to be a very heavy-handed way to deal with the issue. Although it is illegal for any retailer to sell ‘18’ rated games to minors, it is not illegal for children to play such games, or illegal for parents to allow their children to play such games. Many parents need to be educated about the positives and negatives of playing video games but reporting them to the “authorities” is not the right way forward.

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Back in the early 1990s I was probably the only academic in the UK carrying out scientific research on children’s video game playing. In fact, I was proud of my role in getting age ratings onto all video games in the first place, and for writing the text for educational information leaflets for parents (outlining the effects of excessive playing of such games) sponsored by the National Council for Educational Technology. There are many positive benefits of playing video games (something that I wrote about in a previous article for The Conversation).

I know from first-hand experience that children often play games that are age-inappropriate. Two years ago, my (then) 13-year old son said he was the only boy in his class that did not play or own the Call of Duty video game. This is also borne out by research evidence. One study that I was involved in found that almost two-thirds of children aged 11- to 13-years of age (63%) had played an 18+ video game. Unsurprisingly, boys (76%) were more likely than girls (49%) to have played an 18+ video game. Children were also asked about how often they played 18+ video games. Of the two-thirds who had played them, 8% reported playing them “all the time”, 22% reported playing them “most of the time”, 50% reported playing them “sometimes”, 18% reported playing them “hardly ever”. Again, boys were more likely than girls to play 18+ video games more frequently. Children were asked how they got access to 18+ plus video games. The majority had the games bought for them by family or friends (58%), played them at a friend’s house (35%), swapped them with friends (27%), or bought games themselves (5%). This research certainly appears to suggest that parents and siblings are complicit in the playing of age-inappropriate games.

There is a growing amount of scientific literature that has examined the content of video games designed for adults. For instance, a study led by Dr. Kimberley Thompson and published in the Archives of Pediatric and Adolescent Medicine attempted to quantify the depiction of violence, blood, sexual themes, profanity, substances, and gambling in adult (18+) video games and to assess whether the actual game content matched the content descriptor on the packaging. Although content descriptors for violence and blood provided a good indication of content in the 36 games examined, the authors concluded that 81% of the games studied (n=29) lacked content descriptors of other adult content. Other studies carried out by the same research team have found that adult content can be found in lots of games aimed at young children and teenagers.

Another study led by Dr. David Walsh published in Minerva Pediatrica tested the validity of media rating systems (including video games). Results showed that when the entertainment industry rated a product as inappropriate for children, parents also agreed that it was inappropriate. However, parents disagreed with many industry ratings that were designated as containing material as suitable for children. The products rated as appropriate for adolescents by the industry were of the greatest concern to parents.

The issue of children and adolescents playing 18+ games is no different from the debates about children and adolescents watching 18+ films. However, based on anecdotal evidence appears that parents are more likely to adhere to age ratings on films than they are on video games. This is one area that both media researchers and media educators need to inform parents to be more socially responsible in how they monitor their children’s leisure activity. A school sending out a threatening letter to parents is unlikely to change parental behaviour. Education and informed debate is likely to have a much greater effect in protecting our children from the potential harms of video game playing.

Dr. Mark Griffiths, Professor of Behavioural Addiction, International Gaming Research Unit, Nottingham Trent University, Nottingham, UK

 Further reading

Anderson, C.A., Gentile, D.A., & Dill, K.E. (2012). Prosocial, antisocial and other effects of recreational video games. In D.G. Singer, & J.L. Singer (Eds), Handbook of Children and the Media, Second Edition, (pp. 249-272). Thousand Oaks, CA: Sage.

Anderson, C. A., Shibuya, A., Ihori, N., Swing, E. L., Bushman, B.J., Sakamoto, A., Rothstein, H.R., & Saleem, M. (2010). Violent video game effects on aggression, empathy, and prosocial behavior in eastern and western countries: a meta-analytic review. Psychological Bulletin, 136, 151-173.

Bartlett, C. P., Anderson, C.A. & Swing, E.L. (2009). Video game effects confirmed, suspected and speculative: A review of the evidence. Simulation and Gaming, 40, 377-403.

Ferguson, C. J. (2007). Evidence for publication bias in video game violence effects literature: A meta analytic review. Aggression and Violent Behavior, 12, 470-482.

Ferguson, C. J. (2013). Violent video games and the supreme court: Lessons for the scientific community in the wake of Brown v. Entertainment Merchants Association. American Psychologists, 68, 57-74.

Ferguson, C. J., San Miguel, S. & Hartley, T. (2009).  Multivariate analysis of youth violence and aggression: The influence of family, peers, depression and media violence. Journal of Paediatrics, 155, 904-908.

Gentile, D. A. & Stone, W. (2005). Violent video game effects in children and adolescents: A review of the literature. Minerva Pediatrics, 57, 337-358.

Griffiths, M.D. (1998). Video games and aggression: A review of the literature. Aggression and Violent Behavior, 4, 203-212.

Griffiths, M.D. (2000). Video game violence and aggression: Comments on ‘Video game playing and its relations with aggressive and prosocial behaviour’ by O. Weigman and E.G.M. van Schie. British Journal of Social Psychology, 39, 147-149.

Griffiths, M.D. (2010). Age ratings on video games: Are the effective? Education and Health, 28, 65-67.

Griffiths, M.D. & McLean, L. (in press). Content effects: Online and offline games. In P. Roessler (Ed.), International Encyclopedia of Media Effects. Chichester: Wiley.

Grüsser, S.M., Thalemann, R. & Griffiths, M.D. (2007). Excessive computer game playing: Evidence for addiction and aggression?  CyberPsychology and Behavior, 10, 290-292.

Ivory, J.D., Colwell, J., Elson, M., Ferguson, C.J., Griffiths, M.D., Markey, P.M., Savage, J. & Williams, K.D. (2015). Manufacturing consensus in a divided field and blurring the line between the aggression concept and violent crime. Psychology of Popular Media Culture, 4, 222–229.

McLean, L. & Griffiths, M.D. (2013). The psychological effects of videogames on young people. Aloma: Revista de Psicologia, Ciències de l’Educació i de l’Esport, 31(1), 119-133.

McLean, L. & Griffiths, M.D. (2013). Violent video games and attitudes towards victims of crime: An empirical study among youth. International Journal of Cyber Behavior, Psychology and Learning, 2(3), 1-16.

Mehroof, M. & Griffiths, M.D. (2010). Online gaming addiction: The role of sensation seeking, self-control, neuroticism, aggression, state anxiety and trait anxiety. Cyberpsychology, Behavior, and Social Networking, 13, 313-316.

The words and the we’s: When is a new addiction scale not a new addiction scale?

“The words you use should be your own/Don’t plagiarize or take on loans/There’s always someone, somewhere/With a big nose, who knows” (Lyrics written by Morrissey from ‘Cemetry Gates’ (sic) by The Smiths)

Over the last few decades, research into ‘shopping addiction’ and ‘compulsive buying’ has greatly increased. In 2015, I along with my colleagues, developed and subsequently published (in the journal Frontiers in Psychology) a new scale to assess shopping addiction – the 7-item Bergen Shopping Addiction Scale (BSAS) which I wrote about in one of my previous blogs.

We noted in our Frontiers in Psychology paper that two scales had already been developed in the 2000s (i.e., one by Dr. George Christo and colleagues in 2003, and one by Dr. Nancy Ridgway and colleagues in 2008 – see ‘Further reading’ below), but that neither of these two instruments approached problematic shopping behaviour as an addiction in terms of core addiction criteria that are often used in the behavioural addiction field including salience, mood modification, tolerance, withdrawal, conflict, relapse, and problems. We also made the point that new Internet-related technologies have now greatly facilitated the emergence of problematic shopping behaviour because of factors such as accessibility, affordability, anonymity, convenience, and disinhibition, and that there was a need for a psychometrically robust instrument that assessed problematic shopping across all platforms (i.e., both online and offline). We concluded that the BSAS has good psychometrics, structure, content, convergent validity, and discriminative validity, and that researchers should consider using it in epidemiological studies and treatment settings concerning shopping addiction.

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More recently, Srikant Amrut Manchiraju, Sadachar and Jessica Ridgway developed something they called the Compulsive Online Shopping Scale (COSS) in the International Journal of Mental Health and Addiction (IJMHA). Given that we had just developed a new shopping addiction scale that covered shopping across all media, we were interested to read about the new scale. The scale was a 28-item scale and was based on the 28 items included in the first step of BSAS development (i.e., initial 28-item pool). As the authors noted:

“First, to measure compulsive online shopping, we adopted the Bergen Shopping Addiction Scale (BSAS; Andreassen, 2015). The BSAS developed by Andreassen et al. (2015), was adapted for this study because it meets the addiction criteria (e.g., salience, mood modification, etc.) established in the DSM-5. In total, 28 items from the BSAS were modified to reflect compulsive online shopping. For example, the original item – ‘Shopping/buying is the most important thing in my life’ was modified as ‘Online shopping/buying is the most important thing in my life’… It is important to note that we are proposing a new behavioral addiction scale, specifically compulsive online shopping … In conclusion, the scale developed in this study demonstrated strong psychometric, structure, convergent, and discriminant validity, which is consistent with Andreassen et al.’s (2015) findings”.

Apart from the addition of the word ‘online’ to every item, all initial 28 items of the BSAS were used identically in the COSS. Therefore, I sought the opinion of several research colleagues about the ‘new’ scale. Nearly all were very surprised that an almost identical scale had been published. Some even questioned whether such wholescale use might constitute plagiarism (particularly as none of the developers of the COSS sought permission to adapt our scale).

According to the plagiarism.org website, several forms of plagiarism have been described including: “Copying so many words or ideas from a source that it makes up the majority of your work, whether you give credit or not” (p.1). Given the word-for-word reproduction of the 28 item–pool, an argument could be made that the COSS plagiarizes the BSAS, even though the authors acknowledge the source of their scale items. According to Katrina Korb’s 2012 article on adopting or adapting psychometric instruments:

“Adapting an instrument requires more substantial changes than adopting an instrument. In this situation, the researcher follows the general design of another instrument but adds items, removes items, and/or substantially changes the content of each item. Because adapting an instrument is similar to developing a new instrument, it is important that a researcher understands the key principles of developing an instrument…When adapting an instrument, the researcher should report the same information in the Instruments section as when adopting the instrument, but should also include what changes were made to the instrument and why” (p.1).

Dr. Manchiraju and his colleagues didn’t add or remove any of the original seven items, and did not substantially change the content of any of the 28 items on which the BSAS was based. They simply added the word ‘online’ to each existing item. Given that the BSAS was specifically developed to take into account the different ways in which people now shop and to include both online and offline shopping, there doesn’t seem to be a good rationale for developing an online version of the BSAS. Even if there was a good rationale, the scale could have made reference to the Bergen Shopping Addiction Scale in the name of the ‘new’ instrument. In a 2005 book chapter ‘Selected Ethical Issues Relevant to Test Adaptations’ by Dr. Thomas Oakland (2005), he noted the following in relation to plagiarism and psychometric test development:

Psychologists do not present portions of another’s work or data as their own, even if the other work or data source is cited … Plagiarism occurs commonly in test adaptation work (Oakland & Hu, 1991), especially when a test is adapted without the approval of its authors and publisher. Those who adapt a test by utilizing items from other tests without the approval of authors and publishers are likely to be violating ethical standards. This practice should not be condoned. Furthermore, this practice may violate laws in those countries that provide copyright protection to intellectual property. In terms of scale development, a measure that has the same original items with only one word added to each item (which only adds information on the context but does not change the meaning of the item) does not really constitute a new scale. They would find it really hard to demonstrate discriminant validity between the two measures”.

Again, according to Oakland’s description of plagiarism specifically in relation to the development of psychometric tests (rather than plagiarism more generally), the COSS appears to have plagiarized the BSAS particularly as Oakland makes specific reference to the adding of one word to each item (“In terms of scale development, a measure that has the same original items with only one word added to each item … does not really constitute a new scale”).

Still, it is important to point that I have no reason to think that this use of the BSAS was carried out maliciously. Indeed, it may well be that the only wrongdoing was lack of familiarity with the conventions of psychometric scale development. It may be that the authors took one line in our original Frontiers in Psychology paper too literally (the BSAS may be freely used by researchers in their future studies in this field”). However, the purpose of this sentence was to give fellow researchers permission to use the validated scale in their own studies and to avoid the inconvenience of having to request permission to use the BSAS and then waiting for an answer. Another important aspect here is that the BSAS (which may be freely used) consists of seven items only, not 28. The seven BSAS items were extracted from an initial item pool in accordance with our intent to create a brief shopping addiction scale. Consequently, there exists only one version of BSAS, the 7-item version. Here, Dr. Manchiraju and his colleagues seem to have misinterpreted this when referring to a 28-item BSAS.

(Please note: This blog is adapted using material from the following paper: Griffiths, M.D., Andreassen, C.S., Pallesen, S., Bilder, R.M., Torsheim, T. Aboujaoude, E.N. (2016). When is a new scale not a new scale? The case of the Bergen Shopping Addiction Scale and the Compulsive Online Shopping Scale. International Journal of Mental Health and Addiction, 14, 1107-1110).

Dr. Mark Griffiths, Professor of Behavioural Addiction, International Gaming Research Unit, Nottingham Trent University, Nottingham, UK

Further reading

Aboujaoude, E. (2014). Compulsive buying disorder: a review and update. Current Pharmaceutical Design, 20, 4021–4025.

Andreassen, C. S., Griffiths, M. D., Pallesen, S., Bilder, R. M., Torsheim, T., & Aboujaoude, E. (2015). The Bergen Shopping Addiction Scale: reliability and validity of a brief screening test. Frontiers in Psychology, 6, 1374. doi: 10.3389/fpsyg.2015.01374

Christo, G., Jones, S., Haylett, S., Stephenson, G., Lefever, R. M., & Lefever, R. (2003). The shorter PROMIS questionnaire: further validation of a tool for simultaneous assessment of multiple addictive behaviors. Addictive Behaviors, 28, 225–248.

Griffiths, M.D.  (2005). A ‘components’ model of addiction within a biopsychosocial framework. Journal of Substance Use, 10, 191-197.

Griffiths, M.D., Andreassen, C.S., Pallesen, S., Bilder, R.M., Torsheim, T. Aboujaoude, E.N. (2016). When is a new scale not a new scale? The case of the Bergen Shopping Addiction Scale and the Compulsive Online Shopping Scale. International Journal of Mental Health and Addiction, 14, 1107-1110.

Korb, K. (2012). Adopting or adapting an instrument. Retrieved September 12, 2016, from: http://korbedpsych.com/R09aAdopt.html

Manchiraju, S., Sadachar, A., & Ridgway, J. L. (2016). The Compulsive Online Shopping Scale (COSS): Development and Validation Using Panel Data. International Journal of Mental Health and Addiction, 1-15. doi: 10.1007/s11469-016-9662-6.

Maraz, A., Eisinger, A., Hende, Urbán, R., Paksi, B., Kun, B., Kökönyei, G., Griffiths, M.D. & Demetrovics, Z. (2015). Measuring compulsive buying behaviour: Psychometric validity of three different scales and prevalence in the general population and in shopping centres. Psychiatry Research, 225, 326–334.

Maraz, A., Griffiths, M. D., & Demetrovics, Z. (2016). The prevalence of compulsive buying in non-clinical populations: A systematic review and meta-analysis. Addiction, 111, 408-419.

Oakland, T. (2005). Selected ethical issues relevant to test adaptations. In Hambleton, R., Spielberger, C. & Meranda, P. (Eds.). Adapting educational and psychological tests for cross-cultural assessment (pp. 65-92). Mahwah, NY: Erlbaum Press.

Oakland, T., & Hu, S. (1991). Professionals who administer tests with children and youth: An international survey. Journal of Psychoeducational Assessment, 9(2), 108-120.

Plagiarism.org (2016). What is plagiarism? Retrieved September 12, 2016, from: http://www.plagiarism.org/plagiarism-101/what-is-plagiarism

Ridgway, N., Kukar-Kinney, M., & Monroe, K. (2008). An expanded conceptualization and a new measure of compulsive buying. Journal of Consumer Research, 35, 622–639.

Weinstein, A., Maraz, A., Griffiths, M.D., Lejoyeux, M. & Demetrovics, Z. (2016). Shopping addiction and compulsive buying: Features and characteristics of addiction. In V. Preedy (Ed.), The Neuropathology Of Drug Addictions And Substance Misuse (Vol. 3). (pp. 993-1008). London: Academic Press.

The song and binding mode: Musical hallucinations in video game playing

According to a 2015 review in the journal Frontiers in Psychology by Jan Coebergh and colleagues, musical hallucinations (MHs) “are auditory hallucinations characterized by songs, tunes, melodies, harmonics, rhythms, and/or timbres…and that the mechanisms responsible for the mediation of MH are probably diverse”. While Danilo Vitorovic and Jose Biller reported in a 2013 issue of Frontiers in Neurology that the prevalence rate of MHs among the general population is at present unknown and/or rare, ‘involuntary musical imagery’ (INMI) is thought to be more commonplace. For instance, in a 2012 Finnish study in the journal Psychology of Music, Lassi Liikkanen reported that 89% of the total sample (n=12,519) reported experiencing INMI at least once a week. Music hallucination prevalence rates among various groups have been reported including obsessive-compulsive disorder patients (41%; Journal of Clinical Psychiatry, 2004), elderly people with auditory problems (2.5%; International Journal of Geriatric Psychiatry, 2002), and general hospital setting patients (0.16%; Psychosomatics, 1998).

Although Coebergh and colleagues described MHs, they were not explicitly defined. In a review in a 2014 issue of the Journal of Medical Case Reports, Woo and colleagues defined MHs as complex auditory perceptions in the absence of an external acoustic stimulus and are often consistent with previous listening experience” whereas the 2013 review by Vitorovic and Biller (see above) noted that MHs represent a specific form of auditory hallucinations whereby patients experience formed songs, instrumental music, or tunes, without an external musical stimulus”. In a 2015 paper in the journal Psychomusicology: Music, Mind, and Brain, Tim Williams provided a classification of INMI and noted they cover a number of different types of involuntary musical experience (including MHs). Despite the lack of detailed definition, it is known that MHs occur within the context of an individual’s culture and are often viewed by those experiencing them as intrusive and sometimes unpleasant.

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In 2015, Dr. Angelica Ortiz de Gortari and I wrote a commentary paper on musical hallucinations in videogame playing in response to the review by Coebergh and colleagues. As far as we were aware, we noted that no review paper examining musical hallucinations had ever included papers referring to musical hallucinations arising from playing video games. The earliest report in the psychological literature is by Sean Spence (published in 1993 in the Irish Journal of Psychological Medicine) who reported the case of a 20-year-old female patient with a family history of psychosis. She presented with persecutory delusions, suicidal ideation, violent behaviour and third-person auditory hallucinations comprising 48 hours of constant MHs from the Mario Brothers videogame that developed into delusional thoughts. No drugs were found in her urinary system and her EEG was normal when MHs occurred. The MHs from the videogame decreased within 48 hours of treatment (using antidepressants and neuroleptics).

More recently, a series of papers by Dr. Ortiz de Gortari and I examined Game Transfer Phenomena (GTP). GTP research has demonstrated how the videogame can keep on playing even after the game has been turned off. GTP are non-volitional phenomena (e.g., altered perceptions, automatic mental processes, and involuntary behaviors). In an analysis of over 1600 gamers’ self-reports, our research has shown that videogame playing can lead to (i) perceptual distortions of physical objects, environments, and/or sounds, (ii) misperceptions of objects and sounds that are similar to those in the videogame, (iii) interpretation of events in real life contexts that utilize the logic of the videogame, (iv) ghost perceptions and sensations of images, sounds, and tactile experiences, and (v) involuntary actions and behaviors based on experiences from the videogame.

One study that we published in a 2014 issue of the International Journal of Cyber Behavior, Psychology and Learning specifically examined auditory GTP experiences. Gamers’ experiences identified as GTP in one or more modalities (e.g., visual, auditory) were collected from 60 online videogame forums over seven months. Of these, there were 192 auditory experiences from 155 gamers collected. The largest numbers of experiences (90%) were identified as involuntary auditory imagery. This manifested as hearing music (n = 73), sound (n = 83), or voices from within the game (n = 12). Some experiences were triggered by external cues associated with the game, while others were not. Experiences with music included hearing high pitch music in addition to calm and classical music.

Music from the videogames was usually experienced persistently, while sound effects or voices appeared to have occurred more episodically. Hearing the music persistently provoked sleep deprivation, annoyance, and uncertainty. When the music was re-experienced very vividly, the gamers attributed them to external sources associated with the videogame. More specifically, when auditory cues were associated with adverse videogame content, they resulted in irrational thoughts, reactions and changes in behaviour. In many cases, the gamers said that they had been playing intensively (i.e., either playing long sessions or playing frequently). Previous studies have linked hearing music in absence of auditory stimuli with the recent or repeated exposure to music (see ‘Further reading’ below including: Gardner, 1985; Gerra et al., 1998; Hyman et al., 2012).

In our study, one gamer said that he heard the sound of music coming out from the speakers so he stood up to check them while another heard music from Pokémon when vacuuming. It also appears that musical hallucinations can cross sensory modalities. For instance, some gamers have reported hearing music while seeing images from the video game. An online survey about GTP with a convenience sample of 2,362 gamers found that hearing music from videogames when not playing were the more prevalent (74%) than hearing sounds (65.0%) or voices (46%) when not playing (Ortiz de Gortari & Griffiths, 2015b).

Based on what is known empirically, our paper concluded that (i) MHs from videogame playing – although not well documented – appear to be relatively commonplace among gamers and prevalence appears to be higher than found in other populations, (ii) individual interpretation of MHs from videogames are influenced by the meanings and uses of auditory cues in the videogames, (iii) MHs can manifest beyond one sensory modality and has been reported across-sensory channels (e.g., hearing music while seeing ghost images from the game), (iv) there is little evidence that MHs among videogame players are linked to other underlying pathology (e.g., epilepsy, psychiatric disorder, etc.), (v) those researching in the field of MHs and INMI appear to have overlooked the literature on these phenomena related to videogame playing, and (vi) better definitions are needed for MHs and a distinction between MHs and INMI is required.

(Please note: This blog is based on material used in the following paper: Griffiths, M.D. & Ortiz de Gortari, A.B. (2015). Musical hallucinations: Review of treatment effects. Frontiers in Psychology, 6, 1885. doi: 10.3389/fpsyg.2015.01885).

Dr. Mark Griffiths, Professor of Behavioural Addiction, International Gaming Research Unit, Nottingham Trent University, Nottingham, UK

Further reading

Coebergh, J. A. F., Lauw, R. F., Bots, R., Sommer, I. E. C., & Blom, J. D. (2015) Musical hallucinations: review of treatment effects. Frontiers in Psychology, 6, 814.

Cole M.G., Dowson, L., Dendukuri, N., & Belzile, E. (2002). The prevalence and phenomenology of auditory hallucinations among elderly subjects attending an audiology clinic. International Journal of Geriatric Psychiatry (2002) 17, 444–52.

Fukunishi, I., Horikawa, N., & Onai, H. Prevalence rate of musical hallucinations in a general hospital setting. Psychosomatics (1998) 39, 175.

Hermesh H. (2004). Musical hallucinations: prevalence in psychotic and nonpsychotic outpatients. Journal of Clinical Psychiatry, 65, 191–7. doi:10.4088/JCP.v65n0208

Gardner, M. P. (1985). Mood states and consumer behavior: A critical review. Journal of Consumer Research, 12, 281-300.

Gerra, G., Zaimovic, A., Franchini, D., Palladino, M., Giucastro, G., Reali, N., . . . Brambilla, F. (1998). Neuroendocrine responses of healthy volunteers to `techno-music’: relationships with personality traits and emotional state. International Journal of Psychophysiology, 28(1), 99-111.

Griffiths, M.D. & Ortiz de Gortari, A.B. (2015). Musical hallucinations: Review of treatment effects. Frontiers in Psychology, 6, 1885. doi: 10.3389/fpsyg.2015.01885

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