Adoption of digital technology has been rapidly expanding and growing throughout the world, although with some differences in adoption across population demographics (Van Dijk, 2006; Van Dijk & Hacker, 2003). The advancement of technology has had positive impacts on many aspects of our lives (Pinker, 2018). However, there are aspects of using digital technology that have some negative impacts on academic performance (e.g., David et al., 2015; Kuznekoff & Titsworth, 2013), physical fitness (e.g., Mark & Janssen, 2008; Sisson et al., 2010), and psychological well-being (e.g., Cheever et al., 2014; Dhir et al., 2018; Orben & Przybylski, 2019; Scott et al., 2017; Tangmunkongvorakul et al., 2019).

Human interaction with technology, particularly interaction that may lead to harm and require intervention or behavior changes for improved quality of life, has been a growing area of research. As early as 1998, studies have identified a subset of the population that may experience more negative outcomes compared to the general population when engaging with the internet and related technology (Young, 1998). Although some research deems all forms of internet use to be equivalent, other research has focused on engagement with specific digital technologies such as video games and social media (Best et al., 2014; Ferguson & Colwell, 2020; Griffiths, 1998). The difference in topography between these subtypes may have important implications for conceptualization and treatment. This attention to video games in particular, has led to the inclusion of internet gaming disorder in the Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-V), as a “condition for further study” (American Psychological Association, 2013). The World Health Organization (WHO) has also addressed the concern with the addition of gaming disorder in the International Classification of Disease, 11th edition (WHO, 2018). However, the inclusion was met with some backlash, because some researchers believe its inclusion was premature (van Rooij et al., 2018; Wood, 2008). Although other technologies have not had as much attention as gaming, some research suggests that using social media can be associated with problems including addiction (Best et al., 2014; Chóliz et al., 2017).

The study of “problem gaming” and “problem social media” use has been multidisciplinary, with behavioral addictions, psychiatry, clinical psychology, social psychology, media studies, and other fields contributing to its study. Given the multidisciplinary nature and relative newness of the field of study, terminology and definitions have largely varied, in particular around use that may be harmful for the user. Terms such as “internet addiction,” “pathological gaming,” “cyberrelationship addiction,” “smartphone addiction,” “computer addiction,” “excessive gaming,” “dependent gaming,” and “pathological internet” use are examples of terminology used in the literature to describe similar phenomena (Paulus et al., 2018). Many of these terms are related to addiction and pathology, which may contribute to stigma for individuals with problems in these areas (Kelly et al., 2016). Some researchers in other areas of related study have shifted language towards “problem use” (e.g., problem gambling) to address the stigma, as well as encompassing a larger population that may still have problems at subclinical levels (Delfabbro, 2013). We use the terms “problem gaming” and “problem social media use” to describe the repertoire of human behaviors that leads to negative or harmful biopsychosocial consequences when interacting with video games or social media. These terms encompass the spectrum of problems and harm including those that are subclinical.

Gaming may have a wide range of impact on individuals, from healthy, positive, and potentially therapeutic use (e.g., Primack et al., 2012) to disordered use that is described by the DSM-V or International Classification of Diseases-11. Healthy use can have positive impacts on work and school such as potentially increasing interest in fields such as computer science (DiSalvo & Bruckman, 2009), whereas problematic use may affect functioning in these same areas. Individuals may be spending time playing video games instead of attending work or school, or problems may be more subtle where thoughts related to gaming distract individuals from meaningfully engaging in these same areas. The range of outcomes applies to finances as well, where some gamers may be able to monetize their playing through the use of services such as Twitch (Johnson & Woodcock, 2019), whereas others may spend large sums of money that may be outside of their means, in particular playing games with lootbox mechanics (Hanner & Zarnekow, 2015). This continuum of the impact of playing video games on an individual may also apply to other domains such as physical health, mental health, and relationships with family or friends.

Similar to gaming, social media use may have a range of use from healthy to problematic use in the same areas. For example, individuals may use social media to keep in touch with friends and family who are not geographically close, improving their connection to those individuals. Other individuals may find that their connections to friends and family are worsened (Bail et al., 2018). The function of playing video games or using social media may be more important than the topographies when considering problems given the continuum of use. Treatments may be appropriate for problem use of video games and social media, and given the wide range may be applicable for both clinical and subclinical populations.

Problem gaming and problem social media use are areas where behavior analysts could contribute meaningful research insights, both in its conceptualization as well as clinical considerations. Some recent systematic literature reviews and meta-analyses have evaluated the methodological quality of published research on treating internet gaming disorder (e.g., Stevens et al., 2019; Zajac et al., 2020), but those studies did not consider a behavior analytic perspective. Neither of those syntheses included studies with fewer than 10 participants, so single-subject research methods that many behavior analysts may use (e.g., reversal, multiple-baseline, alternating treatment, changing criterion designs) may not have been included.

To understand the current state of research pertaining to the treatment of problem gaming and problem social media use we reviewed published literature in PsycINFO. Our objective with this review was to characterize treatment research on problem gaming and problem social media use and to identify opportunities for behavior analysts to contribute.

Method

Research Questions

Three questions about treatment for problem gaming and problem social media use guided this review. First, what was the nature of treatment for problem gaming and problem social media use? Based on results from previous research (Stevens et al., 2019; Zajac et al., 2020), we categorized each treatment study as pharmaceutical, psychological, or other. Second, who has received treatment for problem gaming and problem social media use? To address this question, we recorded demographic characteristics of participants. Third, how was problem behavior measured? Consensus among behavioral researchers about how to measure problem internet use has not yet emerged (King et al., 2020), so our aim with this question was to determine whether any particular measures have been used in treatment research.

Sources and Search Strategy

We conducted two initial searches in PsycINFO during October, 2020. The search terms and logic for the review were adapted from a recent meta-analysis of cognitive behavioral therapy for internet gaming disorder (Stevens et al., 2019). The terms in the first search were: (1) internet or video or computer or online; and (2) gaming or game* and (3) addic* or disorder, and (4) treat* or intervention or therapy or CBT. The second search used the same criteria as the first except that the second term was “or social med* or social network*,” instead of “gaming or game*.” The search results were limited to peer reviewed journal articles and English language in the PsycINFO database. There were no date restrictions. Figures 1 and 2 illustrate the search and screening process for problem gaming and problem social media use, respectively.

Fig. 1
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Selection of Problem Gaming Studies

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Fig. 2
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Selection of Problem Social Media Use Studies

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Title and Abstract Screening

We reviewed titles and abstracts of the results of the initial searches. Initial searches identified 347 articles for problem gaming and 53 for problem social media use. At this stage, we excluded nonempirical articles (e.g., literature reviews) and those that did not investigate treatment outcomes (e.g., epidemiological research). If it was not clear from the abstract whether the results included treatment outcomes, we included the publication in full-text review. Articles were excluded before full-text review when the abstract indicated the article did not report outcomes for problem gaming or problem social media use treatments. Some of the articles that were excluded at this stage reported survey or interview research to identify possible targets for treatment, evaluation of available screening tools, or commentary about digital technology and mental health.

Full-Text Review and Data Characterization

Based on a review of titles and abstracts, we identified 61 articles as potentially reporting treatment outcomes for problem gaming, and 6 articles for problem social media use. One or both authors reviewed the full text of each article to determine whether it reported treatment outcome data. Of the 61 problem gaming articles, 41 articles met this criterion and were included in the dataset. None of the problem social media use articles reported treatment outcomes for problem social media use. One or both authors recorded the following information about each of the 41 articles: sample size, age, sex, geographic location of sample, study design and treatment, how problem gaming was measured, description of other treatment-relevant measures, and outcomes.

Interobserver Agreement

Twenty articles (49% of articles that met inclusion criteria) were independently coded by both authors. Total agreement on all coded variables was 88%. Instances where there were disagreements were resolved through discussion prior to coding the remaining articles.

Results and Discussion

Treatment for Problem Gaming

Forty-one published articles met the inclusion criteria. Figure 3 shows the number of search results as a function of year of publication. Search results that were excluded from the sample did not report on treatment of problem gaming. The 20 articles that were reviewed at the full-text stage but excluded from the final sample either did not assess problem gaming as an outcome variable, did not report original empirical data, aggregated results across different types of addiction, or did not evaluate treatment outcomes. Some included conceptual analyses or survey research that aimed to operationalize problem gaming (e.g., Khalili-Mahani et al., 2019; Taquet et al., 2017). Others reported results of treatment for other behavioral or psychological issues and identified gaming as a contributing factor to the primary problem (e.g., Cassell & Dubey, 1998; Silić et al., 2019; Yung et al., 2015). The articles that were included in the sample all reported outcomes for treatment of problem gaming. The earliest treatment research was published in 2004, more than a decade after the earliest search result, suggesting that discussion of treatment for problem gaming in peer-reviewed, published literature preceded its implementation by several years.

Fig. 3
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Problem Gaming Search Results by Year of Publication

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Table 1 summarizes the pharmaceutical treatment studies for problem gaming. Eight studies reported outcomes of administering four different drugs: methylphenidate, atomoxetine, escitalopram, and bupropion. All four drugs were shown to decrease problem gaming at posttreatment assessment. The stimulant methylphenidate and selective norepinephrine reuptake inhibitor atomoxetine are commonly prescribed for attention deficit hyperactivity disorder (ADHD; Gilbert et al., 2006). Escitalopram is a serotonin-selective reuptake inhibitor most often prescribed to treat depression and generalized anxiety (Allgulander et al., 2006; Rapaport et al., 2004). Bupropion is a norepinephrine-dopamine reuptake inhibitor and nicotinic receptor agonist. It is prescribed for substance use disorders including smoking cessation (Dwoskin et al., 2006).

Table 1 An Overview of Pharmaceutical Treatment Studies for Problem Gaming
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Bupropion was the most studied pharmaceutical treatment for problem gaming in this review. It was administered in five of the pharmaceutical treatment studies (62.5%) and included as “treatment as usual” in one psychological treatment study (Kim, Han, Lee, & Renshaw, 2012). One bupropion administration study assessed problem gaming at three time points and reported that posttreatment changes related to bupropion were maintained at a 4-week follow up (Han & Renshaw, 2012). Two studies were direct comparisons of bupropion and escitalopram. One (Nam et al., 2017) reported no group differences between bupropion and escitalopram, but the other (Song et al., 2016) reported greater reductions in symptoms with bupropion than escitalopram. Taken together, the eight studies in Table 1 collectively indicate that bupropion may be a more promising treatment for problem gaming than some other pharmaceutical treatments. Compared to atomoxetine, escitalopram, and methylphenidate, bupropion was associated with greater reductions in problem gaming. Moreover, bupropion was the only pharmaceutical treatment with positive evidence that reductions in problem gaming were maintained after treatment was discontinued (Han & Renshaw, 2012).

Table 2 is an overview of the psychotherapy treatment studies for problem gaming. Characteristics of the psychotherapy treatment studies in Table 2 varied widely. Twenty-six studies reported outcomes of a range of group and individual psychotherapies. One case study described treatment with psychodynamic therapy (Essig, 2012). The other 25 studies reported cognitive behavior therapy (CBT) or related cognitive and behavioral treatments. Treatment settings included outpatient clinics, private clinical practices, school-based programs, and residential camps. The duration of treatment ranged from 1 week of therapy at a residential camp (Pornnoppadol et al., 2020) to 4.5 years of psychodynamic therapy (Essig, 2012), with most treatment programs lasting 6–22 sessions occurring over 3–15 weeks. Thirteen studies reported follow-up results, with follow-up periods that ranged from 8 weeks to 5 years. The variation in follow up presents a challenge for comparing the long-term effectiveness of different treatments.

Table 2 An Overview of Psychotherapy Treatment Studies for Problem Gaming
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Thirteen studies delivered treatment to participants with problem gaming in groups. In all the studies appearing in Table 2, group and individual therapies were effective at reducing some symptoms, but no studies compared group versus individual CBT directly. Some of the CBT studies reported treatments that relied on general principles of CBT. Other studies implemented more specific protocols that were related to CBT, including family therapy (Han et al., 2012), mindfulness oriented recovery enhancement (Li et al., 2017; Li, Garland, & Howard, 2018; Li et al., 2018), eye movement desensitization (Bae & Kim, 2012), craving behavior intervention (Liu et al., 2018), or manualized programs of CBT designed to treat internet gaming disorder (Mishra et al., 2020; Torres-Rodríguez et al., 2018; Torres-Rodríguez et al., 2019) or computer game addiction (Wölfling et al., 2019). All studies reported improvements in at least one measure of problem gaming.

Eight unique psychological treatment studies included a comparison group of participants who did not receive the experimental treatment. Comparison group participants attended a general support group (Li et al., 2017), received basic counselling (Li & Wang, 2013), bupropion (Kim, Han, Lee, & Renshaw, 2012), or received no treatment/waitlist control (Du et al., 2010; Liu et al., 2018; Pornnoppadol et al., 2020; Wölfling et al., 2019; Zhang et al., 2016). Reductions in problem gaming occurred for all comparison groups, including those receiving no treatment. These improvements suggest that for some people, problem gaming can resolve in time without specific intervention. Reductions in problem gaming were larger for the experimental treatment groups in six studies (Kim, Han, Lee, & Renshaw, 2012; Li et al., 2017; Liu et al., 2018; Pornnoppadol et al., 2020; Wölfling et al., 2019; Zhang et al., 2016) and the same for experimental and comparison groups in two studies (Du et al., 2010; Li & Wang, 2013). In some studies, clinical measures of anxiety (Du et al., 2010; Li & Wang, 2013), depression (Liu et al., 2018), and negative cognitions (Li, Garland, & Howard, 2018) improved more for experimental treatment groups than comparison groups.

Table 3 is an overview of the other treatment studies included in this review. Between 2012 and 2018, seven studies reported outcomes of interventions involving education, virtual reality, transcranial direct current stimulation, and abstinence. Educational interventions appeared to decrease time spent gaming; however, had no or minimal impact on other reported domains (Kim et al., 2013; Kim, Han, Lee, Kim, & Renshaw, 2012; Walther et al., 2014). Virtual reality therapy (Park et al., 2016) consisted of eight 25-min sessions of relaxation, a simulated high-risk situation, and cognitive reconstruction. A control group participated in eight sessions of group CBT. Both groups experienced significant reductions in problem gaming severity. Lee et al. (2018) also reported significant reductions in gamers’ problem gaming severity and time spent gaming following twelve 30-min sessions of direct current stimulation of the dorsolateral prefrontal cortex. In two studies, asking participants to refrain from gaming voluntarily for several days produced abstinence in 88.9% (King et al., 2017) and 16.7% (King et al., 2018) of participants. Four weeks later, 75% of “successful abstainers” endorsed significantly fewer symptoms of internet gaming disorder (King et al., 2017). Overall, these studies indicate that a wide range of approaches can, in some cases, treat problem gaming effectively.

Table 3 An Overview of Other Treatments for Problem Gaming
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Characteristics of Participants in Problem Gaming Treatment Research

The 41 treatment studies reported results of treating 1,559 unique children, adolescents, and adults with problem gaming. Of those, 169 (10.8%) reported they were female and 1 did not identify as male or female. Twenty-six studies (63.4%) reported data from participants under 18 years of age. Mean ages of participants ranged from 9.3 to 25 years for pharmaceutical treatment studies, 14.2–46 years for psychotherapy treatment studies, and 11.6–24.6 years for other treatment studies. The treatment studies in our sample were conducted in 11 different countries. Two studies (4.9%) recruited globally. Fifteen studies (36.6%) reported results for participants recruited from the same hospital (Chung Ang University Hospital in South Korea). With the exception of one case study (Sattar & Ramaswamy, 2004), all pharmaceutical research was conducted at Chung Ang University Hospital.

Measures of Problem Gaming

The way researchers measure problem gaming in treatment research can affect how effective treatments are considered to be, and the absence of standardization in measures can make comparing treatments difficult. Figure 4 is a Venn diagram showing the number of articles in the sample that used questionnaires, time, and other measures as operational definitions of problem gaming. The number in each cell of the Venn diagram indicates the number of studies belonging in that cell: for example, 15 studies reported questionnaire measures only, 11 reported questionnaires and measures of time (e.g., self-reported time spent gaming per week), and 6 studies reported questionnaires, measures of time, and another type of measure. “Other” measures were clinical assessments and responses to single items. For example, Li et al.’s (2018) study on current craving for video game playing reported on a 10-point visual analog scale, with response options that ranged from 1 (not at all) to 10 (extremely). Twenty-five studies (61.0% of the sample) reported multiple measures of problem gaming, including 21 (51.2%) that used combinations of different types of measures.

Fig. 4
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Measures of Problem Gaming Used in Treatment Studies

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Thirty-three articles in the sample (80.4%) used questionnaires to measure problem gaming. Although problem gaming, not general internet use, was the problem of record in all of the articles we reviewed, a minority of the standardized questionnaires assessed problem gaming specifically. Young’s Internet Addiction Scale (Young, 1998), a 20-item questionnaire designed to measure self-reported dependence on internet use, was the most commonly-used questionnaire. It appeared in 15 studies (36.5%), including 7 of the 8 pharmaceutical treatment studies. Measures used in psychotherapy studies were more varied than those used in pharmaceutical treatment studies, and psychotherapy studies were less likely to rely on questionnaires alone.

Six articles reported scores for standardized questionnaires that assessed problem gaming specifically. A psychological treatment study (Pallesen et al., 2015) reported scores for two standardized measures of gaming. The Game Addiction Scale for Adolescents (Lemmens et al., 2009) queried participants’ gaming addiction during the previous 6 months. The Problem Video Game Playing scale (Tejeiro Salguero & Morán, 2002) was designed to measure the addictive use of video games with items derived from DSM-IV criteria for substance dependence and pathological gambling. Two other psychological treatment studies (Torres-Rodríguez et al., 2018; Torres-Rodríguez et al., 2019) used multiple standardized questionnaires to assess symptoms, including the Internet Gaming Disorder Test (Pontes et al., 2014). Another psychological treatment study (Pornnoppadol et al., 2020) reported scores from a test that assessed problematic gaming behaviors over the previous 3 months. The two abstinence studies (King et al., 2017; King et al., 2018) used the Internet Gaming Cognition Scale, which was designed by the authors to assess maladaptive gaming cognitions. The larger abstinence study (King et al., 2018) also reported scores for questionnaires designed to assess gaming withdrawal and the impact of gaming on quality of life. The Game Addiction Scale for Adolescents, the Problem Video Game Playing scale, the Internet Gaming Cognition Scale, and the Internet Gaming Disorder Test have all been cited by multiple independent researchers in conceptual, psychometric, and correlational research on problem gaming, but there is no standardized gaming questionnaire that is used consistently in problem gaming treatment research (King et al., 2020).

The most reported measure of problem gaming was reported time spent gaming, which was included in 12 out of 26 (46.1%) psychotherapy studies, 3 out of 7 (42%) pharmaceutical studies, and 6 out of 7 (85.7%) other studies, or just over half of the studies in the overall sample. Two studies (King et al., 2017; King et al., 2018) dichotomized time spent gaming by asking participants to report whether they successfully abstained from gaming. In a clinical pilot study of CBT (Wölfling et al., 2014), a case study (Mishra et al., 2020) and one pharmaceutical treatment study (Han et al., 2009), participants reported all time online (not just time spent gaming).

Treatment for Problem Social Media Use

The initial objective of this review was to contrast treatment research for problem gaming with treatment research for problem social media use. However, we did not identify any published research about treating problem social media use that met criteria for inclusion. Excluded articles that appeared in search results reported treatment for something other than social media use, reported survey or interview research, evaluated available screening tools, offered commentary about digital technology and mental health, or presented a case example with no intervention. Figure 5 shows the number of problem social media search results as a function of year of publication. If problem social media use research follows the same trajectory as problem gaming research (Figure 3), articles reporting treatment outcomes may begin to appear soon. Until then, the absence of such research indicates a potential knowledge gap that behavior analysts could address.

Fig. 5
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Problem Social Media Search Results by Year of Publication

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Implications for Behavior Analysis

In this review, we investigated the nature of treatment for problem gaming and problem social media use, the characteristics of participants, and the measures used to operationalize the problem behavior. All studies in the review reported improvements in problem gaming over time or relative to a comparison group, but methodological limitations often meant that the improvement could not be attributed to the intervention definitively. Participants were overwhelmingly male, mostly adolescents and young adults in South Korea. It is not clear how well these participants represent the target population of people with problem gaming. To date, treatment research has relied on self-report measures of time spent gaming or questionnaires that measure internet dependence to assess changes in problem gaming. The variability in assessment methods and treatment approaches, combined with methodological limitations, have constrained the conclusions it is possible to make about problem gaming treatment research at this time (Stevens et al., 2019; Zajac et al., 2020).

The most-studied form of treatment for problem gaming was CBT. The treatments in these studies often included behavioral components as part of multifaceted treatment programs. None of the studies in the sample analyzed different components of CBT (i.e., a component analysis). Component analyses (e.g., Adams et al., 2015) could determine the impact of specific aspects of CBT on treatment outcomes, which would make it possible to determine the “active ingredients” that function to alter problem gaming in CBT.

There is evidence that several different types of treatment can be effective at treating problem gaming, but none of the treatment research studies in our review were primarily behavior-analytic. In one psychological treatment study (Torres-Rodríguez et al., 2019), the authors referred to the study design as an “A-B-A’ withdrawal design,” but the criteria for phase changes were determined by the manualized treatment. They also graphed each participants’ time spent gaming as a function of time in treatment. Although some of the terminology and analyses used in this study would be familiar to behavior analysts, the design and analytic approach were more consistent with clinical case studies. As of this writing, no published research has evaluated behavior-analytic techniques applied to problem gaming or problem social media use.

As this article is written, it is not possible to assess whether the results of treatment research for problem gaming generalize across gender, age, nationality, or other demographics. In spite of a growing body of epidemiological research, it is not clear who among the general global population experience problem gaming (Feng et al., 2017). Therefore, it is difficult to determine whether the participants in these treatment studies were representative of people who experience problem gaming. Behavior-analytic treatments could be particularly well-suited to addressing behavioral issues with ambiguous causes, including problem gaming and problem social media use, because their effectiveness depends on the identification of specific functional relations rather than on population-level regularities.

In addition to the ambiguity in who experiences problem gaming and problem social media use and whether everyone experiences those issues the same way, there is no consensus about how gaming or social media use should be measured in treatment research. In principle, data collection for gaming, social media use, and related behaviors should be more straightforward. Most online behavior creates a detailed digital record of time-stamped responses and metadata. Behavior analysts might be well-suited to creating operational definitions that use practical methods to identify behavioral markers of problems with technology. For example, there is some evidence that passive social media use such as scrolling through social media for long periods of time is associated with symptoms of depression (Aalbers et al., 2019). It may be important to differentiate among possible responses (e.g., scrolling, commenting, liking, or posting images) to distinguish healthy use from problem social media use, and different responses might be indicative of problematic use for different individuals.

Behavior analysts assess characteristics of response patterns in ways that may also be useful in operationalizing certain types of problem behavior. For example, playing video games on the weekend for 8 hr may be less harmful than spending the same amount of time playing late at night on a weeknight when game play could disrupt sleep, school or work, personal hygiene, or time with family. Bout analysis (Shull, 2011; Shull et al., 2001) is a behavior-analytic method of describing response patterns as periods of engagement and nonengagement. The duration or frequency of bouts of engagement, or some aspect of when they occur during the day, might be a better measure of problem gaming or problem social media use than total duration of activity. Behavior analysts may be more inclined to evaluate different response characteristics or response topographies than other researchers, which could enable the development of more sensitive instruments for assessing treatment outcomes.

A harm-reduction approach has been advocated for problem gambling (Delfabbro & King, 2017) and could be appropriate for problem gaming and problem social media use as well. Negative outcomes of problem gaming might include fights with loved ones, lower physical activity, and absences from work or school (King & Delfabbro, 2018). However, some people may spend a large portion of their day playing games online and only experience positive outcomes (e.g., relaxation and socialization). For some people, reducing gaming time may actually cause harm in the form of isolation from peer or social groups (Willoughby, 2008), or reduced physical activity if the person plays active games such as Pokemon Go (LeBlanc & Chaput, 2017). In the absence of evidence that reducing time spent gaming is an effective way of reducing problematic gaming and the potential harms associated with gaming, it may be prudent to record information about consequences of problem gaming in addition to time spent gaming or other analyses such as bout analyses. Recording multiple measures could help distinguish problem gaming from gaming that is healthy or adaptive even though it occupies a lot of time. To the extent that time is reported, it should be in conjunction with other measures.

Another possible contribution of behavior analysis is in the development of standardized questionnaires that assess the function of gaming or social media use. There may be discrepancies between reported and actual behavior (Venuleo et al., 2018), but if self-report measures are worded carefully and corroborated by other measures (Critchfield et al., 1998), they can be used productively in behavior analytic research on treatments for problem gaming and related problem behaviors. In spite of potential issues with accuracy or specificity, self-report measures may be sufficient to identify problem gaming and related issues in situations in which an individual is currently seeking treatment. Similar tools have been created by behavior analysts for gambling (Dixon & Johnson, 2007), problem behavior in individuals with developmental disabilities (Paclawskyj et al., 2000), and mental illness (Singh et al., 2006). For example, the Gambling Functional Assessment (Dixon & Johnson, 2007) identifies whether an individual’s gambling is maintained by tangible reward, sensory experience, social attention, or escape. Gambling maintained by escape is more likely to be associated with gambling-related harms (Miller et al., 2010). Similar patterns may exist for gaming and social media use.

An additional area that behavior analysts might contribute to is the development of research on functional analyses that rely on direct observation and experimental manipulation for problem gaming and problem social media use (Coffey et al., 2020; Iwata & Dozier, 2008). However, doing so would present some additional logistical challenges. For example, using functional analysis to identify functions of problem gaming would require the systematic manipulation of the consequences of gameplay (e.g., presence or absence of social reinforcement for gaming, escaping homework, in-game currency). Arranging such manipulation is likely technically possible, but would require additional consideration, the specific details of which are beyond the scope of this review.

Given that the development of measures for problem gaming and problem social media use are in its infancy, it may be premature to make specific treatment recommendations that are primarily behavior-analytic. It may be valuable to consider how treatment research that has been conducted in similar areas could be adapted for problem gaming and problem social media use. For example, behavior analysts have studied substance use disorders (e.g., Silverman et al., 2011), gambling (e.g., Dixon, 2007; Weatherly, 2012), and other issues of social significance (James & Tunney, 2017). Contingency management, community reinforcement approach, and acceptance and commitment therapy have been used productively to treat a range of substance use and behavioral issues (e.g., Dallery & Raiff, 2011; Lee et al., 2015; Meyers et al., 2011; Nastally & Dixon, 2012; Prendergast et al., 2006; Washington et al., 2014). These behavior-analytic approaches to treatment could be candidates for adaptation to treat problem gaming or problem social media use.

Research on problem gaming and problem social media use is an emerging topic with increased social significance in certain parts of the developing world. Understanding the reinforcing consequences that maintain problem internet use and tailored treatments based on this understanding create many opportunities for study for behavior analysts.