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Abstract

Background:

Gaming and gambling are frequently reported from child and adolescent psychiatry and school health care. Swedish epidemiological data show that 1.3% of the population meet the criteria for gambling disorder. Risk factors are male gender, young age, single status and being born outside Sweden. Both problem gaming and gambling are associated with compulsion, psychiatric and physical symptoms, impaired cognitive development and school performance. Based on the limited knowledge and the need for more research into these behaviours among young individuals, the present study aimed to look at the prevalence of gaming and gambling in patients at the child and adolescent psychiatry department (CAP) in Skåne, a region in the south of Sweden.

Design and methods:

The overall aim is to explore gaming and gambling in a child and youth population. Children aged 8–18 years (N = 144) from CAP in Skåne were assessed with two self-screening instruments: GASA (Game Addiction Scale for Adolescents) and NODS-CLiP (NORC Diagnostic Screen for Gambling Problems). Information were collected regarding type of care, housing situation and diagnosis.

Results:

Thirty-three percent of the study participants showed problem/addictive gaming. Fifty-two percent of the males in the study showed problem/addictive gaming. Forty-four percent of the subjects with ADHD showed problem/addictive gaming. Eleven percent of the study participants showed problem gambling.

Conclusions:

The present study reports hitherto unreported figures of problem gaming and gambling. Our results show the importance of screening children and adolescents for these conditions when admitting subjects to CAP in/outpatient care.

Keywords

Gambling disorder problem gambling internet gaming disorder problem gaming

Introduction

Gaming Disorder (GD) was introduced in the 11th revision of the International Classification of Diseases (ICD 11), defined as a gaming behaviour in sufficient severity to consequence significant impairment in areas of function.¹ The Diagnostic and Statistical Manual of Mental Disorders (DSM-5) identifies Internet Gaming Disorder (IGD) as a ‘condition for further studies’, hence additional clinical experience and research is needed before inclusion as a formal disorder.² The diagnostic criteria for IGD suggested in DSM-5 includes (I) preoccupation with gaming, (II) withdrawal symptoms, (III) increased tolerance to gaming, (IV) unsuccessful attempts to reduce or stop gaming, (V) loss of interest in other hobbies/activities, (VI) continued excessive gaming despite negative consequences, (VII) deceiving others regarding the amount of gaming; (VIII) use of gaming to escape or relieve negative moods and (IX) jeopardizing or losing a significant relationship, job, education or career opportunity because of gaming.²

A meta-analysis across three decades shows a world-wide prevalence of IGD of 1.3%–6.8%.³ Stevens et al.⁴ reported, in a meta-analysis, a prevalence of pathological gaming worldwide of 3.05%. Vadlin et al.⁵ showed that risk factors for problematic computer gaming were male gender, ADHD and depression/anxiety. Problematic computer gaming is reported from child and adolescent psychiatry and school health care.⁶ The clinical picture describes compulsion, psychiatric and physical symptoms and impaired school performance.^3,6 In Király et al.⁶ highlighted the interplay of three key factors in IGD development: structural aspects of computer games, psychological characteristics of the player and motivational aspects of computer game playing.

Gambling disorder is a well-established diagnosis in the diagnostic manual DSM-5.² Vadlin et al.⁵ reported on an association between problematic computer gaming and problematic gambling with money among young individuals. They also showed that problematic computer gaming with money did not predict problematic gambling in adulthood.⁶ Karlsson et al.⁷ found an association between gambling and problematic computer games without money and excessive internet use in adults, but in a non-longitudinal design. Both studies are small and more research is needed. Risk factors for gambling have been shown to be male gender, age (adolescents or young adults), single people and those born outside Sweden.⁸ Thus, even for gambling, there is a possible connection to young people’s online behaviour.

In child and adolescent psychiatry, it has been noted that patients with certain neuropsychiatric disabilities; attention deficit and hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are overrepresented among those who seek treatment for problem gaming.^5,9 It is known that depression, anxiety and ADHD in child and adolescent populations are associated with IGD.^10–12 Studies regarding psychiatric comorbidity among problem gambling adolescents have been published but lack sufficient data from clinical settings for children and adolescents with neuropsychiatric comorbidities.^13,14 Based on the design of the games with repetitions and immediate reinforcement, it can be suspected that patients with ADHD/ASD have an increased risk of developing problem gambling.^3,10

The range of digital games has increased in recent years and is easily accessible to a large part of the young Swedish population. These games are largely aimed at children, adolescents and young adults. The systematic knowledge about prevalence of behavioural addictions in child and adolescent psychiatry is sparse.

The aim of the study was to look at the prevalence of gaming and gambling in patients at a child and adolescent psychiatry department and to correlate these conditions to each other but also to psychiatric diagnosis, as well as gender, age, type of care and housing situation.

Design and methods

The study was performed in Skåne, a county in the south of Sweden with 1.36 million inhabitants, of which 280,000 are individuals under 18 years of age. In 2018 CAP Skåne had 55,000 unique visits. There are seven out-patient child and adolescent psychiatry units in Skåne and one in-patient unit. The out-patient units cater for all types of child and adolescent diagnoses but have no assignment to either diagnose or treat addiction problems. Addiction among children and adolescents in Skåne is treated at special units that are collaborations between psychiatry and social services.

In order to digitalize screening instruments and make administration easier, the Child and Adolescent Psychiatry clinic in Skåne developed an app, ‘Blå appen’.¹⁵ The answers form the basis for diagnosis and further treatment. In the present study, patients coming to the Child and Adolescent Psychiatry clinic (in- and out-patient departments respectively) in Skåne during the study period of 4 months (Feb–May) were asked to participate. We used the NODS-CLiP when screening for gambling¹⁶ and GASA when screening for IGD.¹⁷ Total time required for both forms: 15 min. The following variables were obtained from subjects in the study: NODS-CLiP, GASA, gender, age, housing situation (with whom you live), type of care given at CAP (in-/out-patient care) and diagnosis at CAP.

The study was approved by the Ethics committee (Dnr: 2019-02967). Written informed consent was obtained from all participants and their parents/guardians. The study was performed between Feb 2020 and May 2020.

Measures

Game Addiction Scale

There is no consensus regarding which rating scales should be used for assessment of gaming behaviour and different scales are used both in research and in clinical practice. GASA (Game Addiction Scale for Adolescents) is one of the most frequently used questionnaires for gaming addiction.^17–19 The scale was constructed by Lemmens et al.¹⁷ based on the DSM-5 criteria for pathological gambling. For details see Supplemental Table S1. While the DSM-5 requires half (or more) of their criteria to be met when diagnosing pathological gamblers, scholars within the field of gaming suggest a ranking of the criteria. They describe how the criteria tolerance, mood modification and cognitive salience rather associate to engagement and not necessarily to addiction, while the contrary applies for the criteria withdrawal, relapse, conflict and problems; the ‘core approach’.^18–20

The 7 item GASA applies to gaming behaviour during the last 6 months. Each question covers one criterion (salience, tolerance, mood modification, withdrawal, relapse, conflicts or problems), answered on a five-point scale from 1 = never to 5 = very often. An item should according to Lemmens et al.¹⁷ be considered endorsed when rated 3 or higher.

Aiming to distinguish level of severity within the group of gamers, the core approach was applied whereby individuals meeting all of the core criteria (relapse, withdrawal, conflicts and problems) constituted the group addicted gamers.^18,20 The respondents that endorsed two–three of the core criteria were grouped as problem gamers and those that endorsed all three of the peripheral criteria but not more than one of the core criteria were grouped as engaged gamers,^18,20 items are specified in Supplemental Table S1. Those who remained comprised the fourth and contrasting group, hereafter named remaining study participants (RSP). The RSP group included individuals without gaming behaviour and individuals with gaming behaviour below the cut-off for engaged gaming.

Since both the problem gamers and the addicted gamers were assumed to be associated with more severe gaming behaviour, as well as more negative outcomes, these two groups also constituted one combined group (two–four endorsed core criteria) enabling analyses against the rest of the respondents (=fewer than two endorsed core criteria).

NODS-CLiP

In 1999 Gerstein et al.²¹ developed a 17-item screen instrument for the US national epidemiological and policy study regarding gambling problems – The NORC Diagnostic Screen for Gambling Problems (NODS). NODS has been used in research worldwide.^7,16,21,22 NODS yield a score ranging from 0 to 10, corresponding to the DSM-IV criteria for gambling, where a score of 5 or more qualifies as pathological.² A score of 3–4 corresponds to the subclinical syndrome of problem gambling, and scores of 1–2 an ‘at-risk’ status, with increased likelihood of progression to problem or pathological status.

NODS-CLiP comprises three NODS items that best describe problem gambling, with three NODS questions pertaining to loss of Control, Lying and Preoccupation – the ‘CLiP’.^16,22 The NODS-CLiP items are listed in Supplemental Table S2.

NODS-CLiP requires 1 min to administer and identifies virtually all pathological gamblers and most (90%) problem gamblers captured by the complete NODS.¹⁶ The NODS-CLiP shows excellent sensitivity and specificity for NODS constructs.¹⁶ Answering ‘yes’ on one or more items indicates problem gambling.

Severity measures for correlation analyses

In order to capture the addiction severity for correlation and regression analyses a one factor analysis was performed. The analysis showed reliability factor scores for the two measurements used, reliability coefficient for GASA = 0.90 and NODS-CLiP = 0.65. The f-score values were transformed into a T-score scale with mean 100 and sd 50 (for details see Supplemental Material).

Data preparation

Estimates of frequencies and percentages as well as statistical analysis were performed in SPSS (IBM SPSS statistics version 27).

The demographics and diagnoses were all recoded into binary variables, as shown in Table 1 (details are presented in the Supplemental Material). Each of the diagnoses constituted one binary variable in which the diagnosis listed as either primary or secondary were coded as 1 against the absence of the same diagnose, coded as 0. The diagnoses containing the smallest number of individuals (Eating disorder, OCD, Bipolar disease, Psychosis) were further merged into a new variable labelled ‘other diagnoses’.

Table 1.

Sample characteristics.

	Code	n	(n)	%	(%)
Gender
Female	0	68		49.3
Male	1	70		50.7
Age, years
8–12	0	28		20.3
13–18	1	110		79.7
Type of care
Outpatient care	0	122		88.4
Inpatient care	1	16		11.6
Housing situation
Living with both parents	1	82		59.4
Divorced parents	0	49		35.5
Foster care	0	7		5.1
Diagnosis^°
ADHD	1(/0)	49	(58)	35.5	(42.0)
Depression	1(/0)	42	(52)	30.4	(37.7)
ASD	1(/0)	15	(19)	10.9	(13.8)
Anxiety	1(/0)	15	(29)	10.9	(21.0)
Other diagnosis^#	1(/0)	17	(19)	12.3	(13.8)
Eating disorder		9	(10)	6.5	(7.2)
OCD		4	(4)	2.9	(2.9)
Bipolar disease		3	(4)	2.2	(2.9)
Psychosis		1	(1)	0.7	(0.7)
Social phobia		0	(1)	0.0	(0.7)
Gaming behaviour
Engaged gamers	1(/0)	5		3.6
Problem gamers	1(/0)	31		22.5
Addicted gamers	1(/0)	14		10.1
RSP^§	1(/0)	87		63.0
Missing		1		0.7
Problem gambling
Yes	1	15		10.9
No	0	122		88.4
Missing		1		0.7

Diagnosis listed as primary without parentheses, diagnosis listed as either primary or secondary in parentheses.

Including diagnoses listed below in italics: Eating disorder, OCD, Bipolar disease and Psychosis, Social phobia.

Remaining Study Participants – Non-problem-, Non-addictive-, Non-engaged gamers.

Four gaming categories were created, ‘engaged gamers’ (endorsed the peripheral 3 GASA items and not more than core item), ‘problem gamers’ (endorsing 2 or 3 core items) and ‘addicted gamers’ (endorsed all 4 core items). The problem gamers and addicted gamers were merged into a fourth gaming category of ‘problem-/addictive gamers’. Those that endorsed at least 1 out of the NODS-CliP items constituted the group of ‘problem gamblers’.

Data analysis

For the prevalence part of the study, Fisher’s exact test was used for statistical association analysis between the prevalence of problem-/addictive gamers and gamblers and each of the subcategories in the 0/1 variables such as gender, age categories, housing situation, type of care and diagnoses. For the correlation part of the study, the GASA T-score and the NODS-CLiP T-score were used in the regression analyses as dependent variables. The GASA T-score and the NODS-CLiP T-score was correlation tested against each other and analysed separately for reliability.

The GASA T-score was used as the dependent variable in a regression model analysis with age, type of care, housing situation and each of the diagnoses as independent variables, using depression as reference group (for details see Supplemental Table S6). As the NODS-CLiP T-score variable showed a skewed distribution with only 15 observations with 0/1 item above 0, the number of parameters that could be used in the regression analysis were limited.²³ The prevalence analysis formed the basis for choosing variables to use in the gender divided regression analysis. Age 13 or older, inpatient care and ADHD showed the highest prevalence measures regarding both problem-/addictive gaming and problem gambling, with the exception of the subgroup other diagnosis. A linear regression analysis with GASA T-score and NODS-CLiP T-score as dependent variables was done separately, using age, type of care and ADHD as independent variables in both analyses.

Results

Sample characteristics

The survey was answered by 144 children and adolescents between 8 and 18 years of age. Six individuals participated without sharing social security number which made the collecting of other information (gender, age, housing situation, type of care, diagnosis) impossible. These individuals were excluded from the data file leaving 138 remaining individuals, characteristics specified in Table 1. One individual abstained from answering the GASA-item and one other individual did not answer the NODS-CLiP items, declared missing in the analyses. The gender distribution was even, and a majority were older than 13 years. We got information about the participants housing situation, type of care given and diagnoses from the medical chart (Table 1). The participant’s main as well as secondary diagnosis was registered. The diagnoses were referred to as the Manual of Mental Disorders, 5th edition, describes them.² All patients were assessed in clinical settings by trained psychologists and child and adolescent psychiatrists.

The respondents who endorsed all four core criteria and consequently met the addiction cut-off constituted 10% of the study population. The problem gamers were 23%, the engaged gamers 4%. The respondents who met the cut-off for problem gaming and addictive gaming created a new group named problem-/addictive gamers, comprising 33% of the study population. The remaining 63% comprised the RSP group. A significant majority of both the problem and addictive gamers were male, when compared with the non-problem/addictive gamers (RSP group and engaged gamers). Among the male respondents, 48% were non-problem gamers (RSP group and engaged gamers) and among the female respondents this increased to 87% (n = 33 vs n = 59, p ≤ 0.001). Among the male respondents, 52% were problem-/addictive gamers whereas 13% of the female respondents met the cut-off for at least problem gaming (n = 36 vs n = 9, p ≤ 0.001), for details see Supplemental Tables S4 and S5. The problem gamblers constituted 10%–15% male respondents and 7% female (n = 10 vs n = 5, p = 0.274) (Table 2).

Table 2.

Prevalence of problem-/addictive gamers and problem gamblers among boys versus girls. RSP (Remaining Study Participants) and engaged gamers and non-problem gamblers were set as the reference categories for c² comparisons.

	Boys % (n)	Girls % (n)	p-value
Problem-/addictive gaming	52.2 (36)	13.2 (9)	≤0.001
Problem gambling	14.5 (10)	7.4 (5)	0.274

Problem/addictive gaming

The prevalence of problem/addictive gamers was counted for within subcategories and compared with the prevalence of non-problem/addictive gamers (RSP group and engaged gamers), as Table 3 shows. The prevalence of problem/addictive gaming was significantly overrepresented among individuals diagnosed with ADHD (44%, n = 25, p = 0.027). The regression analysis showed the same tendency but not consistently. The analysis was performed as a model analysis, adding variables step by step and as shown in Supplemental Table S6, ADHD appeared as a significant risk factor in the first two steps. When the background variables (age and gender) were added, the significant association with ADHD disappeared. In the final step, gender appeared as the dominating risk factor for severe gaming. Table 4 shows the gender divided regression analysis presenting a positive association between ADHD and GASA score for both boys and girls. Being 13 years of age or older regardless of gender was also positively associated to severe gaming.

Table 3.

Prevalence of problem-/addictive gaming versus non-problem-/addictive gaming within subgroups, gender divided and entire sample. Fisher’s exact test for x² comparisons of the prevalence of problem behaviour versus non-problem behaviour within subgroups of entire sample (male and female respondents).

	Problem-/addictive gaming				Problem gambling
	Girls	Boys	Entire sample % (n)	p-value^°	Girls	Boys	Entire sample % (n)	p-value ^#
Total	13.2 (9)	51.4 (36)	32.6 (45)		7.4 (5)	14.5 (10)	10.9 (15)
Age 13 or older	12.9 (8)	53.2 (25)	30.3 (33)	0.260	8.1 (5)	17.0 (8)	11.9 (13)	0.736
Cohabiting parents	7.9 (3)	51.4 (18)	28.8 (21)	0.362	2.6 (1)	14.3 (5)	8.2 (6)	0.290
Hospitalized	14.3 (1)	55.6 (5)	37.5 (6)	0.778	28.6 (2)	22.2 (2)	25.0 (4)	0.077
ADHD^§	26.3 (5)	52.6 (20)	43.9 (25)	0.027	5.3 (1)	15.8 (6)	12.3 (7)	0.783
Depression^§	13.2 (9)	47.8 (11)	25.0 (13)	0.138	6.9 (2)	17.4 (4)	11.5 (6)	1.000
ASD^§	–	54.5 (6)	31.6 (6)	1.000	–	9.1 (1)	5.3 (1)	0.694
Anxiety^§	11.1 (2)	45.5 (5)	24.1 (7)	0.373	–	27.3 (3)	10.3 (3)	1.000
Other diagnosis^§	13.3 (2)	75.0 (3)	26.3 (5)	0.606	20.0 (3)	50.0 (2)	26.3 (5)	0.037

Prevalence of problem-/addictive gaming versus non-problem gaming (RSP-group and engaged gamers) within subgroups (Yes or No).

Prevalence of problem gambling versus non-problem gambling within subgroups (Yes or No).

Diagnosis listed as primary or secondary.

Table 4.

Linear regression. Dependent variable GASA T-score.

Gender		Unstandardized coefficients		Standardized coefficients
		B	Standard error	Beta	t	Sig.
Female	Thirteen or older	61.098	5.268	0.723	11.598	≤0.001
	Inpatient care	7.717	14.369	0.031	0.537	0.593
	ADHD	46.547	8.997	0.305	5.174	≤0.001
Male	Thirteen or older	93.838	13.676	0.572	6.861	≤0.001
	Inpatient care	22.038	27.043	0.059	0.815	0.418
	ADHD	67.723	13.860	0.371	4.886	≤0.001

Problem gambling

The prevalence of problem gamblers was likewise counted for within the subcategories and compared with the prevalence of non-problem gamblers. As Table 3 shows, problem gambling was shown to be significantly overrepresented among the merged subgroup of other diagnoses (26%, N = 5, p = 0.037). Specifically, two individuals with eating disorder, two individuals with OCD and one individual with psychosis met the cut off for problem gambling. Table 5 shows the gender divided regression analysis presenting a positive association between severe gambling and ADHD as well as being 13 years of age, regardless of gender.

Table 5.

Linear regression. Dependent variable CLiP T-score.

Gender		Unstandardized coefficients		Standardized coefficients
		B	Standard error	Beta	t	Sig.
Female	Thirteen or older	83.717	4.900	0.838	17.086	≤0.001
	Inpatient care	23.527	13.364	0.079	1.760	0.083
	ADHD	27.666	8.368	0.153	3.306	0.002
Male	Thirteen or older	77.654	13.832	0.510	5.614	≤0.001
	Inpatient care	39.356	27.351	0.113	1.439	0.155
	ADHD	63.178	14.018	0.373	4.507	≤0.001

GASA T-score and CLiP T-score

The correlation analysis between GASA T-score and CLiP T-score showed a significant correlation of 0.291 (p ≤ 0.001). The reliability test showed that the GASA T-score reliability coefficient was 0.90 and the reliability coefficient of the CLiP T-score was 0.65 (see Supplemental Table S3).

Discussion

The present study is to our knowledge the first Swedish study exploring the prevalence of gaming and gambling in a CAP cohort. This study contributes to the understanding of pathological gaming and gambling. Research in this field is scarce and inconsistent in terms of measurement approach and attitudes towards tentative diagnosis. The Manual of Mental Disorders (DSM-5) identifies IGD as a condition requiring further clinical experience and research before inclusion as a formal disorder.² This study presents a prevalence measure of gaming and gambling in a CAP cohort and explores the behaviours in relation to gender, type of care given, housing status and diagnosis.

In our study, male gender was significantly associated with problem/addictive gaming. This has been shown in several other studies where male predominance is a well-known feature of IGD, with a reported male to female ratio of 2.5:1.^3,4 The explanation for the male predominance in IGD is unknown and deserves further exploration. Karlsson et al.⁷ hypthezied on three key factors in IGD development; structural aspects of computer games, psychological characteristics of the player and motivational aspects. Massively Multiplayer Online Role-Playing Games (MMORPGs), have been found to have an addictive potential because of their specific structural characteristics and progression of social interactions and grouping in guilds (a party or raid).^24–26 Lately the Multiplayer Online Battle Arena (MOBA) video games have become the most popular type played worldwide.²⁶ MMORPGs and MOBAs feature similar characteristics of advancement and social interactions.²⁷ Both MMORPGs and MOBAs attract mostly males, compared to story-driven games or constructive games, which attract mostly females . Further, 61% of female MMORPG players played with a romantic partner compared with 24% of men.²⁸ The motives to engage in gaming also appears to differ between genders. Females have been reported that they want to complete challenges or immerse themselves in other worlds, while men give as a main reason for gaming the opportunity to compete or destroy things.^28,29

In a review of 24 studies González-Bueso et al.¹⁴ discuss the relationship between IGD and comorbid conditions. Of the 24 articles, 10 debated the circumstances for children and adolescents (N = 36,124). A high correlation between IGD and depression was reported as well as a correlation between IGD and ADHD.¹⁴ ADHD is a widespread and impairing childhood neurodevelopmental disorder and it is recognized as one of the most common in childhood.²³ The condition is heterogenous with persistent symptoms of hyperactivity, inattention and impulsiveness that impair functioning in multiple settings.² The DSM-5 lists ADHD as a comorbidity of IGD.² The relation can possibly be partly explained by the attention difficulties and impulsivity that individuals with ADHD present. The design and content of the games meets their need for immediate reinforcement when they play^30,31 Accordingly, it is possibly easier to concentrate on a computer game than learning in the classroom at school. Another conceivable explanation for the association between ADHD and pathological gaming is to be found in the dopamine system. There is consensus that an underlying dysfunctions in ADHD can be found in the dopamine system.³² As early as 1998 Koepp et al.³³ reported evidence for high dopamine release during computer gaming. The high rate of IGD in adolescent and young adults with ADHD may reflect a tendency for these individuals to use the game to ‘self-medicate’ deficits in dopamine function. Han et al.³⁴ treated gaming addiction successfully with 8 weeks of methylphenidate, the drug of choice for ADHD; this also speaks in favour of the relationship between ADHD and pathological gaming.

Autism spectrum disorder (ASD) is an impairing and heterogeneous neurodevelopmental disorder with an early onset.^1,2 ASD is characterized by social impairments, communication difficulties, altered sensory processing and repetitive and restricted behaviours.^1,2 Studies have shown possible social gains for online gamers, such as decreased feelings of loneliness, increased feelings of connectedness to friends, increased social capital between players and increased social bridging between players.²⁴ Children with ASD tend to show restricted and repetitive behaviours, interests or activities and if these include gaming they have a potentially higher risk of developing IGD.^1,2 Because of previous research^9,31 we expected a higher prevalence of gaming and/or gambling in the ASD group but we did not find such a relationship. In our study, the numbers of participants with ASD are too small (14%, n = 19) to draw any conclusions.

In our material 15 out of 138 respondents (10.9%, 10 boys and 5 girls) answered affirmative to items on gambling. Games with or without money constitute adjacent phenomena in the sense that money elements, such as so-called loot boxes, are common in computer games or through more computer-game-like virtual environments where games about money take place. One possibility could be that the participants meant games containing such money elements when endorsing items on gambling in the questionnaire.

In the present study gambling was shown to be significantly overrepresented among patients recruited through inpatient care and among individuals within the constructed group other diagnoses. Specifically, two cases of problem gambling were found among individuals with eating disorder (20%), two cases among individuals with OCD (50%) and in one individual with psychosis (100%).

The research on potential relationship between psychosis and gambling is scarce at best but a disproportionate prevalence of psychosis among problem gamblers have been reported.³⁵ Also, previous research suggests an association between compulsivity and behavioural addiction and groups of disordered gamblers have been showed to score high on measures of compulsivity.³⁶ Patients with eating disorders exhibit obsessive-compulsive traits and research describe an overlap with OCD-related conditions.³⁷ The numbers if individuals with psychosis, OCD and eating disorder included in this study is however too small for conclusions to be drawn. One could also speculate about whether the fact that 25% (n = 4) of the patients recruited through inpatient care met the cut off for problem gambling could be a representation of a more severe morbidity related to gambling among children and adolescents. But then again, the sample size was too small for conclusions to be drawn. Also, the gender divided regression analysis showed no association between high NODS-CLiP score and type of care. In the regression analysis, being 13 years of age or older and diagnosed with ADHD appeared as risk factors for problem gambling, this is in line with previous research showing adult individuals with problem gambling as more than four times more likely to have ADHD than controls.³⁸

To our knowledge no previous study has investigated how problem gambling relate to age among children. Possibly, money elements are more common in games preferred by older individuals. However, as gambling is allowed only for adults by Swedish law our findings should be interpreted with great caution.³⁹

The correlation analysis showed that the GASA T-score and the CLiP T-score was significant but moderately associated (Correlation coefficient 0.29, p-value 0.001). Possibly this could be interpreted as a finding in line with previous research, reporting problem gambling and problem gaming as associated^5,7 The fact that the association was so low could be further interpreted as a sign that the measurements used actually managed to capture two separate behaviours, despite the reality that gambling is illegal for children in Sweden.³⁹

Compatible with results from previous research the reliability test showed that GASA exhibited excellent internal consistency (Cronbach’s α 0.9).^18,40 The reliability coefficient of NODS-CLiP was 0.65 which ultimately is pleasing considering the fact that the items reply to 0/1 responses but also with regard to the sample characteristics, children are prohibited from gambling by Swedish law³⁹ and the NODS-CLiP is to the best of our knowledge previously mainly used in adult populations.^7,16,21

The present study has several limitations. One obvious limitation is the sample size, the limited numbers of participants could result in higher variability which affects the reliability of our results.⁴⁰ The sample size partly resulted in custom handling of the data. As the expected frequencies were below five in some cells, Fisher’s exact test was used.⁴⁰ Since the individuals diagnosed with eating disorder, OCD, bipolar disease, psychosis and social phobia were so few in numbers, they were merged into a new group named other diagnosis. This grouping had no empirical or theoretical basis which undoubtedly complicates interpretation of a potential correlate, therefor this subgroup was omitted from further analysis. Another limitation is that the IGD criteria were acquired using a self-assessment questionnaire (GASA),¹⁷ rather than by a standardized structured clinical interview, which would have allowed a more accurate assessment of the DSM-5 diagnostic criteria. However, the use of questionnaires is widespread in psychiatric research including prevalence studies on IGD.^3,4,17 NODS-CLiP is a valuable screening tool for identifying gambling disorders.^16,22 But the instrument is designed to classify just that, not patients at risk of developing problem gambling which would have been valuable particularly in screening children and adolescents. One other limitation is that the NODS-CLiP is previosly mainly examined in an adult population^8,16 and the applicability to a child and adolescent sample is unexplored. However, the reliability coefficient was acceptable and could possibly have been further improved by expanding the Yes/No answer options to continuum scales.⁴⁰ The cross-sectional design of this study does not permit conclusions to be drawn regarding causation; this would require longitudinal investigation. Further, the measures used for this study were based on self-reporting, which implies a risk for recall bias. The sample size is too small to draw any definite conclusions regarding the potential correlates or even lack of correlates. However, the prevalence is notable.

Future research should consider examining differences between the prevalence of IGD and its comorbidities for inpatients, non-inpatients and non-treatment seeking adolescents, respectively. Despite the huge interest in gaming and gambling disorder in both popular science and more clinical and scientific contexts, there is a considerable lack of prevalence studies, especially on the youth population. To our knowledge our study is the first of its kind and provides a unique prevalence measure of problem/addictive gaming as well as problem gambling within different settings in a CAP unit.

Conclusion

Problem/addictive gaming is a common concern among patients seeking treatment at CAP. The main characteristic is male gender and ADHD diagnosis. Gambling should also be considered when assessing children seeking treatment at CAP units.

Supplemental Material

sj-docx-1-phj-10.1177_22799036221104160 – Supplemental material for The prevalence of gaming and gambling in a child and adolescent psychiatry unit

Supplemental material, sj-docx-1-phj-10.1177_22799036221104160 for The prevalence of gaming and gambling in a child and adolescent psychiatry unit by Frida André, Anders Håkansson, Björn Axel Johansson and Emma Claesdotter-Knutsson in Journal of Public Health Research

Footnotes

Author contributions

All authors have contributed significantly and agree with the content of the manuscript.

Declaration of conflicting interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Anders Håkansson has a position as a researcher in addiction medicine at Lund University which is sponsored by the Swedish state-owned gambling operator AB Svenska Spel, as part of AB Svenska Spel’s responsible gambling policies. He also has research funding from the research council of AB Svenska Spel, and from the research council of the Swedish alcohol monopoly (Systembolaget AB) and from the Swedish Sports Federation (Riksidrottsförbundet). All authors have funding from the research council of AB Svenska Spel. None of these organization have been involved in or had any influence on any part of the present work.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by the Svenska Spel Research Council, Fanny Ekdahls Foundation, FoU Regional funds of Region Skane, Craaford foundation and Sigurd and Elsa Goljes Memorial Fund.

Ethics approval and consent to participate

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The study was approved by the Ethics committee (Dnr: 2019-02967).

Patient consent for publication

Written informed consent was obtained from all participants and their parents/guardians.

Informed consent

Written informed consent was obtained from a legally authorized representative for anonymized patient information to be published in this article.

Significance for public health

Internet gaming is a highly common recreational behaviour, mainly without negative consequences. However, most research agree on a pathological potential in gaming and the DSM-5 included Internet Gaming Disorder as a tentative diagnosis and inquires for additional research. Gambling is an established diagnosis and Swedish epidemiological data show that 1.3% of the population meet the criteria for gambling disorder. Both problem gaming and gambling are associated with compulsion, psychiatric and physical symptoms, impaired cognitive development, and school performance. Gaming and gambling are frequently reported from child and adolescent psychiatry and school health care, even though gambling is illegal for children in Sweden. Based on the limited knowledge and the need for more research into these behaviours among young individuals, the present study aimed to look at the prevalence of gaming and gambling in patients at the child and adolescent psychiatry department (CAP).

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Supplemental material

Supplemental material for this article is available online.

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Supplementary Material

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