Adolescents' Game User Type Based on Adaptive and Maladaptive Game Use

Abstract

Objective:

This study classified adolescents into specific game user types based on their adaptive and maladaptive game use and then examined the differences in mental health, academic achievement, and quality of life according to game user type.

Materials and Methods:

This study performed a secondary analysis of data from the ninth analysis of the “Game User Panel” data published by the Korean Creative Content Agency. In addition, an analysis of variance with a post hoc Tukey test was conducted to examine the differences in mental health, academic achievement, and quality of life according to game-use type. This was a retrospective study using secondary deidentified data.

Results:

Among the total respondents, 39.5% of adolescents were classified as general game users (GGUs), 11.3% as adaptive game users (AGUs), 11.2% as maladaptive game users (MGUs), and 38.0% as twofold game users (TGUs). GGUs had the lowest scores for depression and anxiety, followed by higher scores in AGUs, TGUs, and MGUs. In addition, GGUs scored higher on quality of life than the other groups while the AGUs had higher scores on academic achievement than other groups.

Conclusion:

Adolescents experience both adaptive and maladaptive use, and experiencing only adaptive use without maladaptive use has been shown to be relatively infrequent. Therefore, education about online game use for adolescents should not be uniformly provided given the psychological characteristics of each group; instead, it should be customized based on game user type.

Introduction

Online gaming is associated with both negative and positive outcomes, with psychology research mainly focused on the negative aspects of online games, such as addiction. Studies on game addiction mainly measure long-term use, withdrawal, obsession, loss of control, and continuous use.¹ At present, games occupy an important role in the leisure of adolescents and has become a main channel of interaction for people of all ages, from children to adults. Therefore, games are now a natural occurrence in everyday life and are perceived as multidimensional phenomena that have various positive and negative effects.² In addition to the problematic use of online games, various perspectives on the usefulness of gaming have been presented. Since games can have both positive and negative effects on the user, it is also suggested that the dichotomy of problem patterns and positive use in online gaming makes it difficult to fully understand the result of gaming activity.³

Studies highlighting the negative aspects of games report that game addiction generally refers to a compulsory or excessive use of the game that causes unhealthy behavior (e.g., physical and mental problems) or negative consequences (e.g., academic and professional problems).⁴ Adolescents can experience satisfaction, enjoyment, and achievement through the internet and games; however, those who play games for a long time are likely to display depressive symptoms, anxiety, aggressive behavior, impulsivity, feelings of guilt, lethargy, and emotional problems.⁵ Shaffer et al.⁶ believe that symptoms of behavioral addiction, such as morbid gambling and internet addiction, are similar to the existing substance-dependent symptoms. People who are addicted to games have low daily satisfaction,⁷ low self-esteem,⁸ anxiety in social relationships,⁹ depression,¹⁰ and a tendency toward loneliness.¹¹

The adaptive use of games can be analyzed from the “use and gratification perspective” of leisure media based on information and communication technology.^11,12 The use and gratification perspective suggests that there are various positive psychology reasons and behaviors for why users seek or experience adaptive use of mobile games. Previous studies on adaptive game users (AGUs) report that fun and entertainment, rewards, spending time, relieving stress, improving learning ability, satisfying the need to expand social relations, social interaction, and flow are the result of adaptive use of games.^13,14 Studies highlighting the positive aspects of games report findings that the internet and games reduce poor mental health symptoms and consequently have a positive impact on mental health.^15–18

Mental health can be defined as the absence of mental disease or as a state of being that also includes the biological, psychological, or social factors that contribute to an individual's mental state and ability to function within the environment.¹⁹ Mental health includes our emotional, psychological, and social well-being. It affects how we think, feel, and act, and helps determine how we handle stress, relate to others, and make healthy choices.²⁰ A study that systematically reviewed videogames for emotion regulation revealed that they have a positive effect on emotional regulation, and appropriate use improves mental health and psychological function.²¹

Furthermore, a study among adolescents found an indirect association between strategic game play and academic grades; that is, strategic game play predicted higher self-reported problem-solving skills, and, in turn, higher self-reported problem-solving skills predicted higher academic grades.²² Researchers have also found a relationship between playing videogames and persistence when solving problems. Specifically, undergraduate participants who were frequent game players spent a significantly longer amount of time working on unsolved anagrams and riddles compared with infrequent players. The study concluded that persistence during game play may be applied to other forms of problem solving.²³

Another study suggested that students with high academic achievement spend more time playing videogames; indeed, almost as much time as they spend on learning activities while still earning high academic grades.²⁴

Finally, it is important to identify the relationship between playing computer games and quality of life and the extent to which computer games affect the life of adolescents. The World Health Organization defines quality of life as “an individual's perception of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards and concerns.”²⁵ Standard indicators of quality of life include wealth, employment, the environment, physical and mental health, education, recreation and leisure time, social belonging, religious beliefs, safety, security, and freedom.^26–28

A previous study found that adolescents' emotional involvement, enjoyment, and sensory experiences with games positively influenced their quality of life.²⁹ Moreover, a systematic review focused on an adult population report that playing videogames not only improves quality of life, but also improves cognitive and emotional skills.³⁰

The purpose of this study was to compare the mental health, academic achievement, and quality of life of adolescents based on game user type. This study presupposes that game adaptation and maladaptation can coexist with users based on the assumption that the positive and negative consequences of game use are independent.² As such, an approach that includes both positive and negative consequences of game use has the advantage to potentially intervene in groups that use games ineffectively and to increase personal leisure through adaptive use.

In addition, the classification of adaptive and maladaptive use can provide a personalized and effective intervention strategy by detailing psychological and behavioral characteristics that are uniquely expressed or relatively superior to each group.

The research questions to achieve the purpose of this study were as follows: (1) How are adolescent game users classified according to the adaptive and maladaptive use of online games? (2) Are there differences in mental health based on game user type? (3) Are there differences in academic achievement and quality of life based on game user type?

Materials and Methods

Data

This study performed a secondary analysis of data from the ninth analysis of the “Game User Panel” data published by the Korean Creative Content Agency. It includes demographic characteristics, game-use characteristics (e.g., hours of use, machine of use), game behavior types (e.g., adaptive or maladaptive game use), emotional states, academic achievement, and well-being of game users.

We used a stratified rate group sampling method that uses ratio sampling of regional schools after the sample school number is determined to draw a sample from these data. The 778 selected subjects were proportionally assigned, based on region and age, to the 13-, 16-, and 19-year-old group. General demographic characteristics are shown in Table 1.

Table 1.

Demographic Characteristics of the Study Subjects

Characteristics		n (%)
Gender
Male		381 (49.0)
Female		397 (51.0)
Age
13		287 (36.9)
16		272 (35.0)
19		218 (28.1)
Household income per month (unit = KRW^a)
Less than KRW 200		22 (3.5)
KRW 201–400		132 (21.1)
KRW 401–600		262 (41.9)
KRW 601–800		134 (21.4)
KRW 801 and more		76 (12.1)
	Range	Mean (SD)
Time spent gaming (unit = minute/day)	0–480	68.88 (79.89)
Loneliness	1–4	1.62 (0.63)
Depression	1–4	1.36 (0.47)
Anxiety	1–4	1.48 (0.61)
Aggression	1–5	1.99 (0.74)
Academic achievement	1–6	4.25 (1.18)
Quality of life	1–7	3.50 (1.07)

KRW1000 = USD 0.88.

Measurement

The Comprehensive Scale for Assessing Game Behavior (CSG) considers both adaptive and maladaptive aspects of games and was adopted to distinguish game usage types.³¹ The CSG has two subscales: the Adaptive Game Use Scale (AGUS) and the Maladaptive Game Use Scale (MGUS). The items of the AGUS and MGUS were rated on a scale of 0 (never) to 3 (always) with a total score calculated by summing the ratings given for each question.

Loneliness, depression, anxiety, and aggression are factors strongly associated with game use among the mental health characteristics examined in previous studies and were used to compare the mental health of adolescents based on game user type.^5,9,13,18

Loneliness was measured using the UCLA Loneliness scale,³² depression with the Center for Epidemiological Studies-Depression Scale (CES-D),³³ and anxiety with the Generalized Anxiety Disorder scale (The GAD-7).³⁴ Cronbach's α for the above scales was 0.804, 0.894, and 0.939, respectively. Items for the above scales were rated on a scale from 1 (never) to 4 (always), and total scores calculated by summing the ratings.

Aggression was measured using the Short-Form Buss-Perry Aggression Questionnaire (BPAQ-SF)³⁵ with a Cronbach's α = 0.927. Items were rated on a 5-point scale ranging from 1 (nothing at all) to 5 (always). Academic achievements were rated between 1 and 6 based on students' academic performance ranking in their respective academic school year. Students were given a rating of 1 if they ranked in the top 91%–100%, 2 = in the top 71%–90%, 3 = in the top 51%–70%, 4 = in the top 31%–50%, 5 = in the top 11%–30%, and 6 = within the top 10%; a higher rating reflects higher academic achievement.

Quality of life was measured with five items on a 7-point scale using the Subjective Satisfaction Scale.³⁶ Item ratings ranged from 1 (nothing at all) to 7 (very), with Cronbach's α = 0.932.

Analysis

This research classified game user types based on adaptive and maladaptive game use. Specifically, by using the median scores of adaptive and maladaptive uses (adaptive use = 1.381, maladaptive use = 2.150), this research categorized subjects into a highly adaptive use (≥1.381), a low adaptive use (<1.381), a highly maladaptive use (≥2.150), and a low maladaptive use group (<2.150). It further combined the low adaptive and low maladaptive use group into a General Game User (GGU) group, the high adaptive but low maladaptive use group into the AGU group, the low adaptive but high maladaptive use group into the Maladaptive Game User (MGU) group, and the high adaptive and high maladaptive use group into the Twofold Game User (TGU) group.

Analysis of variance with a post hoc Tukey's test was conducted using SPSS 24.0 to examine the differences in mental health (e.g., loneliness, depression, anxiety, and aggression), academic achievement, and quality of life according to game-use type. Tukey's test is a single-step multiple comparison procedure and statistical test. It can be used to find means that are significantly different from each other and compares the means of every treatment to the means of every other treatment; that is, it applies simultaneously to the set of all pairwise comparisons.³⁷ In addition, the effect size was measured with η_p².

Results

Game user type

The items and the subfactor reliabilities of the AGUS and MGUS are shown in Table 2. The AGUS was developed based on previous research on the positive effects of games and the main characteristics of leisure experiences. It comprises seven subfactors that measure adaptive psychological and behavioral experiences following game activity. If these characteristics are present, it can be inferred that the attributes of adaptive game use are strongly achieved.

Table 2.

Measurements of Adaptive and Maladaptive Game Use

	Item	M (SD)	Cronbach's α
Adaptive game use
Life energy	I'm excited when playing the game.	1.90 (0.82)	0.57
	I feel funny during the day if I play the game.	1.06 (0.85)
	After playing games, I have the strength to study.	0.47 (0.68)
Expansion of life experience	I can experience what I haven't known playing the game.	1.24 (0.76)	0.68
	I think I have more imagination playing the game.	0.78 (0.77)
	A new idea comes to my mind when playing the game.	0.65 (0.74)
Feeling and stress control	I feel better if I play the game.	1.69 (0.82)	0.68
	I can stop worrying if I play the game.	0.70 (0.84)
	I stop feeling irritated after I play the game.	0.78 (0.77)
Flow	I become immersed in the game completely when playing the game.	1.50 (0.90)	0.82
	I concentrate only on the game when playing the game.	1.50 (0.89)
	Another idea does not come to my mind when playing the game.	1.26 (0.92)
Challenge	I feel great if I win the game or level up.	1.35 (0.92)	0.70
	I feel proud of myself when I play well during the game.	1.04 (0.89)
	I want to feel challenged by new things when playing the game.	0.89 (0.84)
Increase of control	I have better time control skills through the game.	0.58 (0.75)	0.68
	I learn how to control my impulses through the game.	0.49 (0.68)
	I developed my own self-control while playing the game	1.14 (0.95)
Social interaction	I have much to talk about with my friend when playing the game.	0.97 (0.82)	0.70
	I get better friends playing the game.	0.27 (0.52)
	I get along well with my friends playing the game.	0.79 (0.82)
Maladaptive game use
Resistance	I feel excited only if I play more games.	0.60 (0.79)	0.85
	I feel pleasant if I play the game more.	0.40 (0.67)
	I feel nice only if I play more games.	0.37 (0.64)
Withdrawal	I feel irritated and angry if I cannot play the game.	0.60 (0.77)	0.62
	I cannot study well in class because I want to play the game.	0.08 (0.31)
	I feel out of energy if I don't play the game.	0.22 (0.60)
Waste of time	I always play more games than I wanted to.	0.84 (0.85)	0.74
	I play the other game continuously even while I wait for another opportunity on the smartphone game.	0.28 (0.59)
	I always play the game longer than I planned.	0.63 (0.72)
Damage of control	I cannot stop playing the game by myself.	0.72 (0.89)	0.79
	I usually play more even when I have to stop playing the game.	0.55 (0.67)
	I try to reduce the game time, but I usually play the game for a long time.	0.50 (0.70)
Compulsive use	I think only of the game for a day.	0.25 (0.56)	0.73
	I try to get an opportunity to play the game if I get some time.	0.40 (0.67)
	I cannot think of anything else and usually continue playing getting the game item or leveling up.	0.18 (0.48)
Ignorance of daily life	I usually play the game even when I am with my family.	0.46 (0.67)	0.53
	I have the situation of being late for school or the academy by playing the game.	0.05 (0.24)
	I got into difficulties with my friend because of the game.	0.03 (0.19)
Side effect	I have no time to study because of the game.	0.25 (0.56)	0.71
	I am scolded by my parents because of the game, but I usually do it continuously.	0.35 (0.64)
	I usually play the game continuously even when I'm sick or have sore or tired eyes.	0.17 (0.47)

The subfactors are as follows. (1) Life energy: the degree of feeling energized and experiencing pleasure through the game (Cronbach's α = 0.57). (2) Expansion of life experience: the degree of expanding thought and life experiences through the game (Cronbach's α = 0.68). (3) Feeling and stress control: the degree of resolving stress and engaging in good leisure time with the game (Cronbach's α = 0.68). (4) Flow: the degree of experiencing a state of psychological concentration through the game (Cronbach's α = 0.82). (5) Challenge: the degree to which game technology is demonstrated and an individual experiences validation of ability and feelings of pride through the game (Cronbach's α = 0.70). (6) Increase of control: the degree to which self-control and adjustment can be experienced through game activity (Cronbach's α = 0.68). (7) Social interaction: the degree to which the maintenance and expansion of friendship and social support networks can be experienced through game activity (Cronbach's α = 0.70).

The MGUS measures problematic game use as defined by the American Psychiatric Association and World Health Organization.^4,38 These are universal standards of diagnosing addiction behavior and have their theoretical basis in addiction syndrome research.^6,39 The MGUS measures the following seven subfactors. (1) Resistance: To get the same level of satisfaction, more time is needed to play the game (Cronbach's α = 0.85). (2) Withdrawal: If they stop playing the game suddenly, they may experience uncomfortable symptoms and continue playing the game to avoid them (Cronbach's α = 0.62). (3) Waste of time: the unintentional excessive waste of time (Cronbach's α = 0.74). (4) Damage to self-control: repeated failed attempts to stop or control their game use (Cronbach's α = 0.79). (5) Compulsive use: they spend significant time thinking about or playing the game (Cronbach's α = 0.73). (6) Ignorance of daily life: they give up or reduce their social, professional, and other leisure activities because of the game (Cronbach's α = 0.53). (7) Continued use despite side effects: they continue playing the game despite several side effects caused by the game (Cronbach's α = 0.71).

Game user type

Table 3 shows the types of game users according to adaptive and maladaptive use. Among the total respondents, most (77.5%) were almost equally dispersed across the GGU (39.5%) with low adaptive and maladaptive use scores and the TGU group (38.0%) with high adaptive and maladaptive use scores. The AGU group (11.3%) with high adaptive but low maladaptive use scores and the MGU group (11.2%) with low adaptive and high maladaptive use scores similarly (almost equally) had the least respondents (22.5%). While the frequency of GGU and TGU was relatively high, the frequency of AGU and MGU was low, and this distribution difference was statistically significant (χ² = 235.380, P < 0.000).

Table 3.

Classification of Game User Type

Maladaptive game use	Adaptive game use		χ²	P
Maladaptive game use	Low, n (%)	High, n (%)	χ²	P
Low	General game user307 (39.5)	Adaptive game user88 (11.3)	235.380	0.000
High	Maladaptive game user87 (11.2)	Twofold game user296 (38.0)	235.380	0.000

Characteristics according to game user type

Table 4 shows the characteristics of respondents according to game user type. In terms of gender differences, there was no difference between males and females in the AGU and MGU. However, the gender difference in the GGU and TGU groups was statistically significant with more females (66.8%) than males (33.2%) and more males (64.9%) than females (35.1%), respectively (χ² = 60.885, P < 0.000). There was no difference in the distribution of game user type according to age and household income per month. The difference in time spent gaming by game user type was statistically significant with the TGU group spending the most (90.49 minutes) and the GGU group the least (40.27 minutes) time gaming (F = 28.018, P < 0.000).

Table 4.

Demographic Characteristics According to Game User Type

	General game user	Adaptive game user	Maladaptive game user	Twofold game user	χ²
	n (%)	n (%)	n (%)	n (%)	χ²
Gender					60.885^***
Male	102 (33.2)	46 (52.3)	41 (47.1)	192 (64.9)
Female	205 (66.8)	42 (47.7)	46 (52.9)	104 (35.1)
Age					6.254
13	115 (37.5)	32 (36.4)	32 (36.8)	108 (36.5)
16	115 (37.5)	23 (26.1)	32 (36.8)	103 (34.8)
19	77 (25.1)	33 (37.5)	23 (26.4)	85 (28.7)
Household income per month⁺					8.495
Less than KRW 200	12 (4.9)	1 (1.3)	3 (4.2)	6 (2.6)
KWR 201–400	47 (19.3)	19 (24.7)	14 (19.4)	52 (22.2)
KWR 401–600	107 (44.0)	27 (35.1)	29 (40.3)	99 (42.3)
KWR 601–800	45 (18.5)	21 (27.3)	16 (22.2)	52 (22.2)
KWR 801 and more	32 (13.2)	9 (11.7)	10 (13.9)	25 (10.7)
	Mean (SD)	Mean (SD)	Mean (SD)	Mean (SD)	F
Time spent gaming^‡	40.27 (52.55)^a	66.36 (53.21)^b	62.97 (81.86)^b	90.49 (74.32)^c	28.018^***

In the Tukey test, the significance of the mean difference of each group was verified at the 0.05 level, and the degree of the mean difference for each group was expressed as a<b<c.

KRW1000 = USD 0.88.

‡

Unit = minute/day.

***

P < 0.000.

Mental health according to game user type

The results of adolescents' emotional state based on game user type are presented in Table 5. Statistically significant differences were found between groups for loneliness, depression, anxiety, and aggression.

Table 5.

Mental Health According to Game User Type

	General game user Mean (SD)	Adaptive game user Mean (SD)	Maladaptive game user Mean (SD)	Twofold game user Mean (SD)	Range	F	η_p²
Loneliness	1.39 (0.54)^a	1.47 (0.52)^a	1.91 (0.69)^b	1.82 (0.61)^b	1–4	36.306	0.123
Depression	1.26 (0.37)^a	1.31 (0.43)^b	1.56 (0.61)^c	1.40 (0.51)^bc	1–4	11.486	0.043
Anxiety	1.35 (0.52)^a	1.45 (0.67)^ab	1.78 (1.00)^c	1.54 (0.61)^b	1–4	13.190	0.045
Aggression	1.71 (0.68)^a	1.88 (0.65)^a	2.25 (0.65)^b	2.25 (0.74)^b	1–5	34.449	0.128

In the Tukey test, the significance of the mean difference of each group was verified at the 0.05 level, and the degree of the mean difference for each group was expressed as a<b<c.

According to the Tukey test, for the loneliness domain, the GGU (m = 1.35, SD = 0.54) and AGU (m = 1.47, SD = 0.52) groups had low mental health scores, and the MGU (m = 1.91, SD = 0.69) and TGU (m = 1.82, SD = 61) groups had high mental health scores. In the depression domain, the score for the MGU (m = 1.56, SD = 0.61) group was the highest, followed by TGU (m = 1.40, SD = 0.51), AGU (m = 1.31, SD = 0.43), and GGU (m = 1.26, SD = 0.37) groups.

The anxiety domain also had the highest scores reflected in the MGU (m = 1.78, SD = 1.00) group, followed by the TGU (m = 1.54, SD = 0.61), AGU (m = 1.45, SD = 0.67), and GGU (m = 1.35, SD = 0.52) groups. In the aggression domain, similar to the loneliness domain results, the GGU (m = 1.71, SD = 0.68) and AGU (m = 1.88, SD = 0.65) groups had low mental health scores, and the MGU (m = 2.25, SD = 0.65) and TGU (m = 2.25, SD = 0.74) groups had a high mental health score. Finally, for all variables, the MGU and TGU groups had significantly higher scores compared with the GGU and AGU groups.

Academic achievement according to game user type

The results for the adolescents' academic achievement according to game user type are shown in Table 6. Statistically significant differences were found between groups for academic achievement, with the AGU (m = 4.49, SD = 1.03) group presenting with the highest scores followed by the MGU (m = 4.37, SD = 1.09), GGU (m = 4.34, SD = 1.24), and TGU (m = 4.06, SD = 1.17) groups.

Table 6.

Academic Achievement According to Game User Type

	General game user Mean (SD)	Adaptive game user Mean (SD)	Maladaptive game user Mean (SD)	Twofold game user Mean (SD)	Range	F	η_p²	P
Academic achievement	4.34 (1.24)^ab	4.49 (1.03)^b	4.37 (1.09)^ab	4.06 (1.17)^a	1–6	4.672	0.018	0.003

In the Tukey test, the significance of the mean difference of each group was verified at the 0.05 level, and the degree of the mean difference for each group was expressed as a<b<c.

Quality of life according to game user type

The results for the adolescents' quality of life according to game user type are shown in Table 7. Quality-of-life score differences between groups were statistically significant, with highest scores reported for the AGU (m = 3.62, SD = 1.00) group, followed by the GGU (m = 3.60, SD = 1.13), TGU (m = 3.42, SD = 1.03), and MGU (m = 3.27, SD = 0.99) groups.

Table 7.

Quality of Life According to Game User Type

	General game user Mean (SD)	Adaptive game user Mean (SD)	Maladaptive game user Mean (SD)	Twofold game user Mean (SD)	Range	F	η_p²	P
Quality of life	3.60 (1.13)^b	3.62 (1.00)^b	3.27 (0.99)^a	3.42 (1.03)^ab	1–7	3.189	0.013	0.023

In the Tukey test, the significance of the mean difference of each group was verified at the 0.05 level, and the degree of the mean difference for each group was expressed as a<b<c.

Discussion

This study classified adolescents into game user types by using the adaptive and maladaptive scores of their game usage. We then examined differences in mental health, academic achievement, and quality of life according to game user type.

First, the classification of game user type showed that a relatively high frequency of adolescents had both low adaptive and maladaptive scores or both high adaptive and maladaptive scores. Conversely, the frequency of adolescents who experienced only adaptive use or only maladaptive use was relatively low. In terms of gender differences according to game user type, a higher ratio of females was observed in the GGU group and a higher ratio of males in the TGU group. Most research on gaming disorder focused on males,⁴⁰ and scholars have discussed whether gaming disorder might be a predominantly male disorder.⁴¹ Furthermore, research highlighted that female gamers are typically more casual gamers and play for shorter periods than male gamers.⁴⁰ Therefore, in this study, the high frequency of females observed in the GGU group suggests that this group was more likely to use online gaming casually. In contrast, the high frequency of men in the TGU group suggests that they are more likely to actively use online gaming. Considering the results of time spent gaming, the characteristics of the game user type become more noticeable. The time spent gaming in the TGU group was more than twice as long as that of the GGU group. The findings suggest that individuals in the GGU group do not use games routinely or aggressively, but those in the TGU group seem to play more aggressively.

Regarding mental health, the GGU group had the lowest scores compared with other groups for loneliness, depression, anxiety, and aggression. AGU and GGU had the lowest scores for loneliness and aggression compared with other groups. The depression score for AGU was slightly higher than GGU and lower than MGU and TGU. MGU had the highest scores for loneliness, depression, anxiety, and aggression compared with other groups and closely followed by TGU.

Finally, compared with other groups, GGU was the most stable in terms of mental health, whereas MGU was the most unstable. In addition, the AGU and GGU mental health scores were not significantly different for mental health, and the TGU and MGU groups were not significantly different for loneliness, depression, and aggression.

Many previous studies highlight that problematic game use has a negative effect on mental health. Davis⁴² states that lonely people have a positive assessment of their online space and their own abilities in that space. However, they have a negative perception or evaluation in the offline world and this maladaptive cognition is likely to develop into problematic game play. Mentzoni et al.⁴³ and Peng and Liu⁴⁴ also found that depression is related to game dependence and excessive problem videogames or internet use. A study by Lemmens et al.⁴⁵ reported that male players who overuse online games preferred violent videogames, and Lo et al.⁹ argued that the lower the quality of interpersonal relationships and the greater the level of social anxiety, the more time was spent playing online games. In this study, MGU with high maladaptive use was found to have the highest score for mental health, which is consistent with previous studies. It should be noted, however, that the mental health of the TGU group, who experienced both adaptive and maladaptive use simultaneously, was not significantly different from MGU.

In addition, TGU showed the lowest academic achievement compared with other groups, including MGU. In terms of quality of life, TGU and MGU scores were lower than those of other groups. Eventually, the TGU group would experience mental health instability resulting from the maladaptive game use regardless of the game adaptation experience. Since individuals in the TGU group experience both game inadequacy and adaptation at the same time, they could struggle to identify when they overuse games. Furthermore, the results of this study also showed that the TGU group spent the most time playing games. These findings suggest that the TGU group requires a similar level of intervention and education as the MGU group.

In addition to the TGU group, the most interesting group in this study was the AGU group. The AGU group’ mental health scores were not significantly different from the most stable GGU group. Interestingly, the AGU group scored higher in quality of life than the other groups and had higher academic achievement scores than the other groups. This suggests that the adaptive use of online games can have a positive effect not only on mental health but also on the quality of life and academic achievement of individuals.

Particular attention should be paid to the fact that adaptive game use was related to academic achievement. Previous research has revealed that games have significant advantages as a learning tool.^46–48 However, empirical studies have reported that it is not only the role of the game as a learning tool, but also the experience of immersion and self-esteem formed through the game that increase academic achievement.

Yang and Chang⁴⁹ reported that games are a popular strategy for engaging students by making learning fun. Active involvement in games may have even greater potential for student empowerment through enhancing concentration and engagement,⁴⁹ fostering higher order thinking, and improving learning outcomes. Also, Abbaslou⁵⁰ suggested that game therapy was an effective method in enhancing students' self-esteem and academic achievement and had a positive effect on reducing math and reading problems in students with learning disorders. However, in most cases, adolescents experience both adaptive and maladaptive use, and experiencing only adaptive use without the maladaptive use has been shown to be relatively infrequent.

Furthermore, as leisure activities of adolescents increasingly involve using computers and games, adolescents' game exposure and its influence will similarly increase in the future. Therefore, a longitudinal study to examine the effect of games on the mental health of adolescents should be conducted to explore the age of game use and the expansion of game content.

Data Availability

The data sets generated during and/or analyzed during the current study are available in the Korea Creative Contents Agency repository, http://www.kocca.kr/cop/main.do.

Ethical Statement

This was a retrospective study using secondary deidentified data; formal written consent from study subjects was not required.

Footnotes

Author Disclosure Statement

No competing financial interests exist.

Funding Information

No funding was received for this article.

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