Relationships between Smartphone Addiction and Smartphone Usage Types, Depression, ADHD, Stress, Interpersonal Problems, and Parenting Attitude with Middle School Students

Youl Pyo Hong; Yeon Ok Yeom; Myung Ho Lim

doi:10.3346/jkms.2021.36.e129

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Hong, Yeom, and Lim: Relationships between Smartphone Addiction and Smartphone Usage Types, Depression, ADHD, Stress, Interpersonal Problems, and Parenting Attitude with Middle School Students

Original Article

Psychiatry & Psychology

Journal of Korean Medical Science 2021; 36(19): e129.

Published online: 30 April 2021

DOI: https://doi.org/10.3346/jkms.2021.36.e129

Relationships between Smartphone Addiction and Smartphone Usage Types, Depression, ADHD, Stress, Interpersonal Problems, and Parenting Attitude with Middle School Students

Youl Pyo Hong¹

, Yeon Ok Yeom²

, Myung Ho Lim^1,²

¹Department of Psychology, Graduate School, Dankook University, Cheonan, Korea.

²Departments of Psychology & Psychotherapy, College of Health Science, Dankook University, Cheonan, Korea.

Address for Correspondence: Myung Ho Lim, MD, PhD. Department of Psychology, Environmental Health Center, College of Health Science, Dankook University, 119 Dandae-ro, Dongnam-gu, Cheonan 31116, Republic of Korea. paperose@dku.edu

Received 11 October 2020 Accepted 5 April 2021

The Korean Academy of Medical Sciences

https://creativecommons.org/licenses/by-nc/4.0/

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background

The purpose of this study was to examine the relationships between smartphone addiction of middle school students and smartphone usage types, depression, attention deficit hyperactivity disorder (ADHD), stress, interpersonal problems, and parenting attitude. In particular, we wanted to find out how smartphone usage types predict smartphone addiction when controlling depression, ADHD, perceived stress, interpersonal problems, and parenting attitudes, which are the main predictors of existing smartphone addiction in this study.

Methods

The subjects of this study included 487 local middle school students (253 girls and 234 boys). The measurement instruments used were the smartphone addiction scale, patient health questionnaire-9 (PHQ-9), Korean ADHD rating scales (K-ARS), perceived stress scale (PSS), Short form of the Korean-inventory of interpersonal problems circumplex scales (KIIP-SC), and the parenting attitude scale. We identified the relationships between the variables with correlation analysis and examined the predictors of smartphone addiction with hierarchical multiple regression analysis.

Result

The factors that influence smartphone addiction were sex (β = 3.14, P < 0.01), stress (β = 2.99, P < 0.01), and interpersonal problems (β = 3.81, P < 0.001). In addition, when the confounding variables of smartphone addiction were controlled to examine the effects of smartphone usage types on smartphone addiction, social network service (SNS) (β = 2.66, P < 0.01) and music/videos (β = 2.73, P < 0.01) were found to significantly positively affect smartphone addiction, whereas study (β = −2.54, P < 0.05) had a significantly negatively effect. And these factors explained 29.5% of the variance in smartphone addiction.

Conclusion

The order of the usage types with the highest influence on smartphone addiction was: enjoying music/videos, SNS, and study. This suggests that selective intervention depending on the main smartphone usage type can be effective.

Graphical Abstract

Keywords: Smartphone Addiction, Middle School Students, Smartphone Usage Types, Sex, Stress, Interpersonal Problems

INTRODUCTION

Smartphones are indispensable tools for people of all ages around the world today, and it has become difficult to imagine everyday life without a smartphone. Smartphone use has been changing daily routines, habits, social behaviors, emancipative values, family relationships, and social interactions.1 For adolescents, who are particularly sensitive to new technology and media use, smartphones have become an important part of their lives.2 According to a recent study, the domestic middle and high school students' smartphone ownership rate exceeded 95%, which is about 8% higher than that of all age groups (87.2%).3 Adolescents are more susceptible to smartphone addiction than adults, which can be attributed to the former's use of various contents for interaction with peers and entertainment through their smartphones4 and their neurobiological vulnerability, as discussed in dual processing theory.5 6 A domestic study reported that the smartphone overdependence (addiction) prevalence rate of adolescents was 30.2%, which is higher than that of adults, at 18.8%.7

Smartphones are used for a variety of purposes, including learning, information search, social communication, and entertainment.8 Compared to traditional types of computers, the portability and connectivity of smartphones allow users to easily access information anytime and anywhere. Therefore, users who are vulnerable to behavioral addiction, such as adolescents, are more likely to develop an addiction to smartphones.9 Smartphone addiction is defined as a state involving the excessive use of a smartphone, with withdrawal symptoms of feeling anxious and nervous when without the smartphone. Higher tolerance to smartphone usage, which necessitates increased use to obtain the same level of satisfaction, is observed. Although these symptoms negatively affect the daily life of the person, they cannot control their use of the smartphone. Based on these characteristics, many researchers regard smartphone addiction as one of the behavioral addictions.10 11

Previous studies reported that excessive smartphone use by adolescents is associated with their sex, health problems, psychopathological state (depression, anxiety, high stress, low mood, etc.), and behavioral problems.12 13 14 This is because adolescents are easily affected by external stimuli, interpersonal problems, and emotional changes.2 Excessive smartphone use by adolescents can result in conflict with their parents, low academic achievement, and low satisfaction with life, leading to failure to perform developmental tasks and difficulties in adaptation.15 The focus of previous studies related to smartphone addiction has been on the characteristics of people who use smartphones excessively. These studies have been conducted to investigate smartphone users' addictive use of these devices, their side effects, and factors influencing smartphone addiction.15 16 However, despite smartphones being used for various purposes, only a few studies focused on the relationship between usage types available for smartphones and smartphone addiction.17 18

Jeong et al.14 suggested that more attention is needed to describe smartphone addiction, and that evaluating the role of usage types will help to understand the nature of smartphone addiction. Park and Hwang19 said that the addiction phenomenon of smartphones is closely related to the use of some applications and the media properties of applications. Smartphone usage types can be categorized into playing games, social network service (SNS), news reading, entertainment (music/videos), exchanging text messages, making and receiving calls, commercial transactions, and location-based services.19 Most of the preceding studies have been conducted on the relationship between smartphone use for playing games and SNS and smartphone addiction. However, no prior studies have been conducted in comparison with other key variables that predict usage types and smartphone addiction. Therefore, we aim to investigate the types of smartphone use and the impact of major variables predicting smartphone addiction based on the classification of smartphone usage by Jeong et al.14

Representative variables which explain smartphone addiction were selected as personal psychological factors, home environmental factors, social environmental factors, and technical factors presented by the National Information Society Agency.20 First, the individual psychological factors were depression and ADHD. According to a study of adolescent smartphone addicts visiting outpatients of psychiatry in Korea, many had symptoms of depression and ADHD.21 Depression has a significant impact on smartphone addiction,22 23 and teenagers are known to be addicted to avoid negative emotions such as depression.24 In addition, 40% of ADHD patients were smartphone addicts and were more susceptible to addiction due to poor attention and inhibition.25 The parenting attitude of parents was selected among the family environment factors.26 Among the various variables that affect adolescents' smartphone addiction, those related to family issues were found to have the strongest influence because the parenting attitude of parents considerably influences adolescents whose independence as adults is delayed.27 28 Of the social environment factors, interpersonal problems and stress were selected.29 30 Recent studies reported that interpersonal problems have a positive effect on smartphone addiction.31 This is because adolescence is an important period in which a person prepares for adulthood by developing a healthy self-concept and acquiring various social skills.32 With regard to stress, teenagers avoid psychological conflicts and worries because they are not as good at dealing with stress as adults, or easily concentrate on using smartphones.33 Finally, the type of smartphone use was selected as a technical factor, and we will look at the connection with smartphone addiction with the key variables mentioned earlier.

Therefore, this study aimed to identify two things regarding the use of smartphones by adolescents. We investigated the relationship between smartphone usage types and smartphone addiction. Also, we investigated the effect of smartphone usage types on smartphone addiction when controlling the main predictive factor of smartphone addiction, and attempted to find out how each smartphone usage types affect to smartphone addiction.

METHODS

Participants

We surveyed a total of 487 middle school students, of which 253 were girls and 234 were boys in a region after requesting cooperation from the middle school.

Assessment

Smartphone addiction scale

A youth smartphone addiction self-diagnosis scale developed by the National Information Society Agency was used in this study.34 The 15 items of this scale are divided into four sub-factor categories as follows: 5 items on daily life disorders regarding difficulties in daily life due to excessive smartphone use, 2 items on the pursuit of the virtual world regarding a tendency to cling to the virtual world as it provides more enjoyment than the real world, 4 items on withdrawal symptoms of anxiety and nervousness when not using the smartphone and 4 items on higher tolerance that causes increased use for a sense of satisfaction. The items are constructed based on a 4-point Likert scale.

If a testee receives a total score of 45 points or more, or meets all the conditions including 16 points or more in daily life disorders, 13 points or more in withdrawal symptoms, or 14 points or more intolerance, the person is classified as a high-risk user. Testees who obtain a total score of 42 points or more to 44 points or less, or satisfy any of the following conditions–14 points or more in daily life disorders, 12 points or more in withdrawal symptoms, and 13 points or more intolerance–they are classified as potential risk users, whereas the rest are classified as general users. The overall scale reliability Cronbach's α value as reported by the developers was 0.88, and Cronbach's α in this study 'was 0.89.

Smartphone usage type scale

The smartphone usage type scale used in the study by Jeong et al.14 was used after being modified to reflect the characteristics of the subjects and supplemented with more types. Whereas the existing study divided smartphone usage types into 1) study, 2) entertainment (music/videos), 3) SNS (focused on conversation and social interaction), and 4) games, in this research we added the following three types based on preceding studies12 17: voice calls and text messages, which are the most basic functions of the smartphone; information search; and other (shopping, schedule management, alarms, public transportation use, etc.). In the case of SNS, some platforms such as YouTube and Facebook include the use of music and video content, such as the use of social media for conversation and social interaction, as in the existing study.17 The instruction “Some items regarding the purposes of the app (e.g., Facebook) use may be confusing as the stated purposes can also be considered its functions, but choose an answer that best reflects your main purpose for using the app” was added to the questionnaire, so that the study subjects could distinguish their SNS types. Therefore, this scale was finally constructed with the following seven usage types: 1) calls and text messages, 2) study, 3) information search, 4) music/videos, 5) SNS, 6) games, and 7) other (shopping, schedule management, alarms, public transportation, etc.). Each item was structured on a 5-point Likert scale from “never use” to “use very often” to assess smartphone usage types.

Depression scale

The Korean version of the patient health questionnaire-9 (PHQ-9) developed by Kroenke et al.,35 which was standardized by An et al.,36 was used as a tool to provisionally diagnose depression. PHQ-9 is a self-report test designed to diagnose depression simply and assess the severity of the depression. The grades of depression indicated by each score range are as follows: “not depressive” by a score of 0–4 points, “mild depression” by a score of 5–9 points, “moderate depression” by a score of 10–14 points, “more than moderate depression” by a score of 15–19 points, and “severe depression” by a score of 20 points or more, which is a level at which treatment by a psychiatrist is required. Cronbach's α was 0.89 in the study by Kroenke et al.,35 0.95 in the domestic standardization study, and 0.87 in this study.34

ADHD scale

The Korean version of the attention-deficit hyperactivity disorder rating scale (K-ARS), which is a behavioral assessment scale for children developed by DuPaul et al.,37 was standardized for use in Korea by Kim et al.38 Designed to assess ADHD symptoms in school-age children, the scale has a high discriminant validity for discriminating between the ADHD patient group and the control group. Consisting of 18 questions, it is an efficient tool for distinguishing the three subtypes of ADHD (primarily inattentive, primarily hyperactive/impulsive, and combined). Cronbach's α was 0.90 in the study by DuPaul et al.,37 from 0.77 to 0.89 in the study by Kim et al.,38 and 0.92 in this study.

Parenting attitude scale

The parenting attitude scale devised by Heo39 and modified by panel researchers with expertise in youth issues was used in this study. The original scale was composed of the sub-factors of the supervision, affection, inconsistency, excessive expectations, excessive interference, and rational explanation of parents. In this study, these sub-factors were divided into a positive parenting attitude (supervision, affection, and rational explanation) and a negative parenting attitude (inconsistency, excessive expectations, and excessive interference). In the study by Heo,39 the Cronbach's α values for each factor ranged from 0.68 to 0.86. In this study, the Cronbach's α value for the 10 items related to the three sub-factors of positive parenting attitude was 0.88, and that of the 11 items related to the three sub-factors of negative parenting attitude was 0.87.

Perceived stress scale (PSS)

The PSS developed by Cohen et al.40 and validated by Park and Seo41 for use in Korea was selected to measure stress. The PSS consists of 10 items that were constructed on a 5-point Likert scale with a score range from 10 to 50 points. The higher the score, the higher the perceived stress level. The reliability Cronbach's α value of the original tool was 0.75 and the Cronbach's α values for negative and positive perceptions were 0.77 and 0.74, respectively, in the study by Park and Seo.41 In this study, Cronbach's α was 0.81.

Interpersonal problem scale

The short form of the Korean-inventory of interpersonal problems circumplex scale (KIIP-SC) developed by Hong et al.42 was used in this study. The scale is composed of a total of 40 items and 8 sub-factors including a circumplex of personality, such as domineering, egocentric, cold, socially avoidant, nonassertive, overly-nurturing, self-sacrificing, and intrusive, with 5 questions for each sub-factor. This scale is based on a 5-point Likert scale; the higher the score, the more serious the interpersonal problem. In the study by Hong et al.42 Cronbach's α was found to range from 0.61 to 0.81, and that in this study ranged from 0.78 to 0.89.

Statistical analysis

Data analysis was performed using the Statistical Package for Social Sciences (SPSS/PC), version 15 (SPSS, Chicago, IL, USA), and the predictors of smartphone addiction were examined using correlation analysis and hierarchical multiple regression analysis.

Ethics statement

The Institutional Review Board of Dankook University permitted this study (DKU 2020-09-025). Informed written consents were provided by all participants before participation.

RESULTS

Demographic characteristics of the study subjects

The final study subjects were 487 middle school students, of which 253 (52%) were female students and 234 (48%) were male students. Among the total students, 264 students (54.2%) were in the first grade, 113 (23.2%) in the second grade, and 110 (22.6%) in the third grade. Depending on the family's financial status, 55 of the total students were classified into the high-level group (11.3%), 418 into the mid-level group (85.8%), and 14 into the low-level group (2.9%). The mean and standard deviation of each measurement are as follows (Table 1).

Table 1

Epidemiological characteristics of study subjects

Variables		Frequency (%) or Mean (SD)
Sex (M/F)		234 (48)/253 (52)
Grades (first/second/third)		264 (54.2)/113 (23.2)/110 (22.6)
SES (top/mid/bot)		55 (11.3)/418 (85.8)/14 (2.9)
Total		487
Smartphone addiction		31.59 (7.43)
Depression		5.62 (5.05)
ADHD		10.68 (9.90)
Positive parenting attitude		31.45 (5.36)
Negative parenting attitude		24.17 (6.82)
Perceived stress		26.96 (6.58)
Interpersonal relationship problems		86.43 (26.93)
Smartphone usage type
	Call, text message	3.83 (1.01)
	Study	2.37 (0.91)
	Information finding	3.23 (1.07)
	Music/movies	4.50 (0.80)
	Social network service	4.06 (1.18)
	Game	3.27 (1.30)
	Others (shopping etc.)	2.96 (1.13)

SD = standard deviation, M = male, F = female, SES = socio-economic status, Smartphone addiction = a youth smartphone addiction self-diagnosis scale, Depression = the Korean version of the patient health questionnaire-9, ADHD = attention deficit hyperactivity disorde, Positive parenting attitude = parenting attitude scale devised by Heo, Negative parenting attitude = parenting attitude scale devised by Heo, Stress = the perceived stress scale, Interpersonal relationship problems = the short form of the Korean-inventory of interpersonal problems circumplex scale.

Smartphone usage type scale: call, text message, study, information finding, music/movies, social network service, game, others, shopping etc.

Correlation between smartphone addiction and related variables

Table 2 shows the correlation between smartphone addiction and sex, grade, financial status, smartphone usage type, depression, ADHD, positive parenting attitude, negative parenting attitude, stress, and interpersonal problem. Smartphone addiction was found to have a significant positive correlation (r = 0.09–0.39, P < 0.5) with all variables except for financial status and the three usage types including calls/text messages, information search, and games among the variables used in the study. We also found a negative correlation (r = −0.11 to 0.21, P < 0.5) between smartphone addiction and the two variables study and positive parenting attitude (Table 2).

Table 2

Correlation between smartphone addiction and related variables

	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17
1
2	0.205^**
3	0.089^*	−0.025
4	0.022	0.059	0.110^*
5	0.041	0.161^**	−0.071	0.000
6	−0.114^*	0.117^**	0.050	0.008	0.226^**
7	−0.052	−0.053	0.054	−0.030	0.166^**	0.246^**
8	0.214^**	0.096^*	−0.027	−0.017	0.266^**	−0.034	0.131^**
9	0.199^**	0.217^**	−0.003	−0.002	0.366^**	0.080	0.065	0.380^**
10	0.088	−0.265^**	−0.052	−0.087	0.035	−0.141^**	0.030	0.102^*	0.014
11	0.123^**	0.109^*	0.092^*	−0.014	0.225^**	0.156^**	0.306^**	0.127^**	0.266^**	0.098^*
12	0.284^**	0.192^**	0.090^*	0.095^*	0.035	−0.039	−0.082	0.039	0.017	−0.062	0.136^**
13	0.302^**	0.008	0.105^*	0.040	−0.040	−0.044	−0.067	0.025	0.034	0.054	0.103^*	0.488^**
14	−0.207^**	−0.032	−0.053	−0.139^**	0.142^**	0.099^*	0.127^**	0.013	0.065	0.059	0.000	−0.300^**	−0.183^**
15	0.220^**	−0.096^*	−0.022	0.015	−0.002	−0.064	−0.037	0.008	−0.010	0.050	0.055	0.259^**	0.250^**	−0.426^**
16	0.360^**	0.211^**	0.100^*	0.084	0.051	−0.022	−0.078	0.118^**	0.120^**	−0.023	0.009	0.374^**	0.316^**	−0.255^**	0.241^**
17	0.386^**	0.15^**	0.076	0.130^**	−0.121^**	−0.024	−0.061	0.029	−0.084	0.063	−0.017	0.505^**	0.506^**	−0.352^**	0.323^**	0.466^**
Mean	31.59	−	13.72	1.92	3.83	2.37	3.23	4.50	4.06	3.27	2.96	5.62	10.68	31.45	24.17	26.96	86.43
SD	7.43	−	0.89	0.37	1.01	0.91	1.07	0.80	1.18	1.30	1.13	5.05	9.90	5.36	6.82	6.58	26.93

A hierarchical multiple regression analysis among variables influencing smartphone addiction

Smartphone was set as dependent variables, and Epidemiologic variables, individual and social variables, and smartphone usage types were used as independent variables. A hierarchical multiple regression analysis was performed to determine whether smartphone usage types have a significant additional explanatory power for smartphone addiction (Table 3).

Table 3

Multiple regression analysis among variables influencing smartphone addiction

Independent variables		Model 1			Model 2			Model 3
Independent variables		β	Standard error	t	β	Standard error	t	β	Standard error	t
Epidemiologic variables
	Constant		2.033	12.552^***		3.745	4.747^***		3.998	2.740^**
	Sex	0.207	0.660	4.656^***	0.149	0.633	3.500^***	0.140	0.661	3.140^**
	Grade	0.093	0.405	2.089^*	0.053	0.371	1.288	0.063	0.362	1.574
	SES	−0.001	0.904	−0.011	−0.042	0.830	−1.024	−0.033	0.803	−0.835
Individual, social variables
	Depression				0.008	0.075	0.165	0.004	0.073	0.074
	ADHD				0.119	0.037	2.388^*	0.093	0.036	1.911
	Positive Parenting				−0.036	0.065	−0.777	−0.042	0.064	−0.918
	Negative Parenting				0.090	0.051	1.925	0.078	0.049	1.722
	Stress				0.172	0.053	3.640^***	0.138	0.052	2.985^**
	Interpersonal				0.177	0.015	3.283^***	0.207	0.015	3.806^***
Smartphone usage type
	Call, text message							−0.014	0.329	−0.303
	Study							−0.107	0.342	−2.538^*
	Information Finding							−0.027	0.298	−0.633
	Music, movie							0.118	0.403	2.728^**
	SNS							0.122	0.291	2.660^**
	Game							0.076	0.241	1.802
	Others (Shopping etc.)							0.062	0.291	1.403
F		8.596^***			15.843^***			12.294^***
R²		0.051			0.230			0.295
ΔR²		0.051			0.179			0.065

SES = socio-economic status, ADHD = attention deficit hyperactivity disorder, SNS = social network service.

^*P < 0.05; ^**P < 0.01; ^***P < 0.001.

Dependent variable: smartphone addiction.

First, in Model 1, in which demographic variables were measured, we found that sex (β = 0.207) and grade (β = 0.093) had a statistically significant effect on smartphone addiction (F = 8.596, P < 0.001). In Model 2, depression, ADHD, positive and negative parenting attitudes of parents, stress, and interpersonal problems were added to the demographic variables of Model 1, and sex (β = 0.140), ADHD (β = 0.119), stress (β = 0.172), and interpersonal problems (β = 0.177) showed a positive significant effect (F = 15.843, P < 0.001). The explanatory power of Model 2 was 23%, which was 17.9% higher than that of Model 1. The grade (β = 0.053), which was significant in Model 1, was excluded from Model 2.

Finally, in Model 3, the smartphone usage type was added to the variables of Model 2 (F = 12.294, P < 0.001). Sex (β = 0.140), stress (β = 0.138), interpersonal problems (β = 0.207), and the usage type of study (β = –0.107) had a negatively significant effect, while the usage type of music/videos (β = 0.118) and the usage type of SNS (β = 0.122) were found to have a positive significant effect. The explanatory power of Model 3 was 29.5%, which was 6.5% higher than that of Model 2. ADHD (β = 0.093), which was significant in Model 2, was excluded from Model 3. The results suggested that the level of smartphone addiction is higher among girls, and with the following: higher stress, more serious interpersonal problems, less use of the smartphone for study, more time spent enjoying music and videos, and more use of social media.

DISCUSSION

We investigated the relationship between user characteristics, personal psychological factors, home, and social environment characteristics, and smartphone usage types, which may be predictors of individual smartphone addiction. The results showed that in terms of smartphone usage types, music/videos and social media use had a positive relationship with smartphone addiction, whereas the usage type study showed a negative relationship. Additionally, the contents that affect smartphone addiction were identified as music/videos, social media, and study, in descending order. Games did not show any relationship with smartphone addiction in adolescents, contrary to the results of previous studies. And leading variables showed that in female students, interpersonal problems and stress were positively associated with smartphone addiction.

Among the smartphone usage types, SNS and music/videos positively predicted smartphone addiction. This result is consistent with previous research findings that the use of social media is a predictor of smartphone addiction4 14 43; enjoying music/videos is also a positive predictor of smartphone addiction.14 44 We classified smartphone usage types into social media use and music/videos. In the case of YouTube and Facebook, however, these platforms combined their function related to the formation of social relationships, which is intrinsic to social media and other various functions related to music/videos. Since the two factors that influence smartphone addiction are combined in a complex manner in the two platforms, further exploration of their influence is needed.

Study, as a smartphone usage type, was found to negatively influence smartphone addiction. This is consistent with the results of a previous study that found that smartphone applications intended for user convenience, such as educational programs/dictionaries, have a negative effect on smartphone addiction.19 This suggests that training students on how to use smartphone content in a balanced manner through an educational approach can help prevent smartphone addiction in the future. The results of this study on the relationship between smartphone addiction and games were not consistent with those of previous studies, which reported that games had a positive effect on smartphone addiction.4 14 Lee et al.10 reported that smartphone usage types differ according to age and sex. The subjects of this study were middle school students, whereas those of previous studies4 14 were elementary school students in the fourth to sixth grades. According to the smartphone application use rates in 2018, the use rate of game apps (38.3%) was the highest in elementary school and higher grades, followed by the use rate of instant messenger apps (17.9%). In contrast, the most frequently used apps by middle school students were instant messenger apps (24.3%), followed by game apps (20.1%). These results suggest that smartphone usage patterns differ depending on the grade.3 Higher grades of elementary school are in the stage of “industry vs. inferiority” according to Erikson's psychological development theory, and experiencing a sense of accomplishment by making efforts has an important influence on them. As many of the smartphone games are designed for single players rather than for group players, it seems that smartphone games stimulate a sense of accomplishment in higher grades of elementary school, engrossing them in these games.

As middle school students are in a stage of “identity vs. role confusion” that characterizes adolescence, peer relationships are important to them. Therefore, they seek to be part of a community where they can share culture and play together with their peers rather than engaging in smartphone games that are relatively more focused on a single play. Therefore, computer games in computer cafes where they can interact with their peer community are more preferred by adolescents to smartphone games. This suggests that research on which platform each age group mainly plays games with their developmental age is also necessary.

Similar to the results of previous studies on smartphone addiction and sex,13 45 we found that female students are more likely to be addicted to smartphones. Lin and Chiang13 reported that young women use smartphones to communicate, maintain relationships, achieve socialization, and find information and entertainment. Female users have a strong emotional attachment to these devices and can become dependent on them.13 In addition, women are under higher stress regarding the maintenance of social relationships than men, and such stress is more likely to lead to habitual and addictive smartphone-related behaviors.8 In line with the findings of previous studies on smartphone addiction and interpersonal problems,31 the presence of interpersonal problems was found to affect smartphone addiction in this study. Another study reported that students who are isolated and evasive in relationships with others show a compulsive addiction to smartphones. Those who are cold and closed in interpersonal relationships have a higher tendency to be addicted to smartphones.46 Those with a higher level of social discomfort, a lower level of social problem-solving ability, and more interpersonal problems were found to have a higher tendency to develop Internet addiction.47 Consistent with the results of previous studies on smartphone addiction and stress,14 33 we found that the higher the stress, the stronger its influence on smartphone addiction. When adolescents face stress, they choose passive and superficial solutions rather than actively seeking solutions to the problems, and prefer cyberspaces with guaranteed anonymity and easy access as a space for releasing their stress.28

Depression, ADHD, and the parenting attitudes of parents showed a significant relationship with smartphone addiction. However, these correlations were not observed in the regression analysis. This suggested that interpersonal problems and stress can be stronger predictors of smartphone addiction than depression, ADHD, and parenting attitudes.

First, the subjects of this study were limited to middle school students in an area of North Chungcheong Province, Korea. Differences may exist in students' smartphone addiction levels according to regions, and smartphone addiction levels and usage types in one area cannot represent those in the entire region. Although this study was conducted on middle school students who are younger adolescents, there may be differences in smartphone usage types and levels depending on the developmental stage and adaptation levels of adolescents. In particular, high school students who are preparing for the college entrance exam may have different smartphone experiences from those of middle school students. Therefore, it will be necessary to consider the regional differences among study subjects and to extend the age range of the subjects in future studies. Second, we classified smartphone usage types based on previous studies comparing smartphone use types. The classification was specially performed by supplementing the types presented by Jeong et al.14 However, this approach has limits in classifying the various functions of smartphones simply by type. There were also limits in terms of how the study subjects' smartphone usage types are determined. That is, related items that are relatively simple and the evaluation of use frequency of each type based on a Likert scale suggest that the validity of this approach may be weak. In the future, a more elaborate scale should be developed to classify the types of smartphone usage. Third, since the measurement was carried out using the study subjects' responses based on their perception using a self-report questionnaire, the respondents' subjective interpretations may be included in the survey results. Therefore, there are limits on generalizing the results of this study, and follow-up studies that investigate the perspectives of parents and teachers are needed.

In Model 2 of hierarchical regression, ADHD appeared to be a significant variable for smartphone addiction, consistent with prior research, but did not significantly affect smartphone addiction in Model 3, where content types were added. In the case of depression, it is difficult to distinguish between depression and anxiety in adolescence. In fact, the strong relationship between anxiety and depression has already been reported in various studies.48 49 Some scholars saw that anxiety and depression were on a continuous line and that anxiety preceded it.50 51 This shows that perceived stress variables similar to anxiety are significant. Thus, in this study, perceived stress variables preceded depression, and mixed results appear to have been shown.

Despite these limitations, this study is important as it provides basic data for studies on smartphone addiction and usage types, as few studies have been conducted on these topics. Particularly with the rapid spread of smartphones, although studies on smartphone use and addiction have been actively conducted, there are relatively fewer studies on content related to the unique functions of smartphones, that is, smartphone applications that correspond to smartphone usage types. We focused on the types of smartphone usage and examined the relationship between these types and smartphone addiction to verify the significant differences in the levels of smartphone addiction according to these smartphone usage types when the variables predicting smartphone addiction are controlled. This suggests that selective intervention according to the main smartphone usage type can be effective in preventing addiction. Accordingly, further studies are needed on the relationship between smartphone usage types and smartphone addiction.

ACKNOWLEDGEMENTS

This study was conducted using the research fund of Dankook University in 2019.

Notes

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

Conceptualization: Hong YP, Lim MH.
Data curation: Hong YP.
Formal analysis: Hong YP, Lim MH.
Investigation: Hong YP, Yeom YO, Lim MH.
Methodology: Hong YP, Lim MH.
Validation: Hong YP, Lim MH.
Writing - original draft: Hong YP, Lim MH.
Writing - review & editing: Hong YP, Yeom YO, Lim MH.

References

1. Samaha M, Hawi NS. Relationships among smartphone addiction, stress, academic performance, and satisfaction with life. Comput Human Behav. 2016; 57:321–325.

2. Lee J, Ahn JS, Min S, Kim MH. Psychological characteristics and addiction propensity according to content type of smartphone use. Int J Environ Res Public Health. 2020; 17(7):2292.

3. Korea Information Society Promotion Agency. Analysis of Cell Phone Retention and Usage Behavior of Children and Adolescents. Seoul, Korea: KIDIS STAT Report;2019.

4. Yeo JY, Kang SY, Kim DH. The impact of attachment anxiety on smart phone addiction with a mediating effect of impulsivity and loneliness: focused on the group of SNS and GAME. Korean J Youth Couns. 2014; 22(1):47–69.

5. Adisetiyo V, Gray KM. Neuroimaging the neural correlates of increased risk for substance use disorders in attention-deficit/hyperactivity disorder: a systematic review. Am J Addict. 2017; 26(2):99–111. PMID: 28106934.

6. Sussman CJ, Harper JM, Stahl JL, Weigle P. Internet and video game addictions: diagnosis, epidemiology, and neurobiology. Child Adolesc Psychiatr Clin N Am. 2018; 27(2):307–326. PMID: 29502753.

7. Korea Information Society Promotion Agency. The survey on smartphone overdependence. Seoul, Korea: Korea Information Society Promotion Agency;2019.

8. Van Deursen AJ, Bolle CL, Hegner SM, Kommers PA. Modeling habitual and addictive smartphone behavior: the role of smartphone usage types, emotional intelligence, social stress, self-regulation, age, and gender. Comput Human Behav. 2015; 45:411–420.

9. Demirci K, Orhan H, Demirdas A, Akpinar A, Sert H. Validity and reliability of the Turkish Version of the Smartphone Addiction Scale in a younger population. Klinik Psikofarmakoloji Bülteni-Bull Clin Psychopharmacol. 2014; 24(3):226–234.

10. Lee EJ, Kim YK, Lim SJ. Factors influencing smartphone addiction in adolescents. Child Health Nurs Res. 2017; 23(4):525–533.

11. Billieux J, Maurage P, Lopez-Fernandez O, Kuss DJ, Griffiths MD. Can disordered mobile phone use be considered a behavioral addiction? An update on current evidence and a comprehensive model for future research. Curr Addict Rep. 2015; 2(2):156–162.

12. Lee TY, Song BH. Smart-phone addiction and countermeasure: focusing on ethics education. Korean Crim Psychol Rev. 2017; 13(1):195–226.

13. Lin TT, Chiang YH. Investigating predictors of smartphone dependency symptoms and effects on academic performance, improper phone use and perceived sociability. IJMC. 2017; 15(6):655.

14. Jeong SH, Kim H, Yum JY, Hwang Y. What type of content are smartphone users addicted to?: SNS vs. games. Comput Human Behav. 2016; 54:10–17.

15. Lee CH, Kim KH, Jang SA. A Study on Policy Measures to Protect Youths with the Spread of Smartphone. Seoul, Korea: National Youth Policy Institute;2013.

16. Oh SC, Kim YK, Park JH. Latent profile analysis of smart device usage pattern in children: smart device addiction according to its usage pattern. Korean J Child Stud. 2020; 41(1):47–60.

17. Mahapatra S. Smartphone addiction and associated consequences: role of loneliness and self-regulation. Behav Inf Technol. 2019; 38(8):833–844.

18. Kneer J, Glock S. Escaping in digital games: the relationship between playing motives and addictive tendencies in males. Comput Human Behav. 2013; 29(4):1415–1420.

19. Park SB, Hwang HS. An exploratory study on factors influencing on smart phone addiction: focused on the application use of university students. Korean J Journalism Commun Stud. 2014; 58(4):289–311.

20. Korea Information Society Promotion Agency. Comprehensive Plan to Support Proper Use of Smartphones and Internet for Realization of a Healthy Smart Society. Seoul, Korea: Korea Information Society Promotion Agency;2016.

21. Hwang SM, Lee SA, Han DH. The psychological features by comorbidity in adolescent internet addiction groups: focusing on depression and impulse. Korean J Psychol Gen. 2013; 32(4):989–1002.

22. Thomée S, Härenstam A, Hagberg M. Mobile phone use and stress, sleep disturbances, and symptoms of depression among young adults--a prospective cohort study. BMC Public Health. 2011; 11(1):66. PMID: 21281471.

23. Seo CM, Lee JH, Choi TY, Kim JH, Shin IH, Woo JM. Study for relations between smart-phone addiction level and Korea youth self report. J Korean Soc Biol Ther Psychiatry. 2012; 18(2):223–230.

24. Park SG, Jo HS. The relationship between happiness, depression and smart phone addiction. Korean J Health Psychol. 2018; 23(4):1095–1102.

25. Lee HC, Hong MH, Oh CK, Shim SH, Jun YJ, Lee SB, et al. Smart-phone addiction, depression/anxiety, and self-esteem with attention-deficit hyperactivity disorder in Korean children. J Korean Acad Child Adolesc Psychiatry. 2015; 26(3):159–164.

26. Kim JH. The effects of parenting attitude and peer attachment on the self- regulated learning of adolescents mediated by mobile phone dependency. Forum Youth Cult. 2014; 39:41–61.

27. Lee HJ, Kim SK, Cheon SM. Meta-analysis on the relationship of parent-child relationship variable to youth internet and smartphone addiction. J Rehab Psychol. 2016; 23(2):329–348.

28. Lee EJ, Eo JK. The differences of the impulsivity and parenting attitude of the high school students according to the levels of the smartphone addiction. JLCCI. 2014; 4(1):1–17.

29. Kim BN, Ko EJ, Choi HG. A study on factors affecting smart-phone addiction in university students: a focus on differences in classifying risk groups. Stud Korean Youth. 2013; 24(3):67–98.

30. Lee EJ, Eo JK. The effects of perceived stress and impulsivity of high school students as well as the parents' parenting attitude on smartphone addiction. Fam Ther. 2015; 23(1):1–22.

31. Kim JU, Gawk TE. The mediating effects of job-seeking stress and interpersonal problems between adult attachment and smartphone addiction of college students. JLCCI. 2016; 16(10):1055–1076.

32. Chae YG. The influence of family functioning, marital conflict, self-esteem and spirituality well-being on aggression in adolescence. J Art Psychother. 2005; 1:74–90.

33. Jeon HS, Jang SO. A study on the influence of depression and stress on smartphone addiction among university students: focused on moderating effect of gender. Korean J Youth Stud. 2014; 21(8):103–129.

34. Shin K, Kim D, Jung Y, Lee J, Lee Y, Kim M. Development of Korean Smartphone Addiction Proneness Scale for Youth and Adults. Seoul, Korea: National Information Society Agency (NIA);2011.

35. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001; 16(9):606–613. PMID: 11556941.

36. An JY, Seo ER, Lim KH, Shin JH, Kim JB. Standardization of the Korean version of screening tool for depression (Patient Health Questionnaire-9, PHQ-9). Korean Soc Biol Therap Psychiatry. 2013; 19(1):47–56.

37. DuPaul G, Power T, Anastopoulos A, Reid R, McGoey K, Ikeda M. Teacher ratings of attention deficit hyperactivity disorder symptoms: factor structure and normative data. Psychol Assess. 1997; 9(4):436–444.

38. Kim YS, So Y, Noh J, Choi N, Kim S, Koh Y. Normative data on the Korean ADHD Rating Scales(K-ARS) for parents and teacher. J Korean Neuropsychiatr Assoc. 2003; 42(3):352–359.

39. Huh MY. The study for the development and Validation of Parenting Behavior Inventory perceived by adolescent (Doctor's dissertation). Seoul: Ewha Women's University;1999.

40. Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav. 1983; 24(4):385–396. PMID: 6668417.

41. Park J, Seo Y. Validation of the Perceived Stress Scale (PSS) on samples of Korean university students. Korean J Psychol Gen. 2010; 29(3):611–629.

42. Hong SH, Park E, Kim YH, Kwon JH, Cho Y, Kim Y. Short form of the Korean Inventory of Interpersonal Problems Circumplex scales (KIIP-SC). Korean J Clin Psychol. 2002; 21(4):923–940.

43. Salehan M, Negahban A. Social networking on smartphones: when mobile phones become addictive. Comput Human Behav. 2013; 29(6):2632–2639.

44. Bian M, Leung L. Smartphone addiction: linking loneliness, shyness, symptoms and patterns of use to social capital. Media Asia. 2014; 41(2):159–176.

45. Lee C, Lee SJ. Prevalence and predictors of smartphone addiction proneness among Korean adolescents. Child Youth Serv Rev. 2017; 77:10–17.

46. Han SS, Oh KS. A study on cellular phone addiction symptom dependent on interpersonal relationship types and using inclination - focus on the case of undergraduates. Korean J Broadcast Telecommun Stud. 2006; 20(4):371–405.

47. Park JS. Relationship of social, interpersonal adjustment and internet use of college student: the practical use in counselling. Korean J Health Psychol. 2010; 15(2):357–367.

48. Joiner TE Jr, Catanzaro SJ, Laurent J. Tripartite structure of positive and negative affect, depression, and anxiety in child and adolescent psychiatric inpatients. J Abnorm Psychol. 1996; 105(3):401–409. PMID: 8772010.

49. Essau CA, Conradt J, Petermann F. Frequency and comorbidity of social phobia and social fears in adolescents. Behav Res Ther. 1999; 37(9):831–843. PMID: 10458047.

50. Dobson KS. The relationship between anxiety and depression. Clin Psychol Rev. 1985; 5(4):307–324.

51. Kendall PC, Brady EU. Comorbidity in the anxiety disorders of childhood: Implications for validity and clinical significance. In : Craig KD, Dobson KS, editors. Anxiety and Depression in Adults and Children. Thousand Oaks, CA, USA: Sage;1995. p. 3–36.

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