online ML Comm ORIGINAL ARTICLE ISSN 1225-879 / eissn 5-7571 Korean J Biol Psychiatry 14;21(4):161-167 차의과학대학교분당차병원정신건강의학교실 이성재 김보라 최태규 이상혁 육기환 Associations between Smartphone Addiction Proneness and Psychopathology Sung-Jae Lee, MD, Borah Kim, MD, Tai Kiu Choi, MD, Sang-Hyuk Lee, MD, Ki-Hwan Yook, MD Department of Psychiatry, CHA Bundang Medical Center, CHA University, Seongnam, Korea ObjectivesZZThis study aims to investigate possible associations between proneness toward smartphone addiction and certain psychopathological variables to evaluate the psychopathological meaning of smartphone addiction. MethodsZZQuestionnaires were assigned to 755 adults between September and November 12. We used the Korean Smartphone Addiction Proneness Scale (), the Beck Depression Inventory (BDI), the Beck Anxiety Inventory (BAI), the Obsessive-Compulsive Inventory-Revised (OCI-R), and the Barratt Impulsivity Scale-11 (BIS-11). Subjects were classified into two groups according to the scores of the ; the addiction proneness group and the normal-user group. ResultsZZThe addiction proneness group had significantly higher scores than the normal-user group in the BDI, BAI, OCI-R, and BIS-11. The scores of the were positively correlated with the scores of the BDI, BAI, OCI-R, and BIS-11. Using logistic regression analysis, smartphone addiction proneness showed a significant association with BIS-11. ConclusionsZZThe results of this study suggest that smartphone addiction proneness may be associated with depression, anxiety, obsessive-compulsive symptoms, and impulsivity. Furthermore, impulsivity could be a vulnerability marker for smartphone addiction proneness. Key WordsZZSmartphone ㆍ Addiction ㆍ Psychopathology ㆍ Impulsivity. Received: September 23, 14 / Revised: September 29, 14 / Accepted: October 1, 14 Address for correspondence: Ki-Hwan Yook, MD Department of Psychiatry, CHA Bundang Medical Center, CHA University, 59 Yatap-ro, Bundang-gu, Seongnam 463-712, Korea Tel: +82-31-78-5874, Fax: +82-31-78-5862, E-mail: cha99@cha.ac.kr 스마트폰은휴대폰에컴퓨터의기능을더한것으로인터넷및게임등여러기능을가지고있다. 스마트폰은휴대폰고유의연락기능뿐만아니라더빠르고더편한인터넷접근을가능하게하여, 기존의휴대폰이나컴퓨터보다일상에서더중요한역할을하고있다. 과거의컴퓨터, 휴대폰, 게임기가사용자들의건강에미친유해효과와마찬가지로스마트폰의건강에대한유해효과에대해관심이커지고있다. Kim과 Chae 1) 는스마트폰사용자세에대해생체역학연구를수행하였다. 이연구에서스마트폰사용자세가엄지손가락의운동범위와근육활동에영향을줄수있는것으로나타났다. 그리고 Choi 등 2) 은스마트폰중독이대학생들의정신건강, 학교생활, 그리고대인관계에부정적인영향을미칠수있다고보고하였다. 중독의개념으로스마트폰사용과관련된문제점들을설명할수도있다. 전통적인중독의개념은의학적모델을기반으로하여물질에대한신체적의존과심리적의존을설명하였지만, 행동양식에대한것은아니었다. 3) 그러나최근에는중독에대한개념을행동에대한중독까지포함하는것으로확장시켜야한다는주장들이제기되고있다. 4-6) 인터넷서핑, 텔레비전시청, 컴퓨터게임등그목적이나종류에상관없이특정기기의과다한사용은신체적또는심리적문제를초래할수있기때문에중독이라고불려야한다는주장들이있다. 7)8) Copyright 14 Korean Society of Biological Psychiatry 161
Korean J Biol Psychiatry 14;21(4):161-167 그동안인터넷중독은몰두, 내성, 금단, 조절의실패, 의도한것보다길어진사용시간, 기능적장애, 거짓말, 도피등으로정의되었다. 9) 정신질환진단및통계편람제4판개정판에서는충동조절장애의개념으로문제성인터넷사용에대한진단기준이제시되었다. 1) 또한휴대폰중독은의존, 내성, 불안및갈망, 금단, 지속적몰입, 강박, 생활장애, 통제력상실, 생활력상실등으로정의되기도하였다. 3) 아직까지스마트폰중독에대한보편적정의는없다. 하지만최근한국정보화진흥원은일상생활장애, 가상세계지향성, 금단, 내성등 4가지요인에따라스마트폰중독을정의하고스마트폰중독진단척도를개발하였다. 스마트폰은컴퓨터와휴대폰의특성을모두가지고있기때문에, 스마트폰중독은인터넷중독과휴대폰중독의증상을모두가지고있을것으로보인다. 몇몇연구에서인터넷중독또는문제성인터넷사용은우울, 11-13) 강박증상, 13)14) 충동성 15) 등과연관이있는것으로나타났다. 또한휴대폰중독또는문제성휴대폰사용은우울, 16)17) 불안 18) 등과연관이있는것으로보고되었다. 지금까지스마트폰사용이정신건강에미치는유해효과를조사하는연구는없었다. 그래서본연구는스마트폰중독과정신병리와의연관성을조사하여스마트폰중독의정신병리적의미를파악하고향후스마트폰중독의생물학적기전을밝히는데도움이되고자한다. 저자들은스마트폰중독이우울, 불안, 강박증상, 충동성과연관이있을것으로가정하였다. 구성된자기보고식척도이다. 모든문항은 1~4점의평정척도이다. 대상자들은고위험사용자군, 잠재적위험사용자군, 일반사용자군등세군으로분류되고, 고위험사용자군과잠재적위험사용자군을합쳐중독위험군으로분류된다. 한국정보화진흥원이보고한 의신뢰도는 Cronbach s α =.814 이다. 정신병리척도벡우울척도 (Beck s Depression Inventory, 이하 BDI) )21) 는 21문항으로구성된자기보고식설문지로지난 1주간의우울증상에대해평가한다. 모든문항은 ~3점의평정척도이며 BDI 한국판의신뢰도는 Cronbach s α =.92이다. 벡불안척도 (Beck s Anxiety Inventory, 이하 BAI) 22)23) 는 21문항으로구성된자기보고식설문지로지난 1주간의불안증상에대해평가한다. 모든문항은 ~3점의평정척도이며 BAI 한국판의신뢰도는 Cronbach s α =.93이다. 강박척도 (Obsessive-Compulsive Inventory-Revised, 이하 OCI-R) 24)25) 는 18문항으로구성된자기보고식설문지로강박으로인한고통에대해평가한다. 모든문항은 ~4점의평정척도이며 OCI-R 한국판의신뢰도는 Cronbach s α =.9이다. Barratt 충동성검사 (Barratt Impulsiveness Scale-11, 이하 BIS-11) 26)27) 는 23문항으로구성된자기보고식설문지로충동성에대해평가한다. 모든문항은 1~4점의평정척도이며 BIS-11 한국판의신뢰도는 Cronbach s α =.81이다. 대상자 본연구에는정신과적질환이없는대학생과직장인이참가하였다. 대상자는 세이상 6세이하였고, 조사당시스마트폰을사용하고있었다. 12년 9월과 11월사이에스마트폰을사용하는 755명이연구에등록되었다. 모두서면동의를구하였고, 연구에대한설문지가배부되었다. 본연구는차의과학대학교연구윤리심사위원회를거쳐진행되었다. 평가스마트폰중독진단척도 (Smartphone Addiction Proneness Scale) 19) 대상자들의스마트폰중독을평가하기위해 12년 3월현재시점에서가장많은전문가들이참여하고가장큰규모로척도의신뢰도와타당성을검증한스마트폰중독진단척도 (Smartphone Addiction Proneness Scale, 이하 ) 를사용하였다. 한국정보화진흥원이개발한 는 15문항으로 자료분석모든통계분석은 Statistical Package for the Social Sciences ( 이하 SPSS) version. for Windows(SPSS Inc., Chicago, IL, USA) 를사용하였다. 중독위험군과일반사용자군의범주형변수비교는 χ 2 검정을이용하였고, 연속형변수비교는 t 검정을이용하였다. 점수와정신병리변인점수와의관련성을평가하기위하여 Pearson s correlation coefficient 를산출하였다. 스마트폰중독과정신병리변인사이의인과성을알아보기위하여다변량로지스틱회귀분석을수행하였다. 유의수준은 p-value.5 이하로하였다. 인구학적변인과스마트폰사용 전체대상자중여성이 71.5% 였고, 전체대상자의평균나이는 24.8 ± 5.세였다. 전체대상자중미혼이 86.6% 이고, 대학생은 78.7% 이며, 평균교육기간은 15.23 ± 1.8년이었다. 중독위험군은전체대상자의 19.5%( 고위험사용자군 3.2%, 잠재적위험사용자군 16.3%) 였다 (Table 1). 전체대상자의한 162
Smartphone and Psychopathology Lee SJ, et al 달평균발신통화시간과하루평균스마트폰사용시간은각각 244.17 ± 318.93 분과 27.89 ± 263.3 분이었다. 전체대상자의메신저를포함한한달평균발신문자건수는 1552.78 ± 5111.29 건이었다 (Table 2). 중독위험군은일반사용자군에비해통계적으로유의하게어리고 ( 각각 23.31 ± 4.71 세, 25.21 ± 5.5세, p <.1), 교육기간이짧았다 ( 각각 14.71 ± 1.67 년, 15.38 ± 1.8 년, p <.1). 또한중독위험군에서일반사용자군에비해미혼자비율이유의하게높았다 ( 각각 93.1%, 84.9%, p =.14). 하지만두군사이에성비와고용상태는유의한차이가없었다 (Table 3). 중독위험군은일반사용자군에비해한달평균발신문자건수 ( 각각 2589.78 ± 5292.59 건, 1287.84 ± 554.75 건, p =.15) 와하루평균스마트폰사용시간 ( 각각 37.19 ± 285.27분, 246.95 ± 252.58, p <.1) 이유의하게높았다. 하지만두 Table 1. Demographic for all subjects Subjects 755 (1) Sex Male 214 (28.5) Female 537 (71.5) Age (years) 24.8 ± 5. Marital status Married 1 (13.4) Unmarried 645 (86.6) Education span (years) 15.23 ± 1.8 Occupation College students 583 (78.7) Employed 154 (.8) Unemployed 4 (.5) Smartphone use Normal-user group 598 (8.5) Addiction proneness group 145 (19.5) Values are mean ± SD or n (%). SD : standard deviation 그룹사이에한달평균발신통화시간은유의한차이는없었 다. 스마트폰사용의주된목적중두그룹사이에유의하게차 이를보이는항목은음악, 인터넷, 소셜네트워크서비스, 게임, 쇼 핑, 일정관리, 교육및학습등이었다 (all p <.5)(Table 4). 중독위험군과일반사용자군의정신병리변인의비교 중독위험군은일반사용자군과비교하여 BDI, BAI, OCI-R, Table 2. Smartphone use pattern of all subjects Total time of making calls per month (minutes) Frequency of sending messages per month Total time using a smartphone per day (minutes) Main purpose of using a smartphone [n (%)] n = 755 244.17 ± 318.93 1552.78 ± 5111.29 27.89 ± 263.3 Chatting on messenger 624 (83.3) Making calls & messages 6 (8.2) Listening to music 565 (75.4) News 511 (68.2) Web surfing 448 (58.9) SNS 393 (52.5) Video games 354 (47.3) Shopping 314 (41.9) Scheduling 249 (33.2) GPS 234 (31.2) Internet banking 231 (3.8) TV, movies, radio 6 (27.5) Online community 156 (.8) Education 19 (14.6) Reading 51 (6.8) Pornography 8 (1.1) Gambling 2 (.3) Values are mean ± SD or n (%). GPS : global positioning system, SNS : social networking services, SD : standard deviation Table 3. Demographic for the normal-user group and the smartphone addiction proneness group Sex Normal-user group (n = 598) Addiction proneness group (n = 145) p Male 158 (26.6) 5 (34.7) Female 437 (73.4) 94 (65.3) <.63 χ 2 = 3.824 Age (years) 25.21 ± 5.5 23.31 ± 4.71 <.1 t = 3.811 Marital status Married 89 (15.1) 1 (6.9) Unmarried 5 (84.9) 134 (93.1) <.14 χ 2 = 6.65 Education span (years) 15.38 ± 1.8 14.71 ± 1.67 <.1 t = 4.87 Occupation College students 464 (79.2) 19 (75.7) Employed 118 (.1) 35 (24.3) Unemployed 4 (.7) (.) Values are mean ± SD or n (%). * : Fisher s exact test. SD : standard deviation <.426* F = 1.566 journal.biolpsychiatry.or.kr 163
Korean J Biol Psychiatry 14;21(4):161-167 Table 4. Smartphone use pattern of the normal-user group and the smartphone addiction proneness group Normal-user group Addiction proneness group p Total time making calls per month (minutes) 249.8 ± 343.3 223.56 ± 197.2.398 t =.845 Frequency of sending messages per month 1287.84 ± 554.75 2589.78 ± 5292.59.15 t = -2.438 Total time using a smartphone per day (minutes) 246.95 ± 252.58 37.19 ± 285.27 <.1 t = -4.847 Main purpose of using a smartphone [n (%)] Chatting on messenger 493 (83.) 124 (86.1).384 χ 2 =.8 Making calls & messages 479 (8.8) 114 (79.2).725 χ 2 =.191 Listening to music 437 (73.6) 121 (84.).9 χ 2 = 6.875 News (67.3) 16 (73.6).162 χ 2 = 2.115 Web surfing 338 (56.9) 16 (73.6) <.1 χ 2 = 13.51 SNS 3 (5.5) 89 (61.8).16 χ 2 = 5.938 Video games 259 (43.6) 91 (63.2).1 χ 2 = 17.842 Shopping 238 (.1) 74 (51.4).15 χ 2 = 6.88 Scheduling 184 (31.) 62 (43.1).8 χ 2 = 7.61 GPS 184 (31.) 48 (33.3).617 χ 2 =.299 Internet banking 178 (3.) 51 (35.4).228 χ 2 = 1.69 TV, movies, radio 156 (26.3) 47 (32.6).145 χ 2 = 2.363 Online community 117 (19.7) 38 (26.4).87 χ 2 = 3.128 Education 76 (12.8) 32 (22.2).6 χ 2 = 8.246 Reading 41 (6.9) 1 (6.9) 1. χ 2 =. Pornography 4 (.7) 4 (2.8) Gambling 2 (.3) (.) Values are mean ± SD or n (%). GPS : global positioning system, SNS : social networking services, SD : standard deviation Table 5. Comparison of psychopathological variables between the normal-user group and the smartphone addiction proneness group Normal-user group Addiction proneness group p t BDI 7.91 ± 6.79 11.23 ± 8.83 <.1-4.156 BAI 6.89 ± 7.14 9.91 ± 9.87 <.1-3.421 OCI-R 11.62 ± 1.59 15.86 ± 12.67 <.1-4.59 BIS-11 49.76 ± 7. 53.29 ± 8.6 <.1-4.43 Values are mean ± SD. BAI : Beck Anxiety Inventory, BDI : Beck Depression Inventory, BIS-11 : Barratt Impulsivity Scale-11, OCI-R : Obsessive-Compulsive Inventory-Revised, SD : standard deviation BIS-11 점수가통계적으로유의하게높았다 (BDI, t = -4.156, p <.1 ; BAI, t = -3.421, p <.1 ; OCI-R, t = -4.59, p <.1 ; BIS-11, t = -4.43, p <.1)(Table 5). 스마트폰중독과정신병리사이의관계 점수와정신병리변인점수와의관련성을평가하기위하여시행한 Pearson correlation 분석상 점수와 BDI, BAI, OCI-R, BIS-11 점수사이에통계적으로유의한양의상관관계가나타났다 (BDI, r =.284, p <.1 ; BAI, r =.21, p <.1 ; OCI-R, r =.2, p <.1 ; BIS-11, r =.249, p <.1)(Fig. 1). 스마트폰중독과정신병리사이의인과성을알아보기위하여, BDI, BAI, OCI-R, BIS-11, 성별, 나이, 교육기간, 결혼상태에대해다변량로지스틱회귀분석을수행하였다. 유의한연관성은오직 와 BIS-11 사이에서만나타났다 [β =.5, confidence interval(ci) : 1.22-1.82, p <.1] (Table 6). 본연구는스마트폰중독과정신병리사이의상관관계를알아본첫번째연구이다. 본연구에서중독위험군은 BDI, BAI, OCI-R, BIS-11 점수가일반사용자군에비해유의하게높았다. 또한 점수는 BDI, BIA, OCI-R, BIS-11 과양의상관관계를보였다. 이러한결과들은스마트폰중독이우울, 불안, 강박증상, 충동성등과연관되어있음을시사한다. 본연구에서스마트폰중독은정신병리와연관되어있을것으로시사되었다. 그동안인터넷중독과정신과적증상사이의양방향성상호작용이제시되어왔는데, 28) 이러한양방향성상호작용을스마트폰중독과정신과적증상사이의관계를설명 164
Smartphone and Psychopathology Lee SJ, et al 5 r =.284, p <.1 6 r =.21, p <.1 3 BDI BAI 1 6 6 6 r =.2, p <.1 1 r =.249, p <.1 8 OCI-R BIS-11 6 6 6 Fig. 1. Pearson s correlation analysis between scores of the Smartphone Addiction Proneness Scale () and psychopathological variables. BAI : Beck s Anxiety Inventory, BDI : Beck s Depression Inventory, BIS-11 : Barratt Impulsivity Scale-11, OCI-R : Obsessive-Compulsive Inventory-Revised. Table 6. Logistic regression analysis evaluating factors associated with smartphone addiction proneness β Wald Odds ratio 95% CI p BDI.29 2.859* 1.29.995-1.64.91 BAI.1.398* 1.1.979-1.42.528 OCI-R.13 1.697* 1.13.993-1.34.193 BIS-11.5 11.933* 1.51 1.22-1.82.1 * : logistic regression analysis was conducted to investigate the effects of the psychopathological variables, sex, age, education, and marital status on smartphone addiction proneness. An association only between the scores of the BIS-11 and the smartphone addiction proneness scale was observed. BAI : Beck s Anxiety Inventory, BDI : Beck s Depression Inventory, BIS-11 : Barratt Impulsivity Scale-11, CI : confidence interval, OCI-R : Obsessive-Compulsive Inventory-Revised 하는데적용할수있을것이다. 즉, 한편으로는과다한스마트폰사용이정신과적증상을경감시키는데사용될수있지만, 다른한편으로는비적응적스마트폰사용이정신과적증상을초래하거나악화시킬수도있다는것이다. 본연구에서중독위험군은일반사용자군에비해유의하게높은 BDI 점수를보였다. 이결과는이전의인터넷중독에관한연구들과일치한다. Ko 등 29) 은그들의 2년간의전향적설문조사에서우울증이있는청소년들이대조군에비해인터넷중독에걸릴가능성이유의하게높다고보고하였다. 인터넷은청소년들에게사회적지지, 성취감, 통제감, 현실세계의정서적어려움을탈출할수있는가상세계를제공하기때문에, 우울증이있는청소년들은자신들의증상을경감시키기위 해서우울증이없는청소년들에비해인터넷을더많이사용하게되고, 이로인해인터넷중독에빠질위험성이더높은것으로보인다. 3-32) Kraut 등 33) 은 Rich get richer 모델을제시하였는데, 이미잘적응하고있는사람에게는인터넷이더많은편익을제공하지만, 적응에어려움을겪는우울증이있는사람에게는인터넷이오히려유해한영향을미쳐서악순환을만들수있다는것이다. 이와같은모델이스마트폰중독과우울과의연관성을설명하는데적용될수있을것이다. 본연구에서중독위험군은일반사용자군에비해유의하게높은 BAI 점수를보였다. 이는휴대폰중독이불안과유의한연관성을보인다는연구와, 18) 인터넷중독이사회불안장애와연관이높다는이전의보고와일치한다. 13) 또한 Ko 등 29) journal.biolpsychiatry.or.kr 165
Korean J Biol Psychiatry 14;21(4):161-167 은그들의 2년간의전향적설문조사에서사회불안증상이인터넷중독의발생을예측할수있는요인이라고제시했다. 이들은인터넷이사회적지지를제공하기때문에, 사회불안증상이있는청소년이다른사람과대면하는스트레스를피하면서사회적지지를받기위해사회불안증상이없는청소년에비해인터넷에더몰두한다고제시하였다. 34) 본연구에서중독위험군은일반사용자군에비해유의하게높은 OCI-R 점수를보였다. 인터넷중독이강박행동과동반하는경우가많다는이전의보고와일치한다. 1) 사실, 중독은강박행동으로발현하는뇌질환이라고정의되기도하고, 유해한것을알지만특정물질이나행동을강박적이고지속적으로하는것으로정의되기도한다. 35) 따라서스마트폰중독이강박증상과연관이있을것이다. 인터넷중독은충동조절장애와연관이있는것으로보고되었다. 그래서충동조절장애의개념으로문제성인터넷사용의진단기준이제시되기도하였다. 1) 또한인터넷중독이있는청소년이대조군에비해충동성이더높은것으로보고되기도하였는데, 충동성이인터넷중독발생의위험요인으로제시되기도하였다. 36) 인터넷중독에대한신경생물학적접근으로서, Park 등 37) 은인터넷과다사용자들이대조군에비해양전자방출단층촬영술상충동조절및보상처리와관련된안와전두엽등의뇌영역에서포도당대사가증가되어있다고보고하였다. 본연구에서회귀분석상스마트폰중독은충동성과상관관계를보여스마트폰중독을충동조절장애의개념으로이해하는게타당할수도있을것으로보인다. 하지만아직은근거가부족하므로이후연구에서는이를증명하기위하여스마트폰중독자들의충동성을평가할수있는지연할인, 행동억제, 의사결정등과관련된신경심리학적검사가필요할것으로보인다. 본연구에서스마트폰중독의생물학적기전은조사하지않았으나, 스마트폰중독이인터넷중독의특성을반영한다는것을고려하여그생물학적기전을추측해볼수있겠다. Lee 등 38) 은세로토닌기능에관여하는 5HTTLPR 다형성의짧은대립인자와인터넷과다사용과의연관성을보고하였다. 또한 Koepp 등 39) 은피험자들이비디오게임을하는동안선조체의도파민이분비된다고보고하였는데, 이러한분비는피험자들이게임에집중할수있게하고더좋은수행능력을보여주는데도움이된다고한다. Ko 등 ) 은온라인게임중독군과대조군에게게임장면을보여주며기능자기공명영상법으로뇌영상을촬영하였다. 이연구에서온라인게임중독군은대조군에비해우측안와전두엽, 우측측중격핵, 양측앞쪽대상회와안쪽전두엽, 우측뒤가쪽전전두엽, 우측미상핵등에서더활성화되는것으로나타났다. 이러한결과들을고려 하여, 향후스마트폰중독의생물학적기전을밝히기위해서 는스마트폰중독과연관성을보이는정신과적질환및증상 의생물학적기전을바탕으로접근하는것이필요하겠다. 본연구에는몇가지제한점이있다. 첫째, 전체대상자중여 성이 71.5% 이다. 비록군사이의비교에서성비의차이는없었 지만, 추후연구에서는성비의균형을맞추는것이필요하겠 다. 둘째, 본연구에서는스마트폰중독과정신병리사이의인 과관계를밝힐수없었다. 이를보완하기위해향후종단연구 가필요하겠다. 셋째, 본연구에서는간단한자기보고식평가 만이루어졌다. 보다정확하고다양한평가를위해추후연구 에서는자기보고식이외의객관적검사가필요하겠다. 또한 가스마트폰중독을평가하는데다소단순한문항으로 구성되었다는지적이있는데, 41) 추후연구에서는좀더정교화 된최근의척도를사용하는것이도움이되겠다. 결론적으로, 본연구는한국의공인된기관에서고안한스 마트폰중독진단척도를이용하여스마트폰중독과정신병리 사이의상관관계를기술한첫번째연구이다. 본연구에서 스마트폰중독은우울, 불안, 강박증상, 충동성등과연관이 있음이시사된다. 특히, 충동성은스마트폰중독의위험요인 으로고려될수있으므로향후이에대한보완연구가필요할 것이다. 중심단어 : 스마트폰 중독 정신병리 충동성. Acknowledgments 논문작성에도움을준이지은선생님, 최아영선생님, 김민국선 생님, 김민경선생님께감사의마음을전합니다. Conflicts of interest The authors have no financial conflicts of interest. REFERENCES 1) Kim DS, Chae WS. Biomechanical analysis of a smartphone task with different postures. Korean J Sport Biomech 12;22:253-259. 2) Choi HS, Lee HK, Ha JC. The influence of smartphone addiction on mental health, campus life and personal relations- focusing on K university students. J Korean Data Inf Sci Soc 12;23:15-115. 3) Leung L. Linking psychological attributes to addiction and improper use of the mobile phone among adolescents in Hong Kong. J Child Media 8;2:93-113. 4) Shaffer HJ. Understanding the means and objects of addiction: Technology, the internet, and gambling. J Gambl Stud 1996;12:461-469. 5) Orford J. Excessive appetites: a psychological view of addictions. 2nd ed. Chichester: Wiley;1. 6) Lemon J. Can we call behaviors addictive? Clinical Psychologist 5;6:44-49. 7) Griffiths MD. Internet addiction: Does it really exist? In: Gackenbach J, editor. Psychology and the Internet: intrapersonal, interpersonal and transpersonal applications. New York: Academic Press;1998. p.61-75. 8) Shotton MA. Computer addiction?: a study of computer dependency. London: Taylor & Francis;1989. 166
Smartphone and Psychopathology Lee SJ, et al 9) Young KS. Internet addiction: The emergence of a new clinical disorder. Cyberpsychol Behav 1998;1:237-244. 1) Shapira NA, Lessig MC, Goldsmith TD, Szabo ST, Lazoritz M, Gold MS, et al. Problematic internet use: proposed classification and diagnostic criteria. Depress Anxiety 3;17:7-216. 11) Kim K, Ryu E, Chon MY, Yeun EJ, Choi SY, Seo JS, et al. Internet addiction in Korean adolescents and its relation to depression and suicidal ideation: a questionnaire survey. Int J Nurs Stud 6;43: 185-192. 12) Ko CH, Yen JY, Chen CS, Chen CC, Yen CF. Psychiatric comorbidity of internet addiction in college students: an interview study. CNS Spectr 8;13:147-153. 13) Bernardi S, Pallanti S. Internet addiction: a descriptive clinical study focusing on comorbidities and dissociative symptoms. Compr Psychiatry 9;5:51-516. 14) Ha JH, Yoo HJ, Cho IH, Chin B, Shin D, Kim JH. Psychiatric comorbidity assessed in Korean children and adolescents who screen positive for Internet addiction. J Clin Psychiatry 6;67:821-826. 15) Yen JY, Ko CH, Yen CF, Wu HY, Yang MJ. The comorbid psychiatric symptoms of Internet addiction: attention deficit and hyperactivity disorder (ADHD), depression, social phobia, and hostility. J Adolesc Health 7;41:93-98. 16) Song EJ. The relationship between the using mobile phone and mental health state of high school students. J Korean Acad Psych Mental Health Nurs 6;15:325-333. 17) Jang SH, Cho KD. Effects of depression scale, interaction anxiety and school adjustment on cellular phone addiction of teenagers. J Korea Contents Assoc 1;1:285-293. 18) Jang SH, Oh EJ. The relationship among cellular phone addiction, depression, anxiety, and school life satisfaction perceived by students. J Fish Mar Sci Edu 9;21:259-268. 19) Korea National Information Society Agency. Development of Korean Smartphone Addiction Proneness Scale For Youth and Adults. Seoul: Korea National Information Society Agency;11. ) Beck AT, Ward CH, Mendelson M, Mock J, Erbaugh J. An inventory for measuring depression. Arch Gen Psychiatry 1961;4:561-571. 21) Lee MK, Lee YH, Park SH, Sohn CH, Jung YJ, Hong SK, et al. A standardization study of beck depression inventory (I): Korean version (K-BDI): reliability land factor analysis. Korean J Psychopathol 1995;4:77-95. 22) Beck AT, Steer RA. Manual for the Beck anxiety inventory. San Antonio: Psychological Corporation;199. 23) Yook SP, Kim ZS. A clinical study on the Korean version of Beck Anxiety Inventory: comparative study of patient and non-patient. Korean J Clin Psychol 1997;16:185-197. 24) Foa EB, Huppert JD, Leiberg S, Langner R, Kichic R, Hajcak G, et al. The Obsessive-Compulsive Inventory: development and validation of a short version. Psychol Assess 2;14:485-496. 25) Lim JS, Kim SJ, Jeon WT, Cha KR, Park JH, Kim CH. Reliability and validity of the Korean version of Obsessive-Compulsive Inventory-Revised in a non-clinical sample. Yonsei Med J 8;49:99-916. 26) Barratt ES. Impulsiveness subtraits: Arousal and information processing. In: Spence JT, Izard CE, editors. Motivation, Emotion and Personality. Amsterdam: Elsevier Science Amsterdam, Inc.;1985. p.137-146. 27) Lee HS. Impulsivity Test. Seoul: Korean Guidance;1992. 28) Yen JY, Ko CH, Yen CF, Chen SH, Chung WL, Chen CC. Psychiatric symptoms in adolescents with Internet addiction: Comparison with substance use. Psychiatry Clin Neurosci 8;62:9-16. 29) Ko CH, Yen JY, Chen CS, Yeh YC, Yen CF. Predictive values of psychiatric symptoms for internet addiction in adolescents: a 2-year prospective study. Arch Pediatr Adolesc Med 9;163:937-943. 3) Tichon JG, Shapiro M. The process of sharing social support in cyberspace. Cyberpsychol Behav 3;6:161-17. 31) Suler JR. To get what you need: healthy and pathological internet use. Cyberpsychol Behav 1999;2:385-393. 32) Leung L. Net-generation attributes and seductive properties of the internet as predictors of online activities and internet addiction. Cyberpsychol Behav 4;7:333-348. 33) Kraut R, Kiesler S, Boneva B, Cummings J, Helgeson V, Crawford A. Internet paradox revisited. J Social issue 2;58:49-74. 34) Ko CH, Yen JY, Yen CF, Chen CS, Chen CC. The association between Internet addiction and psychiatric disorder: a review of the literature. Eur Psychiatry 12;27:1-8. 35) Leshner AI. Addiction is a brain disease, and it matters. Science 1997; 278:45-47. 36) Cao F, Su L, Liu T, Gao X. The relationship between impulsivity and internet addiction in a sample of Chinese adolescents. Eur Psychiatry 7;22:466-471. 37) Park HS, Kim SH, Bang SA, Yoon EJ, Cho SS, Kim SE. Altered regional cerebral glucose metabolism in internet game overusers: a 18F-fluorodeoxyglucose positron emission tomography study. CNS Spectr 1;15:159-166. 38) Lee YS, Han DH, Yang KC, Daniels MA, Na C, Kee BS, et al. Depression like characteristics of 5HTTLPR polymorphism and temperament in excessive internet users. J Affect Disord 8;19:165-169. 39) Koepp MJ, Gunn RN, Lawrence AD, Cunningham VJ, Dagher A, Jones T, et al. Evidence for striatal dopamine release during a video game. Nature 1998;393:266-268. ) Ko CH, Liu GC, Hsiao S, Yen JY, Yang MJ, Lin WC, et al. Brain activities associated with gaming urge of online gaming addiction. J Psychiatr Res 9;43:739-747. 41) Kwon M, Lee JY, Won WY, Park JW, Min JA, Hahn C, et al. Devel+opment and validation of a smartphone addiction scale (SAS). PLoS One 13;8:e56936. journal.biolpsychiatry.or.kr 167