Figures
Abstract
Background and Aims
Internet addiction (IA) could be a major concern in university medical students aiming to develop into health professionals. The implications of this addiction as well as its association with sleep, mood disorders and self-esteem can hinder their studies, impact their long-term career goals and have wide and detrimental consequences for society as a whole. The objectives of this study were to: 1) Assess potential IA in university medical students, as well as factors associated with it; 2) Assess the relationships between potential IA, insomnia, depression, anxiety, stress and self-esteem.
Methods
Our study was a cross-sectional questionnaire-based survey conducted among 600 students of three faculties: medicine, dentistry and pharmacy at Saint-Joseph University. Four validated and reliable questionnaires were used: the Young Internet Addiction Test, the Insomnia Severity Index, the Depression Anxiety Stress Scales (DASS 21), and the Rosenberg Self Esteem Scale (RSES).
Results
The average YIAT score was 30 ± 18.474; Potential IA prevalence rate was 16.8% (95% confidence interval: 13.81–19.79%) and it was significantly different between males and females (p-value = 0.003), with a higher prevalence in males (23.6% versus 13.9%). Significant correlations were found between potential IA and insomnia, stress, anxiety, depression and self-esteem (p-value < 0.001); ISI and DASS sub-scores were higher and self-esteem lower in students with potential IA.
Conclusions
Identifying students with potential IA is important because this addiction often coexists with other psychological problems. Therefore, interventions should include not only IA management but also associated psychosocial stressors such as insomnia, anxiety, depression, stress, and self-esteem.
Citation: Younes F, Halawi G, Jabbour H, El Osta N, Karam L, Hajj A, et al. (2016) Internet Addiction and Relationships with Insomnia, Anxiety, Depression, Stress and Self-Esteem in University Students: A Cross-Sectional Designed Study. PLoS ONE 11(9): e0161126. https://doi.org/10.1371/journal.pone.0161126
Editor: Andrea Romigi, University of Rome Tor Vergata, ITALY
Received: March 31, 2016; Accepted: July 30, 2016; Published: September 12, 2016
Copyright: © 2016 Younes et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: The authors received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Internet use has grown exponentially worldwide to more than 2.5 billion active users [1, 2] with the majority being adolescents and young people [3]. Paralleling the rapid growth in internet access is a rise in internet addiction, especially among adolescents, gaining increased attention from the popular media, government authorities, and researchers [4].
Excessive internet use is defined as when internet use has become excessive, uncontrolled, and time-consuming to the point of timelessness and severely disrupting people’s lives [5]. Internet addiction is characterized by a maladaptive pattern of internet use leading to clinically significant impairment or distress [6].
The terms “problematic internet use” [7], pathological internet use [8–10] and “internet addiction” [11–13] are usually considered synonyms of internet dependence [14]. Young et al [15–17] proposed diagnostic criteria for internet addiction (IA) in which withdrawal, poor planning abilities, tolerance, preoccupation, impairment of control, and excessive online time were defined as core symptoms.
Worldwide prevalence of IA ranged from 1.6%-18% [18]. 10.7% of adolescents in South Korea present IA according to Yong’s internet addiction scale [19]. 11% in Greece, based on the same test [20]; 10.7–13.9% of European adolescents are at-risk for addictive use, based on Young's instruments [21] and 4% in high school students in the USA [22].
IA prevalence may vary according to age, sex and ethnicity, and it prevails more commonly among college students [23].
A high rate of personality disorders are found in individuals with IA [24–27].
Heavy internet use was also reported to be associated to mood disorders [28], poor sleep quality [28, 29], low self-esteem [30], impulsivity [31], suicide [32, 33], lower levels of physical activity [29], and health problems (migraines, back pain, obesity) [34].
Our hypothesis were that IA could be a major concern in university medical students, and that examining its association with sleep, mood disorders and self-esteem is important, so that appropriate measures can be taken to address this issue.
For medical students aiming to develop into health professionals, the implications of this addiction can hinder their studies and impact their long-term career goals and can have wide and detrimental consequences on society as a whole.
The objectives of this study were to: 1) Assess potential IA in students at the Campus of Medical Sciences (CMS) at Saint-Joseph University in Lebanon, as well as socio-demographic factors associated with it; 2) Assess the relationships between potential IA, insomnia, depression, anxiety, stress and self-esteem while accounting for the simultaneous exposure to insomnia, stress, anxiety and depression in students.
Materials and Methods
Ethical considerations
The protocol of the study was approved by the ethics committee of Saint-Joseph University (Ref USJ-2015-28, June 2015). Informed written consent was obtained from all individuals participating in the study.
Survey procedure and sampling
Our study was a cross-sectional questionnaire-based survey conducted among students of three faculties: medicine, dentistry and pharmacy at Saint-Joseph University, from September to December 2015 (4 months). Inclusion criteria were: students aged 18 years and above, and willing to participate in the study. Exclusion criteria were: age under 18 years and presence of a chronic disease. Students were randomly selected within each class using a random number table to ensure the representativeness of the sample. This random selection was proportional to the number of students in each class. Students selected were approached by two trained research assistants usually at the end of their courses before leaving the classroom and asked if they were willing to participate on the condition that they didn’t present any exclusion criteria. A written formal consent was then obtained.
Data collection
Data were collected during a face-to-face interview using a self-administered standardized survey tool based on four internationally validated and reliable questionnaires, namely the Young Internet Addiction Test, the Insomnia Severity Index, the Depression Anxiety Stress Scales (DASS 21), and the Rosenberg Self Esteem Scale. The duration of interviews ranged from 15 to 25 minutes.
Measures
Participants.
Personal data about age, gender and faculty were collected. Furthermore, information about living alone or not, tobacco (cigarette or water pipe), and alcohol use were also obtained.
Internet addiction.
The Young Internet Addiction Test (YIAT) is validated among adolescents and adults and widely used [15, 16, 35]. It is a 20-item self-report scale assessing a respondent’s productivity at work, school, or home (3 questions), social behaviors (3 questions), emotional connection to and response from using the internet (7 questions), and general patterns of Internet use (7 questions). Participants respond to the 20 YIAT items on a 6-point Likert measure (“does not apply” to “always”), which produced an overall score between 0 and 100. The following cut-off points to the total YIAT score were applied: (1) normal internet use: scores 0–49 and (2) potential internet addiction: scores over 50 [36, 37].
Insomnia.
The ISI is a 7-item self-report questionnaire assessing the nature, severity, and impact of insomnia. The evaluated domains are: severity of sleep onset, sleep maintenance, early morning awakening problems, sleep dissatisfaction, interference of sleep difficulties with daytime functioning, perception of sleep difficulties by others, and distress caused by the sleep difficulties. A 5-point Likert scale was used to rate each item (0 to 4 where 0 indicates no problem and 4 corresponds to a very severe problem), yielding a total score ranging from 0 to 28. The total score was interpreted as follows: absence of insomnia (0–7); sub-clinical or mild insomnia (8–14); moderate insomnia (15–21); and severe insomnia (22–28). Furthermore, clinically significant insomnia was detected when the total score was >14 [38, 39].
Self-esteem.
The Rosenberg Self Esteem Scale (RSES) is commonly used and its internal consistency and reliability were confirmed in many previous studies [40]. It comprises 10 statements. Participants rate the extent to which they agree with each statement on a four-point Likert scale, (0) strongly disagree to (3) strongly agree for items 1, 2, 4, 6 and 7 and opposite rating for items 3, 5, 8, 9 and 10. A total score is obtained by summing all responses and may range from 0 to 30, with higher scores indicating higher self-esteem [41].
Anxiety, depression and stress.
The Depression Anxiety Stress Scales (DASS) is a widely used measure of negative affect in adults [42]. An important and unique feature of the DASS is its inclusion of a tension/Stress scale in addition to the depression and anxiety scales. The DASS 21 is a short version of the 42-item original scale. Both are reliable and valid measures of depression, anxiety and tension/stress in clinical and non-clinical populations of adults [43–45].
It is a 21-item scale measured on a 4-point Likert scale (0–3), “0” denoting “did not apply to me at all” and “3” denoting “applied to me very much, or most of the time”.
The following cut-off scores are used for each subscale: depression: normal 0–4, mild 5–6, moderate 7–10, severe 11–13 and extremely severe 14+; anxiety: normal 0–3, mild 4–5, moderate 7–10, severe 11–13 and extremely severe 10+; stress: normal 0–7, mild 8–9, moderate 10–12, severe 13–16 and extremely severe 17+.
Statistical analysis.
The statistical analysis was carried out using SPSS software for Windows (version 18.0, Chicago, IL, USA). The significance level was set at 0.05. Sample characteristics were summarized using the mean and the standard deviation (SD) for continuous variables and percentage for categorical variables. Insomnia and internet addiction prevalence rates were calculated using descriptive data, along with corresponding 95% confidence interval (CI). The Kolmogorov-Smirnov tests were used to assess the normality of the distribution of each variable.
Internet addiction categories were grouped as normal internet users and potential internet addiction.
Multivariate analysis were required to determine the impact of multiple explicative explanatory variables presented simultaneously and to determine which of the explanatory factors act independently on the internet addiction.
In the initial stages, the univariate analysis of categorical and continuous variables were carried out using respectively the Chi-square independence tests or Fisher Exact test and the Student’s t-test or Mann-Whitney test. Subsequently, logistic regression analysis were performed with the dichotomized internet addiction (<50, ≥50) as the dependent variable. Participants’ characteristics and scores (ISI, DASS A, DASS S, DASS D, RSES) that showed associations with p-value <0.25 in univariate analysis, were candidates for the multivariate model, according to the Enter method. Collinearity among independent variables was also tested. Independent variables highly correlated were excluded.
It has been suggested not to include two independent variables where there is a correlation of 0.64 or more. Anxiety, stress and depression were not entered in the same model, since they were highly correlated together, indicated by the Spearman and Pearson correlation coefficients. Finally, three logistic regression analysis were conducted and the independent variables included in the model were gender, tobacco smoking, ISI score, RSES score and the DAS score for stress, anxiety and depression in each of the three model.
Results
Socio-demographic characteristics of the participants
A total of 780 students were approached to participate in the study, of whom 600 (77%) consented. Our study population comprised 182 (30.3%) male and 418 (69.7%) female students. Age ranged between 18 and 28 years with a mean of 20.36 ± 1.83 years.
The sample included 219 students from the Faculty of medicine (FM), 109 from the Faculty of dentistry (FD) and 272 from the Faculty of pharmacy (FP). Table 1 summarizes participants’ characteristics.
Internet addiction prevalence (YIAT)
The average YIAT score was 30 ± 18.47 (Table 2); Potential internet addiction prevalence rate was 16.80% with a 95% CI of 13.81–19.79%. “S1 Table” summarizes average scores for each of the 20 items of the YIAT.
Univariate analysis.
The univariate analysis showed that potential internet addiction was significantly different between males and females (p-value = 0.003), with a higher prevalence in males (23.60% versus 13.90%). Tobacco smoking was significantly related to potential internet addiction (p-value = 0.046); however, neither age, faculty, regular alcohol intake, nor living alone, was significantly related to internet use (Table 3).
Insomnia prevalence and severity (ISI)
Insomnia was evaluated according to the ISI questionnaire. The mean ISI score of the sample was 9.31 ± 3.76. Prevalence of clinically significant insomnia was 9.80% with a 95% CI ranging between 7.42 and 12.18% (Table 2).
Anxiety, depression and stress (DASS-21)
Anxiety: DASS A. The average DASS A score was 4.77 ± 3.79. 44.70% of participants presented a normal DASS A score (Table 2).
Depression: DASS D. The average DASS D score was 5.43 ± 4.43. The majority of the participants presented a normal DASS D score (Table 2).
Stress: DASS S. The average DASS S score was 6.99 ± 4.46 and 33.20% of participants presented a normal DASS S score (Table 2).
Associations between internet addiction, insomnia, low self-esteem, anxiety and depression
A significant relationship was found between potential internet addiction and insomnia (p-value < 0.00001) (Table 4).
The average ISI score was 8.99 ± 3.65 for normal internet users versus 10.89 ± 3.90 in the potential internet addiction group (p<0.0001) (Table 5).
Moreover, a significant relationship was found between potential internet addiction and anxiety, depression and stress (Tables 4 and 5). DASS average scores were significantly higher in the potential internet addiction group for anxiety, depression and stress.
As for self-esteem, a significant correlation was found between YIAT and RSES scores with low self-esteem being associated to potential internet addiction (Tables 4 and 5).
Logistic regression model
The logistic regression model showed that gender, ISI, DASS A, S and D, and RSES scores were significantly associated with internet addiction. Once the explicative variables were controlled for in multivariate analysis, the association between tobacco smoking and internet addiction was no longer significant (p > 0.05), (Table 6).
Discussion
We aimed to determine the prevalence of potential IA in Lebanese university medical students, to evaluate relationships between IA and participants’ characteristics (mainly age, gender, smoking habits, alcohol intake), and to explore possible associations between IA, insomnia, anxiety, depression, stress, and self-esteem.
Our study revealed that potential IA was significantly related to gender and higher among males. 16.80% of participants suffered from potential IA, with a mean YIAT score of 30. These results are comparable to those previously reported for young adults [1, 4, 6, 13]. Some studies reported that IA prevalence was higher in males [46], while others did not find difference between genders [34].
When examining insomnia, our results also showed that 9.8% of participants suffered from clinically significant insomnia and a strong correlation was found between potential internet addiction and insomnia. Insomnia prevalence reported in this study is consistent with the nature of the sample studied (young students) and is comparable to what is reported in young adults aged 20 to 29 (9.1%) [47, 48] and in college students (12–13%) [49].
Sleep problems are usually considered negative outcomes or complications of internet addiction [50], but reverse causation is also possible since sleep problems predicted a longer time spent on social networking sites among young university students [51]. In a systematic review of the literature, addictive gaming was found to be associated to poorer sleep quality and problematic internet use was associated with subjective insomnia and poor sleep quality [52]. However, study designs as well as questionnaires used were very heterogeneous and it was mainly sleep quality that was explored, much less insomnia.
Furthermore, a strong correlation was found in this study between potential internet addiction and anxiety, stress, and depression: the percentage of students suffering from anxiety, depression or stress is higher among potential internet addicts. Previous published studies have already indicated a potential correlation between pathological internet use and depression [53, 54] and anxiety [55]; however, data has been contradictory [56] and studies examined pathological internet use and not addiction as defined by Young.
Finally, an important finding of our study was that self-esteem is significantly related to internet addiction as well as to the psychological profile of students: RSES scores were inversely correlated to ISI, DASS A, DASS S, DASS D and YIAT scores. A decrease in self-esteem seems associated to increased insomnia, anxiety, depression, stress and to potential IA.
Self-esteem is described as the evaluation one has of his/herself, how one feels about his/herself in almost all situations [40, 41]. When social integration and support are low, the level of self-esteem will accordingly decrease [57].
Detecting factors associated to low esteem in students is of considerable importance because an inverse relation exists between self-esteem and depression and anxiety [58, 59] and the decrease in the feeling of self-esteem may lead to an increase in suicidal ideation [60].
Strength and limitations
Our findings should be interpreted in the context of the study’s design and limitations. The results of our survey rely on self-reported behavior. Self-reporting questionnaires remain the most widely used tools in community surveys for physical and mental health evaluation [61, 62, 63]. The self-report method reflects the interviewee’s own perspective, which may be more suitable for reporting subjective disorders. The questionnaires were formulated in a “multiple-choice” and scale pattern to facilitate response and have shorter interview duration in order to avoid disturbing the students, in the hope that the simplicity of the questionnaire would make it easier for the respondents to give accurate information. Chronic use of drugs was not assessed since the presence of any chronic disease was among the exclusion criteria of this study. Finally, the study did not examine the repercussion of internet addiction on achievements, in terms of grades, failure or success, which could have been interesting.
Notwithstanding these limitations, the findings observed in this study are important and warrant further investigations.
To the best of our knowledge, this was the first study that assesses the relation between five different psychosocial stressors: insomnia, anxiety, depression, stress, self-esteem, and IA in university students.
Our findings denote the importance of identifying and offering help to students with potential IA because this addiction often coexists with other psychological problems, and IA could be one visible tip of a complex iceberg.
Supporting Information
S1 Table. This is the individual and complete data for all participants (SPSS sheet).
https://doi.org/10.1371/journal.pone.0161126.s001
(DOCX)
Acknowledgments
We are grateful to all the students who participated in the study and to Mrs Tatiana Papazian for helping in editing.
Author Contributions
- Conceived and designed the experiments: LRK HJ.
- Performed the experiments: FY GH.
- Analyzed the data: AH NEO LK.
- Wrote the paper: LRK.
References
- 1.
Internet World Stats. Internet users of the world: Distribution by world regions 2014 [February 27, 2016.]. Available from: www.internetworldstats.
- 2.
Social networking reaches nearly one in four around the world. [February 20, 2016]. Available from: www.emarketer.com/Article/Social-Networking-Reaches-Nearly-One-Four-Around-World/1009976.
- 3. Bremer J. The internet and children: advantages and disadvantages. Child Adolesc Psychiatr Clin N Am. 2005;14(3):405–28, viii. pmid:15936666
- 4. Christakis DA, Moreno MA. Trapped in the net: will internet addiction become a 21st-century epidemic? Arch Pediatr Adolesc Med. 2009;163(10):959–60. pmid:19805719
- 5. Kraut R, Patterson M, Lundmark V, Kiesler S, Mukopadhyay T, Scherlis W. Internet paradox. A social technology that reduces social involvement and psychological well-being? Am Psychol. 1998;53(9):1017–31. pmid:9841579
- 6. Weinstein A, Lejoyeux M. Internet addiction or excessive internet use. Am J Drug Alcohol Abuse. 2010;36(5):277–83. pmid:20545603
- 7. Davis RA, Flett GL, Besser A. Validation of a new scale for measuring problematic internet use: implications for pre-employment screening. Cyberpsychol Behav. 2002;5(4):331–45. pmid:12216698
- 8. Block JJ. Issues for DSM-V: internet addiction. Am J Psychiatry. 2008;165(3):306–7. pmid:18316427
- 9. Pies R. Should DSM-V Designate "Internet Addiction" a Mental Disorder? Psychiatry (Edgmont). 2009;6(2):31–7.
- 10. Holden C. Psychiatry. Behavioral addictions debut in proposed DSM-V. Science. 2010;327(5968):935. pmid:20167757
- 11. Young KS. Psychology of computer use: XL. Addictive use of the Internet: a case that breaks the stereotype. Psychol Rep. 1996;79(3 Pt 1):899–902. pmid:8969098
- 12. Young KS, Case CJ. Internet abuse in the workplace: new trends in risk management. Cyberpsychol Behav. 2004;7(1):105–11. pmid:15006175
- 13. Young K, Pistner M, O'Mara J, Buchanan J. Cyber disorders: the mental health concern for the new millennium. Cyberpsychol Behav. 1999;2(5):475–9. pmid:19178220
- 14. van den Eijnden RJ, Spijkerman R, Vermulst AA, van Rooij TJ, Engels RC. Compulsive internet use among adolescents: bidirectional parent-child relationships. J Abnorm Child Psychol. 2010;38(1):77–89. pmid:19728076
- 15.
Young KS. Caught in the Net: How to Recognize the Signs of Internet Addiction—and a Winning Strategy for Recovery. New York, NY: Wiley; 1998.
- 16. Young KS. Internet Addiction: The Emergence of a New Clinical Disorder. CyberPsychology & Behavior. 2009;1(3):237–44.
- 17. Widyanto L, Griffiths MD, Brunsden V. A psychometric comparison of the Internet Addiction Test, the Internet-Related Problem Scale, and self-diagnosis. Cyberpsychol Behav Soc Netw. 2011;14(3):141–9. pmid:21067282
- 18. Shaw M, Black DW. Internet addiction: definition, assessment, epidemiology and clinical management. CNS Drugs. 2008;22(5):353–65. pmid:18399706
- 19. Park SK, Kim JY, Cho CB. Prevalence of Internet addiction and correlations with family factors among South Korean adolescents. Adolescence. 2008;43(172):895–909. pmid:19149152
- 20. Siomos KE, Dafouli ED, Braimiotis DA, Mouzas OD, Angelopoulos NV. Internet addiction among Greek adolescent students. Cyberpsychol Behav. 2008;11(6):653–7. pmid:18991535
- 21. Durkee T, Kaess M, Carli V, Parzer P, Wasserman C, Floderus B, et al. Prevalence of pathological internet use among adolescents in Europe: demographic and social factors. Addiction. 2012;107(12):2210–22. pmid:22621402
- 22. Liu TC, Desai RA, Krishnan-Sarin S, Cavallo DA, Potenza MN. Problematic Internet use and health in adolescents: data from a high school survey in Connecticut. J Clin Psychiatry. 2011;72(6):836–45. pmid:21536002
- 23. Pujazon-Zazik M, Park MJ. To tweet, or not to tweet: gender differences and potential positive and negative health outcomes of adolescents' social internet use. Am J Mens Health. 2010;4(1):77–85. pmid:20164062
- 24. Dalbudak E, Evren C, Aldemir S, Evren B. The severity of Internet addiction risk and its relationship with the severity of borderline personality features, childhood traumas, dissociative experiences, depression and anxiety symptoms among Turkish university students. Psychiatry Res. 2014;219(3):577–82. pmid:25023365
- 25. Kim EJ, Namkoong K, Ku T, Kim SJ. The relationship between online game addiction and aggression, self-control and narcissistic personality traits. Eur Psychiatry. 2008;23(3):212–8. pmid:18166402
- 26. Floros G, Siomos K, Stogiannidou A, Giouzepas I, Garyfallos G. Comorbidity of psychiatric disorders with Internet addiction in a clinical sample: the effect of personality, defense style and psychopathology. Addict Behav. 2014;39(12):1839–45. pmid:25129172
- 27. Floros G, Siomos K, Stogiannidou A, Giouzepas I, Garyfallos G. The relationship between personality, defense styles, internet addiction disorder, and psychopathology in college students. Cyberpsychol Behav Soc Netw. 2014;17(10):672–6. pmid:25225916
- 28. An J, Sun Y, Wan Y, Chen J, Wang X, Tao F. Associations between problematic internet use and adolescents' physical and psychological symptoms: possible role of sleep quality. J Addict Med. 2014;8(4):282–7. pmid:25026104
- 29. Kim JH, Lau CH, Cheuk KK, Kan P, Hui HL, Griffiths SM. Brief report: Predictors of heavy Internet use and associations with health-promoting and health risk behaviors among Hong Kong university students. J Adolesc. 2010;33(1):215–20. pmid:19427030
- 30. Naseri L, Mohamadi J, Sayehmiri K, Azizpoor Y. Perceived Social Support, Self-Esteem, and Internet Addiction Among Students of Al-Zahra University, Tehran, Iran. Iran J Psychiatry Behav Sci. 2015;9(3):e421. pmid:26576175
- 31. Lee HW, Choi JS, Shin YC, Lee JY, Jung HY, Kwon JS. Impulsivity in internet addiction: a comparison with pathological gambling. Cyberpsychol Behav Soc Netw. 2012;15(7):373–7. pmid:22663306
- 32. Lin IH, Ko CH, Chang YP, Liu TL, Wang PW, Lin HC, et al. The association between suicidality and Internet addiction and activities in Taiwanese adolescents. Compr Psychiatry. 2014;55(3):504–10. pmid:24457034
- 33. 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. 2006;43(2):185–92. pmid:16427966
- 34. Fernandez-Villa T, Alguacil Ojeda J, Almaraz Gomez A, Cancela Carral JM, Delgado-Rodriguez M, Garcia-Martin M, et al. Problematic Internet Use in University Students: associated factors and differences of gender. Adicciones. 2015;27(4):265–75. pmid:26706809
- 35.
Internet Addiction Test (IAT) [April, 2016]. Available from: http://netaddiction.com/internet-addiction-test./.
- 36. Khazaal Y, Billieux J, Thorens G, Khan R, Louati Y, Scarlatti E, et al. French validation of the internet addiction test. Cyberpsychol Behav. 2008;11(6):703–6. pmid:18954279
- 37. Alpaslan AH, Soylu N, Avci K, Coskun KS, Kocak U, Tas HU. Disordered eating attitudes, alexithymia and suicide probability among Turkish high school girls. Psychiatry Res. 2015;226(1):224–9. pmid:25619436
- 38. Cho YW, Song ML, Morin CM. Validation of a Korean version of the insomnia severity index. J Clin Neurol. 2014;10(3):210–5. pmid:25045373
- 39. Gagnon C, Belanger L, Ivers H, Morin CM. Validation of the Insomnia Severity Index in primary care. J Am Board Fam Med. 2013;26(6):701–10. pmid:24204066
- 40. Sinclair SJ, Blais MA, Gansler DA, Sandberg E, Bistis K, LoCicero A. Psychometric properties of the Rosenberg Self-Esteem Scale: overall and across demographic groups living within the United States. Eval Health Prof. 2010;33(1):56–80. pmid:20164106
- 41. Rosenberg M. The association between self-esteem and anxiety. J Psychiatr Res. 1962;1:135–52. pmid:13974903
- 42. Lovibond PF, Lovibond SH. The structure of negative emotional states: comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behav Res Ther. 1995;33(3):335–43. pmid:7726811
- 43. Taylor R, Lovibond PF, Nicholas MK, Cayley C, Wilson PH. The utility of somatic items in the assessment of depression in patients with chronic pain: a comparison of the Zung Self-Rating Depression Scale and the Depression Anxiety Stress Scales in chronic pain and clinical and community samples. Clin J Pain. 2005;21(1):91–100. pmid:15599136
- 44. Brown TA, Chorpita BF, Korotitsch W, Barlow DH. Psychometric properties of the Depression Anxiety Stress Scales (DASS) in clinical samples. Behav Res Ther. 1997;35(1):79–89. pmid:9009048
- 45. Edmed S, Sullivan K. Depression, anxiety, and stress as predictors of postconcussion-like symptoms in a non-clinical sample. Psychiatry Res. 2012;200(1):41–5. pmid:22709538
- 46. Ni X, Yan H, Chen S, Liu Z. Factors influencing internet addiction in a sample of freshmen university students in China. Cyberpsychol Behav. 2009;12(3):327–30. pmid:19445631
- 47. Thomas SJ, Lichstein KL, Taylor DJ, Riedel BW, Bush AJ. Epidemiology of bedtime, arising time, and time in bed: analysis of age, gender, and ethnicity. Behav Sleep Med. 2014;12(3):169–82. pmid:23574553
- 48. Choueiry N, Salamoun T, Jabbour H, El Osta N, Hajj A, Rabbaa Khabbaz L. Insomnia and Relationship with Anxiety in University Students: A Cross-Sectional Designed Study. PLoS One. 2016;11(2):e0149643. pmid:26900686
- 49. Gellis LA, Park A, Stotsky MT, Taylor DJ. Associations between sleep hygiene and insomnia severity in college students: cross-sectional and prospective analyses. Behav Ther. 2014;45(6):806–16. pmid:25311289
- 50. Cain N, Gradisar M. Electronic media use and sleep in school-aged children and adolescents: A review. Sleep Med. 2010;11(8):735–42. pmid:20673649
- 51. Tavernier R, Willoughby T. Are all evening-types doomed? Latent class analyses of perceived morningness-eveningness, sleep and psychosocial functioning among emerging adults. Chronobiol Int. 2014;31(2):232–42. pmid:24131151
- 52. Lam LT. Internet gaming addiction, problematic use of the internet, and sleep problems: a systematic review. Curr Psychiatry Rep. 2014;16(4):444. pmid:24619594
- 53. Tsai CC, Lin SS. Internet addiction of adolescents in Taiwan: an interview study. Cyberpsychol Behav. 2003;6(6):649–52. pmid:14756931
- 54. te Wildt BT, Putzig I, Zedler M, Ohlmeier MD. [Internet dependency as a symptom of depressive mood disorders]. Psychiatr Prax. 2007;34 Suppl 3:S318–22. pmid:17786892
- 55. Bernardi S, Pallanti S. Internet addiction: a descriptive clinical study focusing on comorbidities and dissociative symptoms. Compr Psychiatry. 2009;50(6):510–6. pmid:19840588
- 56. Carli V, Durkee T, Wasserman D, Hadlaczky G, Despalins R, Kramarz E, et al. The association between pathological internet use and comorbid psychopathology: a systematic review. Psychopathology. 2013;46(1):1–13. pmid:22854219
- 57. Garaigordobil M, Perez JI, Mozaz M. Self-concept, self-esteem and psychopathological symptoms. Psicothema. 2008;20(1):114–23. pmid:18206073
- 58. Moksnes UK, Espnes GA. Self-esteem and emotional health in adolescents—gender and age as potential moderators. Scand J Psychol. 2012;53(6):483–9. pmid:23170865
- 59. Sowislo JF, Orth U. Does low self-esteem predict depression and anxiety? A meta-analysis of longitudinal studies. Psychol Bull. 2013;139(1):213–40. pmid:22730921
- 60. Creemers DH, Scholte RH, Engels RC, Prinstein MJ, Wiers RW. Implicit and explicit self-esteem as concurrent predictors of suicidal ideation, depressive symptoms, and loneliness. J Behav Ther Exp Psychiatry. 2012;43(1):638–46. pmid:21946041
- 61. Fischer A, Fischer M, Nicholls RA, Lau S, Poettgen J, Patas K, et al. Diagnostic accuracy for major depression in multiple sclerosis using self-report questionnaires. Brain Behav. 2015;5(9):e00365. pmid:26445703
- 62. Ortega-Montiel J, Posadas-Romero C, Ocampo-Arcos W, Medina-Urrutia A, Cardoso-Saldana G, Jorge-Galarza E, et al. Self-perceived stress is associated with adiposity and atherosclerosis. The GEA Study. BMC Public Health. 2015;15:780. pmid:26271468
- 63. White K, Scarinci IC. Comparison of Self-Rated Health Among Latina Immigrants in a Southern U.S. City and a National Sample. Am J Med Sci. 2015;350(4):290–5. pmid:26263236