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Prevalence of insomnia in a sample of Internet addicts in different age groups in Abu Dhabi, UAE
Middle East Current Psychiatry volume 30, Article number: 27 (2023)
Internet addiction, insomnia, and depression have a major health concern. The association of these problems can severely affect education, work productivity with negative outcomes for society. Internet addiction has been reported to be associated with insomnia and depression that may differ by age. The aim of the study is to assess the prevalence of insomnia and depression and their correlation with Internet addiction in all age groups and to detect age differences. A total of 386 participants were recruited. Data were collected using an online survey that contains questions about sociodemographic, Insomnia Severity Index (ISI), Internet Addiction Test (IAT), and Patient Health Questionnaire (PHQ9) to measure insomnia, Internet addiction, and depression, respectively.
The overall prevalence of clinically significant insomnia was 22.5% while subthreshold insomnia was present in 38.5%. There was a significant negative correlation of age with IAT and PHQ9. Also, a highly significant positive correlation was found between ISI, IAT, and PHQ9 within the total sample and each age group (p = 0.000).
A great proportion of the general population suffers from insomnia, Internet addiction, and depression, and their prevalence rates differ by age. Also, there is a strong association between them. Identifying these problems is important, and interventions should include the three problems.
Insomnia, a common sleep disturbance, is a public health problem, as it can result in physical and mental exhaustion . Typical clinical symptoms of insomnia include inability to initiate sleep, maintain sleep, and/or early-morning waking with difficulty to return to sleep .
The prevalence of insomnia differs greatly in the general population across studies, to be from 6 to 50% [3, 4]. Insomnia has a negative impact on well-being and quality of life of people in all age groups . It also has a considerable public health and social challenges, in the form accidents, decreased social, work, academic performance, and reduced work productivity [5, 6].
Internet use has increased exponentially all over the world to greater than 2.5 billion active users [7, 8]. Excessive Internet use is diagnosed when Internet use has come to be excessive, uncontrolled, and timewasting to the point of extremely disrupting people’s lives .
Sleep difficulties are usually considered negative consequences of Internet addiction . Excessive Internet use was also recorded to be related to mood disorders , decreased self-esteem , impulsivity , reduced levels of physical fitness , and medical problems (migraines, increased body weight, back pain) .
Relation between insomnia and depression has more than one direction. Literature shows that insomnia leads to depressive symptoms; some evidence characterize insomnia as a residual clinical symptom of depression, and yet, other studies propose that both have bidirectional relation [16,17,18].
Our hypothesis is that insomnia and depression have a major concern in society, and they have a relationship with Internet addiction that may differ by age. Detecting this association is important so that appropriate measures can be taken to address this problem. Thus, the aim of the study is to detect the prevalence of insomnia and depression and their correlation with Internet addiction in all age groups and to find age differences.
Study design and setting
It is a cross sectional study. An online survey using google form was created to collect data in Abu Dhabi, UAE. The survey was distributed through different social network applications (WhatsApp, Facebook groups, and LinkedIn) and emails via a link. Follow up reminder was sent to increase the response rate. The survey was sent in English due to the presence of multiple nationalities. The study was conducted from 1 September 2022 until 2 November 2022.
The study included people who were 18 years old or older, living in Abu Dhabi, and willing to participate. Individuals who had a medical disease, a history of diagnosed psychiatric illness (including a history of substance abuse) or were on medications were excluded from the study.
Based on the study done by Al Karaki et al. 2020 prevalence of insomnia in general population was 47.1% . Therefore, a minimum sample size of 383 subjects will be needed to reach a 95% confidence level and a margin of error 5%.
The online survey included the following data.
Demographic characteristics were assessed, such as age, gender, marital status, educational level, occupation, and residency.
Insomnia Severity Index (ISI)
It is a 7-item self-administrated scale investigating the nature, intensity, and impact of insomnia during the past 2 weeks. Its total score ranging from 0 to 28 and interpreted as follows: no insomnia (0–7), subthreshold insomnia (8–14), moderate severity insomnia (15–21), and severe insomnia (22–28) . Clinical significant insomnia was classified if the total score was above 14 [21, 22].
Internet addiction test
It consists of 20 items, its score ranges from 0 to 100 during the past month and interpreted as follows: the normal user (total score ≤ 30), the mild user (score between 31 and 49), the moderate user (score between 50 and 79), and severe or excessive user (total score ≥ 80) [23, 24].
Patient Health Questionnaire 9 (PHQ9)
It is a self-report, 9-item scale for detecting depressive symptoms and investigating the severity of these symptoms in the last two weeks: no depressive symptoms (0 to 4), mild (5 to 9), moderate (10 to14), moderately severe (15 to19), and severe depression (20 to 27). PHQ9 scoring ≥ 10 was used to define depression .
Data was entered and statistically analyzed on the Statistical Package of Social Science Software program, version 25 (IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY: IBM Corp.). Data was presented using median and range for quantitative variables and frequency and percentage for qualitative ones. Comparison between groups for qualitative variables was performed using chi-square or Fisher’s exact tests (if expected counts were less than 5) while for quantitative variables the comparison was conducted using Mann–Whitney test (if 2 groups) or Kruskal Wallis test (if > 2 groups). Correlation between different quantitative or ordinal variables were assessed using Spearman correlation test. P values less than or equal to 0.05 were considered statistically significant.
Ethical approval was taken from the Ethical and Research Committee, Al Dhannah Hospital, Abu Dhabi, UAE (reference number EA/005/09/2022). Informed consent was obtained from all participants as it was the first question in the survey.
Table 1 shows sociodemographic data of the participants. A total of 386 participants were included in the study. Of those, 261 (67.6%) were females and most responses were done by age group ranged from 30 to 39 (50.3%). Most of participants were married (63.5%), graduated (86.8%), and had a private work (50.8%).
The overall prevalence of clinically significant insomnia, Internet addiction, and depression was 22.5%, 48.4%, and 45.1%, respectively while subthreshold insomnia was 38.6%. Age groups ranged from 30 to 39 and from 18 to 29 scored higher prevalence of insomnia, Internet addiction, and depression than other groups while participants 50 years or more had the least prevalence and it reached statistically significant difference on IAT scale (Table 2).
Median score of ISI scale was 9 ranged (0–27), males scored significantly higher than females on the ISI scale (p = 0.008). There was a statistically significant relation between occupation and insomnia as detected by ISI scale, where unemployed participants had more insomnia (p = 0.003) (Table 3).
Table 4 shows the relation between IAT and sociodemographic data where median score of IAT scale was 29 ranged (0–98), higher Internet addiction scores were in age group (18–29) (p = 005). Also, males scored significantly higher than females on IAT scale (p = 000). Besides, Internet addiction score was significantly greater in participants who were unemployed or had government jobs (p = 0.000), unmarried (p = 0.044), and those live in urban areas (p = 0.021).
Median score of PHQ9 was 9 ranged (0–27), younger age groups (18–29) and (30–39) scored significantly higher than older age groups on depression scale (PHQ9) (p = 0.002). Higher depression scores were found in unemployed participants or those with government jobs (p = 0.000) (Table 5).
Table 6 shows there was statistically significant negative correlation of age with IAT, and PHQ9 scales (p = 0.001, 0.002), respectively. Participants who were older had less Internet addiction and depression.
There was a highly statistically significant positive correlation between ISI, IAT, and PHQ9 within the total sample and each age group (p = 0.000). Participants who had more Internet addiction developed more insomnia and depression (Table 7).
This study aimed to detect the prevalence of insomnia and depression and their correlation with Internet addiction among different age groups. This cross-sectional study included 386 subjects who responded through online self-reported survey. Subjects who were 18 years old and more were enrolled in the study.
Most of the subjects were females (67.6%), married (63.5%), with university level education (86.8%). Most of the participants were working whether government employment (30.1%) or private employment (50.8%), 50.3% of the subjects were ranging from 30 to 39 years old.
The current study showed that most of the participants (61.1%) had problems with their sleep where 22.5% of the participants had clinical significant insomnia and 38.6% had subthreshold insomnia. Younger age groups showed higher prevalence of insomnia, Internet addiction, and depression while participants 50 years or more had the least prevalence and it reached a statistically significant difference on IAT and PHQ9 scales (P = 0.005, P = 0.002), respectively.
This might be related to the changes in the pattern of sleep due to working and studying at this age as well as the societal, occupational, and academic needs and stressors disturb their sleep habits making them more prone to develop poor sleep quality . This result was concordant with another study that was done on the prevalence of insomnia in the general population and found that younger adults with mean age less than 43 years suffer from insomnia more than older age and related these results to the rapid urbanization and industrialization that this age often faces, the stressors of society, working needs as they can work for long hours day and night, which affects their biological sleep pattern. Moreover, the widespread use of technology and new media, such as computers and smart phones in younger adults .
This study revealed that there was a statistically significant difference between males and females on ISI where males scored greater than females (P = 0.008). This result was inconsistent with Cao et al. 2017 who found no gender differences and Zeng et al. 2020 who found that the prevalence of insomnia in females was higher than males. These differences might be related to the difference in the methodology as Cao et al. 2017 and Zeng et al. 2020 used meta-analysis study with larger number of participants [27, 28].
Regarding the correlation of employment with ISA, this study showed that insomnia score was significantly greater in unemployed participants (p = 0.003). This might be related to that the participants who are not working might suffer the stressors of socioeconomic circumstances (e.g., difficulty in job seeking, financial problems, responsibilities) that result in lower mental, physical health and sleep disturbances and this was concordant with Lallukka et al. 2012 and Soltani et al. 2012 who found that unemployed participants had higher prevalence of insomnia [29, 30].
In this study 48.4% of the participants suffered from Internet addiction and age was statistically significant related to IAT, where participants with age range from 18–29 had higher median score of Internet addiction (p = 0.005). This could be related to the wide spread of social media, videogames, and smartphones among this age as well as the use of media in work and online working specially after the COVID and COVID quarantine. This result was consistent with meta-analysis study done by Blasco et al. 2022 who found that Internet addiction was higher among the new generations .
Moreover, males scored significantly higher than females on IAT scale (p = 0.000) in the current study. This was in harmony with Dieris-Hirche et al. 2017 and Ceyhan et al. 2019 who found that the prevalence of Internet addiction was higher among male gender [32, 33]. This could be explained by the fact that females are mostly involved in home duties and their children’s care, as well as males are more prone to addiction in general, and their Internet addiction may involve gaming or cyber-sex, which are more common in males as well. Besides, Internet addiction median score was significantly greater in participants who were unemployed (p = 0.000), in a governmental job (p = 0.000) and unmarried (p = 0.044) as they might have more free time to join the Internet and social media as they do not have a lot of work or marital life, as well as suffer more from loneliness and psychological problems, which might lead them to spend more time on the Internet, as shown by other studies where they found that emotional instability, psychological problems, and negative socioeconomic factors were associated with Internet addiction [31,32,33]. Also, being unemployed or in a governmental job adds more stress on individuals, as this may affect their subjective feeling of achievement with unemployment or the economic burden of a governmental job, which will affect their mood and may lead to Internet addiction.
As regards PHQ9, 22.5% of the participants in this study were suffering from depression and age was significantly related to PHQ9 scale where those aged 18–29 and 30–39 years showed higher median score of depression (p = 0.002). Also, greater depression scores were in unemployed participants or those with government jobs (p = 0.000). This might be related to the nature of this age group (Young adulthood) as this period is considered as a transitional zone with multiple challenges and stresses regarding having independent personality from parents and searching for more stability in social relations, career, income, marital life, financial and emotional stability. This result was consistent with Babajide et al. 2020 who found that the prevalence of depression was higher in young adults 
The current study revealed that there was a strong significant positive correlation between insomnia, Internet addiction, and depression within the total sample and each age group. Participants who suffer from Internet addiction and depression had higher rates and levels of insomnia. Also, those who had Internet addiction were more depressed. These findings were in harmony with other studies that were done on the association between online addiction and emotional instability and depression and revealed that difficulties in emotional regulation, anxiety and depression were statistically significantly contributed to the risk for development of Internet addiction where the severity of Internet addiction was associated not only with a higher rate of psychiatric disorders, but also with a greater severity of their symptoms [31, 33, 35]. Another study done by Parash et al. 2017 showed that there was association of insomnia, Internet addiction, and depression. The association between insomnia and depression is mediated by Internet addiction. Also, the relation between Internet addiction and depression is mediated by insomnia .
An individual may not develop mental disorders just due to Internet use, but if a person does not sleep well because of excessive Internet use that is increasing daily, s/he is at risk of having mental disorders when compared to those who sleep well [36, 37]. Staying up late at night leads to sleep problems in Internet users and continuous sleep deficiency may develop psychiatric disorders such as depressive symptoms, anxiety, affective problems, and distress in general [38, 39].
The survey was a cross-sectional study. It was unable to detect a cause and effect. Assessment was done by an online questionnaire and depended on self-reporting tools.
Insomnia, Internet addiction, and depression are common health problems, and their prevalence rates differ by age. Also, there is a strong correlation between them. Identifying these problems is important, and since the three disorders are found to be linked together, assessment should include all three together if an individual suffers from any one of them.
Availability of data and materials
Insomnia Severity Index
Internet Addiction Test
Patient Health Questionnaire
National Institutes of Health (2011) National institutes of health sleep disorders research plan. US Department of Health and Human Services, National Heart Lung and Blood Institute, National Center on Sleep Disorders Research, Trans-NIH Sleep Research Coordinating Committee, Editors. Report No. DOT HS 808 707
Buysse DJ (2013) Insomnia JAMA 309(7):706. https://doi.org/10.1001/jama.2013.193
Ohayon MM (2002) Epidemiology of insomnia: what we know and what we still need to learn. Sleep Med Rev 6(2):97–111. https://doi.org/10.1053/smrv.2002.0186
Nowicki Z, Grabowski K, Cubala WJ, Nowicka-Sauer K, Zdrojewski T, Rutkowski M, et al (2016) Prevalence of self-reported insomnia in general population of Poland. Psychiatr Pol 50: 165–173.
Walsh JK (2004) Clinical and socioeconomic correlates of insomnia. J Clin Psychiatry 65(8):13–19
Seow LS, Verma SK, Mok YM, Kumar S, Chang S, Satghare P, Hombali A, Vaingankar J, Chong SA, Subramaniam M (2018) Evaluating DSM-5 Insomnia Disorder and the Treatment of Sleep Problems in a Psychiatric Population. J Clin Sleep Med 14(2):237–244
Internet World Stats. Internet users of the world: Distribution by world regions 2014 [February 27, 2016.].
- e Marketer. (n.d.). Social Networking Reaches Nearly One in Four Around the World - eMarketer. Retrieved August 15, 2020. https://www.emarketer.com/Article/Social-Networking-Reaches-Nearly-One-Four-Around-World/1009976
Kraut R, Patterson M, Lundmark V, Kiesler S, Mukopadhyay T, Scherlis W (1998) Internet paradox. A social technology that reduces social involvement and psychological well-being. Am Psychol.53(9):1017–31.
Cain N, Gradisar M (2010) Electronic media use and sleep in school-aged children and adolescents: A review. Sleep Med.11(8):735–42.
An J, Sun Y, Wan Y, Chen J, Wang X, Tao F (2014) Associations between problematic internet use and adolescents' physical and psychological symptoms: possible role of sleep quality. J Addict Med. 8(4):282–7.
Naseri L, Mohamadi J, Sayehmiri K, Azizpoor Y (2015) Perceived social support, self-esteem, and Internet addiction among students of Al-Zahra University, Tehran, Iran. Iran J Psychiatry Behav Sci. 9(3): e421.
Lee HW, Choi JS, Shin YC, Lee JY, Jung HY, Kwon JS (2012) Impulsivity in internet addiction: a comparison with pathological gambling. Cyberpsychol Behav Soc Netw.15(7):373–7.
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.
Fernandez-Villa T, Alguacil Ojeda J, Almaraz Gomez A, Cancela Carral JM, Delgado-Rodriguez M, Garcia-Martin M, et al (2015) Problematic Internet Use in University Students: associated factors and differences of gender. Adicciones. 27(4):265–75.
Chen PJ, Huang CL, Weng SF, Wu MP, Ho CH, Wang JJ et al (2017) Relapse insomnia increases greater risk of anxiety and depression: Evidence from a population-based 4-year cohort study. Sleep Med 38:122–129
Mendlewicz J (2009) Sleep disturbances: Core symptoms of major depressive disorder rather than associated or comorbid disorders. World J Biol Psychiatry 10:269–275
Sivertsen B, Salo P, Mykletun A, Hysing M, Pallesen S, Krokstad S et al (2012) The bidirectional association between depression and insomnia: the HUNT study. Psychosom Med 74:758–765
Al Karaki G, Hallit S, Malaeb D, Kheir N, Sacre H, Salameh P, Hallit R (2020) Prevalence and factors associated with insomnia among a representative sample of the Lebanese population results of across sectional study. Journal of epidemiology and global health 10(2):124–130. https://doi.org/10.2991/jegh.k.200117.001
Bastien CH, Vallières A, Morin CM (2001) Validation of the Insomnia Severity Index as an outcome measure for insomnia research. Sleep Med 2(4):297–307
Cho YW, Song ML, Morin CM (2014) Validation of a Korean version of the insomnia severity index. J Clin Neurol. 10(3):210–5.
Gagnon C, Belanger L, Ivers H, Morin CM (2013) Validation of the Insomnia Severity Index in primary care. J Am Board Fam Med. 26(6):701–10.
Young KS (1998) Internet addiction: The emergence of a new clinical disorder. Cyber Psychology & Behavior 1(3):237–244. https://doi.org/10.1089/cpb.1998.1.237
Young KS, Abreu CN (2011) Internet addiction: a Handbook and Guide to Evaluation and Treatment Hoboken. Wiley, NJ
Kroenke K, Spitzer RL, Williams JB (2001) The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med 16(9):606–613. https://doi.org/10.1046/j.1525-1497.2001.016009606.x
Bhandari PM, Neupane D, Rijal S, Thapa K, Mishra SR (2017) Poudyal AK (2017) Sleep quality, internet addiction and depressive symptoms among undergraduate students in Nepal. BMC Psychiatry 17:106. https://doi.org/10.1186/s12888-017-1275-5
Cao XL, Wang SB, Zhong BL, Zhang L, Ungvari GS, Ng CH, Li L, Chiu HF, Lok GK, Lu JP, Jia FJ, Xiang YT (2017) The prevalence of insomnia in the general population in China: A meta-analysis. J PLoS One. https://doi.org/10.1371/journal.pone.0170772
Zeng LN, Zong QQ, Yang Y, Zhang L, Xiang YF, Ng CH, Chen LG, Xiang YT (2020) Gender difference in the prevalence of insomnia: a meta-analysis of observational studies. Front Psychiatry. 11: 577429. https://doi.org/10.3389/fpsyt.2020.577429 PMCID: PMC7714764.
Lallukka T, Sares-Jäske L, Kronholm E, Sääksjärvi K, Lundqvist A, Partonen T, Rahkonen O, Knekt P (2012) Sociodemographic and socioeconomic differences in sleep duration and insomnia-related symptoms in Finnish adults. BMC Public Health 12:565
Soltani M, Haytabakhsh MR, Najman JM, Williams GM, O’Callaghan MJ, Bor W, Dingle K, Clavarino A (2012) Sleepless nights: the effect of socioeconomic status, physical activity, and lifestyle factors on sleep quality in a large cohort of Australian women. Arch Women Ment Health 15:237–247
Blasco RL, Robres AQ, Sanchez AS (2022) Internet addiction in young adults: A meta-analysis and systematic review. Comput Human Behav J 130: 107201. https://doi.org/10.1016/j.chb.2022.107201
Dieris-Hirche J, Bottel L, Bielefeld M, Steinbüchel T, Kehyayan A, Dieris B, Wildt B (2017) Media use and internet addiction in adult depression: a case-control study. Computers in Human Behavior, 68:96-103. https://doi.org/10.1016/j.chb.2016.11.016
Ceyhan E, Boysan M, Kadak T (2019) Associations between online addiction attachment style, emotion regulation depression and anxiety in general population testing the proposed diagnostic criteria for internet addiction. A Journal of Clinical Neuroscience and Psychopathology. Sleep Hypnosis, 21 (2): 123–139, https://doi.org/10.5350/Sleep.Hypn.2019.21.0181
Babajide A, Ortin A, Wei C, Mufson L (2020) Puarte C (2020) Transition cliffs for young adults with anxiety and depression: is integrated mental health care a solution. J Behav Health Serv Res 47(2):275–292. https://doi.org/10.1007/s11414-019-09670-8PMCID:PMC7028507
Leménager T, Hoffmann S, Dieter J, Reinhard I, Mann K, Kiefer F (2018) The links between healthy, problematic, and addicted Internet use regarding comorbidities and self-concept-related characteristics J Behav Addict, 7(1): 31–43, https://doi.org/10.1556/2006.7.2018.13
Baum KT, Desai A, Field J, Miller LE, Rausch J, Beebe DW (2014) Sleep restriction worsens mood and emotion regulation in adolescents. J Child Psychol Psychiatr 55:180–190
Becker SP, Langberg JM, Byars KC (2015) Advancing a biopsychosocial and contextual model of sleep in adolescence: a review and introduction to the special issue. J Youth Adolescence 2015(44):239–270
Iftikhar M, Tariq S (2014) Self-control, narcissistic tendencies and internet addiction among adolescents. J Arts Soc Sci 1(2):37–52
Shabir, G, Mahmood, Y, Hameed, Y, Safdar, G, & Gilani, S. M. F. The impact of social media on youth: a case study of Bahawalpur City. Asian J Soc Sci Human, 3(4):152–161.
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Ethical approval was obtained from the Ethical and Research Committee, Al Dhannah Hospital, Abu Dhabi, UAE (reference number EA/005/09/2022). Informed consent was taken from all participants as it was the first question in the survey.
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Shaheen, S.H., Abdullah, E.A., Razik, M.M.A. et al. Prevalence of insomnia in a sample of Internet addicts in different age groups in Abu Dhabi, UAE. Middle East Curr Psychiatry 30, 27 (2023). https://doi.org/10.1186/s43045-023-00301-9
- Internet addicts
- Age groups