|Year : 2021 | Volume
| Issue : 2 | Page : 153-157
Internet addiction and sleep quality in medical undergraduates of a university in southern India
Manoj Shettar1, Ravichandra Karkal2, Anil Kakunje3, Rohan Mendonsa4
1 Senior Resident, Department of Psychiatry, SDM College of Medical Sciences and Hospital, Shri Dharmasthala Manjunatheshwara University, Dharwad, Karnataka, India
2 Associate Professor, Department of Psychiatry, Yenepoya Medical College and Hospital, Yenepoya (Deemed to be University), Deralakatte, Mangalore, India
3 Professor, Department of Psychiatry, Yenepoya Medical College and Hospital, Yenepoya (Deemed to be University), Deralakatte, Mangalore, India
4 Senior Registrar, North Western Mental Health, Melbourne Health, Melbourne, Australia
|Date of Submission||22-Apr-2021|
|Date of Acceptance||07-Aug-2021|
|Date of Web Publication||07-Sep-2021|
Dr. Ravichandra Karkal
Department of Psychiatry, Yenepoya Medical College, Deralakatte, Mangaluru, Karnataka
Source of Support: None, Conflict of Interest: None
Background: The Internet has pervaded our daily lives and is well known to lead to addictive behaviors. Internet addiction (IA) and its influence on quality of sleep have not gained much attention from researchers in India.
Aims: We aimed to study the rates of IA and its association with quality of sleep in medical undergraduates.
Settings and Design: It was a cross-sectional study evaluating 158 final-year medical undergraduate students at a university in southern India.
Materials and Methods: The Internet Addiction Test (IAT) and the Pittsburgh Sleep Quality Index (PSQI) were applied on consenting participants after recruitment using purposive sampling.
Statistical Analysis Used: Independent samples t-test was done to evaluate the association of gender with sleep quality. Analysis of variance was done to compare sleep quality in various degrees of IA. Pearson's bivariate correlation was done to see the relationship between the severity of IA and the global sleep quality.
Results: More than half of the participants, i.e. 90 (57.0%), had IA, with 2 (1.3%) having severe IA. The rates of IA were similar in both genders. Forty (25.3%) participants were having poor sleep quality as measured by global PSQI cutoff score >5. Participants with moderate-to-severe IA had significantly poor sleep quality compared to participants with mild IA (P = 0.042*). A positive correlation was seen between IAT scores and global PSQI scores (P = 0.012*).
Conclusions: IA is prevalent in medical undergraduates and has a negative impact on quality of sleep. Severity of IA predicts global sleep quality in the participants.
Keywords: Internet addiction, Pittsburgh Sleep Quality Index, problematic Internet use, sleep quality
|How to cite this article:|
Shettar M, Karkal R, Kakunje A, Mendonsa R. Internet addiction and sleep quality in medical undergraduates of a university in southern India. Arch Ment Health 2021;22:153-7
|How to cite this URL:|
Shettar M, Karkal R, Kakunje A, Mendonsa R. Internet addiction and sleep quality in medical undergraduates of a university in southern India. Arch Ment Health [serial online] 2021 [cited 2022 Aug 11];22:153-7. Available from: https://www.amhonline.org/text.asp?2021/22/2/153/325653
| Introduction|| |
Students spend many hours per day on the Internet for academic purposes, entertainment, gaming, using social networking sites (SNSs) and other communications tools. This puts them at greater risk for developing problematic Internet behaviours. A review looking at prevalence among students of South-East Asia Region (SEAR) reported Internet addiction (IA) ranging from 0% to 47.4%, while the prevalence of possible IA ranged from 7.4% to 46.4%. Another review which looked at pooled prevalence of IA among 3651 medical students globally, revealed a rate of 30.1% (95% confidence interval: 28.5%–31.8%) which is higher than in the general population.
Students who spend excessive time online, especially at night, experience delay of bedtime and shorter sleep duration. Internet use at night leads to emission of blue light from bright screen suppressing melatonin secretion and phase delay of the biological clock. IA has been reported to be associated with delay in onset of sleep, lower sleep efficiency, poor sleep quality, more sleep disturbances, reduced sleep duration, and excessive daytime sleepiness.,, IA and associated sleep impairments can both mediate psychopathology such as stress, depression, anxiety, and suicidality.
The objectives of our study were to explore the rates of IA in medical undergraduates and examine the association between severity of IA and quality of sleep in them. Our research will fill the gaps in understanding of IA and its impact on sleep quality, especially in the student community.
| Materials And Methods|| |
This was a cross-sectional study done in a university in southern India. The participants were 158 medical undergraduate students in their final year recruited in September 2016 through purposive sampling after obtaining institutional ethical clearance. Both male and female undergraduate students above the age of 18 years who gave written informed consent were enrolled in the study. Students with history of psychiatric disorders and medical/neurological disease were excluded based on self-report on the sociodemographic pro forma.
Sociodemographic data were collected using a semi-structured questionnaire. Assessment of Internet use and sleep quality was done using the Internet Addiction Test (IAT) and the Pittsburgh Sleep Quality Index (PSQI). IAT is a reliable and valid measure of addictive use of the Internet, developed by Dr. Kimberly Young. It consists of 20 items with scoring based on a Likert scale from 1 (“not at all”) to 5 (“always”). Total score of IA is divided into different grades with a score of 0–30 being normal Internet use, a score of 31–49 denotes mild degree of IA, a score of 50–79 indicates moderate level of IA and a score of 80–100 signifies severe IA. IAT is a valid and reliable scale, with satisfactory internal consistency (Cronbach's alpha of 0.84).
PSQI is a reliable and valid measure for assessing patterns and quality of sleep in the past 1 month. It is a 9-item scale, with items 5–9 to be rated on a Likert scale from 0 to 3 (0 - not during the past month and 3 - three or more times week) and 5th item having 10 subitems. Seven domains of sleep quality are assessed: (1) subjective sleep quality: self-reported satisfaction with sleep quality in the past 1 month. The higher the score, the more unsatisfactory the subject feels; (2) sleep latency: a higher score suggests a longer time required to fall asleep after going to bed; (3) sleep duration: a higher score signifies a shorter sleep duration; (4) habitual sleep efficiency: a higher score signifies lower sleep efficiency; (5) sleep disturbances: a higher score denotes more severe disturbance; (6) use of sleep medication: a higher score suggests the frequent requirement of medication; and (7) daytime dysfunction: a higher score suggests more severe problems engaging in daily activities due to daytime drowsiness. It has high internal homogeneity, internal consistency, and test–retest reliability with 89.6% sensitivity and an 86.5% specificity using a global PSQI cutoff score of 5.,
Statistical analysis was done using SPSS for Windows, Version 16.0. (SPSS Inc., Chicago, Illinois, US). Descriptive statistics were done with frequencies represented as percentages and continuous variables as means and standard deviation (SD). Independent samples t-test was used to compare the mean scores of IAT and PSQI across gender groups. One-way analysis of variance (ANOVA) with post hoc Tukey's tests was used to compare mean PSQI scores across different degrees of IA. Pearson's bivariate correlation was done to see the relationship between severity of IAT total score and PSQI global score.
| Results|| |
Background data of participants
[Table 1] depicts the sociodemographic characteristics of the participants.
More than half of the participants, i.e. 90 (57.0%), had IA. Among them, 61 (38.6%) had mild IA and were average online users; 27 (17.1%) had moderate IA and could experience frequent problems because of the Internet usage, 2 (1.3%) had severe IA and Internet usage could cause significant problems in their life. Independent samples t-test was done to compare IA scores across gender groups. There was no significant difference in the IA scores for males (M = 37.24, SD = 17.75) and females (M = 34.47, SD = 15.09), t (156) =1.06, P = 0.29.
Global sleep quality
In the present sample, 40 (25.3%) participants were having poor sleep quality as measured by global PSQI cutoff score >5. There was no significant difference in global PSQI scores of males (M = 4.53, SD = 2.50) and females (M = 3.93, SD = 2.05), t (156) =1.65, P = 0.10.
We conducted a one-way ANOVA to compare global PSQI scores across the three groups based on the degree of IA severity (normal [A], mild [B], and moderate to severe [C]). IA severity levels had a significant effect on mean global PSQI scores F (2, 155) =3.23, P = 0.042*. Post hoc comparisons using Tukey's test indicated that the mean PSQI score for the “moderate-to-severe” IA group (M = 5.17, SD = 3.35) was significantly higher than the “mild” IA group (M = 4.03, SD = 2.01). However, the “normal” group (M = 4.20, SD = 1.95) was not significantly different from the “mild IA” group and “moderate-to-severe” group in terms of mean PSQI score [Table 2].
|Table 2: Comparison of mean global Pittsburgh Sleep Quality Index scores across different degrees of Internet addiction|
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Majority of the participants rated subjective sleep quality as good (very good: 42.4%, n = 67; fairly good: 50.0%, n = 79). Among the participants, 10 (6.3%) and 2 (1.3%) rated subjective sleep quality as fairly bad and very bad, respectively. Almost half of the participants (n = 78) slept for less than 7 h (49.4%). Four (2.5%) participants had sleep duration of <5 h, 26 (16.5%) had sleep duration of 5–6 h, 48 (30.4%) had 6–7 h, and 80 (50.6%) had sleep for more than 7 h of sleep. Majority, i.e. 154 (97.5%), had habitual sleep efficiency of more than 85%, 3 (1.9%) had sleep efficiency of 75%–84%, and 1 (0.6%) had sleep efficiency of 65%–74%. One hundred (63.3%) participants scored 1–9 which indicates milder form of sleep disturbances, 12 (7.6%) scored between 10 and 18, and 1 (0.6%) scored 19–27 indicating higher sleep disturbances. One hundred and twenty-six (80%) participants did not use sleeping medications, while 28 (17.7%) participants used over-the-counter sleeping medications less than a week, 4 (2.5%) used sleeping medications once or twice a week, and 1 (0.6%) used sleeping medications for three or more times a week. Majority (n = 130, 82.3%) of the participants did not experience daytime dysfunction. Sixteen (10.1%) had mild form of daytime dysfunction, nine (5.7%) had moderate form of daytime dysfunction, and three (1.9%) had severe form of daytime dysfunction.
Association between Internet addiction and global Pittsburgh Sleep Quality Index score
When Pearson's correlation was done to see the relationship between IA total score and global PSQI score, we found a statistically significant positive correlation between scores (r = 0.198, P = 0.012*) with a small-to-moderate effect size [Table 3] and [Figure 1]. This indicates that participants with higher IA score had poorer sleep quality.
|Table 3: Correlation between internet addiction total scores and Pittsburgh Sleep Quality Index scores|
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|Figure 1: Scatter plot showing positive correlation between internet addiction total score and global Pittsburgh Sleep Quality Index score|
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| Discussion|| |
Our study looked at IA and its association with sleep quality in the final-year medical undergraduate students at a university in southern India. In our study, 57% of the participants had IA which is similar to a report by Chaudhari et al. (58.87%) but higher than rate found by Anand et al. (38.2%) and Nath et al. (46.8%)., Rate of IA in our sample of medical undergraduates was also higher than prevalence rates suggested by a review of IA in students of SEAR and pooled prevalence rate in medical students globally (30.1%). Participants in our sample were staying away from their families in the hostel and had easy access to uninterrupted Internet provided by the university. Negative mood states such as loneliness, boredom, anxiety, and depression and their positive alteration by Internet use may lead to excessive use by negative reinforcement. Many use Internet to gain social support from SNSs such as Facebook to overcome loneliness or prefer online social interaction., Chaudhari et al. and Anand et al. also found a higher rate of IA in students staying in the hostel., The rate of severe IA (1.3%) in our study is similar to findings by Anand et al. (0.8%) and Gedam et al. (1.2%) in medical students. Rates of IA in our sample are also much higher than prevalence in the general population. However, rates of IA vary widely as suggested by various reviews and meta-analysis depending on the participants, sample size, demographics, methodology, and instruments used.,,
Gender did not have a significant effect on IA severity although males scored higher on IAT. This is in contrast with two Indian studies which have highlighted a gender difference in IA with males more likely to have IA., However, Zhang et al. in their meta-analysis on IA in medical students reported that gender may not play a key role in IA because of the equal access of the Internet for online learning and assessment.
Around a quarter of the participants had poor sleep quality which is in line with a study by Bhandari et al. who reported that one in three participants had poor sleep quality. Our results demonstrated that severity of IA also predicted poorer sleep quality. Participants with moderate-to-severe IA had poorer sleep quality than participants with mild IA. Meta-analytic reviews have consistently reported poorer sleep quality in those with higher degree of IA., However, a meager 7.6% of the participants in the study perceived their sleep as poor which may be explained by the attitudes toward sleep practices in our sample.
Around 14% reported prolonged sleep latency and 71.5% had some degree of sleep disturbance. This may be due to prolonged use of screens around bedtime leading to suppression of melatonin secretion and delay of circadian rhythm as well as physiological arousal from using the Internet. Daytime dysfunction was seen in 17.7% of the participants. Daytime dysfunction impacts the academic functioning and quality of life of the students.
To the best of our knowledge, this is the first study from southern India to look at IA and its association with quality of sleep in medical students and would add the existing evidence on IA and its negative influence on sleep. Our study used a standardized questionnaire to assess sleep parameters as self-designed questionnaires underestimate the sleep problems.
The present study was limited by cross-sectional design like most of the research in the field. Study participants were final-year undergraduate students of a single medical college which limits the generalizability of results. As we have used self-report questionnaires for the study, there is a possibility of misreporting or bias. Although we excluded participants with diagnosed psychiatric disorders, we have not applied any instruments to ascertain underlying mental health issues. There is also significant evidence suggesting the mediational effect of stress and mood disorders on IA and quality of sleep., In addition, we have not explored the different motives for using the Internet in the participants, thus effect of different motives on causation of IA and their influence on sleep remains unknown; future studies can investigate these unexplored factors.
| Conclusions|| |
IA among medical undergraduates is a growing concern and has a negative influence on quality of sleep. Severity of IA predicted quality of sleep in the participants. Educating students about addictive tendencies of excessive Internet use and its negative impact on sleep is essential, especially with increasing reliance on online learning.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Balhara YP, Mahapatra A, Sharma P, Bhargava R. Problematic internet use among students in South-East Asia: Current state of evidence. Indian J Public Health 2018;62:197-210.
] [Full text]
Zhang MW, Lim RB, Lee C, Ho RC. Prevalence of internet addiction in medical students: A meta-analysis. Acad Psychiatry 2018;42:88-93.
Lam LT. Risk factors of internet addiction and the health effect of internet addiction on adolescents: A systematic review of longitudinal and prospective studies. Curr Psychiatry Rep 2014;16:508.
Moderie C, Van der Maren S, Dumont M. Circadian phase, dynamics of subjective sleepiness and sensitivity to blue light in young adults complaining of a delayed sleep schedule. Sleep Med 2017;34:148-55.
Alimoradi Z, Lin CY, Broström A, Bülow PH, Bajalan Z, Griffiths MD, et al.
Internet addiction and sleep problems: A systematic review and meta-analysis. Sleep Med Rev 2019;47:51-61.
Lam LT. Internet gaming addiction, problematic use of the internet, and sleep problems: A systematic review. Curr Psychiatry Rep 2014;16:444.
Young KS. Caught in the Net: How to Recognize the Signs of Internet Addiction – And a Winning Strategy for Recovery. New Jersey: John Wiley and Sons; 1998.
Widyanto L, McMurran M. The psychometric properties of the internet addiction test. Cyberpsychol Behav 2004;7:443-50.
Buysse DJ, Reynolds CF 3rd
, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: A new instrument for psychiatric practice and research. Psychiatry Res 1989;28:193-213.
Buysse DJ, Hall ML, Strollo PJ, Kamarck TW, Owens J, Lee L, et al.
Relationships between the Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), and clinical/polysomnographic measures in a community sample. J Clin Sleep Med 2008;4:563-71.
Chaudhari B, Menon P, Saldanha D, Tewari A, Bhattacharya L. Internet addiction and its determinants among medical students. Ind Psychiatry J 2015;24:158-62.
] [Full text]
Anand N, Thomas C, Jain PA, Bhat A, Thomas C, Prathyusha PV, et al.
Internet use behaviors, internet addiction and psychological distress among medical college students: A multi centre study from South India. Asian J Psychiatr 2018;37:71-7.
Nath K, Naskar S, Victor R. Cross-sectional study on the prevalence, risk factors, and ill effects of internet addiction among medical students in Northeastern India. Prim Care Companion CNS Disord 2016;18. doi:10.4088/PCC.15m01909. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4956431/
. [Last accessed on 2020 May 13].
Caplan SE. Preference for online social interaction: A theory of problematic Internet use and psychosocial well-being. Commun Res 2003;30:625-48.
Shettar M, Karkal R, Kakunje A, Mendonsa RD, Chandran VV. Facebook addiction and loneliness in the post-graduate students of a university in southern India. Int J Soc Psychiatry 2017;63:325-9.
Gedam SR, Shivji IA, Goyal A, Modi L, Ghosh S. Comparison of internet addiction, pattern and psychopathology between medical and dental students. Asian J Psychiatr 2016;22:105-10.
Kuss DJ, Griffiths MD, Billieux L, Billieux J. Internet addiction: A systematic review of epidemiological research for the last decade. Curr Pharm Des 2014;20:4026-52.
Chia DX, Ng CW, Kandasami G, Seow MY, Choo CC, Chew PK, et al.
Prevalence of internet addiction and gaming disorders in Southeast Asia: A meta-analysis. Int J Environ Res Public Health 2020;17:E2582.
Bhandari PM, Neupane D, Rijal S, Thapa K, Mishra SR, Poudyal AK. Sleep quality, internet addiction and depressive symptoms among undergraduate students in Nepal. BMC Psychiatry 2017;17:106.
Peach HD, Gaultney JF, Ruggiero AR. Direct and indirect associations of sleep knowledge and attitudes with objective and subjective sleep duration and quality via sleep hygiene. J Prim Prev 2018;39:555-70.
Grandner MA. Sleep, health, and society. Sleep Med Clin 2017;12:1-22.
[Table 1], [Table 2], [Table 3]