Prevalence of Internet Addiction and its Impact on Psychological Health and Sleep Quality: A Descriptive Study on Nursing Students of Western U.P, India
Kusum Lata1, Surat Ram Kudi2*
1College of Nursing, AIIMS Jammu India.
2College of Nursing, AIIMS Jammu India.
*Corresponding Author Email: kusum.bhardwaj.133@gmail.com, srjat789@gmail.com
ABSTRACT:
Background: Internet has had a profound impact on nearly every aspect of modern life. Internet addiction can have serious consequences for individuals' physical health, mental health, academic or work performance, and social life. Aim: To find prevalence of internet addiction and its association with depression, sleep, anxiety and sleep quality among nursing students. Material and methods: It was a descriptive web-based survey. Data has been taken from 451 nursing students. Standardized tool Young’s Internet Addiction Test, Depression Anxiety and Stress Scale-21 (DASS-21) and the Pittsburgh Sleep Quality Index were used. Results: 76.7% participants had internet addiction. 49.9% participants had mild addiction, 24.8% have moderate and 2% have severe Internet addiction. 36.2% participants had mild to extremely severe depression, 53.5% had mild to extremely severe anxiety and 38 % had mild to extremely severe stress. More than half of participants (53.9%) had poor sleep quality. The Odds of sleep quality was 1.78 times, depression 0.87, anxiety 1.21 and stress is 1.22 with internet addiction. Conclusion: Students had internet addiction and it influences their psychological health. Awareness program should be organized to teach students about responsible internet use and the potential risks of excessive screen time
KEYWORDS: Anxiety, Depression, Internet addiction, Sleep quality, Stress.
INTRODUCTION:
Internet has had a profound impact on nearly every aspect of modern life, shaping how we communicate, learn, work, shop, entertain ourselves, and interact with the world around us. According to the Internet World Stats, the Internet users' population worldwide had increased from 360 million (Dec 2000) to 4536 million (June 2019), showing the worldwide internet penetration rate of 58.8%. In India, there were about 560 million internet users in June 2019, as compared to only 5 million in Dec 2000, depicting the rate of internet penetration to be 40.9% of the population, which represents 24.3% of internet users in Asia.1
A recent meta-analytic review of 507 studies before October 2021 shows the global prevalence of internet addiction at 14.22%.2 While the prevalence of IA among Indian students is 19.9%.3 The addiction rate is increasing gradually among students. Addiction affects males and females equally. The world statistics report on internet use shows 27% of women and 25% of men report being near-constant internet users. Education increases the risk, with 34% of college grads reporting being online almost constantly, compared to only 20% of those with a high school education or less.4
Internet addiction, also known as problematic internet use (PIU) or internet addiction disorder (IAD), refers to excessive and compulsive use of the internet leading to negative consequences in various aspects of life such as social interaction, work, school, and mental well-being. The Psychologist Mark Griffiths, one of the widely recognized authorities in the sphere of addictive behavior, is the author of the most frequently quoted definition: “Internet addiction is a non-chemical behavioral addiction, which involves human-machine (computer-Internet) interaction”.5 Internet addiction can manifest in various forms, including gaming addiction, social media addiction, online gambling addiction, and compulsive online shopping, among others. WHO included internet gaming disorder in the chapter of substance and behavioural addiction in the 11th edition of the International Classification of Diseases and Related Health Problems (ICD-11).6
Internet addiction can be influenced by various factors, and its development is often multifaceted. Anxiety, Attention deficit hyperactivity disorder (ADHD), depression, low self-esteem, loneliness make individual more prone to developing addiction-like behaviours stress.7,8 Certain environmental conditions like easy access to the internet through smartphones, tablets, and computers, social pressures and norms within peer groups also play a significant role in internet addiction.9 Neglection in family, family conflict, lack of supervision may contribute to the development of internet addiction, especially in adolescents.10 Technological factors such as design features of websites, apps, and online games, such as gamification elements, notifications, and rewards systems, can encourage prolonged engagement and contribute to addiction. Easy accessibility can be one contributing factor for internet addiction.11 Availability of online content on the internet, including entertainment, social networking, gambling, gaming and pornography, can appeal to different individuals and influence addictive behaviors.
Internet addiction can have serious consequences for individuals' physical health, mental health, academic or work performance, and relationships. It can lead to social isolation, sleep disturbances, poor academic or work performance, financial problems (due to excessive online shopping or gambling), and mental health issues such as depression and anxiety.12 Students are a particularly vulnerable to internet addiction in this digital era. To find internet addiction among nursing students and to explore impact of internet addiction on psychological health and sleep quality researcher did this study.
MATERIALS AND METHODS:
A descriptive web-based survey was done to conduct this study. Google form was prepared to conduct the data for the study along with consent form. Snowball sampling technique was used to choose participants. For calculating sample size, prevalence rate (19.9%) is considered from systematic review and meta-analysis conducted on Indian studies, considered 95% confidence interval and 5% precision. 237 sample size was calculated using formula z2pq/d2. It was mandatory to fill all questions before final submission, which eliminated risk of submitting incomplete data form. We get responses from 451 participants and included all of them in analysis. Researcher had taken data from diploma holders, and graduate nursing students of selected nursing colleges in Uttar Pradesh. Students who were 18 or above 18 years, given consent to do study were selected. Students don’t understand English language were excluded from study.
Tools:
· Tool I: Self developed Information sheet for eliciting details about Demographic datasheet of participants and Internet usages like duration and purpose of usage.
· Tool II: Standardized “Young’s Internet Addiction Test” is 20 items, 6-point scale (0 = does not apply, 5 = always) and scores can range from 0-100. Scores between 0-30 points considered normal level of internet usage, 31-49 indicate mild level Internet addiction, 50-79 moderate level addiction and 80-100 severe level Internet addiction.13
· Tool III: Standardized “Depression Anxiety and Stress Scale-21 (DASS-21)” It is 21 item scale. Score ranges 0-4, 0-3 and 0-7 considered normal for depression, anxiety and stress respectively. Score ranges 5-6, 4-5, 8-9 reflects mild depression, anxiety and stress respectively. Score between 7-10, 6-7, 10-12 indicates moderate depression, anxiety and stress respectively and score between 11-13, 8-9, 13-16 reflects severe depression, anxiety and stress respectively. Score 14+, 10+, 17+ reflects extremely severe depression, anxiety and stress respectively.14
· Tool IV: Standardized “The Pittsburgh Sleep Quality Index”. Total score is calculated by summation of seven sub categories i.e duration of sleep, sleep disturbance, sleep latency, day dysfunction due to sleepiness, sleep efficiency, overall sleep quality, need medicine to sleep. Total minimum score is 0 (better) and maximum score is 21 (worse). TOTAL < 5 associated with good sleep quality and score > 5 associated with poor sleep quality.15
Data collection procedure:
Ethical clearance was obtained from ethical committee of LLRM Medical College, Meerut, U.P. The questionnaire was shared through google form and each participant was asked to share the same with their known subjects. The participants entry was restricted to western U.P areas only. The link to google form was open till March 2024 by 10:00pm. After clicking on the respective link in google form, the participants were automatically guided to the information related to consent form. Consent form comprises the information related to purpose of the study, and it was informed to the participant that they can choose any option which belongs to them as there are nothing right and wrong responses. Participants had given right to reject or discontinue their application any time without any penalty. Confidentiality and anonymity of the participants was maintained by not disclosing the name and other information of the participant except its requirement in the study. On being agreed to fill the consent form participants were directed to fill sociodemographic data followed by information related to internet usage than to Standardized “Young’s Internet Addiction Test”, than Standardized “Depression Anxiety and Stress Scale”, and lastly toStandardized “The Pittsburgh Sleep Quality Index”.
After data collection, data were coded and entered in IBM SPSS, version 23. Data were analysed by using statistical methods frequency, mean, standard deviation and chi square. Association between internet addiction and depression, anxiety, stress and sleep quality was obtained by using Odd’s ratio. Statistical significance was considered at p<0.05.
Mean age of the participants was 20 and SD± 6.56. 86.5% participants were females and 13.5% were male. Half of the participants, 53.4% were studying BSCN, 46.6% GNM courses. 54.3% belong to urban area. 55.4% were residing in hostel and 44.6% were day scholar. Majority (62%) participants were using cellular data only, 36% were using wifi and cellular data both and 2% were using wifi only. More than half of the participants (53%) using internet for Entertainment, shopping, social networking, 38% participants were using internet for study purpose only, and 9% were using for playing online games. Majority of participants (85%) were using Internet for 3-6 hours, 10% were using for ≤2 hours, and 5% were using for >6 hours/day.
Table 1: Association between sociodemographic characteristics and internet addiction among participants N=451
|
Demographic Variables |
Internet addiction (n) |
No Internet addiction (n) |
Chi square |
|
|
Gender |
Male |
41 |
20 |
3.86* |
|
Female |
305 |
85 |
||
|
Education |
GNM |
165 |
45 |
0.755 |
|
BSCN |
181 |
60 |
||
|
Residing In |
Hostel/PG |
185 |
65 |
2.32 |
|
Home |
161 |
40 |
||
|
Source of Internet |
Cellular |
210 |
68 |
2.18 |
|
Wifi |
9 |
2 |
||
|
Wifi and cellular both |
127 |
35 |
||
|
Internet Usage is done for |
Only Studying |
135 |
36 |
5.073 |
|
Gaming |
30 |
12 |
||
|
Entertainment, Shopping, social networking |
181 |
57 |
||
|
Total time spent on Internet/day |
≤2 hours |
36 |
9 |
4.12* |
|
3-6hours |
291 |
92 |
||
|
>6 hours |
19 |
4 |
||
*statistically significant at p<0.05
Table 1 showing the association between sociodemographic characteristics and internet addiction among participants. A statistically significant association has been found between Internet addiction and gender and total time spent on Internet/day at p<0.05 level of significance.
Table 2: Frequency of Internet addiction among participants using Youngs Internet Addiction Test Scale N=451
|
Internet addiction |
Frequency |
Percentage |
|
Normal usage |
105 |
23.3 |
|
Mild Addiction |
225 |
49.9 |
|
Moderate addiction |
112 |
24.8 |
|
Severe Addiction |
9 |
2 |
Table 2 shows Internet addiction using Youngs Internet Addiction Test Scale. 23.3% participants using internet in normal range. 49.9% participants had mild addiction. 24.8% have moderate addiction and 2% have severe Internet addiction.
The mean score of Internet addiction on Youngs Internet Addiction Test Scale was 44.08 and SD± 8.76.
Table 3: Psychological Impact of Internet addiction on participants using DASS scale and Pittsburg Scale N=451
|
|
Mean |
SD |
|
Depression |
8.20 |
0.46 |
|
Anxiety |
9.18 |
0.43 |
|
Stress |
12.53 |
0.44 |
|
PSQI |
6.12 |
0.14 |
Table 3 depicts Psychological Impact of Internet Addiction. Mean score of participants for depression was 8.20±0.46, Anxiety 9.18±0.43, Stress 12.53±0.44. Mean score of PSQI was 6.12±0.14.
Table 4: Frequency and Percentage Distribution of depression, anxiety, stress and sleep quality N=451
|
Level of Variable |
Depression |
Anxiety |
Stress |
|||
|
f |
% |
f |
% |
f |
% |
|
|
Normal |
288 |
63.9 |
210 |
46.6 |
280 |
62.1 |
|
Mild |
56 |
12.4 |
90 |
20 |
54 |
12 |
|
Moderate |
40 |
8.9 |
71 |
15.7 |
58 |
12.9 |
|
Severe |
40 |
8.9 |
44 |
9.8 |
40 |
8.9 |
|
Extremely Severe |
27 |
6.0 |
36 |
8 |
19 |
4.2 |
|
Sleep Quality |
||||||
|
|
f |
% |
||||
|
Good |
208 |
46.1 |
||||
|
Poor |
243 |
53.9 |
||||
|
Variable |
Odd’s Ratio |
CI (95%) |
|
Sleep quality |
1.78 |
1.14-2.77 |
|
Depression |
0.87 |
0.55-1.37 |
|
Anxiety |
1.21 |
0.78-1.87 |
|
Stress |
1.22 |
0.76-1.93 |
Table 4 shows frequency and Percentage Distribution of depression, anxiety, stress and sleep quality. 12.4% participants had mild depression, 8.9% had moderate and severe depression each, and 6% had extremely severe depression. 20% had mild anxiety, 15.7% had moderate, 9.8% had severe and 8% extremely severe anxiety level. 12% had mild stress, 12.9% had moderate, 8.9% had severe stress and 4.2% had extremely severe. More than half of participants had poor sleep quality and 46.1% had good sleep quality.
Table 5 shows association between Internet addiction and Depression, anxiety, stress level and quality of sleep. Odds of poor sleep quality is 1.78 times, depression, 0.87, anxiety 1.21 and stress is 1.22 with internet addiction.
DISCUSSION:
In our study 86.5% participants were females and 13.5% were male. Female participants had higher internet addiction (78%) than male (67%). More than half of the participants (53%) using internet for Entertainment, shopping, social networking, 38% participants were using internet for study purpose only, and 9% were using for playing online games. This is supported by Saikia et al found majority of participants use internet for social networking followed by study.16 In this study we found majority of participants, 85% were using internet for 3-6 hours/day, 10% were using for ≤2 hours, and 5% were using for >6 hours/day. Saikia et al found in their study most students (42.1%) used the Internet 3–6 h/day. A relatively fewer number of students (11.5%) used the Internet for >9 h/day.16 The variation in the finding could be due to the study sample as in this study participants were higher secondary schools/college students and in our study, participants are studying a professional course.
In our study prevalence of internet addiction was 76.7%. This finding is approximately similar to study conducted by the Rashmi et.al who found 78.7% prevalence.8 While the prevalence of internet addiction was 80.7% among students studying non-professional courses.16 In the present study 49.9% participants had mild addiction, 24.8% have moderate addiction and 2% have severe Internet addiction. While Jaiswal et.al found prevalence of internet addiction mild in 42.3%, moderate in 48.2 and severe in 3.3%.17 Grover et al. reported the prevalence of internet addiction among resident doctors of a tertiary care hospital of North India as mild in 54% and moderate in 8.2%.18 None had severe addiction. Saikia et al. found the prevalence of internet addiction was mild in 65.4%, moderate in 13.5% and severe in 1.9%.16 Another study the prevalence of internet addiction as mild in 62.9%, moderate in 13% and severe in 1%.19 In our study mean YIAT score was 44.08 and SD±8.76. Raghav Gupta et.al found the average YIAT score was 33.16±15.68.20 The variation in our study findings may be partly attributed to differences in data collection methods and participant characteristics.
In our study mean score of participants for depression was 8.20±0.46, anxiety 9.18±0.43 and stress was 2.53± 0.44. 12.4% participants had mild depression, 8.9% had moderate and severe depression each, and 6% had extremely severe depression. 20% had mild anxiety, 15.7% had moderate, 9.8% had severe and 8% extremely severe anxiety level. 12% had mild stress, 12.9% had moderate, 8.9% had severe stress and 4.2% had extremely severe. Javeed et. Al found in his study that 11.9% had mild depression, 20% moderate depression, 17.6% had severe and 19% had extremely depression. 46.2% had extremely severe anxiety, 15.2% severe anxiety, 21.4% had moderate anxiety and 1.9% had mild anxiety. 22.4% had mild stress, 15.7% had moderate stress, 17.6% had severe stress and 2.4% had extremely severe stress.21
In our study mean PSQI score was 6.12±0.14. More than half of participants (53.9%) had poor sleep quality and 46.1% had good sleep quality. Our findings are contradicted with study conducted by Awasthi et.al who found 66.1% sleep quality was good and 33.9% was poor.22 Paudel et.al found poor sleep quality in 42.3%. Internet addiction was associated with sleep quality which was supported by study conducted by Paudel et.al.23 In our study odds of poor sleep quality is 1.78 times. In this study we found the odds of anxiety 1.21 and stress is 1.22 and depression 0.87, with internet addiction while Saika et.al found odds of stress, depression and anxiety as 12, 14, 3.3 respectively.16 Difference between the finding could be due to the different study population. The variation in our study findings may be partly attributed to differences in data collection methods and participant characteristics.
Limitation Cross-sectional study design was used to conduct this study which is weak design in itself. Although we had taken sample beyond calculated sample size but in descriptive study larger sample size leads to better generalizability. Other variable can also be assessed such as education status of parents, socio-economic status, substance use behavior and educational variables such as academic year, impact of internet addiction on academics.
CONCLUSION:
In this study prevalence of internet addiction was 76.7%. Female participants had higher internet addiction than male. Majority of the participants had mild to moderate level of internet addiction and this further influenced psychological health of the participants, developed stress, anxiety and depression among participants and lead to poor sleep quality. Considering findings of the study awareness program should be organized to teach students about responsible internet use and the potential risks of excessive screen time. Students should be encouraged to explore hobbies and interests that don't involve screens, such as reading, art, music, or outdoor activities in their leisure time. It is necessary to promoting a balanced and healthy relationship with technology.
ACKNOWLEDGEMENT:
We are thankful to Mr. Pankaj Kumar for editing this manuscript. We are thankful to the participants who spare their valuable time.
CONFLICTS OF INTEREST:
There are no conflicts of interest.
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Received on 14.01.2026 Revised on 04.02.2026 Accepted on 24.02.2026 Published on 30.04.2026 Available online from May 02, 2026 Asian J. Nursing Education and Research. 2026;16(2):102-106. DOI: 10.52711/2349-2996.2026.00021 ©A and V Publications All right reserved
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