A Study to Assess the Correlation between Internet Addiction and Daytime Sleepiness among Youth in a selected College at Kollam district
Chikku S, Jiss James, Shahana N, Aquilin K S, Akhila Sunny, Sonnet Maria S,
Merlene Manoj, Deepthy Thankachan, Aneeta K R
Associate Professor, Medical Surgical Nursing Department, Third Year Bsc Nursing Students,
HolyCross College of Nursing, Kottiyam.
*Corresponding Author E-mail: daschikku826@gmail.com
ABSTRACT:
A correlational study was done to assess the correlation between internet addiction and daytime sleepiness among youth in a selected college at Kollam District. The objectives were to assess the internet addiction among youth in selected college at Kollam district, to assess the daytime sleepiness among youth in a selected college at Kollam district, to find out the correlation between the internet addiction and daytime sleepiness among youth in a selected college at Kollam district. Conceptual framework used was health promotion model by Nola J. Pender. Quantitative research approach was selected with correlational prospective design. Study was conducted at Don Bosco College Kottiyam, with sample size of 60 based on non-probability convenience sampling technique. Tools contained two sections, section A: socio demographic data and section B: 2 five-point Likert scale. The data was analyzed using correlational statistics. The findings were 45% of the samples have moderate internet addiction and mild daytime sleepiness, 25% of the samples have moderate internet addiction and moderate day time sleepiness, 10% of the samples have mild internet addiction and moderate daytime sleepiness, 8.37% of the samples have mild internet addiction and mild daytime sleepiness, 6.67% of the samples have severe internet addiction and moderate daytime sleepiness, 3.34% of the samples have severe internet addiction and severe daytime sleepiness, 1.64% of the samples have severe internet addiction and mild daytime sleepiness. Hence according to Karl Pearson’s correlation coefficient, there is moderate positive correlation [0.418], between internet addiction and daytime sleepiness among youth.
KEYWORDS: Assess, Correlation, Internet addiction, Daytime sleepiness, youth.
INTRODUCTION:
Background of the Problem:
Internet addiction and other problematic internet use behaviors can have important influence on the sleep-wake program. Heavy internet usage will lead to sleep disorders like insomnia or excessive daytime sleep. The likelihood of the relation between internet addiction and sleep disorders is on the rise. Excessive use of the internet tragically deteriorates the circadian rhythm of the body.1
Circadian rhythms are physical, mental and behavioral changes that follow a 24 -hour cycle. It includes processes of the body such as alertness or sleepiness, appetite, and body temperature. It is a 24-hour internal clock in our brains. The circadian rhythm plays a large role in this sleep-wake cycle, telling the body when it's time to sleep and wake up for the day.2
Circadian rhythm disorder, also known as sleep -wake cycle disorders, is a problem that occurs when your body's internal clock, which determines the time to sleep or wake, is out of sync with the environment. Many studies have proven the influence of the internet on sleep problems. Sleep is the optimum method for people to rest. People feel energized and ready for a new day after waking up following a good night's sleep.3
A cross sectional study was conducted on prevalence of excessive daytime sleepiness and its determinants among college students in Chengalpattu District, Tamil Nadu. The 422 samples were selected through convenience sampling technique. Data was collected with the help of pre-tested, pre-designed, and modified questionnaire along with Epworth sleepiness scale. The result shown that the prevalence of daytime sleepiness among the study participants were 32% and most of them were facing sleep disturbances due to mobile and laptop usage, stress and over thinking and other reasons like night studies, spending time with friends was 56% respectively.4
A Cross sectional study was conducted to assess the knowledge regarding the trend of internet use among medical students in selected government medical college at Jammu. The 300 medical students were selected by convenience sampling technique. Data was collected with the help of structured knowledge questionnaire. The result shown that about 88% of PG students and 65% of UG students were reported to use computers. However, majority (53%) of the PG students were taking help of others (p<0.0001); whereas, 55% of UG students were using computers on their own.5
A descriptive study was conducted in 2022 to assess the sleep deprivation among school going adolescents in an urban setting at Thiruvananthapuram, Kerala. The 426 subjects were selected based on random sampling technique. Researchers collected data using self-administered structured questionnaires focusing on sleep habits, study habits, before bed screen time and parental sleep habits was administered to them. Univariate analysis showed adolescents above 15 years (odds ratio [OR] = 2203, 95% confidence interval [CI]: 1.380-3.517), students of 11th and 12th (OR = 2.205, 95% CI: 1.380-3.523), sleeping after 10 PM (OR = 19.617, 95% CI: 10.586-36.355), waking up before 6 AM (OR = 2.586, 95% CI: 1.554-4.304), sleeping after parents (OR = 2.356, 95% CI: 1.415-3.922) had significant risk .On multivariate analysis, students of 11th and 12th (adjusted OR [aOR] = 3.197, 95% CI: 1.107-9.234), going to bed after 10 PM(aOR = 51.49, 95% CI: 20.211-131.180), waking up before 6 AM (aOR = 51.49, 95% CI: 20.211-131.180), and sleeping after parents (aOR = 1.927, 95% CI: 1.011-3.673) were significant.6
Sleep deprivation can have dangerous outcomes such as decreased academic performance, elevated accidents, and alleviated coping mechanisms. Against this background, we are planning to conduct a study to assess the correlation between internet addiction and daytime sleepiness among youth.
NEED AND SIGNIFICANCE OF THE STUDY:
As the number of internet addicts continues to grow, clinicians should be proactive in examining internet addiction in cases of excessive daytime sleepiness. Future studies should focus on studying daytime sleep as a mediator between Internet addiction and the performance of youth. Internet addiction has been shown to impair a range of abilities such as impulse control, planning, and sensitivity to rewards.
A cross sectional study was conducted in 2018 to assess internet addiction and its determinants among students in medical College at Wayanad District Kerala. The 729 subjects were all the undergraduate medical students of the college. Researchers collected data using predesigned and pretested self-administered questionnaire and Young's Internet Addiction Test (IAT). The study revealed that prevalence of internet addiction among the study subjects was 94.5% which is more compared to other studies 58.87% and 76.84%. The probable reason for this difference could be the differences in the socio demographic statuses of the study subjects. Furthermore, the entry into the market of a private telecom service provider which has been providing internet services at very low prices since late 2016 could be another contributory factor. 60.8%, 31.3%, and 2.5% of the subjects were found to have mild, moderate, and severe internet addiction respectively.7
A cross sectional study was conducted on mobile phone dependence and its association with sleep quality among post graduate medical students in a tertiary care center in Kochi Kerala in 2019.The 233 samples was selected through convenience sampling technique. Data was collected with the help of test of mobile phone dependence and Pittsburgh sleep quality index. The result shown that the prevalence of mobile phone addiction in this study was found to be 44.9% and 51.9% were found to be poor sleepers as per their PSQI scores. A statistically significant association was found between overall test of mobile phone dependency and Pittsburgh sleep quality index scores.8
The present meta-analysis examined the prevalence of IA in 31 nations across seven world regions. The findings yielded an overall prevalence estimate of 6.0%. In Indian context, general population studies have shown the prevalence of internet addiction to be 1.3% which increases almost 10-fold in college students. The overall prevalence of internet addiction among college students in India is 40.7%. The prevalence of internet addiction among youth in Kerala is 2%.9
With the reference of the above evidence and details, to find or prove a correlation between internet addiction and day time sleepiness among youth to take over the problem as research study.
OBJECTIVES:
1. To assess the internet addiction among youth in selected college at Kollam district.
2. To assess the day time sleepiness among youth in a selected college at Kollam district.
3. To find out the correlation between internet addiction and day time sleepiness among youth in a selected college at Kollam district.
HYPOTHESIS:
● H1; There will be significant correlation between internet addiction and day time sleepiness among youth.
METHODS AND MATERIALS:
RESEARCH DESIGN:
Correlational prospective research design.
SETTING OF THE STUDY: The study setting is the location in which the research is conducted. The study was conducted in youth in a selected college at Kollam district.
RESEARCH APPROCAH: Research Approach is a plan and procedure that consists of the steps of broad assumptions to detailed method of data collection, analysis, and interpretation. In this study a quantitative research approach was adopted to assess the correlation between internet addiction and daytime sleepiness among youth in a selected college at Kollam district.
POPULATION AND SAMPLE: The entire set of individuals or objects having some common characteristic selected for a research study. In this study selected population is youth in a selected college at Kollam district. In this study the sample size is 60.
SAMPLING TECHNIQUES: The sample for the study was selected by using convenience sampling technique.10 The samples were selected based on the inclusion and exclusion criteria.
TOOLS / INSTRUMENTS: A tool is a device used to measure the concept of interest in a research project that a researcher used to collect. 41 The following tool was used for the present study.
Section A:
Socio demographic data is structured interview scheduled was used to collect information regarding the sample, it includes the demographic data.
Section B:
Five-point Likert scales were used to assess the internet addiction and daytime sleepiness.
DESCRIPTION OF TOOL:
Section A:
The section A was developed after extensive review of literature and based on expert opinion. It includes Socio demographic data is a structured interview schedule to collect information regarding the sample. It includes data like age in years, gender, area of residence, type of family, education of parents, occupation of parents, monthly income.
Section B:
The section B was developed after extensive review of literature and based on expert opinions. Researcher prepared two five-point Likert scales to assess internet addiction and daytime sleepiness among youth age 18-24. It consists of 15 questions each.
SECTION-A: Distribution of socio demographic variables.
Table 1: Frequency and percentage distribution of sample based on socio demographic variable.
VARIABLES |
FREQUENCY |
PERCENTAGE |
|
1. |
AGE a) 18-20 b) 21-23 c) 24 and above |
56 4 0 |
93.33% 6.67% 0% |
2. |
GENDER a) Male b) Female c) Transgender |
35 25 0 |
58.34% 41.67% 0% |
3. |
AREA OF RESIDENCE a) Urban b) Rural
|
39 21 |
65% 35% |
4. |
TYPE OF FAMILY a) Joint family b) Nuclear family c) Extended family |
21 38 1 |
35% 63% 1.6% |
5. |
EDUCATION OF PARENTS a) Illiterate b) Primary education c) Secondary education d) Higher education e) Graduate |
1 7 11 25 16 |
1.67% 11.67% 18.34% 41.67% 26.67% |
6. |
OCCUPATION OF PARENTS a) Unemployed b) Government employee c) Private employee d) Retired e) Others |
1 5 29 1 24 |
1.67% 8.34% 48.34% 1.67% 40% |
7. |
MONTHLY INCOME a) Below 10,000 b) 10,000-30,000 c) Above 30,000 |
9 28 23 |
15% 46.67% 38.34% |
SECTION B: To assess the correlation between internet addiction and daytime sleepiness.
Table 2: Comparison of frequency and percentage according to pretest score of internet addiction.
Sl no |
Level of addiction |
Frequency |
Percentage |
A. |
Normal |
0 |
0% |
B. |
Mild |
11 |
18.34% |
C. |
Moderate |
42 |
70% |
D. |
Severe |
7 |
11.67% |
Fig 1: shows the graphical representation of pretest score of internet addiction. The data above shows that 70% of youth have moderate internet addiction, 18% of youth have mild internet addiction ,12% of youth have severe internet addiction.
B. DAYTIME SLEEPINESS:
Table 3: Comparison of frequency and percentage according to pretest score of daytime sleepiness.
Sl no: |
Level of sleepiness |
Frequency |
Percentage |
A. |
Normal |
0 |
0% |
B. |
Mild |
32 |
53.34% |
C. |
Moderate |
26 |
43.34% |
D. |
Severe |
2 |
3.33% |
Fig 2: shows the graphical representation of pre test score of daytime sleepiness. The data above shows that 54% of youth have mild daytime sleepiness, 43% have moderate daytime sleepiness, 3% have severe daytime sleepiness.
Table 4: Comparison of frequency and percentage distribution of internet addiction and daytime sleepiness pretest.
Sl No |
Categories |
Frequency |
Percentage |
1. |
Mild internet addiction and mild daytime sleepiness |
5 |
8.34% |
2. |
Mild internet addiction and moderate daytime sleepiness |
6 |
10% |
3. |
Mild internet addiction and severe daytime sleepiness |
0 |
0% |
4. |
Moderate internet addiction and mild daytime sleepiness |
27 |
45% |
5. |
Moderate internet addiction and moderate daytime sleepiness |
15 |
25% |
6. |
Moderate internet addiction and severe daytime sleepiness |
0 |
0% |
7. |
Severe internet addiction and mild daytime sleepiness |
1 |
1.64% |
8. |
Severe internet addiction and moderate daytime sleepiness |
4 |
6.67% |
9. |
Severe internet addiction and severe daytime sleepiness |
2 |
3.34% |
The above shows that 45% of the samples have moderate internet addiction and mild daytime sleepiness, 25 % of the samples have moderate internet addiction and moderate daytime sleepiness, 10% of the samples have mild internet addiction and moderate day time sleepiness, 8.34% of the samples have mild internet addiction and mild daytime sleepiness, 6.67 % of the samples have severe internet addiction and moderate daytime sleepiness, 3.34 % of the samples have severe internet addiction and severe daytime sleepiness, 1.64 % of the samples have severe internet addiction and mild daytime sleepiness.
Table 5: Mean, Karl Pearson’s correlation value of pretest.
Variables |
Mean |
Karl Pearson’s correlation value |
Internet addiction |
36.183 |
r= 0.418 |
Daytime sleepiness |
45.25 |
The data in the table shows that Karl Pearson’s correlation test was used to compare internet addiction and daytime sleepiness, hence according to scale of Karl Pearson’s correlation coefficient, there is moderate correlation [0.418], between internet addiction and daytime sleepiness among youth. Hence, the research hypothesis [H1] was accepted.
DISCUSSION:
The present study was conducted to assess the correlation between internet addiction and daytime sleepiness among youth at selected college in Kollam district. The convenience sampling techniques were used to select 60 samples of college students.
The findings recorded that 45% of the samples have moderate internet addiction and mild daytime sleepiness, 25% of the samples have moderate internet addiction and moderate daytime sleepiness, 10% of the samples have ,mild internet addiction and daytime sleepiness, 8.34% of the samples have mild internet addiction and mild daytime sleepiness, 6.67% of the samples have severe internet addiction and moderate daytime sleepiness, 3.34% of the samples have severe internet addiction and severe daytime sleepiness, 1.64% of the samples have severe internet addiction and mild daytime sleepiness.
CONCLUSION:
In this study 45% of the samples have moderate internet addiction and mild daytime sleepiness, 25% of the samples have moderate internet addiction and moderate daytime sleepiness, 10% of the samples have ,mild internet addiction and daytime sleepiness, 8.34% of the samples have mild internet addiction and mild daytime sleepiness, 6.67% of the samples have severe internet addiction and moderate daytime sleepiness, 3.34% of the samples have severe internet addiction and severe daytime sleepiness, 1.64% of the samples have severe internet addiction and mild daytime sleepiness.
RECOMMENDATIONS:
On the basis of research study conducted, certain suggestions were given for the future studies:
1. The study can be conducted on a wider range of population so that the study findings can be generalized.
2. The study can be conducted in a community setting.
3. A similar study can be conducted for longer duration so that it could have produced great effectiveness.
REFERENCES:
1. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5182270/
2. https://www.nigms.nih.gov/education/fact-sheets/Pages/circadian-rhythms.aspx
3. https://www.nhlbi.nih.gov/health/circadian-rhythm-disorders
4. https://www.msjonline.org/index.php/ijrms/article/view/11439
5. https://www.jkscience.org/archive/Volume82/trend.pdf
6. https://journals.sagepub.com/doi/10.1177/09731342221122841
7. https://www.researchgate.net/publication/329140497_Internet_Addiction_and_its_determinants_among_the_Students_of_a_Medical_College_in_Kerala
8. https://www.researchgate.net/publication/360672618_Mobile_Phone_Dependence_and_its_Association_with_Sleep_Quality_among_Postgraduate_Medical_Students_in_a_Tertiary_Care_Center_in_Kochi_Kerala_India
9. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4267764/
10. Suresh K Sharma. Nursing Research and Statistics. 4rd ed. Elsevier Publishers; 2018.Page number:185, 172,201,227,228,231,237,243
Received on 17.04.2024 Revised on 08.06.2024 Accepted on 25.07.2024 Published on 30.11.2024 Available online on December 31, 2024 A and V Pub Int. J. of Nursing and Med. Res. 2024; 3(4):141-145. DOI: 10.52711/ijnmr.2024.32 ©A and V Publications All right reserved
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