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| ORIGINAL ARTICLE |
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| Year : 2019 | Volume
: 63
| Issue : 4 | Page : 313-317 |
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Psychological risk factors associated with internet and smartphone addiction among students of an Indian dental institute
Sumeet Bhatt1, Ambika Gaur2
1 Reader, Department of Public Health Dentistry, Himachal Institute of Dental Sciences, Paonta Sahib, Himachal Pradesh, India
2 Tutor, Department of Psychiatric Nursing, Himachal Institute of Nursing, Paonta Sahib, Himachal Pradesh, India
| Date of Web Publication | 18-Dec-2019 |
Correspondence Address:
Dr. Sumeet Bhatt
Department of Public Health Dentistry, Himachal Institute of Dental Sciences, Paonta Sahib, Himachal Pradesh
India
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/ijph.IJPH_330_18
 Abstract | | |
Background: The internet and smartphones have a strong potential for addiction. Excessive use of these technologies can have adverse effects on psychological well-being of the users. Objectives: The objective of the study is to find out the effects of internet addiction (IA) and smartphone addiction (SA) on psychological outcomes of dental students in an Indian institute. Methods: In this cross-sectional study, 320 dental students were assessed for their internet and smartphone habits using the Young's IA test (YIAT) and the SA scale (SAS), respectively. Psychological outcomes were evaluated using the insomnia severity index, the Depression, Anxiety and Stress Scale, and the Rosenberg Self-Esteem Scale. Results: The median YIAT score was 35 and interquartile range (IQR) of 24–49 with 23% subjects reporting potential IA. The median SAS score was 108 (IQR 91.25–128). Both IA and SA were significantly associated with participants' year of the study. Significant correlations were observed between IA and SA with psychological parameters. Conclusions: The association of IA and SA with psychological parameters shows how these habits can affect the users' mental well-being. Public especially young adults should be made aware about potential harmful effects of the internet and smartphones.
Keywords: Internet addiction, psychological outcomes, smartphone addiction
How to cite this article:
Bhatt S, Gaur A. Psychological risk factors associated with internet and smartphone addiction among students of an Indian dental institute. Indian J Public Health 2019;63:313-7 |
How to cite this URL:
Bhatt S, Gaur A. Psychological risk factors associated with internet and smartphone addiction among students of an Indian dental institute. Indian J Public Health [serial online] 2019 [cited 2023 Mar 20];63:313-7. Available from: https://www.ijph.in/text.asp?2019/63/4/313/273357 |
| Introduction | |  |
The advent of the internet has revolutionized the way people use technology, access information, communicate with each other, or simply entertain themselves; and smartphones have certainly changed the way people use internet. The Internet and Mobile Association of India (IMAI) has reported that there were more than 430 million internet users in India as on December 2016. This number is estimated to increase in the subsequent years. The IMAI report also states that the primary medium to access internet in India is smartphone, both in urban (77%) and rural (92%) regions.[1] Internet and smartphones have become complementary to each other in the way people use these services.
However, the use of these technologies can have some seriously detrimental effects. Because these services are readily available, easy to use, and provide a passive entertainment, they have a strong addictive potential. The excessive use of smartphones and internet has been known to cause a variety of physical symptoms such as pain in neck [2] and hands [3],[4] as well as psychological problems such as insomnia, depression, anxiety, and stress.[5],[6],[7] Studies have shown that college students are most vulnerable to internet addiction (IA) and smartphone addiction (SA) compared to older adults.[8],[9] As a result, the greatest impacts of addiction to internet and smartphones often expressed as psychological disturbances could be expected in this group.
In India, studies have assessed the internet and smartphone usage patterns among medical and dental students,[10],[11],[12] but information on their psychological effects is limited and sparse. India is a growing market for internet and smartphones, and the potential for addictive behaviors is manifold. Hence, it would be of significance to know the psychological disorders associated with internet and SA among college students in India. We hypothesized that the excessive use of internet and smartphones could be correlated with insomnia, depression, anxiety, stress, and low self-esteem in dental students. This is important because a dependence on these technologies can restrict the students from acquiring relevant knowledge and skills in dentistry and the situation could be worsened by associated psychological outcomes. This could hinder the overall professional development of a dental student. Hence, this study was carried out to find out the pattern of internet and smartphone use among dental students and its correlation with insomnia, depression, anxiety, stress, and low self-esteem.
| Materials and Methods | | |
This cross-sectional study was conducted on 320 dental students, those who were eligible from 1st year to postgraduation in Himachal Institute of Dental Sciences, Paonta Sahib, Himachal Pradesh, on June 2018. The study began after approval from the Institutional Ethics Committee of Himachal Institute of Dental Sciences. Permission for the study was obtained from the head of the institute, and a written informed consent was taken from each participant. The study followed the guidelines for human research given by the World Medical Association Declaration of Helsinki.
The inclusion criteria of the study were dental students in Himachal Institute of Dental Sciences. Students not willing to take part in the study, and those with chronic physical or mental illness were excluded from the survey. One student undergoing treatment for clinical depression was excluded from the study. The students from first to final year were contacted in their classrooms at the end of their lectures. Dental students in internship and postgraduation were contacted in their respective departments. After the students agreed to participate in the study, they were given a brief description about the purpose of the study and that it involves assessment of dental students' internet and smartphone usage and associated psychological measurements through the completion of a questionnaire. The students were informed that participation in the study is entirely voluntary and they can choose not to take part in the study, though none of the students declined participation. The questionnaire forms were distributed to the participants, and they completed the questionnaire anonymously in the classroom or respective departments. The average duration of completing the questionnaire was 20–25 min.
Data on internet and smartphone usage, insomnia, depression, anxiety, stress, and self-esteem were collected using a self-administered survey tool based on five internationally validated and reliable questionnaires, namely the Young's IA test (YIAT),[13] the SA scale (SAS),[14] the insomnia severity index (ISI),[15] the Depression, Anxiety and Stress Scale (DASS),[16] and the Rosenberg Self-Esteem Scale (RSES).[17]
Young's internet addiction test
YIAT is a widely used tool for measuring IA. It has been internationally validated among adolescents and adults and has been found to be a reliable measure of IA. It consists of 20 questions based on the respondent's internet habits with possible answers ranging from 0 (does not apply) to 5 (applies always). The total possible score for YIAT ranges from 0 to 100, and cutoff for IA is 51 and above.[6]
Smartphone addiction scale
SAS is an internationally validated questionnaire for measuring SA. It consists of 33 questions regarding smartphone usage with answers on a Likert scale ranging from “strongly disagree” to “strongly agree.” The responses are summed together to get a final score. There is no clear cutoff score for SA like YIAT. The greater the score more is the addiction to smartphones.
Insomnia severity index
It is a 7-item questionnaire with responses arranged in a Likert scale based on the severity of sleep problems and are summed together to get a total score which can be used to assess the severity of insomnia as follows: 0–7 = no clinically significant insomnia, 8–14 = subthreshold insomnia, 15–21 = clinical insomnia (moderate severity), and 22–28 = clinical insomnia (severe).
Depression, Anxiety, and Stress Scale
DASS is a 21-item questionnaire for measuring depression, anxiety, and stress in adults. It asks the respondents to indicate how much a statement applied to them over the past week. The responses follow a 4-point Likert scale pattern with values ranging from 0 (did not apply to me at all) to 3 (applied to me very much or most of the time). The score is multiplied by 2, and the cutoff scales for each subscale are as follows:
Depression: 0–9 = normal, 10–13 = mild, 14–20 = moderate, 21–27 = severe, 28 and above = extremely severe
Anxiety: 0–7 = normal, 8–9 = mild, 10–14 = moderate, 15–19 = severe, 20 and above = extremely severe
Stress: 0–14 = normal, 15–18 = mild, 19–25 = moderate, 26–33 = severe, 34 and above = extremely severe.
Rosenberg Self-Esteem Scale
RSES is a 10-item scale that measures self-worth by measuring both positive and negative feelings about the self. All the items are answered using a 4-point Likert scale format ranging from “strongly agree” to “strongly disagree.” The total score can range from 0 to 30, with higher scores indicating higher self-esteem.
Statistical analysis
Statistical analysis was done using the Statistical Package for Social SciencesSPSS Statistics for Windows, version 16 (SPSS Inc., Chicago, Ill., USA). The normality of the data was assessed using the Kolmogorov–Smirnov tests. Descriptive statistics were calculated for all variables in terms of median and interquartile range (IQR) for continuous variables and percentages for categorical variables. IA categories were grouped as normal internet users (score 0–50) and potential IA (score 51–100). Comparisons of YIAT and SAS scores with gender and the year of study were done using the Mann–Whitney U-test and Kruskal–Wallis test followed by Mann–Whitney U-test for multiple comparisons, respectively. Spearman's correlation analysis was used to evaluate associations among continuous variables. Subsequently, linear regression analyses were performed with IA and SA as independent or predictor variables and insomnia, self-esteem, depression, anxiety, and stress as dependent or outcome variables to determine whether IA and SA could significantly predict these psychological parameters. It has been suggested that independent variables with a correlation of 0.64 or above should not be entered in the same model.[6] Keeping this in view, internet and SAs were not entered together because of high correlation. The level of statistical significance for the present study was fixed at a P < 0.05.
| Results | | |
The study population comprised of 68 (21.3%) men and 252 (78.8%) women. The median age of study participants was 21 years with an IQR of 20–22 years. The year of study of the participants showed a statistically significant association with IA as well as SA [Table 1]. | Table 1: Relationship of internet and smartphone addictions with participants' characteristics
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Internet and smartphone use by participants and their psychological parameters are presented in [Table 2]. | Table 2: Distribution of participants based on the use of internet and smartphone and psychological parameters
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Internet addiction
The median YIAT score was 35 with IQR of 24–49. There were about 23% of students reported to have potential IA.
Smartphone addiction
The median SAS score reported was 108 with IQR of 91.25–128.
Insomnia
The median ISI score of the participants was 9 (IQR 6–13). Majority of the participants reported subthreshold insomnia. Clinically significant insomnia was detected in about 20% of participants.
Self-esteem
The median RSES score reported by the participants was 18 (IQR 15–20).
The median depression score was 10 with an IQR of 6–18. Majority of the participants had normal scores. Extremely severe depression was reported by about 9% of participants.
The median anxiety score was 14 (IQR 8–20). About 29% of participants exhibited extremely severe anxiety scores.
The median stress score was 14 (IQR 8–20). Majority of the participants were in the normal range for stress. About 5% reported extremely severe stress scores.
Both YIAT and SAS were found to be significantly correlated with ISI, RSES, and DASS. RSES showed a negative correlation, whereas ISI and DASS were positively correlated with YIAT and SAS. Furthermore, YIAT and SAS also showed a high positive correlation with each other [Table 3]. | Table 3: Correlation matrix of internet and smartphone addiction with psychological variables
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Regression analysis showed that IA and SA were significant predictors of insomnia, depression, anxiety, stress, and low self-esteem. All the models were statistically significant, as evident from significant ANOVA table [Table 4]. | Table 4: Linear regression analysis of the relation between internet and smartphone use with insomnia, self-esteem, depression, anxiety, and stress
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| Discussion | | |
The present study evaluated internet and SA among dental students with the hypothesis that these behaviors could be associated with insomnia, depression, anxiety, stress, and low self-esteem.
In our study, the prevalence of IA was found to be about 23%. Previous studies have reported IA based on Young's criteria in the range of 7%–34% among adolescents and college students.[6],[8],[12],[18],[19] The median SAS score reported in this study was 108. Kwon et al., in their study on the validation of SAS, found a similar score in an adult population.[14] Other studies have reported a comparatively lower SAS scores.[7],[20],[21] Both IA and SA were significantly associated with participants' year of study. As the course progressed, IA and SA scores decreased significantly, with least scores reported by the postgraduate students. Younes et al., in their study on IA found no association of the year of study with IA.[6]
Our correlation analysis showed that IA and SA were significantly correlated with psychological parameters. There was a positive correlation between insomnia and SA as well as IA. Linear regression analysis also showed that IA and SA were significant predictors of insomnia. With easy availability of the internet and ease of taking smartphones anywhere, including bed, the time spent using internet on smartphone during the night might have increased. The global trend indicates that sleep duration and quality are on a decline [22] and addiction to these technologies might be one of the reasons for this change.[23] However, as suggested by Younes et al.,[6] it is important to take into account the temporality of this association as reverse could also be true. People with preexisting sleep problems could be using smartphones and internet because they are unable to sleep at night. More research is recommended in this area.
IA and SA showed a significant positive correlation with depression, anxiety, and stress in our study subjects. This is in agreement with various studies in this area which have found a similar association.[6],[7] Moreover, regression analyses demonstrated that IA and SA are independent predictors of depression, anxiety, and stress.
Finally, RSES showed a negative correlation with IA and SA. Younes et al. found a similar correlation between self-esteem and IA.[6] There are no studies on the effect of SA on self-esteem to make a comparison with our findings. Self-esteem is the perception of the person's value and satisfaction about self. Higher the self-esteem, better a person perceives himself/herself.[24] In our study, higher IA and SA were associated with lower self-esteem. Constant indulgence and comparison with others on internet-based platforms like social media could lower a person's perception of self-worth. Phenomena like “facebook envy” are examples of how internet and smartphones could affect a person's psychological parameters.[25]
There are some limitations to the present study. First of all, the cross-sectional nature of this survey does not give information regarding the causality of the association between the parameters. Future research is warranted in this area to establish the causal association of internet and SA with psychological variables. Second, this is a single-center study, so it may be difficult to generalize these findings to other population groups. Finally, the survey involved the use of self-administered questionnaires, and the results were drawn on the assumption that all the participants answered the questions honestly. However, the behavioral research has been routinely conducted using self-reported measures. Despite these limitations, our study addresses an important and growing subject of addiction to technology, which is of global concern. As the use of these technologies will increase in developing and under-developed countries, there might be an associated effect on public's psychological well-being. With so many potential adverse outcomes, we recommend the idea of introducing labels on the packaging of these devices, warning the user of their possible harmful effects.
| Conclusion | | |
Internet and SA are highly prevalent among dental students, significantly being associated with the year of study. There was also a significant correlation with psychological characteristics. There is a growing need to discourage excessive internet and smartphone use by increasing awareness about their possible association with psychological problems.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
| References | | |
| 1. | Internet and Mobile Association of India. Internet in India-2016. An IAMAI and KANTAR IMRB Report. New Delhi: Internet and Mobile Association of India; 2016.  |
| 2. | Korpinen L, Pääkkönen R, Gobba F. Self-reported neck symptoms and use of personal computers, laptops and cell phones among Finns aged 18-65. Ergonomics 2013;56:1134-46. |
| 3. | Ashurst JV, Turco DA, Lieb BE. Tenosynovitis caused by texting: An emerging disease. J Am Osteopath Assoc 2010;110:294-6. |
| 4. | Fernandez-Guerrero IM. “WhatsAppitis”. Lancet 2014;383:1040. |
| 5. | Matar Boumosleh J, Jaalouk D. Depression, anxiety, and smartphone addiction in university students – A cross sectional study. PLoS One 2017;12:e0182239. |
| 6. | Younes F, Halawi G, Jabbour H, El Osta N, Karam L, Hajj A, et al. Internet addiction and relationships with insomnia, anxiety, depression, stress and self-esteem in university students: A cross-sectional designed study. PLoS One 2016;11:e0161126. |
| 7. | Demirci K, Akgönül M, Akpinar A. Relationship of smartphone use severity with sleep quality, depression, and anxiety in university students. J Behav Addict 2015;4:85-92. |
| 8. | Krishnamurthy S, Chetlapalli SK. Internet addiction: Prevalence and risk factors: A cross-sectional study among college students in Bengaluru, the Silicon Valley of India. Indian J Public Health 2015;59:115-21. [ PUBMED] [Full text] |
| 9. | Smetaniuk P. A preliminary investigation into the prevalence and prediction of problematic cell phone use. J Behav Addict 2014;3:41-53. |
| 10. | Prasad M, Patthi B, Singla A, Gupta R, Saha S, Kumar JK, et al. Nomophobia: A cross-sectional study to assess mobile phone usage among dental students. J Clin Diagn Res 2017;11:ZC34-9. |
| 11. | Subba SH, Mandelia C, Pathak V, Reddy D, Goel A, Tayal A, et al. Ringxiety and the mobile phone usage pattern among the students of a medical college in South India. J Clin Diagn Res 2013;7:205-9. |
| 12. | 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. [ PUBMED] [Full text] |
| 13. | Young KS. Internet addiction: The emergence of a new clinical disorder. Cyberpsychol Behav 1998;1:237-44. |
| 14. | Kwon M, Lee JY, Won WY, Park JW, Min JA, Hahn C, et al. Development and validation of a smartphone addiction scale (SAS). PLoS One 2013;8:e56936. |
| 15. | Bastien CH, Vallières A, Morin CM. Validation of the insomnia severity index as an outcome measure for insomnia research. Sleep Med 2001;2:297-307. |
| 16. | 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;33:335-43. |
| 17. | Rosenberg M. The association between self-esteem and anxiety. J Psychiatr Res 1962;1:135-52. |
| 18. | Tenzin K, Dorji T, Gurung MS, Dorji P, Tamang S, Pradhan U, et al. Prevalence of internet addiction and associated psychological co-morbidities among college students in Bhutan. JNMA J Nepal Med Assoc 2018;56:558-64. |
| 19. | Goel D, Subramanyam A, Kamath R. A study on the prevalence of internet addiction and its association with psychopathology in Indian adolescents. Indian J Psychiatry 2013;55:140-3. [ PUBMED] [Full text] |
| 20. | Choi SW, Kim DJ, Choi JS, Ahn H, Choi EJ, Song WY, et al. Comparison of risk and protective factors associated with smartphone addiction and internet addiction. J Behav Addict 2015;4:308-14. |
| 21. | Demirci K, Orhan H, Demirdas A, Akpinar A, Sert H. Validity and reliability of the Turkish version of the smartphone addiction scale in a younger population. Bull Clin Psychopharmacol 2014;24:226-34. |
| 22. | Hoyos C, Glozier N, Marshall NS. Recent evidence on worldwide trends on sleep duration. Curr Sleep Med Rep 2015;1:195. |
| 23. | Khan MN, Nock R, Gooneratne NS. Mobile devices and insomnia: Understanding risks and benefits. Curr Sleep Med Rep 2015;1:226-31. |
| 24. | Zhang H, Guan L, Qi M, Yang J. Self-esteem modulates the time course of self-positivity bias in explicit self-evaluation. PLoS One 2013;8:e81169. |
| 25. | Pera A. Psychopathological processes involved in social comparison, depression, and envy on Facebook. Front Psychol 2018;9:22. |
[Table 1], [Table 2], [Table 3], [Table 4]
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