|Year : 2021 | Volume
| Issue : 1 | Page : 22-28
Assessment of Correlation between Smartphone Addiction, Social Anxiety, and Self-Esteem: A Cross-Sectional Study
Pandurang Vithal Thatkar1, Jeevan P Tonde2, Rajesh K Dase3, Dattatray D Pawar4, Rajavannian Chidambaram5
1 Department of Community Medicine, Andaman and Nicobar Islands Institute of Medical Sciences, Port Blair, Andaman and Nicobar Islands
2 Department of Computer Science, MIT Arts, Commerce & Science College, Pune, Maharashtra, India
3 Department of Community Medicine, MGM Medical College & Hospital, Aurangabad, Maharashtra, India
4 Department of Statistics, Nanded Education Society’s Science College, Snehnagar, Nanded, Maharashtra, India
5 SRM College of Physiotherapy Kattankulathur, Tamilnadu, India
|Date of Submission||23-Sep-2020|
|Date of Decision||04-Jan-2021|
|Date of Acceptance||04-Jan-2021|
|Date of Web Publication||16-Mar-2021|
Dr. Pandurang Vithal Thatkar
Department of Community Medicine, Andaman and Nicobar Islands Institute of Medical Sciences, Above Syndicate Bank, DHS Block, Atlanta Point, Port Blair 74410, Andaman and Nicobar Islands.
Andaman and Nicobar Islands
Source of Support: None, Conflict of Interest: None
Background: Research on smartphone and Internet addiction has increased rapidly, indicating its clinical and social significance. This study aimed at exploring the possible relationship between smartphone addiction, self-esteem, and social anxiety. Materials and Methods: A total of 464 young college-going adults participated in this study [male = 175 (37.71%), female = 289 (62.3%)] between the ages of 18 and 28 years old. The study participants were asked to complete a survey having three different questionnaires, namely “Smartphone Addiction Scale,” “Rosenberg’s Self-Esteem Scale,” “and the Interaction Anxiousness Scale.” This study also focuses on gender and age differences regarding smartphone addiction. Karl Pearson’s correlation coefficient and unpaired t-test were used to test the significance of the relationship among study variables. Regression analysis was performed to predict smartphone addiction by using Age, Rosenberg’s Self-Esteem Score, and Interaction Anxiousness Score. Results: The mean smartphone addiction scale (SAS) total score was higher in males as compared with females (P = 0.01). No significant difference was observed in RSE total score and IAS total score among males and females (P > 0.05). Significant correlations were observed among SAS total, RSE total, IAS total, and Age (in years) (P < 0.05). Regression analysis was applied to predict SAS total score by using independent variables such as age, RSE total, and IAS total. The coefficients for age and RSE total score were significant (P < 0.01), and the IAS total was not significant (P > 0.05). Conclusion: Males reported having higher smartphone addiction levels as compared with females. A positive correlation was observed between Social Anxiety and Smartphone addiction. A negative correlation was observed between self-esteem and smartphone addiction, which indicates that the lower the self-esteem, the higher will be the smartphone addiction. Age was negatively correlated with smartphone addiction score and social anxiety score, whereas age was positively correlated with self-esteem.
Keywords: Depression, Internet addiction, prevalence, self-esteem, smartphone addiction, social media
|How to cite this article:|
Thatkar PV, Tonde JP, Dase RK, Pawar DD, Chidambaram R. Assessment of Correlation between Smartphone Addiction, Social Anxiety, and Self-Esteem: A Cross-Sectional Study. MGM J Med Sci 2021;8:22-8
|How to cite this URL:|
Thatkar PV, Tonde JP, Dase RK, Pawar DD, Chidambaram R. Assessment of Correlation between Smartphone Addiction, Social Anxiety, and Self-Esteem: A Cross-Sectional Study. MGM J Med Sci [serial online] 2021 [cited 2023 Mar 29];8:22-8. Available from: http://www.mgmjms.com/text.asp?2021/8/1/22/311387
| Introduction|| |
Today, smartphone users are rapidly increasing all over the world. Around the world, smartphones were used by 1.85 billion people in 2014, 2.32 billion in 2017, which is expected to be 2.87 billion in 2020. The smartphone provides its users with “internet-based communication, business trading, education, entertainment media, and even clinical applications.” Smartphone has become an essential aspect of its users’ daily life. From being a useful technological device, the smartphone has become a social object. Today, the smartphone is equipped with many refined features such as high-quality cameras, a huge number of applications (inbuilt and available to download for free), online gaming, navigation apps, social networking, online shopping, video streaming, audio and video players, 3G, 4G, and 5G, wireless Internet, etc. Over the past few years, there has been a huge increase in user-friendly, inexpensive, applications, and growth in broadband and Internet providers. As of the first quarter of 2020, Android is leading with 2.56 million apps followed by Apple’s App Store (1.85 million) apps for iOS.
All entities that can stimulate a person can be an addiction. Once a habit is transformed into a compulsion, it becomes an addiction. With numerous advantages, the smartphone has made our lives easier, but at the same time, its addiction has resulted in physical effects along with psychological and academics effects. Sleep deficit, anxiety, stress, and depression are associated with smartphone usage.
The positive impact of smartphone usage
Due to the advanced features of a smartphone, its users can stay connected with people, places, and interests at all times. In human life, there are many positive impacts of the smartphone. The smartphone can provide a variety of benefits to society, with the availability of a wide range of applications in categories such as Communication, Gaming, Multimedia, Productivity, Travel, and Utilities. In recent years, smartphones are widely used for providing online therapies (telemedicine), clinical support, online shopping, work from home, and free video calling.
The negative impact of smartphone usage
The huge increase in smartphone usage has resulted in many harmful effects on society. Some of the well-known negative impacts of smartphone usage are dangerous driving, the harmful effects of radiation emitted from the device, reduction in concentration, physical health risks, forgetfulness, headaches, and other psychological problems. In Hong Kong, Choi et al. investigated the impacts of using dating applications, and their results suggested that users had greater sexual risks.
Smart phone addiction
Many researchers have mentioned that smartphone addiction may cause numerous psychological problems. As investigated by Oulasvirta et al. for many smartphone users, the smartphone is the first thing they look at in the morning and the last thing they look at before going to sleep. High levels of smartphone usage may result in mental overload, disturbed sleep, and feeling busy and nervous. In a study conducted among college students in Cheonan, Kim et al. concluded that smartphone addiction is positively correlated with depression, aggression, and impulsion.
The risk of anxiety and depression may increase with the excessive use of smartphones along with anxiety, dependency, and a negative attitude. An online survey on students’ behavior was conducted by Jones: It was noted that students seemed to be addicted to their smartphones and it concluded that excessive smartphone use had a negative psychological effect.
Many researchers established a significant relationship and were able to correlate anxiety with smartphone addiction. A cross-sectional study among 668 random Lebanese undergraduate students was conducted by Matar Boumosleh and Jaalouk to investigate the contribution of anxiety and depression independently in smartphone addiction. The results revealed that the depression scores and anxiety scores were positive predictors of smartphone addiction. The study also concluded that the depression scores were a more powerful predictor as compared with anxiety scores.
Self-esteem has been defined in numerous ways. Self-esteem is defined as the level at which an individual values, approves, and likes oneself. Self-esteem is also defined as self-respect, self-worth, and self-acceptance. According to Rosenberg, self-esteem in a person is closely linked with their peer relationship with those who link their self-worth with the consent of others. The innovation of smartphone technology allows Internet access, which enables its users to access social networking sites (SNS) anywhere and anytime. This makes it extremely plausible that self-esteem will be affected.
Schlenker and Leary defined anxiety as “a cognitive and affective response described by apprehensions about an impending, potentially negative outcome that one thinks one is unable to avert.” They further expressed that social anxiety is one of several types of anxiety. A few studies show no significant evidence of a relationship between mobile phone addiction and anxiety. In their study, Ehrenberg et al. did not find a significant relationship between self-esteem and smartphone addiction. These contradictory outcomes necessitate advanced research in this area.
Aim of the study
To investigate the correlation between smartphone addiction and psychological problems such as self-esteem and social anxiety.
- To investigate the age and gender differences in smartphone addiction.
- To predict smartphone addiction by using the variables social anxiety and self-esteem.
| Materials and methods|| |
A cross-sectional quantitative study was conducted in a random sample of 464 participants. The study participants were undergraduate and postgraduate students of various faculties such as Medicine, Arts, Commerce, and Science. To collect the information for this study, three questionnaires, namely the SAS, Rosenberg’s Self-Esteem Scale (SES), and Interaction Anxiousness Scale (IAS), were administered to a group of randomly selected students. The other variables added in the study were the demographic variables such as age, gender, and mobile data used per day (Mb), monthly expenditure on data plans (in Rupees), and whether the student was a day scholar or hosteller. Informed consent was obtained from all study participants. The questionnaires were sent to selected study participants (via Google forms) who were above the age of 18 years and who agreed to participate in the study.
The Smartphone Addiction Scale (SAS-SV0)
Kwon et al. developed a 40-item SAS in South Korea. In this study, the scale used to assess the level of smartphone addiction was a 10-item shortened version of the original scale. Each question can be rated on a six-point scale (1 = “strongly disagree” to 6 = “strongly agree”). The total score of this scale ranges between 10 and 60. A higher score indicates a higher level of smartphone addiction. The final 10 questions were chosen about content validity, and the original SAS-SV showed content and concurrent validity and internal consistency (Cronbach’s alpha: 0.91).
Rosenberg’s SES is a 10-item scale used to measure self-worth by measuring positive and negative feelings about self. The scale is an extensively used instrument for assessing individual self-esteem. Each question of the scale can be rated on a four-point Likert scale (1 = “strongly agree” to 4 = “strongly disagree”). The original reliability of the scale is 0.72 and it has proven to be highly reliable and consistent, as well as to exhibit convergent and discriminant validity. The scale items 2, 5, 6, 8, and 9 were reversed at the time of scoring. The total score of the scale ranges from 0 to 30. The individual scores above 15 indicate a normal level of self-esteem and scores below 15 suggest a low level of self-esteem.
The IAS is a 15-item scale that is used to measure global interaction anxiousness. To measure the level of interaction anxiousness, the study participants were asked to rate themselves on how characteristic each of the items is to them. Each of the 15 questions of the IAS scale can be rated on a five-point scale (1 = “not at all” to 5 = “extremely characteristic to them”). Items 2, 3, 6, 10, and 15 are reversed while scoring.
The data were collected into an MS-Excel file, cleaned, and finally imported to IBM SPSS Statistics 23.0 software. The normality of data was tested by using the Kolmogorov Smirnov test. The quantitative variables were presented by using Mean ± SD, and nominal and ordinal scale variables were presented by using frequency and percentage. Correlation between age, SAS, Rosenberg’s SES, and IAS was calculated by using the Karl Pearson correlation coefficient. Unpaired t-test was used to compare mean scores of SAS, Rosenberg’s SES, and IAS between males and females. Regression analysis was performed to predict smartphone addiction (dependent variable) by using independent variables such as Rosenberg’s SES and IAS.
| Results|| |
A total of 464 young college-going adults participated in this study (male = 175(37.71%), female = 289 (62.3%)) between the ages of 18 and 28 years old [Figure 1]. The average monthly data usage (in Mb) for females was 1048 ± 523 and in males it was 1021 ± 458, and there was no significant difference (t = 0.59, P = 0.55). The average monthly expenses toward data plans (in INR) for females were 228 ± 100 and for males it was 232 ± 104 [Table 1]. The boxplots for the quantitative study variables Age (in years), SAS (SAS_total), Rosenberg’s SES (RSE_total), and IAS (IAS_total) are presented in [Figure 2]. All study variables appear to be approximately normally distributed, which is also evident from the Anderson Darling test of Normality [Table 2].
|Figure 2: Boxplots showing descriptive statistics for AGE, SAS total, RSE total, and IAS total score|
Click here to view
A gender-wise comparison of SAS, Rosenberg’s SES, and IAS by using an unpaired t-test is presented in [Table 3]. The results comprise a significantly higher average SAS among males (33.81 ± 9.21) as compared with females (31.4 ± 11.1) (t = −2.57, P = 0.010). The average Rosenberg’s SES in females was 17.16 (±6.43) and in males, it was 16.1 (±5.8) and there was no significant difference (t = 1.82, P = 0.069). The average IAS among females was 40.5 (±16.0) and in males it was 39.2 (±15.3), and there was no significant difference (t = 0.92, P = 0.558).
The pairwise correlations between Age, SAS (SAS_total), Rosenberg’s SES (RSE_total), and IAS (IAS_total) are shown in [Table 4]. Age is significantly correlated with SAS_total (r = −0.581), RSE_total (0.397), and IAS_total (−0.300). Age is negatively correlated with SAS_total and IAS_total score, and it is positively correlated with RSE_total score. SAS_total score is negatively correlated with RSE_total, and it is positively correlated with IAS_total (r = 0.250). RSE_total score is negatively correlated with IAS_total score (r = −0.719). The results are also shown in a scatter diagram [Figure 3].
|Figure 3: Scatter diagram showing correlation between age, SAS total, and RSE total score|
Click here to view
Regression analysis was performed to predict smartphone addiction level (SAS_total) by using Age, Rosenberg’s SES, and IAS as independent variables. The regression equation was obtained as: SAS_total = 88.09 − 2.222 Age − 0.3379 RSE_total − 0.0327 IAS_total. The coefficients and the corresponding significance values are illustrated in [Table 5]. The regression coefficients of Age and Rosenberg’s SES are significant (p<.05), whereas the coefficient for the IAS is not significant. The overall model was significant (F = 86.98, P < 0.01). The overall r2 (adjusted) was 35.78%, which indicates that the 35.78% variation in SAS is explained by the independent variables age, Rosenberg’s SES, and IAS [Table 5].
| Discussion|| |
One of the objectives of this study was to examine the correlation between smartphone addiction, social anxiety, self-esteem, age, and gender. The dependent variable was smartphone addiction and the independent variables were age, self-esteem, and social anxiety. A significant correlation was found among quantitative variables Age, self-esteem, smartphone addiction, and social anxiety.
Self-esteem is affected by the frequency at which adolescents use social networking sites. Niemz et al. concluded that the lack of confidence enabled many youths to use the Internet as an alternative type of communication and they found a significant correlation between high Internet use and low self-esteem. It has been observed by Young that a person with low self-esteem uses the Internet as a distraction. Valkenburg and Peter found that individuals suffering from social anxiety prefer the online mode of interaction such as messaging and social networking sites, which gives them sufficient time to construct and be prepared for interaction. Reid and Reid and Yen et al. researched that messaging and social networking sites are useful tools of communication for individuals suffering from social anxiety, as they do not have to engage in the face-to-face interface. Kim et al. explored a significant association between smartphone addiction, depression, aggression, and impulsion among college students in Cheonan. Social anxiety is described as anxiety resulting from the potential or presence of personal evaluation or judgment in real or imagined situations. Those suffering from severe social anxiety tend to keep themselves in isolation and away from social affairs. Research has evidence that anxious individuals feel better from an online interaction.
This study reports that males have more smartphone addiction scores as compared with females. In contradiction, Bianchi and Phillips found that females have higher smartphone addiction as compared with males. In the case of self-esteem, the results of this study are in line with those of the study conducted by Hong et al. They reported a significant correlation between smartphone addiction and self-esteem. Also, a few studies were carried out by Valkenburg et al. and Vogel et al. showed a diverse correlation between self-esteem and smartphone addiction. However, the results of a study conducted by Shaw and Gant state that self-esteem and smartphone usage are positively correlated, which contradicts the results of this study.
| Conclusion|| |
A significant negative correlation was observed between smartphone addiction total score and Age. This indicates that smartphone addiction is higher in lower age groups. The significant positive correlation between Rosenberg’s self-esteem total score and Age indicates that self-esteem increases with age, and the significant negative correlation between interaction anxiousness scale total score and age indicates that social anxiety is lower in higher age groups. The higher smartphone addiction score was observed among males as compared with females. Gender-wise, no significant differences were observed in self-esteem scores and social anxiety scores. The regression analysis indicates that the Age, smartphone addiction total score, and Rosenberg’s self-esteem total score are predictors of SAS.
Limitations of the study
Some of the questions in the questionnaire were very personal. In such a case, the accuracy of the results fully depends on the honesty and integrity of the respondents. The authors tried to minimize this error by briefing the full questionnaire and by giving an assurance of confidentiality of data to each participant.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Choi EP, Wong JY, Lo HH, Wong W, Chio JH, Fong DY The impacts of using smartphone dating applications on sexual risk behaviours in college students in Hong Kong. PLoS One 2016;11:e0165394.
Srivastava L Mobile phones and the evolution of social behavior. Behav Inf Technol 2005;24:111-29.
Statista. App stores: Number of mobile app downloads worldwide in 2016, and 2017, by store |Statista. 2020. Available from: https://www.statista.com/statistics/263795/number-of-available-apps-in-the-apple-app-store/.[Accessed on August 20, 2020].
Alavi SS, Ferdosi M, Jannatifard F, Eslami M, Alaghemandan H, Setare M Behavioral addiction versus substance addiction: Correspondence of psychiatric and psychological views. Int J Prev Med 2012;3:290-4.
Gutierrez J, Fonseca F, Rubio G Cell-phone addiction: A review. Front Psychiatry 2016;7:175. doi:10.3389/fpsyt.2016.00175
Grant WD, Scinta W Increased interaction with weight loss app increases likelihood of sustained weight loss. Endocrinol Metab Syndr 2015;04:1-4. doi: 10.4172/2161-1017.1000210
Oulasvirta A, Rattenbury T, Ma L, Raita E Habits make smartphone use more pervasive. Pers Ubiquit Comput 2012;16: 105-14.
Kim M, Kim H, Kim K, Ju S, Choi J, Yu M Smartphone addiction: (focused depression, aggression, and impulsion) among college students. Indian J Sci Technol 2015;8:1-8.
Rosen LD, Whaling K, Carrier LM, Cheever NA, Rokkum J The media and technology usage and attitudes scale: An empirical investigation. Comput Human Behav 2013;29:2501-11.
Jones T Students’ cell phone addiction and their opinions. Elon J Undergrad Res Commun 2014;5:74-80.
Bianchi A, Phillips JG Psychological predictors of problem mobile phone use. Cyberpsychol Behav 2005;8: 39-51.
Matar Boumosleh J, Jaalouk D Depression, anxiety, and smartphone addiction in university students- A cross sectional study. PLoS One 2017;12:e0182239.
Robinson JP, Shaver PR, Wrightsman LS, Andrews FM Measures of Personality and Social Psychological Attitudes, vol. 1, 1st ed. San Diego: Academic Press; 1991. p. 753.
Rosenberg M Society and the Adolescent Self-image. Princeton, NJ: Princeton University Press; 1965. p. 326.
Hong F, Chiu S, Huang D A model of the relationship between psychological characteristics, mobile phone addiction, and use of mobile phones by Taiwanese university female students. Comput Hum Behav 2012;28:2152-9.
Schlenker BR, Leary MR Social anxiety and self-presentation: A conceptualization and model. Psychol Bull 1982;92:641-69.
Whiteside SP, Lynam DR The five-factor model and impulsivity: Using a structural model of personality to understand impulsivity. Pers Individ 2001;30:669-89. http://dx.doi.org/10.1016/S0191-8869(00)00064-7.
Ehrenberg A, Juckes S, White KM, Walsh SP Personality and self-esteem as predictors of young people's technology use. Cyberpsychol Behav 2008;11:739-41.
Kwon M, Kim DJ, Cho H, Yang S The smartphone addiction scale: Development and validation of a short version for adolescents. PLoS One 2013;8:e83558. https://doi.org/10.1371/journal.pone.0083558
Leary MR Social anxiousness: The construct and its measurement. J Pers Assess 1983;47:66-75.
Valkenburg PM, Peter J, Schouten AP Friend networking sites and their relationship to adolescents’ well-being and social self-esteem. Cyberpsychol Behav 2006;9:584-90.
Niemz K, Griffiths M, Banyard P Prevalence of pathological internet use among university students and correlations with self-esteem, the general health questionnaire (GHQ), and disinhibition. Cyberpsychol Behav 2005;8:562-70.
Young KS Internet addiction: Symptoms, evaluation, and treatment innovations in clinical practice (Vol. 17). In: VandeCreek L, Jackson TL, editors. Sarasota, FL: Professional Resource Press; 1999.
Valkenburg PM, Peter J Social consequences of the internet for adolescents: A decade of research. Curr Dir Psychol Sci 2009;18:1-5. doi: 10.1111/j.1467-8721.2009.01595.x.
Reid DJ, Reid FJM. Text or talk? Social anxiety, loneliness, and divergent preferences for cell phone use. Cyberpsychol Behav 2007;10:424-35.
Yen CF, Tang TC, Yen JY, Lin HC, Huang CF, Liu SC, et al
. Symptoms of problematic cellular phone use, functional impairment and its association with depression among adolescents in southern Taiwan. J Adolesc 2009;32:863-73.
Vogel E, Rose J, Roberts L, Eckles K Social comparison, social media, and self-esteem. Psychol Pop Media Cult 2014;3:206-22.
Shaw LH, Gant LM In defense of the internet: The relationship between internet communication and depression, loneliness, self-esteem, and perceived social support. Cyberpsychol Behav 2002;5:157-71.
[Figure 1], [Figure 2], [Figure 3]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]