Abstract
Aims:
The study aims to assess the prevalence of smartphone addiction and its effects on sleep quality among medical students.
Study Setting and Design:
A cross-sectional study was carried out by convenience sampling of medical students at a tertiary care hospital in South India.
Materials and Methods:
Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, 4th Edition, Text Revision axis I disorders research version was used for screening past and current psychiatric illness. A semi-structured pro forma was used to obtain demographic details. Smartphone Addiction Scale-Short Version was used to assess smartphone addiction in the participants. Sleep quality was assessed using Pittsburgh's Sleep Quality Index (PSQI).
Results:
Among 150 medical students, 67 (44.7%) were addicted to smartphone usage. Despite the preponderance of male students (31 [50%]) being addicted, there was no statistically significant gender difference in smartphone addiction (P = 0.270). The PSQI revealed poor sleep quality in 77 (51.3%) which amounts to half of the participants. Smartphone addiction was found to be statistically significantly associated with poor sleep quality (odds ratio: 2.34 with P < 0.046).
Conclusions:
The prevalence of smartphone addiction among younger population is higher compared to those of contemporary studies. No gender difference in smartphone addiction could be made out in the current study. Smartphone addiction was found to be associated with poor sleep quality. The findings support screening for smartphone addiction which will be helpful in early identification and prompt management.
Keywords: Gender difference, medical students, prevalence, sleep quality, smartphone addiction
Smartphones are mobile phones which are used for many functions such as Internet browsing, gaming, and social media participation apart from the conventional calling and sending messages.[1] Smartphones have become an inseparable part of life and play a vital role even in routine activities of the day in the current global scenario.
Although smartphone addiction has not been included in the conventional classificatory systems, evidence is on the rise for the presence of smartphone abuse and addiction in the field of behavioral addictions. The prevalence of smartphone addiction ranges from 0% to 38% based on the instruments used for assessment.[2]
Recent studies have observed that smartphone overuse has been associated with disturbances in sleep, daytime activity, and performance among students, which encompass the domains of biorhythms.[3,4,5,6] Longer smartphone screen time has been associated with shorter sleep duration and poor sleep efficiency.[7]
The current study aims to assess the prevalence of smartphone addiction in medical students from the southern part of India and to evaluate any gender differences in smartphone usage among them. The study will also evaluate the effects of smartphone usage on sleep quality.
MATERIALS AND METHODS
Study setting
The participants comprised medical students currently pursuing internship at a tertiary care hospital in South India. Institutional ethics committee approval was obtained. Those participants fulfilling the inclusion and exclusion criteria were recruited. Inclusion criteria were age >18 years, pursuing medical education, and currently using smartphones. Those participants with a past history of psychiatric illness were excluded from the study using Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, 4th Edition, Text Revision axis I disorders research version.[8]
Sample calculation
The participants were recruited by convenience sampling. Based on a previous survey, the prevalence of smartphone addiction was reported at 8.4%;[9] the sample size was calculated to be 119, with 80% power, α error of 0.05, and confidence interval of 95%. With correction for 10% loss for completion, the sample population was estimated to be 150 (Source: Results from OpenEpi, Version 3, online accessed on 10.01.2019).
Scales used
Semi-structured pro forma: A semi-structured pro forma was used to assess the demographic details of the participants
Smartphone Addiction Scale-Short Version (SAS-SV):[10] The SAS-SV scale comprises of ten questions with a 6-point Likert scale. The SAS-SV is a self-rated scale with cutoffs of 31 in males and 33 in females
The Pittsburgh Sleep Quality Index (PSQI):[11] The PSQI scale assesses various components of sleep such as subjective sleep quality, duration, latency, sleep efficiency, sleep disturbances, daytime dysfunction, and use of medications for sleep. Each component is measured from 0 to 3, and a global score is calculated. Higher global scores indicate lower sleep quality.
Procedure
After obtaining informed consent, participants were recruited. The participants were administered a semi-structured pro forma to assess the demographic details. Following which, SAS-SV and PSQI were administered to the participants and the results were computed.
Statistical analysis
Descriptive statistics were carried out in the form of percentages and mean for the demographic data, smartphone usage, and sleep quality. Comparative analysis between gender was carried out by Chi-square test and unpaired t-test based on the distribution of the variables. Odds ratio (OR) was used to assess the association between smartphone usage and sleep quality.
RESULTS
A total of 150 medical students were recruited for the current study. Among the participants, 62 (41.3%) were males and 88 (58.7%) were females [Table 1].
Table 1.
Distribution of the study participants based on gender
| Gender | Total number of the study participants, n (%) |
|---|---|
| Male | 62 (41.3) |
| Female | 88 (58.7) |
| Total | 150 (100) |
The total mean score (standard deviation) of SAS-SV among participants was 30.2 (±9.7). The mean score among male students was 30.8 (±10.4), whereas it was lower in female students, i.e., 29.8 (±9.2) although no significant difference could be made out between the gender (P = 0.522) [Table 2].
Table 2.
Mean scores of the study participants in the Smartphone Addiction Scale based on gender
| Scale | Mean (SD) |
P* | ||
|---|---|---|---|---|
| Total score | Gender |
|||
| Male | Female | |||
| Smartphone Addiction Scale | 30.2 (9.7) | 30.8 (10.4) | 29.8 (9.2) | 0.522 |
*Unpaired t-test. SD – Standard deviation
Among the participants, 67 (44.7%) qualified for smartphone addiction based on the cutoffs provided for the SAS-SV. Gender-wise analysis revealed that 31 (50.0%) male students and 36 (40.9%) female students qualified for smartphone addiction although no statistically significant difference could be made out (P = 0.270) [Table 3].
Table 3.
Smartphone Addiction Scale based on the gender of the study participants
| Gender | Addiction present, n (%) | No addiction, n (%) | P * |
|---|---|---|---|
| Male | 31 (50.0) | 31 (50.0) | 0.270 |
| Female | 36 (40.9) | 52 (59.1) | |
| Total | 67 (44.7) | 83 (55.3) |
*Chi-square test
Half of the participants (77 [51.3%]) reported poor quality of sleep and rest (73 [48.7%]) had a good quality of sleep [Table 4]. Smartphone addiction was statistically significantly associated with poor sleep quality in the participants (OR: 2.34 with P < 0.046) [Table 5].
Table 4.
Quality of sleep (Pittsburgh’s Sleep Quality Index) based on the gender of the study participants
| Gender | Poor quality of sleep, n (%) | Good quality of sleep, n (%) | P * |
|---|---|---|---|
| Male | 33 (53.2) | 29 (46.8) | 0.697 |
| Female | 44 (50.0) | 44 (50.0) | |
| Total | 77 (51.3) | 73 (48.7) |
*Chi-square test
Table 5.
Association between smartphone addiction and quality of sleep (Pittsburgh’s Sleep Quality Index) and among the study participants
| Mobile phone addiction | Poor quality of sleep | Good quality of sleep | P* |
|---|---|---|---|
| Absent | 25 (30.1) | 58 (69.9) | <0.001 |
| Present | 52 (77.6) | 15 (22.4) | |
| Total | 77 (51.3) | 73 (48.7) |
*Chi-square test
DISCUSSION
Among the participants, the mean score of SAS-SV was found to be closer to the cutoffs for smartphone addiction, especially for male students (30.8 ± 10.4). Female students were found to have comparatively lower scores, although no significant gender difference could be made out.
Nearly half of the participants (44.7%) qualified for smartphone addiction based on the cutoffs provided with the SAS-SV, which is higher when compared to that of contemporary studies in literature.[6,9] This may be explained by different scales and the population evaluated in the other studies.
Although half of the male participants (50%) qualified for smartphone addiction compared to 40.9% of females, no significant gender difference could be made out. This could be possibly explained by lower cutoff scores for males compared to females provided by the SAS-SV.
Poor sleep quality was found in 77 (51.3%) medical students using smartphones. Smartphone addiction was found to be statistically significantly associated with poor sleep quality (OR: 2.34 with P < 0.046), which is in line with previous studies assessing sleep quality.[12,13] Longer average screen times and younger age have been found to be associated with poor sleep quality, which is in line with the current findings.[7] High smartphone usage has been associated with poor academic achievement in addition to poor sleep quality among medical students in a previous study.[12]
The strength of the study was the evaluation of smartphone addiction among medical students, an upcoming behavioral addiction in the current global scenario. Inclusion of the PSQI has enabled evaluation of the subjective component of sleep in the participants.
Limitation of the study can be stated as the absence of a measure of daytime sleepiness scale which could have provided more insights into the dysfunction due to poor sleep. Assessment of the academic performance could have provided inputs regarding the varying effects of sleep on the functioning of medical students. The nature of work during internship encompassing night shifts could have played a confounding role in the assessment of sleep quality.[14]
CONCLUSIONS
Smartphone addiction is frequently being reported in the medical literature and hence warrants the assessment of its prevalence in the population, especially in adolescents and younger adults. Nearly half of the medical students were found to have smartphone addiction more so in the male population although no significant gender difference could be made out. Smartphone addiction was found to be associated with poor sleep quality in the current study. The study needs replication in a larger and more heterogeneous population.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
REFERENCES
- 1.Smartphone | Definition of Smartphone in English by Oxford Dictionaries. [Last accessed on 2018 Dec 17]. Available from: https://en.oxforddictionaries.com/definition/smartphone .
- 2.Pedrero Pérez EJ, Rodríguez Monje MT, Ruiz Sánchez De León JM. Mobile phone abuse or addiction. A review of the literature. Adicciones. 2012;24:139–52. [PubMed] [Google Scholar]
- 3.Matar Boumosleh J, Jaalouk D. Depression, anxiety, and smartphone addiction in university students- A cross sectional study. PLoS One. 2017;12:e0182239. doi: 10.1371/journal.pone.0182239. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Dewi RK, Efendi F, Has EMM, Gunawan J. Adolescents' smartphone use at night, sleep disturbance and depressive symptoms. Int J Adolesc Med Health. 2018 doi: 10.1515/ijamh-2018-0095. pii:/j/ijamh.ahead-of-print/ijamh-2018-0095/ijamh-2018-0095.xml. [DOI] [PubMed] [Google Scholar]
- 5.Chang AK, Choi J. Predictors of sleep quality among young adults in Korea: Gender differences. Issues Ment Health Nurs. 2016;37:918–28. doi: 10.1080/01612840.2016.1235636. [DOI] [PubMed] [Google Scholar]
- 6.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. doi: 10.1556/2006.4.2015.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Christensen MA, Bettencourt L, Kaye L, Moturu ST, Nguyen KT, Olgin JE, et al. Direct measurements of smartphone screen-time: Relationships with demographics and sleep. PLoS One. 2016;11:e0165331. doi: 10.1371/journal.pone.0165331. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.First MB, Spitzer RL, Gibbon M, Williams JW. Struct Clin Interview DSM-IV-TR Axis Disord Res Version Patient Ed-IP. New York: Biometrics Research, New York State Psychiatric Institute; 2002. [Google Scholar]
- 9.Internet Addiction Survey 2011. Seoul: National Information Society Agency; 2011. South Korea National Information Society Agency; pp. 118–9. [Google Scholar]
- 10.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. doi: 10.1371/journal.pone.0083558. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.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. doi: 10.1016/0165-1781(89)90047-4. [DOI] [PubMed] [Google Scholar]
- 12.Ibrahim NK, Baharoon BS, Banjar WF, Jar AA, Ashor RM, Aman AA, et al. Mobile phone addiction and its relationship to sleep quality and academic achievement of medical students at king Abdulaziz university, Jeddah, Saudi Arabia. J Res Health Sci. 2018;18:e00420. [PMC free article] [PubMed] [Google Scholar]
- 13.Touitou Y, Touitou D, Reinberg A. Disruption of adolescents' circadian clock: The vicious circle of media use, exposure to light at night, sleep loss and risk behaviors. J Physiol Paris. 2016;110:467–79. doi: 10.1016/j.jphysparis.2017.05.001. [DOI] [PubMed] [Google Scholar]
- 14.Lin YH, Ho YC, Lin SH, Yeh YH, Liu CY, Kuo TB, et al. On-call duty effects on sleep-state physiological stability in male medical interns. PLoS One. 2013;8:e65072. doi: 10.1371/journal.pone.0065072. [DOI] [PMC free article] [PubMed] [Google Scholar]