|Year : 2021 | Volume
| Issue : 2 | Page : 139-143
Excessive Daytime sleepiness and sleep quality in medical students and their association with smartphone and internet addiction: A cross-sectional study
Harshal Shriram Sathe1, Anantprakash Siddharthkumar Saraf2, Manoj Talapalliwar3, Vrushti Patil1, Vinay Kumar1, Sagar Karia4
1 Department of Psychiatry, Mahatma Gandhi Institute of Medical Sciences, Wardha, Maharashtra, India
2 Department of Psychiatry, BRLSABVM Government Medical College, Rajnandgaon, Chhattisgarh, India
3 Department of PSM, Government Medical College, Gondiya, Maharashtra, India
4 Department of Psychiatry, L.T.M.M.C. and G.H., Mumbai, Maharashtra, India
|Date of Submission||05-May-2021|
|Date of Decision||25-May-2021|
|Date of Acceptance||06-Jun-2021|
|Date of Web Publication||23-Jul-2021|
Dr. Anantprakash Siddharthkumar Saraf
BRLSABVM Government Medical College, Rajnandgaon, Chhattisgarh
Source of Support: None, Conflict of Interest: None
Background: Excessive daytime sleepiness (EDS) and poor sleep quality have shown to be associated with myriad of physical and psychological problems. Increasingly, excessive use of smartphones and Internet, leading to EDS and poor sleep quality, especially among students has become an area of concern. This study was undertaken to check for EDS and sleep quality in the medical undergraduates and its association with smartphone and Internet addiction, psychological distress, depression, and anxiety. Materials and Methods: This is a cross-sectional, observational, and descriptive study done on MBBS students and interns. Semi-structured pro forma was used to collect sociodemographic data and students were asked to complete self-administered rating scales, namely, Epworth Sleepiness Scale (ESS), Pittsburgh Sleep Quality Index (PSQI), Kessler Psychological Distress Scale (K10), Smartphone Addiction Scale-short version (SAS-SV), and Young's Internet Addiction Test (IAT). Results: About 27.4% of students reported EDS and 44.4% fulfilled the criteria of poor sleeper on PSQI. About 56.5% of students reported psychological distress on K10. Almost half (45.3%) of the students reported addictive behavior toward their smartphones and one out of four students at risk of becoming Internet addicted. EDS was significantly (P < 0.05) correlated with PSQI, especially subjective sleep quality and daytime dysfunction component. EDS was also significantly associated (P < 0.05) with psychological distress. EDS was also significantly (P < 0.05) associated with SAS-SV and Young's IAT scores. Poor sleep quality was also significantly (P < 0.05) associated with psychological distress, anxiety, and depression. Conclusion: The impact of smartphone and Internet overuse on sleep quality as well as their association with EDS is significant. We need to be aware of these factors in order to improve the sleep quality of our students.
Keywords: Excessive daytime sleepiness, Internet addiction, sleep quality, smartphone addiction
|How to cite this article:|
Sathe HS, Saraf AS, Talapalliwar M, Patil V, Kumar V, Karia S. Excessive Daytime sleepiness and sleep quality in medical students and their association with smartphone and internet addiction: A cross-sectional study. Ann Indian Psychiatry 2021;5:139-43
|How to cite this URL:|
Sathe HS, Saraf AS, Talapalliwar M, Patil V, Kumar V, Karia S. Excessive Daytime sleepiness and sleep quality in medical students and their association with smartphone and internet addiction: A cross-sectional study. Ann Indian Psychiatry [serial online] 2021 [cited 2022 Sep 25];5:139-43. Available from: https://www.anip.co.in/text.asp?2021/5/2/139/322170
| Introduction|| |
Daytime sleepiness, which interferes with the personal and professional functioning of the individual, is termed as excessive daytime sleepiness (EDS). It is a common symptom affecting around 20% of the adults and is mainly caused by sleep deprivation, use of psychoactive substances and medicines, obstructive sleep apnea, and other conditions affecting mental and physical health. Apart from the serious consequences such as vehicular accidents, EDS has been associated with compromised physical health status and decline in professional performance.,, Moreover, in adolescents, EDS has been linked to laziness, concentration deficits, and poor academic performance. Medical students are exposed to high academic burden due to a vast syllabus covering twenty different subjects, heavy workload in their clinical postings, and rigorous training schedule. Studies show the prevalence of EDS in medical undergraduates to be 25%–30%., Sleep deprivation is a common cause of EDS and a large proportion of medical students have poor sleep quality. Excessive use of smartphone and Internet also cause poor sleep quality and EDS in this population.,
With more than 500 million users, India ranks second in the world with respect to people using Internet and smartphone devices., Smartphones provide access to a wide range of services from banking to the entertainment platforms such as gaming and social media, along with communication. Easy Internet access and high information processing capacity have led the sharp rise in the number of smartphone users across the globe. However, the rising consumption of Internet and mobile services has brought the issue of behavioral addictions to the forefront. Main symptoms of smartphone and Internet addiction include the loss of control over the behavior and compulsive indulgence despite negative physical or psychosocial consequences. Intense craving precedes the behavior and the individual prefers the Internet and smartphone use more than other life obligations and uses it for mood regulation and escape from boredom. Nonavailability of the smartphone or Internet devices is likely to produce irritability and restlessness which considered as withdrawal symptoms., A large proportion (>30%) of medical undergraduates are found to have smartphone and Internet addiction in previous studies., Moreover, along with the sleep problems, high use of these devices is linked to depression and anxiety in these students.
Even though there are many studies in medical undergraduates assessing the sleep pattern and Internet and smartphone use, there is a dearth of Indian studies exploring association between them. Hence, the present study was undertaken in a rural medical college of India with the objective to find the proportion of medical students suffering from the problem of excessive sleepiness and disturbed sleep quality and its association with smartphone addiction and Internet addiction. Furthermore, correlation of sleep issues with psychological distress was studied.
| Materials and Methods|| |
This was a cross sectional, observational, and descriptive study. The data were collected from MBBS students and interns after the approval from the Institutional Ethics Committee. All the undergraduate medical students and interns with age more than 18 years, able to understand English and Hindi, with access to the Internet and smartphone, and willing to give informed consent were included in the study through convenient sampling strategy. Sociodemographic data of these students were collected using a semi-structured pro forma. Students were asked to complete self-administered rating scales, namely, Epworth Sleepiness Scale (ESS), Pittsburgh Sleep Quality Index (PSQI), Kessler Psychological Distress Scale (K10), Smartphone Addiction Scale-short version (SAS-SV), and Young's Internet Addiction Test (IAT).
Epworth sleepiness scale
Daytime sleepiness, which interferes with daily activities, is termed EDS and it is measured by the ESS, a self-administered questionnaire with eight questions. Respondents are asked to rate, on a 4-point Likert scale (0–3), their usual chances of dozing of, or falling asleep while engaged in eight different activities. The ESS score (the sum of 8 item scores) can range from 0 to 24. The higher the ESS score, the higher that person's average “daytime sleepiness.” The scale has good reliability and internal consistency (Cronbach's alpha of 0.82).
Pittsburgh sleep quality index
This is an effective self-rating instrument used to measure the quality and patterns of sleep. It differentiates “poor” from “good” sleep by measuring seven domains: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medication, and daytime dysfunction over the last month. Scoring of the answers is based on a 0–3 Likert scale. A global sum of “5”or greater indicates a “poor” sleeper. The PSQI has good internal consistency and reliability (Cronbach's alpha of 0.83).
Kessler psychological distress scale
The K-10 scale has ten questions answered on a 5-point Likert scale to assess the emotional state of a person. The self-rated scale is used as a screening instrument for psychological problems such as depression and anxiety in the general population and higher scores on the scale warrant further clinical assessment in an individual to confirm the diagnosis. The K10 scale was chosen for the current study considering its brief nature, good reliability, and validity. The cutoff scores adopted by the Victorian population health survey were used in statistical analysis to classify the respondents' level of distress in mild, moderate, and severe categories.
Smartphone addiction scale-short version
It is a self-rated scale containing ten items rated on a Likert scale from 1 to 6. The total score ranges from 10 to 60, with the highest score being the maximum presence of “Smartphone addiction” in the past year. The internal consistency and concurrent validity of SAS-SV were found to be good (Cronbach's alpha of 0.91).
Young's Internet addiction test
It is a 20-item self-report scale developed by Young to assess Internet Addiction. The criteria include loss of control, neglecting everyday life, relationships and alternative recreation activities, behavioral and cognitive salience, negative consequences, escapism/mood modification, and deception. It is a Likert scale ranging from 1 to 5. The minimum score that can be obtained is 20, whereas the maximum is 100 points. The internal consistency and reliability of the IAT have been reported as satisfactory, with a Cronbach's alpha of 0.93.
The data were pooled, tabulated, and analyzed using computerized software. Descriptive statistical analysis was used to summarize categorical data as frequency and percentages and continuous variables as mean and standard deviation. Inferential statistical analysis was done using Pearson's correlation for continuous variables. Statistical significance was determined at P < 0.05.
| Results|| |
A total of 227 students participated in the study and filled the pro forma and questionnaires, but after scrutinizing for completeness of data, 223 participants were included for final analysis. The mean age of study participants was 20.85 ± 1.982 years, with age ranging from 18 to 27 years. Almost equal number of male (57%, n = 127) and female (43%, n = 96) students participated in the study. Students from all the professional years of MBBS participated, i.e., 1st MBBS (14.3%, n = 32), 2nd MBBS (45.7%, n = 102), 3rd MBBS (17%, n = 38), final MBBS (17.5%, n = 39), and interns (5.4%, n = 12).
[Table 1] describes the sleep profile of study population. Most of the students did not report EDS on ESS (72.7%), but a significant minority reported EDS (mild 13.9%, moderate 9%, and severe 4.5%). On PSQI, around 11.6% of students self-reported bad subjective sleep quality. A significant number of students had sleep latency of more than 15 min (16–30 min for 35%, 31–60 min for 17.9%, and >60 min for 14.3%). However, majority of students (82.1%) reported adequate duration of sleep >6 h, with only 2.2% sleeping <5 h a night. Most of the students had adequate sleep efficiency of >85% (76.7%), with very poor sleep efficiency of <65% reported by 2.7% of students. Majority had <9 incidents of sleep disturbances; however, 4% and 1.3% of the students reported 10–18 and 19–27 incidents of sleep disturbances on an average night during the past 1 month. Except for a minority (1.7%) of students, no one was using sleeping pills regularly. Significant minority of students reported some form of daytime dysfunction (18.8%). Overall, significant number of students (44.4%) fulfilled the criteria of poor sleeper according to PSQI.
[Table 2] describes the psychological distress and smartphone and Internet addiction profile of study population. Almost half of the students (43.5%) reported no psychological distress on Kessler Psychological Distress Scale (K10). 30.5%, 13.9%, and 12.1% reported mild, moderate, and severe psychological distress, respectively. Almost half (45.3%) of the students reported addictive behavior toward their smartphones, while only 1.8% reported addiction like pattern of use of Internet, with around one out of four students at risk of becoming Internet addicted.
|Table 2: Mental health and smartphone and Internet addiction profile of medical students and interns|
Click here to view
By applying Pearson's correlation, EDS as measured by ESS was found to be significantly (P < 0.05) correlated with PSQI. Significant correlation (P < 0.05) was also found between subjective sleep quality and daytime dysfunction component of PSQI, while no significant correlation was found between sleep latency, duration, efficiency, and other components of PSQI. EDS was also significantly associated (P < 0.05) with psychological distress (as measured by K10). EDS was also found to be significantly (P < 0.05) associated with SAS-SV and Young's IAT scores; along with all the components of Smartphone Young's IAT, namely, salience of use, excessive use, neglect of work, anticipation of use, lack of control, and neglect of social life [Table 3].
|Table 3: Correlation of excessive daytime sleepiness with sleep profile, mental health and smartphone and Internet addiction of medical students and interns|
Click here to view
[Table 4] describes the association of Sleep Quality with Mental Health and Smartphone and Internet Addiction of Medical students and Interns. Poor sleep quality was significantly (P < 0.05) associated with psychological distress. Those students who were addicted to Internet and those who were at risk for Internet addiction, also reported significantly (P < 0.05) poor quality of sleep.
|Table 4: Association of sleep quality with mental health and smartphone and Internet addiction of medical students and interns|
Click here to view
| Discussion|| |
About 61 (27.4%) medical students out of 223 reported EDS in the current study. This finding was similar to studies conducted in IPGMER, Kolkata (24.91%) and Alapuzzha Medical College, Kerala (25.5%)., However, Giri et al. and Goel et al. reported lower proportions of EDS (17.3% and 10%, respectively) among medical undergraduates which could be because of difference in sampling techniques and instruments used.,
Sleep deprivation is a most common cause of EDS and is a common finding in medical students. In our study, nearly 45% of the participants were poor sleepers (PSQI score <5); about 60% of students slept <7 h and 68% took longer than 15 min fall asleep after going to bed. The average sleep duration was less than the recommended healthy sleep duration of 8 h in young adults. Our research echoed findings of a study in north India where more than one-third of the participants were found to have poor sleep quality. The previous findings of poor sleep quality and EDS in medical students were thus reproduced in the current study which further investigated their association with various factors.
In the current study, behavioral addiction to smartphone and Internet among medical students was also studied. About 45% of the students had smartphone addiction. These findings matched with the proportion of smartphone addiction found in medical university of south India (36.8%), thereby highlighting the problem among the medical students. In our study, 24% of the students admitted excessive Internet use at risk of addiction and 1.4% of participants satisfied the criteria of Internet addiction. These finding were higher than those found in a study by Nagori et al., where problematic Internet use was found in 9.3% of study participants and 0.9% of candidates had Internet addiction. The level of addiction to smartphone was far higher than the addiction to Internet. Portability, real-time Internet access and gaming and social media applications available on smartphones are the reasons for higher levels of smartphone use as compared to surfing Internet through personal computers.
Nagori et al. found a significant correlation of Internet addiction with poor sleep quality which matched our study findings. Each of the subscale scores measuring salience, neglect of work, anticipation, loss of control, neglect of social life showed significant correlation with poor sleep quality thereby emphasizing the impact of Internet overuse on sleep.
We could not find significant association between smartphone addiction and poor sleep quality. However, EDS had a significant association with smartphone use. These findings contrasted with those of Kumar et al., who found excessive smartphone use contributed to poor sleep quality. Self-completed questionnaire, leading to difference in interpretation of the questions, can be the reason for this difference.
| Conclusion|| |
The index study had limitations of small sample size and the use of self-rating scales, which affect the generalizability of the data. However, the study highlighted the impact of smartphone and Internet overuse on sleep quality as well as their association with EDS. Further studies with larger sample size and different study designs are warranted to expand our knowledge of the topic.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Pagel JF. Excessive daytime sleepiness. Am Fam Physician 2009;79:391-6.
Ward KL, Hillman DR, James A, Bremner AP, Simpson L, Cooper MN, et al.
Excessive daytime sleepiness increases the risk of motor vehicle crash in obstructive sleep apnea. J Clin Sleep Med 2013;9:1013-21.
King N, Pickett W, Hagel L, Lawson J, Trask C, Dosman JA. Impact of excessive daytime sleepiness on the safety and health of farmers in Saskatchewan. Can Respir J 2014;21:363-9.
Grunstein RR, Banerjee D. The case of “Judge Nodd” and other sleeping judges – media, society, and judicial sleepiness. Sleep 2007;30:625-32.
Pagel JF, Forister N, Kwiatkowki C. Adolescent sleep disturbance and school performance: The confounding variable of socioeconomics. J Clin Sleep Med 2007;3:19-23.
Ramamoorthy S, Mohandas M, Sembulingam P, Swaminathan VR. Prevalence of excessive daytime sleepiness (EDS) among medical students. World J Pharm Res. 2014 Apr 25;3(4).
Basu M, Saha SK, Majumder S, Chatterjee S, Misra R. A Study on Sleeping Pattern among Undergraduate Medical Students of a Tertiary Care Teaching Hospital of Kolkata. International Journal of Medicine and Public Health. 2019;9(4).
Giri P, Baviskar M, Phalke D. Study of sleep habits and sleep problems among medical students of Pravara Institute of Medical Sciences Loni, Western Maharashtra, India. Ann Med Health Sci Res 2013;3:51-4.
] [Full text]
Kadian A, Mittal R, Gupta MC. Mobile phone use and its effect on quality of sleep in medical undergraduate students at a tertiary care hospital. Open J Psychiatry Allied Sci 2019;10:128-31.
Jahan SM, Hossain SR, Sayeed UB, Wahab A, Rahman T, Hossain A. Association between internet addiction and sleep quality among students: A cross-sectional study in Bangladesh. Sleep Biol Rhythms 2019;17:323-9.
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.
Lin YH, Chiang CL, Lin PH, Chang LR, Ko CH, Lee YH, et al.
Proposed diagnostic criteria for smartphone addiction. PLoS One 2016;11:e0163010.
Tao R, Huang X, Wang J, Zhang H, Zhang Y, Li M. Proposed diagnostic criteria for internet addiction. Addiction 2010;105:556-64.
Karki S, Singh JP, Paudel G, Khatiwada S, Timilsina S. How addicted are newly admitted undergraduate medical students to smartphones?: A cross-sectional study from Chitwan medical college, Nepal. BMC Psychiatry 2020;20:95.
Zhang MW, Lim RB, Lee C, Ho RC. Prevalence of internet addiction in medical students: A meta-analysis. Acad Psychiatry 2018;42:88-93.
Ithnain N, Ghazali SE, Jaafar N. Relationship between smartphone addiction with anxiety and depression among undergraduate students in Malaysia. Int J Health Sci Res 2018;8:163-71.
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.
Kessler RC, Barker PR, Colpe LJ, Epstein JF, Gfroerer JC, Hiripi E, et al.
Screening for serious mental illness in the general population. Arch Gen Psychiatry 2003;60:184-9.
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.
Young K. Internet Addiction Test (IAT). Wood Dale, IL: Stoelting; 2016.
Widyanto L, McMurran M. The psychometric properties of the internet addiction test. Cyberpsychol Behav 2004;7:443-50.
Rajendran D, Vinod PB, Karthika M, Prathibha MT. Excessive daytime sleepiness in medical students. J Evol Med Dent Sci 2018;7:747-50.
Goel N, Malhotra V, Tripathi Y. Sleep habits among first year medical students. J. Evolution Med. Dent. Sci. 2016;5(38):2276-2278.
Chaput JP, Dutil C, Sampasa-Kanyinga H. Sleeping hours: What is the ideal number and how does age impact this? Nat Sci Sleep 2018;10:421-30.
Goyal N, Gupta S. Sleep quality among medical students in Moradabad, Uttar Pradesh, India. Int J Community Med Public Health 2019;7:274.
Ammati R, Kakunje A, Karkal R, Nafisa D, Kini G, Chandrashekaran P. Smartphone addiction among students of medical university in South India: A cross-sectional study. Ann Int Med Dent Res 2018;4:1.
Nagori N, Vasava K, Vala A, Ratnani IJ. Association of sleep quality and internet addiction among the medical students. Int J Res Med Sci 2019;7:2703-7.
Kumar VA, Chandrasekaran V, Brahadeeswari H. Prevalence of smartphone addiction and its effects on sleep quality: A cross-sectional study among medical students. Ind Psychiatry J 2019;28:82-5.
] [Full text]
[Table 1], [Table 2], [Table 3], [Table 4]