Internet addiction and sleep quality among medical students during the COVID-19 pandemic: A multinational cross-sectional survey

Muhammad Junaid Tahir; Najma Iqbal Malik; Irfan Ullah; Hamza Rafique Khan; Shahida Perveen; Rodrigo Ramalho; Ahsun Rizwan Siddiqi; Summaiya Waheed; Mahmoud Mohamed Mohamed Shalaby; Domenico De Berardis; Samiksha Jain; Gautham Lakshmipriya Vetrivendan; Harshita Chatterjee; William Xochitun Gopar Franco; Muhammad Ahsan Shafiq; Naira Taiba Fatima; Maria Abeysekera; Qudsia Sayyeda; Shamat Fathi Shamat; Wajeeha Aiman; Qirat Akhtar; Arooj Devi; Anam Aftab; Sheikh Shoib; Chung-Ying Lin; Amir H Pakpour

doi:10.1371/journal.pone.0259594

. 2021 Nov 5;16(11):e0259594. doi: 10.1371/journal.pone.0259594

Internet addiction and sleep quality among medical students during the COVID-19 pandemic: A multinational cross-sectional survey

Muhammad Junaid Tahir ^1,², Najma Iqbal Malik ³, Irfan Ullah ⁴, Hamza Rafique Khan ⁵, Shahida Perveen ³, Rodrigo Ramalho ⁶, Ahsun Rizwan Siddiqi ⁷, Summaiya Waheed ⁸, Mahmoud Mohamed Mohamed Shalaby ⁹, Domenico De Berardis ¹⁰, Samiksha Jain ¹¹, Gautham Lakshmipriya Vetrivendan ¹², Harshita Chatterjee ¹³, William Xochitun Gopar Franco ¹⁴, Muhammad Ahsan Shafiq ¹⁵, Naira Taiba Fatima ¹⁶, Maria Abeysekera ¹⁷, Qudsia Sayyeda ¹⁸, Shamat Fathi Shamat ¹⁹, Wajeeha Aiman ²⁰, Qirat Akhtar ²¹, Arooj Devi ²², Anam Aftab ⁸, Sheikh Shoib ²³, Chung-Ying Lin ^24,^*, Amir H Pakpour ^25,^26,^*

Editor: Forough Mortazavi²⁷

¹Ameer-ud-Din Medical College, Affiliated with University of Health and Sciences, Lahore, Pakistan

²Lahore General Hospital, Lahore, Pakistan

³University of Sargodha, Sargodha, Pakistan

⁴Kabir Medical College, Gandhara University, Peshawar, Pakistan

⁵Quaid-E-Azam Medical College, Bahawalpur, Pakistan

⁶Department of Social and Community Health, School of Population Health, The University of Auckland, Auckland, New Zealand

⁷Wah Medical College, Affiliated with University of Health Sciences, Wah, Pakistan

⁸Dow Medical College, Dow University of Health Sciences, Karachi, Pakistan

⁹Faculty of Medicine, Ain Shams University, Cairo, Egypt

¹⁰Department of Mental Health, NHS, Psychiatric Service for Diagnosis and Treatment, Hospital “G. Mazzini”, Teramo, Italy

¹¹Guntur Medical College, Guntur, Andhra Pradesh, India

¹²Karpaga Vinayaga Institute of Medical Sciences and Research Centre, Maduranthagam, Tamilnadu, India

¹³Texila American University, Georgetown, Guyana

¹⁴University of Guadalajara, Puerto Vallarta, Mexico

¹⁵Holy Family Hospital, Rawalpindi, Pakistan

¹⁶Changsha Medical University, Changsha, Hunan, China

¹⁷Autonomous University of Santo Domingo, Santo Domingo, Dominica

¹⁸Red Crescent Clinic Tampa Bay, Tampa, Florida, United States of America

¹⁹National Ribat University Medical School, Khartoum, Sudan

²⁰Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America

²¹Aziz Bhatti Shaheed Hospital, Gujrat, Punjab, Pakistan

²²Ghulam Muhammad Mahar Medical College, Sukkur, Pakistan

²³Department of Psychiatry, Jawahar Lal Nehru Memorial Hospital, Srinagar, India

²⁴Institute of Allied Health Sciences, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan

²⁵Department of Nursing, School of Health and Welfare, Jönköping University, Jönköping, Sweden

²⁶Social Determinants of Health Research Center, Research Institute for Prevention of Non-Communicable Diseases, Qazvin University of Medical Sciences, ShahidBahounar BLV, Qazvin, Iran

²⁷Sabzevar University of Medical Sciences, ISLAMIC REPUBLIC OF IRAN

Competing Interests: The authors have declared that no competing interests exist.

^✉

* E-mail: amir.pakpour@ju.se (AHP); cylin36933@gmail.com (CYL)

Roles

Muhammad Junaid Tahir: Conceptualization, Data curation, Formal analysis

Najma Iqbal Malik: Data curation, Formal analysis, Investigation

Irfan Ullah: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration

Hamza Rafique Khan: Conceptualization, Investigation, Methodology, Resources

Shahida Perveen: Formal analysis, Investigation, Methodology, Software

Rodrigo Ramalho: Formal analysis, Investigation, Resources

Ahsun Rizwan Siddiqi: Formal analysis, Investigation, Methodology, Validation

Summaiya Waheed: Investigation, Methodology, Resources, Software

Mahmoud Mohamed Mohamed Shalaby: Conceptualization, Data curation, Resources

Domenico De Berardis: Investigation, Methodology

Samiksha Jain: Investigation, Methodology, Project administration

Gautham Lakshmipriya Vetrivendan: Investigation, Methodology

Harshita Chatterjee: Investigation, Methodology

William Xochitun Gopar Franco: Investigation, Methodology

Muhammad Ahsan Shafiq: Investigation, Methodology

Naira Taiba Fatima: Investigation, Methodology

Maria Abeysekera: Investigation, Methodology

Qudsia Sayyeda: Investigation, Methodology, Validation, Visualization

Shamat Fathi Shamat: Investigation, Methodology

Wajeeha Aiman: Investigation, Methodology, Project administration

Qirat Akhtar: Investigation, Methodology, Project administration

Arooj Devi: Investigation, Methodology, Project administration

Anam Aftab: Project administration, Software, Validation

Sheikh Shoib: Data curation, Formal analysis, Investigation, Methodology, Project administration

Chung-Ying Lin: Investigation, Methodology, Resources, Supervision

Amir H Pakpour: Formal analysis, Methodology, Supervision, Validation, Writing – review & editing

Forough Mortazavi: Editor

PMCID: PMC8570473 PMID: 34739502

Abstract

Background

The emergence of the COVID-19 pandemic has affected the lives of many people, including medical students. The present study explored internet addiction and changes in sleep patterns among medical students during the pandemic and assessed the relationship between them.

Methods

A cross-sectional study was carried out in seven countries, the Dominican Republic, Egypt, Guyana, India, Mexico, Pakistan, and Sudan, using a convenience sampling technique, an online survey comprising demographic details, information regarding COVID-19, the Pittsburgh Sleep Quality Index (PSQI), and the Internet Addiction Test (IAT).

Results

In total, 2749 participants completed the questionnaire. Of the total, 67.6% scored above 30 in the IAT, suggesting the presence of an Internet addiction, and 73.5% scored equal and above 5 in the PSQI, suggesting poor sleep quality. Internet addiction was found to be significant predictors of poor sleep quality, causing 13.2% of the variance in poor sleep quality. Participants who reported COVID-19 related symptoms had disturbed sleep and higher internet addiction levels when compared with those who did not. Participants who reported a diagnosis of COVID-19 reported poor sleep quality. Those living with a COVID-19 diagnosed patient reported higher internet addiction and worse sleep quality compared with those who did not have any COVID-19 patients in their surroundings.

Conclusion

The results of this study suggest that internet addiction and poor sleep quality are two issues that require addressing amongst medical students. Medical training institutions should do their best to minimize their negative impact, particularly during the current COVID-19 pandemic.

Background

The internet has completely revolutionized the world in the past few decades, with the 21st century witnessing explosive growth in worldwide internet usage [1]. This global digitalization has provided better opportunities for education, communication, banking, businesses, health-seeking, and social interaction [1]. Unfortunately, uncontrolled use of internet may lead to maladaptive behaviors [2, 3]. One of the maladaptive behaviors is internet addiction (or termed as problematic internet use or pathological internet use [2] with the definition of “excessive or poorly controlled preoccupations, urges or behaviors regarding computer use and internet access that leads to impairment or distress” [4]. Indeed, evidence shows that internet addiction was linked to other psychiatric disorders (e.g., attention deficit and hyperactivity and alcohol abuse) [5]. Thus, prolonged Internet usage may adversely impact both physical and mental health.

Sleep problems had a clear correlation with mental health, psychiatric illnesses, and disorders related to anxiety and mood [6–9]. Moreover, Cellini et al. [10] found that spending ample time on internet was associated with poor sleep quality and may further lead to an increase of psychological distress (i.e., stress, anxiety and depression) among young adults. Additionally, internet addiction resulted in the dysfunction of daily activities, including neglect of household chores and reduced productivity [10]. Therefore, one can tentatively conclude that pathological internet usage can also negatively affect the circadian rhythm causing insomnia and other sleep disturbances [11–14].

However, the increasing popularity of smartphones makes the use of a smartphone before sleep has become a habit for students [15]. Moreover, it was found that teenagers who had trouble falling asleep or sustaining sleep were progressively inclined to have an internet addiction, and people who were dependent on the internet had their basic circadian rhythms altered [16]. Clear-cut impact that lockdown pose on sleep disorders is understudied, although evidence shows that the students spent more time on digital devices before sleep and had irregular sleep pattern, which may lead to poor sleep quality [17]. Therefore, sleep and internet addiction are of concern: Insufficient sleep and poor sleep quality may result in poor memory and weakened learning abilities, which jeopardize the academic performance of students and can also result in other growth and developmental disorders [18–20]. Excessive internet usage may lead to grey matter atrophy, which negatively affects one’s ability to concentrate and hinders their decision-making capacity [21].

The COVID-19 pandemic has led to an inevitable surge in the use of digital technologies due to the physical distancing norms and nationwide lockdowns, including medical schools [22, 23]. Medical schools in both developed and developing countries have utilized modern technologies to bring effective changes in medical education [24]. Over the years, particularly during COVID-19 pandemic, medical students have faced significant changes in their education (e.g., online classes and virtual learning) [25–27]. Due to the decrease in face-to-face social interactions and the increased time spent indoors during COVID-19 pandemic, there is a growing dependence on social media and online entertainment platforms for social interaction [28, 29]. This increased online learning may contribute to internet addiction. Apart from the issue of online learning, prior evidence shows that age [30] gender [6, 17, 30], family system [17], year of MBBS [17], smoking history [30], and physical health status [6, 30] were associated with internet addiction.

Empirically it was evident that excessive internet usage may significantly affect the sleep cycle of the person which leads to insomnia, irregular sleep patterns and excessive daytime sleepiness [14, 31]. Current study evaluated the effect of COVID-19 pandemic on the use of internet and subsequently, sleeping habits among the medical students. It also viewed at how the sleeping habits were altered by the excessive or problematic use of internet. To date, relatively few studies have examined the effect of the current COVID-19 pandemic on internet addiction and sleep quality particularly among medical students. University graduates from all around the world reported a common problem. Azad et al. [6] compared these sleep related issues among medical students and economics and law undergraduates. He mentioned that medical students rank the highest on the prevalence regarding poor sleep and even worse quality of life in comparison of students from other subject groups. Matter of fact that segregate these medical graduates from other peer groups included their overall lifestyle, attitude towards study and more academic pressure. Therefore, it is important to understand internet addiction and sleep among medical students.

This study was thus aimed to investigate the problem of internet addiction and sleep globally; primary focus was to select countries from all continents. However, availability of researcher and collaborators ended up with selection of these countries finally from North America (Mexico, Dominican Republic), South America (Guyana), Africa (Egypt, Sudan) and Asia (Pakistan, India). In these continents, further these countries were identified on basis of statistics provided by WHO regarding COVID-19. The selected countries were on higher risk due to poor health facilities, extreme poverty and low SGD’s indicator. Moreover, COVID-19 statistics of WHO updated on march 2020 revealed higher percentage of COVID-19 confirmed and suspected cases in these regions. The present study aims to fill this gap. With this regard, several hypotheses were made below: (i) internet addiction is positively associated with poor sleep quality among medical students in different countries; (ii) subdomains in the internet addiction are significant predictors for poor sleep quality among medical students in different countries; (iii) students with issues related to COVID-19 have high levels of internet addiction and poor sleep quality among medical students in different countries; and (iv) students with different demographics have different levels of internet addiction and sleep quality among medical students in different countries.

Methods

Study participants

A survey research design was applied, using a non-probability convenience sampling technique. The research team developed a consent form and a questionnaire using shared Google^® forms. Data collection took place between June and July 2020 after study approval from the University of Sargodha, Pakistan. The target population was students aged 15 to 44 years old in medical colleges from the Dominican Republic, Egypt, Guyana, India, Mexico, Pakistan, and Sudan. First, the relevant institutions’ concerned authorities provided their informed consent, authorizing the study to be conducted with students who attended their institutions. Then, potential participants were contacted directly by the researcher through their active email addresses and WhatsApp^® numbers provided by their institute co-coordinators. A web link to the questionnaire was then shared with them, and informed consent was obtained before participating in the study. The bulk of the data collection was from countries which delivered their curriculum in English and they could understand English language easily. Hence, responses from these places were collected in an English version of questionnaire. Translation to Spanish was done for collecting data from only two countries i.e., Dominican Republic and Mexico, in order to reduce the language barrier in their countries. For these two countries, a single questionnaire (file attached) containing both languages were circulated, and participants could read the question in English or Spanish according to their ease. Participants were not given any monetary benefits for participating in the study. To restrict duplication of responses, respondents were asked to provide email address, as only one response can be submitted through one email address. After data collection, prior to data analysis, data was reviewed and cleaned incomplete responses were discarded and only complete responses per subject were analyzed further for hypotheses testing. The ethic committee of University of Sargodha approved the study procedure (SU/PSY/786-S, April 09,2020). Consent was informed by all participants. All participants provided online informed consent. Regarding sample size calculation, guidelines for being more statistically sound about sample size tells sample of 10% of population size is recommend until sample size become 1000. A general rule of thumb is that larger sample size will increase the generalizability of the results; therefore, we tried to keep sample size as large as possible.

Measure

The questionnaire bank included demographic questions and two scales, the Pittsburgh Sleep Quality Index (PSQI) and the Internet Addiction Test (IAT) [32, 33]. The consent form and the questionnaire were developed in two languages, English and Spanish.

Demographics information

Collected demographics information included age, gender, country of residence, whether they lived in an urban or rural area, family system (joint or nuclear family), whether the participant attended a private or a public medical university, and the participants’ year of study and smoking history; plus, the following COVID-19 related questions: (i) do you have any COVID-19 related symptoms? (ii) have you been diagnosed with COVID-19 by a health professional? (iii) have you been living with a person diagnosed with COVID-19? (iv) are you following COVID-19 standard operating procedures (SOPs)?

Pittsburgh Sleep Quality Index (PSQI)

PSQI is a self-rated questionnaire [32] that assesses sleep quality and disturbances over a 1-month time interval through 19 individual items, four open-ended questions, and 14 questions rated on a Likert scale from 0–3 with 3 reflecting the extreme negative. These 19 items generate seven component scores, i.e., subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction, and the sum of scores for these seven components yields a PSQI global score of sleep quality which ranges from 0 to 21 [34]. A total sum of 5 or more indicates “poor” quality of sleep [34]. The scale has acceptable reliability, Cronbach’s α of 0.914 [35] and 0.73 [36]. The Reliability of the PSQI in present was excellent (Cronbach’s α of 0.91). Raniti et al. [36] validated the single factor structure of PSQI in an adolescent sample and mentioned covariation among poor sleep efficiency, latency and poor duration within this age students which is considerable.

Internet Addiction Test (IAT)

The IAT is a 20-item questionnaire that measures characteristics and behaviors associated with compulsive use of the internet [33]. Each item is weighted along a Likert-scale continuum that ranges from 0 = less extreme behavior to 5 = most extreme behavior. Total scores that range from 0 to 30 points are read as representing a normal level of internet usage, a score range of 31 to 49 indicates the presence of a mild level of internet addiction, scores of 50 to 79 indicate a moderate level, and scores of 80 to 100 indicate a severe level of internet addiction. IAT has high Cronbach’s α of 0.93 [37]. The reliability coefficient of the IAT was excellent in present study (Cronbach’s α = 0.77). Significant correlations of subscales confirm the internal consistency of the scales used in this study. Further, this scale consisted of 5 subscales, whose reliability was also calculated. They were salience (Cronbach’s α = 0.77), excessive use (Cronbach’s α = 0.77), neglect work (Cronbach’s α = 0.77), anticipation (Cronbach’s α = 0.77), lack of control (Cronbach’s α = 0.77), neglect social life. (Cronbach’s α = 0.77).

Statistical analysis

All collected data were scored and analyzed. Descriptive statistics including mean, standard deviation (SD), range, skewness, alpha reliability coefficient of all scales and their subscales were computed. Further, mean, standard deviation, range, and skewness were computed for the PSQI and IAT, including their total score and subscale scores. The mean differences in PSQI and IAT (including total and subscale scores) between demographic variables were explored and compared using independent t-tests and effect size (i.e., Cohen’s d). Multiple linear regression and multiple logistic models were constructed to examine the associations between sleep and internet addiction; and the effects of predictors on sleep and internet addiction. More specifically, the total scores of the PSQI and IAT were used to define having a sleep problem and having internet addiction; then, multiple logistic regressions were used for the PSQI and IAT total scores with their cutoffs. The domain scores of the PSQI and IAT do not have a cutoff score, and multiple linear regressions were used for the domain scores. For all the linear and logistic regression models, demographic variables (including age [adolescent vs. adult], gender [male vs. female] residence [rural vs. urban], family system [joint vs. nuclear], medical university sector [public vs. private], year of MBBS [junior vs. senior], smoking history [yes vs. no], health status [poor vs. good], COVID-19 related symptoms [yes vs. no], COVID-19 diagnosis [yes vs. no], live with COVID-19 infected individuals [yes vs. no], and COVID-19 standard operating procedures [yes vs. no]) were included.

Results

The final sample size was comprised of 2749 participants, 991 (36%) male and 1758 (64%) female participants. Further demographic information is presented in Table 1. A majority of the participants were from Pakistan (n = 1009; 36.7%) and India (n = 939; 34.2%). Most participants, 2311(84.1%), resided in urban areas, and 1936 (70.4%) belonged to a nuclear family system. Also, most participants studied at a public university, 1678 (61%), and 2035 (74%) were juniors (1st year to 4th year) and 714 (26%) seniors (5th and 6th year). Regarding the COVID-19 related questions, most participants, 2398 (87.2%) reported following COVID-19 SOPs. Similarly, most participants, 2621 (95.5%), reported no COVID-19 related symptoms, and only 189 (6.9%) had been diagnosed with COVID-19 by a health professional.

Table 1. Participants’ characteristics with comparisons of IAT and PSQI (N = 2749).

	n (%)		IAT			PSQI
	n (%)	Mean (SD)	95% CI	Cohen’s d	Mean (SD)	95% CI	Cohen’s d
Age
Adolescent (15–20)	1065 (38.7)	40.46 (17.78)	-0.18, -0.04	0.07	6.47 (3.14)	-0.18, -0.67	0.13
Adult (21–44)	1684 (61.3)	39.15 (17.86)			6.89 (3.18)
Gender
Male	991 (36.0)	40.98 (17.48)	3.47, 0.69	0.11	6.61 (3.15)	0.06, 0.69	0.05
Female	1758 (64.0)	38.90 (17.99)			6.79 (3.17)
Residence
Rural	438 (15.9)	38.10 (18.79)	-0.01, -3.65	0.10	6.21 (3.45)	-0.26, -0.96	0.18
Urban	2311 (84.1)	39.94 (17.64)			6.82 (3.10)
Family system
Joint family	812 (29.6)	41.19 (18.62)	3.65, 0.73	0.12	6.99 (3.36)	0.64, 0.11	0.11
Nuclear family	1936 (70.4)	39.00 (17.46)			6.62 (3.07)
University sector
Public	1678 (61.0)	38.19 (17.85)	-2.38, -5.11	0.21	6.52 (3.13)	-0.29, -0.77	0.17
Private	1071 (39.0)	41.94 (17.58)			7.06 (3.19)
Year of MBBS
Junior	2035 (74.0)	40.66 (17.83)	5.40, 2.37	0.21	6.65 (3.19)	-0.01, -0.55	0.09
Senior	714 (26.0)	36.77 (17.56)			6.94 (3.07)
Smoking history
Yes	272 (9.9)	42.62 (16.86)	5.52, 1.06	0.18	7.61 (3.37)	1.37, 0.58	0.30
No	2477 (90.1)	39.32 (17.91)			6.63 (3.13)
Health status
Poor	1048 (38.1)	44.24 (17.08)	8.76, 6.07	0.42	7.63 (3.17)	1.69, 1.21	0.47
Good	1701 (61.9)	36.82 (17.71)			6.17 (3.03)
COVID-19 symptoms
Yes	125 (4.5)	42.94 (17.34)	6.64, 0.24	0.19	8.08 (3.33)	1.97, 0.84	0.43
No	2621 (95.5)	39.49 (17.85)			6.66 (3.14)
COVID-19 diagnosis
Yes	189 (6.9)	41.77 (15.74)	6.33, -1.98	0.12	7.94 (3.63)	2.06, 0.42	0.36
No	2690 (93.1)	39.60 (17.88)			6.70 (3.15)
Live with COVID-19 infected individual
Yes	189 (6.9)	43.59 (16.14)	6.64, 1.81	0.24	8.04 (3.16)	1.87, 0.94	0.44
No	2560 (93.1)	39.36 (17.92)			6.63 (3.14)
Follow COVID-19 instructions
Yes	2398 (87.2)	39.46 (17.69)	0.53, -3.45	0.08	6.70 (3.12)	0.16, -0.60	0.06
No	351 (12.8)	40.92 (18.78)			6.92 (3.48)

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Note: IAT = Internet Addiction Test; PSQI = Pittsburgh Sleep Quality Index.

Overall, 67.6% (n = 1859) of the sample scored above 30 in the IAT, indicating the presence of an internet addiction. Among the 1859 participants; 40.0% (n = 1099) scored between 31 and 49 in the IAT, indicating the presence of a mild level internet addiction; 25.5% (n = 700) scored between 50 and 79, indicating a moderate level internet addiction; and 2.2% (n = 60) scored 80 and over, indicating a severe dependence. Additionally, 73.5% (n = 2007) of the total sample had a global score of 5 or more on the PSQI, indicating poor sleep quality. Detailed scores in PSQI and IAT are presented in Table 2.

Table 2. Descriptive information for the Pittsburgh Sleep Quality Index (PSQI), Internet Addiction Test (IAT), and their subscales (N = 2749).

Scales or subscales	Mean	SD	Potential range	Actual range	Skewness
PSQI global	6.73	3.16	0–21	0–19	0.43
Subjective sleep quality	1.09	0.81	0–3	0–3	0.46
Sleep latency	0.80	0.88	0–3	0–3	0.17
Sleep duration	0.80	0.88	0–3	0–3	0.17
Sleep efficiency	1.05	1.12	0–3	0–3	0.90
Sleep disturbance	1.14	0.54	0–3	0–3	0.82
Use of sleep medication	0.19	0.55	0–3	0–3	0.29
Daytime dysfunction	1.07	0.82	0–3	0–3	0.36
IAT total	39.65	17.84	0–100	0–100	0.31
Salience	9.10	5.16	0–25	0–25	0.42
Excessive use	10.82	4.96	0–25	0–25	0.25
Neglect work	5.84	3.64	0–15	0–15	0.38
Anticipation	3.68	2.32	0–10	0–10	0.48
Lack of control	7.16	3.63	0–15	0–15	0.15
Neglect social life	3.02	2.08	0–10	0–10	0.64

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The correlations between PSQI and IAT were found to be significant (r = 0.36; p <0.01). Moreover, Table 3 reports that significant predictors for internet addiction included university sector (AOR = 1.35; 95% CI = 1.13, 1.62), year of MBBS (AOR = 0.74, 95% CI = 0.59, 0.94), smoking history (AOR = 0.73, 95% CI = 0.54, 0.97), and health status (AOR = 0.59, 95% CI = 0.49, 0.70). Table 4 reports that significant predictors for sleep included residence (AOR = 1.50, 95% CI = 1.16, 1.81), health status (AOR = 0.56, 95% CI = 0.47, 0.67), COVID-19 related symptoms (AOR = 0.62, 95% CI = 0.39, 0.99), living with COVID-19 infected individuals (AOR = 0.58, 95% CI = 0.40, 0.85), salience in internet addiction (AOR = 1.03, 95% CI = 1.00, 1.06), and excessive use in internet addiction (AOR = 1.12, 95% CI = 1.09, 1.15).

Table 3. Predictors on internet addiction (N = 2749).

IV	Dependent variable of internet addiction: AOR (95%CI)^a or B (SE)^b
IV	IAT total^a	Sal.^b	EU ^b	NW ^b	Anti. ^b	LoC ^b	NSL ^b
Age	0.96(0.78,1.18)	0.29(0.22)	0.13(0.22)	-0.40(0.16)	-0.07(0.10)	-0.27(0.16)	-0.011(0.09)*
Gender	0.94(0.78,1.14)	-0.58(0.21)**	-0.20(0.20)	-0.24(0.15)	0.004(0.10)	0.14(0.15)	-0.63(0.08)
Residence	1.08(0.84,1.38)	0.24(0.27)	0.77(0.26)**	0.34(0.19)	-0.06(0.12)	0.33(0.19)	0.02(0.11)*
Family	0.89(0.73,1.08)	-0.57(0.22)**	-0.40(0.21)*	0.03(0.15)	-0.25(0.10)*	-0.30(0.15)	-0.11(0.09)
University	1.35(1.13,1.62)	0.86(0.20)**	0.66(0.19)*	0.36(0.14)	0.32(0.09)**	0.24(0.14)	0.36(0.08)
MBBS	0.74(0.59,0.94)	-0.92(0.25)**	-0.29(0.24)	-0.86(0.18)**	-0.29(0.12)*	-0.72(0.18)**	-0.15(0.10)
Smoking	0.73(0.54,0.97)	-0.42(0.33)	-10.12(0.32)*	0.09(0.23)	-0.20(0.15)	-0.02(0.24)	-0.54(0.13)
Health status	0.59(0.49,0.70)	-1.56(0.20)**	-1.76(0.19)**	-1.43(0.22)**	-0.56(0.09)**	-1.10(0.14)	-0.40(0.08)
Symptom	1.00(0.64,1.54)	-0.31(0.48)	-0.61(0.46)	-0.08(0.34)	-0.44(0.22)*	-0.30(0.34)	0.05(0.19)*
Diagnosis	1.71(0.84,3.48)	0.03(0.71)	1.27(0.68)	-0.24(0.50)	-0.29(0.32)	-0.29(0.50)	0.17(0.28)*
Live	0.76(0.54,1.09)	-1.63(0.40)**	-0.75(0.38)*	-0.31(0.22)	-0.13(0.18)	-0.23(0.28)	-0.75(0.16)
Instruction	1.15(0.89,1.50)	0.21(0.29)	0.10(0.28)	0.33(0.20)	0.13(0.13)	-0.16(0.21)	-0.01(0.12)*

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Note. IV = independent variable; PSQI = Pittsburgh Sleep Quality Index; SSQ = subjective sleep quality; SL = sleep latency; S.DUR = sleep duration; SE = sleep efficiency; S.DIST = sleep disturbance; UoSM = use of sleep medicine; D.DYS = daytime dysfunction; Sal. = salience; EU = excessive use; NW = neglect work; Anti. = anticipation; LoC = lack of control; NSL = neglect social life; Family = family system; University = university sector; MBBS = year of MBBS; Smoking = smoking history; Symptom = COVID-19-related symptom; Diagnosis = COVID-19 diagnosis; Live = live with COVID-19 infected individuals; Instruction = follow COVID-19 instructions.

Reference groups were: age (adolescents); gender (males); residence (rural); family system (joint); university sector (public); year of MBBS (junior); smoking history (yes); health status (poor); COVID-19 related symptoms (no); COVID-19 diagnosis (no); live with COVID-19 infected individuals (no); and follow COVID-19 instructions (no).

^a Reported in adjusted odds ratio (AOR) with 95% confidence interval (CI) by logistic regression model.

^b Reported in unstandardized coefficient with standard error (SE) by linear regression model.

Table 4. Predictors on sleep quality (N = 2749).

IV	Dependent variable of sleep quality: AOR (95%CI)^a or B (SE)^b
IV	PSQI global^a	SSQ^b	SL^b	SE^b	S.DUR^b	S.DIST^b	UoSM^b	D.DYS^b
Age	1.19(0.98,1.44)	0.04(0.03)	0.001(0.04)	-0.02(0.05)	0.09(0.04)*	0.05(0.02)*	0.06(0.02)*	-0.003(0.03)
Gender	1.18(0.98,1.41)	0.01(0.03)	0.11(0.04)	0.17(0.05)**	-0.02(0.04)	0.05(0.02)*	0.001(0.02)	0.06(0.03)*
Residence	1.50(1.16,1.81)	0.09(0.04)	0.13(0.05)	0.16(0.06)*	-0.04(0.05)	0.003(0.03)	-0.04(0.03)	0.03(0.04)
Family	1.08(0.90,1.30)	-0.04(0.03)	0.07(0.04)	0.03(0.05)	-0.06(0.04)	-0.03(0.02)	-0.04(0.02)	-0.04(0.03)
University	1.21(0.94,1.30)	0.07(0.03)*	0.01(0.04)	-0.04(0.05)	0.05(0.04)	0.04(0.02)	0.01(0.02)	0.09(0.03)
MBBS	1.15(0.92,1.43)	0.07(0.04)	0.09(0.05)	0.10(0.06)	0.05(0.04)	-0.003 (0.03)	0.02(0.03)	-0.07(0.04)
Smoking	0.77(0.57,1.03)	-0.09(0.05)	-0.03(0.06)	-0.22 (0.07)*	-0.008(0.06)	-0.10(0.03)**	-0.10(0.04)**	-0.08(0.05)
Health status	0.56(0.47,0.67)	-0.24(0.03)**	-0.24(0.04)**	-0.07(0.05)	-0.05(0.04)	-0.11 (0.02)**	-0.05(0.02)*	-0.20(0.03)**
Symptom	0.62(0.39,0.99)	-0.13(0.07)	0.08(0.09)	-0.14(0.11)	0.01(0.08)	-0.19 (0.05)**	-0.16(0.05)**	-0.15(0.07)*
Diagnosis	0.66(0.33,1.31)	-0.14(0.11)	-0.12(0.13)	0.03(0.16)	-0.08(0.12)	-0.11(0.07)	-0.15(0.08)	0.05(0.11)
Live	0.58(0.40,0.85)	-0.10(0.06)	-0.06(0.08)	0.09(0.09)	-0.17(0.07)	0.16(0.04)**	-0.13(0.04)**	-0.24(0.06)
Instruction	1.12(0.87,1.44)	0.02(0.04)	0.02(0.06)	-0.02(0.07)	0.07(0.05)	0.01(0.03)	0.13(0.03)**	0.01(0.04)
Sal.	1.03(1.00,1.06)	-0.004(0.005)	0.01(0.006)	-0.002(0.007)	-0.003(0.005)	0.01(0.003)**	0.01(0.003)**	0.01(0.005)
EU	1.12(1.09,1.15)	0.04(0.005)**	0.05(0.006)**	0.001(0.008)	0.03(0.006)**	0.002(0.003)	0.005(0.004)	0.03(0.005)**
NW	0.99(0.96,1.02)	0.002(0.006)	-0.02(0.007)*	-0.007(0.009)	-0.01(0.01)	0.009(0.004)*	0.002(0.004)	0.04(0.006)**
Anti.	1.01(0.96,1.06)	0.005(0.008)	0.005(0.01)	-0.01(0.01)	0.01(0.01)	0.02(0.06)**	0.005(0.006)	0.01(0.01)
LoC	0.99(0.96,1.03)	-0.004(0.006)	0.0004(0.008)	0.02(0.01)	-0.01(0.01)	-0.003(0.004)	-0.02(0.004)**	0.01(0.01)
NSL	1.02(0.97,1.07)	0.02(0.008)	0.001(0.01)	0.01(0.001)	0.01(0.01)	0.02(0.006)**	0.03(0.006)**	0.002(0.008)

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^a Reported in adjusted odds ratio (AOR) with 95% confidence interval (CI) by logistic regression model.

^b Reported in unstandardized coefficient with standard error (SE) by linear regression model.

Discussion

The present study explored some aspects of the lifestyle (i.e., internet addiction and sleep) among medical college students across seven different countries during the period of COVID-19 pandemic. The prevalence rates of internet addiction and poor sleep were relatively high in the medical students during COVID-19 pandemic. Moreover, the present study found that university sector, year of MBBS, smoking history, and health status were significant predictors for internet addiction. Residence, health status, COVID-19 related symptoms, living with COVID-19 infected individuals, salience in internet addiction, and excessive use in internet addiction were significant predictors for poor sleep.

As previously reported by Garcia-Priego [38], public health emergencies can cause, trigger, or worsen mental health concerns, plus, they can also be related to a high prevalence of low to mild levels of Internet addiction. Regarding COVID-19, a study by Siste et. al. [39] found that mental health issues and sleep disruptions were related to Internet addiction, and it was especially prevalent in groups with proximity to COVID-19. Fear of COVID-19 contraction and rampant misinformation about COVID-19 could have contributed to these results.

In the present study, adolescents and male participants scored higher on Internet addiction when compared to adults and female participants respectively. However, both male and female were non significantly different in terms of their sleep quality. Similar results were reported by Kim et al. [40]. Similar results were found in other studies too [6, 17, 30], where individuals with internet addiction tended to be males of young age and found no significant gender difference in sleep quality. The authors further reported that adults with internet addiction reported high difficulty in initiating and maintaining sleep, had a non-restorative sleep cycle, showed daytime functional impairment, and their duration of sleep was less than 10 hours on weekdays.

The present study found no significant effect of the place of residence on internet addiction. However, it did have a significant effect on sleep quality, as participants living in urban areas had poorer sleep quality when compared to those living in rural areas. Participants living in joint families and those attending a private medical university scored higher on internet addiction and poorer sleep quality when compared to those living in nuclear families and attending a public medical university, respectively. Some opposite results were reported by Jahan et al. [41], who found that living with their family was associated with better sleep quality. In the present study, juniors scored higher on Internet addiction than seniors, while senior students reported poorer sleep quality. Here, a similar result was previously reported by Cheng et al. [42], and Romero-Blanco et al. [17], who found that undergraduates (although not separated in junior or senior years) had poorer sleep quality than postgraduates.

Participants with a smoking history and self-reported poor health scored higher on Internet addiction and also reported poor sleep quality when compared to those who did not smoke and reported good health. Previous studies found similar results in this regard. For example, Jahan et al. [41] also found that non-smokers had a better sleep quality. A study conducted in Taiwan found that students who smoked had altered sleeping patterns and were less likely to have good sleep quality. Liu et al. [42] and Cheng et al. [43] also reported that people who reported poor sleep quality also smoked excessive and indulged in high internet surfing.

Results suggest that the IAT and all its subscales have a significant positive correlation with PSQI, and all its subscales, except sleep efficiency, with which it had a non-significant relationship. In summary, internet addiction was a significant predictor of poor sleep quality. Previous studies have also found similar results. Jahan et al. [41] study with medical college students of Bangladesh also found as internet addiction increased, the level of poor sleep quality also increased. The study by Canan et. al. [14], conducted with Turkish high school students, also reported an association between internet addiction and impaired sleep. Similarly, the study by Tsitsika [44], conducted in seven European countries, found that the prevalence of sleep problems was higher among students with an internet addiction. Finally, a systematic review conducted by Lam [12], found that internet gaming addiction and problematic internet use were related to sleep problems, including insomnia and poor sleep quality, although more so the latter than the former.

The coronavirus has caused an extraordinary crisis in many fields; therefore, many countries are struggling to get out of the crisis. Mulyadi et al. [45] examine the relationship between sleep duration and anxiety and internet usage duration. Their results showed a significant relationship between sleep duration and anxiety; and internet usage duration with anxiety. Another study from the Middle East region (Kuwait, Saudi Arabia) reported by Alheneidi et al. [46] showed an association between loneliness and problematic internet use (PIU), and an association between loneliness and the number of hours spent online. Those who reported greater loneliness also obtained frequent news about the pandemic from social media. Their ANOVA analyses further showed a dose-response between the predictors and PIU. Moreover, Lin [47] mentioned that the COVID-19 outbreak has significantly disrupted normal activities globally. During this epidemic, people around the world were expected to encounter several mental health challenges. Internet addiction may become a serious issue among teenagers. The prevalence of internet addiction was found to be 24.4% during this period [47]. Additionally, previous studies report an increase in gaming addiction and internet use with detrimental impact on psychosocial well-being. Fernandes et al. [48] compared the impact of lockdown on internet use among adolescents via their habits between before and during the pandemic. They found the relationship between gaming addiction, internet use and COVID-19 worries and showed that adolescents generally have increased their use of social media sites and streaming services. Further, those who had a high level of gaming addiction, compulsive internet use and social media use also reported high scores of depression, loneliness, escapism, poor sleep quality and anxiety related to the pandemic. Their findings indicated that, regardless of country of residence, the COVID-19 outbreak has had a significant effect on adolescent internet use and psychosocial well-being.

The present study also found the varied effect of different constructs of internet addiction on different constructs of sleep quality. For example, anticipation, a subscale of the IAT, was a significant predictor of sleep disturbance, and lack of control was a significant predictor of the use of sleep medication. Neglecting social life, a domain of the IAT, was a significant predictor of sleep disturbance, use of sleep medication, and subjective sleep quality. Work neglect, a sub-construct of IAT, was a significant predictor of sleep disturbance, sleep duration, and daytime dysfunction. Lastly, the salience of internet addiction and excessive use of the internet (subscales of IAT) were significant predictors of sleep disturbance, use of sleep medication, subjective sleep quality, sleep duration, daytime dysfunction, and sleep latency. A previous study conducted by Lin et al. [30] also reported that internet addiction was significantly associated with subjective sleep disturbance, use of sleep medication, sleep quality, sleep duration, daytime dysfunction, and sleep latency.

The strength of this study is that it has focused on the area of research where not much work has been done during the COVID-19 pandemic. Other strengths include the large sample size, use of an international sample (increasing generalizability), and the potentially important implications for mental health and sleep interventions among medical students. However, our study had several limitations as follows: Firstly, the convenience sampling method was used and due to voluntary participation, there was a possibility of selection bias. Specifically, those who did not have internet addiction or sleep problems were more likely than those who had such a problem to agree to participate. Therefore, the results of the present study may underestimate the prevalence of internet addiction and sleep problems. Secondly, because this was a cross sectional study, we were unable to establish causal inferences. That is, it is unclear whether internet addiction results in sleep problems, or the other way around. Future studies are thus needed to use a longitudinal design or case-control design to provide causal relationship evidence. Thirdly, most participants belonged to Pakistan and India due to which it is difficult to generalize the results. Specifically, the present study’s results may be prone more to Pakistani and Indians instead of other countries’ participants. Fourthly, the varying prevalence of COVID-19 in different countries accompanied with the subsequent imposition of SOPs and the degree of lockdown individually, could have been a contributor to bias and was not taken into account. Finally, the data collection was via the online mode; therefore, those who did not frequently surf on their internet during the COVID-19 pandemic might not be aware of this study. This may restrict the generalizability of the present findings. That is, those who were at very low level of internet addiction or internet use might not participate in the present study.

Looking at the results, future studies should focus on how to allow the medical students to use internet in a balanced manner so as to prevent the development of internet addiction during such lockdown conditions and, devise measures to improve the quality of sleep among medical students.

Conclusion

The present study partially supports the hypotheses that during COVID-19 pandemic period, internet addiction was positively associated with poor sleep among medical students; salience and excessive use in the internet addiction were significant predictors for poor sleep among medical students; medical students had high levels of internet addiction and poor sleep during COVID-19 pandemic; and some demographic characteristics were associated with internet addiction and poor sleep among medical students. More specifically, the study found that the prevalence of internet addiction was about 67.6% and that of poor sleep was 73.5%. The presence of COVID-19 related symptoms was associated with disturbed sleep and higher scores in the IAT, and a diagnosis of COVID-19 was associated with poor sleep quality. Similarly, living with someone with a COVID-19 diagnosis was associated with a higher score on the IAT and worse sleep quality. These findings suggest the importance of providing medical students with coping strategies that would prevent pathological Internet usage and poor sleep quality. This study highlights the need to design some sort of training to deal with such pandemic situation, which was previously not given to student sample.

Supporting information

S1 File. Dataset.

(SAV)

Click here for additional data file.^{(1.6MB, sav)}

Data Availability

All the relevant data are within the manuscript and its Supporting information files.

Funding Statement

The authors received no specific funding for this work.

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PLoS One. doi: 10.1371/journal.pone.0259594.r001

Decision Letter 0

Forough Mortazavi

16 Aug 2021

PONE-D-21-19421

Internet addiction and sleep quality among medical students during the COVID-19 pandemic: A multinational cross-sectional survey

PLOS ONE

Dear Dr. Pakpour,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Sep 30 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Forough Mortazavi

Academic Editor

PLOS ONE

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Additional Editor Comments (if provided):

Dear Authors,

According to the study title, it is expected that Internet addiction and sleep quality and predictors of both variables be investigated among students; however, the authors rely on simple tests such as t-test rather than complicated tests which could enable them to disclose predictors of the two variables.The statistical analysis presented by the authors is mostly not relevant to the objectives of study. This makes the study uninteresting and reduces its usefulness. The English language of the manuscript is not up to standard. I hope my comments would help the authors improve their manuscript.

Background:

1. Although this section is too long, no proper explanation is provided for conducting this study in several countries and the reasons for selecting the specific countries. PLS summarize this section and add the necessary explanations.

2. In page 10, the English in these sentences requires correction: “Palatty et al. [27] further differentiated economics and Law graduates had more stressful academic demands and responsibilities in their study programs in comparison to students belong to various other academic discipline. This fact explained the reason of poor sleep among medical students compared to other student population.”

3. The declared objectives of the study are too general. PLS define them in such a way as to justify data collection from several countries.

Methods

4. According to the authors, the global score for sleep quality is 0-21. This seems wrong because the score is calculated based on 14 items rated on a Likert scale from 0-3. PLS provide a reference for the cutoff point of 5 for poor quality of sleep.

5. PLS describe scoring of the 5 open-ended items and the calculation of the total score for the PSQI.

6. The details of sample size calculation are not presented.

7. You have conducted multiple regression analyses assessing the associations between sleep and Internet addiction. Please explain why you did not conduct multiple logistic regression because as you mentioned in page 13, the scale has a cut-off value.

Results

8. This section needs extensive revision.

9. Table 1 is redundant because its contents are already presented in table 3 except for the percentages which can be included in table 3.

10. The relationships between family size and internet addiction and sleep quality are valuable; however, what is important and should be emphasized is that the family size is a predictor of internet addiction and sleep quality. In studies such as this the focus must be on the results of multiple regression analysis. Table 4 is also redundant because it presents the results of t-test and not the regression.

11. In table 5, no comparisons between countries are presented. It seems that the author' aim was to collect a large amount of data without comparing the countries. There are no discussions of the results presented in table 5 therefore; we regard this tale as redundant.

12. The range of correlations between two constructs can be presented in the text. PLS remove table 6, its contents bear no relation to the aims of the study.

13. In table 6. The authors controlled for the effects of other variables to determine if internet addiction impacted sleep quality; however in discussing the results presented in the table they cover several other risk factors of sleep quality and internet addiction. In my opinion, considering the large amount of data, the authors must determine predictors of both poor sleep quality and internet addiction. If these variables have cut-off values, PLS conduct multiple logistic regressions.

14. I suggest that the results of table 6 be presented fully; so that, readers can estimate the effects of all significant variables on poor sleep quality.

15. With such a large data set, it is expected and valuable to determine associated factors and predictors of poor sleep quality and internet addiction.

Discussion

16. This manuscript is difficult to follow. It contains so many redundant tables and so much information that the authors cannot manage to discuss fully. In the discussion section, they mostly focus on the results of table 3.

17. PLS provide a summary of significant results in the first paragraph.

18. In page 17, you write, “To compare cutoff with previous cutoff scores, percentile ranks were calculated and results identified no major differences in score range.” This sentence is vague. What do you mean by “previous cutoff scores and percentile ranks.”

19. PLS provide the reference for this sentence: “Superficially, minimum value of range was slightly shifted from 40 to 51 in the total score of IAT. However, the cutoff score is within the range of 40-69.

20. The last lines of the first paragraph and the second paragraph are more suitable for the results section. PLS do not repeat the results in the discussion section.

21. In page 18, paragraph 2, the duration of sleep less than 10 hours should be reported, “their duration of sleep was more than 10 hours on weekdays.”

22. Page 19, “Additionally, this study also indicated significant mean differences on all scales and subscales other than sleep latency, sleep efficiency, and daytime dysfunction.” It is not clear what is meant by this sentence.

23. PLS correct wrong usages of capital letter in the text.

Conclusions

24. This section includes conclusions unsupported by results: In the first lines, the authors write that, “The present study suggests the possibility that the COVID-19 pandemic, along with its public health measures, has had a significant impact on Internet use and sleep quality amongst medical students.” The objective of this study is not a comparison of pre pandemic and pandemic Internet use and sleep quality. The above statement cannot be presented as a conclusion of you study.

25. Furthermore, the authors mentioned that, “psychological mindedness was impacted due to COVID-19 because student did not expected and trained to handle this situation.” This is not investigated by this study too.

26. The last sentence cannot also be concluded from the study.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Firstl,this is a very good sleep data covering a number of countries.Internet addiction was investigated among medical students and sample size is large enough.There are several changes that need to be made.

1.In the discussion part, there is no data support for the results discussed that need to add.

2.There are too many tables in this paper, so I need to provide some statistical graphs.

3.The presentation in Table 7 is not important and needs to be revised.

Reviewer #2: I understood that this study was an epidemiological study that analyzed the relationship between Internet dependence and various parameters of sleep using the method of Internet survey among medical students in several countries.

One of the features of this study was that it was interesting to examine the effects of the presence of COVID-19 patients on Internet dependence and sleep.

However, I think that this study has some serious problems that cannot be ignored in order to be published in an international journal.

The biggest problem is that there are serious questions about the representativeness and reliability of the sample used for the survey. One of the major drawbacks of Internet-based surveys is that the population bias of the population from which the data is collected is assumed to be quite large. Since the survey was conducted using the Internet, there is a high possibility that the data will be biased toward the population that originally spent a lot of effort on the Internet. Also, in this study, there is no information on how many people were invited to participate in the survey. The response rate of the survey is important information for estimating the magnitude of bias in the reporting of such epidemiological studies (if the response rate is low, the nonresponse bias is quite low, and the reliability of the survey is low).

For these reasons, we believe that the value of this study can be enhanced by publishing it in a domestic journal rather than in an international journal.

**********

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Reviewer #1: No

Reviewer #2: No

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Attachment

Submitted filename: PONE-D-21-19421 internet.pdf

Click here for additional data file.^{(1MB, pdf)}

PLoS One. 2021 Nov 5;16(11):e0259594. doi: 10.1371/journal.pone.0259594.r002

Author response to Decision Letter 0

15 Oct 2021

August 18, 2021

Dear Dr. Mortazavi,

Thanks for giving us the opportunity to revise our work “Internet addiction and sleep quality among medical students during the COVID-19 pandemic: A multinational cross-sectional survey (Manuscript ID PONE-D-21-19421)”. After revising the manuscript, we have resubmitted it to be considered for publication on the PLOS ONE. We have systematically addressed the reviewers’ concerns point-by-point, and the revisions are presented in red font in the manuscript. We hope that the paper is now acceptable for publication in the PLOS ONE.

We look forward to your reply. Thank you for considering our manuscript.

Sincerely,

Corresponding Author

Responses to Editor:

Background:

Response: We have now made the Background section concise (from 1250 words to 923 words) and provided the reasons why the study was conducted in these countries. Specifically, the aim of conducting an international study was to study the impact of the pandemic on medical students globally. The reason for selecting these countries in particular was a rather approachable means of data collection and recruitment of collaborators from these countries. We collected data using convenience and snowball methods.

“This study was thus aimed to investigate the problem of internet addiction and sleep globally; primary focus was to select countries from all continents. However, availability of researcher and collaborators ended up with selection of these countries finally from North America (Mexico, Dominican Republic), South America (Guyana), Africa (Egypt, Sudan) and Asia (Pakistan, India). In these continents, further these countries were identified on basis of statistics provided by WHO regarding COVID-19. The selected countries were on higher risk due to poor health facilities, extreme poverty and low SGD’s indicator. Moreover, COVID-19 statistics of WHO updated on march 2020 revealed higher percentage of COVID-19 confirmed and suspected cases in these regions.”

Response: We have now deleted these sentences as they are not relevant to the present study’s population.

3. The declared objectives of the study are too general. PLS define them in such a way as to justify data collection from several countries.

Response: We have now revised the study objectives.

“The present study aims to fill this gap. With this regard, several hypotheses were made below: (i) internet addiction is positively associated with poor sleep quality among medical students in different countries; (ii) subdomains in the internet addiction are significant predictors for poor sleep quality among medical students in different countries; (iii) students with issues related to COVID-19 have high levels of internet addiction and poor sleep quality among medical students in different countries; and (iv) students with different demographics have different levels of internet addiction and sleep quality among medical students in different countries.”

Methods

Response: The scoring of Pittsburgh Sleep Quality Index (PSQI) is quite complicated. Therefore, we have now provided the information here (in the response letter) instead of putting the complicated information in the manuscript to avoid distraction to the potential readers. However, we have cited the reference in the manuscript for readers to understand the scoring method.

The PSQI 19 items were reformed into seven component scores (each component score ranges between 0 and 3). The 0-21 global score is the sum of the seven component scores.

Component 1 score = PSQI item 9 Score

Component 2 score = PSQI item 2 Score (<15min (0), 16-30min (1), 31-60 min (2), >60min (3)) + PSQI item 5a Score (if sum is equal 0=0; 1-2=1; 3-4=2; 5-6=3)

Component 3 score = PSQI item 4 Score (>7(0), 6-7 (1), 5-6 (2), <5 (3)

Component 4 score = (total # of hours asleep) / (total # of hours in bed) x 100; then, recoded into >85%=0, 75%-84%=1, 65%-74%=2, <65%=3 (Note: the hours asleep information is derived from PSQI item 4; the hours in bed information is derived from PSQI items 1 and 3)

Component 5 score = sum of scores of PSQI items 5b to 5j; then, recoded into 0=0; 1-9=1; 10-18=2; 19-27=3.

Component 6 score = PSQI item 6 Score

Component 7 score = sum of PSIQ item 7 Score + PSQI item 8 score; then, recoded into 0=0; 1-2=1; 3-4=2; 5-6=3

Detailed PSQI items and scoring can be found at here: http://www.goodmedicine.org.uk/files/assessment,%20pittsburgh%20psqi.pdf

Reference (for both cutoff at 5 and scoring method):

Buysse,D.J., Reynolds,C.F., Monk,T.H., Berman,S.R., & Kupfer,D.J. (1989). The Pittsburgh Sleep Quality Index (PSQI): A new instrument for psychiatric research and practice. Psychiatry Research, 28(2), 193-213.

5. PLS describe scoring of the 5 open-ended items and the calculation of the total score for the PSQI.

Response: Please see our response to the previous comment.

6. The details of sample size calculation are not presented.

Response: We have now provided the information how we calculated the sample size.

“Regarding sample size calculation, guidelines for being more statistically sound about sample size tells sample of 10% of population size is recommend until sample size become 1000. A general rule of thumb is that larger sample size will increase the generalizability of the results; therefore, we tried to keep sample size as large as possible.”

Response: We have now used multiple logistic regression when appropriate for the data analysis. The tables have been rearranged and updated.

“Multiple linear regression and multiple logistic models were constructed to examine the associations between sleep and internet addiction; and the effects of predictors on sleep and internet addiction. More specifically, the total scores of the PSQI and IAT were used to define having a sleep problem and having internet addiction; then, multiple logistic regressions were used for the PSQI and IAT total scores with their cutoffs. The domain scores of the PSQI and IAT do not have a cutoff score, and multiple linear regressions were used for the domain scores.”

Results

8. This section needs extensive revision.

Response: We have now revised the section substantially according to your comments.

“The final sample size was comprised of 2749 participants, 991 (36%) male and 1758 (64%) female participants. Further demographic information is presented in Table 1. A majority of the participants were from Pakistan (n=1009; 36.7%) and India (n=939; 34.2%). Most participants, 2311(84.1%), resided in urban areas, and 1936 (70.4%) belonged to a nuclear family system. Also, most participants studied at a public university, 1678 (61%), and 2035 (74%) were juniors (1st year to 4th year) and 714 (26%) seniors (5th and 6th year). Regarding the COVID-19 related questions, most participants, 2398 (87.2%) reported following COVID-19 SOPs. Similarly, most participants, 2621 (95.5%), reported no COVID-19 related symptoms, and only 189 (6.9%) had been diagnosed with COVID-19 by a health professional.

Overall, 67.6% (n = 1859) of the sample scored above 30 in the IAT, indicating the presence of an Internet addiction. Among the 1859 participants; 40.0% (n = 1099) scored between 31 and 49 in the IAT, indicating the presence of a mild level Internet addiction; 25.5% (n = 700) scored between 50 and 79, indicating a moderate level Internet addiction; and 2.2% (n= 60) scored 80 and over, indicating a severe dependence. Additionally, 73.5% (n = 2007) of the total sample had a global score of 5 or more on the PSQI, indicating poor sleep quality. Detailed scores in PSQI and IAT are presented in Table 2.

9. Table 1 is redundant because its contents are already presented in table 3 except for the percentages which can be included in table 3.

Response: We have now combined Tables 1 and 3.

Response: We have now removed Table 4 and provided a table on regression analysis results.

Response: We have now removed Table 5.

12. The range of correlations between two constructs can be presented in the text. PLS remove table 6, its contents bear no relation to the aims of the study.

Response: We have now removed Table 6 and reported the correlation between PSQI and IAT scores in the Results section.

Response: We have now revised our statistical methods and rewritten the Results section.

14. I suggest that the results of table 7 be presented fully; so that, readers can estimate the effects of all significant variables on poor sleep quality.

Response: We have now reported all the independent variables in the regression models.

15. With such a large data set, it is expected and valuable to determine associated factors and predictors of poor sleep quality and internet addiction.

Response: We have now conducted multiple logistic regression models to assess potential predictors for poor sleep quality and internet addiction, separately.

Discussion

Response: Thank you for the constructive comments. We have now revised the Discussion based on the revised Results.

17. PLS provide a summary of significant results in the first paragraph.

Response: We have now provided a summary of significant findings in the first paragraph of the Discussion.

“The present study explored the impact of COVID-19 on the lifestyle of medical college students across seven different countries. The prevalence rates of internet addiction and poor sleep were relatively high in the medical students during COVID-19 pandemic. Moreover, the present study found that university sector, year of MBBS, smoking history, and health status were significant predictors for internet addiction. Residence, health status, COVID-19 related symptoms, living with COVID-19 infected individuals, salience in internet addiction, and excessive use in internet addiction were significant predictors for poor sleep.”

Response: We have now deleted this sentence.

Response: Thank you. This is a typo; the cutoff should be 50-79 instead of 40-69.

In our sample the sample score on 75th percentile was calculated as 51. Which means that internet addiction is prevalent at the starting limit of moderate addiction (scores range from 50-79 as described by Young (1998, 2011).

20. The last lines of the first paragraph and the second paragraph are more suitable for the results section. PLS do not repeat the results in the discussion section.

Response: We have now deleted the two paragraphs.

21. In page 18, paragraph 2, the duration of sleep less than 10 hours should be reported, “their duration of sleep was more than 10 hours on weekdays.”

Response: We have now changed “more than 10 hours” to “less than 10 hours”. Thank you for catching this typo.

Response: We have now deleted the sentence.

23. PLS correct wrong usages of capital letter in the text.

Response: The incorrect uses of capital letters were revised.

Conclusions

Response: We have now revised our conclusion.

“The present study partially supports the hypotheses that during COVID-19 pandemic period, internet addiction was positively associated with poor sleep among medical students; salience and excessive use in the internet addiction were significant predictors for poor sleep among medical students; medical students had high levels of internet addiction and poor sleep during COVID-19 pandemic; and some demographic characteristics were associated with internet addiction and poor sleep among medical students. More specifically, the study found that the prevalence of internet addiction was about 67.6% and that of poor sleep was 73.5%. The presence of COVID-19 related symptoms was associated with disturbed sleep and higher scores in the IAT, and a diagnosis of COVID-19 was associated with poor sleep quality. Similarly, living with someone with a COVID-19 diagnosis was associated with a higher score on the IAT and worse sleep quality. These findings suggest the importance of providing medical students with coping strategies that would prevent pathological Internet usage and poor sleep quality. This study highlights the need to design some sort of training to deal with such pandemic situation, which was previously not given to student sample.”

Response: The sentence is deleted.

26. The last sentence cannot also be concluded from the study.

Response: The last sentence of the Conclusion is deleted.

Responses to Reviewer #1:

First of all, this is a very good sleep data covering a number of countries. Internet addiction was investigated among medical students and sample size is large enough. There are several changes that need to be made.

1. In the discussion part, there is no data support for the results discussed that need to add.

Response: Thank you for the positive comment on our manuscript. We also appreciate the specific comments made which helped us to strengthen our paper.

2. There are too many tables in this paper, so I need to provide some statistical graphs.

Response: We have now reduced the number of tables in the revised manuscript.

3. The presentation in Table 7 is not important and needs to be revised.

Response: We have now revised Table 7.

Responses to Reviewer #2:

1. I understood that this study was an epidemiological study that analyzed the relationship between Internet dependence and various parameters of sleep using the method of Internet survey among medical students in several countries.

One of the features of this study was that it was interesting to examine the effects of the presence of COVID-19 patients on Internet dependence and sleep.

However, I think that this study has some serious problems that cannot be ignored in order to be published in an international journal.

Response: Thank you for the positive comment on our manuscript. We also appreciate the specific comments made which helped us to strengthen our paper.

2. The biggest problem is that there are serious questions about the representativeness and reliability of the sample used for the survey. One of the major drawbacks of Internet-based surveys is that the population bias of the population from which the data is collected is assumed to be quite large. Since the survey was conducted using the Internet, there is a high possibility that the data will be biased toward the population that originally spent a lot of effort on the Internet. Also, in this study, there is no information on how many people were invited to participate in the survey. The response rate of the survey is important information for estimating the magnitude of bias in the reporting of such epidemiological studies (if the response rate is low, the nonresponse bias is quite low, and the reliability of the survey is low).

Response: As the anonymized survey was used so we were unable track response rate 100% however, at start of questionnaire, a question given “are you willing to participate in this study? “54 answered “No”, while 2749 answered “yes” and filled complete questionnaire.

3. For these reasons, we believe that the value of this study can be enhanced by publishing it in a domestic journal rather than in an international journal.

Response: We have now substantially revised the manuscript based on the constructive comments made by the editor and reviewers. Therefore, we believe that our study is valuable in an international journal.

Attachment

Submitted filename: Response letter edited (2).doc

Click here for additional data file.^{(56.5KB, doc)}

PLoS One. doi: 10.1371/journal.pone.0259594.r003

Decision Letter 1

Forough Mortazavi

18 Oct 2021

PONE-D-21-19421R1Internet addiction and sleep quality among medical students during the COVID-19 pandemic: A multinational cross-sectional surveyPLOS ONE

Dear Dr. Pakpour,

Please submit your revised manuscript by Dec 02 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Forough Mortazavi

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

Dear authors,

Thank you for revising the manuscript and submitting it to PLOS ONE. A few things remain to be fixed but it has improved by the changes already made. We invite you to submit a revised version of the manuscript that addresses the points below:

The first sentence of the discussion section: “The present study explored the impact of COVID-19 on the lifestyle of medical college students across seven different countries.”

I do not agree with this statement. PLS kindly explain how this study explored the impact of the covid-19 on students’ lifestyle.

Please kindly discuss limitations of the study and potential sources of bias or imprecision. Discuss both direction and magnitude of any potential bias.

Please discuss the generalizability of the study results.

Good Luck

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: No

**********

6. Review Comments to the Author

Reviewer #1: The language of this manuscript needs further improvement.The quality control of network questionnaire has not been further elaborated.Please list the method in this manuscript.

**********

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PLoS One. 2021 Nov 5;16(11):e0259594. doi: 10.1371/journal.pone.0259594.r004

Author response to Decision Letter 1

19 Oct 2021

October 19, 2021

Dear Dr. Mortazavi,

Thanks for giving us the opportunity to revise our work “Internet addiction and sleep quality among medical students during the COVID-19 pandemic: A multinational cross-sectional survey (Manuscript ID PONE-D-21-19421R1)”. After revising the manuscript, we have resubmitted it to be considered for publication on the PLOS ONE. We have systematically addressed the reviewers’ concerns point-by-point, and the revisions are presented in red font in the manuscript. We hope that the paper is now acceptable for publication in the PLOS ONE.

We look forward to your reply. Thank you for considering our manuscript.

Sincerely,

Corresponding Authors

Responses to Editor:

Background:

1. Thank you for revising the manuscript and submitting it to PLOS ONE. A few things remain to be fixed but it has improved by the changes already made. We invite you to submit a revised version of the manuscript that addresses the points below:

Response: Thank you for appreciating our previous revisions and thank you again for providing us additional constructive comments below to improve our work.

2. The first sentence of the discussion section: “The present study explored the impact of COVID-19 on the lifestyle of medical college students across seven different countries.”

I do not agree with this statement. PLS kindly explain how this study explored the impact of the covid-19 on students’ lifestyle.

Please kindly discuss limitations of the study and potential sources of bias or imprecision. Discuss both direction and magnitude of any potential bias.

Please discuss the generalizability of the study results.

Response: We have now revised the first sentence of the Discussion to precisely reflect what the present study investigates. That is, we have removed the claim of “impact” in the sentence as we did not have pre- and post-pandemic information to understand “impact”.

“The present study explored some aspects of the lifestyle (i.e., internet addiction and sleep) among medical college students across seven different countries during the period of COVID-19 pandemic.”

3. Please kindly discuss limitations of the study and potential sources of bias or imprecision. Discuss both direction and magnitude of any potential bias.

Please discuss the generalizability of the study results.

Response: Limitations of the study, including the potential sources of bias/imprecision, and the generalizability of the study results have been added.

“However, our study had several limitations as follows: Firstly, the convenience sampling method was used and due to voluntary participation, there was a possibility of selection bias. Specifically, those who did not have internet addiction or sleep problems were more likely than those who had such a problem to agree to participate. Therefore, the results of the present study may underestimate the prevalence of internet addiction and sleep problems. Secondly, because this was a cross sectional study, we were unable to establish causal inferences. That is, it is unclear whether internet addiction results in sleep problems, or the other way around. Future studies are thus needed to use a longitudinal design or case-control design to provide causal relationship evidence. Thirdly, most participants belonged to Pakistan and India due to which it is difficult to generalize the results. Specifically, the present study’s results may be prone more to Pakistani and Indians instead of other countries’ participants. Fourthly, the varying prevalence of COVID-19 in different countries accompanied with the subsequent imposition of SOPs and the degree of lockdown individually, could have been a contributor to bias and was not taken into account. Finally, the data collection was via the online mode; therefore, those who did not frequently surf on their internet during the COVID-19 pandemic might not be aware of this study. This may restrict the generalizability of the present findings. That is, those who were at very low level of internet addiction or internet use might not participate in the present study.”

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PLoS One. doi: 10.1371/journal.pone.0259594.r005

Decision Letter 2

Forough Mortazavi

22 Oct 2021

Internet addiction and sleep quality among medical students during the COVID-19 pandemic: A multinational cross-sectional survey

PONE-D-21-19421R2

Dear Dr. Pakpour,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Forough Mortazavi

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0259594.r006

Acceptance letter

Forough Mortazavi

29 Oct 2021

PONE-D-21-19421R2

Internet addiction and sleep quality among medical students during the COVID-19 pandemic: A multinational cross-sectional survey

Dear Dr. Pakpour:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Forough Mortazavi

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 File. Dataset.

(SAV)

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Data Availability Statement

All the relevant data are within the manuscript and its Supporting information files.

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PERMALINK

Internet addiction and sleep quality among medical students during the COVID-19 pandemic: A multinational cross-sectional survey

Muhammad Junaid Tahir

Najma Iqbal Malik

Irfan Ullah

Hamza Rafique Khan

Shahida Perveen

Rodrigo Ramalho

Ahsun Rizwan Siddiqi

Summaiya Waheed

Mahmoud Mohamed Mohamed Shalaby

Domenico De Berardis

Samiksha Jain

Gautham Lakshmipriya Vetrivendan

Harshita Chatterjee

William Xochitun Gopar Franco

Muhammad Ahsan Shafiq

Naira Taiba Fatima

Maria Abeysekera

Qudsia Sayyeda

Shamat Fathi Shamat

Wajeeha Aiman

Qirat Akhtar

Arooj Devi

Anam Aftab

Sheikh Shoib

Chung-Ying Lin

Amir H Pakpour

Roles

Abstract

Background

Methods

Results

Conclusion

Background

Methods

Study participants

Measure

Demographics information

Pittsburgh Sleep Quality Index (PSQI)

Internet Addiction Test (IAT)

Statistical analysis

Results

Table 1. Participants’ characteristics with comparisons of IAT and PSQI (N = 2749).

Table 2. Descriptive information for the Pittsburgh Sleep Quality Index (PSQI), Internet Addiction Test (IAT), and their subscales (N = 2749).

Table 3. Predictors on internet addiction (N = 2749).

Table 4. Predictors on sleep quality (N = 2749).

Discussion

Conclusion

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Forough Mortazavi

Roles

Author response to Decision Letter 0

Decision Letter 1

Forough Mortazavi

Roles

Author response to Decision Letter 1

Decision Letter 2

Forough Mortazavi

Roles

Acceptance letter

Forough Mortazavi

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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