Prevalence of internet addiction in healthcare professionals: Systematic review and meta-analysis

Abstract

Background:

Internet addition is becoming increasingly recognised as an important mental health problem.

Aim:

This study examined prevalence of internet addiction in healthcare professionals.

Method:

Systematic literature review was undertaken of June 4, 2020 with goal to identify studies that evaluated prevalence of internet addiction or problematic internet use in medical professionals. Reported associations with other mental health symptoms were also considered.

Results:

Prevalence of internet addiction was studied in medical residents only (n = 770; three studies), faculty members only (n = 69; one study), nurses only (n = 564; one study) and mixed samples of medical professionals (n = 415; three studies). Pooled prevalence rate of internet addiction in 1,818 healthcare professionals was 9.7% (95% confidence interval: 5.8%–13.6%). Internet addiction was associated with greater mental symptom burden and fatigue of healthcare workers.

Conclusion:

Internet addiction is present in healthcare professionals, however, to a lesser extent than in medical students suggesting that increasing clinical responsibilities and increasing age can be important moderators of risk for internet addiction. Internet addiction is associated with greater burden of unfavourable mental health outcomes of healthcare professionals. Further studies exploring global burden of internet addiction in healthcare professionals and possible impact of internet addiction on work performance of healthcare professionals are encouraged.

Keywords

Internet addiction problematic use of internet healthcare

Introduction

Internet use has become an indispensable and integral part of everyday life. With increasing adoption of electronic health records, telemedicine and other online resources, internet has become an important component of modern healthcare. For example, during the COVID-19 pandemic we have witnessed an unprecedented expansion and utilisation of telehealth services that will further increase the importance of telehealth in modern healthcare (Hollander & Carr, 2020; Wosik et al., 2020). However, despite of numerous personal and professional advantages of internet based technologies, there is a growing body of evidence that internet can be addictive, and cause undesirable and maladaptive mental health sequelae in susceptible individuals. Excessive internet use can become uncontrolled and maladaptive, resulting in a myriad of disruptive internet use behaviours that include excessive use of social networks, internet gambling and online shopping among others (Ioannidis et al., 2018). There still remains an ongoing debate about definition and diagnostic criteria of maladaptive internet use, with internet addiction and problematic use of internet being the most commonly used terms (Fernandes et al., 2019; Yellowlees & Marks, 2007). Diagnostic criteria for the Internet addiction were proposed by Young and were based on the diagnostic criteria of pathologic gambling that included impulse control problems, similar features with other dependencies and significant impairment in daily life (Fernandes et al., 2019; Yellowlees & Marks, 2007; Young, 1998). On the other hand, problematic use of internet is a broader construct that defines internet use behaviour as being more similar to impulse control disorders that cause cognitive and behavioural symptoms, which interfere with social, emotional, academic and/or professional functioning (Aboujaoude, 2010; Beard & Wolf, 2001; Caplan, 2002; Shapira et al., 2003).

Internet addiction and problematic use of internet are recognised as important public health problems (WHO, 2018) (Fineberg et al., 2018). Internet gaming disorder is now included in the International Classification of Diseases 11th Revision (ICD-11 - Mortality and Morbidity Statistics, n.d.) and the fifth edition of Diagnostic and Statistical Manual of Mental Disorders (American Psychiatric Association & American Psychiatric Association, DSM-5 Task Force, 2013). Internet addiction is associated with increased risk for numerous adverse behavioural and mental health sequelae, including elevated risk for depressive and anxiety symptoms (El Asam et al., 2019), impaired cognitive functioning (Zhou et al., 2014), greater level of perceived fatigue (Lin et al., 2013), disrupted sleep quality and reduced sleep duration (Lin et al., 2019). These adverse consequences of internet addiction and related behavioural disruptions can be highly relevant for healthcare professionals who often work in high-stress environment that require mental flexibility, optimal cognitive functioning and hand dexterity. Optimal functioning and performance of healthcare professionals is important for patient safety and treatment outcomes.

It is well recognised that healthcare professionals are at greater risk for mental health problems when compared to the general population that include elevated suicide risk (Schernhammer & Colditz, 2004), greater prevalence of depressive symptoms and disorders (Mata et al., 2015) and substance abuse (Sebo et al., 2007). A meta-analysis that included 3,651 medical students reported pooled prevalence rate of internet addiction at level of 30.1% (95% confidence interval (CI) 28.5%–31.8%) (Zhang et al., 2018). The incidence rate of self-reported internet addiction varied across studies as a function of assessment instrument but it was equally distributed across gender and age groups. Timely recognition and appropriate management of internet addiction in healthcare workers can be important to optimise their work performance and mitigate the risk for other adverse mental health outcomes (Andrade et al., 2020; Restrepo et al., 2020; Tang et al., 2020). Given increasing research interest in internet addiction in healthcare professionals, it is important to better understand the prevalence and potential adverse health consequences of internet addiction to better guide intervention strategies and future research of this emerging mental health problem. Healthcare workers comprise a unique group of individuals because modern healthcare heavily relies on the use of electronic medical records and telemedicine (especially during the COVID-19 pandemic); however, work-related use of internet should be distinguished from personal use of internet and considered when evaluating internet addiction in healthcare professionals. To the best of our knowledge, there are no studies systematically exploring prevalence of internet addiction in healthcare workers.

The goals of this study were (1) to systematically review the existing literature examining prevalence of internet addiction in healthcare professionals and (2) to examine risk factors and mental health symptoms associated with internet addiction in healthcare professionals.

Methods

A comprehensive systematic review was performed on June 4, 2020 in accordance with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement (Moher et al., 2015). The goal of the literature review was to identify all available studies that evaluated internet addiction or problematic internet use in medical professionals, including medical residents, board certified physicians and nurses. Articles were identified from the Pubmed/MEDLINE, PsycINFO, Cochrane CENTRAL and Clarivate Analytics databases using the relevant keywords (mesh vocabulary and free text terms): ‘problematic use of internet’, ‘problematic internet use’, ‘internet addiction’, ‘physicians’, ‘doctors’, ‘residents’, ‘nurses’. Additional articles evaluating internet addiction in healthcare professionals were also identified from additional sources and during review process.

Studies were considered for inclusion if they evaluated healthcare providers for either internet addiction or problematic internet use. Studies that evaluated internet addiction or problematic internet use in medical students were excluded. There were no restrictions to country of origin, year of publication, study design nor sample size. All identified original research papers with their abstracts or full texts available in English were considered for the review. Review papers, case reports, commentaries, editorials and meeting abstracts were not included in the analysis. Case-control and prospective cohort studies were included in the analyses if they reported prevalence of internet addiction. References of identified papers were reviewed for other relevant papers.

Study selection/data extraction

Selection of the relevant publications was performed independently by both authors. Initial literature analysis was performed by reviewing titles and abstracts of all identified papers by both researchers (Figure 1). Disagreements were solved by discussing. Thirty-nine articles were excluded with reasons during screening and full-text assessment phases. Eight articles met the study criteria and were analysed. Seven articles provided prevalence rates of internet addiction and were included in the meta-analysis.

Figure 1.

PRISM flow chart of study selection.

Full texts of all selected articles were reviewed by both researchers. The following variables were extracted from the selected papers: year and country of publication, study inclusion criteria, sample size, questionnaire(s) used for assessment of internet addiction, prevalence rate of internet addiction, mean scores on internet addiction questionnaires, participant age and gender. We also extracted information about the associations of internet addiction with other behavioural variables as considered in the selected studies. No other studies meeting the study goal were identified from the reviewed studies. Quality of the identified studies was assessed using the Newcastle-Ottawa scale (NOS) for cross-sectional studies through assessment of selection, comparability and outcome domains (Wells et al., 2015).

Statistical analysis

Statistical analysis was performed using the Jamovi (The Jamovi Project, 2020) and JASP (JASP Team, 2020) statistical packages. Between-study heterogeneity was assessed using the I² statistic, I² values of 75% indicating high, 50% – moderate and 25% – low heterogeneity. For the purpose of meta-analysis, internet addiction was defined based on scores on the Internet Addiction Test (moderate to severe internet addiction), Internet Addiction Test (high-risk or internet addiction) and Chen Internet Addiction Scale (diagnostic case of internet addiction).

Results

Eight studies evaluated internet addiction in healthcare professionals and seven studies reported prevalence of internet addiction in healthcare professionals (Table 1). All selected studies were cross-sectional. Three studies (n = 770) reported internet addiction in medical residents only (Grover et al., 2019; Mobasher et al., 2015; Schmidt et al., 2019), one in faculty members only (n = 69) (Grover et al., 2019), one in nurses only (n = 564) (Lin et al., 2013), while three studies reported internet addiction in mixed cohort of medical professionals that included residents, faculty members, nurses and or administrative workers (n = 415) (Avcı & Şahin, 2017; Prakash, 2017; Sohail et al., 2020). Sample sizes across studies ranged from 69 faculty members (Grover et al., 2019) to 564 nurses (Lin et al., 2013). Internet addiction was evaluated using the Internet Addiction Test, Chen Internet Addiction Scale and Internet Addiction Scale (Chen et al., 2003; Young, 1998).

Table 1.

Characteristics of identified studies.

Reference	Country	Study population	Sample size (n)	Study design	Proportion of women (n [%])	Mean age (years)	Assessment instrument/method of administration	Mean score	Prevalence of internet addiction (instrument cut-off score)
Schmidt et al. (2019)	USA	Medical residents (military)	94	Cross-sectional	n.r.	n.r.	Internet addiction test/via e-mail	28.48 ± 15.35	Moderate to severe addiction (score ⩾50): 0%
Grover et al. (2019)	India	Postgraduate trainees and senior residents/registrars	376	Cross-sectional	104 (29.8%)	28.93 ± 3.00	Internet addiction test/via e-mail	24.81 ± 3.00	Normal users (score <20): 37.8%
									Mild addiction (score 20–49): 54%
									Moderate addiction (score 50–79): 8.24%
									Severe addition (score >80): 0%
		Faculty members	69		n.r.	n.r.		n.r.	None
Prakash (2017)	India	House surgeons (n = 68) and postgraduate students (n = 32) below 30 years of age	100	Cross-sectional	53 (53%)	n.r.	Internet addiction test/paper-and-pencil	n.r.	Moderate addiction (score 50–79): 13%
Prakash (2017)	India		100	Cross-sectional	53 (53%)	n.r.	Internet addiction test/paper-and-pencil	n.r.	Severe addition (score >80): 0%
Mobasher et al. (2015)	Egypt	Intern doctors	300	Cross-sectional	151 (50%)	n.r.	Internet addiction test/semistructured interview	n.r.	Normal: 46%
									Mild: 41.7%
									Moderate: 12%
									Severe: 0.3%
Avcı an Şahin (2017)	Turkey	Resident physician, nurse and administrative staff	228	Cross-sectional	100 (43.8%)	29.02 ± 5.68	Internet addiction scale/interview	n.r.	High-risk (score 68–101): 11.4%
Avcı an Şahin (2017)	Turkey	Resident physician, nurse and administrative staff	228	Cross-sectional	100 (43.8%)	29.02 ± 5.68	Internet addiction scale/interview	n.r.	Internet addiction (score 102–135): 1.4%
Sohail et al. (2020)	Pakistan	Medical and dental consultants and trainees	87	Cross-sectional	40 (56%)	29.40 ± 4.13	Internet addiction test/paper based	n.r.	Normal users (score <30): 21%
									Mild addiction (score 31–49): 36%
									Moderate addiction (score 50–79): 41%
									Severe addition (score >80): 2%
Lin et al. (2013)	Taiwan	Female registered nurses	564	Cross-sectional	100%	n.r.	Chen internet addiction scale/paper based	Median: 52; range: 26–97	Diagnostic cases of IA: 6%
Lin et al. (2013)	Taiwan	Female registered nurses	564	Cross-sectional	100%	n.r.	Chen internet addiction scale/paper based	Median: 52; range: 26–97	Possible cases of IA: 10%
Kaynak et al. (2018)	Turkey	Nurses	203	Cross-sectional	178 (88%)	⩽35 years: 60%	Internet addiction scale/n.r.	n.r.	n.r.
Kaynak et al. (2018)	Turkey	Nurses	203	Cross-sectional	178 (88%)	>36 years: 40%	Internet addiction scale/n.r.	n.r.	n.r.

Note. IA = internet addiction.

Meta-analysis

The pooled prevalence rate of internet addiction among 1,818 healthcare professionals was 9.7% (95% confidence interval: 5.8%–13.6%), df = 7, I² = 93.47%, p < .001. In a subsample of 770 residents, the pooled prevalence rate of internet addiction was 6.8% (95% confidence interval 0.01%–13.4%), df = 2, I² = 95.18%, p < .001. In the total sample of healthcare workers, the meta-regression analysis showed that instrument used for assessment of internet addiction and healthcare job (resident vs nurse vs physician) (p values >.8) were not significant moderators of heterogeneity. Heterogeneity of internet addiction prevalence as a function of study country (Q = 16.172, p = .006) can be explained by high reported prevalence of internet addiction by one study from Pakistan (Sohail et al., 2020). Gender proportion, participant age and IA severity were not included in the meta-regression analyses because these variables were reported only by a small proportion of the identified studies. With regards to publication bias, regression test for funnel plot asymmetry was significant (Z = 6.973, p < .001).

Individual studies

Six studies evaluated internet addiction in medical residents (Avcı & Şahin, 2017; Grover et al., 2019; Mobasher et al., 2015; Prakash, 2017; Schmidt et al., 2019; Sohail et al., 2020). Of those, three studies also considered other healthcare workers (faculty members, nurses and/or administrative staff) (Avcı & Şahin, 2017; Prakash, 2017; Sohail et al., 2020). Prevalence rate of moderate internet addiction according to the Internet Addiction Test ranged from 0% (Schmidt et al., 2019) to 13% (Prakash, 2017). Schmidt with colleagues contacted 1,000 residents from the Naval Medical Center, San Diego, USA and medical and nursing students from the Uniformed Services University of the Health Sciences, USA via e-mail. Ninety-four residents completed an online survey; however, none of the respondents were identified as having moderate internet addiction defined as score on the Internet Addiction Test of ⩾50. In a multivariate regression analysis aiming to identify factors associated with internet addiction, authors found that resident status (vs medical or nursing student status) was associated with score on the Internet Addiction Test independently from other examined lifestyle variables (Schmidt et al., 2019).

Grover with colleague evaluated the association of internet addiction with mental health status in resident physicians working at a tertiary care hospital in India (Grover et al., 2019). Online survey that included assessment of internet addiction (Internet addiction test), depressive symptoms (Patient Health Questionnaire-9), perceived stress (Cohen’s Perceived Stress Scale), burnout (Maslach Burnout Inventory) and health care related outcomes (self-designed questionnaire) was distributed via e-mail to 1,721 physicians of whom 445 (26%) responded, of which 375 were residents and 69 were faculty members. In a cohort of medical residents, the majority (54%) of respondents reported mild internet addiction (Internet addiction test score: 20–49), 8% of respondents reported moderate internet addiction (Internet addiction test score: 50–79) and none of the respondents had severe internet addiction. None of the faculty members were identified as having excessive internet use or internet addiction. In medical residents, moderate internet addiction was associated with greater depressive symptom severity, perceived stress, burnout and alcohol use, higher frequency of watching pornography, higher proportion self-reported physical/verbal abuse and lower perceived empathy to patients (Grover et al., 2019).

Prakash evaluated internet addiction (Interned addiction test) and psychological distress (General Health Questionnaire) in 100 house surgeons and postgraduate students below 30 years of age and found that 13% of respondents had moderate internet addiction (Prakash, 2017). Internet addiction was associated with being from urban area and with more self-reported psychiatric symptoms.

Mobasher with colleagues evaluated internet addiction (Internet addiction test), internet use patterns (Internet Application Checklist) and depressive symptom severity (Beck Depression Inventory-II and Present State Examination-10) in 300 freshly graduated intern doctors in Cairo University, Egypt (Mobasher et al., 2015). The majority (46%) of study participants were normal internet users, while mild and moderate internet addiction was reported by 41.7% and 12% of respondents, respectively. Higher score on the Internet addiction test was associated with greater depressive symptom severity.

Avci with colleagues evaluated the association of internet addiction (Internet Addiction Scale) with burnout in 228 healthcare workers, including resident physicians (45%), nurses (36%) and hospital administrative staff (19%) (Avcı & Şahin, 2017). They found that 11.4% of healthcare workers were at high risk for internet addiction and 1.4% were classified as having internet addiction. Greater internet addiction was associated with younger age, shorter duration of job experience, longer working hours and longer daily sleep duration.

A study from Pakistan in a cohort of 87 medical and dental trainees and consultants found that 41% and 2% of respondents reported moderate and severe internet addiction, respectively (Sohail et al., 2020). Greater severity of internet addiction was associated with greater incidence of unfavourable behavioural patterns, including ignorance of family and responsibility, emotional disturbances, physical disturbances, distraction and preferred virtual interaction.

Two studies investigated internet addiction in nurses (Kaynak et al., 2018; Lin et al., 2013). Lin with colleagues evaluated internet addiction (Chen Internet Addiction Scale) in 564 registered female nurses in Taiwan and found that 6% of participants had diagnostic and 10% had possible cases of internet addiction (Lin et al., 2013). Possible and diagnostic internet addiction was associated with greater self-reported fatigue independently from work unit, shift work, regular self-medication and self-perceived health status. A study from Turkey in 203 nurses reported weak but significant correlation between scores on the Internet Addiction Scale and UCLA Loneliness Scale (r = 0.140, p = .046) (Kaynak et al., 2018). However, the prevalence of internet addiction was not reported.

Scores on the NOS scale of the identified studies ranged from 7 to 9, which can be attributed to the fact that internet addiction and other mental health outcomes were assessed using self-report questionnaires and multivariate analyses were not uniformly evaluated by all studies.

Discussion

This is the first systematic review that examined the prevalence of internet addiction in healthcare professionals. Pooled prevalence rate of internet addiction among 1,818 healthcare professionals was 9.7%, with prevalence rates of moderate internet addiction ranging from 0% to 13% in residents and 16% in nurses. There were no faculty members with moderate internet addiction in one small study. Internet addiction was associated with mental symptoms in medical residents and with greater fatigue in nurses.

Prevalence rate of internet addiction in healthcare professionals was significantly lower when compared to recently reported pooled prevalence rate of internet addiction of 30.1% in 3,651 medical students (Zhang et al., 2018). One study found that severity of internet addiction was lower in residents when compared to medical and nursing students (Schmidt et al., 2019). Another study reported that none of 69 faculty members qualified as excessive internet users while in the same study 8% of medical residents were identified as having moderate internet addiction (Grover et al., 2019). These findings suggest that in healthcare professionals, the prevalence of internet addiction decreases with advancing age and increasing clinical responsibilities. However, due to progressively increasing internet and mobile technology penetration in daily lives and healthcare practices around the world, the incidence of healthcare workers suffering from internet addiction or problematic use of internet is expected to increase in the near future, especially in more junior faculty members and residents. Hence, continuous surveillance of prevalence of internet addiction and problematic internet use in healthcare workers is warranted.

The majority of the identified studies were performed in Asia, two studies were performed in Turkey and there single reports from Egypt and USA. We did not find studies exploring prevalence of internet addiction in European healthcare workers. Penetration of internet and smart mobile devices varies as a function of economic development. Therefore, further studies are needed to better define prevalence of internet addiction and problematic use of internet among healthcare workers practicing in the Western societies.

Internet addiction was associated with greater burden of mental problems in residents and with greater fatigue symptom severity and loneliness in registered nurses (Grover et al., 2019; Kaynak et al., 2018; Lin et al., 2013; Prakash, 2017). Due to cross sectional design, the causal relationship between internet use and the risk for developing other mental health complications in healthcare professionals cannot be established from the identified studies. However, there is strong evidence to suggest that pathological use of the Internet can increase depression risk in adolescents (Lam & Peng, 2010). Results of the reviewed studies suggest that internet addiction can be associated with other mental health problems and symptoms, therefore assessment for co-morbid mental health problems can be considered in individuals presenting with internet addiction.

In the general population, internet addiction is associated with shorter sleep duration and worse sleep quality (Canan et al., 2013; Zhang et al., 2017). Long working hours are common among physicians during training; therefore, internet addiction can interfere with sleep and contribute to further sleep deprivation that can subsequentially impair the level of functioning of healthcare workers. For example, there is some evidence to suggest that sleep deprivation can negatively impact dexterity of surgeons (Alhola & Polo-Kantola, 2007; Banfi et al., 2019). Further studies should better define the possible adverse effects of internet addiction on job performance of healthcare workers.

Other internet addiction related maladaptive behaviours include, but are not limited to, excessive use of social media, internet gaming and pornography watching (Fineberg et al., 2018; Ioannidis et al., 2018). These maladaptive behaviours can also interfere with optimal functioning of healthcare professional and impact patient care. Some of these facets of internet addiction can be especially common in junior doctors working night-shifts and on-call (Chakraborty, 2016). Further studies should attempt to better describe different facets of internet addiction and their potential impact on work performance of healthcare professionals.

Healthcare professionals spend numerous hours online using electronic medical records and also performing telehealth consultations, which are indispensable components of modern healthcare. Adaption of telemedicine has further increased recently during the COVID-19 pandemic (Hollander & Carr, 2020, p. 19). Nevertheless, it is critical to distinguish internet use for delivering patient care from internet use (or misuse) for personal purposes. It remains to be seen if work related internet use can impact personal internet misuse and increase risk for internet addiction. COVID-19 pandemic causes significant stress, anxiety, depression and other maladaptive emotions in healthcare personnel and first respondents (CDC, 2020; Lai et al., 2020; Pappa et al., 2020). These COVID-19 related mental health challenges can subsequentially increase the risk for maladaptive internet use in vulnerable healthcare workers since maladaptive internet use behaviours can be used to reduce stress, anxiety and mood symptoms (Király et al., 2020). It remains to be seen if work related internet use can impact personal internet misuse and increase risk for internet addiction.

Limitations of our study should be acknowledged. Different aspects of internet addiction (surfing, social media use, etc.) were not specifically addressed in the reviewed studies. Generalisability of internet addiction prevalence is mainly limited to Asian countries because there was only one study from the USA and we did not identify studies from other continents. Further studies exploring global prevalence of maladaptive internet use among healthcare professionals are strongly encouraged. Future studies should consider using validated questionnaires for assessment of internet addiction in order to fortify reliability of the study results and optimise replication practices, and also report prevalence and features of internet addiction across healthcare jobs (physicians, nurses, residents), level of training and age, because these factors can be important moderators of internet use practices among healthcare workers. It is also possible that some articles that reported internet addiction in healthcare professionals were published in journals not indexed in the queried databases or were not in English and therefore were not included in our review.

Conclusion

Interest in internet addiction among healthcare professionals is growing. Internet addiction affects approximately 10% of healthcare workers. There were no faculty members with moderate internet addiction in one study. The prevalence of internet addiction in healthcare professionals is significantly lower when compared to previously reported prevalence rate of internet addiction in medical students. Internet addiction is associated with greater burden of other mental symptoms. Further studies exploring global burden of internet addiction in healthcare professionals and possible impact internet addiction on work performance of healthcare professionals are encouraged.

Footnotes

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This work was supported by the Research Council of Lithuania under Grant P-COV-20-50.

Data availability statement

Data is available upon request.

ORCID iD

Adomas Bunevicius

References

Aboujaoude

(2010). Problematic internet use: An overview. World Psychiatry: Official Journal of the World Psychiatric Association (WPA), 9(2), 85–90. https://doi.org/10.1002/j.2051-5545.2010.tb00278.x

Alhola

Polo-Kantola

(2007). Sleep deprivation: Impact on cognitive performance. Neuropsychiatric Disease and Treatment, 3(5), 553–567.

American Psychiatric Association & American Psychiatric Association, DSM-5 Task Force. (2013). Diagnostic and statistical manual of mental disorders: DSM-5. American Psychiatric Association.

Andrade

A. L. M.

Scatena

Bedendo

Enumo

S. R. F.

Dellazzana-Zanon

L. L.

Prebianchi

H. B.

de Lara Machado

de Micheli

(2020). Findings on the relationship between internet addiction and psychological symptoms in Brazilian adults. International Journal of Psychology: Journal International De Psychologie. Advance online publication. https://doi.org/10.1002/ijop.12670

Avcı

D. K.

Şahin

H. A.

(2017). Relationship between burnout syndrome and internet addiction, and the risk factors in healthcare employees. Konuralp Tıp Dergisi, 9(2), 78–85.

Banfi

Coletto

d’Ascanio

Dario

Menciassi

Faraguna

Ciuti

(2019). Effects of Sleep Deprivation on Surgeons Dexterity. Frontiers in Neurology, 10. https://doi.org/10.3389/fneur.2019.00595

Beard

K. W.

Wolf

E. M.

(2001). Modification in the proposed diagnostic criteria for Internet addiction. Cyberpsychology & Behavior: The Impact of the Internet, Multimedia and Virtual Reality on Behavior and Society, 4(3), 377–383. https://doi.org/10.1089/109493101300210286

Canan

Yildirim

Sinani

Ozturk

Ustunel

T. Y.

Ataoglu

(2013). Internet addiction and sleep disturbance symptoms among Turkish high school students. Sleep and Biological Rhythms, 11(3), 210–213 https://doi.org/10.1111/sbr.12022

Caplan

S. E.

(2002). Problematic internet use and psychosocial well-being: Development of a theory-based cognitive–behavioral measurement instrument. Computers in Human Behavior, 18(5), 553–575. https://doi.org/10.1016/S0747-5632(02)00004-3

10.

CDC. (2020, February 11). Coronavirus Disease 2019 (COVID-19). Centers for Disease Control and Prevention. Retrieved August 15, 2020, from https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/testing.html

11.

Chakraborty

(2016). Facebook addiction: An emerging problem. American Journal of Psychiatry Residents’ Journal, 11(12), 7–9. https://doi.org/10.1176/appi.ajp-rj.2016.111203

12.

Chen

Z.-S.

Weng

Y. R.

Yang

Chen

Yang

Chen

T. S.

(2003). Development of a Chinese internet addiction scale and its psychometric study. Chinese Journal of Psychology, 45(3), 279–294.

13.

El Asam

Samara

Terry

. (2019). Problematic internet use and mental health among British children and adolescents. Addictive Behaviors, 90, 428–436. https://doi.org/10.1016/j.addbeh.2018.09.007

14.

Fernandes

Maia

B. R.

Pontes

H. M.

Fernandes

Maia

B. R.

Pontes

H. M.

(2019). Internet addiction or problematic internet use? Which term should be used? Psicologia USP, 30. https://doi.org/10.1590/0103-6564e190020

15.

Fineberg

N. A.

Demetrovics

Stein

D. J.

Ioannidis

Potenza

M. N.

Grünblatt

Brand

Billieux

Carmi

King

D. L.

Grant

J. E.

Yücel

Dell’Osso

Rumpf

H. J.

Hall

Hollander

Goudriaan

Menchon

Zohar

. . . Chamberlain

S. R.

(2018). Manifesto for a European research network into problematic usage of the internet. European Neuropsychopharmacology, 28(11), 1232–1246. https://doi.org/10.1016/j.euroneuro.2018.08.004

16.

Grover

Sahoo

Bhalla

Avasthi

(2019). Problematic internet use and its correlates among resident doctors of a tertiary care hospital of North India: A cross-sectional study. Asian Journal of Psychiatry, 39, 42–47. https://doi.org/10.1016/j.ajp.2018.11.018

17.

Hollander

J. E.

Carr

B. G.

(2020). Virtually Perfect? Telemedicine for Covid-19. New England Journal of Medicine, 382(18), 1679–1681. https://doi.org/10.1056/NEJMp2003539

18.

ICD-11—Mortality and Morbidity Statistics . (n.d.). Retrieved July 6, 2019, from https://icd.who.int/browse11/l-m/en

19.

Ioannidis

Treder

M. S.

Chamberlain

S. R.

Kiraly

Redden

S. A.

Stein

D. J.

Lochner

Grant

J. E.

(2018). Problematic internet use as an age-related multifaceted problem: Evidence from a two-site survey. Addictive Behaviors, 81, 157–166. https://doi.org/10.1016/j.addbeh.2018.02.017

20.

JASP Team. (2020). JASP (Version 0.13.1)[Computer software].

21.

Kaynak

Duran

Karadaş

(2018). Determination of the relationship between internet addiction and the level of loneliness among nurses. Journal of Health and Nursing Management, 5(1), 27–35.

22.

Király

Potenza

M. N.

Stein

D. J.

King

D. L.

Hodgins

D. C.

Saunders

J. B.

Griffiths

M. D.

Gjoneska

Billieux

Brand

Abbott

M. W.

Chamberlain

S. R.

Corazza

Burkauskas

Sales

C. M. D.

Montag

Lochner

Grünblatt

Wegmann

. . . Demetrovics

(2020). Preventing problematic internet use during the COVID-19 pandemic: Consensus guidance. Comprehensive Psychiatry, 100, 152180. https://doi.org/10.1016/j.comppsych.2020.152180

23.

Lai

Wang

Cai

Wei

Chen

Tan

Kang

Yao

Huang

Wang

Liu

(2020). Factors associated with mental health outcomes among health care workers exposed to coronavirus disease 2019. JAMA Network Open, 3(3), e203976. https://doi.org/10.1001/jamanetworkopen.2020.3976

24.

Lam

L. T.

Peng

Z.-W.

(2010). Effect of pathological use of the internet on adolescent mental health: A prospective study. Archives of Pediatrics & Adolescent Medicine, 164(10), 901–906. https://doi.org/10.1001/archpediatrics.2010.159

25.

Lin

P.-H.

Lee

Y.-C.

Chen

K.-L.

Hsieh

P.-L.

Yang

S.-Y.

Lin

Y.-L.

(2019). The relationship between sleep quality and internet addiction among female college students. Frontiers in Neuroscience, 13, 599. https://doi.org/10.3389/fnins.2019.00599

26.

Lin

S.-C.

Tsai

K.-W.

Chen

M.-W.

Koo

(2013). Association between fatigue and internet addiction in female hospital nurses. Journal of Advanced Nursing, 69(2), 374–383. https://doi.org/10.1111/j.1365-2648.2012.06016.x

27.

Mata

D. A.

Ramos

M. A.

Bansal

Khan

Guille

Di Angelantonio

Sen

(2015). Prevalence of depression and depressive symptoms among resident physicians a systematic review and meta-analysis. JAMA - Journal of the American Medical Association, 314(22), 2373–2383. https://doi.org/10.1001/jama.2015.15845

28.

Mobasher

Fouad

Enaba

Shawky

Moselhy

(2015). Impact of depression on pathologic internet use among intern doctors of Cairo University Hospital (Kasr Al-Ainy). Addictive Disorders & Their Treatment, 14(4), 182–187. https://doi.org/10.1097/ADT.0000000000000061

29.

Moher

Shamseer

Clarke

Ghersi

Liberati

Petticrew

Shekelle

Stewart

L. A.

, & PRISMA-P Group. (2015). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic Reviews, 4, 1. https://doi.org/10.1186/2046-4053-4-1

30.

Pappa

Ntella

Giannakas

Giannakoulis

V. G.

Papoutsi

Katsaounou

(2020). Prevalence of depression, anxiety, and insomnia among healthcare workers during the COVID-19 pandemic: A systematic review and meta-analysis. Brain, Behavior, and Immunity, 88, 901–907. https://doi.org/10.1016/j.bbi.2020.05.026

31.

Prakash

(2017). Internet addiction among junior doctors: A cross-sectional study. Indian Journal of Psychological Medicine, 39(4), 422. https://doi.org/10.4103/0253-7176.211746

32.

Restrepo

Scheininger

Clucas

Alexander

Salum

G. A.

Georgiades

Paksarian

Merikangas

K. R.

Milham

M. P.

(2020). Problematic internet use in children and adolescents: Associations with psychiatric disorders and impairment. BMC Psychiatry, 20(1), 252. https://doi.org/10.1186/s12888-020-02640-x

33.

Schernhammer

E. S.

Colditz

G. A.

(2004). Suicide rates among physicians: A quantitative and gender assessment (meta-analysis). American Journal of Psychiatry, 161(12), 2295–2302. https://doi.org/10.1176/appi.ajp.161.12.2295

34.

Schmidt

Valdez

Farrell

Bishop

Klam

W. P.

Doan

A. P.

(2019). Behaviors associated with internet use in military medical students and residents. Military Medicine, 184(11–12), 750–757. https://doi.org/10.1093/milmed/usz043

35.

Sebo

Bouvier Gallacchi

Goehring

Künzi

Bovier

P. A.

(2007). Use of tobacco and alcohol by Swiss primary care physicians: A cross-sectional survey. BMC Public Health, 7, 5. https://doi.org/10.1186/1471-2458-7-5

36.

Shapira

N. A.

Lessig

M. C.

Goldsmith

T. D.

Szabo

S. T.

Lazoritz

Gold

M. S.

Stein

D. J.

(2003). Problematic internet use: Proposed classification and diagnostic criteria. Depression and Anxiety, 17(4), 207–216. https://doi.org/10.1002/da.10094

37.

Sohail

Rauf

Mirza

T. I.

Ashfaq

Aziz

Ibrahim

(2020). The problematic internet use among healthcare professionals: Emerging behavioral patterns in social networking sites addiction. Journal of University Medical & Dental College, 11(1), 15–22.

38.

Tang

Lewis

S. P.

Chen

Clifford

Ammerman

B. A.

Gazimbi

M. M.

Byrne

Chang

Kang

Tiemeier

(2020). Association of internet addiction with nonsuicidal self-injury among adolescents in China. JAMA Network Open, 3(6), e206863. https://doi.org/10.1001/jamanetworkopen.2020.6863

39.

The Jamovi Project. (2020). Jamovi (Version 1.2) [Computer Software]. Retrieved August 15, 2020, from https://www.jamovi.org.

40.

Wells

G. A.

Tugwell

O’Connell

Welch

Peterson

Shea

Losos

(2015). The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomized studies in meta-analyses. Retrieved August 15, 2020, from http://www,ohri.ca/programs/clinical_epidemiology.oxford.htm.

41.

WHO. (2015). Public health implications of excessive use of the internet, computers, smartphones and similar electronic devices: Meeting report, Main Meeting Hall, Foundation for Promotion of Cancer Research, National Cancer Research Centre, Tokyo, Japan, 27–29 August 2014. World Health Organization. https://apps.who.int/iris/handle/10665/184264

42.

Wosik

Fudim

Cameron

Gellad

Z. F.

Cho

Phinney

Curtis

Roman

Poon

E. G.

Ferranti

Katz

J. N.

Tcheng

(2020). Telehealth transformation: COVID-19 and the rise of virtual care. Journal of the American Medical Informatics Association, 27(6), 957–962. https://doi.org/10.1093/jamia/ocaa067

43.

Yellowlees

P. M.

Marks

(2007). Problematic internet use or internet addiction? Computers in Human Behavior, 23(3), 1447–1453. https://doi.org/10.1016/j.chb.2005.05.004

44.

Young

K. S.

(1998). Internet addiction: The emergence of a new clinical disorder. CyberPsychology & Behavior, 1(3), 237–244. https://doi.org/10.1089/cpb.1998.1.237

45.

Zhang

M. W. B.

Lim

R. B. C.

Lee

R. C. M.

(2018). Prevalence of internet addiction in medical students: A meta-analysis. Academic Psychiatry, 42(1), 88–93. https://doi.org/10.1007/s40596-017-0794-1

46.

Zhang

M. W. B.

Tran

B. X.

Huong

L. T.

Hinh

N. D.

Nguyen

H. L. T.

Tho

T. D.

Latkin

R. C. M.

(2017). Internet addiction and sleep quality among Vietnamese youths. Asian Journal of Psychiatry, 28, 15–20. https://doi.org/10.1016/j.ajp.2017.03.025

47.

Zhou

Zhu

Wang

(2014). Internet addictive individuals share impulsivity and executive dysfunction with alcohol-dependent patients. Frontiers in Behavioral Neuroscience, 8, 288. https://doi.org/10.3389/fnbeh.2014.00288