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Section Abstract Introduction Methods Results Discussion Conflict of Interest Acknowledgment Funding Sources References

Clinical Research


Zoom fatigue and its risk factors in online learning during the COVID-19 pandemic

Jonathan Salim,1 Sutiono Tandy,1 Jannatin Nisa Arnindita,2 Jacobus Jeno Wibisono,3 Moses Rizky Haryanto,1 Maria Georgina Wibisono1




pISSN: 0853-1773 • eISSN: 2252-8083

https://doi.org/10.13181/mji.oa.225703 Med J Indones. 2022;31:13–9


Received: August 6, 2021

Accepted: January 18, 2022

Published online: February 22, 2022


Authors' affiliation:

¹Faculty of Medicine, Universitas Pelita Harapan, Tangerang, Indonesia,

²Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia,

³Department of Obstetrics and Gynecology, Siloam Hospital Lippo Village, Tangerang, Indonesia


Corresponding author:

Jonathan Salim

Faculty of Medicine, Universitas Pelita Harapan,

Jalan Jendral Sudirman No. 20, Kelapa Dua, Tangerang, Banten 15810, Indonesia

Telp/Fax: +62-21-54210130/+62-21-54210133

E-mail: dr.jonathan.salim.94@gmail.com




Global nations have enforced strict health protocols because of the COVID-19’s high transmission, infectivity, and mortality. As shown by increased online learning and video conferencing, the employment and education sectors are shifting to home-based activities. Video conferencing as a communication medium has subtly led to zoom fatigue. This study aimed to analyze the risk factors of zoom fatigue for early prevention and treatment.



This cross-sectional study was conducted on 335 Indonesian university students selected by purposive sampling in July 2021. Data were collected using a demographic questionnaire including online courses duration during the COVID-19 pandemic; Pittsburgh sleep quality index; depression, anxiety and stress scale-21; and zoom & exhaustion fatigue (ZEF) scale through Google Form (Google LLC, USA) distributed via social media and student forums. Association and correlation tests were used, and the model was developed using linear regression.



The respondents were aged 21.3 (1.8) years with 12.8 (5.1) months of online courses during the COVID-19 pandemic and a ZEF scale of 2.8 (0.9). Students with higher ZEF had irregular physical exercise, poorer sleep quality, longer video conferencing sessions, longer months of courses during the COVID-19 pandemic, and higher mental illness (i.e., stress, anxiety, and depression). Smoking negatively correlated with fatigue (r = −0.12). The model for ZEF showed good predictability for zoom fatigue (p<0.001, R2 = 0.57).



Daily exposure to video conferencing in educational settings throughout the pandemic has drastically increased zoom fatigue. The stakeholders must act immediately to minimize the risks while providing maximum benefits.



fatigue, Indonesia, mental illness, online education, risk factors



Social and physical distancing have been strictly enforced since the first coronavirus disease 2019 (COVID-19) case was announced on March 2, 2020.1 The pandemic has adversely affected the employment and education sectors as they shifted to home-based activities to minimize the transmission. In 2021, the Ministry of Education and Culture Republic of Indonesia also noted that most educational institutions in Indonesia were at a moderate to high risk of COVID-19 infections (96.54%).2 Consequently, the educational sector widely adopted the online learning technique.3 From early February to late March 2020, there was also an increment of users who actively used video conferencing platforms (e.g., Zoom, Google Meet, and Skype) (17.32–2,859.07%) in Indonesia.4 Therefore, these transitions prominently increase the zoom fatigue risk and prevalence.

University students are heavily affected by the pandemic transition. They collectively had more screen time and video conferencing duration for their educational needs.⁵ This is even more supported by their decent technology competency and greater freedom in choosing their study location.6,7 They are also in the most productive age category (15–35 years old);⁸ hence, less fatigue may boost productivity.

Numerous risk factors could predispose the incidence of zoom fatigue. Disparity on the sex hormones and stress adaptation may augment fatigue susceptibility.9 Moreover, people with older age secrete more pro-inflammatory cytokines and have more mutation and immunosenescence, which negatively impact energy and exhaustion. Poor sleep quality and physical exercise display similar effects with deteriorating cell organelle and anaerobic respiration, which generate by-products and less energy.10

Video conferencing is an increasing trend, and zoom fatigue has diverse risk factors. The previous study only assessed gender, video conferencing duration, and nonverbal cues influences.11 Thus, this study aimed to conduct a more profound analysis and understanding of zoom fatigue and its risk factors for early prevention and management.




Study design

This cross-sectional study recruited 335 online respondents from purposively selected university students across Indonesia. Six respondents (1.79%) were removed due to either incomplete data or having non-binary gender. This study was approved by the Ethics Committee of the Faculty of Medicine, Universitas Pelita Harapan (No: 157/K-LKJ/ETIK/VII/2021) following the institutional review board and Declaration of Helsinki. All respondents understood, agreed, and signed the informed consent before the study.


Sample size

The minimal sample size was 212 participants, calculated from correlation formula with an assumption of 5% alpha (Zα = 1.64), 80% power (Zβ = 0.84), and r = 0.17 obtained from the previous study by Fauville et al.¹¹ Ten percent of additional samples were included to overcome any loss to follow-up or incomplete data.


Subject enrollment

The inclusion criteria of the eligible respondents included (1) Indonesian university students, (2) aged over 17 years, and (3) had participated in classroom video conferencing. However, the respondents were excluded if they had communication difficulties or refused to participate. In July 2021, the respondents were recruited through social media, student forums, and direct contact across Indonesia.


Data collection

Data were collected using a questionnaire on Google Form (Google LLC, USA), which comprised four sections: demographic, mental status, sleep quality, and zoom fatigue. All aspects were measured using a validated and reliable questionnaire to prevent unnecessary face-to-face interaction. The Indonesian version of the questionnaire was used to prevent any language barrier except for the zoom & exhaustion fatigue (ZEF) due to unavailability.


Demographic data

Several demographics, intrinsic, extrinsic, and academic parameters were obtained as the potential risk factors for zoom fatigue. The grade point average was measured in a numerical form (i.e., 0–4), video conferencing duration in minutes, video conferencing frequency in times a day, alcohol consumption in milliliter per day, cigarette consumption in cigars per day, and online courses during the COVID-19 pandemic in months. Physical exercise was regular if performed on ≥3 consecutive days without >2 days in between. Furthermore, body mass index (BMI) was classified following the Asian cut-off points: underweight (<18.5), normal (18.5–22.9), overweight (23.0–24.9), pre-obese (25.0–29.9), and obese (≥30.0).12


Mental status

The depression, anxiety and stress scale (DASS) questionnaire is a validated and reliable 21-item questionnaire to measure an individual’s stress, anxiety, and depression level. Each mental ailment is scored by summing the frequency of the specific event and divided according to its severity by a specific cut-off. Those categories were normal (stress [S] <8, anxiety [A] <4, & depression [D] <5), mild (S <10, A <6, & D <7), moderate (S <13, A <8, & D <11), severe (S <17, A <10, & D <14), and extremely severe (S >16, A >9, & D >13).13

The DASS-21 could screen mental issues within rural communities (0.79–0.81 sensitivity and 0.72–0.77 specificity) and Indonesian general population (0.79–0.80 McDonald’s Ω, = 0.42–0.43, and Bayes factor [BF₁₀] = 2.97 × 10⁴⁶–8.74 × 10⁴⁷), even across various types of administration.14,15 A previous study had shown that the Indonesian version of the DASS-21 had satisfactory validity and reliability, with a 0.91 Cronbach’s alpha, 0.29–0.76 discriminatory capacity, and −0.41–0.37 validity correlation (p<0.001).¹⁶


Sleep quality

The Pittsburgh sleep quality index (PSQI) is the optimal questionnaire for measuring sleep quality. The index comprises 10 questions corresponding to seven domains: quality, latency, duration, efficiency, disturbance, medication, and daytime dysfunction. Students’ sleep quality reached a decent PSQI score, with 0.74 Cronbach’s alpha value and 0.33–0.82 correlation.17 The Indonesian version also had good validity and reliability with Cronbach’s alpha of 0.79, content validity of 0.89, and significant known-group validity (p<0.001).18


Zoom fatigue

The ZEF scale is a novel yet reliable tool to measure zoom fatigue upon the general, visual, social, motivational, and emotional domains with three Likert scale questions. The instrument has exceptional construct validity where each ZEF items have substantial correlations (r = 0.67–0.90, p<0.001), domains reliability (Cronbach’s alpha = 0.85–0.90), and data fitness with 0.96 comparative fit index, 0.95 Tucker-Lewis index, 0.08 root mean square error of approximation, and 0.05 standardized root mean square error.¹⁹ Unfortunately, the Indonesian version of the ZEF scale had not been validated; hence, this study implemented the English version of the ZEF scale, which is available from http://comm.stanford.edu/ZEF.


Bias and blinding techniques

Some techniques have been implemented in the current study to reduce the potential bias. Therefore, the Indonesian version of the questionnaire eliminated any language barrier, except for the ZEF due to unavailability. A social media account was created as an information center to minimize any language barrier of the English version of the ZEF scale. There were also representatives in each location to quickly help those with inquiries. The anonymity and confidentiality of the respondents were insured to limit any acquiescence and desirability bias. The statisticians who analyzed the study and the data curators were blinded. The questionnaire was also arranged from general to more detailed questions to suppress any chance of question-order bias.


Statistical analysis

Microsoft Excel 365 (Microsoft Corporation, USA) was used for data tabulation, and SPSS software version 26 (IBM Corp., USA) was used for the statistical analysis. All numerical factors were tested for their normality using Kolmogorov-Smirnov test. The parametric data were then assessed to the ZEF scale with Pearson correlation, while Spearman correlation was used for the alternative. Meanwhile, Mann–Whitney test was used to analyze mean differences between the ZEF scale among two categories, and Kruskal–Wallis test was used if the variables had more than two categories.

For the multivariate analysis, linear regression was used. All variables with a p-value of <0.25 in the bivariate analysis were included in the multivariate analysis. A variable was considered confounders and excluded if there were changes in R2 or adjusted odds ratio by <10%. The model was created with five different assumptions: existence, independency, linearity, homoscedasticity, and normality, which were all fulfilled by the current model.




A total of 329 Indonesian university students were recruited. The mean age was 21.3 (1.8) years old, BMI was 22.7 (6.97) kg/m2, and online courses duration was 12.8 (5.1) months. The majority were women, and only a few were married. The mean of the ZEF scale was 2.8 (0.9), and there was a high prevalence of mental issues (i.e., stress = 40.9%, anxiety = 66.1%, and depression = 57.3%). The respondents’ characteristics are shown in Table 1.


Table 1. Zoom fatigue and its risk factors in Indonesian university students


Risk factors of zoom fatigue were gender, regular exercise, and mental issues (Table 1). Although many risk factors had weak correlations, sleep quality and mental issues had moderate and strong correlations. A higher ZEF scale was found in female students with irregular physical exercise, poorer sleep quality, longer video conferencing sessions, longer months of courses during the COVID-19 pandemic, and worse mental illness. Interestingly, smoking was not correlated with the ZEF scale (r = −0.119). Meanwhile, BMI, residency, marital status, education level, and grade point average were not related to zoom fatigue.

From the multivariate analysis, online courses duration during the COVID-19 pandemic, video conference duration, sleep quality, gender, and mental issues were significant without any confounders (Table 1). The model also had good predictability on the ZEF scale.




Among the university students, zoom fatigue was positively correlated to months of online courses during the COVID-19 pandemic, video conferencing duration, sleep quality, mental issues, and gender (p<0.05). Maintaining sleep quality and mental conditions are crucial to reducing zoom fatigue. Good attitudes and practices on sleep hygiene and mental calming exercises such as meditation are necessary. Furthermore, academic institutions should consider the course duration and provide adequate break time between the courses.11

Zoom fatigue is not merely a local phenomenon, but it appears globally. A study in the USA¹¹ showed that the average ZEF scale was 3.0 (0.8), which is similar to our study. Moreover, that study also confirmed that video conferencing duration led to zoom fatigue (p<0.001).¹¹ This may be developed through four mechanisms (mirroring, physiologic trap, hyper-gaze, and nonverbal intention). During video conferencing, self-reflection on the screen may trigger mirror anxiety and visual distortions through unwanted yet greater self-focused attention.²⁰ Moreover, the person may feel physically trapped for constantly being on camera instead of offline meeting with no physical restriction. The isolation and limitation of mobility can induce depression and fatigue.21,22

Furthermore, virtual conferencing forces a constant gaze from all attendees regardless of the presenter. This constant gaze triggers negative physiological impacts and cognitive loads.23 Although nonverbal cues may unconsciously be transmitted in face-to-face communication, more effort is required in virtual conferences. For example, being in a gallery view, a person tends to match his/her eye perspective to others, yet people’s gaze may be challenging due to varying camera locations and internet lag. Nonetheless, this escalates the psychological burdens.24,25

In our study, we also found that women were more prone to zoom fatigue. A high presence of zoom fatigue in women is also shown in some studies; for example, investigations in Sweden and the USA reported a 13.8% higher zoom fatigue proportion in women than in men.11 This may be due to women are more affected by mirror anxiety.26,27 Women also express greater awareness of being observed in video conferencing by showing deeper emotional characteristics such as smiling, frowning, and maintaining facial posture than men.28 Males additionally have a lower secretion rate of dopamine in the nucleus accumbens that its instability has a positive effect on fatigue.29,30 Dopamine agonist medications have been proven to relieve fatigue from a head injury, chronic fatigue syndrome, or cancer.31

Physical exercise often requires muscular strength for adequate exercise. Reduced oxygen availability in this condition causes a shift from aerobic to anaerobic metabolism that produces less energy but more lactic acid by-products. Lactate accumulation decreases pH and will denature essential proteins. For instance, deterioration of calcium receptor bonding with troponin results in mitochondrial dysfunction and energy decrement.32 A study in Columbia showed a weak correlation between physical exercise and fatigue (p≤0.01; r = −0.25).33

This study also found that poor sleepers had a higher zoom fatigue. Similarly, a study by Chatlaong et al34 in Thailand found that exhaustion was higher in poor sleepers (86.1% versus 64.3%, p<0.001). Sleep quality reflects overall energy through modulation of mitochondrial function and energy production.35

Mental issues were also the significant factors to zoom fatigue in our study. Solopchuk et al36 in Belgium confirmed that depression was correlated to exhaustion levels. Abnormalities of the neurotransmitter in the central nervous system cause many mental issues, particularly the catecholamines and their derivatives. For example, low dopamine is linked to low motivation through anhedonia, suggesting a major depressive disorder.37

Smoking also has a significant yet poor correlation with zoom fatigue. Ozdogar et al38 found a similar issue regardless of age, gender, and diseases. Cigarettes act as an anxiolytic and antidepressant by controlling serotonin, dopamine, and glutamine secretion via the nicotine to nicotinic acetylcholine receptors.39 However, the harmful effects of smoking (e.g., heart failure, myocardial infarction, and chronic obstructive pulmonary disease) disrupt the oxygen demand and supply balance and its flow to the vital organs. These conditions stimulate dyspnea, anaerobic respiration, reduced energy, and fatigue.40

This study had limitations such as few samples size, inability to generate causal relationships due to the cross-sectional design, and the recall or respondent bias because the data were collected using a selfreported questionnaire. This study also only employed Indonesian university students who could not be generalized to other countries. Accordingly, future investigations with a more varied subjects, cohort or experimental designs, and direct examinations may provide multiplicity analysis with deeper and further acknowledgment of the risk relations.

In conclusion, zoom fatigue in Indonesian university students was significantly influenced by online courses duration during the COVID-19 pandemic, video conferencing duration, sleep quality, mental issues, and gender. Therefore, universities should consider effective time management and lecture duration, while individuals should raise their mental health and sleep behavior awareness.



Conflict of Interest

The authors affirm no conflict of interest in this study.





Funding Sources






  1. Djalante R, Lassa J, Setiamarga D, Sudjatma A, Indrawan M, Haryanto B, et al. Review and analysis of current responses to COVID-19 in Indonesia: period of January to March 2020. Prog Disaster Sci. 2020;6:100091.
  2. Indonesian Ministry of Education and Culture. Educational center quantity stratified by provincial covid-19 epidemiology in Indonesia. Indonesian Ministry of Education and Culture; 2021. Indonesian.
  3. Statistics Indonesia. Community behavior during the emergency PPKM period, results of the community behavior survey during the covid-19 pandemic. Statistics Indonesia; 2021. Indonesian.
  4. Fajrin MU, Tiorida E. Factors affecting interest and behavior of technology utilization (video conference use during physical distancing). In: Universities’ Role as Center of Excellence in Improving The Innovative and Competitive Human Resource. Proceedings of the 11th Industrial Research Workshop and National Seminar (IRWNS); 2020; Bandung. Indonesian.
  5. Guo YF, Liao MQ, Cai WL, Yu XX, Li SN, Ke XY, et al. Physical activity, screen exposure and sleep among students during the pandemic of COVID-19. Sci Rep. 2021;11:8529.
  6. Abad-Alcalá L. Media literacy for older people facing the digital divide: the e-inclusion programmes design. Comunicar. 2014;21(42):173–80.
  7. Caglar E, Bilgili N, Karaca A, Deliceoglu G. Screen time differences among Turkish university students as an indicator of sedentary lifestyle and inactivity. Croat J Educ. 2017;19(4):1105–30.
  8. Wisnumurti AAGO, Darma IK, Suasih NNR. Government policy of Indonesia to managing demographic bonus and creating Indonesia gold in 2045. IOSR J Humanit Soc Sci. 2018;23(1):23–34.
  9. Hasbi A, Nguyen T, Rahal H, Manduca JD, Miksys S, Tyndale RF, et al. Sex difference in dopamine D1-D2 receptor complex expression and signaling affects depression- and anxiety-like behaviors. Biol Sex Differ. 2020;11(1):8.
  10. Pawelec G. Age and immunity: what is “immunosenescence”? Exp Gerontol. 2018;105:4–9.
  11. Fauville G, Luo M, Queiroz ACM, Bailenson JN, Hancock J. Nonverbal mechanisms predict zoom fatigue and explain why women experience higher levels than men. SSRN Electron J. 2021:1–18.
  12. Llido LO, Mirasol R. Comparison of body mass index based nutritional status using WHO criteria versus “Asian” criteria: report from the Philippines. PhilSPEN Online J Parenter Enteral Nutr. 2011;1(5):1–8.
  13. BlackDog Institute. DASS 21 Australia [Internet]. Available from: https://www.hgmc.com.au/pdf/dass.pdf
  14. Tran TD, Tran T, Fisher J. Validation of the depression anxiety stress scales (DASS) 21 as a screening instrument for depression and anxiety in a rural community-based cohort of northern Vietnamese women. BMC Psychiatry. 2013;13:24.
  15. Onie S, Kirana AC, Alfian A, Mustika NP, Adesla V, Ibrahim R. Assessing the predictive validity and reliability of the DASS-21, PHQ-9 and GAD-7 in an Indonesian sample. PsyArXiv. 2020;1–17.
  16. Kinanthi MR, Listiyandini RA, Amaliah US, Ramadhanty R, Farhan M. Indonesian DASS-21 adaptation among university students. In: Indonesian University Students Mental Health: Understanding its Protective Factors. National Seminar on Psychology and Call for Paper UMB Yogyakarta; 2020; Yogyakarta. Indonesian.
  17. Manzar MD, Moiz JA, Zannat W, Spence DW, Pandi-Perumal SR, BaHammam AS, et al. Validity of the Pittsburgh sleep quality index in Indian university students. Oman Med J. 2015;30(3):193–202.
  18. Alim IZ. Test validity and reliability of the instrument pittsburgh sleep quality index Indonesia language version [master’s thesis]. Universitas Indonesia; 2015.
  19. Fauville G, Luo M, Queiroz ACM, Bailenson JN, Hancock J. Zoom exhaustion & fatigue scale. SSRN Electron J. 2021;1–25.
  20. Caputo GB, Bortolomasi M, Ferrucci R, Giacopuzzi M, Priori A, Zago S. Visual perception during mirror-gazing at one’s own face in patients with depression. Sci World J. 2014;2014:946851.
  21. Kim J, Shin JH, Ryu JK, Jung JH, Kim CH, Lee HB, et al. Association of depression with functional mobility in schizophrenia. Front Psychiatry. 2020;11:854.
  22. Mueller-Schotte S, Bleijenberg N, van der Schouw YT, Schuurmans MJ. Fatigue as a long-term risk factor for limitations in instrumental activities of daily living and/or mobility performance in older adults after 10 years. Clin Interv Aging. 2016;11:1579–87.
  23. Schulze L, Renneberg B, Lobmaier JS. Gaze perception in social anxiety and social anxiety disorder. Front Hum Neurosci. 2013;7:872.
  24. Frosina P, Logue M, Book A, Huizinga T, Amos S, Stark S. The effect of cognitive load on nonverbal behavior in the cognitive interview for suspects. Pers Individ Differ. 2018;130:51–8.
  25. Tomprou M, Kim YJ, Chikersal P, Woolley AW, Dabbish LA. Speaking out of turn: how video conferencing reduces vocal synchrony and collective intelligence. PLoS One. 2021;16(3):e0247655.
  26. Butler DL, Mattingley JB, Cunnington R, Suddendorf T. Mirror, mirror on the wall, how does my brain recognize my image at all? PLoS One. 2012;7(2):e31452.
  27. Chandra SR, Issac TG. Mirror image agnosia. Indian J Psychol Med. 2014;36(4):400–3.
  28. Hall JA, Gunnery SD. 21 gender differences in nonverbal communication. In: Hall JA, Knapp ML, editors. Nonverbal communication. Berlin, Boston: De Gruyter Mouton; 2013. p. 639–70.
  29. Warthen KG, Boyse-Peacor A, Jones KG, Sanford B, Love TM, Mickey BJ. Sex differences in the human reward system: convergent behavioral, autonomic and neural evidence. Soc Cogn Affect Neurosci. 2020;15(7):789–801.
  30. Dobryakova E, Genova HM, DeLuca J, Wylie GR. The dopamine imbalance hypothesis of fatigue in multiple sclerosis and other neurological disorders. Front Neurol. 2015;6:52.
  31. Cordeiro LMS, Rabelo PCR, Moraes MM, Teixeira-Coelho F, Coimbra CC, Wanner SP, et al. Physical exercise-induced fatigue: the role of serotonergic and dopaminergic systems. Brazilian J Med Biol Res. 2017;50(12):e6432.
  32. Yetkin-Arik B, Vogels IMC, Nowak-Sliwinska P, Weiss A, Houtkooper RH, Van Noorden CJF, et al. The role of glycolysis and mitochondrial respiration in the formation and functioning of endothelial tip cells during angiogenesis. Sci Rep. 2019;9:12608.
  33. Useche SA, Montoro LV, Ruiz JI, Vanegas C, Sanmartin J, Alfaro E. Workplace burnout and health issues among Colombian correctional officers. PLoS One. 2019;14(2):e0211447.
  34. Chatlaong T, Pitanupong J, Wiwattanaworaset P. Sleep quality and burnout syndrome among residents in training at the Faculty of Medicine, Prince of Songkla University. Siriraj Med J. 2020;72(4):307–14.
  35. Morris G, Stubbs B, Köhler CA, Walder K, Slyepchenko A, Berk M, et al. The putative role of oxidative stress and inflammation in the pathophysiology of sleep dysfunction across neuropsychiatric disorders: focus on chronic fatigue syndrome, bipolar disorder and multiple sclerosis. Sleep Med Rev. 2018;41:255–65.
  36. Solopchuk O, Sebti M, Bouvy C, Benoit CE, Warlop T, Jeanjean A, et al. Locus coeruleus atrophy doesn’t relate to fatigue in Parkinson’s disease. Sci Rep. 2018;8:12381.
  37. Belujon P, Grace AA. Dopamine system dysregulation in major depressive disorders. Int J Neuropsychopharmacol. 2017;20(12):1036–46.
  38. Ozdogar AT, Kahraman T, Ozakbas S. Smoking is associated with walking, fatigue, depression, and health-related quality of life in persons with multiple sclerosis. Tob Induc Dis. 2018;16(Suppl 3):A21.
  39. Garduño J, Galindo-Charles L, Jiménez-Rodríguez J, Galarraga E, Tapia D, Mihailescu S, et al. Presynaptic α4β2 nicotinic acetylcholine receptors increase glutamate release and serotonin neuron excitability in the dorsal raphe nucleus. J Neurosci. 2012;32(43):15148–57.
  40. Himaja J, Rakesh B. Pathophysiological effects of smoking on cardiovascular system and function: the role of nicotine and carbon monoxide and the benefits of smoking cessation. Int J Pharm Pharm Res. 2016;7(3):360–80.