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Zoom Exhaustion & Fatigue Scale

Zoom Exhaustion & Fatigue Scale
Fauville, G.1, Luo, M.2, Queiroz, A. C. M.2,3, Bailenson, J. N.2, & Hancock, J.2
1Department of Education, Communication and Learning, University of Gothenburg, Sweden
2Department of Communication, Stanford University, USA
3Lemann Center, Stanford University, USA
In 2020, video conferencing went from a novelty to a necessity, and usage skyrocketed due to
shelter-in-place throughout the world. However, there is a scarcity of academic research on the
psychological effects and mechanisms of video conferencing, and scholars need tools to
understand this drastically scaled usage. The current paper presents an empirical creation and
validation of the Zoom Exhaustion & Fatigue Scale (ZEF Scale). In one qualitative study, we
developed a set of interview prompts based on previous work on media use. Those interviews
resulted in the creation of 49 survey items that spanned several dimensions. We administered
those items in a survey of 395 respondents and used factor analyses to reduce the number of
items from 49 to 15, revealing five dimensions of fatigue: general, social, emotional, visual, and
motivational fatigue. Finally, in a scale validation study based on 204 respondents, we showed
the reliability of the overall scale and the five factors and demonstrated scale validity in two
ways. First, frequency, duration, and burstiness of Zoom meetings were associated with a higher
level of fatigue. Second, fatigue was associated with negative attitudes towards the Zoom
meetings. The scale is available for download at
Keywords: video conferences, scale development and validation, fatigue
In March 2020, the World Health Organization declared COVID-19 a pandemic, leading
to the declaration of a public health emergency (WHO, 2020). Public health measures, such as
social distancing, quarantine, and closing places of social contact (e.g., schools and businesses)
were adopted by governments around the world to slow down the spread of the virus
(Nussbaumer-Streit et al., 2020). As a consequence, regular activities individuals usually
performed outside of their home had to be conducted at home. For example, Bick and colleagues
(2020) showed a dramatic increase in the percentage of the US workforce that worked entirely
from home, rising from 8.2% in February 2020 to 35.2% in May 2020.
With individuals sheltered at home and trying to remotely conduct their daily activities
(Nguyen et al., 2021), video conferencing has become a crucial tool for education (Lowenthal et
al., 2020), healthcare (Feijt et al., 2020), and business (Bloom et al., 2021). A prime example is
the rapid rise in the use of Zoom, a video conferencing app, from approximately 10 million daily
Zoom meeting participants in December 2019 to 200 million in March 2020 and 300 million in
April 2020 (Iqbal, 2020; Chawla, 2020).
This thirty-fold increase in video conferences may be part of a growing concern about
exhaustion, with the term “Zoom Fatigue” catching on quickly in the popular media. To our
knowledge, there is little empirical research examining the psychological effects of this uptick in
Zoom usage. Early research (Hinds, 1999) demonstrated that video conferencing increased
cognitive load, compared to voice calls. Moreover, Bailenson (in press) outlines four possible
explanations for nonverbal causes of Zoom Fatigue: extraordinary amount of eye gaze at a close
distance, limited physical mobility, constant viewing of self-video, and increased cognitive load
for senders and receivers. However, to test these hypothetical causes, it is important to create a
rigorous scale to measure fatigue associated with video conferencing. Although objective
outcomes such as behavioral and physiological measures are generally considered more reliable
than self-report measures, a reliable and valid questionnaire is an obvious starting point, and has
benefits in terms of scalability and ease of administering.
Given that the ubiquity of the Zoom platform in video conferences has resulted in
genericization, with many using the word “Zoom” as a verb to replace video conferencing,
similar to “Googling,” we will use the term Zoom Fatigue to refer to fatigue experienced during
or after video conferencing with any platform. For this purpose, we define Zoom fatigue as a
feeling of exhaustion from participating in video conference calls. In the present paper, we
develop the Zoom Exhaustion & Fatigue Scale (ZEF Scale).
Overview of Studies
The goal of the current research is to design and test a scale to measure Zoom Fatigue.
The scale development process involves three phases, guided by the best practices for scale
development by Boateng and colleagues (2018): Item development, scale development and scale
evaluation. Table 1 outlines the five studies from the current project, and how they mapped onto
this framework.
Table 1
Scale Development Overview: Five Studies Across the Three Phases
size (N)
Literature review &
Convenience sample
Pre-testing of items
University research pool
Scale administration
Amazon Mechanical Turk
Test of reliability
University research pool & Lucid
Test of dimensionality
& validity
Convenience sample
Item Generation
Study I: Literature Review and Interviews
The first step of scale development is to define a domain of interest and generate items
that measure different aspects of the defined domain. Study I aims to generate a large and broad
range of potential items for the ZEF Scale that will tap into different dimensions of Zoom
fatigue. To this end, we combined deductive and inductive methods by drawing on theoretical
insights from a literature review and exploring people’s lived experience of Zoom fatigue from
semi-structured interviews.
We created a large pool of potential Zoom fatigue items based on prior literature, the
researchers’ own experience, and existing fatigue scales, such as the Multidimensional Fatigue
Inventory (Smets et al., 1995) and social media fatigue scale (Bright, Kleiser, & Grau, 2015).
Next, we conducted interviews with 10 heavy video conference users (5 women and 5 men) to
identify additional factors that have not been covered in the proposed scale.
Interviewees were between 20 to 59 years old (M = 37.4, SD = 13.8) and included the
following racial/ethnic demographics: three African or African-American or Black, three White,
one Hispanic or LatinX, and three participants identifying with more than one race. The lead
author conducted 10 one-on-one interviews online, with an average duration of 43 minutes (min
= 23, max = 70, SD = 13.3). Participants were compensated with $30 Amazon gift cards.
Transcripts of the interviews were created using the software and then anonymized. In
line with IRB guidelines, audio recordings were destroyed after the study.
At the beginning of each interview, the researcher reiterated the goal of the study and
explained how the interview would be conducted. The researcher shared her screen and
presented a series of slides. Each slide included 4 to 5 questions designed to capture a specific
dimension of Zoom fatigue (e.g., mental fatigue, physical fatigue). For each slide, the
participants were asked to (1) think about how the questions worked together around a given
aspect of Zoom fatigue, (2) suggest items that could be removed, (3) comment on the clarity of
each item. Participants were also prompted to share their own video conferencing experiences.
We followed Willis (2005)’s strategy to conduct two rounds of interviews. We reviewed the
transcripts of the first 5 interviews and revised the initial Zoom fatigue items based on the
feedback. The second round of interviews followed the same procedure to test the revised set of
questions with the other 5 participants. After ten interviews, researchers decided to stop as they
started to observe similar feedback - an indicator of content saturation.
An in-depth literature review and interviews produced a pool of 49 items gathered in 9
thematic constructs related to Zoom fatigue. This large number was consistent with the
recommended number of the initial pool of questions (i.e., two to five times as large as the items
in the final scale; Kline, 1993; Schinka et al., 2012). The initial scale included nine fatigue-
related constructs. The first five constructs were adapted from the Multidimensional Fatigue
Inventory (Smets, et al., 1995). General fatigue (1) refers to the superordinate experience of
being tired (e.g., feeling drained); physical fatigue (2) refers to one’s physical sensation related
to tiredness (e.g., feeling physically only able to do a little); mental fatigue (3) refers to cognitive
symptoms related to fatigue (e.g., feeling hard to concentrate on things); reduced motivation (4)
refers to a lack of motivation to start an activity (e.g., dread having to do things); reduced activity
(5) refers to a tendency to be less active (e.g., get little done). Visual fatigue (6) is defined by the
National Research Council Committee on Vision as "any subjective visual symptom or distress
resulting from use of one's eyes" (1983, p.153) and is measured by Tyrrell & Leibowitz (1990)
with items such as “my vision seems blurry”. Vocal fatigue (7) refers to issues related to
speaking, including the throat, and was adapted from the Vocal Fatigue Index (VFI)
(Nanjundeswaran et al., 2015) with items such as “My voice feels tired when I talk more”.
Emotional fatigue (8), defined as “the state of feeling overwhelmed, drained and used up”
(Maslach, 1982, p.2), occurs after interactions with other people (Wright & Cropanzano, 1998)
and includes items based on emotional symptoms related to fatigue, such as moodiness and
irritability (Department of Health & Human Services, 2015). Social fatigue (9) refers to feelings
of wanting to be alone, which is derived from the interview and researchers’ experiences.
Scale Development
In this phase, our goal was to statistically examine the 49 created items, reduce items, and
test models of the ZEF scale.
Study II: Pre-testing of items
We conducted this pilot study to assess the readability of the 49 items created in Study I.
The 49 items were piloted with 52 Stanford students to get additional feedback on the survey
experience as a whole. The survey was administered through the Qualtrics platform. Participants
(50% female, 50% male) were between 18 and 27 years old (M = 20.35, SD = 1.81). The
distribution of ethnic backgrounds was: 40.4% of White (n = 21), 15.4% of Asian or Asian-
American (n = 8), 13% of African or African-American or Black (n = 7), 3.9% of Hispanic or
LatinX (n = 2), 9.6% Native Hawaiian or Pacific Islander (n = 5), 17.3% identifying with more
than one race (n = 9).
In addition to the 49 fatigue questions, participants were asked to provide additional
comments on the clarity and readability of items and to indicate their use frequency of video
conferencing. Since some of our sample used video conferencing less than once a day, we
decided in the remaining studies to focus on people who use video conferences at least once a
day to increase the likelihood of capturing one’s Zoom fatigue experience. Therefore, in future
studies, we added a screening question at the beginning of the survey to reflect this change, and
only included participants who attend video conferences on a typical day.
Study III: Scale Administration
The purpose of this study was to reduce the number of items and perform confirmatory
factor analyses (CFA) to test our proposed structural model.
A total of 395 participants were recruited online through Amazon’s Mechanical Turk
worker system. This sample size was consistent with the recommended size in prior literature
(Comrey, 1988; Guadagnoli & Velicer, 1988). Each participant was compensated $2.50 for
completing the questionnaire. The sample included 37% female (n = 148), 62% male (n = 243)
and 1% of participants who identified neither as male nor female (n = 4). The age ranged from
18 to 70 years old (M = 30.05, SD = 9.13). The distribution of race/ethnic backgrounds was:
56.7% of White (n = 224), 16% of Asian or Asian-American (n = 63), 10.4% of African or
African-merican or Black (n = 41), 8.1 % of Hispanic or LatinX (n = 32), 4.5% identifying with
more than one race (n = 18), 2% declined to answer (n = 8), 1.5% Middle Eastern (n = 6), 0.5%
Native Hawaiian or Pacific Islander (n = 2), 0.25%, and one Indigenous or Native American
participant (n = 1). Forty-five percent of the sample reported using video conferences once a day
(n = 176) whereas 55% reported using video conferences multiple times a day (n = 219).
Upon consenting to participate, participants were initially asked how often they used
video conferences. A minimum of attending video conferences daily was required to proceed
with the study. Participants who failed the attention check questions or used video conferences
less than daily were removed from data analysis, leaving a final sample of 395.
Participants were then introduced to the 49-item ZEF scale (see Appendix 1) and asked to
indicate their level of fatigue on a five-point Likert-type scale from 1 = “Not at all” to 5 =
“Extremely”. The order of the items was randomized.
All analyses were conducted in statistical language in R software (version 1.3.1093).
First, item reduction analysis was performed to develop a parsimonious scale with internally
consistent items (Thurstone, 1947; Boateng et al., 2018). We followed the Classical Test Theory
(CTT) to exclude items based on their inter-item and item-total correlations. Out of the 49 items,
8 were removed due to their low item-total correlation (<.3). Then, we calculated the mean inter-
item correlation to test whether the remaining items were reasonably homogeneous while
containing sufficient unique variance. The mean inter-item correlation was within the acceptable
range from .2 to .4 (r = .33).
Second, we conducted a series of iterative second-order confirmatory factor analyses
(CFA) to test our theoretical structural model. The predicted nine-factor model with the
remaining 41 items was tested. In the first CFA, 18 items with loadings lower than .7 were
removed. Since all the items from vocal fatigue were removed, this construct was removed as
well. A new model with 8 constructs and 24 items was tested. Nine additional items were
removed due to low factor loadings and the 15 remaining items focused on 5 constructs: general,
visual, social, motivational, and emotional fatigue. The remaining two items from the general
fatigue construct (gen_1 and gen_5) were merged with the remaining item from the mental
fatigue construct (men_1), creating the construct general fatigue. The two remaining items from
the reduced motivation construct (redmot_2 and redmot_4) were merged with the only remaining
item from the reduced activity construct (redac_5), creating the construct of motivational fatigue.
This resulted in the following CFA model with good fit metrics: CFI = .942, TLI = .929,
RMSEA = .086 and SRMR = .039, X2 (85) = 332.1. Finally, Cronbach's alphas were calculated
for each of the 5 remaining constructs, which indicated good reliability (all ɑ > .8; see Table 2).
Table 2
Descriptive Statistics, Factor Loadings and Cronbach Reliability of the 15 Items in the ZEF
General Fatigue
I feel tired
I feel exhausted
I feel mentally drained
Visual Fatigue
my vision gets blurred
my eyes feel irritated
I experience pain around
my eyes
Social Fatigue
I avoid social situations
I just want to be alone
I need time by myself
I dread having to do things
I don’t feel like doing
I often feel too tired to do
other things
I feel emotionally drained
I feel irritable
I feel moody
Note. The prompt for the items was “After video conferencing…”
With the reduction of the scale from 49 to 15 items, we reworded the items to become
individual questions with construct-specific response options. These 15 items across 5 constructs
constitute the final ZEF Scale and are presented in Table 3. All items are measured on 5- point
Likert-scale ranging from 1 = “Not at all”, 2 = “Slightly”, 3 = “Moderately”, 4 = “Very” to 5 =
“Extremely” except for the two frequency questions (marked with asterisks) from 1 = “Never”,
2 = “Rarely”, 3 = “Sometimes”, 4 = “Oftento 5= “Always”.
Table 3
Survey Questions for the ZEF Scale
How tired do you feel after video conferencing?
How exhausted do you feel after video conferencing?
How mentally drained do you feel after video conferencing?
Visual Fatigue
How blurred does your vision get after video conferencing?
How irritated do your eyes feel after video conferencing?
How much do your eyes hurt after video conferencing?
Social Fatigue
How much do you tend to avoid social situations after video conferencing?
How much do you want to be alone after video conferencing?
How much do you need time by yourself after video conferencing?
How much do you dread having to do things after video conferencing?
How often do you feel like doing nothing after video conferencing? *
How often do you feel too tired to do other things after video conferencing? *
How emotionally drained do you feel after video conferencing?
How irritable do you feel after video conferencing?
How moody do you feel after video conferencing?
Scale Evaluation
Study IV: Test of Reliability
Study IV aims to assess the internal consistency of the revised version of the ZEF scale
using independent samples.
Participants were recruited from the Lucid platform - an aggregator of survey respondents
from multiple sources – and a student research pool at Stanford University. Participants were
qualified to answer the survey if they reported using video conferences more than “once a day”
in a screening question. Participants who failed the attention check question were directly
terminated and no data was recorded for them. A total of 114 participants took part in this study
(47 students, 57 recruited from Lucid). The participants (58% female, 41% male, 1% identifying
neither as female nor male) were between 18 and 62 years old (M = 29.35, SD = 11.45). The
distribution of race or ethnic backgrounds among participants was as follows: 47% of White (n =
49), 15.4% of African or African-American or Black (n = 16), 19.2% of Asian or Asian-
American (n = 20), 7.7% of Hispanic or LatinX (n = 8), 7.7% of participants identifying with
more than one race (n = 8), 1.9% of Middle Eastern (n = 2) and 1% of Native Hawaiian or
Pacific Islander (n = 1). Participants who failed the two attention check questions were removed
from the data analysis.
The Cronbach’s alphas were calculated for each of the five constructs of fatigue (each
including three items). The reliability for each construct was above .8 (general fatigue: ɑ = .88,
visual fatigue: ɑ = .88, social fatigue: ɑ = .84, motivational fatigue: ɑ = .83, emotional fatigue: ɑ
= .86), indicating a good scale reliability.
The ZEF Score is the averaged rating across the 15 fatigue items and showed high
reliability (ɑ = .95), which is significantly correlated with each of the five constructs of the scale
(see Table 4 for the bivariate correlations).
Table 4
Means, SDs, and Bivariate Correlations Among the ZEF Score and 5 Constructs of Zoom
1. ZEF Score
2. General
3. Emotional
4. Visual
5. Motivational
6. Social
Note. N = 104, ZEF Score = average scoring of 15 items.
*** p < .001 (two-tailed)
Study V: Test of Dimensionality and Validity
The final study aims to assess the validity of the ZEF scale. We investigated if Zoom
fatigue is correlated to two theoretically similar constructs – frequency of use and attitude
towards video conferencing – to evaluate the scale’s convergent validity. Prior literature
suggested a positive association between fatigue and the use of the given technology, such as the
duration of internet use (Dol, 2016) and social media overuse (Sanz-Blas, Buzova, & Miquel-
Romero, 2019). Therefore, we predicted that longer and more frequent use of video conferencing
may be associated with higher levels of fatigue. We also predicted that individuals who feel more
fatigued will have more negative attitudes towards the medium than those who feel less fatigued.
Although feelings of fatigue may not necessarily correspond to negative affect (i.e., a rewarding
day of work or a long walk can be tiring and positive at the same time), our qualitative
interviews demonstrated that people who felt overusing Zoom tend to view video conferencing
A total of 204 participants were recruited through the snowball sampling technique.
Members of the current research team distributed the online survey via email to their students
and colleagues, who were in turn referred to their networks of video conferencing users.
Participants (68% female, 30% male, 1.5% identifying neither as female nor male, 1% declining
to answer) were between 18 and 75 years old (M = 38.3, SD = 10.7). The distribution of ethnic
backgrounds was: 61.7% of White (n = 126), 15.7% of Asian or Asian-American (n = 32), 4.9%
of Hispanic or LatinX (n = 10), 1.6% of Middle Eastern (n = 3), 10.8% of participants
identifying with more than one race (n = 22), 3.4% of African or African-American or Black (n =
7), and 2.1% declined to answer (n = 4). Participants who failed the two attention check
questions were removed from the data analysis.
In addition to the 15-item multidimensional ZEF scale (see Table 3 for all items),
attitudes toward video conferencing, and three measures of the use of video conferencing were
also included in the survey.
Attitudes. Attitude toward video conferences was measured on a three-item Likert-scale
(i.e., “How much do you like participating in video conferences”, “How much do you feel like
video conferences are a burden?”, and “How much do you enjoy video conferences”) ranging
from 1 = “Not at all” to 5 = “Extremely”.
Frequency. Participants were asked to indicate “On a typical day, how many video
conferences do you participate in” on a 7-point Likert-scale ranging from 1 = “1” to 7 = “7 and
Duration. Participants were asked to indicate “on a typical day, how long does a typical
video conference last on a 5-point Likert-scale ranging from 1 = “Less than 15 minutes”, 2 =
“15 to 30 minutes”, 3 = “30 to 45 minutes”, 4 = “45 minutes to an hour”, and 5 = “More than
an hour”.
Burstiness. Participants were asked to indicate “on a typical day, how much time do you
have between your video conferences?” As frequency, duration and burstiness are used to
measure the level of intensity of the video conferences experience, burstiness was reversed coded
as less time between meetings indicating high burstiness. The response options range from 1 =
“More than an hour”, 2 = “45 minutes to an hour”, 3 = “30 to 45 minutes”, 4 = “15 to 30
minutes”, and 5 = “Less than 15 minutes”.
Factor analysis of the ZEF scale. To test the dimensionality of the scale, a confirmatory
factor analysis was firstly used to examine the model’s goodness of fit. A second-order 5-factor
(i.e., general, visual, social, motivational, and emotional fatigue) model was tested. The model
revealed a good fit and supported the 5-factor structure in this diverse adult sample: CFI = .958,
TLI = .949, RMSEA = .076 and SRMR = .050, X2 (85) = 185.17. The loading of each item onto
their construct and of each construct onto the ZEF score are presented in Table 5 along with the
reliability of each construct and their means and standard deviations.
Table 5
Descriptive Statistics, Factor Loadings, and Cronbach Alphas of the ZEF Scale Items
How tired do you feel after video
How exhausted do you feel after video
How mentally drained do you feel after
video conferencing?
How blurred does your vision get after
video conferencing?
How irritated do your eyes feel after video
How much do your eyes hurt after video
How much do you tend to avoid social
situations after video conferencing?
How much do you want to be alone after
video conferencing?
How much do you need time by yourself
after video conferencing?
How much do you dread having to do
things after video conferencing?
How often do you feel like doing nothing
after video conferencing?
How often do you feel too tired to do
other things after video conferencing?
How emotionally drained do you feel after
video conferencing?
How irritable do you feel after video
How moody do you feel after video
Note. N = 204
Analysis of reliability. Similar to Study IV, the ZEF Score and each factor of the ZEF
scale are significantly correlated, suggesting high internal reliability of the scale (see Table 6 for
the bivariate correlation matrix).
Table 6
Means, SDs, and Bivariate Correlations Among the ZEF Score and Each Construct of Zoom
1. ZEF Score
2. General
3. Emotional
4. Visual
5. Motivational
6. Social
Note. N = 204, *** p < .001
Scale validity. To assess convergent validity, the correlations between the ZEF Score,
which is the average rating of all items on the ZEF scale, video conference attitude, and video
conference use were examined. As shown in Table 7, attitude was significantly negatively
correlated to the ZEF Score [r(202) = -.57, p < .001], suggesting that a higher level of Zoom
fatigue corresponds to a lower positive attitude toward video conferences. Similarly, consistent
with our hypotheses, the ZEF Score was positively correlated to the three measures of video
conferencing use: a higher level of fatigue is associated with having more meetings (frequency, r
(202) = .23, p < .005), longer meetings [duration, r(202) = .17, p <.05], and the tendency to
cluster meetings together without breaks in between [burstiness; r(202) = .17, p <.05], suggesting
high convergent validity.
Table 7
Means, SDs, and Bivariate Correlations Among the ZEF Score and Variables for Validity Tests
1. ZEF Score
2. Attitude
3. Frequency
4. Duration
5. Burstiness
Note. N = 204; * p < .05, ** p < .01, *** p < .001,
Finally, we used a linear regression to predict the ZEF Score with the three measures of
video conferencing use, frequency, duration and burstiness, as predictors. The omnibus model
was significant, F (3, 200) = 7.99, p < .001, 95% CI [.47, 1.90], adjusted R2 = .094. Controlling
for the other two types of video conferences use, both duration (β = .28, SE = .08, p < .001) and
frequency (β = .16, SE = .05, p = .002) were significant predictors of the ZEF Score, whereas
burstiness was not significant (β = -.02, SE = .05, p = .65). To examine the potential interactions
of video conferencing use measures, another linear regression was modeled with a three-way
interaction to predict the ZEF Score. A comparison between the full and reduced model suggests
a non-significant interaction effect, F(4, 196) = 1.16, p = .33, 95% CI [-2.37, 2.49].
General Discussion
Current research outlines the process and results of the development and validation of the
ZEF Scale (freely available for use). In four studies, which included over 700 participants, we
created a scale examining Zoom fatigue and provided initial evidence for the scale validity. The
final scale involves 15 items measuring 5 aspects of fatigue experienced in video conferences,
which were found reliable across multiple studies. Moreover, the ZEF scale has been validated
by both frequency of video conferencing use and attitudes towards video conferencing. People
who have more and longer meetings tend to feel more fatigued than those with fewer and shorter
meetings. Moreover, people who feel more fatigued after a video conference tend to have a more
negative attitude towards it.
The current research has limitations. First, while we employed a number of strategies to
ensure a diverse population of respondents, such as recruiting participants from several sources,
some races or ethnic groups were underrepresented. Second, the five dimensions of the scale
highly correlate with one another, and thus are likely to be dependent. Finally, the current
research did not examine all types of validities, such as predictive validity and discriminant
In addition to a systematic assessment of scale validity, future work could also employ
the ZEF scale to examine the potential causes and outcomes of Zoom fatigue. For example, our
initial qualitative interview suggested a few potential predictors of Zoom fatigue, such as
perceived gaze, self-presentation concerns, and immobility. We plan to empirically test these
hypotheses as the next step. Future work could also explore how people in different contexts
(e.g., work vs. socializing, size of the video conferencing) or individual differences (e.g., gender,
personalities) may experience Zoom fatigue differently. We also want to empirically investigate
the cost-benefit ratio of video conferencing, given it is one of the main channels people have for
social interaction.
In sum, the present research provides a valid and reliable measure for the Zoom Fatigue
that is available to employ by other researchers interested in this field. In the emerging media
era, the fact that increasing people have seamlessly integrated Zoom and other video
conferencing technologies into their work and social lives has posed important questions such as
when, how, and why Zoom fatigue occurs, as well as how to mitigate the fatigue effectively. We
encourage more future work on this topic to advance this new line of research because it will
have practical implications on interpersonal communications in video conferences and interface
designs of the platforms.
This research was partially supported by two National Science Foundation grants (IIS-1800922
and CMMI-1840131) and by the Knut och Alice Wallenbergs Stiftelse #20170440. Moreover,
we are thankful for assistance in this research from Jet Toner, Tobin Asher, Sunny Liu
and Carlyn Strang.
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Appendix 1
Table 1
Original 49 Items Tested
After participating in a video conference:
(not at all - Slightly - Moderately - Very - Extremely)
General Fatigue
gen_1: I feel tired
gen_2: I feel rested (Reversed)
gen_3: I feel energized (Reversed)
gen_4: I feel refreshed (Reversed)
gen_5: I feel exhausted
gen_6: I need to take a nap
Physical Fatigue
phy_1: can take on only a little physically
phy_2: I can take on a lot physically (Reversed)
phy_3: I feel restless
phy_4: my back hurts
phy_5: my neck hurts
phy_6: my body feels tired
Mental Fatigue
men_1: I feel mentally drained
men_2: I can concentrate well (Reversed)
men_3: it takes a lot of effort to concentrate on my next tasks
men_4: my thoughts easily wander
men_5: I am able to think clearly (Reversed)
Visual Fatigue
vis_1: I often get a headache
vis_2: my vision gets blurred
vis_3: my eyes feel fine (Reversed)
vis_4: my eyes feel irritated
vis_5: I experience pain around my eyes
vis_6: I experience a burning or pricking sensation in the eyes
Vocal Fatigue
voc_1: I feel like talking (Reversed)
voc_2: my voice feels tired
voc_3: I tend to generally limit my talking
voc_4: my throat aches with voice use
voc_5: my voice feels strong (Reversed)
voc_6: my voice gets hoarse
voc_7: it feels like work to use my voice
Social Fatigue
soc_1: I avoid social situations
soc_2: I just want to be alone
soc_3: I crave seeing other people (Reversed)
soc_4: I feel like engaging with other people is effortless (Reversed)
soc_5: I need time by myself
Reduced Activity
redac_1: I feel very active (Reversed)
redac_2: I feel like I can do a lot (Reversed)
redac_3: I get little done
redac_4: I need to take a break
redac_5: I often feel too tired to do other things
Reduced Motivation
redmot_1: I feel like doing all sorts of things (Reversed)
redmot_2: I dread having to do things
redmot_3: I feel like making plans (Reversed)
redmot_4: I don’t feel like doing anything
Emotional Fatigue
emo_1: I feel emotionally drained
emo_2: I feel irritable
emo_3: I feel moody
emo_4: I feel excited (Reversed)
emo_5: I feel happy (Reversed)
... One study has developed an online survey to measure fatigue that comes with VCs. 6 This questionnaire was formed on the assumption that there are 4 nonverbal elements (eye gaze, cognitive load, all day mirror, and physical mobility constraints) that may contribute to VCF, emphasizing that these elements are amplified in virtual interface as opposed to face-to-face (FTF) interactions. 14 Online meetings were presumed to increase load on eye gaze in that there is decreased interpersonal distance (referring to the space between the user and device monitor), increased size of the faces from the grid configuration, and increased duration of eye contact in zoom meetings. ...
... 17,18 Inspired by these arguments, a 15-item questionnaire Zoom Fatigue and Exhaustion (ZEF) tool was designed to measure fatigue experienced by consumers after attending VCs. 6 The rigorous scale development process involved literature review and interviews that initially produced 49 items with 9 thematic constructs related to VCF. 6 Some themes, namely general fatigue, physical fatigue, mental fatigue, reduced motivation, and reduced activity, were adapted from the Multidimensional Fatigue Inventory. 19 Other themes included visual fatigue, vocal fatigue, emotional fatigue, and social fatigue. ...
... 17,18 Inspired by these arguments, a 15-item questionnaire Zoom Fatigue and Exhaustion (ZEF) tool was designed to measure fatigue experienced by consumers after attending VCs. 6 The rigorous scale development process involved literature review and interviews that initially produced 49 items with 9 thematic constructs related to VCF. 6 Some themes, namely general fatigue, physical fatigue, mental fatigue, reduced motivation, and reduced activity, were adapted from the Multidimensional Fatigue Inventory. 19 Other themes included visual fatigue, vocal fatigue, emotional fatigue, and social fatigue. ...
... There are also some solutions from similar studies, such as avoiding multitasking, enough rest, and using the speaker view feature to keep the focus on the person that is talking . Even though some solutions have already been discovered for the problems before, previous studies tend to discuss only the cause and effect of Zoom Fatigue on students' health (Fauville et al., 2021b). Therefore, this study discusses the aspect that hasn't been discussed in the previous study, which is about the impact of Zoom Fatigue on health, such as physical and mental health, including exhaustion, saturation, inconvenience and anxiety moreover, eyesight issues, spine problems, also headache and learning loss with the decrease of productivity, motivation, understanding, concentration, also academic value as an indicator in student. ...
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Covid-19 pandemic causes all face-to-face activities converted to distance or virtual meetings, including educational learning system. These days, the majority educational institutions in Indonesia still implement distance learning via video conference platforms. This primary research aims to discover how Zoom Fatigue affects health and causes learning loss in university students across Java. The methodologies used in this research are qualitative and quantitative. To specify this research, the population is set to students who study at several universities in Java, with 50 samples being Multimedia Nusantara University students and the other 50 sample are being students from other universities in Java. The techniques used to collect data are observation and questionnaire via Google Forms. After analyzing responses, the result shows that most students experience health issues such as eyesight issues, exhaustion, and saturation. Moreover, most students also go through some issues when it comes to the learning process, they experience a decrease of learning motivation and have trouble concentrating. Nevertheless, their academic records are stable and some even stated that their grades are increasing when compared to face-to-face learning systems, this fact shows that in-class learning is not the only source of information used by students to support their academic performance, but there are other sources of information such as internet where students can look up information from online article and scientific journals.
... El método empleado es de carácter cuantitativo, empleándose como instrumento el cuestionario, para evaluar la fatiga digital través de la escala Zoom Exhaustion & Fatigue Scale (ZEF) (Fauville et al., 2021). ...
Conference Paper
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Los modelos son utilizados para referirse a prototipos o un punto de referencia que actúa como un “patrón” para recrear objetos. En el campo educativo, los modelos tienen la función de encontrar soluciones a problemas recurrentes en las experiencias de enseñanza-aprendizaje. Por tanto, los modelos facilitan la creación de un patrón en las soluciones y forman un lenguaje común para la creación de nuevos modelos. En el campo del diseño de itinerarios personales de aprendizaje, un modelo para su formalización facilitaría a los docentes encontrar soluciones a problemas frecuentes en su diseño. El objetivo de esta contribución es reportar los resultados preliminares sobre la percepción del alumnado universitario respecto a la incorporación de itinerarios personales de aprendizaje con el modelo ACDGE. Este trabajo corresponde a un avance de la tesis doctoral DISEÑO DE UN MODELO PARA LA FORMALIZACIÓN DE ITINERARIOS PERSONALES DE APRENDIZAJE.
... El método empleado es de carácter cuantitativo, empleándose como instrumento el cuestionario, para evaluar la fatiga digital través de la escala Zoom Exhaustion & Fatigue Scale (ZEF) (Fauville et al., 2021). ...
Conference Paper
Full-text available
Las tendencias en educación centradas en el alumno, en términos generales, están diseñadas para que el alumno pueda elegir cuando, de qué modo aprender, y a su vez, construir su propia definición de aprendizaje. Una estrategia que favorece esta tendencia son los itinerarios personales de aprendizaje. Los itinerarios son potentes organizadores de los temas/conceptos a aprender, los objetos de aprendizaje a utilizar y la evaluación a desarrollar. Están conformados por secuencias de aprendizaje que el propio alumno elige según sus características individuales (necesidades, estilo de aprendizaje, etc.). Esta contribución tiene por objetivo reportar los resultados preliminares de un estudio de validación por juicio de expertos aplicado a un modelo para gestionar itinerarios personales de aprendizaje, en el marco de la tesis doctoral DISEÑO DE UN MODELO PARA LA FORMALIZACIÓN DE ITINERARIOS PERSONALES DE APRENDIZAJE.
... This could be even more so after the outbreak of the COVID-19 pandemic required workers to avoid face-to-face meetings at all, contributing to a less important role of social support as potential job resource as we found in this research. Another explanation could be offered by the phenomenon of zoom fatigue (Fauville et al., 2021). Workers are exhausted by the number of video calls that are scheduled already and think twice if they would start another call to ask for social support. ...
The digitalized work environment poses challenges to the workforce, such as the meaningful use of ever-new technological advances, dealing with increasingly complex tasks, or effective collaboration in dispersed work groups. The individual worker needs to adapt to rapidly increasing demands due to far-reaching changes in the workplace, to complete their everyday work tasks. However, there is an increasing discrepancy between the existing and required digital competencies in the workforce. Due to the urgent need to expand the scientific knowledge on this important topic, the main focus of this dissertation is on the development and measurement of the construct of digital competencies at work. In the scientific literature, a comprehensive framework that integrates the perspectives of prior research and practitioners in a work context has not been developed yet. Additionally, a common definition of digital competencies at work was still lacking although many wordings have been used for the concept. Modern work practices, such as the ubiquity of remote work for office workers emphasize the importance of digital communication and collaboration competencies at work. Yet, to date, there was no measurement tool for individual digital communication and collaboration competencies at work that is needed to conduct more scientific research on the construct. Another research gap derived from the results of the prior studies in this dissertation measuring digital competencies: The high mean values in all collected data sets led to the assumption that office workers might over-estimate their digital competencies. However, the research question of how the self-assessment of workers’ digital communication and collaboration competencies can be influenced by varying instructions has not yet been explored in an experimental study. Moreover, to further explore the nomological net of the construct, the relationship between digital communication and collaboration competencies and the motivation to train those were investigated. In my dissertation, I realized the collection of quantitative and qualitative data in nine samples and conducted a literature review to address the outlined research gaps. By integrating perspectives from research and practice and combining diverse methods, a coherent and detailed framework of digital competencies at work was created and a definition of the concept was provided in Paper 1. As depicted in Paper 2, building on the theoretical framework and prior research, digital communication and collaboration competencies were identified as dimensions with particular relevance to the challenges of today’s work environments. By using mixed methods, a measurement tool for digital communication and collaboration competencies was developed. The role of those competencies as potential resources in a gain spiral with social support, ultimately boosting work engagement in the unique setting of a pandemic that fundamentally altered the way of work worldwide based on the Job Demands-Resource model (Demerouti et al., 2001) and the conservation of resources theory (Hobfoll, 2011) was explored. Although results did not support the assumption of a gain spiral, we found that digital competencies, social support, and work engagement were stable and high during the crisis. The findings add knowledge about the motivational processes of workers in times of crisis. Subsequently, in Paper 3 the initial measurement tool was refined into a reliable and valid short-scale of digital communication and collaboration competencies at work. In several studies, the short-scale was validated and the nomological net of the constructs was explored. The last part of my dissertation is dedicated to the systematic examination of the effect that varying instructions have on workers’ self-assessment of digital communication and collaboration competencies and the motivation to train those. The results imply that the self-assessment of competencies and the motivation to train those cannot be influenced easily by varying instructions. Nevertheless, workers with high levels of digital communication and collaboration competencies also showed high motivation to train those. The findings of this dissertation provide a solid base for further theory building and extension in research on digital competencies at work. The insights gained from the studies of this dissertation comprise theoretical and practical implications for training development and human resource management. Overall, the results of this dissertation imply that digital competencies at work could be an important benefit in meeting the challenges of today’s digital work environments. The concept of digital competencies at work deserves more attention in future research.
Hybrid meetings are the standard solution to balance remote and on-site work. They are built around on-site participants, and online and on-demand (offline) participants are at risk of being ignored. This paper set out to determine how hybrid meetings could be more engaging and interactive. As a solution, the Tribrid-Meeting-Setup (TMS) is presented. The TMS allows online participants to have an active role during the meeting by using a Telepresence-Robot (TPR) and give an interactive perspective of the meeting to the on-demand (offline) participants by recording the meeting in a 360° video format. An iterative prototype development approach was implemented for designing the TMS. This article summarizes how to build a TMS, the results of the first test using the TMS, and further recommendations for the TMS.KeywordsRobotsAvatars and Virtual HumanRemote WorkHybrid MeetingsTelepresence RobotsInteractive Video Conferencing
One of the most important things for successful projects is communication. Especially in agile software development face-to-face communication plays a central role. Face-to-face conversations are also one of the most direct and most emotional forms of human interaction. Facial expressions communicate emotions to others and can have a strong impact on conversations, perception, and decision-making. Due to the Covid-19 pandemic and digitalization more and more meetings and workshops take place virtually or hybrid. Many social cues and non-verbal signals are more difficult to share in a virtual set-up or are getting lost completely. Since face-to-face communication and facial expressions are so important for the success of a project and the perception of emotions, we are trying to find ways to bridge the gap of emotion recognition between on-site meetings and videoconferences. We are investigating, how software could support participants of videoconferences to improve virtual communications efficiently with empirical and qualitative research.In this paper, we present the results of two experimental user studies of facial expression recognition (FER) during videoconferences. In one study we did an analysis of emotion recognition after the meeting with a FER-tool, a human observer, and self-assessments of the participants. In another study, we visualized emotional states in real-time during a business meeting with the help of human observers and a simulation. We realized that both, humans, and the FER-tool, had difficulties to recognize emotions in specific situations and that there is room for improvement. In general, positive emotions were easier to identify than negative ones. Most participants were open to visualizing emotions and considered the information during a meeting as helpful, but also mentioned that it can be distracting. Therefore, a suitable form of emotion recognition and visualization which takes the productivity and wellbeing of the participants into account needs to be developed and evaluated.KeywordsFacial expression recognitionfacial expressionshuman emotionsprofessional environmentvirtual human interactionvideoconferences
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El uso continuo de los sistemas de videoconferencia para llevar a cabo el proceso de enseñanza-aprendizaje en las instituciones de educación superior, durante la pandemia derivada de la Covid-19, ha influido negativamente en el aprendizaje de los estudiantes universitarios, provocando fatiga digital. Esta fatiga afecta principalmente a la vista, estado emocional, motivacional y social. El objetivo de este trabajo fue determinar el grado de fatiga digital derivada de la exposición prolongada a sistemas de videoconferencia de los estudiantes universitarios. Para ello, se aplicó un diseño de estudio transversal a partir de la distribución de una encuesta en línea. En el estudio participaron un total de 613 estudiantes universitarios, con edades comprendidas entre los 18 y 35 años (M = 21,54; DT = 3,85). Los resultados obtenidos revelaron que: 1) el grado de prevalencia de la fatiga digital en los estudiantes universitarios fue medio-alto; 2) las variables sociodemográficas vinculadas a ser mujer, cursar estudios de la rama de Artes y Humanidades, pasar más tiempo frente a un dispositivo electrónico y conectarse a través de un ordenador portátil fueron indicadores de una mayor tasa de fatiga digital; 3) el sexo y el consumo elevado de horas fueron variables predictores de la fatiga visual, fatiga social, fatiga motivacional y fatiga emocional; 4) la fatiga visual, fatiga motivacional y fatiga emocional, junto a la rama de conocimiento de los estudios cursados, influyeron de forma significativa en la fatiga general. Finalmente, se discuten las futuras líneas de investigación de este trabajo, destacando la riqueza de los datos obtenidos para avanzar en el conocimiento sobre la fatiga digital y su influencia en el aprendizaje universitario.
Face-to-face education activities were transferred to online environments for the last two years. It is important to determine the exhaustion and fatigue levels of students in the education performed with video conferencing systems. The validity and reliability study was conducted to measure screen fatigue of students in the risk group of “Zoom Exhaustion and Fatigue Scale”. This methodological and descriptive study were conducted with 317 students. Psychometric analyses of the scale and its adaptation to Turkish were carried out. A total of 317 students, 82.3% (n=261) female participated in the research. The mean age was 20.02±1.52. The validity and reliability analyzes of the scale were determined to consist of 15 items and 5 subscales. Confirmatory factor analysis results indicated acceptable goodness-of-fit indices. The Cronbach's Alpha Coefficient of the scale was found to be 0.89. Turkish version of the scale is an appropriate tool which can be used to measure the zoom fatigue that may occur because of the video conferencing systems used in courses in the education of students.
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The COVID-19 pandemic forced colleges and universities to move all in-person courses to a remote or online learning format. As a result, many faculty, including teacher educators , opted to transition their courses to live synchronous web meetings using web conferencing tools like Zoom. Despite benefits of synchronous communication, there are constraints with the use and overuse of synchronous live meetings (which many teacher educators ended up experiencing during the pandemic). In this paper, we describe the experiences of how four different faculty, at four different universities, used asynchronous video to maintain connection and engagement during the COVID-19 pandemic. We conclude with implications for practice and future research.
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Background: Coronavirus disease 2019 (COVID-19) is a rapidly emerging disease classified as a pandemic by the World Health Organization (WHO). To support the WHO with their recommendations on quarantine, we conducted a rapid review on the effectiveness of quarantine during severe coronavirus outbreaks. Objectives: To assess the effects of quarantine (alone or in combination with other measures) of individuals who had contact with confirmed or suspected cases of COVID-19, who travelled from countries with a declared outbreak, or who live in regions with high disease transmission. Search methods: An information specialist searched the Cochrane COVID-19 Study Register, and updated the search in PubMed, Ovid MEDLINE, WHO Global Index Medicus, Embase, and CINAHL on 23 June 2020. Selection criteria: Cohort studies, case-control studies, time series, interrupted time series, case series, and mathematical modelling studies that assessed the effect of any type of quarantine to control COVID-19. We also included studies on SARS (severe acute respiratory syndrome) and MERS (Middle East respiratory syndrome) as indirect evidence for the current coronavirus outbreak. Data collection and analysis: Two review authors independently screened abstracts and titles in duplicate. Two review authors then independently screened all potentially relevant full-text publications. One review author extracted data, assessed the risk of bias and assessed the certainty of evidence with GRADE and a second review author checked the assessment. We used three different tools to assess risk of bias, depending on the study design: ROBINS-I for non-randomised studies of interventions, a tool provided by Cochrane Childhood Cancer for non-randomised, non-controlled studies, and recommendations from the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) for modelling studies. We rated the certainty of evidence for the four primary outcomes: incidence, onward transmission, mortality, and costs. Main results: We included 51 studies; 4 observational studies and 28 modelling studies on COVID-19, one observational and one modelling study on MERS, three observational and 11 modelling studies on SARS, and three modelling studies on SARS and other infectious diseases. Because of the diverse methods of measurement and analysis across the outcomes of interest, we could not conduct a meta-analysis and undertook a narrative synthesis. We judged risk of bias to be moderate for 2/3 non-randomized studies of interventions (NRSIs) and serious for 1/3 NRSI. We rated risk of bias moderate for 4/5 non-controlled cohort studies, and serious for 1/5. We rated modelling studies as having no concerns for 13 studies, moderate concerns for 17 studies and major concerns for 13 studies. Quarantine for individuals who were in contact with a confirmed/suspected COVID-19 case in comparison to no quarantine Modelling studies consistently reported a benefit of the simulated quarantine measures, for example, quarantine of people exposed to confirmed or suspected cases may have averted 44% to 96% of incident cases and 31% to 76% of deaths compared to no measures based on different scenarios (incident cases: 6 modelling studies on COVID-19, 1 on SARS; mortality: 2 modelling studies on COVID-19, 1 on SARS, low-certainty evidence). Studies also indicated that there may be a reduction in the basic reproduction number ranging from 37% to 88% due to the implementation of quarantine (5 modelling studies on COVID-19, low-certainty evidence). Very low-certainty evidence suggests that the earlier quarantine measures are implemented, the greater the cost savings may be (2 modelling studies on SARS). Quarantine in combination with other measures to contain COVID-19 in comparison to other measures without quarantine or no measures When the models combined quarantine with other prevention and control measures, such as school closures, travel restrictions and social distancing, the models demonstrated that there may be a larger effect on the reduction of new cases, transmissions and deaths than measures without quarantine or no interventions (incident cases: 9 modelling studies on COVID-19; onward transmission: 5 modelling studies on COVID-19; mortality: 5 modelling studies on COVID-19, low-certainty evidence). Studies on SARS and MERS were consistent with findings from the studies on COVID-19. Quarantine for individuals travelling from a country with a declared COVID-19 outbreak compared to no quarantine Very low-certainty evidence indicated that the effect of quarantine of travellers from a country with a declared outbreak on reducing incidence and deaths may be small for SARS, but might be larger for COVID-19 (2 observational studies on COVID-19 and 2 observational studies on SARS). Authors' conclusions: The current evidence is limited because most studies on COVID-19 are mathematical modelling studies that make different assumptions on important model parameters. Findings consistently indicate that quarantine is important in reducing incidence and mortality during the COVID-19 pandemic, although there is uncertainty over the magnitude of the effect. Early implementation of quarantine and combining quarantine with other public health measures is important to ensure effectiveness. In order to maintain the best possible balance of measures, decision makers must constantly monitor the outbreak and the impact of the measures implemented. This review was originally commissioned by the WHO and supported by Danube-University-Krems. The update was self-initiated by the review authors.
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The outbreak of the COVID-19 pandemic has necessitated sudden and radical changes in mental health care delivery, as strict social distancing and lockdown measures were imposed in the early phases of the pandemic. Almost overnight, practitioners were forced to transfer their face-to-face care practice to online means. In order to understand the implications of this drastic change for mental health care, and to improve the online care offerings, an online qualitative survey was held amongst mental health care professionals in the Netherlands (N=51). Our findings indicate that technological and usability problems pose a significant challenge, as do difficulties to establish rapport with clients. Moreover, not all mental health issues and treatment forms are equally amenable to online interaction. On the other hand, in many instances, practitioners were positive about the effectiveness of treatment, and reported flexibility, a lower threshold for contact, and lack of travel time as advantages. Their most prominent needs concern better technological, organizational and logistical support. It is critical that these needs are acted upon by institutions and governments. Additionally, current results inform future research on the improvement of e-mental health technologies.
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Purpose Today’s society interest in mobile photography drives consumers’ and brands’ growing usage of Instagram. This paper aims to address the consequences of excessive use of Instagram on the negative feeling of losing information when not connected and the emotional fatigue resulting from an overcharge with new information. The mediating role of addiction between Instagram overuse and the two outcomes is also analyzed. Design/methodology/approach Data from 342 active Instagram users were used to test the proposed model, applying the partial least square equation modeling method (SmartPLS 3). Findings Addiction partially mediates the impact of overuse on emotional fatigue and instastress. Addiction to Instagram was mainly due to respondents’ lack of control over the time spent on it resulting in incapability to reduce its usage. Social implications social networking site managers, educators, families and public institutions should promote an adequate use of Instagram, making users (especially the young) aware of the potential threats of its excessive usage. The control on the amount of time devoted to Instagram is a key factor for detaining overuse and addiction, as well as avoiding the negative outcomes analyzed in this research. Originality/value The findings contribute to the extant knowledge on the negative side of the digitization of the individual, as little is known about it to the best of the authors’ knowledge.
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Scale development and validation are critical to much of the work in the health, social, and behavioral sciences. However, the constellation of techniques required for scale development and evaluation can be onerous, jargon-filled, unfamiliar, and resource-intensive. Further, it is often not a part of graduate training. Therefore, our goal was to concisely review the process of scale development in as straightforward a manner as possible, both to facilitate the development of new, valid, and reliable scales, and to help improve existing ones. To do this, we have created a primer for best practices for scale development in measuring complex phenomena. This is not a systematic review, but rather the amalgamation of technical literature and lessons learned from our experiences spent creating or adapting a number of scales over the past several decades. We identified three phases that span nine steps. In the first phase, items are generated and the validity of their content is assessed. In the second phase, the scale is constructed. Steps in scale construction include pre-testing the questions, administering the survey, reducing the number of items, and understanding how many factors the scale captures. In the third phase, scale evaluation, the number of dimensions is tested, reliability is tested, and validity is assessed. We have also added examples of best practices to each step. In sum, this primer will equip both scientists and practitioners to understand the ontology and methodology of scale development and validation, thereby facilitating the advancement of our understanding of a range of health, social, and behavioral outcomes.
Theoretical and empirical work on digital media use and social connectedness has often considered face-to-face communication to be an available option. But how do various digital media uses relate to social connectedness when face-to-face communication is not, or much less, possible? Drawing on survey data from 2925 US adults during the early months of the COVID-19 pandemic, we find that different digital communication methods display different relationships with social connectedness under stay-at-home circumstances with limited in-person interactions outside the home. Overall, digital communication relates to lower social connectedness. In line with notions from social presence theory, especially digital media lower in social presence (e.g. email, social media, and online games, and to some extent text messaging) relate negatively to social connectedness, while this is not the case for higher social presence media (e.g. voice and video calls). Our study has implications for theorizing about digital media use and social connectedness in times when face-to-face communication is less available.