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 http://comm.stanford.edu/ZEF.
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
Scale Development Overview: Five Studies Across the Three Phases
Literature review &
Pre-testing of items
University research pool
Amazon Mechanical Turk
Test of reliability
University research pool & Lucid
Test of dimensionality
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
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 Otter.ai 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.
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).
Descriptive Statistics, Factor Loadings and Cronbach Reliability of the 15 Items in the ZEF
I feel tired
I feel exhausted
I feel mentally drained
my vision gets blurred
my eyes feel irritated
I experience pain around
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
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 = “Often” to 5= “Always”.
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?
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?
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?
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).
Means, SDs, and Bivariate Correlations Among the ZEF Score and 5 Constructs of Zoom
1. ZEF Score
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
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.
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
How blurred does your vision get after
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
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
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).
Means, SDs, and Bivariate Correlations Among the ZEF Score and Each Construct of Zoom
1. ZEF Score
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.
Means, SDs, and Bivariate Correlations Among the ZEF Score and Variables for Validity Tests
1. ZEF Score
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].
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
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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Original 49 Items Tested
After participating in a video conference:
(not at all - Slightly - Moderately - Very - Extremely)
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
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
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)
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
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
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
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
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
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)