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Eustress or distress: an empirical study of perceived stress in everyday college life

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Eustress is literally the "good stress" that associated with positive feelings and health benefits. Previous studies focused on general stress, where the concept of eustress has been overlooked. This paper presents a novel approach towards stress recognition using data collected from wearable sensors, smartphones, and computers. The main goal is to determine if behavioral factors can help differentiate eustress from another kind of stress. We conducted a natural experiment to collect user smartphone and computer usage, heart rate and survey data in situ. By correlation and principle component analysis, a set of features could then be constructed. The performance was evaluated under leave-one-subject-out cross-validation, where the combined behavioral and physiological features enabled us to achieve 84.85% accuracy for general stress, 71.33% one kind of eustress as an urge for better performance, and 57.34% for eustress as a state of better mood. This work provided an encouraging result as an initial study for measuring eustress.
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Eustress or Distress: An Empirical
Study of Perceived Stress in
Everyday College Life
Chun-Tung Li
Department of Computing
The Hong Kong Polytechnic
University
Hong Kong, China
csctli@comp.polyu.edu.hk
Jiannong Cao
Department of Computing
The Hong Kong Polytechnic
University
Hong Kong, China
csjcao@comp.polyu.edu.hk
Tim M. H. Li
Department of Paediatrics &
Adolescent Medicine
The University of Hong Kong
Hong Kong, China
timlmh@hku.hk
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Owner/Author. Copyright is held by the owner/author(s).
Ubicomp/ISWC’16 Adjunct , September 12-16, 2016, Heidelberg, Germany
ACM 978-1-4503-4462-3/16/09.
http://dx.doi.org/10.1145/2968219.2968309
Abstract
Eustress is literally the ”good stress” that associated with
positive feelings and health benefits. Previous studies
focused on general stress, where the concept of eustress
has been overlooked. This paper presents a novel
approach towards stress recognition using data collected
from wearable sensors, smartphones, and computers. The
main goal is to determine if behavioral factors can help
differentiate eustress from another kind of stress. We
conducted a natural experiment to collect user smartphone
and computer usage, heart rate and survey data in situ.
By correlation and principle component analysis, a set of
features could then be constructed. The performance was
evaluated under leave-one-subject-out cross-validation,
where the combined behavioral and physiological features
enabled us to achieve 84.85% accuracy for general stress,
71.33% one kind of eustress as an urge for better
performance, and 57.34% for eustress as a state of better
mood. This work provided an encouraging result as an
initial study for measuring eustress.
Author Keywords
Eustress, Stress, mHealth, Ubiquitous Computing
ACM Classification Keywords
H.1.2. [User/Machine Systems Subjects: Software
psychology]: Miscellaneous.
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Introduction
Stress as one of the major attribute to mental health has
received growing interest from both industry and
academia. Numerous studies suggest that stress is a
health crisis, which associated with several diseases such
as cardiovascular diseases, anxiety, and depression. A
recent survey found that about half of the Americans
experienced major stressful events in the last year [11].
Many of them reported they suffer from stress-related
behavioral responses including lack of sleep, losing
appetite and desire to exercise. Nowadays, the term stress
is generally referring to negative stress (distress) in our
daily conversation. The adverse impact of stress has been
studied extensively, whereas the positive aspect of stress
has also attracted rising attention. For example, the
business and management community aims at maximizing
individual productivity by managing work stress. However,
the concept of positive stress (eustress) is incomplete.
Lacking of knowledge about eustress obstructed the
development of positive stress.
Typically, stress was assessed through questionnaires or
clinical assessment by a psychiatrist. In the last two
decades, researchers tried to measure stress through
physiological marker including heart rate, blood pressure,
galvanic skin response, etc. The result of these methods is
promising in a rigorous laboratory environment, however,
not applicable to detect stress in daily life. Moreover, the
concept of eustress has been overlooked in the past
decades. In the light of advanced mobile and wearable
technology, data can be collected ubiquitously and less
obtrusively, that enabled continuous stress assessment
using ubiquitous sensing technology. To address these
problems, we conducted a natural experiment and
evaluated the classification result on the features
extracted. We showed that ubiquitous computing is a
potential method for evaluating eustress.
Background
The word stress was coined by Selye back in 1965, who
defined stress as “the non-specific responses of the body
to any demand for change” [13]. In general, it refers to
the physiological responses caused by any stressful event
(stressor). These responses are triggered by the
Autonomic Nervous System (ANS), which influence
internal organs and regulating heart rate, respiratory rate,
blood vessel, galvanic skin response, and so on. ANS is
divided into two subsystems, namely Sympathetic Nervous
System (SNS) and Parasympathetic Nervous System
(PNS). When stressful event arises, higher activity rate in
SNS, which signals the adrenal glands to release stress
hormones (e.g. adrenaline and cortisol). These hormones
led to physiological changes, also known as the fight or
flight response. Alternatively, activity in PNS increases
during the restful event.
Selye introduced the concept of positive stress, namely
eustress in 1974 [14]. He extended his work in stress to
distinguish eustress and distress in terms of adaptiveness
toward stress response, where eustress is “healthy,
positive, constructive results of stressful events and stress
response” [8]. Lazarus considers eustress as a positive
cognitive response to a stressor, which associated with
positive feelings and a healthy physical state [9].
Another dominating approach for understanding eustress
was developed on the Yerkes-Dodson Law [3]. It suggests
that stress is beneficial to performance until some optimal
level is reached, after which performance will decline,
which follow the inverted U shape diagram.
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Related Work
Owing to the unclear criteria to distinguish eustress from
others, existing analysis focused on general stress. Various
stress measurement methods using computational
technology have been proposed in the last two decades
[15]. These methods can be classified into two categories:
physiological measures and physical measures. The former
one evaluate mental stress by monitoring different
physiological responses including skin conductivity, heart
activity, brain activity, blood pressure, etc. The later one
collect physical characteristics (e.g. body gesture, facial
expression, voice, etc.) that are sensitive to stress, and
using machine learning methods to develop a
computational model for stress recognition. Among all
different types of input, Sharma & Gedeom suggested
that heart rate variability (HRV) rank the top among
different primary measure for assessing mental stress in
terms of accuracy and non-intrusiveness.
Sun et al. consider stress assessment as a detection
problem, which takes accelerometer data into account to
filter the effect of motion artifact [6]. In [12], the authors
collected data from wearable sensors and mobile phone in
situ, which accuracies range from 75-87.5% for 2-class
classification problem with the different feature set. Their
work was extended in [5] with a larger population and
longer period, which achieving classification accuracies
range from 67-92%, showing that behavioral features are
possible to recognize mental stress on a daily basis.
Existing methods investigated the pattern of physical and
physiological sensory data under general stress. In our
work, we study the feasibility of measuring eustress by
HRV, smartphone and computer usage data. To the best
of our knowledge, we are the first who proposing
classification model toward eustress.
Research Questions
On the basis of previous work, general stress can be
recognized by physiological signal with high accuracy, and
suggested that stress is related to a number of behavioral
factors such as multitasking, application usages, and
physical activities. Recall one of the explanations of
eustress regarding performance, multitasking lead to the
task-switching cost which associated with a decrement in
performance [10]. It is obvious that smartphone and
computer use are the major sources of interruption, and
closely related to multitasking. Therefore, we designed the
experiment to investigate the possibility of using
physiological and behavioral signal together to build an
accurate classifier of eustress recognition. Since there has
no single domination definition towards eustress, we assess
eustress in twofold: 1) Higher self-reported performance
along with moderate stress level [3]; 2) Higher
self-reported mood along with moderate stress level [9].
It is not difficult to realize that too much or too few stress
might not trigger “eustress” in terms of the definitions
mentioned in the previous section. Therefore, we assume
eustress must be under a moderate physiological stress
level. To answer these questions, we study the pattern
whether these behavioral features is able to correlate to
these situations.
Study Protocol
We designed the in situ study and recruited 7 physically
healthy subjects (5 males and 2 females) with ages
ranging from 22 to 28, in which all of them are either
research students and staffs. We collected data from each
participant on 5 days during their waking hours. During
the study, three sources of data are collected from (1)
sensor and application on smartphone, (2) application on
the personal computer, and (3) wearable heart rate
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sensor. These data can be categorized into heart rate,
usage, and survey measure respectively.
We developed StressSurvey, which is an application for
Android smartphone to collect smartphone activities and
other sensory data. It connects the heart rate sensor
automatically in the background, and recording heart rate
data transmitted. It also captures smartphone screen and
call activities. Every hour in between 8 AM to 12 PM, the
application reminds the participant to report the survey by
notification. The detail of data acquisition process is
described in the following section.
Figure 1: Control Panel for
Heart Rate Measurement.
Figure 2: An Example of
Periodic Survey.
Heart rate measure. Heart rate variability is collected
using Polar H7 heart rate sensor [1], wearing with a chest
band to record beat-to-beat interval and average heart
rate. The heart rate data is measured by ECG sensor and
preprocessed within the H7 device. Then it transmits the
record in 1000ms via Bluetooth to the Android
smartphone. Since the connection is using Bluetooth 4.0
(BLE), the smartphone is required at least Android
version 4.3 with BLE enabled (e.g. Nexus 5, Galaxy S3).
The data transmitted complies with the BLE specification,
where the characteristic specified the format of the record.
Each record is either 8 or 16 bit int format, indicated by
the first bit of data (0 for 8-bit int, 1 for 16-bit int). Bit 1
and 2 indicate whether sensor contact feature supported
and the sensor contact status. Bit 3 is the indicator for
energy status that indicates if energy expended data is
presented. Bit 4 indicate if RR-interval data is presented,
and the interval is represented in 1/1024 sec. We shift the
reading byte by checking the flag data. Each record is
stored with UNIX timestamp on the smartphone in
common separated values (CSV) format.
In order to eliminate the effect of heart rate due to the
human artifact, motion data was collected along with
heart rate measure, obtained from the accelerometer on
the android smartphone. Each motion data contains a
three-dimensional vector, which was calculated after
removing the influence of the force of gravity.
Smartphone and computer usage measure. The usage log
is collected via commercial application RescueTime [2].
Participants are asked to install the RescueTime client
application on both computer and smartphone, each of
them is assigned to seven prepared user accounts:
hkpu.stresssurvey.#@gmail.com where # is an integer id
from one to seven. Data can be downloaded through the
public API, each row contains the timestamp, application
name, category, duration, and estimated productive index
ranging from -2 to 2. We collected the most fine-grained
record in five minutes’ interval for each participant.
Screen on and off events and the state of smartphone call
are collected directly by StressSurvey. Each record comes
with an event indicator and timestamp and stored locally
in CSV format.
Survey measure. This study using experience sampling
method (ESM) to capture self-reported survey from time
to time. During the day time, the application sends out
the notification to remind participant to complete a
survey every hour. The survey consists of several
questions and provided an integer scale ranging from one
to five, asking the perceived stress, performance, and
mood. Participant completed the end-of-day survey rated
the same scale according to the daily basis.
Data Overview
Over 7 participants, one was excluded from the analysis
because the heart rate sensor was disconnected most of
the time. We collected 5,058,233 accelerometer data,
1,410,109 heart rate data, 10,851 screen activity data,
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878 call activity, 14,746 smartphone and computer usage
and 252 self-reported survey data in raw format.
By removing incomplete data, there are 143 survey data
combined with sensory, usage and survey data aggregated
in hourly basis. Statistic of the reported survey is shown
in table 1, where we found that each participant has their
own preference of reporting their values.
Table 1: Statistic of each participant
Subj. Survey # of reports (1-5) Total
1
Stress 11 1 3 1 0
16Mood 1 1 3 3 8
Performance 2 5 5 1 3
3
Stress 10 3 2 2 0
17Mood 0 1 6 8 2
Performance 4 7 4 2 0
4
Stress 1 6 6 1 0
14Mood 0 1 7 6 0
Performance 0 4 9 1 0
5
Stress 3 9 22 4 2
40Mood 0 5 19 14 2
Performance 0 10 20 9 1
6
Stress 7 17 5 1 0
30Mood 0 2 17 11 0
Performance 4 9 9 8 0
7
Stress 2 3 1 9 11
26Mood 9 13 1 3 0
Performance 5 8 12 0 1
In general, perceived stress is positively associated with
performance and inversely for mood as shown in figure 3.
The average use of smartphone and computer increase
starting from 6AM to 11AM and reach the first peak in
the morning. After which it slightly drop during 12PM to
1PM. The use of computer and smartphone at night
decreased significantly.
Figure 3: Average of inter-subject computer and smartphone
usage (duration) and survey value.
Feature Extraction
Data especially heart rate measure requires cleaning and
transformation prior to classification. First of all, we
remove obvious error (e.g. heart rate <40), and
RR-interval that is more than 20% different from the
previous one. Then, the value is interpolated by the
moving average. The summary of features extracted is
shown in table 2.
Heart Rate Measure
Heart rate measure (HRM) including average heart rate
data and actual R-R interval obtained from the heart rate
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sensor. The average heart rate data were aggregating in
60-minute windows, in which standard deviation of heart
rate (SDHR) and the average of heart rate (AVHR) were
derived. Heart rate variability features can also be
extracted from the windows including standard deviation
of NN-interval (SDNN), average of NN-interval (AVNN),
percentage of adjacent NN-intervals differing by more
than 50ms (pNN50), and root-mean-square differences of
successive R-R intervals (RMSSD). For frequency domain
features, since the sampling rate deviate because of the
system operation, and the number of samples is not
necessary to be the product of two. Therefore, we employ
the Lomb-Scargle Periodogram [7] that is capable of
analyzing unevenly sampled time-series and data sets with
missing values.
Table 2: Summary of extracted features
Modality Features
Heart rate measure AVHR, SDHR, AVNN, SDNN,
RMSSD, PNN50, VLF, LF, HF,
LF/HF
Motion AVMI, SDMI
Screen Duration of screen on time (secs),
frequency of screen on event
Call Number of call, answered call; Du-
ration of off-hook
Application Duration of each category: social,
entertainment, internet, commu-
nication, study, email
Then the power spectrum obtained is sum up to three
separate bin, grouped by very low frequency (VLF) <0.04
Hz, low frequency (LF) 0.04 - 0.15 Hz and high frequency
(HF) 0.15 - 0.4 Hz respectively. In addition, the
accelerometer data was collected during heart rate
measure is available. Then the motion intensity (MI) was
defined by 1
3(|ACCx|+|AC Cy|+|AC Cz|), where
average and standard deviation of MI were calculated in
the 60-minute windows aligned to the HRM features.
Smartphone and Computer Usage
Usage log including smartphone screen, call state, and
application used are captured from smartphone and
computer. For screen and call activities, duration and
frequency are extracted from raw data. For the
application usage, records are aggregated in hourly basis.
Each record consists of the name of application, time of
the usage recording, and duration of each application.
Some other information such as category and estimated
productivity provided by RescueTime were not used. The
usage records are then labeled manually into the following
categories: internet, email, social, communication, study,
and entertainment. Then the sum of duration of
application used from the same category were calculated.
In order to eliminate the individual difference among
different participants, the categorized data was used to
derive three ratios namely: social, productive and
non-productive ratio. Then we perform the dimension
reduction by using Principle Component Analysis (PCA),
to further eliminate linearly dependent features.
Classification Result
In this section, we present the process of training classifier
and the result of different approaches. We use the R
(programming language) to build various classifiers using
well-known learning methods: Multinomial Logistic
Regression (MLR), Support Vector Machine (SVM) and
Random Forest (RF), to evaluate the predictive power of
linear classifier, non-linear classifier, and ensemble
classifier respectively.
First of all, we perform inter-subject z-score normalization
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Figure 4: Classification Result
on the features in order to increase generality of the
model. Then we calculate the correlation matrix to
eliminate redundant features, which has coefficient greater
than 0.75. Then the features were selected by exhaustive
search with 10-fold cross-validation using Random Forest.
Then we apply Synthetic Minority Over-Sampling
Technique (SMOTE) [4] to the training data set to avoid
over-fitting and deal with unbalanced data distribution.
For each classification problem, we partition all features
into to two subsets of features: physiological features,
physical features. We tested every problem with any set of
features before and after dimension reduction using PCA.
The performance was evaluated under
leave-one-subject-out cross-validation. For each learning
method, the model was built using repeated
cross-validation. We also fine-tuning the parameters of
the model using greedy approach in terms of accuracy.
General Stress Recognition
Prior to the eustress recognition, we tested our features
on two-class general stress recognition with the above
setting. Whereas the self-reported survey collected during
the study was ranging perceived stress from one to five,
then the value was normalized within subject and the class
”stressed” is defined by z-score >0 where the alternative
is ”not stressed”.
On average, we achieved 82.75% accuracy and 96.93%
recall for two-class stress recognition problem using all
features by applying PCA; More specifically, the best
result was obtained by Support Vector Machine with
83.22% accuracy and 97.9% recall. For physiological
features alone the accuracy reached 81.59% and 96.27%
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recall, where behavioral features obtained 84.85%
accuracy and 99.03% recall. Our results show that we
achieved competitive classification accuracy comparing to
the state of the art.
Eustress Recognition
In this study, we have several assumptions: 1) eustress is
the “right” amount of stress that improves performance
[3]; 2) eustress associated with positive feeling. Therefore,
we define eustress in twofold: Eustress is the combination
of moderate stress with high performance, and eustress is
the combination of moderate stress with high mood. We
consider moderate stress as 1 standard deviation away
from 0 (both positive and negative direction) for z-score
normalized stress. Mood as a subjective measure as stress
was applied the same normalization technique as stress,
where the distribution of performance is more consistent
over different subjects, we considered high performance
strictly greater than 3.
For eustress in terms of perceived performance, the
accuracy achieved 67.13% with recall only 42.75% using
all PCA features. For eustress defined by perceived mood,
the accuracy has only 55.25% and recall 56.22% using
physiological PCA features. It shows that the highly
unbalanced data result in a poor recall rate on eustress
classification.
Discussion and Conclusion
Existing work studied general stress in both laboratory
and natural environment. However, there are only a few
works contributed to eustress since the concept has been
proposed in the 70’s. Our work studies the possibility of
using ubiquitous sensing technologies for eustress
recognition. We conducted a natural experiment and
recruited 7 participants over 5 days. With an Android
based application developed, heart rate and smartphone
usage data were collected to constructed a set of features
using correlation and principle component analysis. We
estimated the robustness of the features by three standard
learning algorithms.
The result showed that heart rate variability, computer
and smartphone usage can be used for general stress
classification as literature suggested. The recognition
accuracy also remains consistent over different learning
algorithms. On the other hand, the accuracy of eustress in
terms of performance is higher than mood, since perceived
performance is highly related to application used on
smartphone and computer. However, the recall rates are
low for both cases showing that the generality of the
model still requires further study. The gap between
general stress and eustress mainly due to the solid
background of general stress that facilitated the feature
engineering process and results in better classification
performance.
Notice that the accuracy comparing to the existing work
may seem quite low, however, is reasonable since the
previous studies assess mental stress in rigorous laboratory
or aggregated the data by days. In contrast, our natural
experiment approach and finer granularity of time-series
result in noisier data which leads to decrement of
performance. We agreed that there is room for
improvement, further study is required to achieved better
recognition accuracy and recall rate.
To conclude, eustress as a widely accepted psychological
phenomenon should receive more interest from the
academia. As an initial study, our work provided
encouraging result of eustress recognition, which can
facilitate research on this problem in the near future.
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Limitation
This work as a preliminary study of eustress has several
limitations. Firstly, the sample size is limited to 6, where a
larger scale study is required for further study. Secondly,
self-report surveys are considered as ground truth in this
work, where it may suffer from inconsistent between
different subjects. Lastly, the concept of eustress is
unclear, where a more accurate model can be achieved by
introducing a more concrete definition of eustress.
References
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[2] Rescuetime : Time management software for staying
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[3] Benson, H., and Allen, R. How much stress is too
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[4] Chawala, N., Bowyer, K., Hall, L., and Kegelmeyer,
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[8] Kupriyanov, R., and Zhdanov, R. The eustress
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[9] Lazarus, R. From psychological stress to the
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Purpose. To assess the effectiveness of using adventure tourism for the prevention of stress-related states in students during wartime. Materials and methods. The study involved 30 students who took part in short-term adventure programs organized during wartime (October 10-26, 2023 and May 22-28, 2024) in Ukraine. The age of the participants was 22.0 (20.0; 31.0) years. Of these, 73.3% were male and the rest were females. 73.3% lived in Kyiv and Kyiv region, the rest were from Lviv, Dnipro, and Khmelnytskyi. 66.7 % indicated that they had no negative experience of being directly in the area of active hostilities. A sample of 194 students from the main group of 1901 students who took part in the study during the same period as the participants of the adventure program was used as a comparison group. Inclusion criteria: studying in a higher education institution in Ukraine; motivation to participate in a short-term adventure program; voluntary consent to participate in the study; and medical clearance. The study used a short version of the questionnaire ‘Reaction of Ukrainian students to hostilities in the country’ that was developed using Google Forms at the National University of Physical Education and Sports of Ukraine supplemented by the question “How did participation in the adventure program affect your condition? In addition to a block of demographic information questions, the questionnaire included blocks of questions aimed at determining psychophysiological indicators (activity, mood, sleep, appetite, performance, and well-being; measured with the 5-point Likert scale from 1 (very poor) to 5 (very good); Cronbach’s alpha – 0.837); factors that can enhance or mitigate the impact of military stress (gender: male – 1 and female – 0; participation in the adventure program – Yes, comparison group – No; measured with a categorical scale); stress assessment by V. Y. Shcherbatykh (measured with a ratio scale); anxiety scores were assessed using the Spielberg-Hanin Inventory and the risk of PTSD was assessed using the Mississippi Scale (measured with an ordinal scale). Results. The study analyzed the impact of short-term adventure programs on reducing stress, anxiety, and the risk of post-traumatic stress disorder (PTSD) in students during wartime. Using GLM modeling, it was found that participation in the program is a statistically significant predictor of an increase in all three indicators (p<0.05). At first glance, this seems to contradict the intuitive expectation of effects from such a program. However, when gender was taken into account, the opposite trend was observed: unlike female students, in male students, participation in the program was associated with a significant reduction in stress, anxiety, and the risk of PTSD. The program had a particularly significant impact on reducing the risk of post-traumatic stress disorder: the reduction was 1.35 standard deviations. Conclusions. This study is one of the first to examine the impact of adventure tourism on students’ mental health during wartime and makes a new contribution to understanding the mechanisms of influence of extreme physical activities on stress-related states in higher education students. Significant gender differences in the impact of short-term adventure programs were found: while male students showed a significant reduction in stress, anxiety, and risk of PTSD, female students, on the contrary, had higher scores of stress-related states. The results of the study demonstrate that participation in the program had the greatest impact on reducing the risk of developing PTSD in male students, which emphasizes its potential as an effective preventive intervention. The data obtained can act as a starting point for further research on gender differences in reactions to military stress and extreme types of PA, as well as their impact on stress-related states in higher education students
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... The research on stress is a near-exclusively negatively biased, hence there is a lack of research on positive eustress. To counteract this negative emphasis, a more balanced approach is required, to fully take into account both the negative and positive aspects of the stress [26], [17]. The principal goal of the study was therefore to account for both eustress and distress, as well as their relationship to emotion. ...
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... It causes gentle excitement and improves performance for carrying out regular daily tasks. A reasonable amount of stress that causes excitement is termed eustress or positive stress 7 . On the other hand, undesirable stress is called distress or negative stress. ...
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