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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
Hong Kong, China
Jiannong Cao
Department of Computing
The Hong Kong Polytechnic
Hong Kong, China
Tim M. H. Li
Department of Paediatrics &
Adolescent Medicine
The University of Hong Kong
Hong Kong, China
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Ubicomp/ISWC’16 Adjunct , September 12-16, 2016, Heidelberg, Germany
ACM 978-1-4503-4462-3/16/09.
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.
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.
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.
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
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: 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,
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
Stress 11 1 3 1 0
16Mood 1 1 3 3 8
Performance 2 5 5 1 3
Stress 10 3 2 2 0
17Mood 0 1 6 8 2
Performance 4 7 4 2 0
Stress 1 6 6 1 0
14Mood 0 1 7 6 0
Performance 0 4 9 1 0
Stress 3 9 22 4 2
40Mood 0 5 19 14 2
Performance 0 10 20 9 1
Stress 7 17 5 1 0
30Mood 0 2 17 11 0
Performance 4 9 9 8 0
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
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,
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
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%
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
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
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.
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.
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Objectives:The aim of this study is to analyze the psychometric properties of Adolescent Distress Eustress Scale developed by Branson, Dry, Palmer and Turnbull, such as validity and reliability, and to carry out adaptation studies to Turkish.Materials and Methods:In the 2019-2020 academic year, a total of 366 high school students, 261 female and 105 male, studying at different official secondary schools in Tuzla, Istanbul, participated in the study. In order to examine the psychometric properties of the Turkish version of the scale, linguistic equivalence, criterion concordance validity, calculation of internal consistency coefficient and confirmatory factor analysis were performed.Results:The construct validity of the scale was examined by confirmatory factor analysis and the dual structure, which constituted the subdimensions of the Adolescent Boredom Estress scale (ADES), was confirmed. As a result of the criterion compliance validity study, negative correlations were found between the ADES-Distress sub-dimension and the General Self-Efficacy scale and positively significant correlations were found with the Perceived Stress Scale. Positive significant relationships were detected between ADES-Eustress sub-dimension and General Self-Efficacy scale, and negative significant relationships were found with the Perceived Stress Scale. As a result of the internal consistency analysis, the reliability Cronbach alpha coefficient was calculated as .81 for the ADES-Distress sub-dimension and .84 for the ADES-Eustress sub-dimension.Conclusion:Based on the findings of the research, it has been revealed that the Turkish version of the Adolescent Distress Eustress Scale is a valid and reliable measurement tool.
... Stress is one of the main reasons behind variation in intellectual and emotional health (Hampel & Petermann, 2006;Li et al., 2016). The psychological response to the pandemic was observed in the form of stress, fear, anxiety and panic (Torales et al., 2020). ...
... Although individuals vary significantly in the way they perceive and react to the same stressor, long term exposure causes chronic stress, as well as mental and physical health issues [8]. Interestingly enough, stress may also stem from pleasant events, occurring positive sentiments, or performance increase [17]. Because of the preceding, predicting stress in places such as work using wearable, unobtrusive sensors, including GSR, and building personalized stress detection algorithms have received growing attention [13][9] [24]. ...
Conference Paper
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Being confronted with a dulling perception and challenges to be creative when working at home, I imagined my home office to sense my state of arousal and search together with me for inspirational moments by infusing its familiar appearance with distortion. In two prototypical human-home office interaction systems, we use Virtual Reality (VR), Deep Reinforcement Learning (DRL) and Galvanic Skin Response (GSR) to enhance a home office’s sensibility towards its user’s level of arousal as well as to enlarge its textural action space. Although physiological feedback in machine learning faces low learning rates, the resulting interaction offers a fresh perspective on our human-home office relation.
... where eustress refers to a positive or good stress as it is beneficial to the cell, while distress refers to a negative or bad stress as it is harmful to the cell (Li et al., 2016). ...
... It involves tachycardia, muscle contraction and psychosomatic disorders reflected in non-lifethreating everyday activities [53]. From a psychological point of view, the Stress may not always be considered as a negative affective state (distress), in fact 'Eustress' [39,54] can be referred as the phenomenon of positive stress able to enhances physical or mental abilities [59] allowing mental preparing and recover methods useful, for example, in context like entrepreneurs work-related activities [30]. ...
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This work exploits Touch Dynamics to recognize affective states of a user while using a mobile device. To the aim, the acquired touch pattern is segmented in swipes, successively a wide set of handcrafted features is computed to characterize the swipe. The affective analysis is obtained through machine learning techniques. Data have been collected developing a specific App designed to acquire common unlock Android touch patterns. In this way the user interaction has been preserved as the more natural and neutral possible in real environments. Affective state labels have been obtained adopting a well-known psychological questionnaire. Three affective states have been considered: anxiety, stress and depression. Tests, performed on 115 users, reported an overall accuracy of 73.6% thus demonstrating the viability of the proposed approach.
... Most of the researchers concluded that stress was found to be affected by a variety of personal and situational factors and the perception of an individual of those factors. Chun -Tung Li, et al., (2016) presented a unique approach towards the recognition of stress using survey data from wearable sensors, smartphones, and computers, to establish if behaviour components can help differentiate good stress from another kind of stress. This in situ surveys where data was collected based on smartphone and computer usage, heartbeat rate. ...
... Most of the researchers concluded that stress was found to be affected by a variety of personal and situational factors and the perception of an individual of those factors. Chun -Tung Li, et al., (2016) presented a unique approach towards the recognition of stress using survey data from wearable sensors, smartphones, and computers, to establish if behaviour components can help differentiate good stress from another kind of stress. This in situ surveys where data was collected based on smartphone and computer usage, heartbeat rate. ...
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Purpose: In the aftermath of national lockdown due to Covid-19, several organizations were forced to opt remote working, which provides several challenges and opportunities to the employees and employer. The reason for carrying out this empirical study is that the subject is new, challenging, and occupational stress exists everywhere; also, an inadequate research has been reported on such type of studies. This empirical study reports the results of the effect of occupational stress and remote working on employees' psychological well-being in the Information Technology industry. Methodology: The effect of seven independent occupational stress-causing factors including workload, peer, physiological factors, role ambiguity, organization climate, psychological factors and job satisfaction, and an independent factor, remote working, on the dependent factor of psychological well-being of employees in Information Technology industry was measured. The psychological well-being was measured with six subscales-environment mastery, positive growth, positive relations, self-acceptance, autonomy, and purpose of life. The independent factors were measured using a survey instrument, a structured undisguised questionnaire, whereas dependent factors were measured with a shortened version of 18-item Ryff's scale. The inferences of the outcome were made using appropriate statistical procedures. Findings: The multiple regression analysis results revealed independent factors like peer, role ambiguity, organization climate, and job satisfaction are significantly influencing the psychological well-being of the employees in the Information Technology Industry. There are minor statistically significant gender and age group differences that are affecting the psychological well-being of employees as observed. Implications: The study implies that wherever possible, the remote working options need to be worked out by the employer, in all the sectors to reduce the stress and enhance the psychological well-being of employees. Originality: Till now, no researcher has reported such type of empirical study, and the available literature is limited to occupational stress in general, without suggesting how remote working affects the psychological well-being of employees in particular.
... Understood as the physiological demand placed on the body when one must adapt, cope, or adjust [10], stress responses are triggered by the autonomic nervous system, which influences internal organs, heart rate, respiratory rate, blood vessel, galvanic skin response, and so on [11]. Lavender aroma may reduce stress and produce relaxation via the limbic system, particularly the amygdala and hippocampus [1]. ...
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Introduction: Aromatherapy is prominent in complementary and alternative medicine. Little endocrinological evidence, however, of the effects of aromatherapy has yet been presented. We used salivary stress marker chromogranin A (CgA) to examine the effects of lavender aroma on women watching a stressful video. Methods: Healthy female university students (n = 23) aged 20-22 years old were randomly assigned to two groups: an aroma group exposed to lavender and an unexposed control group. Both groups watched a stressful video for 10 min. During the protocol, the aroma group was exposed to lavender aroma. Samples of salivary chromogranin A (CgA) were collected immediately before and after watching the video, and at 5 and 10 min after that. Results: In the aroma group, the levels of CgA statistically significantly decreased throughout the experimental period. In the control group, there was no such change. Conclusion: The findings suggest that lavender aroma may reduce the stress effects of watching a stressful video.
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A stress group should be subdivided into eustress (low-stress) and distress (high-stress) groups to better evaluate personal cognitive abilities and mental/physical health. However, it is challenging because of the inconsistent pattern in brain activation. We aimed to ascertain the necessity of subdividing the stress groups. The stress group was screened by salivary alpha-amylase (sAA) and then, the brain’s hemodynamic reactions were measured by functional near-infrared spectroscopy (fNIRS) based on the near-infrared biosensor. We compared the two stress subgroups categorized by sAA using a newly designed emotional stimulus-response paradigm with an international affective picture system (IAPS) to enhance hemodynamic signals induced by the target effect. We calculated the laterality index for stress (LIS) from the measured signals to identify the dominantly activated cortex in both the subgroups. Both the stress groups exhibited brain activity in the right frontal cortex. Specifically, the eustress group exhibited the largest brain activity, whereas the distress group exhibited recessive brain activity, regardless of positive or negative stimuli. LIS values were larger in the order of the eustress, control, and distress groups; this indicates that the stress group can be divided into eustress and distress groups. We built a foundation for subdividing stress groups into eustress and distress groups using fNIRS.
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The concepts of eustress and allostasis are discussed in terms of their interrelationship and penetration. Historical research on general adaptation syndrome is examined from the standpoint of an interdisciplinary approach in which the effects of stress are manifested in four areas: physiology, behaviour, subjective experience and cognitive functions. This article provides a review of eustress concepts. The key problems and contradictions of eustress concept are disclosed and discussed. The authors propose an approach to eustress from the position of the adaptation process. The definition of eustress is substantiated as a form of stress after which a person's adaptive capacity increases. The ways and mechanisms of eustress are described. The role of eustress is reviewed in the context of allostasis, a form of adaptation more complex than homeostasis. The authors propose the working hypothesis of allostatic states to describe the phenomena of distress and eustress.
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An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of "normal" examples with only a small percentage of "abnormal" or "interesting" examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space) than varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
Conference Paper
In this study, we aim to find physiological or behavioral markers for stress. We collected 5 days of data for 18 participants: a wrist sensor (accelerometer and skin conductance), mobile phone usage (call, short message service, location and screen on/off) and surveys (stress, mood, sleep, tiredness, general health, alcohol or caffeinated beverage intake and electronics usage). We applied correlation analysis to find statistically significant features associated with stress and used machine learning to classify whether the participants were stressed or not. In comparison to a baseline 87.5% accuracy using the surveys, our results showed over 75% accuracy in a binary classification using screen on, mobility, call or activity level information (some showed higher accuracy than the baseline). The correlation analysis showed that the higher-reported stress level was related to activity level, SMS and screen on/off patterns.
Stress is a major growing concern in our day and age adversely impacting both individuals and society. Stress research has a wide range of benefits from improving personal operations, learning, and increasing work productivity to benefiting society - making it an interesting and socially beneficial area of research. This survey reviews sensors that have been used to measure stress and investigates techniques for modelling stress. It discusses non-invasive and unobtrusive sensors for measuring computed stress, a term we coin in the paper. Sensors that do not impede everyday activities that could be used by those who would like to monitor stress levels on a regular basis (e.g. vehicle drivers, patients with illnesses linked to stress) is the focus of the discussion. Computational techniques have the capacity to determine optimal sensor fusion and automate data analysis for stress recognition and classification. Several computational techniques have been developed to model stress based on techniques such as Bayesian networks, artificial neural networks, and support vector machines, which this survey investigates. The survey concludes with a summary and provides possible directions for further computational stress research.
Stress is “the nonspecific response of the body to any demand made upon it,” that is, the rate at which we live at any one moment. All living beings are constantly under stress and anything, pleasant or unpleasant, that speeds up the intensity of life, causes a temporary increase in stress, the wear and tear exerted upon the body. A painful blow and a passionate kiss can be equally stressful.
The "stress" or "general adaptation" syndrome? developed by Dr. Selye, is note an important and fundamental concept in medical theory and underlies much of the research being carried on in such diseases as rheumatoid arthritis, hypertension, and cardiac necrosis. In essence, the stress concept postulates that the body responds to stress of any kind with a unified defense mechanism characterized by specific structural and chemical changes. This reaction can raise the resistance to stressful agents and can also be used to protect against disease. But, when the reaction is faulty or overly prolonged, is can also produce disease.
"There is no question that a certain amount of stress is good," says one of the chief executives quoted in this article. "If I have a particularly easy week, I can feel an ache or pain, but if I get really busy, I feel really much better." But when managers feel themselves under too much stress, the executive adds pessimistically, then "not only will they burn out in time, but they get erratic and their judgment goes all to hell." These insights reflect one of the authors' main themes: medical research finds stress productive up to a point (which of course varies with the manager), but beyond that point it can be disastrous. The trouble in corporate life seems to be that leaders appreciate the first part of the relationship but not the second. As a consequence, both individiuals and organizations suffer--and suffer greatly. This penalty is unnecessary, the authors believe, because a newly tested, proved, and relatively simple approach to managing stress is available to any corporation that wants to use it.