Relaxation
4.1. unobtrusive stress detection system with smart bands.
Our stress detection system developed in [ 32 ] allows users to be aware of their stress levels during their daily activities without creating any interruption or restriction. The only requirement to use this system is the need to wear a smart band. Participants in this study wore the Empatica E4 smart band on their non-dominant hand. The smart band provides Blood Volume Pressure, ST, EDA, IBI (Interbeat Interval) and 3D Acceleration. The data are stored in the memory of the device. Then, the artifacts of physiological signals were detected and handled. The features were extracted from the sensory signals and fed to the machine learning algorithm for prediction. In order to use this system, pre-trained machine learning models are required. For training the models, feature vectors and collected class labels were used.
The body sweats when emotional arousal and stress are experienced and, therefore, skin conductance increases [ 40 ]. This makes EDA a promising candidate for stress level detection. Intense physical activity and temperature changes contaminate the SC (Skin Conductance) signal. Therefore, affected segments (artifacts) should be filtered out from the original signal. In order to detect the artifacts in the SC signal, we used an EDA toolkit [ 41 ] which is 95% accurate on the detection of the artifacts. While developing this tool, technicians labeled the artifacts manually. They trained a machine learning model by using the labels. In addition to the SC signal, 3D acceleration and ST signals were also used for artifact detection. We removed the parts that this tool detected as artifacts from our signals. We further added batch processing and segmentation to this tool by using custom software built-in Python 2.7.
After the artifact removal phase, features were extracted from the EDA signal. This signal has two components phasic and tonic; features from both components were extracted (see Table 2 ). The cvxEDA tool [ 42 ] was used for the decomposition of the signal into these components. This tool uses convex optimization to estimate the Autonomic Nervous System (ANS) activity that is based on Bayesian statistics.
EDA features and their definitions.
Feature | Description |
---|---|
Quartdev Tonic | Quartile deviation (75 percentile–25 percentile) of the phasic component |
Strong Peaks Phasic | The number of strong peak per 100 s |
Peaks Phasic | The number of peaks per 100 s |
Perc20 | 20th percentile of the phasic component |
Perc80 Tonic | 80th percentile of the phasic component |
Mean Tonic | Mean of the phasic component |
SD Tonic | Standard deviation of phasic component |
The tonic component in the EDA signal represents the long-term slow changes. This component is also known as the skin conductance level. It could be regarded as the indicator of general psychophysiological activation [ 43 ].
The phasic component represents faster (event-related ) differences in the SC signal. The Peaks of phasic SC component as a reaction to a stimulus is also called Skin Conductance Response [ 43 ]. After we decompose the phasic component from the EDA signal, peak related features were extracted.
Heart activity (or, more specifically, HRV) reacts to changes in the autonomic nervous system (ANS) caused by stress [ 44 ] and it is, therefore, one of the most commonly used physiological signal for stress detection [ 40 ]. However, vigorous movement of subjects and improperly worn devices may contaminate the HRV signal collected from smartwatches and smart bands. In order to address this issue, we developed an artifact handling tool in MATLAB programming language [ 45 ] that has batch processing capability. First, the data were divided into 2 min long segments with 50% overlapping. Two-minute segments were selected because it is reported that the time interval for stress stimulation and recovery processes is around a few minutes [ 46 ]. The artifact detection percentage rule (also employed in Kubios [ 47 ]) was applied after the segmentation phase. In this rule, each data point was compared with the local average around it. When the difference was more than a predetermined threshold percentage, (20% is commonly selected in the literature [ 48 ]), the data point was labeled as an artifact. In our system, we deleted the inter-beat intervals detected as the artifacts and interpolated these points with the cubic spline interpolation technique which was used in the Kubios software [ 47 ]. The time-domain features of HRV are calculated. In order to calculate the frequency domain features, we interpolated the RR intervals to 4 Hz. Then, we applied the Fast Fourier Transform (FFT). These time and frequency domain features (see Table 3 ) were selected because these are the most discriminative ones in the literature [ 30 , 49 , 50 ].
HRV features and their definitions [ 32 ].
Feature | Description |
---|---|
Mean RR | Mean value of the inter-beat (RR) intervals |
STD RR | Standard deviation of the inter-beat interval |
pNN50 | Percentage of the number of successive RR intervals varying more than 50 ms from the previous interval |
RMSSD | Root mean square of successive difference of the RR intervals |
SDSD | Related standard deviation of successive RR interval differences |
HRV triangular index | Total number of RR intervals divided by the height of the histogram of all RR intervals measured on a scale with bins of 1/128 s |
TINN | Triangular interpolation of RR interval histogram |
LF | Power in low-frequency band (0.04–0.15 Hz) |
HF | Power in high-frequency band (0.15–0.4 Hz) |
pLF | Prevalent low-frequency oscillation of heart rate |
pHF | Prevalent high-frequency oscillation of heart rate |
VLF | Power in very low-frequency band (0.00–0.04 Hz) |
LF/HF | Ratio of LF-to-HF |
Research has shown that movements of the human body and postures can indeed be employed as a means to detect signs of different emotional states. The dynamics of body movement were investigated by Castellano et al. who used multimodal data to identify human affective behaviors. Specific movement metrics, such as the amount of movement, intensity and fluidity, were used to help deduct emotions, and it was found that the amount of movement was a major factor in distinguishing different types of emotions [ 51 ]. Melzer et al. investigated whether movements comprised of collections of Laban movement components could be recognized as expressing basic emotions [ 52 ]. The results of their study confirm that, even when the subject has no intention of expressing emotions, particular movements can assist in the perception of bodily expressions of emotions. Accelerometer sensors may be used to detect these movements and different types of affect. The accelerometer sensor data are used for two different purposes in our system. Firstly, we extracted features from the accelerometer sensor, for detecting stress levels. We also selected the features to be used as described in Table 4 [ 53 ] and, as mentioned above, this sensor was also employed to clean the EDA signal in the EDAExplorer Tool [ 41 ].
ACC features and their definitions.
Feature | Description |
---|---|
Mean X | Mean acceleration over axis |
Mean Y | Mean acceleration over axis |
Mean Z | Mean acceleration over axis |
MeanAccMag | Mean acceleration over acceleration magnitude |
Energy | FFT energy over mean acceleration magnitude |
A skin temperature signal is used for the artifact detection phase of the EDA signal in the EDAExplorer Tool [ 41 ]. After we divide our data into segments, different modalities were merged into one feature vector. The heart activity signal started with a delay (to calculate heartbeats per minute at the start) and all signals were then synchronized. We included start and end timestamps for each segment, and each modality was merged with a custom Python script.
The Weka machine learning toolkit [ 54 ] is used for identifying stress levels. The Weka toolkit has several preprocessing features before classification. Our data set was not balanced when the number of instances belonging to each class was considered. We solved this issue by removing samples from the majority class. We selected random undersampling because it is the most commonly applied method [ 55 ]. In this way, we prevented classifiers from biasing towards the class with more instances. In this study, we employed five different machine learning classification algorithms to recognize different stress levels: MultiLayer Perceptron (MLP), Random Forest (RF) (with 100 trees), K-nearest neighbors (kNN) ( n = 1–4), Linear discriminant analysis (LDA), Principal component analysis (PCA) and support vector machine (SVM) with a radial basis function. These algorithms were selected because they were the most commonly applied and successful classifiers for detecting stress levels [ 30 , 48 ]. In addition, 10-fold stratified cross-validation was then applied and hyperparameters of the machine learning algorithms were fine-tuned with grid search. The best performing models have been reported.
We applied correlation-based feature selection (CBFS) technique which is available in the Weka machine learning package for combined signal [ 56 ]. The CBFS method removes the features that are less correlated with the output class. For every model, we selected the ten most important features. This method is applied for MLP, RF, kNN and LDA. In order to create an SVM based model, we applied PCA based dimensionality reduction where the covered variance is selected as 0.95 (the default setting).
The CBFS method computes the correlation of features with the ground truth label of the stress level. Insights about the contribution of the features to the stress detection performance can be obtained from Figure 1 and Figure 2 . Three of the best features (over 0.15 correlation) are frequency domain features. These features are high, low and very-low frequency components of the HRV signal (see Figure 1 ). When we examine the EDA features, peaks per 100 s feature are the most important and distinctive feature by far. Since the EDA signal is distorted under the influence of the stimuli, the number of peaks and valleys increases. Lastly, when the acceleration signal is investigated, the most discriminative feature is mean acceleration in the z -axis (see Figure 2 b). This could be due to the nature of hand and body gestures which are caused by stressed situations.
Top-ranking features selected for the HRV signal.
Top-ranking features selected for the EDA and ACC signals.
Context is a broad term that could contain different types of information such as calendars, activity type, location and activity intensity. Physical activity intensity could be used to infer contextual information. In more restricted environments such as office, classrooms, public transportation and physical activity intensity could be low, whereas, in outdoor environments, physical activity intensity could increase. Therefore, an appropriate relaxation method will change according to the context of individuals.
For calculating physical activity intensity, we used the EDAExplorer tool [ 41 ]. The stillness metric is used for this purpose. It is the percentage of periods in which the person is still or motionless. Total acceleration must be less than a threshold (default is 0.1 [ 41 ]) for 95 percent of a minute in order for this minute to count as still [ 41 ]. Then, the ratio of still minutes in a session can be calculated. For the ratio of still minutes in a session, we labeled sessions below 20% as still, above 20% as active and suggested relaxation method accordingly (see Figure 3 ).
The whole system diagram is depicted. When a high stress level is experienced, by analyzing the physical activity based context, the system suggests the most appropriate reduction method.
The proposed stress level monitoring mechanism, for real-life settings, was evaluated during an eight day Marie Skłodowska-Curie Innovative Training Network (ITN) training event in Istanbul, Turkey, for the AffecTech project. AffecTech is a program funded by Horizon 2020 (H2020) framework established by the European Commission. The AffecTech project is an international collaborative research network involving 15 PhD students (early stage researchers (ESR)) with the aim of developing low-cost effective wearable technologies for individuals who experience affective disorders (for example, depression, anxiety and bipolar disorder).
The eight-day training event included workshops, lectures and training with clearly defined tasks and activities to ensure that the ESR had developed the required skills, knowledge and values outline prior to the training event. At the end of the eight-day training, ESRs were required to deliver a presentation about their PhD work to two evaluators from the European Union where they received feedback about their progress (see Figure 4 for raw physiological signals at the start of the presentation). For studying the effects of emotion regulation on stress, yoga, guided mindfulness and mobile-based mindfulness, sessions were held by a certified instructor.
Sample data belong to a presentation session. The increase in EDA, ST and IBI could be observed when the subject started the presentation.
During the training, physiological and questionnaire data were collected from the 16 ESR participants (9 men, mean age 28); 15 ESRs and one of the AffecTech project academics, all of whom gave informed consent to participate in the study. Participants were from different countries with diverse nationalities (two from Iran, two from Spain, two from Italy, one from Argentina, one from Pakistan, one from China, one from Switzerland, one from Belarus, one from France, one from England, one from Barbados, one from Turkey and one from Bulgaria). Due to the fault of one of the Empatica E4 devices, it was not possible to include data from one participant. The remaining 15 participants completed all stages of the study successfully.
During the eight days of training and presentations, psychophysiological data were collected from 16 participants during the training event from Empatica E4 smart band while they are awake. For studying the effects of emotion regulation on stress, yoga, guided mindfulness and mobile-based mindfulness sessions were held by a certified instructor. The timeline of the event is shown in Figure 5 .
Time-line depicting eight days of the training event. Presentations, relaxations and lectures are highlighted.
The psychophysiological signal data were collected using the Empatica E4 smart band whilst participants were awake throughout the eight days of the AffecTech training. Physiological data included IBI, EDA, ACC (Accelerometer) and ST and stored in different csv files. In addition, 27.39% of the data are obtained from free times (free day and after training until subjects slept 5:00 p.m.–10:00 p.m.), 43.83% of the data comes from lectures in the training, 11.41% is the presentation session and relax sessions consist of 17.35% of the data. As mentioned previously, we randomly undersampled (most commonly applied method [ 55 ] ) the data to overcome the class imbalance problem. The participants’ blood pressure (BP) was also recorded using CE(0123) Harvard Medical Devices Ltd. automated sphygmomanometer prior to and after each stress reduction event (yoga and mindfulness), in order to demonstrate whether the participants stress levels were modified. On each occasion that the participants’ BP was recorded, the mean of three recordings was used as the final BP. A reduction in the participants’ blood pressure and/or pulse rate may be seen, which demonstrates a reduction in stress level.
The procedure used in this study was approved by the Institutional Review Board for Research with Human Subjects of Boğaziçi University with the approval number 2018/16. Prior to data acquisition, each participant received a consent form describing the experimental procedure and its benefits and implications to both the society and the subject. The procedure was also explained verbally to the subject. All of the data are stored anonymously.
A session-based self-report questionnaire comprised of six questions based on the Nasa Task Load Index (NASA-TLX) [ 57 ]. The frustration scale was specifically used to measure perceived stress levels [ 32 ]. We asked the following question to the participants for each session:
How irritated, stressed and annoyed versus content, relaxed and complacent did you feel during the task?
Questionnaires were completed daily (at the end of the day) and, after each presentation, lecture and stress reduction event (such as yoga and mindfulness).
During the eight day training, it is assumed that the participants’ stress levels are likely to have increased day by day because they were required to give a presentation (perceived as a stressful event) reporting their PhD progress to the EU project evaluators at the end of the training.
Underpinned by James Gross’s Emotion Regulation model (see Figure 6 ) [ 4 ], we modified the situation to help the participants to reduce their thoughts of the end of the training presentation. To help participants manage their stress levels, we applied Yoga and mindfulness sessions on two separate days (day three and day four, respectively). These sessions lasted approximately 1 h and, throughout the sessions, participants wore an Empatica E4 smartband. In addition to the physiological signals coming from the Smartbands, participants’ blood pressure values were also recorded before and after the yoga and mindfulness sessions.
Application of James Gross’s Emotion Regulation model [ 4 ] in the context of stress management.
5.1. statistical data analysis, 5.1.1. validation of different perceived stress levels by using the self-reports.
In order to validate that the participants experienced different perceived stress levels in different contexts (lecture, relaxation, presentation), we used the Frustration item (see Section 4.5) from the NASA-TLX [ 57 ]. The distribution of answers is demonstrated in Figure 7 . Our aim is to show that the perceived stress levels (obtained from self-report answers) differ in relaxation sessions considerably when compared to the presentation session (high stress). To this end, we applied the t -test (in R programming language) to the perceived stress self-report answers of yoga versus presentation, mindfulness versus presentation and pause (mobile mindfulness) versus presentation session pairs. The paired t -test is used to evaluate the separability of each session. The degree of freedom is 15. We applied the variance test to each session tuple; we could not identify equal variance in any of the session tuples. Thus, we selected the variance as unequal. We used 99.5% confidence intervals. The t -test results’ ( p -values and test statistics) are provided in Table 5 . For all tuples, the null hypothesis stating that the perceived stress of the relaxation method is not less than the presentation session is rejected. The perceived stress levels of participants for all meditation sessions are observed to be significantly lower than the presentation session (high stress).
Visual representation of the frustration scores collected in different types of sessions.
T -test results for session tuple comparison of perceived stress levels using self-reports.
Session Tuple | -Test Statistic | -Value |
---|---|---|
Yoga—Presentation | −4.0027 | < 0.005 |
Guided Mindfulness—Presentation | −5.4905 | < 0.005 |
Mobile Mindfulness—Presentation | −4.2677 | < 0.005 |
In this section, we compared the effect of stress management tools such as yoga and mindfulness on blood pressure. It is expected that blood pressure sensors will be part of unobtrusive wrist-worn wearable sensors soon. We plan to integrate a blood pressure (BP) module to our system when they are available. Therefore, by using the measurements of a medical-grade blood pressure monitor, we provided insights about how stress reaction affects BP. We further applied and tested the prominent emotion regulation model of James Gross by analyzing these measurements in the context of stress management. We measured the diastolic and systolic BP and pulse using a medical-grade blood pressure monitor before and after the yoga and mindfulness sessions. In order to ensure that the participants were relaxed and that an accurate BP was recorded, BP was measured three times with the mean as the recorded result. A one-sample t -test was applied to the difference between mean values. The results are shown in Table 6 .
The difference between the mean diastolic blood pressure, the mean systolic blood pressure and the mean pulse, before and after sessions of guided mindfulness and guided yoga. (* p < 0.05).
Activity | Systolic | Diastolic | Pulse |
---|---|---|---|
Guided Mindfulness | −1.31% | 1.75% * | −5.75% * |
Guided Yoga | −5.81% * | −1.93% | 8.06% * |
Mindfulness decreased the systolic BP, –1.13% (ns), increased diastolic BP, +1.75% ( p < 0.05) and decreased the pulse –5.75% ( p < 0.05). Medicine knows that systolic blood pressure (the top number or highest blood pressure when the heart is squeezing and pushing the blood around the body) is more important than diastolic blood pressure (the bottom number or lowest blood pressure between heartbeats) because it gives the best idea of the risk of having a stroke or heart attack. In this view, the significant reduction of systolic BP after mindfulness is an important result.
Moreover, the difference between systolic and diastolic BP is called pulse pressure. For example, 120 systolic minus 60 diastolic equals a pulse pressure of 60. It is also known that a pulse pressure greater than 60 can be a predictor of heart attacks or other cardiovascular diseases, while a low pulse pressure (less than 40) may indicate poor heart function. In our study, pulse pressure was lower after mindfulness (we had both a significant reduction in systolic BP and an increase in diastolic BP), but its value was higher than 40 (42.69 mean difference before the mindfulness and 40.48 mean difference after the mindfulness), suggesting that this result can also be considered clinically positive.
During yoga, there was a decrease in systolic BP by −5.81% ( p < 0.05), diastolic BP by −1.93% (ns) and increase in pulse +8.06% ( p < 0.05). Yoga appears to be more effective than mindfulness at decreasing systolic and diastolic blood pressure, although mindfulness seems to be more effective than yoga for decreasing the pulse due to the activity involved in yoga.
We tested our system by using the known context labels of sessions as the class label. We used Lecture (mild stress), Yoga and Mindfulness (relax) and Presentation in front of the board of juries (high stress) as class labels by examining perceived stress self-report answers in Figure 6 . We investigated the success of relaxation methods, different modalities and finding the presenter.
We evaluated the effect of using the interbeat-interval, the skin conductance and the accelerometer signals separately and in a combined manner on two and three class classification performance. These classes are mild stress, high stress and relax states from mindfulness and yoga sessions. The results are shown in Table 7 , Table 8 and Table 9 . For the three-class classification problem, we achieved a maximum accuracy of 72% by using MLP on only HRV features and 86.61% with only accelerometer features using the Random Forest classifier and 85.36% accuracy combination of all features with LDA classifier (see Table 7 ). The difficulty in this classification task is a similar physiological reaction to relax and mild stress situations. However, since the main focus of our study is to discriminate high stress from other classes to offer relaxation techniques in this state, it did not affect our system performance. We also investigated high-mild stress and high stress-relax 2-class classification performance. For the discrimination of high and mild stress, HRV outperformed other signals with 98% accuracy using MLP (see Table 8 ). In the high stress-relax 2-class problem, only HRV features with RF achieved a maximum accuracy of 86%, whereas ACC features with MLP achieved a maximum of 94% accuracy. In this problem, the combination of all signals with RF achieved 92% accuracy which is the best among all classifiers (see Table 9 ). For all models, EDA did not perform well. This might be caused by the loose contact with EDA electrodes in the strap due to loosely worn smartbands.
Effect of different modalities and their combination on the system performance. Note that the number of classes is fixed at 3 (high stress, mild stress and relax).
Algorithm | Accuracy, % | |||
---|---|---|---|---|
HRV | EDA | ACC | Combined | |
MLP | 72.14 | 36.61 | 74.29 | 82.68 |
RF | 67.86 | 36.96 | 86.61 | 85.18 |
kNN | 65.00 | 29.82 | 70.89 | 78.39 |
LDA | 69.82 | 31.96 | 73.39 | 85.36 |
SVM | 47.14 | 30.54 | 58.57 | 46.96 |
Effect of different modalities and their combination on the system performance. Note that the number of classes is fixed at 2 (high stress and mild stress).
Algorithm | Accuracy, % | |||
---|---|---|---|---|
HRV | EDA | ACC | Combined | |
MLP | 98.00 | 60.00 | 64.00 | 98.00 |
RF | 98.00 | 42.00 | 72.00 | 98.00 |
kNN | 94.00 | 44.00 | 58.00 | 94.00 |
LDA | 94.00 | 40.00 | 54.00 | 94.00 |
SVM | 66.00 | 54.00 | 54.00 | 66.00 |
Effect of different modalities and their combination on the system performance. Note that the number of classes is fixed at 2 (high stress and relax).
Algorithm | Accuracy, % | |||
---|---|---|---|---|
HRV | EDA | ACC | Combined | |
MLP | 82.00 | 66.00 | 96.00 | 90.00 |
RF | 86.00 | 60.00 | 94.00 | 92.00 |
kNN | 82.00 | 66.00 | 88.00 | 90.00 |
LDA | 78.00 | 64.00 | 92.00 | 88.00 |
SVM | 78.00 | 62.00 | 52.00 | 74.00 |
We applied three different relaxation methods to manage stress levels of individuals. In order to measure the effectiveness of each method, we examined how easily these physiological signals in the relaxation sessions can be separated from high stress presentations. If it can be separated from high stress levels with higher classification performance, it could be inferred that they are more successful at reducing stress. As seen in Table 10 and Table 11 , mobile mindfulness has lower success in reducing stress levels. Yoga has the highest classification performance with both HR and EDA signals.
The classification accuracy of the relaxation sessions using stress management methods and stressful sessions using EDA.
Algorithm | Accuracy, % | ||
---|---|---|---|
Guided Mindfulness | Yoga | Mobile Mindfulness | |
MLP | 65.71 | 78.57 | 75.00 |
RF | 67.14 | 87.14 | 67.64 |
kNN | 64.29 | 82.86 | 77.94 |
LDA | 65.71 | 80.00 | 51.47 |
SVM | 70.00 | 72.86 | 58.82 |
The classification accuracy of the relaxation sessions using stress management methods and stressful sessions using HRV.
Algorithm | Accuracy, % | ||
---|---|---|---|
Guided Mindfulness | Yoga | Mobile Mindfulness | |
MLP | 90.00 | 97.50 | 93.94 |
RF | 97.50 | 95.00 | 87.89 |
kNN | 90.00 | 90.00 | 93.93 |
LDA | 87.50 | 87.50 | 75.75 |
SVM | 85.00 | 80.00 | 81.82 |
In this study, by using our automatic stress detection system with the use of Empatica-E4 smart-bands, we detected stress levels and suggested appropriate relaxation methods (i.e., traditional or mobile) when high stress levels are experienced. Our stress detection framework is unobtrusive, comfortable and suitable for use in daily life and our relaxation method suggestion system makes its decisions based on the physical activity-related context of a user. To test our system, we collected eight days of data from 16 individuals participating in an EU research project training event. Individuals were exposed to varied stressful and relaxation events (1) training and lectures (mild stress), (2) yoga, mindfulness and mobile mindfulness (PAUSE) (relax) and (3) were required to give a moderated presentation (high stress). The participants were from different countries with diverse cultures.
In addition, 1440 h of mobile data (12 h in a day) were collected during this eight-day event from each participant measuring their stress levels. Data were collected during the training sessions, relaxation events and the moderated presentation and during their free time for 12 h in a day, demonstrating that our study monitored daily life stress. EDA and HR signals were collected to detect physiological stress and a combination of different modalities increased stress detection, performance and provided the most discriminative features. We first applied James Gross ER model in the context of stress management and measured the blood pressure during the ER cycle. When the known context was used as the label for stress level detection system, we achieved 98% accuracy for 2-class and 85% accuracy for 3-class. Most of the studies in the literature only detect stress levels of individuals. The participants’ stress levels were managed with yoga, mindfulness and a mobile mindfulness application while monitoring their stress levels. We investigated the success of each stress management technique by the separability of physiological signals from high-stress sessions. We demonstrated that yoga and traditional mindfulness performed slightly better than the mobile mindfulness application. Furthermore, this study is not without limitations. In order to generalize the conclusions, more experiments based on larger sample groups should be conducted. As future work, we plan to develop personalized perceived stress models by using self-reports and test our system in the wild. Furthermore, attitudes in the psychological field constitute a topic of utmost relevance, which always play an instrumental role in the determination of human behavior [ 58 ]. We plan to design a new experiment which accounts for the attitudes of participants towards relaxation methods and their effects on the performance of stress recognition systems.
We would like to show our gratitude to the Affectech Project for providing us the opportunity for the data collection in the training event and funding the research.
Y.S.C. is the main editor of this work and made major contributions in data collection, analysis and manuscript writing. H.I.-S. made valuable contributions in both data collection and manuscript writing. She was the yoga and mindfulness instructor in the event and contributed the related sections regarding traditional and mobile methods. She also led the blood pressure measurement efforts before and after relaxation methods. D.E. and N.C. contributed equally to this work in design, implementation, data analysis and writing the manuscript. J.F.-Á., C.R. and G.R. contributed the experiment design and provided valuable insights into both emotion regulation theory. They also contributed to the related sections in the manuscript. C.E. provided invaluable feedback and technical guidance to interpret the design and the detail of the field study. He also performed comprehensive critical editing to increase the overall quality of the manuscript. All authors have read and agreed to the published version of the manuscript.
This work has been supported by AffecTech: Personal Technologies for Affective Health, Innovative Training Network funded by the H2020 People Programme under Marie Skłodowska-Curie Grant Agreement No. 722022. This work is supported by the Turkish Directorate of Strategy and Budget under the TAM Project number DPT2007K120610.
The authors declare no conflict of interest.
Students are often asked to write an essay on Stress in their schools and colleges. And if you’re also looking for the same, we have created 100-word, 250-word, and 500-word essays on the topic.
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Understanding stress.
Stress is a common feeling that everyone experiences. It’s your body’s reaction to demanding situations or pressures. It can be caused by both good and bad experiences.
Stress can be caused by various things. For example, homework, exams, moving houses, or losing a loved one. It’s different for everyone – what stresses one person may not stress another.
Stress affects your body and mind. It can lead to headaches, upset stomach, or trouble sleeping. It can also make you feel anxious, irritable, or depressed.
It’s important to manage stress. Exercise, relaxation, and talking about your feelings can help. Remember, it’s okay to ask for help if stress becomes overwhelming.
Stress, a ubiquitous aspect of modern life, is the body’s response to perceived threats or challenges. It is a psychological and physiological reaction that can significantly impact an individual’s well-being.
Stress is regulated by the hypothalamic-pituitary-adrenal (HPA) axis, a complex set of interactions among the hypothalamus, the pituitary gland, and the adrenal glands. When a stressor is encountered, the HPA axis is activated, leading to the release of cortisol, the primary stress hormone.
Short-term stress can enhance the body’s adaptive capabilities, improving cognitive function and physical performance. However, chronic stress can lead to a plethora of health issues, including cardiovascular diseases, mental health disorders, and impaired immune function.
Effective stress management is crucial in today’s fast-paced world. Techniques include mindfulness practices, physical exercise, adequate sleep, and a balanced diet. Moreover, seeking professional help when necessary should not be stigmatized but encouraged.
Understanding and managing stress is a crucial skill in the modern world. By recognizing the signs of stress and implementing effective coping strategies, we can mitigate its negative impacts and enhance our overall well-being.
The physiology of stress.
The human body is designed to respond to stress through the “fight or flight” response. When faced with a stressful situation, the adrenal glands release hormones such as adrenaline and cortisol. These hormones increase heart rate, blood pressure, and blood glucose levels, preparing the body for immediate action. However, when this response is triggered too frequently or for prolonged periods, it can lead to a variety of health problems, including cardiovascular diseases, diabetes, and weakened immune system.
Psychologically, stress can affect mood, behavior, and cognition. It can lead to feelings of anxiety, depression, irritability, and restlessness. Behaviorally, stress can result in changes in eating and sleeping patterns, social withdrawal, and substance abuse. Cognitively, it can cause problems with concentration, memory, and decision-making.
Fortunately, there are several effective strategies for managing stress. These include lifestyle changes such as regular physical activity, a healthy diet, adequate sleep, and avoidance of alcohol, caffeine, and nicotine. Mindfulness and relaxation techniques such as yoga, meditation, and deep breathing can also be beneficial.
It’s crucial to recognize when stress becomes overwhelming and to seek professional help when needed. Therapies such as cognitive-behavioral therapy (CBT) and stress management training can be highly effective. Medication may be necessary in some cases.
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Home — Essay Samples — Nursing & Health — Psychiatry & Mental Health — Stress
Hook examples for stress essays, "the modern epidemic: unmasking stress's grip" hook.
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Stress, in psychology and biology, is any environmental or physical pressure that elicits a response from an organism.
Stress may be acute, chronic, or traumatic. Acute stress is characterized by immediate danger that occurs within a short span of time. Chronic stress is characterized by the persistent presence of sources of frustration or anxiety that a person encounters every day. Traumatic stress is characterized by the occurrence of a life-threatening event that evokes fear and helplessness.
In psychology, researchers generally classify the different types of stressors into four categories: 1) crises/catastrophes, 2) major life events, 3) daily hassles/microstressors, and 4) ambient stressors.
Stress causes muscular aches and tightness. Stress can impact mental performance. Women appear more prone to stress than men. Chronic stress can cause substance abuse.
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23 Pages Posted: 30 Aug 2024
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Two consecutive devastating earthquakes occurred in Turkey on February 6. While some studies have examined the long-term traumatic effects of devastating earthquakes on victims, no study to date has examined the consequences on adults who have migrated from the earthquake area. Therefore, this study aims to investigate the traumatic consequences of the earthquake among migrants in relation to sociodemographic factors. Additionally, the mediating effects of earthquake stress coping strategies on the relationship between intolerance of uncertainty (IU) and post-traumatic stress disorder (PTSD) were also determined. 1877 individuals participated in this study (Mage = 23.86; SD = 6.35). The findings revealed that approximately 2/3 of the adults had PTSD symptoms. The results show that there are significant relationships between IU, coping with earthquake stress, and PTSD levels. In addition, the relationship between IU and PTSD was mediated by coping strategies. Previous studies have mainly focused on the mental health of individuals living in earthquake zones. However, mental health service providers in different areas in Turkey should focus more on interventions that contribute to reducing the stress experienced by migrants from earthquake zones.
Note: Funding declaration: No financial support was received during the research process. Conflict of Interests: The authors declare that they have no conflict of interest. Ethical Approval: This study was approved by the Atatürk University Educational Sciences Unit Ethics Board (meeting number: 04, and decision number: 14).
Keywords: Intolerance of uncertainty, Coping with earthquake stress, Post-traumatic stress disorder, Earthquake survivors
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Get original essay. Body Paragraph 1: One of the most important ways to cope with stress is to practice mindfulness and relaxation techniques. Engaging in activities such as meditation, deep breathing exercises, and yoga can help individuals reduce their stress levels and create a sense of calmness and inner peace.
Conlcusion. There are other effective coping strategies, which even though I have not used, I would consider applying. Self-nurturing is such "effective way of coping with stress" (Aldwin, 2007). Creating time for fun and relaxing, enhance our ability to copy with life's unending stressors. It is therefore prudent for an individual to ...
The essay "Coping Up with Stress" provides a useful overview of different coping strategies for dealing with stress. However, there are a few shortcomings that detract from the overall quality of the writing. For example, in the second paragraph, the author writes, "Stress is a part of life, and one cannot deny it." ...
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Life without stress is hard to imagine. However, it is easy to manage stress and take the steps with the help of which human potential can be discovered and used in many different ways. Works Cited. American Psychological Association. Stress in America: The State of Our Nation. 2017. Web. Chew, Delena. "7 Tips for Fighting Stress.
Stress and anxiety both contribute to nervousness, poor sleep, high blood pressure, muscle tension, and excess worry. Experiencing anxiety can make it more difficult to cope with stress and may contribute to other health issues, including increased depression, susceptibility to illness, and digestive problems.
Healthy coping strategies include exercise, relaxation techniques, social support, and Cognitive-Behavioral Therapies (CBT). Exercise has been shown to have numerous health benefits, including stress reduction, improved mood, and enhanced cognitive function (Sui et al., 2019).
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By examining the intersection of stress and mental health, we will shed light on the importance of self-care and coping mechanisms in navigating the demands of everyday life. Ultimately, this essay aims to provide insight into the pervasive nature of stress and offer guidance on how individuals can cultivate resilience and well-being in the ...
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The experience of stress can be either acute or chronic. Acute stress usually occurs in response to a short-term stressor, like a car accident or an argument with your spouse. Acute stress can be very distressing, but it passes quickly and typically responds well to coping techniques like calming breathing or brisk physical activity.
She offers the following ways to reduce or manage stress: Relaxation techniques. These are activities that trigger the relaxation response, a physiological change that can help lower your blood pressure, heart rate, breathing rate, oxygen consumption, and stress hormones. You can achieve this with activities such as meditation, guided imagery ...
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12 essay samples found. An essay on stress management can explore strategies and techniques for coping with stress in modern life. It can discuss the physical and psychological effects of stress, mindfulness practices, time management, and the importance of work-life balance in reducing stress and promoting overall well-being.
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The essay on stress management will throw light on the very same thing. Stress is a very complex phenomenon that we can define in several ways. Stress management refers to a wide spectrum of techniques and psychotherapies for controlling a person's stress level, especially chronic stress. The essay on stress management will throw light on the ...
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By recognizing the signs of stress and implementing effective coping strategies, we can mitigate its negative impacts and enhance our overall well-being. ... 500 Words Essay on Stress Understanding Stress. Stress is a ubiquitous and universal human experience. It is a physiological and psychological response to perceived threats, challenges, or ...
Essay grade: Excellent. 3 pages / 1401 words. Stress, as defined in the Longman Dictionary, is the continuous feeling of worry about your work or personal life that prevents one from relaxing or feeling at ease. Every student and adult faces stress at one point or another in their life.
Additionally, the mediating effects of earthquake stress coping strategies on the relationship between intolerance of uncertainty (IU) and post-traumatic stress disorder (PTSD) were also determined. 1877 individuals participated in this study (Mage = 23.86; SD = 6.35). The findings revealed that approximately 2/3 of the adults had PTSD symptoms.